Search Results (65,982)

Pinyon–juniper (PJ) woodlands, one of the most extensive mature and old-growth woodland types in the Western United States, provide critical ecological, cultural, and economic benefits but face increasing threats from climate change, altered disturbance regimes, invasive species, and pests. We developed the PJ Woodland Climate Adaptation Management Menu, a decision support tool designed to guide adaptive, climate-informed management of PJ ecosystems, particularly within the Colorado Plateau ecoregion. The menu was created through an iterative, collaborative process involving literature review, integration of strategies from existing adaptation frameworks, and extensive input from scientists, land managers, and community partners during workshops and focus groups. The menu links specific, evidence-based approaches to each of six broad strategies, including soliciting community input, mitigating disturbance, enhancing and maintaining biodiversity, conserving ecotones, timing actions for optimal outcomes, and accepting climate-driven changes when appropriate. It is intended for use with the Adaptation Workbook to help managers connect local goals and climate vulnerabilities to tailored management tactics. Hypothetical scenarios demonstrate the menu’s application to contrasting PJ woodland conditions, from die-off events to old-growth maintenance. Lessons learned during development underscore the value of early stakeholder engagement, cross-sector collaboration, and balancing diverse ecological objectives. This menu offers a flexible, transferable framework to strengthen climate resilience in PJ woodlands and serves as a model that could improve adaptation planning in other dryland forest ecosystems. Full article

Urban heat islands present a significant obstacle to climate adaptation strategies, yet the interplay between surface and atmospheric thermal elements is not fully understood. This research investigates the spatial relationship between land surface temperature (LST) and near-surface air temperature (TAIR) across Zagreb’s 218 local councils during the summer of 2024, assessing the premise that these constitute separate thermal dimensions with varying land cover correlations. Landsat 8/9-derived LST and CERRA-derived TAIR, temporally aligned to the Landsat overpass slot (09:00 UTC), were examined through spatial autocorrelation (Moran’s I, Getis–Ord Gi*), correlation analysis, and Fisher’s z-tests to compare the effects of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). The findings indicated partial coupling (r = 0.537, R² = 0.288), with 71.2% of the variance remaining unexplained, suggesting considerable surface-atmospheric decoupling. Furthermore, hot spot overlap analysis revealed limited convergence (11.9% of neighborhoods), while 44.5% displayed divergent thermal extremes. Land cover showed much stronger connections with LST (NDVI: r = −0.970, R² = 0.941; NDBI: r = +0.973, R² = 0.947) than with TAIR (NDVI: r = −0.478; NDBI: r = +0.496), representing reductions in explained variance of 63–64% (p < 0.001). These findings suggest that surface and atmospheric urban heat are related but distinct thermal aspects. Full article

Background: Carbonic Anhydrases (CAs) represent regulators of cell adaptation to hypoxia, pH regulation, and metabolic fitness. Among cancers, multiple myeloma (MM) is a plasma cell malignancy sustained by hypoxia-driven metabolic adaptation, extracellular acidification, and redox imbalance. Tight regulation of tumor extracellular pH, mediated by Carbonic Anhydrases IX and XII, is crucial for myeloma survival, progression, and stemness, making these isoforms attractive therapeutic targets. Methods: We designed and synthesized a library of terpenoid-based hybrids by derivatizing chlorothymol and 4-isopropyl-3-methylphenol with either the natural coumarin umbelliferon or the 2,2′-dipicolylamine (DPA) scaffold. This chemical strategy aimed to selectively inhibit tumor-associated CAs IX/XII through coumarin- or DPA-mediated recognition, while terpenoid fragments were introduced to enhance lipophilicity, membrane permeability, and potential redox-modulating properties. The compounds were tested by a Stopped-Flow assay for CA inhibition, in cell-based assays for antiproliferative properties and by means of several antioxidant assays. Results: The most active compounds, connecting the coumarin core to a terpenoid tail, inhibited the targeted CAs in the nanomolar range, showing up higher selectivity over off-target isoforms (I and II). In studies performed on MM cell lines, selected derivatives reduced viability (IC₅₀ = 15.8–85.4 µM) and displayed favorable selectivity over normal cells. In silico investigations suggested that the compounds were able to interact selectively with the target enzymes. Conclusions: Collectively, these results support a dual-targeting strategy in which selective inhibition of tumor-associated CAs, combined with redox modulation, interferes with adaptive mechanisms of MM cells, providing a rational framework for the development of multifunctional agents against metabolically resilient hematological malignancies. Full article

Fully leveraging the four-wheel independent drive characteristics of distributed-drive electric vehicles has become essential for enhancing their driving range. However, conventional regenerative braking strategies applied to such vehicles often fail to consider individual wheel slip ratios, which can easily lead to wheel lock and low energy recovery efficiency. To address these issues, this paper proposes a novel energy management method that integrates hybrid braking control with intelligent connected speed planning. A hierarchical control strategy for the hybrid braking system is first developed, explicitly accounting for the slip ratio of each wheel. The upper-level controller calculates the slip ratio for each wheel based on vehicle speed and wheel speed information and subsequently determines the braking torque distribution between the front and rear axles. The lower-level controller then allocates the motor braking torque and hydraulic braking torque to each wheel, subject to system constraints such as battery status and motor torque limits. Building on this framework, vehicle state and road information are incorporated as inputs to formulate a Markov decision process, which optimizes traffic efficiency, energy economy, and ride comfort as multiple objectives. The deep deterministic policy gradient (DDPG) algorithm is employed to achieve collaborative optimization of speed planning and energy management. Simulation results demonstrate that the proposed DDPG-based control strategy outperforms both rule-based control methods and classical dynamic programming algorithms in terms of comprehensive performance across traffic efficiency, energy consumption, and ride comfort. These findings validate its superiority in complex traffic conditions. Full article

Grid-scale Battery Energy Storage Systems (BESSs) are becoming essential components of modern power grids undergoing rapid decarbonisation. This review examines how nine jurisdictions—Great Britain, Germany, Spain, Italy, France, California (USA), Australia, Singapore, and China—are enabling the growth of BESSs, focusing on market access and revenue streams, investment risks and mitigation strategies, support mechanisms, and regulatory conditions. A central finding is that batteries typically become investable only when they can stack revenue from multiple sources, including energy arbitrage, ancillary services, and capacity markets. Regulation proves as important as technology: frameworks that fail to recognise storage as a distinct asset class expose projects to double charging, unclear licensing, and limited market access. Grid connection delays, declining revenues in saturating ancillary service markets, and safety compliance represent significant practical barriers. International experience indicates that BESSs scale fastest when decarbonisation policy is credible and market rules enable diversified, financeable revenue streams. Full article

This study presents a data-driven and explainable artificial intelligence (XAI) framework for quantitatively analyzing research trends in the seismic performance of timber structures. Unlike conventional bibliometric approaches based on descriptive statistics, the framework integrates large-scale literature mining, natural language processing, topic modeling, network analysis, and SHAP-based machine learning to enable structural and temporal interpretation. A dataset of 248 journal articles from OpenAlex was processed through a unified pipeline, including domain-specific filtering, text preprocessing, and temporal balancing. Topic modeling identified eight research themes spanning traditional component-level mechanics and emerging areas such as cross-laminated timber (CLT), hybrid systems, and performance-based design. Network analysis revealed a highly interconnected structure centered on key concepts such as shear walls, connections, stiffness, and cyclic behavior. SHAP-based analysis further showed that research evolution follows a layered and cumulative pattern rather than simple topic replacement: classical themes remain foundational, while newer concepts such as CLT and structural capacity have become increasingly influential. The proposed framework provides a reproducible and scalable method for quantitatively mapping research structures and temporal dynamics in timber seismic engineering. Full article

To accurately predict the fishing intensity of the Northwest Pacific squid fishing grounds and address the limitations of traditional models in capturing long-term temporal and spatial correlations and neglecting the coupling relationships of deep environmental factors, this study constructs a 3DCNN model and three fusion models incorporating residual, attention, and Transformer mechanisms. Using the 2017–2024 AIS fishing data and ocean environmental variables from the North Pacific squid fishing industry, the models’ performance is compared at 12 different temporal and spatial scales, and key core environmental variables are identified. The results show that the ResNet3D model exhibits the best overall performance, achieving an F1 score of 0.7909 at the 1.0°-7 days temporal–spatial scale. The residual connections effectively mitigate the gradient vanishing problem, balancing prediction accuracy and stability. The optimal spatial resolution is 1.0°, and the key environmental variables include S₁₀₀, Chl-a₁₀₀, PP₁₀₀, and DO₁₀₀. S₁₀₀ is the core driving variable, consistently exhibiting the highest feature importance value at all time scales. It should be noted that Chl-a is considered an indirect indicator of primary productivity, which may influence squid distribution through trophic transfer processes rather than direct biological effects. This study demonstrates the prediction accuracy and applicability of the multi-mechanism fusion 3DCNN model, reveals the temporal and spatial distribution patterns of fishing intensity in the Northwest Pacific squid fishing grounds, and provides scientific methods and technical support for dynamic monitoring, intelligent management, and sustainable utilization of squid resources. Full article

The rising market penetration of new energy vehicles (NEVs) is transforming urban traffic into a heterogeneous mix of battery electric (BEVs), hybrid electric (HEVs), and conventional fuel vehicles (FVs). For analytical brevity, traditional internal combustion engine vehicles (ICEVs) are hereafter referred to as ‘fuel vehicles (FVs)’ in the discussion of New Energy Vehicle (NEV) networks. This research investigates the efficacy of centralized coordination for NEVs within a localized region, as opposed to individualized speed control, in enhancing the mitigation of traffic congestion. Evaluating traffic efficiency and decarbonization strategies in such settings often requires extensive random sampling and Monte Carlo simulations over a large set of parameter combinations. However, conventional microscopic traffic simulators (e.g., SUMO), which rely on fine-grained modeling of vehicle dynamics and signal control, incur prohibitive computational time when scaled to large networks and numerous experimental scenarios. In this study, battery electric vehicles and hybrid electric vehicles are designed as density-aware vehicles, whose movement speed is adaptively adjusted according to the regional traffic density in their vicinity and the control parameter β. In contrast, fuel vehicles adopt a stochastic movement speed and, together with other vehicle types, exhibit either movement or stoppage in the lattice environment. This density-driven speed-adaptive control and lattice arbitration mechanism is intended to reproduce, in a simplified yet extensible manner, changes in mobility and traffic-flow stability under high-density traffic conditions. The simulation results indicate that, under the same Manhattan road network and vehicle-density conditions, tuning the β parameter of new energy vehicles to reduce their movement speed in high-density areas and to mitigate abrupt position changes can suppress traffic-flow oscillations, delay the onset of the congestion phase transition, and promote spatial equilibrium of traffic flow. Meanwhile, this study develops simplified energy-consumption and carbon emission models for battery electric vehicles, hybrid electric vehicles, and fuel vehicles, demonstrating that incorporating a speed-adaptive density strategy into mixed traffic flow not only helps alleviate abnormal congestion but also reduces potential energy use and carbon emissions caused by congestion and stop-and-go behavior. From a sensing and practical perspective, the proposed framework assumes that future connected and autonomous vehicles (CAVs) can estimate vehicle states and local traffic density through GNSS–IMU multi-sensor fusion and V2X communications, indicating methodological consistency between the proposed model and real-world CAV sensing capabilities and making it a suitable and effective experimental platform for investigating the relationships among new energy vehicle penetration, density-control strategies, and carbon footprint. Full article

High-synchrotron-peaked (HSP) BL Lac objects are extreme particle accelerators whose synchrotron emission peaks at high frequencies, typically in the UV-to-X-ray band (${\nu }_{\mathrm{peak}}>{10}^{15}$ Hz; ${\nu }_{\mathrm{peak}}\ge {10}^{17}$ for EHSPs), implying electron Lorentz factors of order ${10}^5$–${10}^6$. Their relative proximity ($z\lesssim 0.5$), clean radiation environments, and favorable Hillas parameters make them prime candidates for ultra-high-energy cosmic ray (UHECR) acceleration beyond ${10}^{19}$ eV and for neutrino production above 100 TeV. The 2017 association of IceCube-170922A with the flaring blazar TXS 0506+056 provided compelling evidence for blazars as neutrino sources, while an archival neutrino flare from 2014–2015 with no clear electromagnetic counterpart (13 events) revealed additional complexity in the emission mechanism. This review examines HSP physical properties, identifies them through WISE-based infrared selection (the 2WHSP and 3HSP catalogs, ∼2000 sources), and contrasts leptonic synchrotron self-Compton models with hadronic alternatives. We assess the observational evidence linking HSPs to high-energy neutrinos and UHECRs, finding that extreme baryonic loading ($L_p/L_e\sim {10}^3$–${10}^5$) strains energetic budgets, Auger composition measurements favor heavy nuclei over proton-dominated scenarios, and the near-isotropy of UHECR arrival directions is difficult to reconcile with rare beamed sources. Potential resolutions involving magnetic reconnection, structured jets, and duty cycle effects are discussed. Next-generation facilities, including IceCube-Gen2, KM3NeT, CTAO, IXPE, and AugerPrime/TA × 4, will probe key observables to either establish HSP BL Lacs as sources of the highest-energy cosmic particles or redirect the search toward alternative accelerator classes. Full article

Artificial intelligence (AI) tools are increasingly integrated into mathematics education, yet most reviews emphasize achievement rather than how AI shapes mathematical thinking. This scoping review mapped literature published between 2020 and 2026 on AI-supported mathematics learning through three cognition frameworks: APOS (Action–Process–Object–Schema), Sfard’s process–object duality and reification, and Conceptual Image theory. Searches were conducted in Scopus, Web of Science, ERIC, PsycINFO, Education Source, and IEEE Xplore, followed by duplicate removal and Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR)-aligned screening. Twenty-one peer-reviewed studies met inclusion criteria (18 empirical studies plus three theoretically oriented studies). Evidence growth accelerated after 2022, with most studies situated in secondary and higher education. Large language models (LLMs) and Intelligent Tutoring Systems (ITS) were the most frequently investigated modalities. Across studies, AI commonly supported theoretically inferred action-level execution and procedural management (APOS) via adaptive feedback, hinting, and stepwise scaffolding, and it often broadened learners’ conceptual images through multiple representations and generated explanations. However, these interpretations were necessarily cautious, because very few studies directly operationalized theory-linked conceptual mechanisms such as process internalization, object encapsulation, reification, or alignment between conceptual images and formal definitions. In LLM-supported contexts, gains in explanation quality coexisted with risks of procedural outsourcing when students relied on generated solutions without prior reasoning. By contrast, ITS-based environments more often supported tightly structured procedural engagement, suggesting that different AI modalities afford different forms of cognitive support and risk. Overall, AI’s conceptual impact appears to depend less on tool availability and more on instructional orchestration (task design, prompting, and teacher mediation). The findings also suggest that sustainability-related dimensions—particularly learner agency, transparency of AI support, and equitable participation—are closely connected to whether AI use promotes durable conceptual learning rather than superficial performance gains. Future research should operationalize cognitive transitions, assess structural understanding, and report AI-use conditions transparently to support cumulative, theory-driven synthesis. Full article

Ecological networks provide an important spatial framework for maintaining regional ecological security in fragmented landscapes. However, structural comparison of ecological network evolution at the provincial scale remains relatively limited, especially in cold-region contexts. Taking Heilongjiang Province in Northeast China as the study area, this study applies a structure-oriented workflow integrating ecological sensitivity assessment, the Minimum Cumulative Resistance (MCR) model, and edge-betweenness-based backbone corridor extraction to examine ecological network change in 2000, 2010, and 2020. The results show that 16, 18, and 17 ecological source areas were identified in 2000, 2010, and 2020, respectively, with a relatively stable spatial distribution concentrated in forest- and wetland-dominated regions. The total length of potential ecological corridors decreased from 12,634 km in 2000 to 11,985 km in 2020. Quantitative topological indicators further indicate that the 2010 ecological network was the most compact and densely connected of the three periods, whereas the 2020 network remained connected but exhibited lower structural compactness. Backbone ecological corridors retained only a limited proportion of the full corridor network while preserving overall connectivity, indicating that a relatively small subset of structurally important corridors supported the main network framework. Spatially, structural weakening was more evident in the Harbin–Daqing region, whereas the northwestern and southeastern parts of the province maintained relatively stable ecological foundations. These patterns were broadly consistent with land-use dynamics, particularly grassland decline and built-up land expansion. Overall, this study provides an applied structure-oriented workflow for examining ecological network evolution at the provincial scale and offers a spatial basis for ecological conservation and territorial planning in cold-region provinces. Full article

The shift from the central place paradigm to the network paradigm in regional relation research emphasizes the need to elucidate the factors and mechanisms driving urban network dynamics. Leveraging firm-level big data—including a headquarters–branch relationships database (29,359 headquarters and 114,679 branches) and an investment relationships database (21,843 investing firms and 69,733 recipients)—this study constructs an urban network integrating both vertical and horizontal enterprise connections. Using exponential random graph models (ERGMs), it analyzes the influencing factors and driving mechanisms of urban network dynamics in the Yellow River Basin (YRB). This study found that the urban network in the YRB is characterized by multiple isolated “core–periphery” radial networks. Strong connections are concentrated within each province’s major cities and their immediate surroundings, while horizontal connections across provincial borders are weaker. From 2000 to 2020, the urban network has evolved from isolated “core–periphery” radial networks to corridor networks where some core nodes are interconnected. The urban network dynamics in the YRB result from the combined influences of the preferential attachment mechanism, the network self-organization mechanism, the multi-dimensional proximity mechanisms, and the geographical boundary effect. Enterprises tend to establish branches or investments in cities with spatial proximity and larger economic scales. Reciprocal and transitive structures significantly facilitate urban network formation. Additionally, institutional proximity, geographical proximity, cultural proximity, cognitive proximity, and geomorphological division all exert varying degrees of influence on enterprise connections between cities. Full article

Manufacturing tolerances inevitably induce cell-to-cell inconsistencies. These inconsistent cells are connected in series and parallel to form battery packs, which will affect the safety and reliability of the battery system. This study presents a novel optimization framework integrating the multi-level physical model with machine learning to improve battery consistency from the manufacturing perspective. The multi-level physical modeling approach is applied to establish the link between the parameter deviations of the calendering process and the battery inconsistency performance. Based on the multi-level physical model, the Monte Carlo method is used to describe parameter deviations and generate datasets of electrochemical properties. The coefficients of variations in battery capacity and resistance are calculated as the consistency evaluation index based on these datasets. The proposed optimization approach applies machine learning to reduce the computational cost of the multi-level physical simulations due to lots of Monte Carlo simulations. Combined with the multi-level physical model and neural network model, the multi-objective particle swarm optimization algorithm is adopted to provide the optimal calendering process parameter deviations by achieving the trade-off between battery consistency performance and manufacturing cost. Results indicate that the battery consistency performance is improved by controlling the precision of the calendering process and manufacturing cost. This approach can effectively give feedback and guidance to the inverse design of the manufacturing process. Full article

Aging profoundly alters hematopoiesis by impairing stem cell self-renewal, skewing lineage differentiation, and remodeling immune and stromal compartments within the bone marrow. Consequently, these changes contribute to an increased susceptibility to leukemia. Conversely, leukemia contributes to systemic aging. Although the connection between hematopoietic aging and leukemogenesis has been well-recognized, the precise molecular and microenvironmental mechanisms underlying this association remain poorly elucidated. In recent years, emerging studies have identified altered clonal dynamics, chronic inflammation, and niche-dependent metabolic remodeling as major contributors to malignant transformation. Building on these findings, we synthesize current insights into how aging reprograms the hematopoietic ecosystem to promote leukemic initiation and progression, and furthermore, discuss potential strategies to counteract these processes by targeting aging-related pathways. Full article

Sulfur–soybean oil polymers with tunable thermal insulation properties were synthesized via inverse vulcanization of elemental sulfur and soybean oil and reinforced with biochar (BC) derived from spent barley biomass. Biopolymer films (F-BPs) with sulfur contents ranging from 20 to 80 wt% were prepared, and biochar-filled biocomposites (F-BP-Cs) were obtained using different filler loadings and processing routes. Their structural, morphological, thermal, mechanical, and surface properties were systematically analyzed to establish structure–property relationships, with particular focus on thermal transport behavior. Differential scanning calorimetry (DSC) revealed that sulfur contents ≤50 wt% favored the chemical incorporation of elemental sulfur into the polymer network via covalent bonding, significantly reducing the presence of free crystalline sulfur in the material. SEM images and porosity analysis revealed that BC incorporation and processing conditions significantly affected microstructural connectivity and air-filled porosity. As a result, F-BP-C materials exhibited low thermal conductivities, reaching values of ~0.033–0.039 W/(m·K), comparable to commercial insulating materials such as cork and polymeric foams. This reduction was attributed to increased structural disorder, high interfacial density, and enhanced phonon scattering within the heterogeneous polymer–BC–air system. These findings demonstrate the potential of these biocomposites as sustainable thermal insulating materials derived from industrial and agricultural waste. Full article