Search Results (837)

There is a growing need for comprehensive and transparent frameworks in bibliometric evaluation that support fairer assessments and capture the multifaceted nature of research performance. This study proposes a novel methodology for identifying top-performing researchers based on a composite performance index (CPI). Unlike existing rankings, this framework presents a multidimensional approach by integrating sixteen weighted bibliometrics metrics, spanning research productivity, citation, publications in top journal percentiles, authorship roles, and international collaboration, into a single CPI, enabling a more nuanced and equitable evaluation of researcher performance. Data were retrieved from SciVal for 1996–2025. Two ranking exercises were conducted with Kazakhstan as the analytical unit. Subject-specific rankings identified the top 1% authors within different research areas, while subject-independent rankings highlighted the overall top 1%. CPI distributions varied markedly across disciplines. A comparative analysis with the Stanford/Elsevier global top 2% list was conducted as additional benchmarking. The results highlight that academic excellence depends on a broad spectrum of strengths beyond just productivity, particularly in competitive disciplines. The CPI provides a consistent and adaptable tool for assessing and recognizing research performance; however, future refinements should enhance data coverage, improve representation of early-career researchers, and integrate qualitative aspects. Full article

We present a comprehensive quantum field theoretical analysis of graviton self-energy and mass generation in 3+1 dimensional BTZ black hole spacetime, incorporating axion interactions within the framework of dark matter theory. Using a novel mathematical approach based on ultrahyperfunctions, generalizations of Schwartz tempered distributions to the complex plane, we derive exact quantum relativistic expressions for graviton and axion self-energies without requiring ad hoc regularization procedures. Our approach extends the Gupta–Feynman quantization framework to BTZ gravity while introducing a new constraint that eliminates unitarity violations inherent in previous formulations, thereby avoiding the need for ghost fields. Through systematic application of generalized Feynman parameters, we evaluate both bradyonic and tachyonic graviton modes, revealing distinct quantum correction patterns that depend critically on momentum, energy, and mass parameters. Key findings include (1) natural graviton mass generation through cosmological constant interactions, yielding $m^2=2\left|\Lambda \right|/\kappa (1-\kappa )$; (2) qualitatively different quantum behaviors between bradyonic and tachyonic modes, with bradyonic corrections reaching amplitudes 6 times larger than their tachyonic counterparts; (3) the discovery of momentum-dependent quantum dissipation effects that provide natural ultraviolet regulation; and (4) the first explicit analytical expressions and graphical representations for 17 distinct graviton self-energy contributions. The ultrahyperfunction formalism proves essential for handling the non-renormalizable nature of the theory, providing mathematically rigorous treatment of highly singular integrals while maintaining Lorentz invariance. Our results suggest observable consequences in gravitational wave propagation through frequency-dependent dispersive effects and modifications to black hole thermodynamics, potentially bridging theoretical quantum gravity with experimental constraints. Full article

Malware analytics suffer from scarce, delayed, and privacy-constrained labels, limiting fully supervised detection and hampering responsiveness to zero-day threats. We propose SMAD, a Semi-supervised Android Malicious App Detector that integrates a segmentation-oriented backbone—to extract pixel-level, multi-scale features from APK imagery—with a dual-branch consistency objective that enforces predictive agreement between two parallel branches on the same image. We evaluate SMAD on CICMalDroid2020 under label budgets of 0.5, 0.25, and 0.125 and show that it achieves higher accuracy, macro-precision, macro-recall, and macro-F1 with smoother learning curves than supervised training, a recursive pseudo-labeling baseline, a FixMatch baseline, and a confidence-thresholded consistency ablation. A backbone ablation (replacing the dense encoder with WideResNet) indicates that pixel-level, multi-scale features under agreement contribute substantially to these gains. We observe a coverage–precision trade-off: hard confidence gating filters noise but lowers early-training performance, whereas enforcing consistency on dense, pixel-level representations yields sustained label-efficiency gains for image-based malware detection. Consequently, SMAD offers a practical path to high-utility detection under tight labeling budgets—a setting common in real-world security applications. Full article

This paper investigates the role of symmetry and asymmetry in the learning process of modern machine learning models, with a specific focus on feature representation and optimization. We introduce a novel symmetry-aware learning framework that identifies and preserves symmetric properties within high-dimensional datasets, while allowing model asymmetries to capture essential discriminative cues. Through analytical modeling and empirical evaluations on benchmark datasets, we demonstrate how symmetrical transformations of features (e.g., rotation, mirroring, permutation invariance) impact learning efficiency, interpretability, and generalization. Furthermore, we explore asymmetric regularization techniques that prioritize informative deviations from symmetry in model parameters, thereby improving classification and clustering performance. The proposed approach is validated using a variety of classifiers including neural networks and tested across domains such as image recognition, biomedical data, and social networks. Our findings highlight the critical importance of leveraging domain-specific symmetries to enhance both the performance and explainability of machine learning systems. Full article

For any point P in the Euclidean plane of a triangle $\Delta$, the six parallelians of P lie on a single conic, which shall be called the parallelian conic of P with respect to $\Delta$. We provide a synthetic and an analytic proof of this fact. Then, we studied the shape of this particular conic, depending on the choice of the pivot point P. This led to the finding that the only circular parallelian conic is the first Lemoine circle. Points on the Steiner inellipse produce parabolae, and those on a certain central line yield equilateral hyperbolae. The hexagon built by the parallelians has an inconic $\mathcal{I}$ and the tangents of $\mathcal{P}$ at the parallelians define some triangles and hexagons with several circum- and inconics. Certain pairings of conics, together with in- and circumscribed polygons, give rise to different kinds of porisms. Further, the inconics and circumconics of the triangles and hexagons span exponential pencils of conics in which any pair of subsequent conics defines a new conic as the polar image of the inconic with regard to the circumconic. This allows us to construct chains of nested porisms. The trilinear representations of the centers of the appearing conics, as well as the perspectors of some deduced triangles, depending on the indeterminate coordinates of P, define some algebraic transformations that establish algebraic relations between well- and lesser-known triangle centers. We completed our studies by compiling a list of possible porisms between any pair of conics. Further, we describe the possible loci of pivot points so that the mentioned conics allow for porisms of polygons with arbitrary numbers of vertices. Full article

This study employs a finite element approach to investigate wellbore stability in interbedded weak formations, such as unconsolidated layers, with a focus on the failure-tendency method, which is derived according to the principle of Mohr–Coulomb theory. The numerical model is successfully verified through analytical solutions for stress distributions around a borehole. Through finite element modeling, the method captures critical shear failure thresholds, exemplifying how variations in horizontal stress anisotropy, orientation of interbedded weak layers, and mechanical properties of layered geological formations impact wellbore stability in stratified formations. Results indicate that the potential unstable regions, aligned in the direction of minimum principal stress, and the range of unstable regions gradually enlarge as the internal cohesive strength decreases. By modeling heterogeneous rock sequences with explicit representation of interbedded weak layers and stress anisotropy, the analysis reveals that interbedded weak layers are prone to shear-driven borehole breakouts due to stress redistribution and relatively lower internal cohesive strength. As compressive stresses concentrate at interfaces between stiff and compliant layers, breakouts are induced at those weak layers along the interfaces; this type of failure is also manifested through a field borehole breakout observation. Simulation results reveal the significant influences of the mechanical properties of layered formations and in situ stress on the distribution of instability regions around a borehole. The study underscores the necessity of layer-specific geomechanical models to predict shear failure in complex layered geological formations and offers insights for optimizing drilling parameters to enhance wellbore stability in anisotropic, stratified subsurface environments. Full article

This paper develops an information-theoretic reliability inference framework for consecutive $r$-out-of-$n$:G systems by employing the concept of residual extropy, a dual measure to entropy. Explicit analytical representations are established in tractable cases, while novel bounds are derived for more complex lifetime models, providing effective tools when closed-form expressions are unavailable. Preservation properties under classical stochastic orders and aging notions are examined, together with monotonicity and characterization results that offer deeper insights into system uncertainty. A conditional formulation, in which all components are assumed operational at a given time, is also investigated, yielding new theoretical findings. From an inferential perspective, we propose a maximum likelihood estimator of residual extropy under exponential lifetimes, supported by simulation studies and real-world reliability data. These contributions highlight residual extropy as a powerful information-theoretic tool for modeling, estimation, and decision-making in multicomponent reliability systems, thereby aligning with the objectives of statistical inference through entropy-like measures. Full article

The primary aim of this study is to establish new Wirtinger-type inequalities involving fractional derivatives, which are essential tools in analysis and applied mathematics. We derive generalized Wirtinger-type inequalities incorporating the $(k,\psi )$-Caputo fractional derivatives using Taylor’s expansion. The inequalities are derived in $L_p$ spaces $(p>1)$ through Hölder’s inequality. A detailed analytical discussion is provided to further examine the derived inequalities. The theoretical findings are validated through numerical examples and graphical representations. Furthermore, the novelty and applicability of the proposed technique are demonstrated through the applications of the resulting inequalities to derive new results related to the arithmetic–geometric mean inequality. Full article

This paper proposes a method for assessing societal risk along a traffic link by integrating a stochastic formulation of the fundamental diagram. The approach accounts for uncertainty in vehicle speed due to user heterogeneity, vehicle characteristics, and environmental conditions. The risk index is decomposed into occurrence, vulnerability, and exposure components, with the occurrence probability modeled as a function of stochastic speed. The inverse gamma distribution is adopted to represent speed variability, enabling analytical tractability and control over dispersion. Numerical results show that urban and suburban environments exhibit distinct sensitivity to model parameters, particularly the gamma shape parameter η and the composite parameter c = β · v₀ obtained by the product of the occurrence parameter β and the free speed flow v₀. Graphical representations illustrate the impact of uncertainty on risk estimation. The proposed framework enhances existing deterministic methods by incorporating probabilistic elements, offering a foundation for future applications in traffic safety management and infrastructure design. Full article

Building Energy Modelling (BEM) practitioners play a crucial role in delivering energy-efficient buildings by analysing building performance using simulation tools. However, their experiences while using BEM software to predict building energy performance are understudied. In addition, research that directly engages with practitioners and stakeholders is particularly lacking in the Global South (GS), where the bulk of new building construction takes place. This study explores the implementation challenges and opportunities associated with BEM software among South African industry practitioners, focusing on their experiences in utilising BEM tools. Structured interviews were conducted with 19 South African industry specialists, supplemented by quantitative data collected through a questionnaire. Qualitative data from the interviews were analysed using MAXQDA 24 Analytics Pro to identify key themes, while quantitative data were visualised to compare software preferences. The analysis indicated that DesignBuilder is widely used, followed by BSIMAC. These tools highlight the largest opportunities for supporting active South African practitioners. The respondents highlighted the need for user-friendly interfaces, standardised methodologies, and improved training to address entry barriers and inconsistent simulation outcomes. Mixed opinions exist regarding the preference for tools with visual representations of 3D geometry, primarily influenced by the field of specialisation and how it impacts client engagement. The research concludes that while BEM software is critical for advancing sustainable design, its effective implementation is hindered in South Africa and potentially in the GS. Recommendations include developing more intuitive software interfaces, establishing standardised modelling approaches, and creating structured training programmes and professional forums to enhance practitioner proficiency, knowledge transfer across contexts, and industry-wide adoption. Full article

The primary objective of this study is to provide a more precise and beneficial mathematical model for assessing corruption dynamics by utilizing non-local derivatives. This research aims to provide solutions that accurately capture the complexities and practical behaviors of corruption. To illustrate how corruption levels within a community change over time, a non-linear deterministic mathematical model has been developed. The authors present a non-integer order model that divides the population into five subgroups: susceptible, exposed, corrupted, recovered, and honest individuals. To study these corruption dynamics, we employ a new method for solving a time-fractional corruption model, which we term the q-homotopy analysis transform approach. This approach produces an effective approximation solution for the investigated equations, and data is shown as 3D plots and graphs, which give a clear physical representation. The stability and existence of the equilibrium points in the considered model are mathematically proven, and we examine the stability of the model and the equilibrium points, clarifying the conditions required for a stable solution. The resulting solutions, given in series form, show rapid convergence and accurately describe the model’s behaviour with minimal error. Furthermore, the solution’s uniqueness and convergence have been demonstrated using fixed-point theory. The proposed technique is better than a numerical approach, as it does not require much computational work, with minimal time consumed, and it removes the requirement for linearization, perturbations, and discretization. In comparison to previous approaches, the proposed technique is a competent tool for examining an analytical outcomes from the projected model, and the methodology used herein for the considered model is proved to be both efficient and reliable, indicating substantial progress in the field. Full article

Artificial intelligence and machine learning have increasingly transformed optometry, enabling automated classification and predictive modeling of eye conditions. In this study, we introduce Optometry Random Forest, an artificial intelligence-based system for automated classification and forecasting of optometric data. The proposed methodology leverages Random Forest models, trained on academic optometric datasets, to classify key diagnostic categories, including Contactology, Dry Eye, Low Vision, Myopia, Pediatrics, and Refractive Surgery. Additionally, an autoRegressive integrated moving average based forecasting model is incorporated to predict future research trends in optometry until 2030. Comparing the one-shot and epoch-trained Optometry Random Forest, the findings indicate that the epoch-trained model consistently outperforms the one-shot model, achieving superior classification accuracy (97.17%), precision (97.28%), and specificity (100%). Moreover, the comparative analysis with Optometry Bidirectional Encoder Representations from Transformers demonstrates that the Optometry Random Forest excels in classification reliability and predictive analytics, positioning it as a robust artificial intelligence tool for clinical decision-making and resource allocation. This research highlights the potential of Random Forest models in medical artificial intelligence, offering a scalable and interpretable solution for automated diagnosis, predictive analytics, and artificial intelligence-enhanced decision support in optometry. Future work should focus on integrating real-world clinical datasets to further refine classification performance and enhance the potential for artificial intelligence-driven patient care. Full article

Physics-Informed Neural Networks (PINNs) represent a transformative approach to solving partial differential equation (PDE)-based boundary value problems by embedding physical laws into the learning process, addressing challenges such as non-physical solutions and data scarcity, which are inherent in traditional neural networks. This review analyzes critical challenges in PINN development, focusing on loss function design, geometric information integration, and their application in engineering modeling. We explore advanced strategies for constructing loss functions—including adaptive weighting, energy-based, and variational formulations—that enhance optimization stability and ensure physical consistency across multiscale and multiphysics problems. We emphasize geometry-aware learning through analytical representations—signed distance functions (SDFs), phi-functions, and R-functions—with complementary strengths: SDFs enable precise local boundary enforcement, whereas phi/R capture global multi-body constraints in irregular domains; in practice, hybrid use is effective for engineering problems. We also examine adaptive collocation sampling, domain decomposition, and hard-constraint mechanisms for boundary conditions to improve convergence and accuracy and discuss integration with commercial CAE via hybrid schemes that couple PINNs with classical solvers (e.g., FEM) to boost efficiency and reliability. Finally, we consider emerging paradigms—Physics-Informed Kolmogorov–Arnold Networks (PIKANs) and operator-learning frameworks (DeepONet, Fourier Neural Operator)—and outline open directions in standardized benchmarks, computational scalability, and multiphysics/multi-fidelity modeling for digital twins and design optimization. Full article

Extracting key features for phenotype classification from high-dimensional and complex mass spectrometry (MS) data presents a significant challenge. Conventional data representation methods, such as traditional peak lists or grid-based imaging strategies, are often hampered by information loss and compromised signal integrity, thereby limiting the performance of downstream deep learning models. To address this issue, we propose a novel data representation framework named MSIMG. Inspired by object detection in computer vision, MSIMG introduces a data-driven, “density-peak-centric” patch selection strategy. This strategy employs density map estimation and non-maximum suppression algorithms to locate the centers of signal-dense regions, which serve as anchors for dynamic, content-aware patch extraction. This process transforms raw mass spectrometry data into a multi-channel image representation with higher information fidelity. Extensive experiments conducted on two public clinical mass spectrometry datasets demonstrate that MSIMG significantly outperforms both the traditional peak list method and the grid-based MetImage approach. This study confirms that the MSIMG framework, through its content-aware patch selection, provides a more information-dense and discriminative data representation paradigm for deep learning models. Our findings highlight the decisive impact of data representation on model performance and successfully demonstrate the immense potential of applying computer vision strategies to analytical chemistry data, paving the way for the development of more robust and precise clinical diagnostic models. Full article

We derive an analytical solution to the one-dimensional linearized Boussinesq equation with mixed boundary conditions (Dirichlet–Neumann), formulated to describe drainage in porous media. The solution is obtained via the unified transform method (Fokas method), extending its previous applications in infiltration problems and illustrating its utility in soil hydrology. An explicit integral representation is constructed, considering different types of initial conditions. Numerical examples are presented to demonstrate the accuracy of the solution, with direct comparisons to the classical Fourier series approach. Full article