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In recent years, deep-learning methods have significantly improved the classification results in the field of plant-leaf recognition. However, limited by the model input, the original image needs to be compressed to a certain size before it can be input into the convolutional neural network. This results in great changes in the shape and texture information of some samples, thus affecting the classification accuracy of the model to a certain extent. Therefore, a minimum enclosing quadrate (MEQ) method is proposed to standardize the sample datasets. First, the minimum enclosing rectangle (MER) of the leaf is obtained in the original image, and the target area is clipped. Then, the minimum enclosing quadrate of the leaf is obtained by extending the short side of the rectangle. Finally, the sample is compressed to fit the input requirements of the model. In addition, in order to further improve the classification accuracy of plant-leaf recognition, an EC-ResNet50 model based on transfer-learning strategy is proposed and further combined with the MEQ method. The Swedish leaf, Flavia leaf, and MEW2012 leaf datasets are used to test the performance of the proposed methods, respectively. The experimental results show that using the MEQ method to standardize datasets can significantly improve the classification accuracy of neural networks. The Grad-CAM visual analysis reveals that the convolutional neural network exhibits a higher degree of attention towards the leaf surface features and utilizes more comprehensive feature regions during recognition of the leaf samples processed by MEQ method. In addition, the proposed MEQ + EC-ResNet50 method also achieved the best classification results among all the compared methods. This experiment provides a widely applicable sample standardization method for leaf recognition research, which can avoid the problem of sample deformation caused by compression processing and reduce the interference of redundant information in the image to the classification results to a certain degree.

The majority of the energy required for human survival is derived either directly or indirectly from solar radiation, thus it is important to investigate the periodic fluctuations in global solar radiation over time. In this study, six cities—Harbin, Shenyang, Beijing, Shanghai, Wuhan, and Guangzhou—located in the utilizable areas of solar energy in China, were selected, and the periodicity of the daily global solar radiation of these six cities with time were investigated by means of wavelet power spectrum analysis. Furthermore, Harbin, which has the lowest monthly average of global solar radiation in the cold of winter, was selected to explore the temporal evolution relationship between global solar radiation and other meteorological factors, and air quality factors by wavelet coherence analysis. Among the meteorological factors, the correlation between global solar radiation and sunshine duration exhibits the highest level of consistency. Global solar radiation demonstrates a positive association with atmospheric temperature/wind speed/precipitation over the annual cycle. Conversely, it displays a negative correlation with atmospheric pressure during this time frame. Additionally, on shorter timescales, global solar radiation is negatively correlated with air humidity and precipitation. Among air quality factors, the relationship between global solar radiation and CO/NO2/O3/PM2.5/PM10/SO2 primarily manifests in the broader annual cycle time scale. Within this context, global solar radiation shows a positive correlation with O3, while displaying negative associations with the other five air quality indicators. These findings contribute to urban environmental planning and the effective management and utilization of solar radiation, thereby providing valuable insights to guide the future development of cross-regional comprehensive energy utilization projects under diverse climatic and geographical conditions. Additionally, they serve as a reference for subsequent research aimed at enhancing the accuracy of global solar radiation prediction models.

Accurate identification of apple leaf diseases is of great significance for improving apple yield. The lesion area of the apple leaf disease image is small and vulnerable to background interference, which easily leads to low recognition accuracy. To solve this problem, a lightweight bilinear convolutional neural network (CNN) model named BLSENet based on attention mechanism is designed. The model consists of two subnetworks, and each subnetwork is embedded with a Squeeze-and-Excitation (SE) module. By using the feature extraction ability of the two subnetworks and combining the bilinear feature CONCAT operation, the multiscale features of the image are obtained. Compared with the unimproved model LeNet-5 (84.63%), BLSENet has higher accuracy in the test set, which indicates that SE module and bilinear feature fusion have a positive effect on the performance of the model, and BLSENet has the ability to identify apple leaf diseases. The model has achieved the expected goal and can provide technical support for accurate identification and real-time monitoring of apple disease images.

The detection of defects in wood is valuable for promoting the efficient exploitation of wood. So it is significant to further increase the accuracy of the detection of wood defects and enhance the real-time detection. In this paper, the YOLOv5 convolutional neural network is applied to wood defects detection, and the model is modified for both the YOLOv5n and YOLOv5m scales. The SimAM attention model was first incorporated into the network, and the learning rate decay strategy was replaced with CosLR, with Ghost convolution employed to minimize the model parameters. Finally, the modified network was tested for five types of wood defects, including live-knot, resin, dead-knot, knot-with-crack, and crack. It is demonstrated that the improvements resulted in a 1.5% increase in mAP0.5:0.95 for YOLOv5n-C3Ghost and a 1.6% increase in mAP0.5:0.95 for YOLOv5m-C3Ghost. In addition, there is a 51% and 63% difference in the number of model parameters, and a decrease in inference time and floating point operations respectively. The experiments indicate that our improved method not only enhances the accuracy of YOLOv5 in detecting wood defects, but also enables a reduction in the volume and computational cost of the model parameters.

Wood is an indispensable non-renewable resource and plays an important role in our life. So, it is a necessity to apply accurate classification techniques. In this paper, a novel model of deep one-dimensional convolutional neural network NIR-VGGNet19 is proposed to classify seven pius wood by combining near-infrared spectroscopy. NIR-VGGNet19 uses a deep convolutional neural network to automatically extract features, eliminate noise, and solve the problem of spectral overlapping peaks; initially eliminates random noise by adding $1\times 7$ convolutional kernels; then uses an attention mechanism to extract more accurate features. According to the results, when NIR-VGGNet19 was tested against other methods, NIR-VGGNet19 had the highest classification accuracy, achieving 98.41% on the test set. In contrast, the accuracy of LeNet, AlexNet, VGGNet-19, ResNet-34, back propagation neural network, and support vector machine were 70.95%, 92.54%, 93.02%, 96.67%, 76.19%, and 71.26%, respectively. Thus, it suggests that the NIR-VGGNet19 can improve the accuracy of classifying wood of the homogeneous genus.

AimsUrbanization plays an important role in individuals' health. However, it is difficult to isolate healthy migrant effect between urbanization and health. This study examined the effects of urbanization on depressive symptoms and its possible pathways among Chinese middle-aged and older adults independent of the influence of health-selective migration.MethodsUsing the baseline survey of the China Health and Retirement Longitudinal Study, this study compared the depressive symptoms among three groups (urbanized rural residents, rural non-migrants and urban non-migrants). The 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) short form was used to measure depressive symptoms. Logistic regression models and Structural Equation Model (SEM) were applied to examine the association between urbanization and depressive symptoms and the corresponding potential mechanisms.ResultsOur final sample contained 11,156 respondents with an average age of 58.91 (SD = 9.48), with 5,142 males (46.09%) and 6,014 females (53.91%). Compared with urbanized rural residents, rural residents were more likely to have depressive symptoms (OR = 1.19, 95% CI = 1.07, 1.32), and urban residents were associated with a decreased risk of depressive symptoms (OR = 0.81, 95% CI: 0.70, 0.94). A large proportion of the association between urbanization and depressive symptoms were mainly mediated by social participation, income and living conditions.ConclusionsPlanned urbanization had an independent impact on decreased depressive symptoms. Improvements in social participation, income and living conditions are the main drivers behind this relationship. Additionally, urbanization compensates for the negative impact of depressive symptoms from disadvantaged early life conditions, but it cannot eliminate the gap between urbanized rural people and urban non-migrants.

An open-set recognition scheme for tree leaves based on deep learning feature extraction is presented in this study. Deep learning algorithms are used to extract leaf features for different wood species, and the leaf set of a wood species is divided into two datasets: the leaf set of a known wood species and the leaf set of an unknown species. The deep learning network (CNN) is trained on the leaves of selected known wood species, and the features of the remaining known wood species and all unknown wood species are extracted using the trained CNN. Then, the single-class classification is performed using the weighted SVDD algorithm to recognize the leaves of known and unknown wood species. The features of leaves recognized as known wood species are fed back to the trained CNN to recognize the leaves of known wood species. The recognition results of a single-class classifier for known and unknown wood species are combined with the recognition results of a multi-class CNN to finally complete the open recognition of wood species. We tested the proposed method on the publicly available Swedish Leaf Dataset, which includes 15 wood species (5 species used as known and 10 species used as unknown). The test results showed that, with F1 scores of 0.7797 and 0.8644, mixed recognition rates of 95.15% and 93.14%, and Kappa coefficients of 0.7674 and 0.8644 under two different data distributions, the proposed method outperformed the state-of-the-art open-set recognition algorithms in all three aspects. And, the more wood species that are known, the better the recognition. This approach can extract effective features from tree leaf images for open-set recognition and achieve wood species recognition without compromising tree material.

Macrocycles have been widely studied for their structure-specific and highly selective recognition properties. Here the authors review the design, synthesis and applications of photo-, pH- and redox-responsive macrocycles.

Recently, a number of non-destructive testings equipment (NDTs) which can partially replace human-conducted onsite inspections has been implemented for detecting modern civil architectural structures. However, the situation of implementing 3D laser scanning measurement technology worldwide is not optimistic: several inconvenient flaws is troubling users (e.g., heavy, costly, hard to move, hard to inspect and etc.). Therefore, a new equipment to fix those problems is urgently demanded. This paper aims to thoroughly analyze the issue and then develop a feasible. A 3D laser scanning system composed of high precision elevating platform and small 2D laser ranging sensor is designed. Manufacture Process of 3D laser scanning system is describes in detail. Based on the bending fatigue loading test, the scanning designed efficiency and feasibility had been proved by comparing and analyzing traditional measurement results. The designed 3D laser scanning system can quickly capture the information of micro-damage and depth of the structure. Its detecting performance is better than the traditional methods. To overcome the challenges of the 3D laser scanning on-site measurement technology, this article proposed the manufacture process of the 3D scanning system with a high precision, miniaturization and lightweight.

Idiopathic pulmonary fibrosis (IPF) is serious chronic lung disease with limited therapeutic approaches. Inflammation and immune disorders are considered as the main factors in the initiation and development of pulmonary fibrosis. Inspired by the key roles of macrophages during the processes of inflammation and immune disorders, here, we report a new method for direct drug delivery into the in-situ fibrotic tissue sites in vitro and in vivo. First, liposomes containing dexamethasone (Dex-L) are prepared and designed to entry into the macrophages in the early hours, forming the macrophages loaded Dex-L delivery system (Dex-L-MV). Chemokine and cytokine factors such as IL-6, IL-10, Arg-1 are measured to show the effect of Dex-L to the various subtypes of macrophages. Next, we mimic the inflammatory and anti-inflammatory microenvironment by co-culture of polarized/inactive macrophage and fibroblast cells to show the acute inflammation response of Dex-L-MV. Further, we confirm the targeted delivery of Dex-L-MV into the inflammatory sites in vivo, and surprisingly found that injected macrophage containing Dex can reduce the level of macrophage infiltration and expression of the markers of collagen deposition during the fibrotic stage, while causing little systematic toxicity. These data demonstrated the suitability and immune regulation effect of Dex-L-MV for the anti-pulmonary process. It is envisaged that these findings are a step forward toward endogenous immune targeting systems as a tool for clinical drug delivery.

The composite of nano-metal materials and wood to prepare multifunctional metallized wood is of great significance for increasing the added value of wood and expanding the field of wood applications. In this paper, the veneer of Pinus sylvestris L. var. mongholica Litv was used as the research object and the wood veneer was treated by simple physical methods. A copper thin film was grown on the surface of the wood veneer by the magnetron sputtering method to realize the preparation of “environmentally friendly” metalized wood. The preparation method of “environmentally friendly” metallized wood was explored, and the mechanical properties, electrical conductivity, and fluorescent effect of metallized wood were studied. The nano-indenter, square resistance tester, and fluorescence microscope were used to characterize the mechanical properties, electrical conductivity, and fluorescent effect of the “environmentally friendly” metallized wood surface. The results show that as the sputtering time increases, the load–displacement curve shifts gradually and the elastic modulus and hardness of the metallized wood veneers also gradually increase. The loading displacement of magnetron sputtered samples for 15 min (200 °C) is 269.9 nm. The loading displacement decreases by more than 60%, the average elastic modulus increases by 6.7 times, and the average hardness increases by 6.6 times. The transverse grain square resistance on the surface of metallized wood is about 2–3 times the parallel grain square resistance. As the sputtering time increases, the natural fluorescence intensity of the metallized wood surface gradually decreases and the wood surface is completely covered by the copper film.

The density and its spatial distribution of wood-plastic composites (WPCs) were tested by the computer tomography (CT) method. Based on the correlation among the attenuation coefficient, density, and CT number, a mathematical model between CT number and density of WPCs was established. This contributed to the realization of fast and nondestructive detection of WPC density and to the determination of the quality of wood-plastic composite products. Taking pine and poplar wood powder WPCs as subjects, the density was higher near the edge and lower in the middle of the sample, which resembled a "V" shape in the width direction. Compared with the density variations of poplar wood powder WPCs, those of pine wood powder had more of a "V" distribution.

The combination of nano-metal and wood to prepare copper-coated wood-based composite materials has important research value and practical significance for improving the function of wood, expanding the application field of wood, and adding added value. In this paper, 31-year-old wood (Pinus sylvestris L. var. mongholica Litv) veneer was taken as the research object. The wood veneer was pretreated by ultrasonic wave, and copper film was deposited on the surface of the wood veneer by magnetron sputtering to prepare “environmentally friendly” copper-plated wood-based composite materials. The microstructure and hydrophobic properties of Cu-coated wood-based composites were characterized and studied. With the increase in coating time, the diffraction peak intensity of wood cellulose gradually decreased, and the diffraction peaks of Cu (111), Cu (200), and Cu (220) of metallic copper appeared. Under the sputtering condition of a substrate temperature of 200 °C, the copper film deposit on the surface of the wood was uniform and densely arranged. The surface water contact angle reached 149.9°. Ultrasonic treatment increases the porous structure of wood, and the rough metal copper film interface was constructed on the surface of wood by magnetron sputtering to transform the surface wettability of the wood from hydrophilic to super-hydrophobic. The lotus leaf effect was realized on the wood surface.

A number of intelligent warning techniques have been implemented for detecting underwater infrastructure diagnosis to partially replace human-conducted on-site inspections. However, the extensively varying real-world situation (e.g., the adverse environmental conditions, the limited sample space, and the complex defect types) can lead to challenges to the wide adoption of intelligent warning techniques. To overcome these challenges, this paper proposed an intelligent algorithm combing gray level co-occurrence matrix (GLCM) with self-organization map (SOM) for accurate diagnosis of the underwater structural damage. In order to optimize the generative criterion for GLCM construction, a triangle algorithm was proposed based on orthogonal experiments. The constructed GLCM were utilized to evaluate the texture features of the regions of interest (ROI) of micro-injury images of underwater structures and extracted damage image texture characteristic parameters. The digital feature screening (DFS) method was used to obtain the most relevant features as the input for the SOM network. According to the unique topology information of the SOM network, the classification result, recognition efficiency, parameters, such as the network layer number, hidden layer node, and learning step, were optimized. The robustness and adaptability of the proposed approach were tested on underwater structure images through the DFS method. The results showed that the proposed method revealed quite better performances and can diagnose structure damage in underwater realistic situations.

In order to improve the mechanical properties of the wood surface and explore the mechanical effect of wood veneer surface metallization, the 31-year-old Pinus sylvestris is taken as the research object and Cu is deposited on the wood surface by magnetron sputtering to achieve wood veneer metallization. Based on X-ray diffraction (XRD) and nanoindentation, a research on the gradual process of the structures and mechanical properties of wood veneer metallization was carried out. The results indicate that wood veneer metallization does not affect the crystallization zone of wood, there are still wood cellulose characteristic peaks and the crystalline structure of the wood cellulose is not damaged; the thickness of the copper thin film increases with the increase of the deposition time, the cellulose characteristic peak strength gradually decrease, and the relative crystallinity also decreases; the characteristic diffraction peaks of Cu (111), Cu (200), and Cu (220) appear near the diffraction angle 2θ which is equal to 43.3°, 50.4°, and 74.1°, and the diffraction peak intensity increases with increase of deposition time, the copper film of the metal wood veneer crystallizes well; the load⁻displacement of wood veneer decreases significantly with the increase of deposition time, while the moduli of elasticity and hardness increase rapidly. The load⁻displacement of the samples which were coated for 15 min decreased by 80%, while the moduli of elasticity and hardness of these samples increased by 24.1 times and 17.3 times, respectively. From the results of Scanning Electron Microscope (SEM) measurement of the metallization of wood veneer, it can be seen that the uniform and continuous copper film can be formed on the wood veneer surface by using the magnetron sputtering method. This paper provides a basis for wood veneer surface metallization, which is of great significance for the functional improvement of wood, the expansion of wood application fields, and the enhancement of added value.

The ground state structures, HOMO and LUMO energy levels, band gaps ΔH-L, ionization potentials (IP), and electron affinities (EA) of three types of copolymer P1 and its derivatives P2, P3, and PBDT-BTA were investigated by using density functional theory (DFT) with B3LYP and 6-31G (d) basis set. On the base of optimized structures of ground states, their absorption spectra were obtained by using TD-DFT//Cam-B3LYP/6-31 G (d). Research shows that with the increasing conjugated units, HOMO energy levels increased, LUMO energy levels decreased, and band gaps decreased gradually. Moreover, their ionization potentials decreased and electron affinities increased along with the increase of conjugated chains, and absorption spectra red-shifted. In addition, the side chain has a significant effect on the properties of ground and excited states. In order to investigate the influence of conjugated units and side chain on the charge transport, their hole and electron reorganization energies were calculated, and the results indicated that Pb have a good hole transport capability. Considering the practical application, the HOMO and LUMO energy levels, band gaps, and absorption spectra under external electric field were studied, and the results proved that the external electric field has an effect on the optical and electronic properties.

A modified Hopfield neural network with a novel cost function was presented for detecting wood defects boundary in the image. Different from traditional methods, the boundary detection problem in this paper was formulated as an optimization process that sought the boundary points to minimize a cost function. An initial boundary was estimated by Canny algorithm first. The pixel gray value was described as a neuron state of Hopfield neural network. The state updated till the cost function touches the minimum value. The designed cost function ensured that few neurons were activated except the neurons corresponding to actual boundary points and ensured that the activated neurons are positioned in the points which had greatest change in gray value. The tools of Matlab were used to implement the experiment. The results show that the noises of the image are effectively removed, and our method obtains more noiseless and vivid boundary than those of the traditional methods.

The issue of plastic pollution has attracted widespread social attention. Gelatin is valued as a degradable bio-based material, especially as an edible active packaging material. However, the commonly used solution-casting film-forming technology limits the mass production of gelatin films. In order to improve the production efficiency and enhance the commercial value of gelatin films, in this study, fish gelatin (FG) particles were successfully blended with essential oils (EOs) to prepare active films by melt extrusion technique, a common method for commercial plastics, and applied to bread preservation. FG and EOs showed good compatibility with each other. The elongation at break was enhanced in all samples of films containing EOs. The addition of EOs weakened the oxygen barrier properties of the FG films. The gelatin films containing clove EO showed the highest DPPH radical scavenging rate of 39.38%. The films containing oregano EO showed the highest antibacterial activity, with 98.84% and 99.86% against S. aureus and E. coli, respectively. The bread preservation results showed a slower microbial growth rate in the samples preserved by the active films. The bread samples preserved in commercial PE film showed mold on day 3, and the bread samples preserved in active gelatin film showed mold visible to the naked eye by day 7. This study demonstrates the potential of gelatin-based activated films for commercial application in baked goods preservation. [ABSTRACT FROM AUTHOR]

Abstract A decarbonised future grid should couple technological novelty with innovative market models to efficiently capture the value of grid‐edge decarbonised assets. The transactive energy (TE) concept inverts the centralised grid model by leveraging the evolution of consumers to prosumers to prosumagers. The principal TE market design challenge is transactive control—using market and pricing mechanisms to coordinate autonomous peer interactions, to optimally allocate power and incentivise peers. Peer attraction, incentivisation and retention are all critical for practical TE implementation along three adoption stages, starting from independent peer transactions with the centralised market; to decentralised peer coordination; towards distributed peer‐to‐peer trading. Addressing gaps in related scholarship, the authors investigate the economic positions of distinct peer roles in each adoption stage and two local pricing strategies. Using a real market dataset, trading decisions are simulated over a 1‐year horizon at hourly granularity. Coordinated action achieves better transactive control for the community, with economic superiority over centralised and distributed mechanisms. Distinct peer incentives should equitably align with their contribution to market functionality, such as the value ascribed to prosumagers' flexibility in local pricing and the constrained bargaining power of prosumers in distributed bilateral negotiations.

Member 5 of the Upper Triassic Xujiahe Formation (T3X5) in central Sichuan Basin has made a breakthrough in exploration recently. However, this new stratum has not been investigated sufficiently with respect to basic geology, making its types and distribution of sedimentary facies unclear, which severely restricts its subsequent exploration evaluation. In this study, types of sedimentary microfacies in the first sand group of T3X5 (T3X51) are clarified through core observation and logging interpretation using core, log and seismic data, and then distribution of sedimentary microfacies in T3X51 is determined according to seismic waveform features and seismic prediction. The results show that T3X51 in the Dongfengchang area is mainly composed of deltaic deposits of several microfacies, such as delta front underwater distributary channel, sheet sand, and interdistributary bay. On seismic sections, different microfacies are significantly different in waveform features, the underwater distributary channel is characterized by one trough between two peaks, while diversion bay exhibits chaotic reflections between T6 and T51. The sedimentary microfacies varied greatly during the depositional period of T3X51 in the Dongfengchang area, this is because that the sediment supply was mainly controlled by the southwest and southeast provenance regions. Three superimposed underwater distributary channels are developed in the Dongfengchang area. The phase-1 superimposed underwater distributary channel in the northwest transition to sheet sand in the northeast, the phase-2 superimposed underwater distributary channel in the south extends shortly, the phase-3 superimposed underwater distributary channel in the northeast has a large development scale. These research findings are helpful to guide the subsequent exploration of T3X5 gas reservoir and also theoretically significant for investigating the depositional evolution of the Xujiahe Formation in central Sichuan Basin.

Abstract Bone marrow stromal/stem cells (BMSCs) are generally considered as common progenitors for both osteoblasts and adipocytes in the bone marrow, but show preferential differentiation into adipocytes rather than osteoblasts under aging, thus leading to senile osteoporosis. Accumulated evidences indicate that rejuvenation of BMSCs by autophagic enhancement delays bone aging. Here we synthetized and demonstrated a novel autophagy activator, CXM102 that could induce autophagy in aged BMSCs, resulting in rejuvenation and preferential differentiation into osteoblasts of BMSCs. Furthermore, CXM102 significantly stimulated bone anabolism, reduced marrow adipocytes, and delayed bone loss in middle-age male mice. Mechanistically, CXM102 promoted transcription factor EB (TFEB) nuclear translocation and favored osteoblasts formation both in vitro and in vivo. Moreover, CXM102 decreased serum levels of inflammation and reduced organ fibrosis, leading to a prolonger lifespan in male mice. Our results indicated that CXM102 could be used as an autophagy inducer to rejuvenate BMSCs and shed new lights on strategies for senile osteoporosis and healthyspan improvement.

Abstract Although there is increasing evidence suggesting that DNA methylation regulates seed development, the underlying mechanisms remain poorly understood. Therefore, we aimed to shed light on this by conducting whole-genome bisulfite sequencing using seeds from the large-seeded cultivar 'HZ' and the abortive-seeded cultivar 'NMC'. Our analysis revealed that the 'HZ' seeds exhibited a hypermethylation level compared to the 'NMC' seeds. Furthermore, we found that the genes associated with differentially methylated regions (DMRs) and differentially expressed genes (DEGs) were mainly enriched in the reactive oxygen species (ROS) metabolic pathway. To investigate this further, we conducted nitroblue tetrazolium (NBT) and 2,7-Dichlorodihydrofluorescein (DCF) staining, which demonstrated a significantly higher amount of ROS in the 'NMC' seeds compared to the 'HZ' seeds. Moreover, we identified that the gene LcGPX6, involved in ROS scavenging, exhibited hypermethylation levels and parallelly lower expression levels in 'NMC' seeds compared to 'HZ' seeds. Interestingly, the ectopic expression of LcGPX6 in Arabidopsis enhanced ROS scavenging and resulted in lower seed production. Together, we suggest that DNA methylation-mediated ROS production plays a significant role in seed development in litchi, during which hypermethylation levels of LcGPX6 might repress its expression, resulting in the accumulation of excessive ROS and ultimately leading to seed abortion.

Crime type identification is crucial for improving public safety through more accurate prevention and efficient responses. However, practical applications often suffer from a significant lack of effective samples features, making it difficult to focus on the most informative aspects during identification. This study addresses these challenges by proposing a novel crime type identification method that leverages a deep neural network enhanced with multiple attention mechanisms. The approach includes a tailored data processing method involving target encoding to convert categorical data into numerical form, L2 normalizer to standardize data and ensure balanced feature contribution, and variance threshold feature selection to remove low-variance features. Additionally, a High-Order Deep Residual Network with Multiple Attention (HO-ResNet-MA) is developed, featuring an optimized Huta68 block (Huta-6(8)-MA ResBlock) with an enhanced Contextual Transformer (CoT) unit for local attention and a queue-and-exclusion layer for global attention. To validate the effectiveness of the proposed method, homicide reports data and Chicago crimes data are processed and fed into the crime type identification model, resulting in accuracies of over 84.1% and 99.5%, respectively. This study makes contributions to the field of crime analysis by validating the practical applicability of these approaches, and enhancing the efficiency of public safety workers.

The low-carbon agenda promotes the increasing penetration of renewable energy, this however also results in a rapid decrease in the flexibility of power systems with less controllable generation. There is an urgent requirement to explore user-side resources to provide flexibility for the power system. The regional integrated energy system (RIES) contains a large number of multi-energy coupling devices with abundant flexibility. However, considering the multi-energy complementary characteristics and complex multi-energy networks, as well as the uncertainty of the renewable energy, it is difficult to quantify the energy purchasing demand and flexibility support ability of RIES in the market, making it challenging to identify the RIES feasible region. This paper proposes a novel approach to tackle this challenge by introducing a projection method based on second-order cone programming. The approach aims to describe the energy purchasing and flexible reserve feasible regions of an RIES. Specifically, a max–min RIES operating model is constructed with the goal of minimizing the distance from the boundary of the feasible region in the worst case. Subsequently, the strong duality principle and Karush-Kuhn-Tucker (KKT) optimality conditions are leveraged to reformulate the max–min optimization problem into a solvable second-order cone programming problem. Additionally, the chance constraint is introduced to address and express the risk associated the uncertainty of the renewable energy output in RIES. Moreover, for the convenience of subsequent solving, the deterministic equivalent method is used to convert it into a deterministic constraint. Case studies conducted on an RIES incorporating a 33-bus distribution network and a 7-node natural gas network demonstrate the effectiveness and efficiency of the proposed approach in projecting the feasible region in terms of both operating costs and computational time.

IntroductionBrain diseases, particularly the classification of gliomas and brain metastases and the prediction of HT in strokes, pose significant challenges in healthcare. Existing methods, relying predominantly on clinical data or imaging-based techniques such as radiomics, often fall short in achieving satisfactory classification accuracy. These methods fail to adequately capture the nuanced features crucial for accurate diagnosis, often hindered by noise and the inability to integrate information across various scales.MethodsWe propose a novel approach that mask attention mechanisms with multi-scale feature fusion for Multimodal brain disease classification tasks, termed M3, which aims to extract features highly relevant to the disease. The extracted features are then dimensionally reduced using Principal Component Analysis (PCA), followed by classification with a Support Vector Machine (SVM) to obtain the predictive results.ResultsOur methodology underwent rigorous testing on multi-parametric MRI datasets for both brain tumors and strokes. The results demonstrate a significant improvement in addressing critical clinical challenges, including the classification of gliomas, brain metastases, and the prediction of hemorrhagic stroke transformations. Ablation studies further validate the effectiveness of our attention mechanism and feature fusion modules.DiscussionThese findings underscore the potential of our approach to meet and exceed current clinical diagnostic demands, offering promising prospects for enhancing healthcare outcomes in the diagnosis and treatment of brain diseases.

A virtual power plant (VPP) refers to an active aggregator of heterogeneous distributed energy resources (DERs), which creates a promising pathway to expand renewable energy and demand-side electrification for deep decarbonization. The VPP market is projected to have a significant growth potential, with the global investment surging from $6.47 billion in 2022 to $16.90 billion by 2030. Up to now, VPPs still face technical challenges in dealing with the inherent uncertainty of DERs, and data emerge as a promising and essential resource to handle this issue. This paper makes the first effort to review the development of VPP technologies from a data-centric perspective, and then analyze the major role of data within every decision phase of VPPs. We examine the VPP energy management through a data lifecycle lens, and extensively survey the progress in data creation, data communication, data-driven decision support, data sharing and privacy, as well as technical solutions stemming from reinforcement learning, peer-to-peer sharing, blockchain, and market participation. In addition, we offer a unique overview of open data and recent real-world projects around the world to showcase the latest VPP practices. We finally discuss the major challenges and future opportunities in detail, with a focus on topics such as public benchmarks, internet of things, 5G, explainable artificial intelligence, and federated learning. We highlight the need for technical advances in data management and support systems for the growing scale of future VPP systems, and suggest VPPs delivering more ancillary grid services in the future.

Bone marrow stromal/stem cells (BMSCs) are primitive and heterogeneous cells that can be differentiated into osteoblasts, adipocytes and other subsets. Their bone-fat lineage commitment is responsible for the homeostasis of bone marrow microenvironment. However, there are little effective methods and evidence to simultaneously visualise the lineage commitment of BMSCs. Here we provide a bivalent differentiation medium that can enable BMSCs differentiation into osteoblasts and adipocytes in vitro, and establish a method to simultaneously distinguish osteoblasts or adipocytes from the heterogeneous BMSCs based on Alizarin red S and Oil red O staining, which have been used for detection of specific mineralized nodules and lipid droplets, respectively. This assay provides a specifically simple but effective and low-cost method to evaluate the efficiency of osteo-adipogenic (OA) allocation of BMSCs. ► Researchers can utilize the bivalent differentiation medium to evaluate the efficiency of osteogenic and adipogenic differentiation of BMSCs in vitro.

Abstract Hydraulic fracturing technology has played a pivotal role as a key technology in shale gas development, but the geological environmental problems caused have been worrying the public. This paper proposes an advanced step rectangular pulse hydraulic fracturing technique, which can achieve higher pressure and more complex fractures by pumping lower energy fluid. The results indicate that the pressure propagation of step rectangular pulse hydraulic fracturing can be divided into stable and unstable propagation stages. Compared with conventional hydraulic fracturing, longer unsteady propagation time, higher pressure and more complex pressure distribution can be obtained. Step pulse parameters are discussed. We found that the pulse pressure increases linearly with the increase in pulse amplitude. There are optimal pulse frequencies and comprehensive damping coefficients to get optimal fluid pressure in different shale reservoirs. These research results will help reduce geological environmental hazards, and can also provide guidance for the optimization of step rectangular pulse hydraulic fracturing technology in shale reservoirs.

The continued growth of the industrial Internet of Things (IoT) paves the way for the fifth industrial revolution (Industry 5.0), which is expected to further enhance the wireless connectivity and human machine collaboration. Nonetheless, Industry 5.0 will inevitably face the great challenges such as large scale connectivity and explosive data traffic because of the proliferation of IoT devices. To meet these challenges, we propose a novel non-orthogonal multiple access (NOMA)-based overlay cognitive radio (OCR) network for Industry 5.0. Specifically, the hybrid full-duplex (FD)/ half-duplex (HD) mode at the secondary user (SR) is adopted and in-phase and quadrature phase imbalance (IQI) is introduced to better comply with the practical scenarios. We investigate the outage probability (OP) and ergodic sum rate (ESR) of the considered network with the Nakagami-m channels. Extensive simulation experiments are performed to verify the accuracy of the analysis. From the simulation results, we can conclude: i) Increasing the signal-to-noise ratio (SNR), OP decreases but ESR increases and tends to a fixed value at high SNR regimes, indicating that the existence of IQI leads to a ceiling for the ESR; ii) The primary system has higher reliable performance at low SNR regions when SR is in FD mode, and for ergodicity, the system with FD relay is superior to HD relay; iii) The reliable performance of OMA system for primary users will be better since there is no interference at the relay.

Nanohydrogels show great potential as efficient drug carriers due to their biocompatibility, low toxicity, and high water absorbability. In this paper, we prepared two O-carboxymethylated chitosan (OCMC)-based polymers functionalized with β-cyclodextrin (β-CD) and amino acid. The structures of the polymers were characterized by Fourier Transform Infrared (FTIR) Spectroscopy. Morphological study was carried out on a Transmission Electron Microscope (TEM), and the results indicated that the two polymers had irregular spheroidal structure with some pores distributed on their surface. The average particle diameter was below 500 nm, and the zeta potential was above +30 mV. The two polymers were further used for preparing nanohydrogels loaded with anticancer drugs lapatinib and ginsenoside Rg1, and the resulting nanohydrogels showed high drug loading efficiency and pH-sensitive (pH = 4.5) drug release behavior. In vitro cytotoxicity investigation revealed that the nanohydrogels exhibited high cytotoxicity against lung cancer (A549) cells. In vivo anticancer investigation was performed in a transgenic Tg(fabp10:rtTA2s-M2; TRE2:EGFP-krasV12) zebrafish model. The results showed that the synthesized nanohydrogels significantly inhibited the expression of EGFP-krasv12 oncogene in zebrafish liver, and the L-arginine modified OCMC-g-Suc-β-CD nanohydrogels loading lapatinib and ginsenoside Rg1 showed the best results.

The rapid rise of glacioeustatic change is the most extreme paleoenvironment alteration in the aftermath of Snowball Earth. Although geologists conducted a lot of multi-subdiscipline research on this issue previously, there still exists the potential room for further discussions of the process in detail. For decades, the practice proved that the Fischer plot is a simple and robust tool to illustrate the fluctuations of accommodation patterns v.s. cycle sets or strata depth which could be interpreted as relative sea-level changes. This research simulates the Fischer plot to unravel the sea-level change in the aftermath of Marinoan glaciation by measuring the lower Ediacaran Doushantuo Formation in Shennongjia, South China. The result shows that 131 fifth-order cycles and nine third-order cycles help us propose a completed second-order cycle variation of ice melting-forced sea-level change, i.e., (1) early high-frequency and slow to rapid stepwise rising, and (2) followed by a stable decrease in the latter. In addition, the vertical sedimentary facies of the lower Doushantuo Formation display, in ascending order, (1) intertidal carbonate rock, (2) subtidal clastics with turbidite, and (3) intertidal lagoon fine clastics, indicating the process of the relative waxing and waning of sea-level. Such interpretation of the Fischer plot and the sedimentary facies’ vertical evolution is beneficial for studying the high-frequency sea-level change and paleogeographic reconstruction of post-Marinoan deglaciation.

Realizing the reversible transformation of the supramolecular hydrogels is essential for their application as smart materials. The reversible sol-gel transition of the polypseudorotaxane (PPR) hydrogels is achieved through adding a CO2-switchable guest molecule into the PPR hydrogel of poly(ethylene glycol) (PEG) and α-cyclodextrins (α-CDs). Alternating treatments with CO2 and heating drive such reversible transition due to the competitive complexation as evidenced by 2D NOESY 1H NMR and isothermal titration calorimetry as well as X-ray diffraction. The present work describes a straightforward way to obtain switchable PPR hydrogels triggered by a compatible stimulus of CO2. These stimuli-responsive PPR hydrogels have potential applications as biomaterials. [ABSTRACT FROM AUTHOR]

Polysaccharides are the most important effective components of Lycii fructus, which has a variety of biological activities and broad application prospects in the fields of medicine and food. In this study, we reported a novel arabinogalactan LFP-80-W1 with potential immunostimulatory activity. LFP-80-W1 was a continuous symmetrical single-peak with an average molecular weight of 4.58 × 104 Da and was mainly composed of arabinose and galactose. Oligosaccharide sequencing analyses and NMR data showed that the LFP-80-W1 domain consists of a repeated 1,6-linked β-Galp main chain with branches arabinoglycan and arabinogalactan at position C-3. Importantly, we found that LFP-80-W1 could activate the MAPK pathway and promote the release of NO, IL-6, and TNF-α cytokines in vitro. Therefore, our findings suggest that the homogeneous arabinogalactan from Lycii fructus, can be used as a natural immunomodulator.

Summary: The depletion of fossil fuels and the increasingly severe environmental pollution caused by massive fossil fuel consumption has driven the quick development of emerging renewable energy technologies. As the most extensive renewable carbon resource, lignocellulose is the potential substitute of fossil resources because of its sustainability and carbon-neutral features. Efficient lignocellulose conversion based on photocatalysis is a promising topic because of sustainable solar energy and the mild condition. This review highlights state-of-the-art photocatalytic technologies for lignocellulosic biomass conversion, focusing on the electricity generation, hydrogen production, and high-value-added biomass derivatives production. Moreover, the progress, challenge, and perspectives of related photocatalytic technologies are specifically discussed. It is recommended that developing more robust and efficient photocatalysts suitable for the complex structure of lignocellulose is necessary to promote the oxidation the biomass. Design and development of novel photochemical reactors and photoelectrochemical cells are also important for demonstration of light-driven lignocellulose conversion at larger scale.

Diabetic kidney disease (DKD) is a common diabetic complication. Salvia miltiorrhiza has significant therapeutic effects on diabetes complications, although the mechanism remains unclear. Here, biochemical indicators and pathological changes were used to screen out the optimal Salvia miltiorrhiza multi-bioactive compounds combination. Metabolomics, transcriptomics and proteomics were used to explore the pathogenesis of DKD. RT-PCR and parallel reaction monitoring targeted quantitative proteome analysis were utilized to investigate treatment mechanisms of the optimal Salvia miltiorrhiza multi-bioactive compounds combination. The db/db mice showed biochemical abnormalities and renal lesions. The possible metabolic pathways were steroid hormone biosynthesis and sphingolipid metabolism. The 727 differential genes found in transcriptomics were associated with biochemical indicators via gene network to finally screen 11 differential genes, which were mainly key genes of TGF-β/Smad and PI3K/Akt/FoxO signaling pathways. Salvia miltiorrhiza multi-bioactive compounds combination could significantly regulate the Egr1, Pik3r3 and Col1a1 genes. 11 differentially expressed proteins involved in the two pathways were selected, of which 9 were significantly altered in db/db mice compared to db/m mice. Salvia miltiorrhiza multi-bioactive compounds combination could callback Q9DBM2, S4R1W1, Q91Y97, P47738, A8DUK4, and A2ARV4. In summary, Salvia miltiorrhiza multi-bioactive compounds combination may ameliorate kidney injury in diabetes through regulation of TGF-β/Smad and PI3K/Akt/FoxO signaling pathways.

This paper proposes a novel bi-level optimization model to study the strategic retail pricing and demand bidding problems of an electricity retailer that considers the interactions between demand response and market clearing process. In order to accurately forecast the day-ahead demand bids submitted by the retailer, a novel deep learning framework based on convolutional neural networks and long short-term memory is proposed that can capture both local trends and long-term dependency of the forecasting data. In addition, uncertainties about the retailer’s served demand, rivals’ demand bids, and wind power generation are incorporated using the data-driven uncertainty set constructed from data. We further propose chance-constrained programming that introduces a set of chance constraints to represent the operational risk associated with the market uncertainties. To solve this problem, we first reformulate chance-constrained programming as a tractable second-order conic programming and then convert it into a single-level mathematical program with equilibrium constraints by using its Karush Kuhn Tucker conditions. The scope of the examined case studies is four-fold. First, they evaluate the benefits of the proposed forecasting framework in terms of higher accuracy and expected profit compared to the conventional forecasting methods. Second, they demonstrate how demand flexibility affects the retailer’s strategies and its business cases. Third, they highlight the added value of the proposed bi-level model capturing the market clearing process by comparing its outcomes against the state-of-the-art bi-level model with exogenous market prices. Finally, they analyze the retailer’s strategies and business cases at different confidence levels regarding the imposed chance constraints.

Surfactant-templating approach is used for the novel synthesis of sandwich structured mesoporous silica microspheres with a silica-coated magnetite core and ordered mesoporous silica shell with perpendicularly oriented channels. The magnetic microspheres have very high magnetization, high surface area, large pore volume and uniform accessible mesopores.