Search Results (6,879)

Understanding the spatial organization of the new materials industry chain is essential for promoting sustainable industrial development. However, existing research rarely examines it as an integrated intercity network spanning multiple segments and specialized sub-sectors. To address this gap, this study constructs the New Materials City Association Network (NM-CityNet) using firm-level cross-regional equity investment data for 294 Chinese cities from 2010 to 2024. NM-CityNet includes two dimensions: segment networks (upstream, midstream, downstream) and sub-sector networks (advanced basic materials, critical strategic materials, and frontier new materials). A chain-lock model is applied, combined with social network analysis and the quadratic assignment procedure. Location quotients are integrated with weighted degree to capture specialized division-of-labour patterns. Using these methods, this study reveals the regional distribution, network structure, specialization patterns, and formation mechanisms of NM-CityNet. Results show that: (1) upstream core cities cluster in eastern China, midstream activities diffuse toward central and western regions, and downstream activities concentrate along the south-eastern coast; (2) NM-CityNet remains sparse and shows clear community structures, while different segments form differentiated spatial organization mechanisms; (3) sub-sectors exhibit clear specialization, with critical strategic materials showing broader spatial coverage; (4) drivers are heterogeneous: administrative proximity promotes link formation; government S&T financial-support differences are positively associated with link formation, although this association may partly reflect selective investment effects; economic and transport disparities inhibit link formation; innovation differences matter only in the midstream segment; and resource-endowment differences matter upstream and downstream. Full article

Fracture characterization is crucial to constrain a realistic subsurface reservoir model. They are key elements, affecting fluid flow, permeability and consequently recovery factor and productivity. Considering a proper assessment of fracture network from subsurface investigation is often difficult; in recent years, the application of Digital Photogrammetry (DP) has become popular for fracture network characterization. In this paper, we combined DP and Discrete Fracture Network modeling (DFN) to assess the fluid circulation analysis of the Monte Antola Formation (Northern Apennines, Italy). Thanks to the application of DP, it is possible to reconstruct Digital Outcrop Models (DOMs) and acquire high-precision fracture measurements such as size, location, and orientation. Utilizing quantitative measurements, we performed DFNs to simulate rock mass permeability. The primary findings from the DFNs indicate that fluid circulation is primarily influenced by (1) regions with a high density of fractures, which are associated with the primary structural features observed throughout the study area, and (2) locally, by the orientation of the dominant and persistent fracture set. The proposed approach highlights the importance of the use of DOMs for better reconstruction of the fracture network and defining an important number of relevant parameters; such quantitative information remarkably improves the reliability of DFNs. Full article

The formation and development of gullies are pervasive drivers of hillslope degradation, yet forecasting where and at what elevation gullies begin remains challenging. This study proposes a morphometric–energetic framework to anticipate gully-initiation zones in catchments developed on low-permeability lithologies and limited tectonic control across contrasting climatic and geomorphic settings. Using GIS analyses and morphometric parameters, with some derived from hypsometric curves, our objective is to link basin-scale morphology and energy distribution to the propensity for linear incision, thereby defining a statistically representative initiation belt and stream network positions most susceptible to gully initiation. The study results show that the altitudinal range most susceptible to gully development is at the mean basin’s elevation, and that this range can be associated with an energy potential (Şen’s “Energy Index”) similar to those used to calculate hydroelectric potential in a river basin. Furthermore, the study highlights that the contributing area required to activate these erosive processes varies within fairly narrow limits, between 1 and 3 ha. The framework is designed to be quantitative, transferable among landscapes, and parsimonious in data requirements, even if applicable, as mentioned, in basins with low-permeability lithology and limited tectonic control, and as a first-level predictive tool. By prioritizing diagnostics that can be computed from standard topographic datasets, the approach aims to support land-use planning and sediment-risk mitigation, offering a practical pathway for early identification and management of areas vulnerable to gullying. Full article

Multistage hydraulic fracturing in horizontal wells induces significant spatial heterogeneity in fracture networks because of the complex interactions between reservoir geological characteristics and fracturing operations. This heterogeneity is difficult to capture using conventional productivity models, even when realistic fracture geometry is considered, resulting in large prediction deviations. To address this issue, this study proposes a physics-data hybrid productivity prediction model optimized by a production-informed loss function, with a tight sandstone gas reservoir in the Ordos Basin used as the case study. By integrating field data with asymmetric-fracture numerical simulations, eight controlling parameters, including formation pressure and reservoir thickness, were identified through weighted sensitivity analysis. On this basis, a refined productivity forecasting model was established by combining the Goose Optimization Algorithm (GOOSE) with long short-term memory (LSTM). Four physics-data hybrid models were developed based on the GOOSE-LSTM framework. Among them, the loss-function-optimized model exhibited the best performance, achieving an R² of 0.953 and a prediction error below 0.05. The proposed methodology provides a refined decision-making tool for fracturing scheme optimization and development plan formulation, thereby improving productivity forecasting accuracy and supporting the efficient development of target horizontal-well intervals. Full article

Repeated heat–moisture treatment (RHMT) is an efficient approach for modifying starch. However, the role of treatment temperature, a critical parameter, remains poorly understood. Therefore, this study investigated the effects of RHMT temperatures (80, 100, 120 °C) and cycles (1, 3, 5, 7) on the multi-scale structure and in vitro digestibility of hard proso millet starch, using native starch as a control. Compared with the severe 120 °C treatment, processing at 100 °C better preserved double-helical organization (supported by moderately retained enthalpy, ΔH) and short-range order, while maintaining granule integrity. These structural retentions restricted swelling, improved pasting stability, and reinforced the macroscopic gel network. Furthermore, multivariate analysis suggested that the rigidified internal granular architecture delayed initial enzymatic hydrolysis, maximizing slowly digestible starch (SDS) formation (47.44% in 100-RHMT-5). Conversely, 120 °C caused severe granular collapse and a drastic drop in ΔH, diminishing gel elasticity and triggering a surge in rapidly digestible starch (RDS, 59%). Overall, 100 °C RHMT yields an SDS-enriched starch, which may be a promising ingredient for the development of starch-based foods with slower in vitro digestibility. Full article

Pollen exposure drives allergic disease in millions of Americans, yet no standardized, publicly available national pollen dataset has existed until now. We describe the first nationally standardized and publicly available dataset of pollen season timing and airborne pollen concentrations. The data were derived from the National Allergy Bureau™ (NAB™), the only pollen and mold measuring network in the United States certified by the American Academy of Allergy, Asthma & Immunology (AAAAI), and curated, processed, and disseminated by the U.S. Centers for Disease Control and Prevention (CDC). The 2003–2024 dataset provides standardized measures of (1) taxa-specific historical average main pollen season (MPS) concentrations and timing (e.g., start dates, peak dates, end dates, season length); (2) taxa-specific yearly MPS concentrations and timing; (3) grouped weekly MPS concentrations, levels, and timing; and (4) grouped daily pollen levels and MPS timing. Pollen concentrations are reported as pollen grains per cubic meter (PPCM). MPS timing is computed using a 3-day consecutive method: season start occurs after the first occurrence of three consecutive days with concentrations > 1.0 PPCM; season peak is the day of maximum concentration; and season end occurs after the first occurrence of three consecutive days with concentrations < 1.0 PPCM after the peak. Historical average timing is calculated in a 365-day-of-year format and converted to calendar dates using 2024 as a reference year for display and consistency. By combining long-term data from monitoring sites across the country, this dataset shows how pollen levels vary over time and across geographic locations. This resource supports tracking pollen trends, linking pollen with weather and climate factors, and informing public health action, clinical care, and communication about population exposure and the impact to allergic diseases such as asthma and hay fever. Full article

Background: Oil-in-water nanoemulsions (NEs) represent versatile platforms for the delivery of hydrophobic compounds and find a wide range of applications in different fields such as food, cosmetics, agriculture, pharmaceutics, and oil and gas industries. Various methodologies can be applied for the preparation of NEs as low-energy and high-energy methods. Among them, high-energy ultrasonication (HEU) is a popular technique in research laboratories or small manufacturing facilities. However, a clear gap remains in understanding how, and to what extent, experimental parameters and the chemical and physical characteristics of the components affect the formation and properties of NEs through HEU. Methods: In this work, a comprehensive screening of factors (oil viscosity and density, surfactant type, processing parameters, and formulation composition) affecting NEs formation and quality was performed and an artificial neural network (ANN) was applied to determine the relative relevance of each parameter. Results: Oil viscosity revealed to be the primary factor affecting droplet size (Z_avg) and polydispersity index (PDI), with high-viscosity oils leading to poor emulsification into nanosized droplets. Higher processing temperatures improved NE formation by reducing viscosity during sonication. Ultrasound amplitude and pulse mode influenced NE characteristics, particularly under challenging conditions. Surfactant type and oil content had, instead, minor effects on the NEs’ features. ANN modelling accurately predicted NEs’ properties and identified critical viscosity limits for successful nanosized emulsification (Z_avg < 300 nm and PDI < 0.4). Conclusions: These findings provide a predictive basis for rational NE design under HEU, serving as a guide for researchers working in different fields. Full article

We develop a categorical and operadic semantics for the diagrammatic proof system underlying the Greimas semiotic square. Building on a prior proof-theoretic formulation, we extract a typed signature of diagrammatic inference rules and construct the corresponding free coloured operad ${\mathcal{O}}_{\Sigma }$, whose elements correspond to proof trees. This establishes a precise correspondence between diagrammatic derivations and operadic terms, making explicit the compositional structure implicit in the original system. We then interpret these terms as wiring diagrams in a symmetric monoidal setting, yielding a graphical semantics in which intermediate semantic configurations are represented as flows through a network of operations. Within this framework, the construction of the semiotic square is realised as a single composite operation $\Omega$, obtained by operadic substitution of generators corresponding to negation, implication, and meta-term formation. Finally, we consider semantic interpretations of this structure as algebras of ${\mathcal{O}}_{\Sigma }$, yielding a category $\mathbf{Alg}\left({\mathcal{O}}_{\Sigma }\right)$, whose morphisms capture structure-preserving translations between interpretations. This provides a formal account of the extensibility of the square across domains such as seme-level analysis, modality, and narratology, and recasts it as a compositional semantic schema rather than a static relational diagram. Full article

Laser powder bed fusion (LPBF) is a widely utilized metal additive manufacturing (AM) process for fabricating intricate geometries with high mechanical strength. However, achieving defect-free parts remains challenging due to complex thermodynamics and process variability. Component quality is primarily determined by mel-pool morphology, which depends on key process parameters such as laser power, scan speed, and layer thickness. Improper parameter selection causes defects like porosity (keyhole and lack of fusion), balling, and residual stresses, compromising structural integrity. Optimizing these parameters is crucial but difficult due to the multi-scale, multi-physics nature of the process, which traditionally relies on costly, time-intensive experimental trials. We present results from a data-driven approach using machine learning (ML) models to predict and optimize LPBF melt-pool characteristics, reducing reliance on trial-and-error experimentation. We find that laser power and scan speed predominantly influence the melt-pool formation. Higher scan speeds produce more favorable melt pools, whereas excessive laser power at low scan speeds leads to deep keyhole defects. To predict and classify melt pools efficiently, several ML models are deployed, including logistic regression, decision trees, ensemble learning, and fully connected neural networks. The standard neural network achieved the highest cross-validated macro-F1 score of 0.978 ± 0.014, while the weighted neural network achieved the highest recall for the rare optimal melt-pool class, 0.967 ± 0.050. These findings show that class-weighted learning provides a recall-oriented strategy for identifying suitable LPBF process windows, while avoiding overreliance on single train-test split performance. The findings underscore the effectiveness of ML in accurately classifying LPBF melt pools to rapidly identify optimal process parameters. Full article

The rapid growth of digital data has created an urgent need for innovative storage solutions. Transactional data, with its diverse formats and row-based structure, is a significant contributor to this challenge. Existing storage methods struggle to compress heterogeneous structured data efficiently due to limited redundancy exploitation across mixed data types. This study introduces Pixelated, a novel lossless data transformation and storage framework that converts structured transactional data into pixel representations stored in Portable Network Graphics (PNG) format. Pixelated introduces a new data representation strategy that enables the existing DEFLATE compression mechanism within PNG to exploit patterns and redundancy in heterogeneous transactional datasets more effectively. Designed for datasets containing numerical, categorical, and datetime values, Pixelated achieves average compression rates exceeding 90%. The framework is evaluated across benchmark and real-world datasets, demonstrating competitive performance against ZIP, Apache Parquet, and Python Pickle. Detailed methodology, redundancy characteristics, limitations, and performance results are presented. Full article

Stretchable conductive composites are important for soft electronics, wearable systems, and adaptive electromechanical devices, yet the mechanisms governing strain-dependent electrical transport remain insufficiently understood, particularly in hybrid filler systems. In this work, the strain-dependent electromechanical behavior of graphite–silicone and hybrid graphite–copper–silicone composites was investigated under uniaxial tensile deformation up to 60% strain. Electrical measurements revealed distinct transport behaviors governed by filler composition and conductive network structure. Graphite-only composites containing 50 wt% and 60 wt% graphite exhibited monotonic resistance increases with increasing strain due to progressive widening of inter-particle tunneling gaps between neighboring graphite platelets. In contrast, hybrid graphite–copper composites showed monotonic resistance decreases under deformation, which is attributed to Poisson-ratio-driven transverse contraction, tunneling-gap reduction, and strain-assisted formation of Cu–Cu and Cu–graphite conductive pathways. Representative volume element (RVE)-based simulations further supported the proposed transport interpretation. From an engineering design perspective, the results show that filler composition and conductive network architecture can be used as design variables to tune strain-dependent electrical responses in stretchable conductive composites. These findings provide design guidance for developing silicone-based conductive composites with tunable electromechanical functionality for soft electronics, wearable sensors, and adaptive devices. Full article

The increasing global demand for dietary protein has intensified the search for functional and sustainable plant-based ingredients. Chickpea flour is a promising candidate owing to its high nutritional quality and rich bioactive content. This study evaluated the use of microfluidization as a non-thermal strategy to enhance the physicochemical and functional properties of chickpea flour. Microfluidization induced particle fragmentation and led to protein denaturation, producing more irregular and porous surface morphologies. These structural modifications increased surface hydrophobicity, enhancing emulsifying and foaming capacities. Enhanced surface hydrophobicity also led to marked improvements in oil-holding capacity (up to 210% increase over control, after microfluidization at 200 MPa for three passes), likely due to stronger interactions with non-polar solvents. In parallel, microfluidization facilitated greater protein-water interactions, resulting in a 210% increase in protein solubility and 40% improvement in water-holding capacity after microfluidization at 200 MPa for one pass, compared to control. Increased surface area additionally contributed to higher in vitro protein digestibility (about 45% higher than control for all microfluidized samples) and the formation of a stronger network. Overall, these results demonstrate that microfluidization is an effective approach for improving the functional performance of whole chickpea flour, supporting its potential application in plant-based food systems. Full article

To clarify the anomalous corrosion behavior in Cu-containing CoCrNi-based medium-entropy alloys, in which an enhanced corrosion driving force is accompanied by a reduced overall corrosion rate, the phase constitution, microstructure, electrochemical behavior, post-corrosion morphology, and surface chemical states of CoCrNi, CoCrNiCu, and CoCrNiCuFe alloys were systematically compared. The results show that Cu addition induces pronounced phase separation in the CoCrNi matrix, leading to the formation of a Cu-depleted FCC1 phase, a continuous Cu-rich FCC2 intergranular network, and dispersed nanoscale Cu-rich precipitates, with an FCC2 area fraction of about 0.145. In 3.5 wt.% NaCl solution, CoCrNiCu exhibits a stronger thermodynamic tendency for corrosion, whereas its overall corrosion rate does not increase, but instead shows the lowest corrosion current density and higher impedance, indicating an anomalous electrochemical response. Post-corrosion SEM morphology, EDS elemental mapping, and XPS valence-state analyses further reveal that corrosion is mainly concentrated in the Cu-rich phases and their adjacent narrow regions, while the Cu-rich phases themselves remain relatively stable as non-sacrificial cathodes. Semi-quantitative thermodynamic and mass-transport calculations indicate that although Cu-induced phase separation enhances the micro-galvanic corrosion driving force, with an estimated interphase potential difference of about 0.337 V, the overall corrosion rate remains constrained by the oxygen diffusion supply during cathodic oxygen reduction on the Cu-rich regions. Therefore, the anomalous corrosion response of CoCrNiCu can be attributed to the synergistic effect of the enhanced micro-galvanic corrosion driving force caused by Cu-induced phase separation and the restricted cathodic oxygen supply. Full article

Co- and Ni-doped mullite–spinel ceramics were synthesized via a sol–gel method followed by high-temperature sintering in order to investigate the influence of dopant type on the phase evolution, microstructure, and optical properties. X-ray diffraction analysis confirmed the formation of a multiphase system consisting of mullite and spinel phases, with a residual amorphous fraction, the amount of which decreases with increasing temperature. FTIR and Raman spectroscopy indicate progressive structural ordering of both spinel and aluminosilicate networks during thermal treatment, with differences in crystallization behavior between Co- and Ni-containing system. UV–Vis spectroscopy revealed characteristic absorption bands arising from d–d electronic transitions of Co²⁺ and Ni²⁺ ions in the ceramic matrix, reflecting differences in their local coordination environments and optical behavior. Colorimetric analysis showed that Co-doped samples exhibit intense blue coloration, whereas Ni-doped ceramics display greenish-blue hues. The temperature-dependent evolution of the L*, a*, and b* parameters correlate with structural changes. The results suggest that the type of additive influences the phase evolution and optical response in mullite–spinel ceramics, in agreement with structural and spectroscopic analyses. Full article

Inflorescence degeneration in Areca catechu L. is characterized by growth arrest, tissue shrinkage and browning, ultimately compromising functional inflorescence formation and yield stability. To investigate its developmental window and regulatory basis, inflorescences from different leaf positions at the full-bloom stage were analyzed using anatomical observation, morphological measurements, carbohydrate and hormone assays, and RNA-seq-based transcriptomic analysis with qRT-PCR validation. Inflorescence degeneration was mainly concentrated in axillary inflorescences at the third and fourth leaf positions (BY3 and BY4). Compared with adjacent normal inflorescences, degenerated inflorescences showed reduced sucrose, starch and trehalose contents, increased ABA, JA and MeJA levels, and decreased cZR levels. Transcriptomic analysis revealed clear separation between degenerated and normal inflorescences, and differentially expressed genes were enriched in starch and sucrose metabolism, plant hormone signal transduction and transcriptional regulation. Co-expression network analysis identified modules associated with the degeneration window and key physiological traits, highlighting six candidate hub genes: AcAHP2, AcTIFY4B, AcTPS9-2, AcHXK2, AcWRKY3 and AcMPK1. These findings suggest that inflorescence degeneration is closely associated with carbon metabolic imbalance, hormone network remodeling and co-expression network reprogramming within a specific developmental window, providing a basis for future mechanistic studies and control strategies. Full article