Search Results (9,856)

Against the backdrop of China’s “dual carbon” goals, investigating whether market-based environmental regulations can effectively induce technological upgrading is critical for achieving a sustainable low-carbon transition. This study adopts a staggered difference-in-differences (DID) approach within a two-way fixed-effects framework, supplemented by propensity score matching (PSM-DID), to identify the causal impact of the carbon emission trading (CET) pilot policy. The research utilizes a comprehensive panel dataset of A-share listed companies in heavy-polluting industries from 2010 to 2024, incorporating IPC-matched green patent application data to provide a granular assessment of corporate innovation performance. The empirical findings reveal a structural divergence: while the CET policy promotes green innovation in state-owned enterprises (SOEs), it exhibits a potential “crowding-out” effect on private enterprises, a relationship further explained by the mechanisms of carbon information disclosure and financing constraints. These results suggest that the “Porter Effect” in emerging markets is highly conditional on institutional resource endowments, implying that policymakers must complement market incentives with differentiated financial support and enhanced transparency standards to foster a more equitable innovation ecosystem. Full article

The rally to mitigate growing carbon emissions and climate change necessitates decarbonization strategies, with hydrogen emerging as a key candidate option across multiple sectors. This review examines the current state of the hydrogen economy, including production, implementation, and associated risks. Hydrogen’s versatility in industry, transportation, and energy storage is highlighted, alongside the challenges of transitioning from fossil fuel-based production. It explores the current state of hydrogen technologies, differentiating between green, blue, and gray hydrogen production methods, and highlights advancements in production techniques like thermochemical water splitting. Key findings show that while green hydrogen offers the cleanest pathway, high production costs and infrastructure limitations remain significant barriers to widespread adoption. This study also addresses safety concerns and public perception, emphasizing the need for robust risk assessment methodologies and management approaches. Furthermore, this paper underscores the importance of technological innovations, such as high-temperature electrolysis and synergies with renewable energy sources, to enhance efficiency and sustainability. Policy recommendations include financial incentives, regulatory frameworks, and international cooperation to accelerate hydrogen adoption and balance its development with other low-carbon solutions. Full article

Tumor oxygenation is a key determinant of cancer biology and treatment response, correlating with angiogenesis, recurrence, and malignant progression. Hypoxia is a defining feature of glioblastoma (GBM) and adult diffuse gliomas, generating low-oxygen niches that promote invasion, stem-like states, immune suppression, and resistance to radiotherapy and temozolomide, contributing to poor outcomes. Measuring tissue partial pressure of oxygen (pO₂) and mapping its spatial heterogeneity can, therefore, inform mechanistic understanding and therapeutic development, including hypoxia-activated prodrugs, hypoxia-responsive gene therapy, and optimized radiotherapy planning. Although direct pO₂ assessment is challenging, invasive probes and multimodal imaging can characterize regional hypoxia pre-operatively, support patient stratification, monitor treatment effects, and improve outcome prediction. This review summarizes oxygen dynamics in GBM; analyzes causes of hypoxia (rapid growth outpacing supply, diffusion-limited hypoxia, and abnormal/chaotic vasculature); compares methods to quantify oxygenation from direct measurements to noninvasive imaging surrogates; and evaluates preclinical and clinical strategies that target hypoxia to enhance standard therapy, including barriers to translation. We further integrate oxygenation with cell signaling and redox biology: oxygen gradients are transduced via hypoxia-inducible factor programs and redox-sensitive pathways (NRF2/KEAP1, NOX-derived ROS, nitric oxide/S-nitrosylation, and sulfur metabolic routes), shaping mesenchymal-like transitions and cell-death programs such as ferroptosis. Framing oxygenation as both a microenvironmental and redox-signaling variable positions oxygen imaging as an entry point to biomarker-guided therapies that exploit oxidative vulnerabilities. Full article

Background/Objectives: MSS-CRC comprises a heterogeneous group of tumors generally considered “immune cold” due to limited neoantigen generation and T-cell exclusion or inactivation. Current evidence indicates that the composition of T and B immune cells within the tumor microenvironment represents a prognostically relevant factor, significantly associated with both tumor expression profiles and molecular subtypes. Methods: We conducted an exploratory analysis to identify prognostically relevant immune cell components in this group of tumors and to investigate corresponding differences in RNA-based bulk expression and high-resolution spatial transcriptomic profiles. Results: A total of 254 localized mismatch repair-proficient colorectal cancer cases were evaluated. Our findings revealed PD-L1 expression as a robust independent prognostic biomarker associated with favorable outcomes in this specific population. Bulk RNA expression analysis showed that PD-L1-negative tumors exhibited an expression profile consistent with abundant cancer-associated fibroblast infiltration, increased matrix stiffness, and impaired immune activation—features consistent with tumor progression and poorer clinical outcomes. In contrast, PD-L1-positive tumors displayed stromal programs enriched in immune activation and controlled remodeling, consistent with an immunologically active microenvironment. Spatial transcriptomics added an additional layer of evidence, revealing that epithelial to mesenchymal transition-related programs can dominate stromal niches in PD-L1-negative tumors, particularly within macrophage-enriched stromal regions. Conclusions: Our observations suggest an association between PD-L1 expression on immune cells and immune-activated versus mesenchymal-dominant states, potentially occurring within macrophage-enriched stromal niches. These results provide insight into the biological mechanisms underlying disease progression and highlight tumor-associated macrophages as a potential therapeutic target to overcome immune resistance, particularly in PD-L1-negative MSS-CRC tumors. Full article

Large-scale integration of photovoltaics (PV) introduces complex source-load temporal volatility and grid-connection/off-grid transitions. Traditional static reliability assessments fail to capture these dynamics, resulting in “considerable deviations” in system indices. This paper proposes a reliability evaluation framework that couples temporal source-load trajectories with a multi-stage fault recovery process. Unlike traditional methods that rely on a single static snapshot, the proposed model evaluates the system state across a continuous 5-h restoration window. The novelty lies in the unique integration of a Dynamic Time Warping (DTW)–Kmedoids method to preserve temporal phase-shifts and a multi-stage Mixed-Integer Linear Programming (MILP) model to simulate PV grid-connection transitions throughout this window. By capturing the intra-outage evolution of sources and loads, the framework fundamentally corrects the “considerable deviations” of static assessments. Case studies demonstrate high precision with an error of less than 0.71% and a 20-fold speedup. Crucially, the framework corrects the 22.31% risk underestimation bias inherent in static models by tracking real-time source-load evolution. This confirms that temporal coordination performance is the primary determinant of the reliability ceiling in active distribution networks. The findings reveal that the precise alignment of intermittent generation and fluctuating demand defines the actual operational safety margin, providing a superior quantitative foundation for grid resilience enhancement. Full article

Planar microwave (MW) sensors offer high-resolution, non-invasive technology for monitoring critical soil properties, serving as a support for modern precision agriculture. While laboratory studies confirm their exceptional sensitivity, the widespread adoption of these sensors is severely impeded by critical translational challenges that constitute a defining “lab-to-field gap”. These barriers include high sensor-to-sensor variability, debilitating thermal cross-sensitivity, soil heterogeneity necessitating unique site-specific calibration, and the enduring tension between high-performance and cost-effective scaling. This review systematically synthesizes the current state of planar permittivity MW technology, moving beyond technical mechanisms to critically assess these operational limitations. We detail advanced architectural strategies designed to bridge this gap, focusing particularly on the transition toward more robust solutions. The key strategies analyzed include the adoption of differential sensor designs using microstrip patch antennas to mitigate common-mode environmental errors, the integration of ultra-compact metamaterial structures such as split-ring resonators (SRRs) and complementary split-ring resonators (CSRRs) for enhanced field robustness and deep soil sensing, and the necessity of multi-parameter sensing capabilities (moisture, pH, and salinity). By establishing a comprehensive roadmap that prioritizes field stability, cost efficiency, and seamless IoT integration, this review demonstrates that planar MW sensors are poised to become reliable and scalable tools. Addressing these critical translational hurdles will ensure optimal resource management, significantly enhance crop productivity, and enable sustainable practices within smart farming ecosystems. Full article

This study presents a systematic topological progression and multi-objective optimization of a Permanent Magnet-assisted Non-overlapping Winding Line-Start Synchronous Reluctance Motor (PMaNWLS-SynRM) for industrial applications. To explicitly highlight the core contribution, the research establishes a rigorous comparative framework evaluating the transition from a conventional 4-pole/36-slot distributed winding (DW) to a 2 × 12-slot non-overlapping winding (NW) architecture. Baseline results demonstrate that the NW configuration shortens end-turns, successfully reducing total electromagnetic losses from 417 W to 349 W and improving steady-state efficiency from 93.7% to 95.1%. To overcome the inherent starting limitations of pure synchronous reluctance machines, an aluminum squirrel-cage is integrated to enable robust direct-on-line (DOL) synchronization, while NdFeB permanent magnets are embedded within the rotor flux barriers to mitigate asynchronous spatial harmonics and elevate torque density. Finite element analysis (FEA) confirms this magnetic assistance raises the average synchronous torque to 65.8 Nm while suppressing absolute torque ripple to 1.37 Nm. Finally, an evolutionary genetic algorithm is deployed across 440 iterative configurations to resolve geometric multi-physics conflicts. The finalized optimized design achieves a 13.2 kW output power at 1800 rpm, maximizing average torque to 70.12 Nm and strictly dampening absolute torque ripple to an industry-acceptable 1.04 Nm. Operating with an aggregated total loss of 1382 W, the optimized PMaNWLS-SynRM yields a 90.5% operational efficiency, definitively validating its suitability as an ultra-premium IE4/IE5 alternative to conventional induction motors. Full article

Multiple myeloma (MM) and osteosarcoma (OS) are two biologically distinct osseous malignancies with similar molecular networks that present translational challenges for their computational modeling. This comparative research analyzes MM and OS biology relevant to in silico approaches, focusing on PI3K-AKT-mTOR signaling, the RANK-RANKL-OPG axis, angiogenic factors (VEGF, TGFs), and immune mediators in MM, alongside the transcription factors (SOX9, RUNX2), signaling pathways (PI3K-AKT-mTOR, NOTCH), immune cell state (TAM2), and interleukins in OS. Based on this pathophysiologic foundation, the review outlines five computational paradigms: (i) mechanistic models; (ii) data-driven/machine learning schemes; (iii) hybrid mechanistic approaches; (iv) digital twins/virtual cohorts, and (v) MIDD/PBPK models for real-world applications. A cross-cancer comparison section summarizes common and distinct biological axes and their computational translation as well as the overlapping features from the bone microenvironment. For both MM and OS, the research assesses strengths, limitations, and data needs of current models, outlining the strategic objectives for next-generation multiscale, AI-enabled models providing a roadmap for tissue engineers, oncology scientists, and translational researchers to design clinically relevant preclinical tests and accelerate safer, more effective strategies for tumor-affected bones. The differences between MM and OS impose distinct biological constraints, so their comparisons are rare. Combining all these features with artificial intelligence capabilities will underpin a promising transition in the development of in silico adaptive and learning models. Full article

The present paper addresses the experimental measurement of vibration frequencies using an earth pressure sensor embedded in a full-scale (1:1) test structure. The vibration frequencies within the tested structure were induced by static load tests carried out at different elevation levels (corresponding to varying thicknesses of the crushed aggregate layer) in accordance with the methodology applied on German railways (DIN 18 134). The aim of the research was to verify the stress state at individual partial levels of the tested structure on the basis of the measured vibration frequencies, and to determine the depth of influence and the load dispersion angle generated by the static load test (SLT). The measured parameters also serve as input data for parallel research focused on the assessment of transition zones between railway embankments and artificial structures along railway lines. The results presented in this paper indicate that the stress induced by the SLT decreases with increasing structural thickness of the tested construction. For a structural layer thickness of 150 mm, the resulting stress corresponds to approximately 63% of the stress value (force effect) induced on a rigid circular plate (σ = 0.50 MPa), whereas for a layer thickness of 900 mm, the stress corresponds to approximately 12% of that value. The force (stress) effects of the SLT cease to act at a depth between 900 and 950 mm (only stress due to the self-weight of the overlying material was recorded), and the load dispersion angle is approximately 40°. Full article

Brief post-learning wakeful resting periods and local sleep mechanisms have been proposed to support offline memory consolidation processes. Mind-wandering (MW), thought to reflect the occurrence or need for local sleep, has been linked to momentary attentional disengagement and may index transitions toward offline processing states. We hypothesized that resting opportunities administered immediately after probe-caught MW episodes reflecting local sleep need may selectively enhance memory consolidation. In a first experiment, participants learned five blocks of eight paired-associate words; a MW thought probe was administered after each block. In the MW condition, participants were allowed a 3 min quiet, offline pause after the block if they reported MW. In the control condition, no pause was administered. Consolidation was better in the MW than the control condition, supporting the hypothesis. However, Experiment 2 tested the MW-related pause effect by comparing the MW condition to a condition in which pauses were allowed irrespective of MW. Results showed that performance equally improved in both conditions, suggesting that post-learning pause effects would not be MW-specific. However, additional analyses evidenced a positive relationship between MW intensity and memory consolidation in both experiments. Our findings suggest that transient interruption of input during a declarative learning session may favor memory consolidation at wake, partially independently of the attentional state. Full article

Corporate carbon disclosure has become increasingly important in China’s transition toward sustainability and low-carbon development, yet existing research often focuses on isolated determinants and relies mainly on linear empirical models. Using 48,187 observations of Chinese A-share firms from 2012 to 2024, this study identifies the key predictors of corporate carbon disclosure. It develops an interpretable machine learning model and compares its predictive performance with that of linear regression, LASSO, decision tree, random forest, support vector machine, GBDT, and XGBoost. The results show that ensemble methods outperform linear models in both in-sample and out-of-sample predictions. GBDT delivers the best out-of-sample performance, with an R² of 0.5191, suggesting that nonlinear relationships and interaction effects matter in predicting corporate carbon disclosure. The key factors identified are firm size, media attention, environmental policy intensity, market concentration, and executive financial background. The heterogeneity tests show that regulatory and governance factors are more important for firms in heavily polluting industries, state-owned firms, and firms in central and western China, whereas market factors are more important for firms in eastern China, private firms, and firms in less polluting industries. Overall, the paper provides new evidence on the prediction of corporate carbon disclosure and offers practical implications for regulators and firms seeking to improve their sustainability-related disclosure practices. Full article

Almond (Prunus dulcis (Mill.) D.A. Webb) is one of the most economically important nut crops worldwide, valued for its nutritional properties and adaptability to diverse agroecological environments. This review summarizes current knowledge on almond domestication, genetic diversity, production trends, and improvement strategies, with a focus on drought tolerance under climate change. Archaeobotanical and molecular evidence indicate central Asia and the eastern Mediterranean as key centers of origin, where recurrent introgression from wild Prunus species contributed to the high genetic variability of cultivated almond. Global production trends reveal increasing challenges due to prolonged drought, climate variability, and rising water and energy costs, particularly affecting major producers such as the United States. Mediterranean regions are transitioning from traditional low-density orchards to intensive systems, where cultivar and rootstock choice are crucial for sustainability. Self-fertile and late-blooming cultivars improve yield stability, while interspecific hybrid rootstocks enhance water use efficiency and tolerance to drought and poor soils. Drought stress impacts almond physiology and yield, although moderate deficit irrigation can maintain productivity and improve kernel quality. Future improvement relies on germplasm conservation, marker-assisted selection, and genomic tools to develop climate-resilient cultivars integrated with sustainable water management strategies. Full article

Natural gas hydrate is a clean energy resource critical for global energy security and low-carbon transition. Understanding its geological accumulation mechanisms is essential for exploration and development. However, the current research on NGH geological accumulation lacks a systematic and quantitative analysis of its global research evolution, hotspots, and frontiers. To fill this gap, this study conducts a bibliometric analysis of 5891 articles (1999–2025) from the Web of Science Core Collection using CiteSpace and VOSviewer to map research trends, contributions, and frontiers. The results show that annual publications followed a three-stage trajectory: slow initiation, rapid growth, and stable development, with key boosts from production tests in Japan (2013) and China (2017). Marine and Petroleum Geology emerged as the most cited journal. China, the United States, and Germany lead research output, with the Chinese Academy of Sciences serving as the central hub (centrality: 0.46). Core researchers such as Jinqiang Liang have established foundational knowledge through highly cited studies on accumulation theory and resource–environment interactions. Research focus has shifted from early resource assessment to controlling factors, and recently toward production technologies and parameter optimization, highlighting a transition from basic to applied research with strong interdisciplinary integration. While bibliometrics reveals structural evolution and hotspots, limitations in data sources and analytical scope remain. Future efforts should integrate multi-source data and deepen content analysis to address unresolved challenges in NGH geological accumulation. Full article

As a key component in oil drilling, drill pipes are prone to failure in harsh operating service environments. Multiple severe cracks were identified in the S135 drill pipes following field service, with partial crack extensions of ~1 mm detected at the thread roots penetrating into the pipe wall, posing critical threats to structural integrity. This study investigated the failure mechanisms of the drill pipes and examined the potential effects of dynamic rotation on corrosion-assisted cracking. The results showed that this failure was close to the combined results of corrosion and torque. Cl⁻ and S²⁻ in the drilling fluid were the main sources of corrosive substances. Cl⁻ preferentially accumulated on the drill pipe surface, initiating localized pitting corrosion. Under applied stress, these surface pits exacerbated local stress concentration. The synergistic action of S²⁻ then promoted the transition from pitting to stress corrosion cracking. Regarding the corrosion stage, the rotational state of the drill pipe will affect the drilling fluid’s corrosion results. The mud deposition during rotation leads to severe intergranular corrosion, which further causes material peeling. Dynamic rotation at 60 r·min⁻¹ increased the corrosion rate to 0.55 mm·a⁻¹ after 216 h of immersion, 41% higher than under static conditions, while maximum corrosion depth increased from 8.43 μm to 13.86 μm. These results indicate that rotational motion accelerates corrosion-assisted cracking. Full article

Background/Objectives: Alzheimer’s disease (AD) is an irreversible neurodegenerative disorder that imposes a profound burden on global public health. While resting-state functional magnetic resonance imaging (rs-fMRI)-based dynamic functional connectivity (dFC) analysis has demonstrated promise in capturing time-varying brain network abnormalities, existing deep learning methods suffer from three fundamental limitations: (1) an inability to model temporal dependencies across dynamic connectivity windows, (2) reliance on post hoc black-box explainability tools, and (3) misalignment between feature learning and classification objectives. Methods: To address these challenges, we propose MambaKAN, an end-to-end interpretable framework integrating a Variational Autoencoder (VAE), a Selective State Space Model (Mamba), and a Kolmogorov–Arnold Network (KAN). The VAE encodes each dFC snapshot into a compact latent representation, preserving nonlinear connectivity patterns. The Mamba encoder captures long-range temporal dynamics across the sequence of latent representations via input-selective state transitions. The KAN classifier provides intrinsic interpretability through learnable B-spline activation functions, enabling direct visualization of how latent features influence diagnostic decisions without post-hoc approximation. The entire pipeline is trained end-to-end with a joint loss function that aligns feature learning with classification. Results: Evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset across five classification tasks (CN vs. AD, CN vs. EMCI, EMCI vs. LMCI, LMCI vs. AD, and four-class), MambaKAN achieves accuracies of 95.1%, 89.8%, 84.0%, 86.7%, and 70.5%, respectively, outperforming strong baselines including LSTM, Transformer, and MLP-based variants. Conclusions: Comprehensive ablation studies confirm the indispensable contribution of each module, and the three-layer interpretability analysis reveals key temporal patterns and brain regions associated with AD progression. Full article