Search Results (846)

Biomethane is emerging as a key renewable gas in both mature and developing energy systems worldwide. Driven by climate-neutrality objectives, energy-security concerns, and rising waste-to-energy ambitions, global biomethane production is expected to expand rapidly in the coming decade. In Europe, this growth is accelerated by the REPowerEU target of 35 billion m³ by 2030. However, as biomethane production increases and natural gas demand declines over time, distribution networks face growing operational challenges, including pressure build-up and biomethane curtailment caused by supply and demand mismatches. This study evaluates whether surplus biomethane can be converted into electricity as a multi-commodity strategy to alleviate these constraints. Using hourly operational data from two Dutch Distribution System Operators (DSOs), a simulation model was developed to assess the impact of generator-based biomethane-to-power conversion on both gas and electricity distribution networks. The results show that, for RENDO, the approach increases effective biomethane injection by 49.0%, reduces natural gas deliveries from the transmission system by 20.0%, and lowers electricity imports by 9.2%. For Coteq, the corresponding impacts are 106.8%, 30.6%, and 16.2%, respectively. These findings indicate that multi-commodity coupling through biomethane-to-power conversion provides a promising strategy for increasing biomethane injection and renewable electricity generation. Full article

To address the polarization fading noise in coherent detection phase-sensitive optical time-domain reflectometry (Φ-OTDR) for distributed low-frequency vibration sensing, a Φ-OTDR sensing scheme integrating polarization diversity reception and the variational mode decomposition (VMD) algorithm is proposed. The mechanism of polarization fading induced by fiber birefringence and external perturbations is systematically analyzed. A signal–noise mathematical model for polarization diversity reception is established, and the adaptive decomposition capability of the VMD algorithm for non-stationary phase signals is elaborated. This scheme can accurately separate the additional noise introduced by polarization diversity reception from the target low-frequency vibration signals. Experimental results demonstrate that, compared with the single-path detection scheme, the proposed method eliminates the amplitude attenuation of beat frequency signals caused by polarization mismatch at the optical path level. Meanwhile, it effectively suppresses both the additional noise introduced by polarization diversity and the low-frequency phase drift resulting from unstable laser frequency. It achieves precise phase restoration of vibration signals excited at 50 Hz under three typical sensing distances of 5 km, 10 km, and 30 km. Additionally, it successfully restores low-frequency vibration signals as low as 0.6 Hz at the sensing distance of 30 km. Full article

Background: Anatomical variations are inevitable part of studying the human body. Very often, muscles of the limbs may show atypical attachments, extra or fewer muscle bellies. These variations are likely rooted in limb development. Our goal was to thoroughly study and describe the variations in the lateral wrist extensors. Our initial goal was to attempt to explain the developmental processes that occur before the formation of these variations, with a focus on the interconnecting tendons. Methods: We used a standard dissection technique, paying extra attention to the space between the two radial wrist extensors to properly visualize interconnecting tendons. Taking advantage of the chi square test, we compared the observed vs the expected random distribution of interconnecting tendons. Results: In this article, we systematically studied the variations in the interconnecting tendons of the lateral carpal extensors in 58 upper limbs of our cadaver donors used for the education of medical students. The main variation we found is interconnecting tendons between the extensor carpi radialis longus and extensor carpi radialis brevis. The insertion and origin of the interconnecting tendons were consistent: it either originates from the middle of the ECRB body and inserts medial to ECRL tendon or it originates from the ventral side ECRL and inserts ventral to the ECRB tendon. We supplemented them with two dissections of fetal upper limbs (at GW 12 and GW 17). Statistical analysis of the distribution of single vs double interconnecting tendons suggests that they are dependent events, consistent with literature data. Conclusions: Based on our observations and the literature, we propose that oblique muscle division and a mismatch between the muscle fission plane and the initial distal tendon fission plane may result in the observed phenotype. We also suggest that the origin of the extra numerary tenons form ECRL body may play a role when choosing which one to mobilize for tendon transfer. Full article

Frequency response analysis (FRA) is recognized as an effective method in power transformer winding fault diagnosis. However, the traditional numerical index methods focus on the overall features of FRA curves, making it difficult to capture subtle deformations in transformer windings. Similarly, existing digital image processing methods rely on a single feature or a simple feature combination without adaptive fusion. These methods ignore differences in the data distributions of features, leading to feature mismatch, the loss of sensitive fault information, and lower diagnostic accuracy. To solve this problem, a novel adaptive multiple-image-feature fusion method for transformer winding fault diagnosis is proposed. First, a multi-dimensional feature space combining image pixel matrix similarity, morphological features, and image texture features is built to decode the difference in fault of FRA images. Second, the multiple kernel learning (MKL) framework is used to dynamically adjust the fusion weights of different kernels to make features compatible and remove redundant information. Finally, comparative and ablation experiments show that the proposed method outperforms the traditional methods in identifying different types and levels of faults. The method achieves over 99% accuracy in fault type identification across SVM, KNN, and RF classifiers. For radial deformation (RD) severity prediction, the accuracy of the proposed model is 93.37% with SVM and 94.85% with KNN, outperforming the full-feature concatenation method. These results confirm the method’s robustness and diagnostic precision. Full article

In the fields of atomic physics, quantum sensing, and precision measurement, 852 nm lasers are essential for the resonant excitation and manipulation of the cesium (Cs) D₂ transition ($6S_{1/2}\to 6P_{3/2}$). While significant global progress has been made in developing 852 nm laser based on distributed feedback (DFB) lasers and external cavity diode lasers (ECDL), the burgeoning demand for portable and integrated quantum instruments imposes stringent requirements on miniaturization and long-term, maintenance-free operation. To address the challenge of mode competition in Faraday lasers, this work demonstrates a frequency-stabilized semiconductor laser based on an atomic frequency-selective architecture. By utilizing a customized Faraday Anomalous Dispersion Optical Filter (FADOF) for frequency selection, the laser wavelength automatically corresponds to the Cs 852 nm D₂ transition, offering “Plug-and-play” operation. To further enhance integration, we propose and demonstrate a miniaturized Faraday laser architecture that resolves the instability caused by the mismatch between the FADOF transmission bandwidth and the free spectral range (FSR) of the external cavity. By employing a 7000 Gs magnetic field, the FADOF bandwidth is actively broadened to ∼15 GHz, while the cavity length is concurrently compressed to 30 mm to maximize FSR to effectively suppressing unstable mode competition. The resulting laser achieves a highly compact dimension of $102\times 109\times 96{\mathrm{mm}}^3$. Performance testing demonstrates a Lorentzian fitted linewidth of $16.4\mathrm{kHz}$ and a 1-s frequency stability of $3.05\times {10}^{-13}$ after modulation transfer spectroscopy (MTS)-based frequency locking. This robust and autonomous 852 nm laser source provides a critical technological foundation for the miniaturization of high-performance quantum sensors. Full article

Owing to multi-target tracking in scenarios with nonlinearity, uncertain measurement model and high clutter density, the Poisson multi-Bernoulli mixture (PMBM) recursion is prone to unstable covariance propagation under nonlinear dynamics as well as uncertainty in measurement-to-target association caused by mismatched gate that causes erroneous updates from clutters. In the prediction stage, the singular value decomposition (SVD) is used in place of Cholesky factorization to construct and propagate the square-root covariance factor in the square-root cubature Kalman filter (SCKF), yielding a numerically stable square-root implementation. Then, the resulting SVD-SCKF is incorporated into the PMBM prediction step and used to propagate the Gaussian-mixture components of both the Poisson point process (PPP) intensity and the Bernoulli component in the Multi-Bernoulli mixture (MBM), yielding predicted means and covariances under nonlinear dynamics. An adaptive fading factor is determined from innovation statistics, and covariance inflation is performed to improve robustness under target maneuvers and model mismatch. In the update stage, the unknown measurement function is regressed by Gaussian process (GP) using historical state–measurement samples, yielding an equivalent measurement mapping and state-dependent uncertainty. Furthermore, the predicted measurement distribution is generated from the GP-based conditional measurement distribution with state prior approximated by SVD-SCKF cubature points. An adaptive gate is determined from the GP-based conditional measurement distribution, which is approximated by an equivalent ellipsoidal gate via fitting for screening the current measurements and filtering out clutter. Residual in-gate clutter measurements are handled via Bayesian target discrimination, where the posterior probability of measurement originated from target is employed as a weight and incorporated into association weights and update likelihoods. Simulation results further confirm the effectiveness and stability of the proposed filter in complex scenarios. Full article

The primary aim of this study is to validate the utility of Social Media Data (SMD) as a scientifically grounded tool for quantifying the spatial mismatch between objective ecological supply and subjective social demand. Assessing the spatial coupling and mismatch between Habitat Quality (HQ)—representing objective ecological supply—and Ecological Perception (EP)—representing subjective social demand—is essential for developing targeted urban management and development strategies. Focusing on the core urban area of Hangzhou, this study quantified ecological supply using the InVEST HQ model. To reflect social demand, 4958 geolocated Weibo posts were processed using contextual sentiment analysis. A Coupling Coordination Degree model served as a diagnostic tool to evaluate the synergy between these two dimensions. Additionally, a Geodetector model was employed to investigate the factors driving spatial differentiation in this coupling. The findings indicate that: (1) The regional average HQ is 0.56, reflecting a moderate overall level of degradation, while EP shows a preference for natural environments and exhibits a distinct “strip-like” spatial distribution. (2) The overall CCD value is 0.384; high-coupling areas are primarily concentrated in regions with superior natural conditions and dense vegetation, whereas low-coupling areas correspond to zones with intensive urban functions. (3) Driving factor analysis reveals that land-use type exerts the most significant influence on the overall degree of coupling. This study demonstrates that the HQ-EP coupling framework provides a reliable spatial diagnostic tool for urban planners to identify socio-ecological vulnerabilities. The results suggest that an appropriate integration of natural elements enhances coupling outcomes, with the highest synergy observed in environments characterized by high HQ and minimal anthropogenic disturbance. Full article

Populations of several billfish species are declining due to overfishing and bycatch, and fundamental aspects of their biology and population dynamics remain poorly understood. We provide the first assessment of the population genetic structure of longbill spearfish (Tetrapturus pfluegeri) in the western Atlantic Ocean. We screened variation at 12 nuclear microsatellite loci (n = 144) and mitochondrial DNA control region sequences (mtCR, n = 177). Both marker types revealed three genetically differentiated clusters, with mean values for microsatellites showing differentiation of F_ST = 0.136 and D_EST = 0.201, and for mtCR F_ST = 0.645. Microsatellite markers demonstrated moderate-to-high genetic diversity, with a mean allelic richness of 6.73 alleles per locus, moderate heterozygosities (Ho = 0.446, He = 0.604), and a positive inbreeding coefficient (F_IS = 0.22) across the three sample collection sites. The overall estimated effective population size was 789.2 (95% CI: 246.7–∞). The mtCR exhibited 96 haplotypes, with high haplotype (0.989 ± 0.003) and nucleotide (0.025 ± 1.3%) diversities. We found higher mean relatedness within clusters than among them, supporting the interpretation of population subdivision and the Wahlund effect. Tajima’s D and Fu’s Fs were negative across all localities, with significant values observed along the Brazilian coast but not in the Caribbean Sea. These neutrality test results, together with low Harpending’s raggedness indices from DNA sequence mismatch distributions, are consistent with historical demographic expansion. Our findings establish a genetic baseline for fishery monitoring and management, contributing to the conservation of T. pfluegeri populations in the western Atlantic Ocean. Full article

Colorectal cancer (CRC) shows consistent sex-related differences in incidence, anatomic distribution, molecular subtype, immune context, and clinical outcome. However, these differences are often discussed through broad parallel themes such as hormones, genetics, or the microbiome, rather than through the biological settings in which sex meaningfully modifies tumor behavior. This review argues that sex is most informative in CRC when treated as a contextual modifier whose relevance emerges only after integrating tumor sidedness, mismatch repair status, oncogenic background, immune ecology, and age at onset. The clearest signals arise from interaction-based contexts, particularly when sex is interpreted together with tumor sidedness and dMMR/MSI-H or BRAF-linked disease states. Current evidence indicates that women are enriched for proximal or right-sided, microsatellite instability-high, mismatch repair-deficient, CpG island methylator phenotype-high, and BRAF-associated CRC, whereas men more often present with distal disease and a higher overall burden. Mechanistic studies further show that sex-related differences extend beyond hormone exposure to include KRAS–STAT4–KDM5D signaling, site-specific immune-checkpoint programs, metabolic phenotypes, epigenetic biomarker variation, and microbiota–hormone crosstalk. These effects are most evident in defined clinical niches, particularly right-sided CRC, mismatch repair-deficient disease, BRAF-mutated metastatic CRC, and early-onset CRC. A sex-aware, subtype-aware, and location-aware framework therefore offers a more clinically useful interpretation of CRC heterogeneity than descriptive male-versus-female comparisons alone. Full article

In goafs formed by underground mineral resource extraction, the remaining pillars are often subjected to uniaxial loading at different loading rates, and their mechanical responses and failure mechanisms directly affect the long-term stability of the goafs. This study uses rock-like materials to conduct uniaxial compression tests at loading rates ranging from 0.001 mm/min to 0.05 mm/min, combined with acoustic emission (AE) monitoring, to systematically investigate the effects of loading rate on the mechanical properties, energy distribution, constitutive model, and AE characteristics of the material. The results show that an increase in loading rate significantly enhances the stiffness and strength of the material, promotes a transition in failure mode from a shear–tension composite to tension-dominated, intensifies brittle characteristics, and simultaneously inhibits full crack development and fragments generation. In terms of energy evolution, an increased loading rate enhances the pre-peak total strain energy and elastic strain energy storage but reduces the efficiency of energy dissipation, leading to an intensified mismatch between energy storage and dissipation capacities at peak stress. A damage variable induced by loading rate was proposed, and a damage constitutive model considering the loading rate was established, with the theoretical curves showing good agreement with the experimental data. AE characteristic analysis further reveals that an increase in loading rate causes the crack type to transition from shear-dominated to tension-dominated, and the fluctuating increase in the b-value reflects a reduction in pre-peak fracture scale and a decrease in the degree of material fragmentation. The research findings are expected to deepen the understanding of the damage and failure mechanisms of rock materials under different loading rates, thereby laying a research foundation for the stability assessment of goaf pillars and disaster warning. Full article

Rapid urbanization in megacities has exacerbated the spatial mismatch between employment and housing, necessitating effective spatial optimization strategies. However, classical optimization models often rely on the idealized assumption of “proximity maximization,” failing to account for the complex, nonlinear regularities of actual human mobility. To address this disconnect between theoretical modeling and real-world behavior, this study establishes a job–housing balance optimization framework integrated with empirical commuting patterns. Using Shenzhen as a case study, we analyze citywide commuting big data since 2024 to characterize the power law relationship between commuting population size and distance. We propose a novel optimization model that partitions residential areas into “commuting rings” on the basis of observed distance-decay functions rather than simple Euclidean proximity. We applied the proposed method to current and future planning scenarios and successfully generated spatial regulation schemes that decentralize employment functions to peripheral areas while strategically densifying residential zones. By respecting the “heavy-tailed” nature of commuting distributions, this approach offers urban planners a more robust tool for reducing aggregate commuting burdens without violating the behavioral realities of the workforce. Full article

The life insurance sector plays a critical role in financial system stability but is inherently exposed to extreme market fluctuations due to long-term liabilities and asset–liability mismatches. This study investigates extreme losses in the growth rates of the JSE Life Insurance Price Index (LIPI) using the Generalised Extreme Value Distribution (GEVD) and the Generalised Pareto Distribution (GPD) under the Extreme Value Theory (EVT) framework. Monthly data from January 2000 to October 2023 were transformed into a loss series, and extreme events were captured using quarterly block maxima and a POT threshold at the 95th percentile. Model parameters were estimated through Maximum Likelihood Estimation, and downside risk was assessed using return levels, Value-at-Risk (VaR), and Tail Value-at-Risk (tVaR). The GEVD model produced a negative shape parameter, consistent with a bounded Weibull-type tail, while the GPD indicated a heavy-tailed distribution. Return level estimates show escalating loss magnitudes and widening uncertainty over longer horizons, reflecting the challenges of projecting rare events. Kupiec backtesting confirms the adequacy and reliability of the GEVD-based VaR across all confidence levels, whereas the GPD underestimates risk at lower thresholds. These findings indicate significant tail risk within the South African life insurance equity segment and underscore the importance of EVT-based risk measures for capital planning and regulatory oversight. The study contributes to financial risk modelling in the life insurance sector and offers practical insights for strengthening solvency assessment and enterprise risk management frameworks. Full article

Water scarcity, climate variability, and increasing water demands are placing growing pressure on water supply and distribution systems, particularly in water-scarce environments. Optimization-based approaches have become central to improving system design, planning, and operation. This study presents a structured review of optimization techniques applied to water distribution systems under conditions of scarcity, intermittency, or aridity, and introduces a context-aware classification framework incorporating system scale, population, and scarcity severity. PRISMA (“Preferred Reporting Items for Systematic Reviews and Meta-Analyses”) principles are adopted. Relevant studies are identified through Scopus and Google Scholar, screened using criteria focused on system type, optimization relevance, and explicit consideration of scarcity, intermittency, or aridity, and classified by optimization stage, methodological approach, geographical context, and main findings. The review is dominated by benchmark network studies under water scarcity, while real-world applications in arid regions, such as Sub-Saharan Africa and parts of the Middle East, remain underrepresented. Deterministic least-cost designs are inadequate under water scarcity, whereas multi-objective approaches deliver more reliable systems. The review shows a mismatch between the optimization focus of the benchmark studies, which is mainly in the design phase, and the real-world applications, which mainly focus on optimization of the operations of the existing systems. Full article

In the era of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) architectures are gaining significant attention for their ability to ground language generation in reliable knowledge sources. Despite their effectiveness, RAG systems based solely on semantic similarity often fail to ensure factual accuracy in specialized domains, where terminological ambiguity can affect retrieval relevance. This study proposes Entity Linking Enhanced RAG (ELERAG), an enhanced RAG architecture that integrates a factual signal derived from Entity Linking to improve the accuracy of educational question-answering systems in Italian. The system includes a Wikidata-based Entity Linking module and implements a hybrid re-ranking strategy based on Reciprocal Rank Fusion (RRF). To validate our approach, we compared it against standard baselines and state-of-the-art methods, including a Weighted-Score Re-ranking, a standalone Cross-Encoder and a combined RRF + Cross-Encoder pipeline. Experiments were conducted on two benchmarks: a custom academic dataset and the standard SQuAD-it dataset. Results show that, in domain-specific contexts, ELERAG significantly outperforms both the baseline and the Cross-Encoder configurations. Conversely, the Cross-Encoder approaches achieve the best results on the general-domain dataset. These findings provide strong experimental evidence of the domain mismatch effect, highlighting the importance of domain-adapted hybrid strategies to enhance factual precision in educational RAG systems without relying on computationally expensive models trained on disparate data distributions. They also demonstrate the potential of entity-aware RAG systems in educational environments, fostering adaptive and reliable AI-based tutoring tools. Full article

The rapid proliferation of low-altitude drones has led to increasingly congested and heterogeneous electromagnetic environments, posing significant challenges to fine-grained spectrum awareness and reliable drone management. Specific emitter identification (SEI), which exploits inherent hardware-dependent radio frequency fingerprints, provides an effective physical-layer solution for emitter-level discrimination. However, practical SEI systems often suffer from two critical issues: extremely limited labeled samples for newly emerging emitters and heterogeneous data distributions collected by geographically distributed receivers with mismatched label spaces. To address these challenges, this paper proposes a heterogeneous federated learning (HFL)-based framework for few-shot specific emitter identification (FS-SEI). The proposed framework decouples feature embedding learning from task-specific classification and enables collaborative representation learning across distributed receivers without sharing raw signal data. A metric learning-based training strategy is adopted, where only the feature embedding models are aggregated in the federated process, effectively alleviating the impact of label space mismatch by utilizing center loss and an improved triplet loss. Moreover, two federated optimization schemes, namely gradient averaging (GA) and model averaging (MA), are systematically investigated to analyze their effectiveness under fully heterogeneous settings. Extensive experiments conducted on a real-world dataset demonstrate that the proposed HFL framework significantly outperforms isolated local training. In particular, the GA-based scheme achieves a few-shot identification performance that closely approaches centralized learning while preserving data privacy and robustness against data heterogeneity. The results validate the effectiveness of the proposed approach for practical FS-SEI in low-altitude drone management scenarios. Full article