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Developing countries in which infrastructure development is heavily dependent on overseas development aid face significant sustainability challenges, including financing gaps and inadequate maintenance. Increasing private-sector investment is crucial for addressing these challenges. This paper proposes an innovative framework linking environmental, social, and governance (ESG) principles with a revised joint credit mechanism (JCM) to attract private investment in infrastructure development, particularly in Pacific Island countries facing the climate crisis. Under the revised JCM, by allocating generated carbon credits to participating Japanese companies, rather than the Japanese government, corporations can monetise credits through market transactions, creating compelling economic incentives for private-sector engagement. In ESG-advanced markets, credits serve as strategic instruments for corporate value enhancement beyond revenue generation, while corporations require continuous credit acquisition to sustain investor confidence. Our revised framework provides a sustainable solution to both financing gaps and infrastructure maintenance challenges. Our analysis demonstrates that integrating market dynamics and corporate incentives into bilateral climate mechanisms holds substantial potential for mobilising private capital for sustainable climate infrastructure finance. This approach represents a promising departure from traditional donor-dependent models, effectively aligning corporate interests with sustainable development objectives while advancing national emission reduction commitments. Full article

This study proposes a canopy-adaptive TAD-IRRT* (target-biased sampling, artificial potential field, and dynamic step-size informed rapidly-exploring random tree star) algorithm to solve the collision-free 3D path-planning problem for a 6-DOF apple-harvesting robotic arm. To improve computational speed and search directionality, the method integrates target-biased sampling and a distance-regulated artificial potential field (APF) into the Informed-RRT* framework. Furthermore, an obstacle-distance-based dynamic step-size mechanism is introduced to optimize spatial exploration. The generated routes undergo greedy path pruning and cubic B-spline smoothing to ensure kinematic executability. The simulation results in complicated ROS-based scenarios demonstrate that the TAD-IRRT* algorithm achieves a 100% planning success rate, reducing the average computational time and joint-space path length by approximately 60.1% and 15.6%, respectively, compared to the standard Informed-RRT*. Kinematic analysis via Fourier curve fitting ($R^2=0.9849$) confirms continuous angular velocity and acceleration without high-frequency chattering. Physical prototype experiments in the dense-obstacle scenarios show that the proposed method increases the path execution success rate by 36.7% and reduces the average execution time by 41% compared to the standard Informed-RRT* algorithm. The proposed approach effectively balances high-quality path generation with low computational overhead, providing a reliable and safe solution that significantly reduces mechanical wear. Full article

Precision Livestock Farming (PLF) requires real-time control systems that connect high-level Decision Support Systems with resource-constrained edge devices. This paper presents a hybrid Multi-Agent System (MAS) architecture for swine precision feeding designed to address the trade-off between semantic interoperability and real-time operational efficiency. The proposed Controlling Module uses a dual-layer communication strategy: a lightweight character-delimited TCP/IP protocol ensures deterministic performance for embedded controllers, while an XML-serialized format that maps to the FIPA Agent Communication Language preserves semantic interoperability. A custom serialization/deserialization algorithm was developed to process this XML structure within LabVIEW while avoiding the overhead typically associated with generic DOM/SAX parsers. The architecture was validated in a 120 h laboratory test that combined a Digital Twin simulation of 50 virtual feeders with Hardware-in-the-Loop testing of key sensing components. Under these test conditions, no communication failures were observed, all simulated network interruptions were recovered from, and the system operated with a modest resource footprint, including an average CPU use of 15% and a peak memory use of 350 MB. The platform also processed 2590 consumption events without reported data loss during the validation period. These results indicate that the proposed hybrid MAS architecture is a feasible solution for integrating interoperable decision support and deterministic edge control in PLF applications. Full article

Superradiance is a scattering process in which incident waves are amplified by a scatterer, such as a black hole, leading to the extraction of energy from the system. In this work, we study superradiant scattering within Bopp–Podolsky electrodynamics, an extension of Maxwell electrodynamics that introduces higher-derivative terms in the electromagnetic field and a non-minimal coupling to curved spacetime. We analyze the propagation of scalar waves in a static, spherically symmetric wormhole geometry obtained perturbatively from the Reissner–Nordström solution of General Relativity coupled to Maxwell electrodynamics. We demonstrate that superradiant scattering occurs in this background and, through numerical analysis, find that the Podolsky parameter suppresses the amplification. Full article

In recent years, synergistic effects between inorganic clay minerals (e.g., montmorillonite, sepiolite, kaolinite) and bio-based flame retardants (e.g., chitosan-based, lignin-based, phytate-based) have achieved certain progress in the area of polymer flame retardancy. The effects of bio-based flame retardants are exerted through mechanisms such as catalytic char generation and vapour-phase hindrance. However, they have limitations when used alone, including insufficient thermal stability and the need for a high dosage. Inorganic clays form physical barriers through their layered or tubular structures. The high thermal stability of these structures suppresses heat and mass transfer, thereby offsetting the shortcomings of bio-based flame retardants. This synergistic combination greatly improves the flame retardancy of polymer composites, often strengthening their mechanical performance in the process. It therefore offers great potential for the design of multifunctional, eco-friendly flame-retardant polymer composites. Nevertheless, a systematic review of the synergistic mechanisms, fabrication approaches and application progress of different inorganic clay minerals when combined with various bio-based flame retardants is still lacking. Therefore, this article offers a comprehensive review of the current developments of synergistic systems that incorporate various primary clays, such as sepiolite and montmorillonite, with bio-based flame retardants for usage in polymers. Before this, the synergistic flame-retardant mechanism and the key preparation techniques of the composite system were explained in detail. Finally, this article puts forward solutions to the current challenges and sets out prospects for innovation in the designing of flame-retardant materials and the optimisation of processes. The aim is to promote the sustainable growth of efficient, eco-friendly flame-retardant materials. Full article

To improve the sustainability of intelligent building operation and enhance grid adaptability in the presence of uncertainty, this paper presents a coordinated optimization method that jointly exploits virtual energy storage and waste heat recovery. A thermal modeling framework is developed to represent the coupling relationships among air conditioning operation, waste heat utilization, and indoor comfort requirements. On this basis, building thermal inertia is incorporated into an IDM-informed two-stage robust optimization framework, where distributional bounds derived from the Imprecise Dirichlet Model are transformed into data-driven interval uncertainty sets for wind–photovoltaic output and outdoor temperature. To make the model computationally tractable, the column-and-constraint generation method is employed for iterative solution. Numerical results verify that the proposed method can effectively unlock the flexibility of the cooling system and improve the utilization of recoverable heat resources while maintaining acceptable indoor comfort, even under adverse operating conditions. Overall, the proposed strategy strengthens system resilience, reduces carbon-related operational pressure, and provides more dependable demand-side support for secure power system operation. Full article

This paper presents a linguistically constrained neural approach to morphological segmentation for low-resource Turkic languages, with a case study on Turkmen. The proposed method combines large-scale training data generated by a Complete Set of Endings (CSE) model with a neural architecture augmented with explicit phonological inductive biases. Unlike prior FEMSeg-based architectures that rely on convolutional and Transformer layers for implicit feature learning, the proposed model, LCMSeg (Linguistically Constrained Morphological Segmentation), introduces vowel/consonant indicators and harmony-class embeddings, both of which are directly derived from linguistic rules. The constraints are implemented as inductive biases. The CSE framework serves as a data-generation mechanism, producing a segmented corpus of 270k sentences used for training. The neural model learns to approximate the segmentation function induced by the CSE annotations while generalizing beyond the limitations of rule-based methods. Experiments conducted on training sets of 10k to 80k sentences demonstrate consistent improvements, achieving up to 99.76% token accuracy and 99.53% morpheme accuracy. Evaluation on the FLORES-200 benchmark confirms strong generalization under domain shift, with harmony consistency reaching 98.9%. The results show that explicitly encoding phonological structure provides a strong inductive bias, particularly beneficial in low-resource settings. The proposed framework offers a scalable and linguistically grounded solution for morphological segmentation in Turkic languages. Full article

In this paper, we investigate the existence and positivity of solutions for a class of twelfth-order nonlinear boundary value problems that naturally arise in the mathematical modeling of elastic and micro-mechanical systems. The considered model incorporates higher-order derivatives to account for nonlocal and gradient effects that commonly appear in the analysis of micro- and nano-scale elastic structures. By employing the Leray–Schauder nonlinear alternative and fixed point theorems, we establish sufficient conditions for the existence of at least one positive solution. The analysis relies on the explicit construction and properties of the associated Green’s function, which plays a fundamental role in deriving upper and lower bounds for the nonlinear term. The obtained results extend and generalize earlier works on sixth, eighth and tenth-order problems to the twelfth-order case. Finally, numerical examples are presented to illustrate the applicability and accuracy of the theoretical findings. The results provide a rigorous analytical foundation for the study of high-order elastic models and micro-scale structural stability. Full article

The power flow problem is one of the most challenging tasks in power systems, affecting both generation cost and energy quality. Optimal power flow (OPF) further complicates this task by requiring the optimal adjustment of system variables and parameters. This paper adapts the Modified Effective Butterfly Optimizer (MEBO) to solve multi-objective optimal power flow (MOOPF) problems with the contribution of optimized weighting using multiple Pareto archives. MEBO is an advanced optimization algorithm that utilizes population reduction and parameter learning to guide subsequent searches for unconstrained problems. The proposed technique has been tested on IEEE 30 and 57 bus test systems, and the results have been compared with existing methods reported in the literature. In the paper, four single-objective functions, namely generator cost, active power loss, fuel emission, and voltage deviation, are used to construct four multi-objective (MO) problems: cost–loss, cost–voltage, cost-emission, and emission–loss. For the cost-emission case, the proposed MEBO achieved compromised solutions of 791.1951 $/h fuel cost with 0.10873 ton/h emission and 801.8172 $/h fuel cost with 0.10044 ton/h emission under different Pareto-based optimization metrics. In the emission–loss case, the algorithm obtained 0.20539 ton/h emission with 3.1403 MW/h power loss, demonstrating the effectiveness of the proposed approach in balancing conflicting objectives. The Pareto curves of MEBO in achieving MO problems are presented, along with the suggested compromised solutions acquired from the literature. In the literature, this is the first application of MEBO for solving MOOPF problems. The results demonstrate that MEBO performs better than most other alternatives; this shows potential for further improvements with respect to the MOOPF problem. Full article

High-dimensional feature selection aims to identify compact and discriminative feature subsets from large feature spaces. In multi-objective feature selection (MOFS), this task remains challenging because the search space grows exponentially with dimensionality, and conventional binary evolutionary operators may generate ineffective perturbations in sparse high-dimensional spaces. To address these issues, this paper proposes CORAL, a rank-memory search framework for MOFS. CORAL uses a joint continuous score–cardinality representation to model feature priorities and subset sizes and applies Top-K decoding to obtain binary feature subsets. A rank-memory mechanism is introduced to extract feature occurrence information from elite solutions and guide score-space variation. In addition, elite local refinement and feature-number-stratified environmental selection are used to refine candidate subsets and maintain solutions across different sparsity regions. Experiments on 18 benchmark classification datasets show that CORAL achieves balanced performance in terms of solution-set quality, test classification performance, feature compactness, and computational efficiency. Ablation results further demonstrate the complementary roles of rank memory, elite local refinement, and stratified environmental selection. Full article

This paper presents a multibeam hybrid beamforming (MHBF) architecture for microwave power transfer (MPT), enabling wireless power delivery to multiple receivers with a reduced number of RF chains. The proposed architecture decouples beam control into the horizontal and vertical dimensions, where horizontal multibeams are generated in the baseband through digital precoding, while the vertical beam direction is controlled by a Butler-matrix-based analog beamformer. In particular, multibeam transmission is achieved using multi-tone signals with distinct phase weights assigned to each tone, enabling beams to be steered toward different directions, while the Butler-matrix-based analog beamformer provides vertical beam-steering capability. Compared with fully digital beamforming (DBF), MHBF enables simultaneous multibeam formation in the horizontal domain with fewer RF chains, thereby reducing hardware overhead and system complexity. To validate the proposed architecture, a 5.8 GHz prototype was designed and fabricated. The experimental results demonstrate three-beam and four-beam operation under a transmit power of 30.57 dBm, while the average received RF power in the single-beam case was 12.11 dBm at a distance of 1 m. In the three-beam and four-beam cases, average received RF power levels of 7.3 dBm and 6.1 dBm per beam were achieved, respectively. RF-to-DC conversion measurements under 430 $\Omega$ and 680 $\Omega$ load conditions further showed average PCE values of up to 38.77% and 35.05% for the three-beam and four-beam cases, respectively. These results confirm the feasibility of simultaneous multibeam wireless power delivery and its potential as an effective solution for multi-receiver operation with reduced RF-chain requirements. Full article

This paper presents new optimal eighth-order families with weight functions for solving nonlinear systems, obtained as a generalization of the first optimal eighth-order CTT8 method introduced by Cordero, Torregrosa and Triguero-Navarro. The proposed schemes are constructed by combining a Newton-type predictor with high-order correction steps whose weight functions are suitably chosen to preserve optimal convergence while keeping a low computational cost. To the best of our knowledge, this work introduces the first family of optimal eighth-order methods for nonlinear systems, in the sense of the Cordero–Torregrosa conjecture, developed through a weight-function technique. A complete local convergence analysis is carried out under standard smoothness assumptions, proving eighth-order convergence for nondegenerate solutions. The computational efficiency of the proposed methods is also studied and compared with several existing high-order iterative schemes. Numerical experiments on nonlinear systems of different dimensions confirm the theoretical order of convergence and show the robustness of the new families. In addition, a Fredholm integral equation is solved, followed by a semilinear elliptic Dirichlet problem, further illustrating the reliability and computational performance of the proposed weight-function-based methods. Full article

Aiming at the deterioration in Noise, Vibration and Harshness (NVH) performance caused by broadband torsional vibration in the integrated electric drive system (IEDS) of electric vehicles, most existing studies independently focus on electromagnetic excitation suppression or torsional vibration control of mechanical transmissions. Few researchers consider the coupling characteristics between the electromagnetic nonlinearity of motors and the nonlinearity of gear transmissions, making it difficult to realize the coordinated suppression of high- and low-frequency torsional vibration. In this paper, a seven-degree-of-freedom electromechanical coupling dynamic model is firstly established, which incorporates the electromagnetic torque ripple of the motor, the time-varying meshing stiffness of gears, meshing errors, and gear backlash nonlinearity. Through modal analysis and Campbell diagram solution, the natural characteristics and critical speed range of the system are clarified, and the generation mechanism of full-frequency band torsional vibration as well as the high–low frequency coupling characteristics are systematically revealed. On this basis, a coordinated active control strategy based on PD pole placement and harmonic current injection (PD-HCI) is proposed. The PD pole placement controller is adopted to suppress the low-frequency torsional vibration (0–20 Hz) of the transmission system, and the 5th/7th harmonic current injection is used to counteract the high-frequency torque ripple (above 200 Hz) of the motor, thereby achieving the coordinated suppression of broadband torsional vibration. The Matlab/Simulink R2023a simulation results show that the proposed control strategy reduces the torque fluctuation rate from 3.11% to 1.96%, the speed fluctuation rate from 0.10% to 0.03%, and the total harmonic distortion (THD) of stator current from 8.69% to 1.77% under steady-state operating conditions. Under transient operating conditions with sudden load changes, the stabilization time of fluctuations in speed and half-shaft torque is shortened by more than 80%, the impact amplitude is significantly reduced, and there is no loss in the vehicle’s dynamic response and speed tracking performance. Experimental results show that the coefficients of determination R2 of vehicle speed, motor speed, acceleration and torque are 0.9990, 0.9982, 0.9997 and 0.9997, respectively, which verifies the reliability of the established model. Full article

Aiming at the problems of glare interference, local overexposure and detail loss caused by artificial light sources such as vehicle lamps and street lamps in nighttime road scenes, as well as the challenges of existing glare suppression models with large parameters, high computational complexity and difficulty in deploying on edge devices, this paper proposes a lightweight glare suppression network (LGSNet) based on ghost depthwise separable convolution and Lightweight Parallel Attention. Based on the U-Net architecture, the network introduces ghost depthwise separable convolution blocks (GhostDSC) in the encoder and decoder, which generates ghost features through cheap linear transformations by exploiting feature map redundancy, significantly reducing model parameters and computational costs while maintaining feature representation ability. Meanwhile, a Lightweight Parallel Attention (LPA) module is designed in the decoder stage, which integrates channel attention and pixel attention in parallel, enhancing the network’s attention to glare regions and edge details with extremely low parameter increment to improve detail recovery accuracy. In addition, a joint loss function consisting of background loss, glare loss and reconstruction loss is constructed to collaboratively optimize glare suppression and detail preservation. Experimental results on the public Flare7K++ dataset and the self-built nighttime road glare dataset NRGD show that the proposed method has only 7.45 M parameters, much lower than standard U-Net and Uformer. It achieves competitive results on full-reference metrics such as PSNR, SSIM, LPIPS and no-reference metrics such as NIQE, BRISQUE, PIQE, and can effectively suppress various types of glare interference and restore obscured scene details. It achieves a superior trade-off between model complexity and enhancement performance, significantly reducing the parameter count and computational overhead compared to heavy baselines, thereby offering a highly efficient solution for resource-aware glare suppression tasks. Full article

Fractured–vuggy carbonate reservoirs are characterized by highly discrete storage structures, and the number, spatial distribution, and volume of cavities strongly affect well-test responses and reservoir development decisions. This study develops a PEBI-grid finite-volume implementation of a wave–seepage-coupled model for pressure-transient interpretation in reservoirs containing irregular cavities. The objective is not to introduce a new general-purpose finite-volume method but to embed irregular cavities as special control volumes into a locally orthogonal PEBI grid so that the cavity volume, geometry, and well–cavity distance can be represented explicitly in bottom-hole pressure calculations. The model is formulated as a thickness-averaged two-dimensional system in which the fracture–matrix region is treated as an equivalent seepage continuum, and each cavity is assigned a spatially uniform pressure governed by a wave–seepage exchange relation. For the limiting case of zero cavity volume, the numerical bottom-hole pressure agrees closely with the analytical solution and the material-balance estimate. A further cylindrical-cavity benchmark against an analytical wave–seepage solution gives a pressure-drawdown relative L2 error of 4.38%, where the relative L2 error denotes the Euclidean norm of the pressure error vector normalized by that of the reference solution, providing additional validation of the cavity-coupled formulation. Sensitivity analysis shows that increasing the cavity volume delays the characteristic extrema of the pressure derivative and strengthens the contrast between the minimum and maximum, whereas increasing the well–cavity distance mainly shifts the onset of the cavity-dominated response and weakens its amplitude. A field pressure-buildup case from the Fuyuan oilfield is interpreted using the proposed workflow. The matched model indicates a pentagonal cavity with a volume of 169,770 m³, a well–cavity distance of 158.4 m, a permeability of 5.535 md, and an initial reservoir pressure of 86.66 MPa. The results demonstrate that the proposed PEBI-FVM wave–seepage-coupled model can support practical well-test interpretation of irregular cavities, while its reliability depends on the validity of the equivalent-continuum and uniform-cavity-pressure assumptions. Full article