Search Results (43)

While green bonds are increasingly used to channel capital towards environmentally responsible projects, their effectiveness depends not only on market growth, but also on the credibility of the institutional frameworks that support the green label. Through focused bibliometric positioning, scoping synthesis and comparative document analysis, the paper examines how seven major green-bond frameworks organise credibility: the EU European Green Bond Standard, the ICMA Green Bond Principles, the Japan Green Bond Guidelines, the ASEAN Green Bond Standards, China’s catalogue-plus-principles framework, India’s Sovereign Green Bond Framework and China’s Sovereign Green Bond Framework 2025. The findings show that frameworks converge in product grammar, including project selection, management of proceeds, reporting and external review, but diverge substantially in credibility architecture, especially regarding external review, supervision, refinancing governance and environmental additionality. Current frameworks generally make green-bond labels more transparent, comparable and verifiable, but not necessarily more additional in environmental terms. Important challenges therefore remain: fragmented standards, greenwashing risks, information asymmetries, weak impact-reporting comparability and limited safeguards against refinancing existing assets. The paper argues that the contribution of green bonds to the green transition depends on how credibility is institutionally organised. Full article

Dynamic spectrum access (DSA) is an effective technology to exploit spectrum for radio devices in complex electromagnetic environments. Systematic external jamming, such as swept and comb jamming, is a common form of jamming in anti-jamming communication scenarios. Deep reinforcement learning (DRL) is widely utilized to improve the performance of DSA. However, DRL-based DSA methods face challenges in generalizing across different jamming scenarios. In this paper, a DSA scheme based on a Bootstrap ensemble deep Q-network (BEDQN) integrated with an echo state network (ESN), termed ESN-BEDQN, is proposed to achieve fast and reliable access in scenarios where jamming patterns undergo sudden changes. The ESN provides low-complexity temporal memory to capture jamming patterns, while the BEDQN maintains multiple diverse readout heads to achieve fast exploration after ESN-BEDQN reset. Moreover, a jamming pattern change prediction method based on channel idle ratio detection using symmetric KL divergence is proposed to trigger network reset, i.e., Pred-Reset. Simulation results demonstrate that the ESN-BEDQN-based scheme achieves a near-zero collision rate under periodic jamming and recovers substantially faster than conventional deep Q-network (DQN)- and long short-term memory (LSTM)-DQN-based schemes in scenarios with abrupt jamming pattern changes. Furthermore, the Pred-Reset method can correctly capture jamming pattern changes and trigger network resets, achieving faster convergence than other baseline schemes across all tested scenarios. Full article

Accurate correction of gridded numerical weather prediction precipitation forecasts remains challenging because many end-to-end deep learning correction models treat meteorological variables as undifferentiated data channels and therefore provide limited physical interpretability. Inspired by general principles of biological environmental sensing, selective information processing, and regulatory constraint learning, this study proposes PCPNet, a bio-inspired and physically constrained precipitation correction framework. The framework does not imitate a specific biological organ or species; instead, it abstracts three information-processing principles into a meteorological correction task. First, key weather-system cues, including low-level shear lines, trough-ridge effects, upper-level jet-stream forcing, vorticity-divergence-related vertical motion, and water-vapor flux convergence, are quantified as structured diagnostic fields. This transforms the subjective synoptic diagnosis of forecasters into automated grid-based sensing features. Second, these diagnostic cues are fused with numerical weather prediction variables and terrain descriptors in an encoder–attention–decoder network, allowing the model to emphasize dynamically important precipitation-triggering regions. Third, water-vapor conservation and terrain-forcing relationships are embedded as differentiable constraint losses, providing training-time constraint-based regulation that guides the corrected precipitation field toward physically consistent solutions. The method is evaluated from 2021 to 2023 in Hunan Province, China, using hourly numerical weather prediction model outputs as input features, China Meteorological Administration Land Data Assimilation System gridded analysis data as the training target, and independent meteorological station observations for strict cross-validation. PCPNet reduces the mean absolute error by 22.1% compared with the uncorrected China Meteorological Administration Land Data Assimilation System gridded precipitation products and outperforms Linear Regression, Bagging, Boosting, Multi-Layer Perceptron, TabNet, and Tree-based Progressive Regression Models by 12.9%, 13.5%, 16.9%, 10.8%, 14.9%, and 15.9%, respectively. The single-day event analysis provides an initial demonstration of heavy precipitation recovery capability, while comprehensive validation across long-term continuous weather events is planned for future operational deployment to further verify model stability. These results indicate that bio-inspired sensing and regulatory constraint learning can improve both the accuracy and interpretability of precipitation nowcasting correction. Full article

Wireless-power mobile edge computing, SWIPT-MEC, priority-aware WPT scheduling and swarm resource allocation already solve important parts of the energy-management problem. The novelty of WPT-JCCO is not any one of those elements; it is a single swarm-supervisory feasible set that couples decisions which the three adjacent method classes normally separate. Each epoch-level action jointly selects the robot to charge and one of three physically distinct WPT modalities: far-field radio-frequency, resonant near-field and directional lightwave transfer, together with the SWIPT split, local/edge task placement, CPU frequency, bandwidth and transmit power. Relative to SWIPT-MEC, the formulation adds discrete recipient–modality selection with pose, alignment, blockage and dwell-dependent feasibility. Relative to conventional WPT scheduling, charging is not a separate priority or routing stage but is solved jointly with computation and radio allocation. Relative to swarm resource-allocation methods, energy replenishment is endogenous and an individual minimum-battery constraint protects the weakest robot. A fourth coupling makes the centrally generated resource vector admissible only when the complete sense–compute–actuate age fits the one-second supervisory epoch; otherwise a previously feasible or local-safe action is applied. Nonlinear harvesting, partial offloading, priority scoring and augmented-Lagrangian primal–dual updates are treated as established techniques. This paper derives the continuous block updates, keeps the WPT variables binary through candidate screening, and declares convergence only when stationarity, feasibility, merit-change and binary-hold tests are jointly satisfied. Normalised primal steps are safeguarded by backtracking, dual and penalty updates are bounded, and a local tracking bound plus divergence monitor delimit real-time operation without claiming global mixed-integer optimality or closed-loop motion stability. Numerical evaluation over a 20-robot swarm and 30 Monte Carlo runs shows that WPT-JCCO reduces net energy depletion by 23.8% relative to communication–computation optimisation with static WPT and by 49.7% relative to local-only execution, while increasing task success from 93.5% to 97.3%. A released common-trace comparison shows normalised-cost reductions of 11.1%, 11.3% and 5.8% relative to two-stage WPT+CCO, fixed-SWIPT dynamic offloading and an offline Q-learning scheduler. Convergence and one-factor-at-a-time sensitivity studies further examine swarm size, task load, WPT budget, bandwidth, edge capacity, mobility and channel margin. The headline values remain scoped to the nominal independent-task case; mode-specific RF, near-field and lightwave operating envelopes, robust pose/CSI, WPT-safety and task-DAG extensions are formulated but not presented as hardware-validated results. Full article

Rural policy is pivotal to achieving the UN 2030 Agenda amid rapid global urbanization. This study integrates bibliometric analysis, stage-based comparative policy analysis, and quantitative SDG alignment modeling across six economies (USA, Germany, Japan, China, India, South Africa) spanning 79 rural policy documents from 1913 to 2025. Each document was scored against all 17 SDGs using a three-point ordinal scale, with AI-assisted coding validated through independent human review (inter-coder reliability: Cohen’s κ = 0.763, indicating substantial agreement prior to reconciliation). Bibliometric results document a post-2015 shift from sectoral silos to integrated sustainability frameworks. Generalized Linear Models (GLMs) identify a pattern of “aggregate convergence with structural divergence”: the year of policy enactment is the sole significant predictor of overall SDG alignment (p < 0.01), while income stage and development status show no independent effect on total scores, indicating that global discourse diffusion drives the universal rise in SDG coverage. However, per-SDG regressions demonstrate that income stage and the developed–developing divide significantly shape which specific SDGs receive attention: “late-emergence” goals scale with income, while “development-imperative” goals are systematically prioritized in developing countries. Three distinct evolutionary trajectories are proposed as interpretive constructs derived from comparative analysis: a U-shaped remedial path in developed economies, a J-shaped leapfrogging path in developing economies, and China’s unique Compressed Checkmark trajectory. A Research–Policy–Development nexus model suggests that economic stages act as a “filter” channeling governance capacity toward goals aligned with prevailing social needs. The findings suggest that developing countries may benefit from a “late-comer discursive advantage” in policy-text alignment; however, policy-text alignment does not imply implementation capacity, and realizing SDGs depends fundamentally on developmental resources to bridge vision and reality. Full article

We introduce a notion of curvature based on informational holonomy. Let $(M,g)$ be a smooth Riemannian manifold and let $\pi :\mathcal{P}\to M$ be a bundle of state spaces equipped fibrewise with a smooth divergence $D_x$ inducing an information metric $g^{{\mathcal{P}}_x}$. Assuming a connection on $\mathcal{P}$ compatible with this fibrewise information geometry, we measure the deviation of holonomy around small geodesic triangles by transporting a reference state ${\mu }_x$ and comparing it to its image via the induced informational distance $d_x=\sqrt{2D_x}$. Normalizing the resulting distance defect by the geometric area yields a continuous informational holonomy (sectional) curvature$K_{\mathrm{hol}}^{\mathrm{cont}}(x,\mathsf{\Pi })$. We prove that this limit exists for all $(x,\mathsf{\Pi })$ and equals the norm of a vector $W_x(\mathsf{\Pi };{\mu }_x)\in T_{{\mu }_x}{\mathcal{P}}_x$ depending linearly on the curvature of the connection along $\mathsf{\Pi }$. In geometric models induced from the Levi–Civita connection via an isometric representation, $K_{\mathrm{hol}}^{\mathrm{cont}}$ becomes a scalar invariant of $R^g{|}_{\mathsf{\Pi }}$ and, on spaces of constant sectional curvature, reduces to a constant multiple of $|{\sec }_g|$. On the discrete side, we consider quasi-uniform sampling graphs whose edges carry channels approximating parallel transport. Discrete triangle holonomies define a curvature estimator, and under explicit sampling, area-approximation, and channel-consistency assumptions, we establish a discrete-to-continuum convergence theorem with a quantitative error bound controlled by the sampling scale. Full article

Income inequality remains a central impediment to inclusive growth, yet whether government-led digital infrastructure programs mitigate or exacerbate distributional disparities is empirically contested. Exploiting the staggered rollout of China’s “Broadband China” (BBC) demonstration cities as a quasi-natural experiment, this study employs a multi-period difference-in-differences (DID) framework on a panel of 281 prefecture-level cities spanning 2009–2022 to estimate the designation effects of national digital infrastructure policy under the DID identifying assumptions. After parallel-trends validation, permutation-based placebo tests, and propensity score matching, the baseline estimates indicate a dual distributional pattern: BBC designation is associated with a wider urban-rural income gap and lower within-prefecture nighttime-light-based spatial income inequality. Candidate-channel analysis provides evidence consistent with financial deepening and factor mobility as plausible pathways: expanded financial coverage and cross-regional labor reallocation are associated with spatial convergence, whereas asymmetric usage depth and selective labor mobility reinforce urban-rural divergence. Exploratory heterogeneity analysis across four institutional dimensions, officials’ political promotion incentives, local fiscal capacity, traditional infrastructure endowments, and urban hierarchy, further shows that this distributional pattern varies across local contexts. Furthermore, this study extends the analytical lens to the spatial dimension by employing a spatial DID framework. The results identify significant cross-border externalities characterized by cross-prefecture spillovers associated with lower within-prefecture nighttime-light-based spatial income inequality in neighboring cities. These findings provide an integrated policy-evaluation framework that disentangles the complex, multidimensional distributional consequences of digital infrastructure investment, offering actionable insights for designing more equitable digital public policies in developing economies. Full article

The extension of the European Union Emissions Trading System to buildings and road transport (ETS2) raises important questions about the distribution of carbon pricing burdens across Member States. While existing research has primarily focused on income differences or household typologies, the structural heterogeneity of vulnerability across EU countries remains insufficiently explored. This study develops a dual-channel framework of household vulnerability to ETS2, distinguishing between structural carbon exposure and socio-economic sensitivity. Using a balanced panel of 27 EU Member States over 2010–2024, we construct composite indices based on Eurostat data and combine cluster analysis, sigma-convergence tests, and two-way fixed-effects models with Driscoll–Kraay standard errors. The results suggest that the two vulnerability channels are empirically distinct and geographically differentiated across Member States, with no country group simultaneously characterized by high exposure and high sensitivity. Energy productivity and renewable energy expansion are associated with lower structural exposure but higher socio-economic sensitivity, consistent with a transitional burden mechanism. Over time, composite vulnerability exhibits statistically significant divergence, despite partial and uneven convergence across the underlying vulnerability dimensions. These findings highlight the need for differentiated compensation mechanisms and structural policy interventions that address both structural exposure and socio-economic sensitivity, supporting a socially equitable and sustainable energy transition under ETS2. Full article

To address the challenge of imprecise detection and control of fertilizer application rates caused by high granular flow during fertilization operations, a parallel diversion detection method with real-time application rate regulation is proposed. The mechanism of uniform distribution of discrete particles formed by high-throughput aggregated granular fertilizer was elucidated. Key components including the uniform fertilizer tube, sensor detection structure, six-channel diversion cone disc, and fertilizer convergence tube underwent parametric design, culminating in the innovative development of a six-channel parallel diversion detection device. A multi-channel parallel signal detection method was studied, and a synchronous multi-channel signal acquisition system was designed. Through calibration tests, relationship models were established between the measured flow rate of granular fertilizer and voltage, as well as between the actual flow rate and the rotational speed of the fertilizer discharge shaft. A fuzzy PID control model was constructed in MATLAB2023/Simulink. Using overshoot, response time, and stability as evaluation metrics, the control performance of traditional PID and fuzzy PID was compared and analyzed. To validate the control system’s precision, device performance tests were conducted. Results demonstrated that fuzzy PID control reduced the time required to reach steady state by 66.87% compared to traditional PID, while overshoot decreased from 7.38 g·s⁻¹ to 1.49 g·s⁻¹. Divergence uniformity tests revealed that at particle generation rates of 10, 20, 30, and 40 g·s⁻¹, the coefficient of variation for channel divergence consistency gradually increased with rising tilt angles. During field operations at 0–5.0° tilt, the coefficient of variation for channel divergence consistency remained below 7.72%. Bench tests revealed that the fuzzy PID control system achieved an average accuracy improvement of 3.64% compared to traditional PID control, with a maximum response time of 0.9 s. Field trials demonstrated detection accuracy no less than 92.64% at normal field operation speeds of 3.0–6.0 km·h⁻¹. This system enables real-time, precise detection of fertilizer application rates and closed-loop regulation. Full article

Background: Trait convergence or parallelism is widely seen across the animal and plant kingdoms. For example, the evolution of eyes in cephalopods and vertebrate lineages, wings in bats and insects, or shark and dolphin body shapes are examples of convergent evolution. Such traits develop as a function of environmental pressures or opportunities that lead to similar outcomes despite the independent origins of underlying tissues, cells, and gene transcriptional patterns. Our current understanding of the molecular processes underlying these phenomena is gene-centric and focuses on how convergence involves the recruitment of novel genes, the recombination of gene products, and the duplication and divergence of genetic substrates. Scope: Despite the independent origins of a given trait, these model organisms still possess some form of epigenetic processes conserved in eukaryotes that mediate gene-by-environment interactions. These traits evolve under similar environmental pressures, so attention should be given to plastic molecular processes that shape gene function along these evolutionary paths. Key Mechanisms: Here, we propose that epigenetic processes such as histone-modifying machinery are essential in mediating the dialog between environment and gene function, leading to trait convergence across disparate lineages. We propose that epigenetic modifications not only mediate gene-by-environment interactions but also bias the distribution of de novo mutations and recombination, thereby channeling evolutionary trajectories toward convergence. An inclusive view of the epigenetic landscape may provide a parsimonious understanding of trait evolution. Full article

This paper addresses the long-standing question of understanding the origin and evolution of low-frequency unsteadiness interactions associated with shock waves impinging on a turbulent boundary layer in transonic flow (Mach: $1.1$ to $1.3$). To that end, high-speed experiments in a blowdown open-channel wind tunnel have been performed across a convergent–divergent nozzle for different expansion ratios (PR = 1.44, 1.6, and 1.81). Quantitative evaluation of the underlying spectral energy content has been obtained by processing time-resolved pressure transducer data and Schlieren images using the following spectral analysis methods: Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT), as well as coherence and time-lag evaluations. The images demonstrated the presence of increased normal shock-wave impact for PR = 1.44, whereas the latter were linked with increased oblique $\lambda$-foot impact. Hence, significant disparities associated with the overall stability, location, and amplitude of the shock waves, as well as quantitative assertions related to spectral energy segregation, have been inferred. A subsequent detailed spectral analysis revealed the presence of multiple discrete frequency peaks (magnitude and frequency of the peaks increasing with PR), with the lower peaks linked with large-scale shock-wave interactions and higher peaks associated with shear-layer instabilities and turbulence. Wavelet transform using the Morlet function illustrates the presence of varying intermittency, modulation in the temporal and frequency scales for different spectral events, and a pseudo-periodic spectral energy pulsation alternating between two frequency-specific events. Spectral analysis of the pixel densities related to different regions, called spatial FFT, highlights the increased influence of the feedback mechanism and coupled turbulence interactions for higher PR. Collation of the subsequent coherence analysis with the previous results underscores that lower PR is linked with shock-separation dynamics being tightly coupled, whereas at higher PR values, global instabilities, vortex shedding, and high-frequency shear-layer effects govern the overall interactions, redistributing the spectral energy across a wider spectral range. Complementing these experiments, time-resolved numerical simulations based on a transient 3D RANS framework were performed. The simulations successfully reproduced the main features of the shock motion, including the downstream migration of the mean position, the reduction in oscillation amplitude with increasing PR, and the division of the spectra into distinct frequency regions. This confirms that the adopted 3D RANS approach provides a suitable predictive framework for capturing the essential unsteady dynamics of shock–boundary layer interactions across both temporal and spatial scales. This novel combination of synchronized Schlieren imaging with pressure transducer data, followed by application of advanced spectral analysis techniques, FFT, CWT, spatial FFT, coherence analysis, and numerical evaluations, linked image-derived propagation and coherence results directly to wall pressure dynamics, providing critical insights into how PR variation governs the spectral energy content and shock-wave oscillation behavior for nozzles. Thus, for low PR flows dominated by normal shock structure, global instability of the separation zone governs the overall oscillations, whereas higher PR, linked with dominant $\lambda$-foot structure, demonstrates increased feedback from the shear-layer oscillations, separation region breathing, as well as global instabilities. It is envisaged that epistemic understanding related to the spectral dynamics of low-frequency oscillations at different PR values derived from this study could be useful for future nozzle design modifications aimed at achieving optimal nozzle performance. The study could further assist the implementation of appropriate flow control strategies to alleviate these instabilities and improve thrust performance. Full article

To elucidate the temporal dynamics of genetic diversity across successive breeding generations of channel catfish (Ictalurus punctatus) and enhance subsequent breeding efficiency, we systematically evaluated the genetic variation in four consecutive generations using ten highly polymorphic microsatellite loci. The number of alleles (Na), effective alleles (Ne), and Shannon’s index (I) all declined with increasing generations. The mean expected heterozygosity (He) decreased gradually from 0.822 to 0.805 but remained above 0.80, indicating that all generations maintained relatively high genetic diversity. Allele frequency analysis revealed the progressive fixation of alleles potentially linked to target traits, while some rare alleles were gradually lost. Analysis of molecular variance (AMOVA) demonstrated that 98% of the genetic variation occurred within generations, with weak differentiation among generations (Fst = 0.016). UPGMA clustering further indicated that later generations diverged from the base stock, whereas genetic distances among adjacent generations progressively narrowed, suggesting increasing convergence and stabilization of genetic structure. These findings provide both theoretical insights and practical guidance for the continuous selective breeding and germplasm conservation of channel catfish. Full article

This study focuses on understanding the bioconvection in Jeffery–Hamel (JH) flow, which has valuable applications in areas like converging dies, hydrology, and the automotive industry, which make it a topic of practical importance. This research aims to explore Homogeneous–Heterogeneous (HH) chemical reactions in a magnetic Darcy–Forchheimer model with bioconvection in convergent/divergent channels. To analyze the role of porosity, the Darcy–Forchheimer law is applied. The main system of equations is simplified through similarity transformation into ordinary differential equations solved numerically with the help of the NDSolve technique. The results, compared with previous studies for validation, are presented through graphs and tables. The study reveals that in divergent channels, the velocity decreases with higher solid volume fractions, while in convergent channels, it increases. Furthermore, various physical parameters, such as the Eckert number and porosity parameter, increase skin friction in divergent channels but decrease it in convergent channels. These findings suggest that the parameters investigated in this study can effectively enhance homogeneous reactions, providing valuable insights for practical applications. Full article

The rapid evolution of mega-constellation networks and 6G satellite communication systems has ushered in an era of ubiquitous connectivity, yet their sustainability is threatened by the energy-computation dilemma inherent in high-throughput data transmission. Polar codes, as a coding scheme capable of achieving Shannon’s limit, have emerged as one of the key candidate coding technologies for 6G networks. Despite the high parallelism and excellent performance of their Belief Propagation (BP) decoding algorithm, its drawbacks of numerous iterations and slow convergence can lead to higher energy consumption, impacting system energy efficiency and sustainability. Therefore, research on efficient early termination algorithms has become an important direction in polar code research. In this paper, based on information geometry theory, we propose a novel geometric framework for BP decoding of polar codes and design two early termination algorithms under this framework: an early termination algorithm based on Riemannian distance and an early termination algorithm based on divergence. These algorithms improve convergence speed by geometrically analyzing the changes in soft information during the BP decoding process. Simulation results indicate that, when $E_b/N_0$ is between 1.5 dB and 2.5 dB, compared to three classical early termination algorithms, the two early termination algorithms proposed in this paper reduce the number of iterations by 4.7–11% and 8.8–15.9%, respectively. Crucially, while this work is motivated by the unique demands of satellite networks, the geometric characterization of polar code BP decoding transcends specific applications. The proposed framework is inherently adaptable to any communication system requiring energy-efficient channel coding, including 6G terrestrial networks, Internet of Things (IoT) edge devices, and unmanned aerial vehicle (UAV) swarms, thereby bridging theoretical coding advances with real-world scalability challenges. Full article

The characteristics of CO₂ seepage in reservoirs have important research significance in the field of CCS technology application. However, the characteristics of macro-scale seepage are affected by the geometrical characteristics of micro-scale media, such as pore size and particle shape. Therefore, in this work, a series of numerical simulations were carried out using the phase field method to study the effect of pore structure simplification on micro-scale displacement process. The influences of capillary number, wettability, viscosity ratio, interfacial tension, and fracture development are discussed. The results show that the overall displacement patterns of the real pore model and the simplified particle model are almost similar, but the oil trapping mechanisms were totally different. There are differences in flow pattern, number of dominant flow channels, sensitivity to influencing factors and final recovery efficiency. The real pore model shows higher displacement efficiency. The decrease in oil wet strength of rock will change the CO₂ displacement mode from pointing to piston displacement. At the same time, the frequency of breakage will be reduced, thus improving the continuity of CO₂. When both pores and fractures are developed in the porous media, CO₂ preferentially diffuses along the fractures and has an obvious front and finger phenomenon. When CO₂ diffuses, it converges from the pore medium to the fracture and diverges from the fracture to the pore medium. The shape of fracture development in the dual medium will largely determine the CO₂ diffusion pattern. Full article