Search Results (1,104)

This study introduces a new integral operator $I_{p,\mu }^{\delta }$ that extends the traditional Rafid operator to meromorphic p-valent functions. Using this operator, we define and investigate two new subclasses: ${\Sigma }_p^{+}\left(\delta ,\mu ,\alpha \right)$, consisting of functions with nonnegative coefficients, and ${\Sigma }_p^{+}\left(\delta ,\mu ,\alpha ,c\right)$, which further fixes the second positive coefficient. For these classes, we establish a necessary and sufficient coefficient condition, which serves as the foundation for deriving a set of sharp results. These include accurate coefficient bounds, distortion theorems for functions and derivatives, and radii of starlikeness and convexity of a specific order. Furthermore, we demonstrate the closure property of the class ${\Sigma }_p^{+}\left(\delta ,\mu ,\alpha ,c\right)$, identify its extreme points, and then construct a neighborhood theorem. All the findings presented in this paper are sharp. To demonstrate the practical utility of our symmetric operator paradigm, we apply it to a canonical fractional electrodynamics problem. We demonstrate how sharp distortion theorems establish rigorous, time-invariant upper bounds for a solitary electrostatic potential and its accompanying electric field, resulting in a mathematically guaranteed safety buffer against dielectric breakdown. This study develops a symmetric and consistent approach to investigating the geometric characteristics of meromorphic multivalent functions and their applications in physical models. Full article

This paper presents a comprehensive analysis of the dynamic properties of low-power current transformers (LPCTs) in the context of their application in both power systems and electromechanical systems. Momentary changes in external loads occurring in the mechanical parts of systems, affecting their correct operation, cause the appropriate monitoring and control systems, including LPCTs, to operate in transient states where dynamic errors are significant. The issues discussed in this article are therefore important from both an electrical and mechanical engineering perspective. The study focuses on the evaluation of dynamic errors using two complementary performance criteria: the mean squared error and the absolute dynamic error. An equivalent circuit model of the LPCT is formulated and employed to investigate its response under transient conditions representative of modern energy networks as well as electromechanical devices, including drives, converters, and rotating machines operating under variable loads. A key contribution of this work is the determination of the upper bounds of dynamic errors, which establish the ultimate accuracy constraints of LPCTs when subjected to rapid current variations. The obtained results provide quantitative evidence of the impact of dynamic properties on the reliability of current measurements, thereby reinforcing the importance of the proposed error evaluation framework. In this context, the study demonstrates that a rigorous assessment of dynamic errors is essential for improving the functional performance of LPCTs, particularly in applications where steady-state accuracy must be complemented by a reliable transient response. Full article

Safety is a central concern in aviation, where aircraft operations involve complex processes and interactions exposed to multiple hazards. Addressing these hazards requires systematic risk management and the selection of effective safety measures. This study introduces a novel hybrid multi-criteria decision-making (MCDM) framework that integrates the grey Delphi method, the grey Analytic Hierarchy Process (AHP), and the grey Axial-Distance-Based Aggregated Measurement (ADAM) method. The framework provides a rigorous engineering-based approach for evaluating and ranking safety measures under uncertainty and diverse stakeholder perspectives. Application of the model to aircraft operations demonstrates its ability to identify the most effective measures, including the development of critical infrastructure protection plans, rerouting of flight paths from high-risk areas, and strengthening of regulatory oversight. The proposed methodology advances decision-support tools in aviation safety engineering, offering structured guidance for optimizing resource allocation and improving system resilience. Full article

The application of orchard inspection robots has become increasingly widespread. How-ever, achieving autonomous navigation in unstructured environments continues to pre-sent significant challenges. This study investigates the Simultaneous Localization and Mapping (SLAM) navigation system of an orchard inspection robot and evaluates its performance using Light Detection and Ranging (LiDAR) technology. A mobile robot that integrates tightly coupled multi-sensors is developed and implemented. The integration of LiDAR and Inertial Measurement Units (IMUs) enables the perception of environmental information. Moreover, the robot’s kinematic model is established, and coordinate transformations are performed based on the Unified Robotics Description Format (URDF). The URDF facilitates the visualization of robot features within the Robot Operating System (ROS). ROS navigation nodes are configured for path planning, where an improved A* algorithm, combined with the Dynamic Window Approach (DWA), is introduced to achieve efficient global and local path planning. The comparison of the simulation results with classical algorithms demonstrated the implemented algorithm exhibits superior search efficiency and smoothness. The robot’s navigation performance is rigorously tested, focusing on navigation accuracy and obstacle avoidance capability. Results demonstrated that, during temporary stops at waypoints, the robot exhibits an average lateral deviation of 0.163 m and a longitudinal deviation of 0.282 m from the target point. The average braking time and startup time of the robot at the four waypoints are 0.46 s and 0.64 s, respectively. In obstacle avoidance tests, optimal performance is observed with an expansion radius of 0.4 m across various obstacle sizes. The proposed combined method achieves efficient and stable global and local path planning, serving as a reference for future applications of mobile inspection robots in autonomous navigation. Full article

Under the tightening carbon reduction policies, port microgrids face the challenge of optimizing the installed capacity of multiple power generation types to reduce operating costs and increase renewable energy penetration. We develop a bi-level cost-optimization framework in which the upper level decides long-term capacities (PV, wind, gas turbine, bio-fuel unit, and battery energy storage), and the lower level dispatches a multi-energy port microgrid (electricity–heat–cold) on an hourly basis with frequency regulation services. To ensure rigor and reproducibility, we (i) move the methodology upfront and formalize all constraints, (ii) provide a dedicated data–preprocessing pipeline for multi-region 50/60 Hz frequency time series, and (iii) map a policy intensity index to a carbon price and/or an annual cap used in the objective/constraints. The bi-level MILP is solved by a column-and-constraint generation algorithm with optimality gap control. We report quantitative metrics—annualized total cost, CO₂ emissions (t), renewable shares (%), and regulation cycles—across scenarios. Results show consistent cost–carbon trade-offs and robust capacity shifts toward storage and biofuel as policy tightens. All inputs and scripts are organized for exact replication. Full article

A rigorous rate-based absorption model integrated with an improved thermodynamic framework was developed to simulate natural gas desulfurization using TMS–MDEA (Tetramethylene Sulfone–Methyldiethanolamine) aqueous solutions. The model was validated against 50 sets of industrial and experimental data, achieving R² values above 0.98 and average deviations within 5%. The model was formulated for steady-state operation of a trayed absorber integrated with flash and packed-bed regeneration and applicable over industrially relevant ranges (absorber pressure 3–6.4 MPa; gas–liquid ratio 350–720; flash pressure 0.3–0.6 MPa; packing height ≥ 3 m). The results indicate that H₂S can be removed almost completely (>99.9%); CO₂ and COS achieve 70–85% and 75–83% removal, respectively; and CH₃SH removal exceeds 90% under typical conditions. Parametric analysis revealed that higher tray numbers, weir heights, and pressures enhance absorption efficiency, whereas hydrocarbon solubility increases with carbon number and is strongly affected by pressure and the gas–liquid ratio. In the desorption section, flash regeneration efficiently strips light hydrocarbons, with decreasing desorption efficiency from CH₄ to C₆H₁₄. This study provides quantitative insights into the coupled absorption–desorption process and offers practical guidance for process design, solvent selection, and energy-efficient operation in natural gas purification. Full article

This paper presents a novel B-spline wavelet-based scheme for solving multi-term time–space variable-order fractional nonlinear diffusion-wave equations. By combining semi-orthogonal B-spline wavelets with a collocation approach and a quasilinearization technique, we transform the original problem into a system of algebraic equations. To enhance the computational efficiency, we derive the operational matrix formulation of the proposed scheme. We provide a rigorous convergence analysis of the method and demonstrate its accuracy and effectiveness through numerical experiments. The results confirm the robustness and computational advantages of our approach for solving this class of fractional differential equations. Full article

Background: The hemoglobin–albumin–lymphocyte–platelet (HALP) score integrates the immunonutritional and inflammatory status. We evaluated whether baseline HALP predicts the 3-month response after lumbar medial branch radiofrequency ablation (RFA), defined as a Visual Analogue Scale (VAS) reduction of ≥50% and an Oswestry Disability Index (ODI) reduction of ≥40%, and identified a Youden-optimal cut-off. The discrimination and calibration of multivariable models were also assessed. Methods: This single-center retrospective cohort (N = 120) included rigorously selected patients (≥50% pain relief after two comparative medial branch blocks) undergoing standardized RFA. Multivariable logistic regression was adjusted for age, sex, Body Mass Index (BMI), smoking status, paraspinal tenderness, and baseline scores. We quantified the Area Under the Receiver Operating Characteristic Curve (AUC), Hosmer–Lemeshow (HL) goodness-of-fit, Brier score, and calibration slope; optimism was corrected using a 500-bootstrap method. Results: Responses occurred in 64.2% (VAS) and 65.8% (ODI) of participants. HALP independently predicted ODI (OR = 1.06, 95% CI 1.02–1.09; p < 0.001) and VAS (OR = 1.05, 95% CI 1.02–1.08; p = 0.001). As a single predictor, HALP showed fair discrimination (AUC 0.717 [VAS], 0.731 [ODI]). The Youden cut-off of 39.8 yielded high sensitivity (~0.87) with modest specificity (~0.58–0.61). Multivariable AUCs were 0.744 (VAS) and 0.774 (ODI), optimism-corrected to 0.680 and 0.720; calibration was acceptable (HL p > 0.05; slopes ≈ 0.74–0.78; Brier 0.188/0.179). Conclusions: HALP is a simple, low-cost adjunct that independently predicts short-term pain and functional outcomes after lumbar medial branch RFA. Incorporation into post-block triage may refine selection, especially for functional improvement, pending prospective external validation and recalibration of the cut-off. Full article

The aim of this study was to rigorously quantify and analyse the concentrations of atmospheric pollutants (PM₁₀, PM_2.5, SO₂, NO₂, CO, H₂S, and O₃) emitted by artisanal brick kilns in Juliaca City, Peru. The AERMOD dispersion model and a network of low-cost sensors (LCSs) were employed to characterise air quality at specific receptor sites. A georeferenced inventory of kiln operations was created to determine their parameters and operational intensity, providing a foundation for estimating emission factors and rates. Data were obtained from the United States Environmental Protection Agency (EPA) and supplemented with locally gathered meteorological records, which were processed for integration into the AERMOD model. The findings revealed that brick kilns are a principal source of atmospheric pollution in the region, with carbon monoxide (CO) emissions being especially pronounced. The LCSs facilitated the identification of pollutant concentrations at various locations and enabled the quantification of the specific contribution of brick production to ambient aerosol levels. Comparative assessments determined that these sources account for approximately 85% of CO emissions within the study area, underscoring a significant adverse impact on air quality and public health. Background pollutant levels, emission rates, spatial distributions, and concentration patterns were analysed within the assessment zones, resulting in solid model performance. These results provide a sound scientific basis for the formulation and implementation of targeted environmental mitigation policies in urban areas and the outskirts of Juliaca. Full article

Mitigating ground vibrations from sources like vehicles and construction operations poses significant challenges, often relying on computationally intensive numerical methods such as Finite Element Methods (FEM) or Boundary Element Methods (BEM) for analysis. This study addresses these limitations by developing and evaluating Machine Learning (ML) methodologies for the rapid and accurate prediction of Insertion Loss (IL), a critical parameter for assessing the effectiveness of open trenches as vibration barriers. A comprehensive database was systematically generated through high-fidelity numerical simulations, capturing a wide range of geometric, elastic, and physical configurations of a stratified geotechnical system. Three distinct ML strategies—Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forests (RF)—were initially assessed for their predictive capabilities. Subsequently, a Meta-RF stacking ensemble model was developed, integrating the predictions of these base methods. Model performance was rigorously evaluated using complementary statistical metrics (RMSE, MAE, NMAE, R), substantiated by in-depth statistical analyses (normality tests, Bootstrap confidence intervals, Wilcoxon tests) and an analysis of input parameter sensitivity. The results clearly demonstrate the high efficacy of Machine Learning (ML) in accurately predicting IL across diverse, realistic scenarios. While all models performed strongly, the RF and the Meta-RF stacking ensemble models consistently emerged as the most robust and accurate predictors. They exhibited superior generalization capabilities and effectively mitigated the inherent biases found in the ANN and SVM models. This work is intended to function as a proof-of-concept and offers promising avenues for overcoming the significant computational costs associated with traditional simulation methods, thereby enabling rapid design optimization and real-time assessment of vibration mitigation measures in geotechnical engineering. Full article

Helically coiled tube heat exchangers (HCT) are recognized as promising solutions for steam generator applications in Small Modular Reactors (SMRs), where compactness and high thermal performance are crucial. The complex geometry of HCTs, however, substantially increases the difficulty of accurately estimating pressure drops, particularly under two-phase flow conditions. Over the last decade, several predictive correlations have been suggested, and their applicability is often limited to specific ranges of geometry and operating pressure. The present study examines correlations proposed during the previous decade, aiming to clarify their applicability limits. Validation is carried out using experimental datasets from the literature, enabling a rigorous evaluation of predictive accuracy, robustness, and generality. Full article

Climate change affects both the quantity and quality of water resources, amplifying the water crisis, slowing progress toward achieving the Sustainable Development Goals (SDGs), and contributing to the needs of future generations. To address these challenges, this study presents an interdisciplinary synthesis of the literature on the subject, highlighting the impact of climate change on water resources (surface water and groundwater). The escalating global demand for water, driven by factors such as population growth, urbanization, and industrial development, is placing significant pressure on water resources. This situation needs sustainable management solutions to mitigate the environmental impacts associated with increased water consumption and climate change. The methodology included bibliometric analysis using VOSviewer version 1.6.19, a software tool for constructing and visualizing bibliometric networks, and systematic analysis according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. 155 records were used in this review from a total number of 1344 documents searched in Science Direct, Scopus and Google Scholar databases. The results indicate that research on the consequences of climate change on water quality remains in its infancy. This study highlights the effects of climate change on water quality indicators, including physicochemical, microbiological, and micropollutants, as well as the implications for human health and water supply infrastructure. Climatic factors, such as rising temperatures and changing precipitation patterns, are particularly important because they control processes fundamental to sustaining life on the planet. The main conclusions are that climate change accelerates the degradation of drinking water quality and amplifies public health risks. These findings highlight the need for rigorous assessments and the development of integrated adaptation strategies involving collaboration among water operators, decision-makers, the scientific community, and climate change specialists. Full article

This paper presents the first rigorous empirical investigation into a fundamental question of cryptocurrency valuation: Are cryptocurrency prices in line with the prices of fundamental assets? To answer this, we analyze the nine largest cryptocurrencies by market capitalization—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), Binance Coin (BNB), Ripple (XRP), Cardano (ADA), Litecoin (LTC), Tron (TRX), and the stablecoin DAI—against a suite of traditional benchmarks, including major fiat currencies (EUR, CAD, JPY), gold, and the S&P500 index. Our dataset spans from 1 January 2014 to 30 June 2025, with start dates varying for newer cryptocurrencies to ensure robust time series analysis. Guided by the asset pricing theory, we formulate a martingale test: if a cryptocurrency is priced in line with a fundamental numeraire asset, its price ratio relative to that numeraire must follow a martingale process. Our extensive empirical analysis reveals that the prices of major cryptocurrencies (BTC, ETH, SOL, BNB) consistently reject the martingale hypothesis when traditional assets (currencies, gold, equities) serve as the numeraire, indicating a decoupling from fundamental valuation anchors. Conversely, when Bitcoin or Ethereum itself is used as the numeraire, most smaller cryptocurrencies are priced in line with these crypto benchmarks, suggesting an internal valuation ecosystem that operates independently of traditional finance. Full article

Objectives: The potential use of electron density (ED) and effective atomic number (Zeff) derived from dual-energy computed tomography (DECT) as novel quantitative imaging biomarkers for differentiating malignant brain tumors was investigated. Methods: Data pertaining to 136 patients with a pathological diagnosis of brain metastasis (BM), glioblastoma, and primary central nervous system lymphoma (PCNSL) were retrospectively reviewed. The 10th percentile, mean and 90th percentile values of conventional 120-kVp CT value (CTconv), ED, Zeff, and relative apparent diffusion coefficient derived from diffusion-weighted magnetic resonance imaging (rADC: ADC of lesion divided by ADC of normal-appearing white matter) within the contrast-enhanced tumor region were compared across the three groups. Furthermore, machine learning (ML)-based diagnostic models were developed to maximize diagnostic performance for each tumor classification using the indices of DECT parameters and rADC. Machine learning models were developed using the AutoGluon-Tabular framework with rigorous patient-level data splitting into training (60%), validation (20%), and independent test sets (20%). Results: The 10th percentile of Zeff was significantly higher in glioblastomas than in BMs (p = 0.02), and it was the only index with a significant difference between BMs and glioblastomas. In the comparisons including PCNSLs, all indices of CTconv, Zeff, and rADC exhibited significant differences (p < 0.001–0.02). DECT-based ML models exhibited high area under the receiver operating characteristic curves (AUC) for all pairwise differentiations (BMs vs. Glioblastomas: AUC = 0.83; BMs vs. PCNSLs: AUC = 0.91; Glioblastomas vs. PCNSLs: AUC = 0.82). Combined models of DECT and rADC demonstrated excellent diagnostic performance between BMs and PCNSLs (AUC = 1) and between Glioblastomas and PCNSLs (AUC = 0.93). Conclusion: This study suggested the potential of DECT-derived ED and Zeff as novel quantitative imaging biomarkers for differentiating malignant brain tumors. Full article

Bellows compensators are critical components in pipeline systems, designed to absorb thermal expansions, vibrations, and pressure reflections. Ensuring their operational reliability requires accurate prediction of the stress–strain state (SSS) and stability under internal pressure. This study presents a comprehensive mathematical model for analyzing corrugated bellows compensators, formulated as a boundary value problem for a system of partial differential equations (PDEs) within the Kirchhoff–Love shell theory framework. Two numerical approaches are developed and compared: a finite difference method (FDM) applied to a reduced axisymmetric formulation to ordinary differential equations (ODEs) and a finite element method (FEM) for the full variational formulation. The FDM scheme utilizes a second-order implicit symmetric approximation, ensuring stability and efficiency for axisymmetric geometries. The FEM model, implemented in Ansys 2020 R2, provides high fidelity for complex geometries and boundary conditions. Convergence analysis confirms second-order spatial accuracy for both methods. Numerical experiments determine critical pressures based on the von Mises yield criterion and linearized buckling analysis, revealing the influence of geometric parameters (wall thickness, number of convolutions) on failure mechanisms. The results demonstrate that local buckling can occur at lower pressures than that of global buckling for thin-walled bellows with multiple convolutions, which is critical for structural reliability assessment. The proposed combined approach (FDM for rapid preliminary design and FEM for final verification) offers a robust and efficient methodology for bellows design, enhancing reliability and reducing development time. The work highlights the importance of integrating rigorous PDE-based modeling with modern numerical techniques for solving complex engineering problems with a focus on structural integrity and long-term performance. Full article