Search Results (1,478)

Bamboo-derived biochar (BC) is promising for high-salinity wastewater treatment through photothermal evaporation. This study systematically evaluated BCs synthesized at 400–800 °C with residence times of 40 or 70 min. Pyrolysis temperature proved dominant, with 600 °C representing a critical threshold. Below this temperature, BCs maintained high carbon content and polar functional groups but exhibited limited porosity. Above it, structural reorganization enhanced pore development and aromaticity while reducing polar surface groups. Residence time primarily influenced volatile retention, and prolonged pyrolysis led to pore collapse. The optimal BC—produced at 800 °C for 40 min—combined hierarchical porosity with balanced surface chemistry, achieving an evaporation rate of 1.21 kg/m²·h and a photothermal efficiency of 70.45% under high-salinity conditions. Mechanistic analysis indicates that short, high-temperature pyrolysis preserves structural integrity and interfacial activity with minimal energy input. These results establish a thermal processing approach that reconciles carbon stability with surface functionality, offering practical guidance for scaling efficient and sustainable biochar-based wastewater treatment systems. Full article

Solid base catalysts hold significant promise for replacing traditional homogeneous bases with green chemical processes. However, the construction of their strong basic sites typically relies on high-temperature calcination, which often leads to the collapse of the carrier structure and high energy consumption. This study proposes a novel “carrier reducibility tuning” strategy, which involves endowing the carrier with intrinsic reducibility to induce the low-temperature decomposition of alkali precursors via a redox pathway, thereby enabling the mild construction of strong basic sites. Low-valence Cr³⁺ was doped into a mesoporous zirconia framework, successfully fabricating an MCZ carrier with a mesostructure and reducible characteristics. Characterization results indicate that a significant redox interaction between the Cr³⁺ in the carrier and the supported KNO₃ occurs at 500 °C. This interaction facilitates the complete conversion of KNO₃ into highly dispersed, strongly basic K₂O species, while Cr³⁺ is predominantly oxidized to Cr⁶⁺. This activation temperature is approximately 300 °C lower than that required for the conventional thermal decomposition pathway and effectively preserves the structural integrity of the material. In the transesterification reaction for synthesizing dimethyl carbonate, the prepared catalyst exhibits superior catalytic activity, significantly outperforming classic solid bases like MgO and other reference catalysts. Full article

Rheology plays an important role in the 3D concrete printing technology, because it directly governs the flowability and shape retention of the material, impacting both the printing process and the final quality of the obtained structure. Local raw materials such as Portland cement, washed sand, and tap water were used for the preparation of 3D-printed concrete mixtures. The solid-state polycarboxylate ether with an anti-foaming agent was used as superplasticizer. The Portland cement was partially replaced (by volume) with a natural zeolite additive in amounts ranging from 0% to 9% in 3D-printed concrete mixtures. A rotational rheometer with coaxial cylinders was used in this research for the determination of rheological characteristics of prepared 3D-printed concrete mixtures. The Herschel–Buckley model was used to approximate experimental flow curves and assess rheological parameters such as yield stress, plastic viscosity, and shear-thinning/thickening index. The additional experiments and calculations, such as water bleeding test and evaluation of the carbon footprint of 3D-printed concrete mixtures, were performed in this work. The replacement of Portland cement with natural zeolite additive positively influenced rheological and stability-related properties of 3D-printed concrete mixtures. Natural zeolite additive consistently reduced water bleeding, enhanced yield stress under increasing shear rates, and lowered plastic viscosity, thereby improving flowability and mixture transportation during the 3D printing process. As the shear-thinning/thickening index remained stable (indicating non-thixotropic behavior in most cases), higher amounts of natural zeolite additive introduced slight thixotropy (especially under decreased shear rates). These changes contributed to better shape retention, layer stability, and the ability to print taller and narrower structures without collapse, making natural zeolite additive suitable for use in the optimized processes of 3D concrete printing. A significant decrease in total carbon footprint (from 3% to 19%) was observed in 3D-printed concrete mixtures with an increase in the mentioned amounts of natural zeolite additive, compared to the mixture without this additive. Full article

Dryland coastal–mountain systems stand at the frontline of climate change, where steep topographic gradients amplify the balance between resilience and collapse. Mount Elba—Egypt’s hyper-arid coastal–mountain reserve—embodies this fragile equilibrium, preserving a seventy-year climatic record across a landscape poised between sea and desert. Here, we present the first multi-decadal, spatio-temporal assessment (1950–2021) integrating the Standardized Precipitation–Evapotranspiration Index (SPEI-6) with satellite-derived vegetation responses (NDVI) along a ten-grid coastal–highland transect. Results reveal a pervasive drying trajectory of −0.42 SPEI units per decade, with vegetation–climate coherence (r ≈ 0.3, p < 0.05) intensifying inland, where orographic uplift magnifies hydroclimatic stress. The southern highlands emerge as an “internal drought belt,” while maritime humidity grants the coast partial refuge. These trends are not mere numerical abstractions; they trace the slow desiccation of ecosystems that once anchored biodiversity and pastoral livelihoods. A post-1990 regime shift marks the breakdown of wet-season recovery and the rise in persistent droughts, modulated by ENSO teleconnections—the first quantitative attribution of Pacific climate signals to Egypt’s coastal mountains. By coupling climatic diagnostics with ecological response, this study reframes drought as a living ecological process rather than a statistical anomaly, positioning Mount Elba as a sentinel landscape for resilience and adaptation in northeast Africa’s rapidly warming drylands. Full article

Large language models (LLMs) have become central to modern artificial intelligence, yet their internal decision-making processes remain difficult to interpret. As interest grows in making these models more transparent and reliable, topological data analysis (TDA) has emerged as a promising mathematical approach for exploring their structure. This scoping review maps the current landscape of research where TDA tools—such as persistent homology and Mapper—are used to examine LLM components like attention patterns, latent representations, and training dynamics. By analyzing topological features across layers and tasks, these methods provide new ways to understand how language models generalize, respond to unfamiliar inputs, and shift under fine-tuning. The review also considers how TDA-based techniques contribute to broader goals in interpretability and robustness, especially in detecting hallucinations, out-of-distribution behavior, and representational collapse. Overall, the findings suggest that TDA offers a rigorous and versatile framework for studying LLMs, helping researchers uncover deeper patterns in how these models learn and reason. Full article

Seepage–dissolution in carbonate rock fractures serves as the core driver governing the evolution of key engineering projects, including reservoir dam stability, CO₂ geological sequestration, and unstable rock collapse mitigation strategies. While physical heterogeneity (e.g., fracture aperture, mineral distribution) is widely recognized as a critical factor regulating dissolution processes, the specific influence of mineral distribution heterogeneity on dissolution rates still lacks quantitative quantification. To address this gap, this study focuses on limestone fractures and employs multi-component reactive transport numerical simulations to model acidic fluid (pH = 5.0) seepage–dissolution under two Darcy flux conditions (37.8/378 m·yr⁻¹). It investigates the controlling mechanisms of fracture roughness (λ_b = 0.036~0.308) and calcite contents (55%, 75%, 95%) on dissolution dynamics, and analyzes spatial variations in local Darcy velocity, reaction rate, and effective dissolution rate (R_eff,i). Results demonstrate that mineral distribution heterogeneity directly induces pronounced spatial heterogeneity in dissolution behavior: diffusion dominates under low flux (simulation duration: 48.3 days), forming discrete reaction fronts (~15 mm) controlled by mineral clusters; advection prevails under high flux (simulation duration: 4.83 days), generating alternating dissolution–deposition zones (~7.5 mm) with R_eff,i one order of magnitude greater than that under low flux. Notably, 55% calcite content yields the highest R_eff,i (1.87 × 10⁻¹¹ mol·m⁻²·s⁻¹), 0.94 orders of magnitude greater than that at 95% calcite content. A strong linear correlation (R² > 0.98) exists between the Damköhler number (Da_I) and R_eff,i at the same calcite content. Furthermore, the synergistic interaction between fracture aperture and mineral heterogeneity amplifies dissolution complexity, with high roughness (λ_b = 0.308) coupled with 55% calcite content achieving the highest R_eff,i of 2.1 × 10⁻¹¹ mol·m⁻²·s⁻¹. This study provides critical theoretical insights and quantitative data support for fractured rock mass evolution prediction models, geological hazard prevention, and geological carbon sequestration optimization. Full article

Mitochondrial Ca²⁺ signaling is increasingly recognized as a key integrator of synaptic activity, metabolism, and redox balance within the tripartite synapse. At excitatory synapses, Ca²⁺ influx through ionotropic glutamate receptors and voltage-gated channels is sensed and transduced by strategically positioned mitochondria, whose Ca²⁺ uptake and release tune tricarboxylic acid cycle activity, adenosine triphosphate synthesis, and reactive oxygen species (ROS) generation. Through these Ca²⁺-dependent processes, mitochondria are proposed to help set the threshold at which glutamatergic activity supports synaptic plasticity and homeostasis or, instead, drives hyperexcitability and excitotoxic stress. Here, we synthesize how mitochondrial Ca²⁺ dynamics in presynaptic terminals, postsynaptic spines, and perisynaptic astrocytic processes regulate glutamate uptake, recycling, and release, and how subtle impairments in these pathways may prime synapses for failure well before overt energetic collapse. We further examine the reciprocal interplay between Ca²⁺-dependent metabolic adaptations and glutamate homeostasis, the crosstalk between mitochondrial Ca²⁺ and ROS signals, and the distinct vulnerabilities of neuronal and astrocytic mitochondria. Finally, we discuss how disruption of this Ca²⁺-centered mitochondria–glutamatergic axis contributes to synaptic dysfunction and circuit vulnerability in neurodegenerative diseases, with a particular focus on Alzheimer’s disease. Full article

During coal mining, detecting subsurface structures (such as faults, voids, collapse columns, etc.) using radio waves in existing mines is hindered by the absence of effective three-dimensional coal seam medium models and simulation methods, adversely affecting the forward modeling of data analysis. This study establishes a Three-Dimensional Finite-Difference Time-Domain (3D-FDTD) radio wave penetration medium model based on coal seam tunnel penetration working conditions to simulate the electric field intensity characteristics of longitudinal and transverse waves in various coal rock mediums. Firstly, a higher-order finite difference method based on Maxwell’s equations is employed to analyze the electric field characteristics of gas-enriched areas under various geological conditions, enabling the exploration of the relationship between the position and size of the electromagnetic wave field strength in different areas. The electromagnetic wave field strength response data are then analyzed during the actual detection process to determine the specific location, shape, and size of the abnormal area. Finally, by comparing the simulation results with an actual engineering project, electromagnetic wave field strength attenuation data were collected from 158 measuring points at a working face of a coal mine in Anhui. The detection results clearly illustrate the changes in electric field intensity (with attenuation coefficients ranging from 0.41 to 0.77 dB/m) in anomalous areas, enabling the forward simulation to accurately determine the position and size of faults. The novelty of this study lies in the establishment of a conductivity-weighted 3D-FDTD model specifically calibrated for complex coal seam environments, which significantly improves the accuracy of fault boundary detection compared to traditional linear inversion methods. Full article

Chitinases are crucial in the molting process of insects and represent potential targets for the development of RNA interference (RNAi)-based insecticidal strategies. In this study, we identified and characterized 11 chitinase genes (TaChts) in T. absoluta, each harboring at least one GH18 catalytic domain. Phylogenetic analysis placed these proteins into 11 established groups (I-X and h), revealing conserved lineage-specific patterns. Spatiotemporal expression profiling showed that most TaChts peak during key developmental transitions. TaCht1, TaCht2, TaCht3, TaCht5, TaCht6, TaCht7, TaCht10, TaCht-h, and TaIDGF were predominantly expressed in the integument, whereas TaCht8 and TaCht11 showed gut-enriched expression, indicating functional specialization. Stage-tailored RNAi assays demonstrated that silencing TaCht5, TaCht7, TaCht10, and TaIDGF caused pronounced mortality in both larvae and pupae. In larvae, delivery of dsRNA using carbon quantum dots (CQDs) disrupted molting: knockdown of TaCht7, TaCht10, and TaIDGF prevented shedding of the old cuticle, whereas TaCht5 silencing produced severe cuticular shrinkage and blackening. Hematoxylin and eosin staining revealed detachment of epidermal cells from the cuticle, providing cytological evidence of disrupted cuticle–epidermis remodeling. In pupae, microinjection of dsRNA induced cuticular collapse, melanization, and eclosion failure due to persistent adhesion of the pupal case, accompanied by marked reductions in chitinase activity. Together, our findings highlight the critical roles of four key chitinases in endocuticular turnover and metamorphic progression in T. absoluta, offering mechanistic insight into chitin-mediated developmental processes. The identified genes represent strong candidates for RNAi-based, species-specific pest management strategies against this globally significant insect. Full article

Conventional quantum mechanics treats the electron as a point-like particle endowed with intrinsic properties—mass, charge, and spin—that are inserted as axioms rather than derived from first principles. Here, we propose a thermodynamic reformulation of the electron grounded in entropy field dynamics, based on S-Theory. In this framework, the electron is composed of three distinct entropic components: S_core (a collapsed entropy core from configurational mass), S_EM (a structured electromagnetic entropy field from charge), and S_thermal (a diffuse entropy component from ambient interactions). We show that spin emerges as a rotating S_EM shell around S_core, and that electron collapse—as in quantum measurement—can be modeled as a Recursive Amplification of Sfield (RAS) process driven by entropic feedback. Through mathematical formulation and high-resolution simulations, we demonstrate how the S-field components evolve under entropic excitation, culminating in a collapse threshold defined by local entropy density matching. This model not only explains the emergence of quantum properties but also offers a thermodynamic mechanism for electron–photon interaction, wavefunction collapse, and spin generation, revealing the inner structure and dynamics of one of nature’s most fundamental particles. Full article

The Extended Digital Narrative (XDN) research project explores how experimental creative practice with emerging technologies generates critical insights into algorithmic narrativity—the intersection of human narrative understanding and computational data processing. This article presents five case studies demonstrating that direct engagement with AI and Extended Reality platforms is essential for humanities research on new genres of digital storytelling. Lina Harder’s Hedy Lamar Chatbot examines how generative AI chatbots construct historical personas, revealing biases in training data and platform constraints. Scott Rettberg’s Republicans in Love investigates text-to-image generation as a writing environment for political satire, documenting rapid changes in AI aesthetics and content moderation. David Jhave Johnston’s Messages to Humanity demonstrates how Runway’s Act-One enables solo filmmaking, collapsing traditional production hierarchies. Haoyuan Tang’s video game project reframes LLM integration by prioritizing player actions over dialogue, challenging assumptions about AI’s role in interactive narratives. Sérgio Galvão Roxo’s Her Name Was Gisberta employs Virtual Reality for social education against transphobia, utilizing perspective-taking techniques for empathy development. These projects demonstrate that practice-based research is not merely artistic production but a vital methodology for understanding how AI and XR platforms shape—and are shaped by—human narrative capacities. Full article

Reliable probabilistic wind speed forecasts are essential for integrating renewable energy into power grids and managing operational uncertainty. This study compares Quantile Regression Forests (QRF), Bayesian Additive Regression Trees (BART), and Gaussian Process Regression (GPR) under at-site and regional pooled frameworks using 21 years (2000–2020) of daily wind data from eleven stations in New South Wales and Queensland, Australia. Models are evaluated via strict year-based holdout validation across seven metrics: RMSE, MAE, R², bias, correlation, coverage, and Continuous Ranked Probability Score (CRPS). Regional QRF achieves exceptional point forecast stability with minimal RMSE increase but suffers persistent under-coverage, rendering probabilistic bounds unreliable. BART attains near-nominal coverage at individual sites but experiences catastrophic calibration collapse under regional pooling, driven by fixed noise priors inadequate for spatially heterogeneous data. In contrast, GPR maintains robust probabilistic skill regionally despite larger point forecast RMSE penalties, achieving the lowest overall CRPS and near-nominal coverage through kernel-based variance inflation. Variable importance analysis identifies surface pressure and minimum temperature as dominant predictors (60–80%), with spatial covariates critical for regional differentiation. Operationally, regional QRF is prioritised for point accuracy, regional GPR for calibrated probabilistic forecasts in risk-sensitive applications, and at-site BART when local data suffice. These findings show that Bayesian machine learning methods can effectively navigate the trade-off between local specificity and regional pooling, a challenge common to wind forecasting in diverse terrain globally. The methodology and insights are transferable to other heterogeneous regions, providing guidance for probabilistic wind forecasting and renewable energy grid integration. Full article

Streptococcus uberis is a bovine mastitis pathogen with a demonstrated ability to form biofilms. However, the dynamics of this process remain poorly characterized. This study aimed to comprehensively characterize biofilm formation in four S. uberis strains that differed in their biofilm-forming capacity, from weak to strong producers, and in the presence of key virulence-associated genes, such as sua, hasA and hasC. To achieve this, we integrated structural, biochemical, physiological and transcriptional analyses using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FT-IR), spectral flow cytometry and qRT-PCR. The multi-faceted analysis revealed a coordinated maturation peak at 48 h, characterized by a structured architecture with water channels, a distinct biochemical signature rich in polysaccharides and proteins, and a predominantly viable bacterial population. This peak coincided with a marked upregulation of key virulence-associated genes, with sua expression increasing 2.5-fold and hasA increasing 3-fold at 48 h. This mature biofilm conferred high tolerance to antibiotics, with eradication concentrations (>256 µg/mL) exceeding planktonic MICs, although tetracycline was notably effective. At 72 h, the biofilm entered a dispersion phase characterized by structural collapse and reduced viability. These findings establish S. uberis biofilm maturation as a highly coordinated process, providing new insights into the biofilm lifecycle of this important pathogen and identifying key temporal and molecular targets for future interventions. Full article

Covered karst collapse is a key geotechnical hazard in infrastructure construction in karst regions of China. In particular, strata consisting of an overlying clay layer and an underlying sand layer are prone to abrupt collapse induced by sand leakage under construction disturbances, which poses serious risks to pile foundation safety. To clarify the disaster-forming mechanism and develop a quantitative analysis method, this study investigates the mechanical behaviour of the entire collapse process by combining theoretical analysis with numerical simulation. A continuous mechanical analysis framework is established that follows the sequence from sand layer leakage to cavity expansion and then clay layer instability. Within this framework, a calculation model for the angle of repose of the sand layer is proposed that considers seepage and confined pressure effects. Simultaneously accounting for the influence of the casing, stability models for overall and localised collapses are developed using limit equilibrium theory. A comprehensive safety factor criterion K_c based on the critical span (or radius) is then proposed, leading to a linked evaluation method that couples the potential span of the sand layer with the ultimate span of the clay layer. The results show that an increase in Δh/h significantly reduces the angle of repose of the sand layer; the mechanical mechanism is confirmed whereby an increase in the roof span leads to shear stress exceeding the soil’s shear strength, thus triggering instability; the proposed safety factor K_c can effectively predict both overall and localised collapse, and case verification demonstrates that the predicted spans match well with actual collapse dimensions. The results provide a theoretical and technical basis for risk prediction, as well as for the prevention and control of pile foundation construction in karst areas. Full article

The abnormal pore pressure is possibly generated through a comprehensive process including geological, physical, geochemical, or hydrodynamic factors. Generally, all mechanisms are abstracted as four typical categories, namely skeleton deformation, pore fluid mass increase, temperature change, and other mechanisms. Traditional methods for evaluating reservoir overpressure often only consider the influence of a single factor and lack mathematical methods for a comprehensive explanation of reservoir overpressure. Therefore, this article is dedicated to proposing a comprehensive mathematical model, incorporating effective mean stress, shear stress, temperature, pore collapse-induced plastic deformation, time-dependent skeleton deformation, and pore fluid mass increase, to account for pore pressure generation in sedimentary basins. The effects of various factors on pore pressure generation are analyzed, and case studies are conducted. Main conclusions are drawn that both the compressibility of sediments and the porosity at the surface control the pore pressure generation rate and vertical gradient. The pore pressure generation rate and vertical gradient in deep formation are larger than those in shallow formation. The higher compressibility and lower porosity at the surface lead to a greater pore pressure generation rate and vertical gradient during the skeleton deformation. The lower compressibility and a lower porosity at the surface can cause a higher pore pressure change rate and vertical gradient during the pore pressure mass increase and temperature change. By comparison, mechanical loading plays a more important role in pore pressure generation rate and vertical gradient than aquathermal pressuring. Full article