Scientific Domains Research Articles

New Applications of “Abeer-AL-Tememe” Transformation in partial differential equations

The Introduction of integral transforms as a partial differential equation solution technique results from the tremendous significance of differential equations and integral transforms in scientific domains. This study proposes a new integral transform Abeer Al- Tememe transform. The suggested transform applications has demonstrated its capacity to resolve partial differential equations.

Static Magnetic Field Focusing With Neodymium Magnets For Wound Healing: A Numerical Study

Static magnetic fields (SMFs) find widespread applications in diverse scientific, technological, and medical domains. This study explores the potential of neodymium permanent magnets in focusing and controlling SMFs, specifically emphasizing wound healing applications. Numerical simulations using COMSOL Multiphysics create a uniform static magnetic field for wound healing. The study systematically increases the number of neodymium magnets, demonstrating enhanced magnetic flux density and a focused magnetic field. The results affirm the efficacy of neodymium magnets in generating a uniform static magnetic field between 160-600 m Tesla. This research proposes neodymium permanent magnets as a promising tool for wound healing applications, offering a non-invasive and focused therapeutic approach. While the study provides valuable insights, further experimental and clinical validations are necessary to establish the real-world efficacy of this method. The work contributes to the evolving understanding of static magnetic fields as a viable therapeutic modality for various medical conditions, particularly in the context of wound healing.

Rogue-wave structures for a generalized (3+1)-dimensional nonlinear wave equation in liquid with gas bubbles

This study explores the behavior of higher-order rogue waves within a (3+1)-dimensional generalized nonlinear wave equation in liquid-containing gas bubbles. It creates the investigated equation’s Hirota D-operator bilinear form. We employ a generalized formula with real parameters to obtain the rogue waves up to the third order using the direct symbolic technique. The analysis reveals that the second and third-order rogue solutions produce two and three-waves, respectively. To gain deeper insights, we use the Cole-Hopf transformation on the transformed variables ξ and η to produce a bilinear equation. Using the system software Mathematica, the dynamic analysis presents the graphics for the obtained solutions in transformed ξ, η, and original spatial-temporal coordinates x, y, z, t. These visualizations reveal rogue waves’ intricate structure and evolution, capturing their localized interactions and significant presence within nonlinear systems. We demonstrate that rogue waves, characterized by their substantial height and sudden appearance, are prevalent in various nonlinear events. The equation examined in this study offers valuable insights into the evolution of longer waves with smaller amplitudes, which is particularly relevant in fields such as fluid dynamics, dispersive media, and plasmas. The implications of this research extend across multiple scientific domains, including fiber optics, oceanography, dusty plasma, and nonlinear systems, where understanding the behavior of rogue waves is crucial for both theoretical and practical applications.

DETERMINING STUDENT'S ONLINE ACADEMIC PERFORMANCE USING MACHINE LEARNING TECHNIQUES

Predicting student's academic performance during online learning has been considered a major task during the pandemic period. During the online mode of learning, academic activities have been affected in such a way that the management of educational institutions has planned to design support systems for predicting the student's performance to reduce the dropout ratio of the students and bring improvement in academic activities. During COVID-19, the main challenge is maintaining student's grades by predicting their academic performance using different techniques such as Education Data Mining and Learning Analytics. Different features have been identified related to the teaching mechanisms in online learning, which have a great impact on the improvement of academic performance. A high-quality dataset helps us to generate productive results, which in turn helps us to make effective decisions for promoting high-quality education. In this research, five prediction models for predicting academic performance have been proposed by collecting an imbalanced dataset of 350 students from the same computer science domain. After applying pre-processing techniques for cleaning the data, machine learning models have been applied, including K-Nearest Neighbor Classifier, Decision Tree, Random Forest, Support Vector Classifier, and Gaussian Naive Bayes. Results have been predicted for an imbalanced and balanced dataset after feature selection. Support Vector classifier has produced the best results in a balanced dataset with selected features by giving an accuracy of 96.89%.

Assessing semantic interoperability in environmental sciences: variety of approaches and semantic artefacts

The integration and reuse of digital research products can be only ensured through the adoption of machine-actionable (meta)data standards enriched with semantic artefacts. This study compiles 540 semantic artefacts in environmental sciences to: i. examine their coverage in scientific domains and topics; ii. assess key aspects of their FAIRness; and iii. evaluate management and governance concerns. The analyses showed that the majority of semantic artefacts concern the terrestrial biosphere domain, and that a small portion of the total failed to meet the FAIR principles. For example, 5.5% of semantic artefacts were not available in semantic catalogues, 8% were not built with standard model languages and formats, 24.6% were published without usage licences and 22.4% without version information or with divergent versions across catalogues in which they were available. This investigation discusses common semantic practices, outlines existing gaps and suggests potential solutions to address semantic interoperability challenges in some of the resources originally designed to guarantee it.

Understanding and Assessing Scientific Wisdom

ABSTRACT We applied a balance theory of wisdom to thinking in the domain of science. In two studies, we administered maximum-performance scales measuring Scientific Wisdom, Scientific Reasoning, Scientific Creativity, and fluid and crystallized intelligence, and a typical-performance scale of self-assessed wisdom. Our Scientific Wisdom scale, along with the Scientific Reasoning scales and a Scientific Creativity scale, loaded on one clear factor separate from the factor(s) on which the psychometric tests of intelligence, SAT and ACT scores, and GPA loaded. These results suggested that the scientific tests were assessing abilities that were different from g, as well as what the SAT/ACT and academic GPA were assessing. The studies are merely a starting point for a relatively new idea—that schools should consider teaching for and assessing scientific wisdom, and that a full analysis of scientific giftedness should include scientific wisdom.

Examining Dropout Among Graduate and Undergraduate Public Affairs and Political Science Students

Within the domain of public affairs and political science (PAPS), student dropout emerges as a critical concern. This study delves into the factors influencing the decisions of undergraduate and graduate PAPS students to discontinue their programs. Employing a comprehensive mixed-methods approach, we surveyed 384 at-risk undergraduates and conducted in-depth interviews with 18 former graduate students. Thematic analysis of these interviews revealed six pivotal factors influencing graduate student dropout: the financial burden of study (including tuition and limited financial aid), program curricular challenges (such as demanding thesis requirements and inadequate practical components), the delicate balance of multiple responsibilities (work, study, and family), health-related issues (including anxiety and stress), poor academic performance, and personal difficulties (such as lack of family support). At the undergraduate level, descriptive analyses indicated that 38% of PAPS students contemplated dropping out, with 30% considering changing their major. Exploratory Factor Analysis and Multiple Regression highlighted the significance of factors such as family and societal influences, economic considerations, program and curriculum structure, institutional dynamics, academic performance, personal attitudes, and student health in shaping these contemplations. This research not only expands the application of integration and engagement theory and the pull, push, and failing out theory but also validates their effectiveness in predicting dropout within PAPS programs. The study offers valuable insights for both researchers and policymakers, underscoring the imperative for more supportive, flexible, and financially viable educational structures to enhance student retention in the realm of public affairs and political science.

Complexity and phase transitions in citation networks: insights from artificial intelligence research.

In this study, we analyze the changes over time in the complexity and structure of words used in article titles and the connections between articles in citation networks, focusing on the topic of artificial intelligence (AI) up to 2020. By measuring unpredictability in word usage and changes in the connections between articles, we gain insights into shifts in research focus and diversity of themes. Our investigation reveals correspondence between fluctuations in word complexity and changes in the structure of citation networks, highlighting links between thematic evolution and network dynamics. This approach not only enhances our understanding of scientific progress but also may help in anticipating emerging fields and fostering innovation, providing a quantitative lens for studying scientific domains beyond AI.

Influence of external magnetic field on nanofluid dynamics using a two-phase Lattice Boltzmann Mixture Model at low Reynolds numbers

This work examines the behavior of an external magnetic field-induced Al2O3-water nanofluid flow between two parallel plates, with a particular emphasis on low Reynolds number scenarios. Understanding the dynamics of magnetohydrodynamic (MHD) flows in applications including energy systems, cooling systems, and medicinal devices depends on solving this problem. For the first time, a new two-phase lattice Boltzmann method was used to simulate the flow in conjunction with the mixture model. This methodology was specifically designed for nanofluid flow in the presence of magnetic field. Three coupled transport equations for flow velocity, concentration of nanoparticles, and magnetic field intensity are solved using this method. Three various cases were examined numerically. By comparing the simulated velocity profiles with analytical solutions in the first case, model validation was accomplished, demonstrating a strong agreement with less than 1 % variance. The impact of different inflow nanoparticle volume fractions (0.05 and 0.01) on the velocity field at various Reynolds numbers (0.5, 1, 10) and Hartmann numbers (0, 10, 20) was investigated in the second case. The findings showed that for Reynolds numbers of 0.5, 1, and 10, respectively, the velocity magnitude dramatically fell by approximately 75 %, 68 %, and 30 % as the Hartmann number grew from 0 to 20. Alongside this decrease, vortices began to form at Hartmann numbers of 10 and 20. Furthermore, the influence of the magnetic field diminished as the Reynolds number increased from 0.5 to 10, resulting in a noticeable reduction in vortex intensity. In the third case, where the nanoparticle volume fractions at the inlet were closer (e.g., 0.03 and 0.02), the intensity of the vortices decreased compared to the second case. The study demonstrates the robustness of the proposed model and its applicability across scientific and engineering domains. The novelty lies in quantifying MHD effects on nanofluid flow and particle distribution using a two-phase lattice Boltzmann approach, offering more precise and efficient simulations than existing methods. The findings provide new insights into the interaction between magnetic fields and nanofluids, especially at low Reynolds numbers, and emphasize the critical role of nanoparticle distribution in flow dynamics.

Locally Adaptive Shrinkage Priors for Trends and Breaks in Count Time Series

Non-stationary count time series characterized by features such as abrupt changes and fluctuations about the trend arise in many scientific domains including biophysics, ecology, energy, epidemiology, and social science domains. Current approaches for integer-valued time series lack the flexibility to capture local transient features while more flexible models for continuous data types are inadequate for universal applications to integer-valued responses such as settings with small counts. We present a modeling framework, the negative binomial Bayesian trend filter (NB-BTF), that offers an adaptive model-based solution to capturing multiscale features with valid integer-valued inference for trend filtering. The framework is a hierarchical Bayesian model with a dynamic global-local shrinkage process. The flexibility of the global-local process allows for the necessary local regularization while the temporal dependence induces a locally smooth trend. In simulation, the NB-BTF outperforms a number of alternative trend filtering methods. Then, we demonstrate the method on weekly power outage frequency in Massachusetts townships. Power outage frequency is characterized by a persistent low level trend with occasional spikes. These illustrations show the estimation of a smooth, non-stationary trend with adequate uncertainty quantification.

Investigation of Chrysene heterodimers complexes potential energy surface using ab initio computational methods

Numerous chemical and biological entities are greatly impacted by non-covalent interactions in terms of their stability and structure. One such example is the interaction of aromatic rings. These interactions are highly valued in the domains of astrochemistry, biology, chemistry, biochemistry, and material science. The main goals of this study are to explore the Potential Energy Surface (PES) of Chrysene (Chy) heterodimers, identify the most stable configurations among the Chy-Bz, Chy-Np, and Chy-Anth heterodimer complexes, and analyze the inter-molecular interactions between these molecules. This analysis was conducted utilizing ab initio computational techniques. On their PES, the Chy-Np heterodimer exhibited four minima, while the Chy-Bz and Chy-Anth heterodimer complexes showed three. Compared to conformers oriented perpendicularly, co-facial arrangement conformers in Chy heterodimer complexes have stable structures. The global minimum structure of Chy-Bz has been determined to be the face isomer, while Chy-Np and Chy-Anth have global minimum structures of the cross isomer. The binding energies of the structures generated by MP2 are higher than those of DFT-D3, DFT-D4, and SCS-MP2. Optimized geometries and binding energies of larger hydrocarbon aromatic systems are explained in detail by B3LYP-D3 and the recently created B3LYP-D4.

Extension of Meir-Keeler-Khan (ψ − α) Type Contraction in Partial Metric Space

In numerous scientific and engineering domains, fractional-order derivatives and integral operators are frequently used to represent many complex phenomena. They also have numerous practical applications in the area of fixed point iteration. In this article, we introduce the notion of generalized Meir-Keeler-Khan-Rational type (ψ−α)-contraction mapping and propose fixed point results in partial metric spaces. Our proposed results extend, unify, and generalize existing findings in the literature. In regards to applicability, we provide evidence for the existence of a solution for the fractional-order differential operator. In addition, the solution of the integral equation and its uniqueness are also discussed. Finally, we conclude that our results are superior and generalized as compared to the existing ones.

Fast Computer Model Calibration using Annealed and Transformed Variational Inference

Computer models play a crucial role in numerous scientific and engineering domains. To ensure the accuracy of simulations, it is essential to properly calibrate the input parameters of these models through statistical inference. While Bayesian inference is the standard approach for this task, employing Markov chain Monte Carlo methods often encounters computational hurdles due to the costly evaluation of likelihood functions and slow mixing rates. Although variational inference (VI) can be a fast alternative to traditional Bayesian approaches, VI has limited applicability due to boundary issues and local optima problems. To address these challenges, we propose flexible VI methods based on deep generative models that do not require parametric assumptions on the variational distribution. We embed a surjective transformation in our framework to avoid posterior truncation at the boundary. Additionally, we provide theoretical conditions that guarantee the success of the algorithm. Furthermore, our temperature annealing scheme and fine-tuning can prevent being trapped in local optima through a series of intermediate posteriors and weight adjustment. We apply our method to infectious disease models and a geophysical model, illustrating that the proposed method can provide fast and accurate inference compared to its competitors.

Walter Brendel and the dawn of transplantation research in Germany.

Walter Brendel was a physiologist who headed the Institut of Experimental Surgery at the University of Munich (LMU) from 1961 until 1989. His legendary career began with the development of an anti-human lymphocyte globulin (ALG) at his Institute during the late 1960s. The initial successful treatment of a small number of patients culminated in the co-treatment of the first successfully heart-transplanted patient in Capetown, South Africa (successful reversal with ALG of an acute allograft rejection). Walter Brendel was a pioneering personality whose work has laid a wide platform for the promotion of interdisciplinarily conducted innovative research programs in various domains of translational science and medicine. Among the many innovative achievements, the most notable are: discovery of involvement of the alternative pathway of complement activation in hyperacute xenograft rejection; induction of immunological tolerance to horse IgG as a means to prevent anaphylactic reactions during ALG therapy; development and clinical implementation of the extracorporeal shock wave lithotripsy for extracorporeal destruction of renal and ureteral calculi. The legacy of Brendel continues with the foundation of the Walter-Brendel Kolleg für Transplantationsmedizin (i.e., the German Transplant School for Transplantation Medicine), which has been held annually since 1994.

Extracting Semantic Frames from Specialized Corpora for Lexicographic Purposes

This paper focuses on Frame-based Terminology (FBT), a theoretical framework based on cognitive premises that underpin the representation of specialized objects and events. It demonstrates that specialized semantic frames can be inferred from lexical schemas and semantic annotation. This research used corpus techniques to collect data for the representation of multilingual semantic frames. The research focuses on deforestation within the domain of Environmental Science. The methodology involves the encapsulation of corpus queries and semantic annotation to guide the creation of specialized frames associated with the deforestation event. Although the creation of frame-based terminological resources can be time-consuming, our results show that it is facilitated by the use, of e-tools that extract the arguments of specialized predicates, which are often immersed in complex syntactic constructions.

Investigating multi-soliton patterns and dynamical characteristics of the (3+1)-dimensional equation via phase portraits

In this study, we investigate the deeper characteristics of the modified Ito equation, which can be applied across various scientific domains to represent systems influenced by noise and randomness. Multi-solitons, including 1-wave, 2-wave, and 3-wave solitons, have been successfully generated using a multiple exponential-function approach. For visual representation, the outcomes are displayed through 3D, 2D, density, and contour plots. The wave transformation is then applied to convert the studied model into an ordinary differential equation. Following this, the dynamic nature of the model is examined from various viewpoints, including bifurcation, chaotic phenomena, multistability, and sensitivity analysis. Bifurcation shows how the solution of a planar system depends on equilibrium points, and when an outward periodic force is implemented to the unperturbed planar system, it reveals chaotic characteristics. These are analyzed using tools such as 3-dimensional and 2-dimensional plots, time scale plots, and Poincaré maps. Additionally, the model’s sensitivity is assessed with varying initial values. The results underscore the effectiveness and relevance of the proposed approaches for examining solitons within a broad spectrum of nonlinear systems.

Application of an Empirical Model to Improve Maximum Value Predictions in CFD-RANS: Insights from Four Scientific Domains

This study introduces an empirical model designed to predict the maximum values of time-dependent data across four turbulence-related fields: hydrogen combustion in renewable energy systems, urban microclimate effects on cultural heritage, shipping emissions, and road vehicle emissions. The model, which is based on the mean, standard deviation, and integral time scale, employs two parameters: a fixed exponent ‘ν’ (0.3) reflecting time scale sensitivity, and a variable parameter ‘b’ that accounts for application-specific uncertainties. Integrated into the Computational Fluid Dynamics (CFD) framework, specifically the Reynolds-Averaged Navier–Stokes (RANS) methodology, the model addresses the RANS approach’s limitation in predicting extreme values due to its inherent averaging process. By incorporating the empirical model, this study enhances RANS simulations’ ability to predict critical values, such as peak hydrogen concentrations and maximum urban wind speeds, which is essential for safety and reliability assessments. Validation against experimental and numerical data across the four fields demonstrates strong agreement, highlighting the model’s potential to improve CFD-RANS predictions of extreme events. This advancement offers significant implications for future CFD-RANS applications, particularly in scenarios demanding fast and reliable maximum value predictions.

SS-DNN: A hybrid strang splitting deep neural network approach for solving the Allen–Cahn equation

The Allen–Cahn equation is a fundamental partial differential equation that describes phase separation and interface motion in materials science, physics, and various other scientific domains. The presence of interfacial width (ϵ) between two stable phases, associated with a nonlinear term, is a small positive parameter which makes the problem more challenging to solve as ϵ approaches zero. This paper proposes a novel hybrid deep splitting method to efficiently and accurately solve the Allen–Cahn equation in a convex polygonal domain in Rd(d=1,2,3). The method combines the benefits of deep learning and splitting strategies, leveraging the strengths of both approaches. Essentially, a second-order splitting method is employed to split the Allen–Cahn equation into two simpler linear and non-linear sub-problems. While the nonlinear sub-problem can be solved analytically, the deep neural network is utilized to approximate the linear sub-problem. By integrating deep learning into the splitting strategy, we achieve a more efficient and accurate solution for the Allen–Cahn equation, demonstrating promising results. We also derive an error estimate for the proposed hybrid method. Modified space adaptivity and transform learning techniques are employed to enhance the efficiency of the neural network.

Metal alkoxides as models for metal oxides—the concept revisited

Sol-Gel synthesis of metal oxides constitutes a tremendously exciting domain of inorganic chemistry, where molecular and supramolecular science meet the physical chemistry and materials science. Structure and reactivity, especially surface complexation of biologically important molecules on the surface of metal oxide nanoparticles can efficiently be traced through structural studies of metal oxo-paperbags—the product of partial hydrolysis of alkoxide precursors. Paperbag is a recently proposed term to denote oligonuclear complexes not featuring intrinsic metal-metal bonding and thus not qualified to be called “clusters”. Another important insight, provided recently by the studies of heterometallic species, is dealing with visualization of bonding modes of single atom catalysts on metal oxide substrates and reveals possible coordination environments of heteroatoms on doping. The studies of large paperbag aggregates can contribute to understanding of factors influencing the bandgap and photocatalytic activity of related oxides. The use of these species directly as photo or electro catalysts is rather debatable, however, in the view of high reactivity of these alkoxide intermediates, easily transforming them into metal oxide nanoparticles on hydrolysis or thermolysis.Graphical

A Chromatographic Approach for High-Precision Eu Isotope Analysis.

Eu isotopes are promising tracers across various scientific domains such as planetary, earth, and marine science, yet their high-precision analysis has been challenging due to the similar geochemical properties of rare earth elements (REEs). In this study, a novel two-column chromatographic approach was developed utilizing AG50W-X12 and TODGA resins to separate Eu effectively from matrix and interfering elements like Ba, Nd, Sm, and Gd, while ensuring high Eu yields (99.4 ± 0.4%, n = 19) and low blanks (<20 pg). The robustness of this method is evidenced by various rock types and different Eu loading masses. The efficient purification of Eu facilitated the establishment of a high-precision calibration technique with standard-sample bracketing (SSB) and internal normalization (Nd). When a Nu Plasma 1700 multicollector-inductively coupled plasma-mass spectrometry (MC-ICP-MS) instrument was employed, repeated purification and analysis of various Geological Reference Materials (GRMs) confirmed that the long-term external precision of δ153/151Eu is better than 0.04‰ (2 standard deviation (2SD)), which represents a 2-5-fold increase in precision compared to previously reported methods. Additionally, the high-precision Eu isotopic compositions of five GRMs, including basalts, andesite, syenite, and marine sediment, were measured. The high-precision Eu isotope techniques presented herein open up new avenues for Eu isotope geochemistry.