Search Results (139)

Accelerating the construction of an international, modern, and people-oriented city, Suzhou often faces bottlenecks in its peripheral communities. These open spaces suffer from rapid vitality decline, extensive governance, and weak resilience, which directly hinder residents’ daily interactions and community integration. Moving beyond subjective qualitative approaches, this study selects typical peripheral communities in Suzhou and employs a comprehensive methodology combining field surveys, space syntax analysis, and time-segmented spatial trajectory entropy measurement to quantitatively assess the “lack of vitality.” By quantifying spatial form, accessibility, and behavioral patterns, the study examines their coupling relationships with vitality, social interaction, and safety resilience. The results show that the spatial trajectory entropy model effectively captures the spatiotemporal movement patterns of crowds, addressing the gap in dynamic characterization left by traditional methods. Community vitality is significantly positively correlated with accessibility, interface continuity, and facility allocation. These spatial elements also play a crucial role in promoting social interaction and enhancing the sustainable use of spaces. Based on quantitative diagnosis, this paper proposes a governance framework of “digital empowerment–community co-governance–resilience enhancement,” transforming spatial weaknesses into targeted strategies for smart governance. This research provides quantitative analysis methods and localized implementation approaches for the refined governance and sustainable development of communities on the outskirts of cities like Suzhou. Full article

Marine macroalgae represent abundant, renewable reservoirs of structurally unique bioactive compounds, such as sulfated polysaccharides, phlorotannins, and carotenoids, with immense potential for sustainable functional foods. However, their industrial exploitation is severely bottlenecked by complex, repeat-rich genomes, recalcitrant genetic transformation tools, and environmental cultivation variability. Synthesizing evidence from 180 high-quality studies spanning from 1961 to 2026, this review provides a comprehensive synthesis of how artificial intelligence (AI) and synthetic biology may contribute to overcoming these challenges. We highlight key advances across the bioengineering pipeline, including the application of metabolic engineering strategies for enhancing valuable compound production in engineered algal systems. For example, a CrtYB-based metabolic engineering approach achieved β-carotene accumulation of 22.8 mg/g in the microalga Chlamydomonas reinhardtii, providing important insights for future metabolic engineering of marine macroalgae. In addition, AI-assisted approaches show promising potential for enzyme discovery, metabolic pathway prediction, and multi-omics-guided optimization of bioactive compound production. We further discuss critical downstream challenges, including the low gastrointestinal absorption (~14%) and extensive metabolic transformation of seaweed-derived phenolic compounds, as well as the potential application of AI-integrated physiological modeling for improving bioavailability prediction and safety assessment. This review provides a pioneering, data-driven synthesis of how the convergence of AI and synthetic biology is overcoming these roadblocks. Moving beyond generic descriptions, we highlight key empirical milestones across the bioengineering pipeline, including multi-fold yield enhancements in target pigments (up to 22.8 mg/g) and the AI-driven discovery of novel polysaccharide-degrading enzymes. Furthermore, we confront critical downstream challenges, specifically addressing the characteristically low (~14%) gastrointestinal absorption bottleneck and extensive metabolic biotransformation of seaweed phenolics. We demonstrate that integrating digital twins with reinforcement learning-driven physiologically based pharmacokinetic (PB-PK) modeling can compress the R&D cycles of these seaweed functional ingredients by over 60%. Unlike previous reviews that treat these technologies as independent entities, this article proposes a macroalgae-focused approach that delivers a unique, macroalgae-specific computational and experimental framework, providing a future roadmap toward intelligent smart marine biotechnology and sustainable development to drive the global blue bioeconomy. Full article

Under tightening resource and environmental constraints, exploring the synergy between new-type urbanization and water use efficiency is key to high-quality development in Central China. Using panel data for cities in Jiangxi (11) and Hunan (13) from 2013 to 2022, this study measures the spatiotemporal evolution characteristics of water-coupling coordination in the two provinces. It also incorporates feedback mechanisms from Complex Adaptive Systems (CAS) theory to explain the differences in the evolutionary trajectories of the two provinces from three dimensions: signal strength, transmission efficiency, and bottleneck nodes. The results show the following: (1) The urbanization processes in these two provinces have shifted from a phase of rapid growth to one of steady optimization, and water use efficiency has improved in both. Since 2016, Jiangxi Province has experienced fluctuations, moving from a decline to a recovery, while Hunan Province has seen a steady increase; however, disparities among regions within the provinces persist. (2) The evolutionary trajectories of coupling coordination between the two provinces show marked divergence: Hunan exhibits a pattern of “core responsiveness and peripheral lag,” while Jiangxi displays a “multi-point gradual response”. (3) The barriers in the two provinces are highly similar in nature, focusing on economic scale, capital investment, and labor. Although the same constraints exist, different response pathways lead to distinct patterns—Jiangxi’s passive adaptation results in a diffuse equilibrium, while Hunan’s proactive adjustment results in a polarized gradient. These findings identify two distinct pathways for urban-water system evolution in central China’s water-rich areas, advancing theoretical understanding of system adaptation while providing targeted policy references for regional high-quality development. Full article

Rapid global urbanization is increasing the need for accurate, large-scale traffic monitoring to support sustainable transportation and city governance. Satellite video remote sensing offers a unique way to continuously observe urban road networks over large areas. It provides high-resolution spatio-temporal data that is essential for traffic flow analysis, infrastructure assessment, and dynamic urban planning. Moving vehicle detection in satellite video sequences is a basic task that turns raw imagery into useful traffic-state information, supporting these applications. Despite the advantages of satellite video data, detecting moving vehicles in practice remains a tough problem. Objects are extremely small and lack clear appearance details, while low local contrast makes them hard to separate from complex backgrounds. Satellite platform motion also introduces background misalignment and intensity fluctuations, resulting in missed detections and false alarms that hurt monitoring reliability. Furthermore, current methods do not fully exploit temporal motion cues or transform-domain priors, creating a performance bottleneck that restricts their practical use. To solve these problems, this paper proposes a Channel-Attentive Spatio-Temporal-Frequency Alignment (CASTFA) framework to effectively use and combine multi-dimensional features for moving vehicle detection in satellite videos, with the goal of providing high-quality traffic monitoring data to help smart city planning. Specifically, a State Space-Guided Temporal Compression (SSGTC) module first collects information along the time dimension with linear computational complexity, greatly reducing overhead while keeping motion cues that are critical for traffic-state estimation. The compressed temporal features are then processed with a multi-scale Haar wavelet transform to get hierarchical time-frequency representations that capture subtle motion dynamics across different frequency bands. At the same time, a pre-trained backbone network extracts multi-scale spatial features. To allow these different domains to work together, a Cross-Domain Feature Alignment (CDFA) mechanism aligns and combines spatial and time-frequency features through channel-attentive operations. Experimental results on the publicly available satellite video moving vehicle detection dataset show that the proposed CASTFA method consistently outperforms existing approaches, with better precision, recall, and F1-scores across diverse urban scenarios. These results show that CASTFA can provide reliable moving vehicle detection performance under difficult real-world conditions, supporting accurate traffic-flow monitoring and providing valuable geospatial intelligence for smart urban planning, transportation management, and sustainable city development. Full article

Regularization can improve generalization by constraining how a model uses its internal representation. In this paper, we study whether algebraic fixed-point constraints applied to the final LSTM hidden state during training can reorganize the recurrent latent space and improve held-out frame prediction. Rather than modifying the inference-time architecture, we introduce four training-time operators, GlobalHouseholder (reflection), GlobalGivens (rotation), Composition, and Lie Algebra, that bias the hidden state toward geometrically structured regions without changing the decoder pathway. Experiments across four datasets (indoor robot sequences, KITTI driving, Flying Shapes 2D, and Moving 3D Shapes) show that lightweight constraints consistently improve prediction on structured scenes, with GlobalGivens achieving up to $+1.04$ dB PSNR and $-11.3$% MAE over the unconstrained baseline on held-out Indoor sequences. The latent analysis reveals that the operators that generalize best are not those that compress the representation most aggressively but those that redistribute latent energy while preserving broad dimensional participation. Lie Algebra, despite collapsing activation variance by 81–96%, degrades under latent perturbation and does not match the lighter operators on structured datasets identifying over-constraint as a clear failure mode. These results suggest that geometric regularization of a recurrent bottleneck can act as a useful training-time prior without adding any inference overhead. Full article

Wearable biosensing is moving beyond single-signal activity tracking toward multimodal, AI-assisted health monitoring that combines biophysical streams with biochemical information from sweat, interstitial fluid, tears, and other accessible biofluids. Recent work has accelerated progress in flexible optical materials, programmable DNA-based sensing architectures, biosafety-aware sweat patches, and edge AI pipelines capable of denoising, calibration, personalization, and low-latency inference. This review synthesizes current advances across general biosensor platforms, vital-sign monitoring, biochemical sweat sensing, motion and biomechanics sensing, and edge AI/data analytics. Particular attention is given to the translational bottlenecks that now dominate the field, including motion artifacts, sensor drift, biofouling, subject-to-subject variability, limited sweat-to-blood equivalence, insufficient external validation, and uneven regulatory readiness. The central argument of this updated review is that the next phase of progress will not be driven by sensitivity alone but by robust multimodal fusion, clinically anchored validation, interoperable data pipelines, and energy-efficient on-device intelligence. By linking materials, electronics, algorithms, and deployment constraints, the review identifies the wearable biosensing strategies most likely to progress from promising laboratory demonstrations to reliable personalized-health tools. Full article

Agricultural green total factor productivity (AGTFP) is a key indicator for evaluating the level of green development in agriculture. However, conventional approaches to AGTFP measurement often treat intermediate agricultural production processes as a “black box”, overlooking internal system mechanisms and thus leading to biased identification of efficiency bottlenecks. To address this limitation, this study introduces an analysis using a network slack-based measure (NSBM) model to move beyond the traditional single “input–output” transmission framework. By integrating soil sample data from 2017 to 2024, the agricultural production process is decomposed into three sequential stages—material inputs, nutrient transformation, and crop production—to evaluate AGTFP in Baoding, China. The results reveal that AGTFP in Baoding remains at a relatively low level overall, although a steady upward trend is observed over time. Specifically, overall efficiency increased by 18.18%, while the efficiency of converting agricultural inputs into soil nutrients (the input subsystem) improved by 36.13%. The input subsystem serves as the primary driver of AGTFP improvement, with a marginal contribution coefficient of 0.61% to overall efficiency (p < 0.01). Although the efficiency of transforming soil nutrients into agricultural output (the output subsystem) remains relatively high, its growth potential is constrained by biological limits. It is highly sensitive to external factors such as topography and natural disasters. At present, the key bottleneck to enhancing regional AGTFP is the low efficiency with which external inputs are converted into soil nutrients. These findings suggest that policy priorities should shift from simple input reduction to process-oriented management, with an emphasis on improving the conversion efficiency of external inputs into effective soil nutrients, thereby facilitating agricultural green transformation while mitigating non-point source pollution. Based on existing research frameworks, this study supplements and refines the original analytical framework by incorporating soil data from the agricultural production process, providing new empirical evidence for uncovering the “black box” of agricultural production. Full article

Autonomous vehicles (AVs) are moving from pilots to regular urban service, yet the speed of large-scale implementation remains uncertain. While prior research emphasizes technological feasibility and adoption, less attention has been paid to the socio-technical dynamics that constrain deployment. This study examines how unintended consequences shape the pace of AV implementation in cities. Drawing on a mixed-methods design combining a structured scoping review with 18 expert interviews, interrelated dynamics are identified across institutional, behavioral, economic-platform, spatial, and normative-societal domains. The findings indicate that implementation speed is not determined by technology alone but emerges from reinforcing feedback loops that generate systemic frictions, including governance lag, demand rebound, spatial bottlenecks, and legitimacy challenges. The study advances a systems-oriented framework that conceptualizes implementation speed as an emergent property of socio-technical dynamics, highlighting the importance of adaptive and anticipatory governance for sustainable urban mobility transitions. Full article

Intricate natural wood textures and diversified defect morphologies hinder high-precision recognition of visible surface defects on sawn lumber. Six common types of surface defects exist on sawn lumber, including dry knots, edge knots, small knots, sound knots, wavy defects, and splits. Among these defect types, edge knots, small knots, wavy defects, and splits bring great difficulties to detection due to their tiny areas, slender geometric outlines and indistinct boundaries. To accurately identify the above defects, a customized You Only Look Once version 8 medium (YOLOv8m)-based framework was developed for lumber surface inspection. First, the Cross-Stage Partial Bottleneck with Two Convolutions embedded with Efficient Channel Attention (C2f-ECA) and Space-to-Depth Convolution (SPD-Conv) are introduced into the backbone to enhance channel-wise feature representation and preserve fine spatial details during downsampling, while C2f with Deformable Convolution (C2f-DCN) is embedded in the deep feature extraction branch to improve the geometric modeling of irregular defects. Second, a C2f-DCN with Exponential Moving Average Attention (C2f-DCN-EMA) module and dynamic upsampling (DySample) are integrated in the feature-fusion stage to refine multi-scale features and reconstruct local edges. Third, Scaled Intersection over Union (SIoU) loss is used to improve bounding-box regression for defects with extreme aspect ratios. Experiments show that the proposed model achieves 91.8% mean Average Precision at IoU 0.5 (mAP@50) and 69.3% mean Average Precision across IoU thresholds of 0.5–0.95 (mAP@50-95), exceeding the YOLOv8m baseline by 1.0 and 1.5 percentage points, respectively. Full article

Molecularly imprinted membranes (MIMs) have demonstrated tremendous potential in the field of high-efficiency separation due to their specific molecular recognition capabilities. This review aims to elucidate the underlying mechanisms governing MIMs’ performance and, moving beyond traditional classification frameworks, systematically reconstructs the classification system for MIMs from the perspectives of the spatial distribution of imprinted sites, the chemical topology of the matrix, and mass transfer kinetics. The article focuses on the decisive influence of key physical parameters such as pore size, specific surface area, hydrophilicity/hydrophobicity, and swellability on separation efficiency. It provides an in-depth analysis of the spatial matching between pore size and target molecules, the nonlinear relationship between specific surface area and adsorption capacity, and the mechanisms by which mechanical strength and swelling behavior constrain the long-term stability of the membranes. Addressing the common bottlenecks faced by MIMs “high mass transfer resistance and poor accessibility of recognition sites” this paper critically summarizes cutting-edge morphological optimization strategies, such as multi-level pore construction, nanocomposite reinforcement, and surface topological engineering, aiming to elucidate how microstructural regulation can achieve a synergistic enhancement of both high throughput and high selectivity. Finally, by reviewing breakthroughs in MIMs applications for biomedical extraction and environmental pollutant remediation, this review not only clarifies the principles governing material suitability across different scenarios but also provides a systematic technical reference for the development of next-generation, high-performance, industrial-scale MIMs. Full article

This paper proposes a level-based scientific maturation master plan (SMMP) for strengthening research projects prior to manuscript submission. A weak manuscript is often not simply a weak text but an immature project that has been translated too early into publication form. Contemporary research management is better at registering deadlines, deliverables, resources, and visible publication signals than at diagnosing the internal maturity of a scientific object. The result is false readiness: a project may have a topic, structure, literature, methodological vocabulary, and a polished manuscript but still lack a mature problem, a coherent conceptual architecture, a testable design, sufficient evidence, and a disciplined contribution. To address this gap, this paper proposes a nine-level SMMP, moving from thematic impulses to peer review and publication readiness. The model integrates noncompensatory gates, evidence packages, red flags, maturation debt, bottlenecks, the publication maturation gap, and peer-review readiness. Methodologically, the paper is a conceptual design study supplemented by a proof-of-concept documentary application to publicly available CORDIS project biographies. The framework shows how to distinguish publication polish from scientific maturation and how to translate expert criticism into concrete presubmission actions. Full article

Vascularization remains a fundamental bottleneck in tissue engineering, as the absence of functional vascular networks limits oxygen and nutrient delivery, resulting in necrotic cores and poor host integration. While structural scaffold design and cell sourcing have advanced considerably, emerging evidence indicates that paracrine signaling, rather than direct cell contact or scaffold architecture alone, is the primary driver of angiogenesis and vasculogenesis within engineered constructs. Key cell types, including endothelial cells (ECs) and mesenchymal stem cells (MSCs), engage in bidirectional paracrine crosstalk through the secretion of vascular endothelial growth factor (VEGF), angiopoietins, hepatocyte growth factor, and platelet-derived growth factor, among other mediators. While researchers have long focused on improving scaffold structure and cell selection, growing evidence shows that the chemical messages cells send to one another play a far more important role in driving blood vessel formation than previously appreciated. This review explores how cells embedded within engineered scaffolds communicate through secreted signals to coordinate the growth of new blood vessels. Two cell types, MSCs and ECs, are central to this process: cells that line blood vessels and bone marrow-derived stem cells. These cells exchange a variety of chemical messages that instruct neighboring cells to multiply, move, and organize into vessel-like structures. Importantly, the material properties of the scaffold itself, including its stiffness, surface texture, and degradation over time, influence the signals cells produce and how those signals spread through the tissue. Strategies to amplify paracrine signaling include growth factor-loaded delivery systems, hypoxic and genetic preconditioning of MSCs, and perfusion bioreactor culture. In vitro and in vivo evidence consistently demonstrates that coculture systems leveraging paracrine interactions produce superior vascular outcomes compared to single-cell or acellular constructs. Despite this progress, challenges related to signaling complexity, reproducibility, and clinical translation persist. Integration of transcriptomic and proteomic profiling, computational modeling, and machine learning approaches offers a path toward rationally designed scaffolds that recapitulate the spatiotemporal dynamics of native vascular signaling and ultimately support functional tissue regeneration. Full article

Addressing critical bottlenecks in traditional hydrogen refueling station operations—specifically supply–demand imbalances and suboptimal scheduling—this paper presents a systematic review of the advancements and practical implementations of intelligent transportation platforms (ITPs). We explore how these platforms catalyze enhancing operational efficiency within the hydrogen ecosystem. This paper first outlines the technical foundations of Vehicle-to-Everything communication, edge computing, and multi-source data fusion, and provides an in-depth analysis of core challenges, such as demand uncertainty and resource scheduling complexity, as well as existing optimization algorithms. Through typical case studies, the significant value of such platforms in breaking down data silos, reducing equipment idle rates, and achieving end-to-end energy efficiency optimization is demonstrated. This study notes that current bottlenecks include fragmented standards, difficulties in implementing algorithms, commercial challenges, and the retrofitting of existing infrastructure. Moving forward, efforts should shift from isolated technological breakthroughs to ecosystem development. This includes improving demand forecasting accuracy in low-penetration regions, implementing lightweight retrofits to revitalize the existing market, establishing cross-domain data collaboration standards, building a trustworthy cross-platform settlement system, and exploring innovative pathways that integrate “hydrogen, carbon, and computing.” Full article

Nanotechnology offers transformative potential for wastewater treatment, yet its full-scale implementation remains bottlenecked by the “lab–reality gap”. While bench-scale studies using idealized matrices report outstanding pollutant removal efficiencies, performance routinely deteriorates in authentic wastewater due to complex matrix interferences, natural organic matter (NOM) competitive binding, fouling dynamics, and unpredictable nano–bio transformations. Moving beyond traditional reviews that focus heavily on material synthesis and theoretical capacities, this review provides a novel, systems-oriented, and function-driven perspective on environmental nanotechnology. We critically evaluate the operational stability and behavior of nano-enabled systems under realistic conditions, categorizing nanomaterial roles into reactive interfaces, selective barriers, signal generators, and biological modulators. Crucially, this work examines the synergistic integration of nanotechnology with advanced oxidation processes (AOPs), membrane bioreactors, and digital intelligence—including artificial intelligence (AI) and real-time nanosensing—to achieve smart fouling management and circular resource recovery. Finally, we propose a comprehensive, multidimensional evaluation framework that simultaneously assesses technical efficiency, stability, scalability, economic feasibility, environmental safety, and system compatibility. This review delivers a pragmatic roadmap to bridge the chasm between isolated laboratory discovery and robust, sustainable, field-scale wastewater engineering. Full article

Metal oxide nanoparticles serve as crucial drivers in modern biomedical, catalytic, environmental, and energy technologies due to their high surface-to-volume ratios and quantum confinement properties. Traditional chemical and physical synthesis methods remain limited by significant energy footprints, high costs, and the use of hazardous reagents. To address these challenges, bioinspired (“green”) synthesis has emerged as a sustainable paradigm that employs biological systems as nature nanofactories. This critical review provides a provides a comprehensive and systematic analysis of the green synthesis of major metal oxide systems (ZnO, TiO₂, Fe₃O₄/Fe₂O₃, CuO, Co₃O₄, CeO_2, and MnO₂) using diverse biological templates, including plant extracts, bacteria, fungi, algae, and biopolymers. Moving beyond simple descriptive summaries, we critically evaluate the foundational electron-transfer and nucleation mechanism, systematically correlate processing parameters with physical outcomes, and offer a rigorous comparative analysis across different biological kingdoms. Finally, we directly address the underlying challenges facing the field: reproducibility bottlenecks, scalability limits, environmental safety variations, and regulatory hurdles necessary for industrial translation. Full article