Understanding the drivers of carbon emissions from the water-food-energy (WFE) system during urbanization is essential for achieving China's carbon peak and neutrality goals; yet, systematic quantification in this domain remains limited. This study develops a consumption-based accounting framework to estimate WFE carbon emissions for 41 cities in China's Yangtze River Delta (YRD) from 2000 to 2023. By integrating restricted cubic splines, boosted regression trees, and piecewise structural equation modeling, we systematically uncover the nonlinear impacts and multi-path transmission mechanisms of comprehensive urbanization on WFE emissions. The key findings are as follows: (1) WFE carbon emissions (WFEC) in the YRD exhibit a fluctuating pattern with an overall upward tendency since 2000, mainly driven by industrial and residential water use. (2) A significant nonlinear relationship exists between the composite urbanization index (UI) and both WFEC and WFE carbon intensity (WFECI), with a turning point at approximately UI = 0.2. (3) The marginal effects of population, spatial, and social urbanization on WFEC intensify over time, while that of ecological urbanization weakens; effects on WFECI are predominantly negative. (4) Population, economic, and spatial urbanization exert significant direct effects on emissions, alongside indirect effects mediated by resource use intensity and behavioral consumption structure, marking them as the most pivotal and complex dimensions. The present study provides new evidence on consumption-driven WFE emissions and offers theoretical and policy insights for low-carbon transitions in resource-intensive regions.

Rapid urbanization intensifies landscape fragmentation, making the optimization of ecological networks essential for maintaining landscape connectivity. Effective optimization needs a diagnostic understanding of a network’s internal cluster structure. However, clustering methods cannot simultaneously reconcile spatial adjacency with interaction strength, often yielding results that are either geographically fragmented or ecologically incomplete. The objective is to generate spatially coherent clusters while preserving critical functional connections, thereby enabling the identification of structural weaknesses for targeted conservation. The main contribution of this study is developing a spatially constrained method of ecological network clustering and optimization, which involves constructing a Maximum Spanning Tree (MaxST) under Voronoi-based adjacency rules and employing a Genetic Algorithm (GA) to optimally partition this structure. The framework begins by constructing a MaxST under strict spatial adjacency rules to form a contiguous network backbone. A GA then optimally partitions this backbone by maximizing modularity, incorporating interaction strengths from both adjacent and non-adjacent corridors. The method successfully identified ecologically meaningful and spatially contiguous clusters. It revealed that corridors between clusters, despite their greater length, constituted the network's most vulnerable links due to their lower interaction strength. The analysis pinpointed high-priority structural weaknesses for intervention, including key ecological pinch points, barrier areas, and critical bridging corridors. This study introduces a practical method that explicitly addresses the spatial-functional trade-off in ecological network analysis. It shifts the conservation focus from isolated landscape elements to diagnosing and repairing cluster-based structural weaknesses, offering a targeted pathway for enhancing connectivity in fragmented urban landscapes.

Our study assesses multiple environmental inequalities between the Global South and the Global North across more than 10,000 urban centers. Three environmental indicators (CO2 emissions, air pollution (PM2.5) and greenness) are used to represent three distinct environmental roles of urban ecosystems to society and human health, respectively: destruction (cost), victimization (harm) and contribution (benefit). The difference in relative importance, temporal dynamics, and driving patterns of these three indicators is analyzed between the Global South and the Global North. The results indicate that CO₂ emissions in the Global North exceed more than twice those in the Global South, whereas the mean PM₂.₅ concentration is less than half, reflecting significantly higher environmental destruction but lower environmental victimization. Global South and Global North exhibit similar trends in greenness, yet have different causes, with the luxury effect in Global North. Socioeconomic factors shape environmental development in the Global North, while both socioeconomic factors and natural endowments do so in the Global South. Achieving equitable environmental development of global urban centers requires breaking the development dilemma of the Global South and addressing the unequal exchange patterns between the Global North and the Global South.

Gel electrophoresis is used to separate and analyze macromolecules （such as DNA， RNA， and proteins） and their fragments， and highly reproducible and efficient automatic band-detection methods have been developed to analyze gel images. Uneven background， low contrast， lane distortion， blurred band edges， and geometric deformation pose detection-accuracy challenges during automatic band detection. In order to address these issues， various correction algorithms have been proposed； however， these algorithms rely on researcher experience to adjust and optimize parameters based on image characteristics， which introduces human error while qualitatively and quantitatively processing bands. Isoelectric focusing （IEF） gel electrophoresis separates proteins with high-resolution based on isoelectric point （pI） differences. Microarray IEF （mIEF） is used for the auxiliary diagnosis of diabetes and adult β-thalassemia owing to operational ease， low sample consumption， and high throughout. This diagnostic method relies on accurately positioning and precisely determining protein bands. To avoid errors associated with correction algorithms during band analysis， this paper introduces a method for rapidly recognizing bands in gel electrophoresis patterns that relies on a deep learning object detection algorithm， and uses it to quantify and classify the IEF electrophoresis pattern of hemoglobin （Hb）. We used mIEF experiments to collect 1 665 pI-marker-free Hb IEF images as a model dataset to train the YOLOv8 model. The trained model accepts a Hb IEF image as input and infers band bounding boxes and classification results. Using inference data， the gray intensities of the pixels in each band area are summed to determine the content of each protein. The background and foreground of the image need to be separated prior to summing the abovementioned gray intensities， and the threshold method is used to achieve this. The threshold is defined as the average intensity of the background area， which is obtained by summing and averaging the background intensities of gel areas between the detection bounding boxes of each protein band. The baseline band areas are unified after removing the background. This method only requires the input image， directly outputs the corresponding electrophoretic band information， and does not rely on the experience of professionals nor is it affected by factors such as lane distortion or band deformation. In addition， the developed method does not depend on pI markers for qualitatively determining bands， thereby reducing experimental costs and improving detection efficiency. YOLOv8n delivered a detection accuracy of 92.9% and an inference time of 0.6 ms while using limited computing resources. Using Hb A2 as an example， we compared its content measured using the developed method with clinical data. The quantitative results were subjected to regression analysis， which delivered a linearity of 0.981 2 and a correlation coefficient of 0.980 0. We also used the Bland-Altman analysis method to verify that these two values are highly consistent. Compared with the traditional automatic band detection methods， the method developed in this study is fast， accurate， more repeatable， and stable， and can be used to determine the Hb A2 content in clinical practice， thereby potentially assisting in the auxiliary diagnosis of adult β-thalassemia.

BackgroundHemoglobin (Hb) is an important protein in red blood cells and a crucial diagnostic indicator of diseases, e.g., diabetes, thalassemia, and anemia. However, there is a rare report on methods for the simultaneous screening of diabetes, anemia, and thalassemia. Isoelectric focusing (IEF) is a common separative tool for the separation and analysis of Hb. However, the current analysis of IEF images is time-consuming and cannot be used for simultaneous screening. Therefore, an artificial intelligence (AI) of IEF image recognition is desirable for accurate, sensitive, and low-cost screening.ResultsHerein, we proposed a novel comprehensive method based on microstrip isoelectric focusing (mIEF) for detecting the relative content of Hb species. There was a good coincidence between the quantitation of Hb via a conventional automated hematology analyzer and the one via mIEF with R2 = 0.9898. Nevertheless, our results showed that the accuracy of disease diagnosis based on the quantification of Hb species alone is as low as 69.33%, especially for the simultaneous screening of multiple diseases of diabetes, anemia, alpha-thalassemia, and beta-thalassemia. Therefore, we introduced a ResNet1D-based diagnosis model for the improvement of screening accuracy of multiple diseases. The results showed that the proposed model could achieve a high accuracy of more than 90% and a good sensitivity of more than 96% for each disease, indicating the overwhelming advantage of the mIEF method combined with deep learning in contrast to the pure mIEF method.SignificanceOverall, the presented method of mIEF with deep learning enabled, for the first time, the absolute quantitative detection of Hb, relative quantitation of Hb species, and simultaneous screening of diabetes, anemia, alpha-thalassemia, and beta-thalassemia. The AI-based diagnosis assistant system combined with mIEF, we believe, will help doctors and specialists perform fast and precise disease screening in the future.

Electrophoresis titration chip (ETC) is a versatile tool for onsite and point -of -care quantification analyses because it affords naked -eye detection and a straightforward quantification format. However, it is vulnerable to changes in environmental temperature, which regulates the electrophoretic migration by affecting the ion mobility and the target recognition by influencing the enzyme activity. Therefore, the quantification accuracy of the ETC tests was severely compromised. Rather than using the dry bath or heating/cooling units, we proposed a facile model of dual calibration standards (DCS) to mathematically eliminate the effects of temperature on quantification accuracy. To verify our model, we deployed the ETC device at different temperatures ranging from 5 to 40 degrees C. We further utilized the DCS-ETC to determine the protein content and uric acid concentration in real samples outside the laboratory. All the experimental results showed that our model significantly stabilized the quantification recovery from 35.31-153.44 % to 99.38-103.44 % for protein titration; the recovery of uric acid titration is also stable at 96.25-106.42 %, suggesting the enhanced robustness of the ETC tests. Therefore, DCS-ETC is a field -deployable test that can offer reliable quantification performance without extra equipment for temperature control. We envision that it is promising to be used for onsite applications, including food safety control and disease diagnostics.

Studying carbon emissions is critical to mitigating global climate change, guiding policy development, promoting economic and technological development, and improving public health. To verify whether technological progress accelerates peak carbon dioxide emissions in megacities, we constructed a genetic algorithm (GA)-optimized back-propagation neural network (BPNN) model to predict carbon dioxide emissions in Shanghai from 2022 to 2043 under business as usual (BAU), technological progress (TP), and technological progress with energy substitution (TE) scenarios. Technological progress had a relatively small impact on CO2 emissions. Heavy industrial production, population, and industrial structure had major impacts on carbon dioxide emissions in Shanghai. Technological progress had a rebounding effect. There was an inverted U-shaped relationship between technological progress and CO2 emissions. Technological progress has accelerated peaking CO2 emissions in megacities. The TP and TE scenarios peaked five years earlier than the BUA scenario. The BUA scenario was projected to achieve peak carbon emissions in 2029. Energy substitution could reduce the amount of CO2 emissions. Technological progress in Shanghai has promoted the decoupling of carbon emissions from economic development. The continuous development of science and technology is an effective way to advance carbon peaking and reduce carbon dioxide emissions.

Human activities and regional land development have considerably contributed to the degradation of ecosystems and the growing contradiction between the supply and demand of ecosystem services (ESs) in Jiangsu Province, China. However, few studies have applied a comprehensive approach to elucidate the patterns and evolutionary characteristics of ESs over long periods. This study investigated the spatial-temporal evolution of the supply, demand, and supply-demand relationships of six individual ESs and comprehensive ESs in Jiangsu Province from 2000 to 2020. It revealed the key drivers of the changes in comprehensive ecosystem services supply-demand relationship (CESSD) using a geo-detector model and a geographically and temporally weighted regression model to address the shortcomings of previous studies. The results showed that at the provincial level, the CESSD presented a surplus while the supply-demand state tended to be imbalanced. At the 1-km2 grid scale, the spatial heterogeneity of CESSD was obvious, with surplus supply-demand zones primarily distributed in the Taihu Lake watershed, hilly areas, riverside zones, the watershed from Hongze Lake to Gaoyou Lake, and the eastern coastal zone, while deficit supply-demand zones were mainly clustered in areas with concentrated and contiguous construction land. From 2000 to 2020, the area of deficit in CESSD increased from 626 to 3257 km2 and the degree of deficit gradually deepened, with socioeconomic factors having the greatest influence on changes in CESSD. Our findings reveal the balance/imbalance between human society and natural ecology in Jiangsu Province and highlight the need for effective management of regional ecosystems.

Land-use conflicts have become increasingly intense in the process of urbanisation and industrialisation. Previous studies on land-use conflict pay little attention to historical land-use changes and consideration of multi-objective constraints when coordinating conflicts. In this study, we propose a framework for identifying and coordinating land-use conflicts in the construction‒agricultural‒ecological space. In this framework, a conflict intensity index of construction‒agricultural‒ecological land use was created with the local dominance of occurrence probability, which was predicted by spatial dependence logistic regression with historical land-use change. Then, multi-objective constraints based on comparative advantages were used for scenario coordination of construction–agricultural–ecological land-use conflicts. The case study showed that land-use conflicts in Changzhou City exhibited a circular pattern around urban areas, and that agricultural spaces were at risk of encroachment. Five land-use zones were divided using the grouping analysis method. Scenario coordination provided a reference for land-use conflict coordination at different stages of development. Protecting the baseline of food and ecological security as well as controlling urban sprawl can help mitigate land-use conflicts.

Reconciling carbon emissions and GDP development is a challenge for most Chinese cities in the sustainable development process. Taking the Yangtze River Delta of China as an example, this study calculated carbon-emission inventories for five typical years and assigned anthropogenic carbon emissions to land-use types. A multi-objective particle swarm algorithm was used to consider three functions of land-use allocation: maximizing GDP, minimizing carbon emissions, and maximizing suitability. Thereafter, the technique for order of preference by similarity to ideal solution (TOPSIS) method was combined to find the optimal solution for carbon emission and economic development trade-offs. It is observed that Jiangsu has the largest carbon emissions in the Yangtze River Delta region; energy consumption is the main source of the carbon emissions, and industrial land is the most carbon intensive region. The land-use allocation obtained by multi-objective optimization with the TOPSIS evaluation method increases GDP and reduces carbon emissions, which is worthy of reference for land planners. Land use optimization from the perspective of balancing carbon emissions and economic development can provide a useful reference for the rational use of land resources and socio-economic sustainable development.

Urban expansion is one of the most important drivers of carbon emissions. It not only causes the invasion of terrestrial carbon sinks but also aggravates carbon emissions through social and economic activities. Because the effect of urban expansion on carbon emissions varies at different stages of urban development, spatiotemporal analysis of the dynamic relationship between urban expansion and carbon emissions is critical for low‐carbon urban planning. By combining nighttime light remote sensing and panel data, this study explored the spatiotemporal coupling characteristics of urban expansion and carbon emissions in a case study of Zhejiang in China. Spatial overlap analysis was used to measure the consistency of the spatial movement trajectories, and the coupling coordination degree model was used to dynamically investigate the interaction between urban land expansion and carbon emission increase. In this way, the dynamic coupling is represented from both a temporal and spatial perspective. The results showed that both the carbon emission and urban expansion growth rates first increased and then decreased, and their spatial coupling became increasingly close. Significantly, the coordinated development level between carbon emissions and urban expansion presented an inverted U‐shaped curve, which is consistent with the Kuznets curve theory. Overall, our research revealed the regularity of carbon emissions associated with land urbanization, which can help policymakers and urban planners to achieve sustainable land management.

Intrinsic fluorescence imaging (IFI) has been used for the stain-free detection of proteins in slab gel. However, complicated detection setups and small irradiation area limited the development of facile, online, and portable imaging of the whole slab gel. We here designed a quadruple UV LED array to produce even and powerful area light for direct irradiation of gel electrophoresis chip (GEC) at 275 nm. In addition, we only used a filter of 365 nm, a UV camera lens, and a CCD for IFI detection. We integrated the simple detection setup with the small GEC to construct the IFI-GEC device with a portable size of 15 × 15 × 38 cm. We detected three model proteins to demonstrate the good evenness of the LED array and the online imaging of the whole GEC. Furthermore, the reproducible IFI-GEC detection was completed within 10 min and the LOD was as low as 40 ng for lysozyme detection. All results indicated the potential of the IFI-GEC device for online and portable detection of proteins without staining.

In this work, by combining the microcolumn isoelectric focusing (mIEF) and similarity analysis with the earth mover's distance (EMD) metric, we proposed the concept of isoelectric point (pI) barcode for the identification of species origin of raw meat. At first, we used the mIEF to analyze 14 meat species, including 8 species of livestock and 6 species of poultry, to generate 140 electropherograms of myoglobin/hemoglobin (Mb/Hb) markers. Secondly, we binarized the electropherograms and converted them into the pI barcodes that only showed the major Mb/Hb bands for the EMD analysis. Thirdly, we efficiently developed the barcode database of 14 meat species and successfully used the EMD method to identify 9 meat products thanks to the high throughput of mIEF and the simplified format of the barcode for similarity analysis. The developed method had the merits of facility, rapidity and low cost. The developed concept and method had evident potential to the facile identification of meat species.

Elastic scaling in/out of operator parallelism degree is needed for processing real time dynamic data streams under low latency and high stability requirements. Usually the operator parallelism degree is set when a streaming application is submitted to a stream computing system and kept intact during runtime. This may substantially affect the performance of the system due to the fluctuation of input streams and availability of system resources. To address the problems brought by the static parallelism setting, we propose and implement a machine learning based elastic strategy for operator parallelism (named MeStream) in big data stream computing systems. The architecture of Me-Stream and its key models are introduced, including parallel bottleneck identification, parameter plan generation, parameter migration and conversion, and instances scheduling. Metrics of execution latency and process latency of the proposed scheduling strategy are evaluated on the widely used big data stream computing system Apache Storm. The experimental results demonstrate the efficiency and effectiveness of the proposed strategy.

Diffuse axonal injury (DAI) is the most severe pathological feature of traumatic brain injury (TBI). However, how primary axonal injury is induced by transient mechanical impacts remains unknown, mainly due to the low temporal and spatial resolution of medical imaging approaches. Here we established an axon-on-a-chip (AoC) model for mimicking DAI and monitoring instant cellular responses. Integrating computational fluid dynamics and microfluidic techniques, DAI was induced by injecting a precisely controlled micro-flux in the transverse direction. The clear correlation between the flow speed of injecting flux and the severity of DAI was elucidated. We next used the AoC to investigate the instant intracellular responses underlying DAI and found that the dynamic formation of focal axonal swellings (FAS) accompanied by Ca2+ surge occurs during the flux. Surprisingly, periodic axonal cytoskeleton disruption also occurs rapidly after the flux. These instant injury responses are spatially restricted to the fluxed axon, not affecting the overall viability of the neuron in the acute stage. Compatible with high-resolution live microscopy, the AoC provides a versatile system to identify early mechanisms underlying DAI, offering a platform for screening effective treatments to alleviate TBI.

Intrinsic fluorescence Imaging (IFI) circumvents the time-consuming process of staining or labelling of proteins, while its sensitivity is limited by the weak fluorescence emission due to the low quantum yield. Herein, we designed a quadruple deep-UV LED array to excite the tryptophan (Trp) in proteins for the portable gel electrophoresis chip (GEC) developed in our previous work. The quadruple LED array significantly enhanced the efficiency and uniformity of excitation without sacrificing the merits of good portability and rapid detection of GEC. We developed IFI-GEC method for three model proteins to demonstrate that: (i) our quadruple deep-UV LED array improved the intrinsic fluorescence and led to the comparable and even higher sensitivity compared to the Coomassie Brilliant Blue (CBB) staining in slab gel; (ii) the compact design of LED array and the insertion of GEC into the IFI device realized the small size (38 × 15 × 15 cm) of IFI-GEC system; and (iii) the short run time of 6 min on GEC diminished the photobleaching of Try resulting from the high excitation power. Therefore, the IFI avoided the tedious staining of protein bands, and meanwhile the GEC compensated the decrease of sensitivity due to the photobleaching when real-time analysis is required. Thanks to the reliable sensitivity, the small size, and the rapid detection, the integration of IFI and GEC techniques showed great potential for the on-site analysis of proteins and can be expanded to the detection of nucleic acids in the future.

Synthesis of polymeric nanoparticles (NPs) through self-assembly of di-block copolymers have attracted substantial interest in the past decades for drug delivery and controlled release. Microfluidics offers a facile approach for making such NPs and drug encapsulation. However, a fundamental understanding of the drug encapsulation process is lacking. In this paper, we report a combined computational fluid dynamics (CFD) and experimental approach to illustrate the fundamental principle that governs the encapsulation of a drug in polymeric NPs through microfluidic nanoprecipitation. Taking a drug curcumin and a polymer poly (ethylene glycol)-block-poly (D, L-lactide-co-glycolide) (PEG-PLGA) as a model system, we demonstrated the different precipitation times of curcumin and PEG-PLGA as well as their mixing times in the microfluidic device. The big difference in their mixing times led to very low drug loading. This study provides a new perspective in understanding and controlling the formation of drug-loaded polymeric NPs and offers a new design rule for selecting the right combinations of drugs, polymers, solvents, and devices. (C) 2021 Elsevier Ltd. All rights reserved.

Since 1973, studies have explored ocean power generation from different perspectives. However, in the past 45 years, few studies have attempted to comprehensively review the existing studies on ocean power generation using wave energy, tidal current energy, ocean thermal energy, salinity gradient energy, bio-mass energy, and gas hydrates. In this study, we collected 5262 studies published from 1973 to 2018 for scientometric visualization analysis and drew a knowledge map of the ocean power generation field. The results show that the most important contributions to the research of ocean power generation mainly came from the United States, China, Britain, Italy, Spain, Japan, Norway, Germany, France, and Denmark. Ocean power generation research is mainly divided into two stages. From 1973 to 2007, there were relatively few studies and no obvious hot topics. From 2008 to 2018, the knowledge fields mainly focused on ocean biomass power generation, the exploitation of natural gas hydrates, the utilization of wave energy and tidal energy, the research and optimization of energy generators, the storage and management of ocean energy, and numerical simulations of marine climates. In addition, the joint utilization of wind energy and wave energy is also a current research topic of interest, including joint assessment of the two energy potentials, the research and development of equipment, and numerical simulations of joint power generation projects.

Most mammalian neurons have a narrow axon, which constrains the passage of large cargoes such as autophagosomes that can be larger than the axon diameter. Radial axonal expansion must therefore occur to ensure efficient axonal trafficking. In this study, we reveal that the speed of various large cargoes undergoing axonal transport is significantly slower than that of small ones and that the transit of diverse-sized cargoes causes an acute, albeit transient, axonal radial expansion, which is immediately restored by constitutive axonal contractility. Using live super-resolution microscopy, we demonstrate that actomyosin-II controls axonal radial contractility and local expansion, and that NM-II filaments associate with periodic F-actin rings via their head domains. Pharmacological inhibition of NM-II activity significantly increases axon diameter by detaching the NM-II from F-actin and impacts the trafficking speed, directionality, and overall efficiency of long-range retrograde trafficking. Consequently, prolonged NM-II inactivation leads to disruption of periodic actin rings and formation of focal axonal swellings, a hallmark of axonal degeneration.

The Organic Rankine Cycle (ORC) has become a leading thermodynamic technique to extract more energy by benefiting from the application of dense gas. As the connecting component to the ORC turbine outlet, dense gas diffusers are key components designed to improve the efficiency of ORC. However, investigations in the robust optimal design of dense gas diffusers are lacking, which restricts the improvement of overall ORC efficiency. An advanced and robust framework coupling an Uncertainty Quantification (UQ) approach with Computational Fluid Dynamics (CFD) and NIST REFPROP is proposed to effectively implement sensitivity analysis of dense gas conical diffusers. R143a, a potential dense gas is employed in this analysis. Both operating and geometric parameters have significant impact on the performance of conical diffusers, and thus a performance analysis is conducted using the proposed framework. This paper is the first attempt to quantify the influence of coupled and multiple uncertain parameters on a dense gas conical diffuser. It is shown that the swirl velocity has more impact than inlet axial velocity on pressure recovery under various geometric conditions regarding length and angle of the dense gas conical diffuser. This study highlights the need to achieve a robust optimal dense gas diffuser design in order to improve overall ORC efficiency.