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Found 6273 publications
Thermal characteristics of magnetic blood-based hexa-hybrid nanofluids in stenotic arteries with heat source/sink by applying Caputo-Fabrizio fractional derivatives
Publication Name: Results in Surfaces and Interfaces
Publication Date: 2026-08-01
Volume: 24
Issue: Unknown
Page Range: Unknown
Description:
The current examination explores the magnetohydrodynamic flow and transport behavior of a Casson-based blood-derived hexa-hybrid nanofluid via a vertically oriented, mildly stenotic artery using a fractional-order framework. The hexa-hybrid nanofluid is formulated by dispersing Au, Cu, ZnO, Ag, MgO and TiO2 nanoparticles into blood, and the flow is considered highly pulsatile. Mathematical modelling is developed from the conservation laws of mass, momentum, and energy, followed by nondimensionalization under the mild-stenosis approximation. To extend the classical model to its fractional form, the Caputo–Fabrizio fractional derivative is incorporated, enabling closed-form analytical expressions for velocity and temperature through combined Laplace and Hankel transforms. The graphical results highlight the influence of key physical factors on velocity, temperature, and entropy production. The inclusion of hexa-hybrid nanoparticles notably enhances the thermal characteristics of blood due to the substantial rise in effective thermal conductivity. The velocity increases with higher Casson parameter values, whereas temperature decreases as the fractional-order parameter intensifies. Furthermore, entropy generation is found to rise with increasing thermodynamic parameters, while the Bejan number correspondingly decreases, reflecting dominant irreversibility effects within the system.
Open Access: Yes
Energy poverty dynamics and geostrategic shocks: Moderation of financial markets
Publication Name: Energy Policy
Publication Date: 2026-08-01
Volume: 215
Issue: Unknown
Page Range: Unknown
Description:
Universal energy poverty is a key ingredient to social inequality, education barrier and poor health outcome. Therefore, it is crucial for policymakers to identify the factors that mitigates energy poverty. Present study examined the influence of geopolitical risk on energy poverty, focusing on financial market depth, access and efficiency in 42 economies, spanning 2000 to 2022, using instrument variable two stage least square (2SLS), three stage least square (3SLS) approach and double panel threshold regression. The estimation provides following observations. First, geopolitical risk significantly intensify energy poverty over time. Second, natural disasters is a more serious hindrance to energy access. Third, financial markets significantly moderates the favourable spillover effects of geopolitical risk on energy poverty, dampens negative effect of geopolitical risk, improving household energy access, and reducing vulnerability to external shocks. Alongside this, the research provide similar pattern in urban and rural concentration, indicating the severe effect of geopolitical and natural disaster risk in rural areas. Moreover, the research explored several other factors and prioritizes digitalization, economic growth and political liberty as the major attributes for mitigating energy poverty. Hence, this research, provides stronger support for the roles of financial markets and digitalization in mitigating the energy poverty in the long run. This paper further delves into the policy implications arising from the findings.
Open Access: Yes
A Modified Metaheuristic Optimization Approach for Forecasting the Lifecycle of Rechargeable Lithium-Ion Batteries
Publication Name: Smart Grids and Sustainable Energy
Publication Date: 2026-08-01
Volume: 11
Issue: 2
Page Range: Unknown
Description:
The global shift toward renewable energy is driven by the dual imperatives of rising energy demand and the need to reduce environmental harm caused by fossil fuels. However, renewables like wind and solar power pose unique challenges, particularly due to their intermittent generation and current limitations in energy storage technologies. Battery banks, commonly used to store surplus energy, degrade over time, making accurate forecasting of their remaining usable lifecycles critical for maintaining system reliability and efficiency. This study proposes a novel approach for forecasting battery health using an optimized long short-term memory (LSTM) network. To address the complexity of deep learning hyperparameter selection, a modified metaheuristic optimization algorithm is developed and integrated into a broader optimization framework aimed at improving model performance while minimizing overfitting. The method is benchmarked against several state-of-the-art optimizers, with results validated through comprehensive simulations and statistical analysis. This work contributes a scalable forecasting methodology, an effective optimization strategy, and interpretable results to support sustainable energy storage solutions.
Open Access: Yes
Does industrial digital-real integration enhance both environmental pollution and carbon emission?
Publication Name: Gondwana Research
Publication Date: 2026-08-01
Volume: 156
Issue: Unknown
Page Range: 264-279
Description:
Clarifying how industrial digital-real integration (IDRI) drives the synergistic control of environmental pollution and carbon emissions (SCPC) is critical for advancing digital ecological civilization and implementing China's “Dual Carbon” goals. Using panel data for 285 Chinese prefecture-level cities from 2014 to 2022, this study employs panel regression models to estimate the impact of IDRI on SCPC. The results show that SCPC improved steadily across the sample period, while IDRI exhibited an “N”-shaped evolution, with substantial regional heterogeneity in both variables. IDRI is found to significantly enhance SCPC, and this effect remains robust after addressing endogeneity and conducting a battery of robustness tests. Mechanism analysis indicates that IDRI promotes SCPC through green technological innovation (GTI) and improved ecological governance capacity (EGC). Further, the vitality of environmental governance (VEG) positively moderates the IDRI–SCPC relationship. This study offers important policy implications for developing countries aiming to build a green and intelligent digital ecological civilization.
Open Access: Yes
Magneto-bioconvective stagnation point flow of a three-dimensional Casson nanofluid over a rotating Riga surface with exponential heat source: Homotopy analysis method
Publication Name: Results in Surfaces and Interfaces
Publication Date: 2026-08-01
Volume: 24
Issue: Unknown
Page Range: Unknown
Description:
The analytical results presented here not only deepen the understanding of coupled magneto-bioconvective transport phenomena but also highlight the possibility of various applications including microelectronic cooling, renewable energy systems, electromagnetic flow control, biomedical transport, microbial fuel cells, and advanced nanofluid-based thermal technologies. The present study investigates a three-dimensional Casson nanofluid flow over a Riga surface at stagnation point under the influence of an applied magnetic field, an exponential heat source, and a rotating frame. This study explores how these combined physical mechanisms influence velocity, temperature, nanoparticle concentration, and microorganism distributions. Also, it assesses whether the Homotopy analysis method (HAM) is capable of yielding precise analytical solutions for such a highly nonlinear transport model. The original nonlinear partial differential equations representing magneto-bioconvective Casson nanofluid flow are first converted to a dimensionless system of ordinary differential equations by using appropriate similarity transformations. The coupled system thus obtained is then solved analytically by the HAM. The solutions achieved through this method are checked against results from the literature to ensure their validity. The finding shows that enhancement in the Casson fluid parameter, magnetic parameter, and mass Grashof number leads to a notable decrease in velocity field as a result of increased flow resistance. In contrast, the higher Hartmann numbers produced by the Riga surface aid fluid motion via electromagnetic forcing. A stronger heat source and larger Biot number cause temperature distribution to rise, whereas thermophoresis lowers nanoparticle concentration. Also, higher activation energy affects concentration transport, but an increase in Peclet number boosts microorganism distribution and bioconvection strength.
Open Access: Yes
Optimal techno-economic framework for the design and control of off-grid solar-battery EV charging stations in Benban-Egypt
Publication Name: Journal of Energy Storage
Publication Date: 2026-07-30
Volume: 167
Issue: Unknown
Page Range: Unknown
Description:
This study presents a full design and validation method for an independent PV-battery-based Electric Vehicle (EV) charging station in Benban, Aswan. Using HOMER Pro, the system's techno-economic sizing is done. MATLAB/Simulink simulations are used to check its dynamic operation and MPPT performance. A realistic EV profile is considered (600 kWh per day, with a peak demand of 48.9 kW). Simulink uses population-based algorithms like Gazella Optimization Algorithm (GOA), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO) to improve dual PI controllers. The study looks at how well the system works for both the economy and the environment. The best setup has an Levelized Cost of Energy (LCOE) of $0.111/kWh, a Net Present Cost (NPC) of $430,468, and saves about 212 tons of CO₂ per year, with an unmet load of less than 2%. The results of the simulation show that the DC bus voltage is stable, the MPPT tracking is efficient, and the battery can be charged and discharged reliably even when the sun is not shining. In general, the results show that the proposed off-grid EV charging system is both technically sound and economically competitive for areas with high solar potential.
Open Access: Yes
Memory-sampled data controller for exponential synchronization of Markovian jump neural networks with mixed delays and partially unknown transition probabilities
Publication Name: Physica A Statistical Mechanics and Its Applications
Publication Date: 2026-07-15
Volume: 694
Issue: Unknown
Page Range: Unknown
Description:
The current study investigates the exponential synchronization (ES) problem for a class of Markovian jump neural networks (MJNNs), which are susceptible to distributive and additive time-varying delays and are managed by a memory-sampled data controller (MSDC). The transition probabilities in question are thought to be partially unknown. The information of time delay and sampling instants is captured by enhanced Lyapunov-Krasovskii functionals (LKFs). A novel modified integral inequality is utilized, which provides a potent framework for studying dynamical systems, and also made a foundation of this study. Adequate requirements for the ES of proposed system are obtained in the form of linear matrix inequalities (LMIs) by incorporating these integral inequalities. Under these circumstances, the hybrid closed-loop system's mean square input-to-state stability (ISS) is ensured. Lastly, the accuracy of the proposed ISS synchronization mechanism is verified and illustrated with numerical examples.
Open Access: Yes
Assessing the influence of financial repression on Bangladesh's financial development
Publication Name: Multidisciplinary Science Journal
Publication Date: 2026-07-08
Volume: 8
Issue: 3
Page Range: Unknown
Description:
We investigate how financial repression affects financial development of Bangladesh over the period 1980-2022. Employing VECM, we find that repression policies negatively affect financial development, meaning that controlling the financial sector counteracts financial progress. Following the results, we recommend some policies. To accelerate financial progress, policymakers need to rethink on these restrictive policy instruments. For emerging nations like Bangladesh, this paper offers the first empirical data on the connection between financial repression and financial development.
Open Access: Yes
Hybrid fertilized particle swarm optimization for engineering design with application to vibration control
Publication Name: Applied Soft Computing
Publication Date: 2026-07-01
Volume: 198
Issue: Unknown
Page Range: Unknown
Description:
Structural vibration control is a critical challenge in engineering systems such as tower cranes, where excessive oscillations compromise safety and operational accuracy. This paper proposes a Fertilized Particle Swarm Optimization (FRPSO) algorithm that hybridizes Particle Swarm Optimization with Flower Fertilization Optimization via a dual-phase global-best update to enhance the exploration–exploitation balance. FRPSO is evaluated on 26 problems (20 non-convex constrained cases, four 1000-variable large-scale benchmarks, structural optimization, and a tower crane vibration-control case study) and is compared against 18 metaheuristic optimizers. Across the benchmark suites, FRPSO achieves solution quality with consistent run-to-run stability, achieving near-optimal objective values with very low dispersion in the 1000-variable tests under the reported experimental protocol. In structural optimization, FRPSO reduces the weight of the 72-bar truss from 381.91 lb (PSO) to 379.63 lb. For the tower crane boom modeled as a 3D beam structure under transient dynamic loading, FRPSO yields designs that achieve effective vibration attenuation, as evidenced by the rapid decay of boom-tip vertical displacement responses, while satisfying stress and displacement constraints. Non-parametric statistical comparisons based on the Wilcoxon signed-rank test indicate that the observed improvements are consistent across repeated runs for selected benchmark cases. Overall, the reported results suggest that FRPSO can be effectively applied to vibration-aware structural design of crane booms and other flexible beam-type structures.
Open Access: Yes
