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To investigate diquark correlation in baryons, the baryon spectra with different light–heavy quark combinations are calculated using Gaussian expansion method within both the naive quark model and the chiral quark model. By computing the diquark energies and separations between any two quarks in baryons, we analyze the diquark effect in the $ud$-q/Q, $us$-Q, $ss$-q/Q, and $QQ$-q/Q systems (where $q=u,d$, or s; $Q=c,b$). The results show that diquark correlations exist in baryons. In particular, for $qq$-Q and $QQ$-q systems, the same type of diquark exhibits nearly identical energy and size across different baryons. In the orbital ground states of baryons, scalar–isoscalar diquarks have lower energy and a smaller size compared to vector–isovector diquark, which qualifies them as “good diquarks”. In $QQ$-q systems, a larger mass of Q leads to a smaller diquark separation and a more pronounced diquark effect. In $qq$-Q systems, the separation between the two light quarks remains larger than that between a light and a heavy quark, indicating that the internal structure of such diquarks must be taken into account. A comparison between the naive quark model and the chiral quark model reveals that the introduction of meson exchange slightly increases the diquark size in most systems. Full article

This study is designed to investigate the dynamic risk transmission processes between clean energy ETFs and ESG indices in the BRICS countries—Brazil, India, China, and South Africa—while excluding Russia due to the lack of consistent data availability during the study period, which coincides with the Russia–Ukraine conflict. The analysis is conducted on daily data obtained from DataStream, spanning from 27 October 2021 to 5 January 2024. By applying a time-varying parameter vector autoregression (TVP-VAR) modeling framework, we considered examining the global market conditions and economic shocks’ effects on these indices’ interconnectedness, including COVID-19 and geopolitical tensions. In this context, clean energy ETFs turned out to stand as net shock transmitters throughout volatile market spans, while ESG indices proved to act as net receivers. Moreover, we undertook to estimate both of the minimum variance and minimum connectedness portfolios’ hedging efficiency and performance. The findings highlight that introducing clean energy indices into investment strategies helps boost financial outcomes while maintaining sustainability goals. Indeed, the minimum connectedness portfolio consistently delivers superior risk-adjusted returns across varying market circumstances. In this respect, the present study provides investors, regulators, and policymakers with practical insights. Investors may optimize their portfolios by integrating clean energy and ESG indexes, useful for achieving financial and sustainability aims. Similarly, regulators might apply the findings to establish reliable green investment norms and strategies. Thus, this work underscores the crucial role of dynamic portfolio management in optimizing risk and return in the globally evolving green economy. Full article

A spacecraft is a typical rigid–flexible–thermal coupled multibody system, and the study of such rigid–flexible–thermal coupled systems has important engineering significance. The dissipation effect of material damping has a significant impact on the response of multibody system dynamics. Owing to the increasing multitude of computational dimensions, computational efficiency has remained a significant bottleneck hindering their practical applications in engineering. However, due to the fact that the stiffness matrix is a highly nonlinear function of generalized coordinates, traditional methods of modal truncation are difficult to apply directly. In this study, the absolute nodal coordinate formulation (ANCF) is used to uniformly describe the modeling of rigid–flexible–thermal coupled multibody systems with large-scale motion and deformation. The constant tangent stiffness matrix and damping matrix can be obtained by locally linearizing the dynamic equation and heat transfer equations, which are based on the Taylor expansion. The dynamic and heat transfer equations obtained by reducing the order of complex modes are transformed into a unified first-order equation, which is solved simultaneously. The orthogonal complement matrix of the constraint equation is proposed to eliminate the nonlinear constraints. A strategy based on energy preservation was proposed to update the reduced-order basis vectors, which improved the calculation accuracy and efficiency. Finally, a systematic method for rigid–flexible–thermal coupled viscoelastic multibody systems via modal truncation with complex global modes is developed. Full article

The escalating global demand for primary energy—still predominantly met by conventional carbon-based fuels—has led to increased atmospheric pollution. This underscores the urgent need for alternative energy strategies capable of reducing carbon emissions while meeting global energy requirements. Hydrogen, as a clean combustible fuel, offers a promising alternative to hydrocarbons, producing neither soot, CO₂, nor unburned hydrocarbons. Although nitrogen oxides (NO_x) are the primary combustion by-products, their formation can be mitigated by controlling flame temperature. This study investigates the viability of hydrogen as a clean energy vector by simulating an unsteady, turbulent, non-premixed hydrogen jet flame interacting with an air co-flow. The numerical simulations employ the Unsteady Reynolds-Averaged Navier–Stokes (URANS) framework for efficient and accurate prediction of transient flow behavior. Turbulence is modeled using the Shear Stress Transport (SST k-ω) model, which enhances accuracy in high Reynolds number reactive flows. The combustion process is described using a presumed Probability Density Function (PDF) model, allowing for a statistical representation of turbulent mixing and chemical reaction. The simulation results are validated by comparison with experimental temperature and mixture fraction data, demonstrating the reliability and predictive capability of the proposed numerical approach. Full article

This paper deals with static stability in planar grasps of an object by multiple fingers. Differently from previous research, we focus on the case that each finger has redundant links and joints. Based on contact constraints between the object and fingers, the relationships among displacements of object’s pose, contact positions, and joint positions are formulated. Using the constraints, the redundant joints are reduced to independent parameters. The relationship between the displacement and reaction torque of each joint is modeled as a linear spring, and potential energy of the grasp is formulated. Not only for frictionless sliding contact but also for pure rolling contact, we derive stable conditions on the contact positions and joint positions. Based on the conditions, partially differentiating the potential energy, a wrench (force and moment) vector and a stiffness matrix applied to the object by each finger are derived. Summing up the wrenches and matrices of all the fingers, we obtain a wrench vector and a stiffness matrix of the grasp, and we evaluate the grasp stability. Because of our analytical formulation, grasp parameters such as local curvatures at contact points, joint stiffnesses, etc., are explicitly included in the derived matrices. Partially differentiating the wrenches and matrices by the grasp parameters, we clarify effects of the parameters on the stability. Moreover, the difference between the frictionless sliding contact and pure rolling contact is derived in the wrench vector and the stiffness matrix. Using numerical examples, we validate our analysis. Full article

The increasing reliance on renewable energy sources, such as photovoltaic (PV) systems, is pivotal for achieving sustainable development and addressing global energy challenges. However, short-term power forecasting for distributed PV systems often faces accuracy limitations, hindering their efficient grid integration. To address this, a novel hybrid prediction model is proposed, combining multivariate variational mode decomposition (MVMD) with a gated recurrent unit (GRU) network, an attention mechanism (ATT), and an enhanced vector weighted average algorithm (cINFO). The MVMD first decomposes historical data to reduce volatility. The INFO algorithm is then improved by integrating the crested porcupine optimizer (CPO), forming the cINFO algorithm to optimize GRU-ATT hyperparameters. An attention mechanism is incorporated to accentuate key influencing factors. The model was evaluated using the DKASC Alice Springs dataset. Results demonstrate high predictive accuracy, with mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) values of 0.0249, 0.0693, and 99.79%, respectively, under sunny conditions, significantly outperforming benchmark models. This confirms the model’s feasibility and superiority for short-term PV power forecasting. Full article

Thermal bridging through cold-formed steel (CFS) studs significantly reduces the thermal performance of light gauge steel frame (LSF) wall systems, particularly in climates demanding higher thermal resistance (R-value). While thermal breaks are commonly used, they increase material costs and construction complexity. According to NCC 2022, the minimum total R-value requirement for external walls ranges between 2.8 and 3.8 m²·K/W depending on the climate zone and building class. This study therefore evaluated web-perforated steel studs as a passive strategy to enhance thermal resistance of LSF walls, analysing 120 configurations with validated 3D finite element models in Abaqus and benchmarking in THERM. The results showed that web perforations consistently improved R-values by 14 to 20%, as isotherm contours and heat flux vectors demonstrated disruption of direct heat flow through the stud, thereby mitigating thermal bridging. Although the axial compression capacity of web-perforated CFS studs decreased by 29.5%, the use of 4 mm hole-edge stiffeners restored 96.8% of the original capacity. The modified NZS 4214:2006 and ASHRAE Modified Zone methods, incorporating steel area reduction and heat flux redistribution, closely matched Abaqus predictions, with coefficients of variation (COV) below 0.009, corresponding to less than 1% relative deviation between analytical and numerical R-values. Furthermore, application of web-perforated CFS studs in five external wall systems demonstrated improved thermal resistance, ensuring compliance with NCC 2022 R-value requirements across all Australian climate zones. Overall, the findings establish web-perforated studs as an effective solution for improving the energy performance of LSF building envelopes. Full article

The growing energy demand has increased the number of wind turbines, raising the need to monitor blade health. Since blades are prone to damage that can cause severe failures, early detection is crucial. Machine learning-based monitoring systems can identify and locate cracks without interrupting energy production, enabling timely maintenance. This study provides a comparative analysis and approach to the application and effectiveness of different vibration-based machine learning algorithms to detect the presence of cracks, identify the cracked blade, and locate the zone where the crack occurs in rotating blades of a small wind turbine. The datasets comprise root vibration signals, derived from healthy and cracked blades of a wind turbine in operational conditions. In this study, the blades are not considered identical. The sampling set dimension and the number of features were variables considered during the development and assessment of different models based on decision tree (DT), support vector machine (SVM), k-nearest neighbors (KNN), and multilayer perceptron algorithms (MLP). Overall, the KNN models are the clear winners in terms of training efficiency, even as the sample size increases. DT is the most efficient algorithm in terms of test speed, followed by SVM, MLP, and KNN. Full article

In this paper, an energy efficiency (EE)–spectral efficiency (SE) trade-off scheme is investigated for the distributed reconfigurable intelligent surface (RIS)-assisted backscatter vehicle-to-infrastructure (V2I) communication system. Firstly, a multi-objective optimization framework balancing EE and SE is established using the linear weighting method, and the quadratic transformation is utilized to recast the optimization problem as a strictly convex problem. Secondly, an alternating optimization (AO) approach is applied to partition the original problem into two independent subproblems of the BS and RIS beamforming, which are, respectively, designed by the weighted minimization mean-square error (WMMSE) and the Riemannian conjugate gradient (RCG) algorithms. Finally, according to the trade-off factor, the power reflection coefficients of backscatter devices (BDs) are dynamically optimized with the BS beamforming vectors and RIS phase shift matrices, considering their activation requirements and the vehicle minimum quality of service (QoS). The simulation results verify the effectiveness of the proposed algorithm in simultaneously improving SE and the EE in practical V2I applications through rational optimization of the BD power reflection coefficient. Full article

Parameter estimation for solar photovoltaic panels is a popular research topic in green energy. Model parameters can be used for fault diagnosis in solar panels. Artificial neural network (ANN) approaches have been developed to estimate the model parameters of solar panels. In this study, an ANN and Adaptive Particle Swarm Optimization (APSO) approach for model parameter estimation of solar panel is proposed. Load perturbation is injected at the output of the solar PV panel, and the load voltage and current time series are measured. The current and voltage vectors are used as inputs for an ANN, which is used as a classifier for the ranges of the model parameters. The population of the APSO is initialized according to the results of the ANN classifier, and the APSO algorithm is then used to estimate the model parameters of the PV panel. Simulations and experimental studies show that the proposed method has better performance than conventional PSO, and it requires a smaller number of generations to achieve an average parameter estimation error of less than 5%. Full article

In the present research note, we explore the nature of the conformal Ricci solitons on the energy–momentum squared gravity model $\mathcal{F}(\mathcal{R},{\mathcal{T}}^2)$ that is a modification of general relativity. Furthermore, we deal with a subcase of the $\mathcal{F}(\mathcal{R},{\mathcal{T}}^2)=\mathcal{R}+\lambda {\mathcal{T}}^2$-gravity model coupled with a perfect fluid, which admits conformal Ricci solitons with a time-like concircular vector field. Using the steady conformal Ricci soliton, we derive the equation of state for the perfect fluid in the $\mathcal{F}(\mathcal{R},{\mathcal{T}}^2)$-gravity model. In this series, we convey an indication of the pressure and density in the phantom barrier period and the stiff matter era, respectively. Finally, using a conformal Ricci soliton with a concircular vector field, we study the various energy constraints, black holes, and singularity circumstances for a perfect fluid coupled to $\mathcal{F}(\mathcal{R},{\mathcal{T}}^2)$-gravity. Lastly, employing conformal Ricci solitons, we formulate the first law of thermodynamics, enthalpy, and the particle production rate in $\mathcal{F}(\mathcal{R},{\mathcal{T}}^2)$-gravity and orthodox gravity. Full article

This study presents the design and experimental evaluation of a 10 MHz voltage regulator module (VRM) that incorporates a solenoid inductor embedded within a printed circuit board (PCB). To verify the performance of the inductor, a test PCB was fabricated and characterized using a vector network analyzer (VNA), with measurement data processed through 2x-thru de-embedding technique. A 10 MHz VRM was then implemented to assess the impact of the embedded inductor on system efficiency. Comparative measurements were conducted between two VRMs—one employing a surface-mounted (SMT) inductor and the other a PCB-embedded inductor. The SMT-based system achieved a peak efficiency of 65.24% at a load current of 800 mA, whereas the PCB-embedded inductor version reached 70.43% at 900 mA, reflecting an improvement of 5.19%. The VRM with an embedded inductor experienced less efficiency degradation under heavy load conditions, demonstrating superior energy delivery stability. These findings confirm the practical benefits of integrating solenoid inductors within a PCB for high-frequency, high-efficiency power conversion. Full article

WSNs consist of numerous energy-constrained Sensor Nodes (SNs), making energy efficiency a critical challenge. This paper presents a novel multipath routing model designed to enhance network lifetime by simultaneously optimizing energy consumption, node connectivity, and transmission distance. The model employs an Improved Particle Swarm Optimization (IPSO) algorithm to dynamically determine the optimal weight coefficients of a cost function that integrates three parameters: residual energy, link reliability, and buffer capacity. A compressed Bloom filter is incorporated to improve packet transmission efficiency and reduce error rates. Simulation experiments conducted in the NS2 environment show that the proposed approach significantly outperforms existing protocols, including Reinforcement Learning Q-Routing Protocol (RL-QRP), Low Energy Adaptive Clustering Hierarchical (LEACH), On-Demand Distance Vector (AODV), Secure and Energy-Efficient Multipath (SEEM), and Energy Density On-demand Cluster Routing (EDOCR), achieving a 7.45% reduction in energy consumption and maintaining a higher number of active nodes over time. Notably, the model sustains 19 live nodes at round 800, whereas LEACH and APTEEN experience complete node depletion by that point. This adaptive, energy-aware routing strategy improves reliability, prolongs operational lifespan, and enhances load balancing, making it a promising solution for real-world WSN applications. Full article

This study tackles the issue of ensuring secure communications in vehicular ad hoc networks (VANETs) under dynamic eavesdropping threats, where eavesdroppers adaptively reposition to intercept transmissions. We introduce a scheme utilizing a partitioned reconfigurable intelligent surface (RIS) to assist in the joint transmission of confidential signals and artificial noise (AN) from a source station. The RIS is divided into segments: one enhances legitimate signal reflection toward the intended vehicular receiver, while the other directs AN toward eavesdroppers to degrade their reception. To maximize secrecy performance in rapidly changing environments, we introduce a joint optimization framework integrating meta-learning for RIS partitioning and reinforcement learning (RL) for reflection matrix optimization. The meta-learning component rapidly determines the optimal RIS partitioning ratio when encountering new eavesdropping scenarios, leveraging prior experience to adapt with minimal data. Subsequently, RL is employed to dynamically optimize both beamforming vectors as well as RIS reflection coefficients, thereby further improving the security performance. Extensive simulations demonstrate that the suggested approach attain a 28% higher secrecy rate relative to conventional RIS-assisted techniques, along with more rapid convergence compared to traditional deep learning approaches. This framework successfully balances signal enhancement with jamming interference, guaranteeing robust and energy-efficient security in highly dynamic vehicular settings. Full article

Alpha therapy (TAT) relies on combining alpha-emitting radionuclides with specific cell-targeting vectors to deliver a high payload of cytotoxic radiation capable of destroying tumor tissues. TAT efficacy comes from the tissue selectivity of the targeting vector, the high linear energy transfer (LET) of the radionuclide, and the short range of alpha particles in tissues. Recent research studies have been directed to evaluate TAT on a preclinical and clinical scale, including evaluating damage to tumor tissues with minimal toxic radiation effects on surrounding healthy tissues. This review highlights the use of Actinium-225/Bismuth-213 radionuclides as promising candidates for TAT. Herein, we begin with a discussion on the production and supply of [²²⁵Ac]Ac/[²¹³Bi]Bi followed by the formulation of [²²⁵Ac]Ac/[²¹³Bi]Bi-radiopharmaceuticals using different radiolabeling techniques. Finally, we have summarized the preclinical and clinical evaluation of these potential radiotheranostic agents. Full article