Search Results (2,568)

Compared to commuting, grocery shopping trips, despite their profound implications for mixed land use and transportation planning, have received limited attention in travel behavior research. Drawing upon a travel diary survey conducted in a fast-growing metropolitan region of the United States, i.e., Salt Lake County, UT, this research investigated a variety of influential factors affecting mode choices associated with grocery shopping. We analyze how built environment (BE) characteristics, measured at seven spatial scales or different ways of aggregating spatial data—including straight-line buffers, network buffers, and census units—affect travel mode decisions. Key predictors of choosing walking, biking, or transit over driving include age, household size, vehicle ownership, income, land use mix, street density, and distance to the central business district (CBD). Notably, the influence of BE factors on mode choice is sensitive to different spatial aggregation methods and locations of origins and destinations. The straight-line buffer was a good indicator for the influence of store sales amount on mode choices; the network buffer was more suitable for the household built environment factors, whereas the measurement at the census block and block group levels was more effective for store-area characteristics. These findings underscore the importance of considering both the spatial analysis method and the location (home vs. store) when modeling non-work travel. A multi-scalar approach can enhance the accuracy of travel demand models and inform more effective land use and transportation planning strategies. Full article

With the growing severity of environmental pollution, people are paying increasing attention to their health. However, naturally occurring wood with health benefits and applications in human healthcare is still scarce. Natural wood exhibits a limited negative oxygen ion release capacity, and this release has a short duration, failing to meet practical application requirements. This study innovatively developed a humidity-responsive, healthy wood material with a high negative oxygen ion release capacity based on fast-growing poplar. Through vacuum cyclic impregnation technology, hexagonal stone powder was infused into the pores of poplar wood, endowing it with the ability to continuously release negative oxygen ions. The healthy wood demonstrated a static average negative oxygen ion release rate of 537 ions/cm³ (peaking at 617 ions/cm³) and a dynamic average release rate of 3,170 ions/cm³ (peaking at 10,590 ions/cm³). The results showed that the particle size of hexagonal stone powder in suspension was influenced by the dispersants and dispersion processes. The composite dispersion process demonstrated optimal performance when using 0.5 wt% silane coupling agent γ-(methacryloxy)propyltrimethoxysilane (KH570), achieving the smallest particle size of 8.93 μm. The healthy wood demonstrated excellent impregnation performance, with a weight gain exceeding 14.61% and a liquid absorption rate surpassing 165.18%. The optimal impregnation cycle for vacuum circulation technology was determined to be six cycles, regardless of the type of dispersant. Compared with poplar wood, the hygroscopic swelling rate of healthy wood was lower, especially in PEG-treated samples, where the tangential, radial, longitudinal, and volumetric swelling rates decreased by 70.93%, 71.67%, 69.41%, and 71.35%, respectively. Combining hexagonal stone powder with fast-growing poplar wood can effectively enhance the release of negative oxygen ions. The static average release of negative oxygen ions from healthy wood is 1.44 times that of untreated hexagonal stone powder, and the dynamic release reaches 2 to 3 times the concentration of negative oxygen ions specified by national fresh air standards. The water-responsive mechanism revealed that negative oxygen ion release surged when ambient humidity exceeded 70%. This work proposes a sustainable and effective method to prepare healthy wood with permanent negative oxygen ion release capability. It demonstrates great potential for improving indoor air quality and enhancing human health. Full article

Background/Objectives: Celiac disease (CD) is an autoimmune disorder characterized by small intestinal enteropathy triggered by gluten ingestion, often associated with gut dysbiosis. The most effective treatment is strict adherence to a gluten-free diet (GFD), which alleviates symptoms. This study uniquely integrates taxonomic, functional, and resistance profiling to evaluate the gut microbiota of women with CD on a GFD. Methods: To evaluate the long-term impact of a GFD, this study analyzed the gut microbiota of 10 women with CD on a GFD for over a year compared to 10 healthy controls with unrestricted diets. Taxonomic diversity (16S rRNA gene sequencing and the analysis of α and β-diversity), metabolic functionality (Biolog EcoPlates^®), and antibiotic resistance profiles (Cenoantibiogram) were assessed. Results: Metagenomic analysis revealed no significant differences in taxonomic diversity but highlighted variations in the abundance of specific bacterial genera. Women with CD showed increased proportions of Bacteroides, Streptococcus, and Clostridium, associated with inflammation, but also elevated levels of beneficial genera such as Roseburia, Oxalobacter, and Paraprevotella. Despite no significant differences in metabolic diversity, higher minimum inhibitory concentrations (MICs) in women in the healthy control group suggest that dietary substrates in unrestricted diets may promote the proliferation of fast-growing bacteria capable of rapidly developing and disseminating antibiotic resistance mechanisms. Conclusions: These findings indicate that prolonged adherence to a GFD in CD supports remission of gut dysbiosis, enhances microbiota functionality, and may reduce the risk of antibiotic resistance, emphasizing the importance of dietary management in CD. Full article

Redwood (Sequoia sempervirens (Lamb. ex D. Don) Endl.) is a fast-growing, long-lived conifer native to a narrow coastal zone along the western seaboard of the United States. Redwood can accumulate very high amounts of carbon in plantation settings and continuous cover forestry (CCF) represents a highly profitable option, particularly for small-scale forest growers in the North Island of New Zealand. We evaluated the profitability of conceptual CCF regimes using two case study forests: Blue Mountain (109 ha, Taranaki Region, New Zealand) and Spring Creek (467 ha, Manawatu-Whanganui Region, New Zealand). We ran a strategic harvest scheduling model for both properties and used its results to guide a tactical-spatially explicit model harvesting small 0.7 ha units over a period that spanned 35 to 95 years after planting. The internal rates of return (IRRs) were 9.16 and 10.40% for Blue Mountain and Spring Creek, respectively, exceeding those considered robust for other forest species in New Zealand. The study showed that small owners could benefit from carbon revenue during the first 35 years after planting and then switch to a steady annual income from timber, maintaining a relatively constant carbon stock under a continuous cover forestry regime. Implementing adjacency constraints with a minimum green-up period of five years proved feasible. Although small coupes posed operational problems, which were linked to roading and harvesting, these issues were not insurmountable and could be managed with appropriate operational planning. Full article

Against the backdrop of China’s fast-growing digital economy and its financial inclusion agenda, there is still little city-level evidence on whether the e-CNY pilot accelerates financial deepening at the grassroots. Using a balanced panel of 271 prefecture-and-above cities for 2016–2022, this study employs a staggered difference-in-differences (SDID) design augmented by the synthetic control method (SCM) to rigorously identify the policy effect of the e-CNY pilot. The results show that the pilot program significantly improves urban financial inclusion, contributing to more equitable access to financial services and supporting inclusive socio-economic development. Mechanism analysis suggests that the effect operates mainly through two channels, a merchant-coverage channel and a transaction-scale channel, with the former contributing the majority of the overall effect. Incorporating a migration-based mobility index shows that most studies’ focus on the merchant-coverage effect is amplified in cities under tight mobility restrictions but wanes where commercial networks are already saturated, whereas the transaction-scale channel is largely insensitive to mobility shocks. Heterogeneity tests further indicate stronger gains in non-provincial capital cities and in the eastern and central regions. Overall, the study uncovers a “penetration-inclusion” network logic and provides policy insights for advancing sustainable financial inclusion through optimized terminal deployment, merchant incentives, and diversified scenario design. Full article

The growing scale of big data and artificial intelligence (AI)-based models has heightened the urgency of developing real-time digital twins (DTs), particularly those capable of simulating personalized behavior in dynamic environments. In this study, we examine the personalization of AI-based digital twins (DTs), with a focus on overcoming computational latencies that hinder real-time responses—especially in complex, large-scale systems and networks. We use bibliometric analysis to map current trends, prevailing themes, and technical challenges in this field. The key findings highlight the growing emphasis on scalable model architectures, multimodal data integration, and the use of high-performance computing platforms. While existing research has focused on model decomposition, structural optimization, and algorithmic integration, there remains a need for fast DT platforms that support diverse user requirements. This review synthesizes these insights to outline new directions for accelerating adaptation and enhancing personalization. By providing a structured overview of the current research landscape, this study contributes to a better understanding of how AI and edge computing can drive the development of the next generation of real-time personalized DTs. Full article

Power distribution systems are witnessing a growing deployment of distributed, inverter-based renewable resources such as solar generation. This poses certain challenges such as rapid voltage fluctuations due to the intermittent nature of renewables. Volt-Var control (VVC) methods have been proposed to utilize the ability of inverters to supply or consume reactive power to mitigate fast voltage fluctuations. These methods usually require a detailed power network model including topology and impedance data. However, network models may be difficult to obtain. Thus, it is desirable to develop a model-free method that obviates the need for the network model. This paper proposes a novel model-free cooperative control method to perform voltage regulation and reduce inverter aging in power distribution systems. This method assumes the existence of time-series voltage and load data, from which the relationship between voltage and nodal power injection is derived using a feedforward artificial neural network (ANN). The node voltage sensitivity versus reactive power injection can then be calculated, based on which a cooperative control approach is proposed for mitigating voltage fluctuation. The results obtained for a modified IEEE 13-bus system using the proposed method have shown its effectiveness in mitigating fast voltage variation due to PV intermittency. Moreover, a comparative analysis between model-free and model-based methods is provided to demonstrate the feasibility of the proposed method. Full article

Construction wood has a high economic value, and its construction waste also has multiple consumption values. Natural wood has many advantages, such as thermal, environmental, and esthetic properties; however, wood sourced from artificial fast-growing forests is found to be deficient in mechanical strength. This shortcoming makes it less competitive in certain applications, leading many markets to remain dominated by non-renewable materials. To address this issue, various modification methods have been explored, with a focus on enhancing the plasticity and strength of wood. Studies have shown that hydrogen bonds in the internal structure of wood have a significant impact on its operational performance. Whether it is organic modification, inorganic modification, or a combination thereof, these methods will lead to a change in the shape of the hydrogen bond network between the components of the wood or will affect the process of its breaking and recombination, while increasing the formation of hydrogen bonds and related molecular synergistic effects and improving the overall operational performance of the wood. These modification methods not only increase productivity and meet the needs of efficient use and sustainable environmental protection but also elevate the wood industry to a higher level of technological advancement. This paper reviews the role of hydrogen bonding in wood modification, summarizes the mechanisms by which organic, inorganic, and composite modification methods regulate hydrogen bond networks, discusses their impacts on wood mechanical properties, dimensional stability, and environmental sustainability, and provides an important resource for future research and development. Full article

Cellulose nanocrystals (CNCs) have attracted growing interest in optics and electronics, extending beyond their traditional applications. They are considered key materials due to their fast computing, sensing adhesion, and emission of circularly polarized luminescence with high dissymmetry factors. This interest arises from their unique chemical structure, which gives rise to structural color, a chiral nematic phase, and high mechanical strength. In this perspective, we first introduce the definition, sources, and fundamental properties of CNCs to explain the basis for their unique and effective use in optics and electronics. Next, we review recent research on the application of CNCs in these fields. We then analyze the current limitations that hinder further advancement. Finally, we offer our own perspective on future directions for the CNC-enabled advanced optics and electronics. Full article

This study proposes a novel LK-BP-AFPSO model for the nondestructive evaluation of wood mechanical properties, combining a backpropagation neural network (BP) with adaptive fractional-order particle swarm optimization (AFPSO) and Liang–Kleeman (LK) information flow theory. The model accurately predicts four key mechanical properties—longitudinal tensile strength (SPG), modulus of elasticity (MOE), bending strength (MOR), and longitudinal compressive strength (CSP)—using only nondestructive physical features. Tested across diverse wood types (fast-growing YKS, red-heart CSH/XXH, and iron-heart XXT), the framework demonstrates strong generalizability, achieving an average prediction accuracy (R²) of 0.986 and reducing mean absolute error (MAE) by 23.7% compared to conventional methods. A critical innovation is the integration of LK causal analysis, which quantifies feature–target relationships via information flow metrics, effectively eliminating 29.5% of spurious correlations inherent in traditional feature selection (e.g., PCA). Experimental results confirm the model’s robustness, particularly for heartwood variants, while its adaptive fractional-order optimization accelerates convergence by 2.1× relative to standard PSO. This work provides a reliable, interpretable tool for wood quality assessment, with direct implications for grading systems and processing optimization in the forestry industry. Full article

This comprehensive review maps the fast-evolving landscape in which artificial intelligence (AI) and deep-learning (DL) techniques converge with the Internet of Things (IoT) to manage energy, comfort, and sustainability across smart environments. A PRISMA-guided search of four databases retrieved 1358 records; after applying inclusion criteria, 143 peer-reviewed studies published between January 2019 and April 2025 were analyzed. This review shows that AI-driven controllers—especially deep-reinforcement-learning agents—deliver median energy savings of 18–35% for HVAC and other major loads, consistently outperforming rule-based and model-predictive baselines. The evidence further reveals a rapid diversification of methods: graph-neural-network models now capture spatial interdependencies in dense sensor grids, federated-learning pilots address data-privacy constraints, and early integrations of large language models hint at natural-language analytics and control interfaces for heterogeneous IoT devices. Yet large-scale deployment remains hindered by fragmented and proprietary datasets, unresolved privacy and cybersecurity risks associated with continuous IoT telemetry, the growing carbon and compute footprints of ever-larger models, and poor interoperability among legacy equipment and modern edge nodes. The authors of researches therefore converges on several priorities: open, high-fidelity benchmarks that marry multivariate IoT sensor data with standardized metadata and occupant feedback; energy-aware, edge-optimized architectures that lower latency and power draw; privacy-centric learning frameworks that satisfy tightening regulations; hybrid physics-informed and explainable models that shorten commissioning time; and digital-twin platforms enriched by language-model reasoning to translate raw telemetry into actionable insights for facility managers and end users. Addressing these gaps will be pivotal to transforming isolated pilots into ubiquitous, trustworthy, and human-centered IoT ecosystems capable of delivering measurable gains in efficiency, resilience, and occupant wellbeing at scale. Full article

A considerable amount of scientific evidence demonstrates that the regime of magnitude, direction, and/or frequency of wall shear stress (WSS) modulates endothelial cell function and structure, influencing vascular biology in health and disease. Advances in computational fluid dynamics (CFD) and fluid–structure interaction (FSI) simulations in cardiovascular medicine have enabled accurate WSS quantification, correlating flow behavior and its interaction with the vessel wall with disease progression. To effectively analyze and interpret the results of numerical simulations, various descriptors of blood flow were defined. Such indicators allow researchers to quantify and characterize key aspects of blood flow, facilitating the study of healthy and pathological conditions, medical device design, and treatment planning. However, a very fast-growing collection of hemodynamic metrics were defined and used: whether called indicators, parameters, metrics, or indexes, they will be here referred to as hemodynamic descriptors. This narrative review was aimed at synthesizing scientific literature about the descriptors used to analyze blood flow in computational cardiovascular studies, highlighting their significance, applications, and advancements. Full article

This study aims to provide a reference for the fire protection design and fire emergency response strategies for fuel islands in high-speed railway stations and other transportation buildings. By using an industrial calorimeter, this paper analyzes the combustion characteristics of a fuel island. For the fuel island setup in this test, the fuel island fire development cycle was relatively long, and the maximum fire source heat release rate reached 4615 kW. Before the fire source heat release rate reaches the maximum peak, the HRR curve slowly fluctuates and grows within the first 260 s after ignition. Within the time range of 260 s to 440 s, the fire growth rate resembled that of a t² medium-speed fire, and within the time range of 400 s to 619 s, it more closely aligned with a t² fast fire. It is generally suggested that the growth curve of t² fast fire could be used for the numerical simulation of fuel island fires. The 1 h fire separation method adopted in this paper demonstrated a good fire barrier effect throughout the combustion process. Full article

With the growing global demand for clean energy, the accuracy of wind-power forecasting plays a vital role in ensuring the stable operation of power systems. However, wind-power generation is significantly influenced by meteorological conditions and is characterized by high uncertainty and multiscale fluctuations. Traditional recurrent neural network (RNN) and long short-term memory (LSTM) models, although capable of handling sequential data, struggle with modeling long-term temporal dependencies due to the vanishing gradient problem; thus, they are now rarely used. Recently, Transformer models have made notable progress in sequence modeling compared to RNNs and LSTM models. Nevertheless, when dealing with long wind-power sequences, their quadratic computational complexity (O(L²)) leads to low efficiency, and their global attention mechanism often fails to capture local periodic features accurately, tending to overemphasize redundant information while overlooking key temporal patterns. To address these challenges, this paper proposes a wind-power forecasting method based on dimension-transformed collaborative multidimensional multiscale attention (DTCMMA). This method first employs fast Fourier transform (FFT) to automatically identify the main periodic components in wind-power data, reconstructing the one-dimensional time series as a two-dimensional spatiotemporal representation, thereby explicitly encoding periodic features. Based on this, a collaborative multidimensional multiscale attention (CMMA) mechanism is designed, which hierarchically integrates channel, spatial, and pixel attention to adaptively capture complex spatiotemporal dependencies. Considering the geometric characteristics of the reconstructed data, asymmetric convolution kernels are adopted to enhance feature extraction efficiency. Experiments on multiple wind-farm datasets and energy-related datasets demonstrate that DTCMMA outperforms mainstream methods such as Transformer, iTransformer, and TimeMixer in long-sequence forecasting tasks, achieving improvements in MSE performance by 34.22%, 2.57%, and 0.51%, respectively. The model’s training speed also surpasses that of the fastest baseline by 300%, significantly improving both prediction accuracy and computational efficiency. This provides an efficient and accurate solution for wind-power forecasting and contributes to the further development and application of wind energy in the global energy mix. Full article

The main objective of this study was to reveal the molecular mechanism of the albinism in Schima superba and to identify the related functional genes to provide theoretical support for the optimization of S. superba seedling nursery technology. Combining third-generation SMRT sequencing with second-generation high-throughput sequencing technology, the transcriptomes of normal seedlings and albinism seedlings of S. superba were analyzed and the sequencing data were functionally annotated and deeply resolved. The results showed that 270 differentially expressed transcripts were screened by analyzing second-generation sequencing data. KEGG enrichment analysis of the annotation information revealed that, among the photosynthesis-antenna protein-related pathways, the expression of LHCA3 and LHCB6 was found to be down-regulated in S. superba albinism seedlings, suggesting that the down-regulation of photosynthesis-related proteins may affect the development of chloroplasts in leaves. Down-regulated expression of VDE in the carotenoid biosynthesis leads to impaired chlorophyll cycling. In addition, transcription factors (TFs), such as bHLH, MYB, GLK and NAC, were closely associated with chloroplast development in S. superba seedlings. In summary, the present study systematically explored the transcriptomic features of S. superba albinism seedlings, screened out key genes with significant differential expression and provide a reference for further localization and cloning of the key genes for S. superba albinism, in addition to laying an essential theoretical foundation for an in-depth understanding of the molecular mechanism of the S. superba albinism. The genes identified in this study that are associated with S. superba albinism will be important targets for genetic modification or molecular marker development, which is essential for improving the cultivation efficiency of S. superba. Full article