Search Results (531)

With the rapid depletion of natural graphite, the synthesis of artificial graphite from high-carbon precursors has garnered growing interest. However, conventional artificial graphitization typically requires extremely high temperatures. This study demonstrates that natural halloysite mineral can serve as an effective catalyst to lower the graphitization temperature threshold of anthracite. The results show that halloysite exerts a pronounced catalytic effect within the temperature range of 1400–2300 °C. The enhancement in graphitization is primarily attributed to the formation and subsequent decomposition of intermediate phases between halloysite and the carbon matrix. From 1400 to 1700 °C, the interlayer spacing decreases significantly with halloysite as a catalyst due to the nucleation of highly ordered “multilayer graphene” structures surrounding intermediates. However, these graphene layers exhibit a confined and curved morphology that spatially restricts crystallite growth, resulting in relatively small in-plane (L_a) and stacking (L_c) crystallite dimensions. Moreover, multilayer graphene originating from intermediate crystal corners tends to generate numerous dislocation defects. From 1700 to 2300 °C, significant increases in both L_a and L_c are observed, accompanied by a marked improvement in structural order. This evolution is driven by the progressive inward decomposition of intermediate phases, which causes the “circular-shaped” graphene domains to collapse at the dislocation defects and subsequent straightening of the curved graphene layers. These findings provide new microstructural insights into mineral-catalyzed graphitization mechanisms in anthracite and present a promising pathway toward energy-efficient production of synthetic graphite. Full article

Biological decision-making under uncertainty relies on somatic markers, which are affective signals that bias choices without exhaustive computation. This study biomimetically translates the Somatic Marker Hypothesis (SMH) into synthetic somatic markers (SSMs), a minimal and interpretable evaluative mechanism that assigns a scalar valence to compressed environmental states in the high-dimensional discrete grid-world Pixelverse, without modelling subjective feelings. SSMs are implemented as a lightweight Python routine in which agents accumulate valence from experience and use a simple threshold rule (θ = −0.5) to decide whether to keep the current trajectory or reset the environment. In repeated simulations, agents perform few resets on average and spend a higher proportion of time in stable “good” configurations, indicating that non-trivial adaptive behaviour can emerge from a single evaluative dimension rather than explicit planning in this small stochastic grid-world. The main conclusion is that, in this minimalist 3 × 3 Pixelverse testbed, SMH-inspired SSMs provide an economical and transparent heuristic that can bias decision-making despite combinatorial state growth. Within this toy setting, they offer a conceptually grounded alternative and potential complement to more complex affective and optimisation model. However, their applicability to richer environments remains an open question for future research. The ethical implications of deploying such bio-inspired evaluative systems, including transparency, bias mitigation, and human oversight, are briefly outlined. Full article

The transition toward decentralized energy systems has amplified interest in peer-to-peer electricity trading. However, research on prosumer behavior in such markets remains fragmented, hindered by a lack of benchmarkable experimental infrastructure. Addressing this gap, the LabChain system was developed—a modular, interactive prototype designed to study human behavior in synthetic P2P electricity markets under controlled laboratory conditions. This system integrates real-world technologies, such as blockchain-based transaction backends, flexibility market interfaces, and asset control tools, allowing fine-grained observation of strategic and perceptual dimensions of prosumer activity. The research followed an iterative design approach to develop the infrastructure for experimental energy economics research, and to assess its effectiveness in aligning participant experience with design intentions. Based on the meta-requirements generality, affordance-centric design, and technological grounding, 13 detailed peer-to-peer market, software, and system requirements that allow for system evaluation were developed. As a proof of concept, seven participants simulated prosumer behavior over a week through interaction with the system. Their interaction with the system was analyzed through simulation data and focus group interviews, using a modified thematic content analysis with a hybrid inductive–deductive coding approach. The main achievements are (i) the design and implementation of the LabChain system as a modular infrastructure for P2P electricity market experiments, (ii) the development of an associated experimental workflow and research design, and (iii) its demonstration through an illustrative, proof-of-concept evaluation based on thematic content analysis of a single focus group session focusing on interaction and perceptions. The behavioral results from an initial session are limited, exploratory, and demonstrative in nature and should be interpreted as illustrative only. They nevertheless revealed tension between system flexibility and cognitive usability: while the system supports diverse strategies and market roles, limitations in interface clarity and information feedback constrain strategic engagement. Full article

Many fluid-injection sequences display burst-like seismicity with approximate power-law event-size distributions whose exponents drift between catalogs. Classical percolation models instead predict fixed, dimension-dependent exponents and do not specify which geometric mechanisms could underlie such b-value variability. We address this gap using two loopless invasion percolation variants—the constrained Leath invasion percolation (CLIP) and avalanche invasion percolation (AIP) models—to generate synthetic burst catalogs and quantify how burst geometry modifies size–frequency statistics. For each model we measure burst-size distributions and an interference fraction, defined as the proportion of attempted growth steps that terminate on previously activated bonds. Single-burst clusters recover the Fisher exponent of classical percolation, whereas multi-burst sequences show systematic, dimension-dependent drift of the effective exponent with a burst number that is strongly correlated with the interference fraction. CLIP and AIP are indistinguishable under these diagnostics, indicating that interference-driven exponent drift is a generic feature of burst growth rather than a model-specific artifact. Mapping the size-distribution exponent to an equivalent Gutenberg–Richter b-value shows that increasing interference suppresses large bursts and produces b value ranges comparable to those reported for injection-induced seismicity, supporting the interpretation of interference as a geometric proxy for mechanical inhibition that limits the growth of large events in real fracture networks. Full article

The study assesses the level and concentration of revitalisation measures in Poland’s county-level cities across two dimensions: spatial–cultural and social. We compiled comparable indicators from the Local Data Bank (2020–2023) and municipal revitalisation programmes for 63 cities, constructing ten stimulus variables (five spatial–cultural; five social). Indicators were normalised to (0–1) and aggregated into two synthetic indices—IRSC (spatial–cultural) and IRS (social)—followed by a standard-deviation-based classification into four types/groups. Results show pronounced inter-city variation with no clear voivodeship pattern. Several cities emerge as consistent leaders across dimensions, while others perform unevenly—e.g., cases with high IRSC but moderate IRS, and vice versa—highlighting different strategic emphases of programmes. We also note large disparities in financial effort (per area and per resident) and low counts of actions per unit in many cities, contrasted with a few high-activity cases. The findings indicate that roughly one-third of cities leverage revitalisation effectively in both dimensions. The study advocates complementing synthetic, comparative assessment with practice-informed models that adapt solutions proven in top-performing cities, rather than relying solely on unified, centrally framed approaches. Full article

The significance of access to energy sources for fostering innovation is increasing. Regions should, however, base their competitiveness not merely on innovation, but also on social cohesion and ecological ambitions. In this context, the objective of this article is to evaluate the sustainable competitiveness potential of Polish regions from the perspective of energy-related factors, as well as to identify the trends and the disparities observed over the past decade. The study employs a multidimensional comparative analysis (MCA), operationalized through the development of a Synthetic Measure of Potential (SMP) constructed from ten disaggregated indicators encompassing resource-related, economic, environmental, and social dimensions of energy. This approach is complemented by a cluster analysis using Ward’s method to identify patterns and groupings within the data. The empirical results demonstrate that sustainable competitiveness potential with regard to energy factors has generally increased, although it was not a linear process. The most favorable trend was observed for the generation of energy from renewable sources. An interesting side effect of transformation was observed in the energy balance. Further, despite the significant decrease in industrial electricity consumption per unit of gross value added, the energy poverty level increased. The study offers several practical implications for advancing the green transformation, emphasizing the uneven regional impacts of this process and underscoring the necessity of a coordinated policy framework to support the energy transition. Full article

In this paper, we present LJ-TTS, a large-scale single-speaker dataset of real and synthetic speech designed to support research in text-to-speech (TTS) synthesis and analysis. The dataset builds upon high-quality recordings of a single English speaker, alongside outputs generated by 11 state-of-the-art TTS models, including both autoregressive and non-autoregressive architectures. By maintaining a controlled single-speaker setting, LJ-TTS enables precise comparison of speech characteristics across different generative models, isolating the effects of synthesis methods from speaker variability. Unlike multi-speaker datasets lacking alignment between real and synthetic samples, LJ-TTS provides exact utterance-level correspondence, allowing fine-grained analyses that are otherwise impractical. The dataset supports systematic evaluation of synthetic speech across multiple dimensions, including deepfake detection, source tracing, and phoneme-level analyses. LJ-TTS provides a standardized resource for benchmarking generative models, assessing the limits of current TTS systems, and developing robust detection and evaluation methods. The dataset is publicly available to the research community to foster reproducible and controlled studies in speech synthesis and synthetic speech detection. Full article

Urban welfare can deteriorate over a few weeks, yet most official indicators are only updated quarterly. This mismatch in time scales leaves city administrations effectively blind to the early stages of emerging crises, especially in areas where vulnerable residents generate few administrative or digital records. We cast urban socioeconomic monitoring as a systems problem: a six-dimensional welfare state on a spatial grid, observed through sparse delayed administrative data and noisy digital traces whose reliability declines with digital exclusion. On top of this latent state, we design a four-layer cyber–physical–social (CPSS) architecture centered on a stochastic state-space model with empirically guided couplings. This is supported by a semantic sensing layer where large language models (LLMs) convert daily geo-referenced public text into noisy welfare indicators. These signals are then fused with quarterly administrative records via an extended Kalman filter (EKF). Finally, a lightweight convex post-processing layer enforces fairness, differential privacy, and minimum representation as hard constraints. A key ingredient is a state-dependent noise model in which the LLM observation variance grows exponentially with digital exclusion. Under this model, we study finite-horizon observability and obtain an exclusion threshold beyond which several welfare dimensions become effectively unobservable over 30–60 day horizons; EKF error bounds scale with the same exponent, clarifying when semantic sensing is informative and when it is not. Finally, a 100,000-agent agent-based model of a synthetic city with daily shocks suggests that, relative to a quarterly-only baseline, the LLM-augmented fusion pipeline substantially reduces detection lags and multi-dimensional cascade failures while keeping estimation error bounded and satisfying the explicit fairness and privacy constraints. Full article

Mixed palletizing remains a core challenge in distribution centers and modern warehouse operations, particularly within robotic handling and automation systems. Progress in this domain has been hindered by the lack of realistic, freely available datasets for rigorous algorithmic benchmarking. This work addresses this gap by introducing MixedPalletBoxes, a family of seven synthetic datasets designed to evaluate algorithm scalability, adaptability and performance variability across a broad spectrum of workload sizes (500–100,000 records) generated via an open source Python script. These datasets enable the assessment of algorithmic behavior under varying operational complexities and scales. Each box instance is richly annotated with geometric dimensions, material properties, load capacities, environmental tolerances and handling flags. To support dynamic experimentation, the dataset is accompanied by a FastAPI-based tool that enables the on-demand creation of randomized daily picking lists simulating realistic inbound orders. Performance is analyzed through metrics such as pallet count, volume utilization, item distribution per pallet and runtime. Across all dataset sizes, the distributions of the physical attributes remain consistent, confirming stable generation behavior. The proposed framework combines standardization, feature richness and scalability, offering a transparent and extensible platform for benchmarking and advancing robotic mixed palletizing solutions. All datasets, generation code and evaluation scripts are publicly released to foster open collaboration and accelerate innovation in data-driven warehouse automation research. Full article

Synthetic Aperture Radar (SAR) images of the sea surface reveal a variety of oceanic and atmospheric phenomena. Automatically detecting and identifying these phenomena is essential for understanding ocean dynamics and ocean–atmosphere interactions. This study selected 2383 Sentinel-1 Wave (WV) mode images and 2628 Interferometric Wide swath (IW) mode sub-images to construct a semantic segmentation dataset covering 12 typical oceanic and atmospheric phenomena, with a balanced distribution of approximately 400 sub-images per category, culminating in a comprehensive dataset of 5011 samples. The images in this dataset have a resolution of 100 m and dimensions of 256 × 256 pixels. We propose Segformer-OcnP model based on Segformer for the semantic segmentation of these multiple oceanic and atmospheric phenomena. Experimental results demonstrate that Segformer-OcnP outperforms classic CNN-based models (U-Net, DeepLabV3+) and mainstream Transformer-based models (SETR, the original Segformer), achieving 80.98% mDice, 70.32% mIoU, and 86.77% Overall Accuracy, verifying its superior segmentation performance. Full article

This study introduces an amplitude-based method that applies Spatially Variant Apodization (SVA) to reduce sidelobes in Synthetic Aperture Radar (SAR) data. Unlike conventional approaches, the filter is applied only to the amplitude while preserving the original interferometric phase, thereby enabling accurate Persistent Scatterer Interferometry (PSI) for dam deformation monitoring in Stanford Method for Persistent Scatterers (StaMPS) software. The SVA filter is integrated as an additional processing step within the Sentinel Application Platform (SNAP) for the SentiNel Application Platform to Stanford Method for Persistent Scatterers (SNAP2StaMPS) workflow. Filtering is performed in two dimensions (azimuth and range) separately on the In-phase (I) and Quadrature (Q) components of the coregistered data using a Python-based implementation via SNAP-Python (snappy). By recombining the SVA-filtered and original I and Q components, the method modifies only the amplitude while leaving the phase unchanged. The approach is evaluated in a proof-of-concept case study of the Sorpe Dam in Germany, where an Electronic Corner Reflector - C Band (ECR-C) produces sidelobe artifacts that degrade the Sentinel-1 (S-1) descending time series. The results demonstrated a successful integration of SVA filtering into the SNAP2StaMPS framework, achieving sidelobe reduction and improved Persistent Scatterer (PS) detection without compromising phase quality. The number of sidelobe-affected PS decreased by 39.26% after SVA filtering, while the amplitude-based approach preserved the original phase and deformation values, with a Root Mean Square Error (RMSE) of approximately 0.38 mm. Overall, this novel amplitude-based SVA approach extends the SNAP2StaMPS workflow by reducing strong sidelobes while preserving phase information for dam monitoring at the Sorpe dam site. Full article

In the rapid development of rural tourism, multiple disturbances, such as capital reorganization, uneven resource distribution, and the marginalization of farmers as the main body, have emerged. This has led to the dual challenges of increased vulnerability and insufficient resilience of farmers’ livelihood systems in the face of risk shocks. Based on survey data of the “Agri-Tourism–Commerce–Culture Integration” demonstration zone in China, this study integrates the Pressure–State–Response model into the analysis of livelihood resilience and constructs a “vulnerability–adaptability–recuperability” tri-dimensional framework. Through methods such as the entropy weight method, the synthetical index method, grey relational degree analysis, and the obstacle degree model, this study measures the levels of different livelihood types of farmers in each dimension of livelihood resilience and their influencing factors. The research findings indicate that the overall livelihood resilience of farmers in the study area was at a medium level, with vulnerability making the most significant contribution, reflecting that the current livelihood system is dominated by risk resistance. Different types of farmers exhibit heterogeneity in resilience, with tourism-oriented farmers showing the highest resilience and agriculture-oriented farmers the lowest. However, tourism-oriented farmers also display the most prominent vulnerability, revealing the tension between short-term efficiency enhancement and long-term risk diversification in single livelihood strategies. Key factor analysis reveals that vulnerability correlates most strongly with livelihood resilience. The most correlated indicators are the price increase rate, proportion of migrant workers, and neighborhood trust in the vulnerability, adaptability, and recuperability dimensions. Diagnosis of obstacle factors reveal that loan accessibility, land resource dependency, and agricultural risk perception rank as the top three common obstacles, with tourism-driven farmers exhibiting higher obstacle degrees than other farmer categories. These findings not only validate the empowering effect of rural tourism on farmers’ livelihoods but also reveal the different livelihood strategies chosen by various farmers. Based on the results, this study proposes policy recommendations of “common optimization + individual adaptation” to enhance farmers’ livelihood resilience. This is conducive to transforming external support into farmers’ endogenous resilience capabilities and provides a useful reference for achieving the deep integration of rural tourism and farmers’ livelihood systems. Full article

Background: Glioblastoma (GBM) is among the most aggressive and morphologically heterogeneous brain tumors. Beyond static imaging biomarkers, its structural organization can be viewed as a nonlinear dynamical system. Characterizing morphodynamic attractors within such a system may reveal latent stability patterns of morphological change and potential indicators of morphodynamic organization. Methods: We analyzed 494 subjects from the multi-institutional BraTS 2020 dataset using a fully automated computational pipeline. Each multimodal MRI volume was encoded into a 16-dimensional latent space using a 3D convolutional autoencoder. Synthetic morphological trajectories, generated through bidirectional growth–shrinkage transformations of tumor masks, enabled training of a contraction-regularized Neural Ordinary Differential Equation (Neural ODE) to model continuous-time latent morphodynamics. Morphological complexity was quantified using fractal dimension (DF), and local dynamical stability was measured via a Lyapunov-like exponent (λ). Robustness analyses assessed the stability of DF–λ regimes under multi-scale perturbations, synthetic-order reversal (directionality; sign-aware comparison) and stochastic noise, including cross-generator generalization against a time-shuffled negative control. Results: The DF–λ morphodynamic phase map revealed three characteristic regimes: (1) stable morphodynamics (λ < 0), associated with compact, smoother boundaries; (2) metastable dynamics (λ ≈ 0), reflecting weakly stable or transitional behavior; and (3) unstable or chaotic dynamics (λ > 0), associated with divergent latent trajectories. Latent-space flow fields exhibited contraction-induced attractor-like basins and smoothly diverging directions. Kernel-density estimation of DF–λ distributions revealed a prominent population cluster within the metastable regime, characterized by moderate-to-high geometric irregularity (DF ≈ 1.85–2.00) and near-neutral dynamical stability (λ ≈ −0.02 to +0.01). Exploratory clinical overlays showed that fractal dimension exhibited a modest negative association with survival, whereas λ did not correlate with clinical outcome, suggesting that the two descriptors capture complementary and clinically distinct aspects of tumor morphology. Conclusions: Glioblastoma morphology can be represented as a continuous dynamical process within a learned latent manifold. Combining Neural ODE–based dynamics, fractal morphometry, and Lyapunov stability provides a principled framework for dynamic radiomics, offering interpretable morphodynamic descriptors that bridge fractal geometry, nonlinear dynamics, and deep learning. Because BraTS is cross-sectional and the synthetic step index does not represent biological time, any clinical interpretation is hypothesis-generating; validation in longitudinal and covariate-rich cohorts is required before prognostic or treatment-monitoring use. The resulting DF–λ morphodynamic map provides a hypothesis-generating morphodynamic representation that should be evaluated in covariate-rich and longitudinal cohorts before any prognostic or treatment-monitoring use. Full article

Synthetic Aperture Radar (SAR) target detection faces significant challenges including speckle noise interference, weak small object features, and multi-category imbalance. To address these issues, this paper proposes LSDA-YOLO, an enhanced SAR target detection framework built upon the YOLO architecture that integrates Large Kernel Attention and SimAM dual attention mechanisms. Our method effectively overcomes these challenges by synergistically combining global context modeling and local detail enhancement to improve robustness and accuracy. Notably, this framework leverages the inherent symmetry properties of typical SAR targets (e.g., geometric symmetry of ships and bridges) to strengthen feature consistency, thereby reducing interference from asymmetric background clutter. By replacing the baseline C2PSA module with Deformable Large Kernel Attention and incorporating parameter-free SimAM attention throughout the detection network, our approach achieves improved detection accuracy while maintaining computational efficiency. The deformable large kernel attention module expands the receptive field through synergistic integration of deformable and dilated convolutions, enhancing geometric modeling for complex-shaped targets. Simultaneously, the SimAM attention mechanism enables adaptive feature enhancement across channel and spatial dimensions based on visual neuroscience principles, effectively improving discriminability for small targets in noisy SAR environments. Experimental results on the RSAR dataset demonstrate that LSDA-YOLO achieves 80.8% mAP50, 53.2% mAP50-95, and 77.6% F1 score, with computational complexity of 7.3 GFLOPS, showing significant improvement over baseline models and other attention variants while maintaining lightweight characteristics suitable for real-time applications. Full article

This article presents research on developing a synthetic measure to assess the readiness of individual EU countries to store energy from renewable energy sources. The authors developed individual measures that describe both the technical aspects of energy storage and the systemic and strategic aspects related to energy security and energy transition. These measures enabled the development of a synthetic measure, the Energy Storage Readiness Index (ESRI-BESS), and scenarios for the development of energy storage facilities in the European Union. TOPSIS and Monte Carlo methods were used. In the research presented, the authors focused their analyses on how the system interacts with storage facilities, rather than on what is installed. A quantitative set of indicators was constructed, embedded in the 4A energy security model. The resulting indicator measures not only whether storage facilities exist but also whether the system is prepared to ensure the country’s energy security. The results obtained indicate the need to build a flexible regulatory framework adapted to the growing role of storage facilities, that is, to facilitate and accelerate the process of connecting storage facilities to the grid. In the context of 4A, it is important to note that energy storage facilities can strengthen all four pillars of energy security when infrastructure development is paralleled by reforms and grid integration. The ability to store and flexibly manage energy is becoming a new dimension of energy transformation. Full article