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Item response theory (IRT) is a modern psychometric framework for estimating respondents’ latent traits (e.g., ability, attitude, and personality) based on their responses to a set of questions in psychological tests. The current study adopted an item response tree (IRTree) method, which combines the tree model with IRT models for handling the sequential process of responding to a test item, to score individual moral inclination for the CNI test—a broadly adopted model for examining humans’ moral decision-making with three parameters generated: sensitivity to moral norms, sensitivity to consequences, and inaction preference. Compared to previous models for the CNI test, the resulting EIRTree-CNI Model is able to generate individual scores without increasing the number of items (thus, less subject fatigue or compromised response quality) or employing a post hoc approach that is deemed statistically suboptimal. The model fits the data well, and the subsequent test also supported the concurrent validity and the predictive validity of the model. Limitations are discussed further.

Male sterility is a common phenomenon in higher plants and often plays an important role in the selection of superior offspring. ‘Xiang Yun’ is a mutant of Lagerstroemia indica that does not bear fruit after flowering, and its flowering period is significantly longer than that of normal L. indica. To explore the timing and molecular mechanisms of sterility in ‘Xiang Yun’, this study determined the period of sterility through anatomical observation and compared the content of nutrients and the activity of antioxidative enzymes at different stages of flower development. Finally, sequence alignment and qPCR were used to analyze the differences in pollen development genes between ‘Xiang Yun’ and ‘Hong Ye’. The results showed that the anthers of ‘Xiang Yun’ dispersed pollen normally, but the pollen grains could not germinate normally. Observations with scanning electron microscopy revealed that the pollen grains were uneven in size and shriveled in shape. Further observation of anther sections found that abnormal development of the microspores began at the S2 stage, with the callose wall between microspores of ‘Xiang Yun’ being thicker than that of ‘Hong Ye’. In addition, during the flower development of ‘Xiang Yun’, the contents of soluble sugar, soluble protein, free proline, and triglycerides were deficient to varying degrees, and the activities of POD, SOD, and MDA were lower. Sequence alignment and qPCR showed that there were several mutations in EFD1, TPD1, and DEX1 of ‘Xiang Yun’ compared with ‘Hong Ye’, and the expression levels of these genes were abnormally elevated in the later stages of development. Our results clarified the timing and phenotype of male sterility in ‘Xiang Yun’. This provides solid and valuable information for further research on the molecular mechanism of sterility in ‘Xiang Yun’ and the genetic breeding of crape myrtle.

The charging load of electric vehicles (EV) is one of the most rapidly increasing loads in current power distribution systems. It may cause distribution transformer/feeder overload without proper coordination or control, especially in residential area where household load and EV charging load are sharing transformer capacity. Existing smart meter-based orderly charging control (OCC) approaches commonly require costly but unreliable communication schemes to control EV charging behavior. In this work, a smart meter-based distributed controller is designed to establish a meter-to-EV communication interface with low cost and enhanced reliability, based on the state-of-the-art charging standard. An event-driven OCC algorithm is developed, and then, deployed in the data hub (concentrator) of the AMI with an easy-to-implement optimization formulation. The effectiveness of the proposed approach is validated using a numerical case study and a practical field test in Hangzhou, China. Both results indicate promising advantages of the proposed OCC approach in reducing the peak load of emerging EV charging demand by more than 30%.

By systematically analyzing the natural gas composition, carbon isotopes, and source rock characteristics in the Yongfeng sub-sag of the Bogda Mountain front belt, natural gas characteristics were determined, and the genetic types and sources of natural gas were investigated. The research results indicate that methane is the main component of natural gas in the Yongfeng sub-sag, with low levels of heavy hydrocarbons and a high drying coefficient. These characteristics make it dry gas, which refers to natural gas with a methane content of over 95%. The ethane carbon isotope δ13C2 of natural gas is −28.5‰ and belongs to oil type gas. The methane carbon isotope δ13C1 of natural gas is −58.6‰~−59.4‰, has a relatively depleted methane carbon isotope value, shows significant differences from the surrounding natural gas methane carbon isotope, and belongs to the category of biogenic gas. The Permian Lucaogou Formation is the main source rock in the study area, with good organic matter abundance. The microscopic components of kerogen are mainly composed of sapropelic formations and the organic matter type is I–II1. The source rock has a high maturity and has reached the mature stage, mainly consisting of oil and wet gas. The ethane carbon isotope of natural gas in the Yongfeng sub-sag shows as oil type gas, which is consistent with the kerogen type of the Lucaogou Formation source rocks, indicating that the natural gas mainly comes from the Lucaogou Formation source rocks. Based on comprehensive data and information on natural gas composition, carbon isotopes, and burial history of the source rocks, it is believed that some of the crude oil generated from the Lucaogou Formation in the early stage underwent biodegradation due to tectonic uplift, resulting in biogenic methane and the formation of crude oil biodegraded gas.

Laser powder bed fusion (L-PBF) manufacturing technology is an emerging field of research that focuses on evaluating constraints in printed products. This study highlights the importance of considering various factors, such as mechanical properties and support structures, during the design phase, particularly in the context of microchannel heat exchangers where all limiting factors are critical. This paper presents a methodology for analyzing channel pressure limitations and examines the impact of pipe porosity on the loss of mechanical properties. A combination of simulation experiments and pressure capacity tests is used to elucidate the pressure distribution characteristics of microchannel flat tubes and their true pressure capacity. This study also explores potential methods for improving the performance of L-PBF-printed microchannel flat tubes. The results and the development of the experimental setup are summarized.

Photoregulation of biomolecules has become crucial tools in chemical biology, because light enables access under mild conditions and with delicate spatiotemporal control. The control of enzyme activity in a reversible way is a challenge. To achieve it, a facile approach is to use photoswitchable inhibitors. This review highlights recent progress in photoswitchable inhibitors based on azobenzenes units. The progress suggests that the incorporation of an azobenzene unit to a known inhibitor is an effective method for preparing a photoswitchable inhibitor, and with these photoswitchable inhibitors, the activity of enzymes can be regulated by optical control, which is valuable in both basic science and therapeutic applications.

Drawing inspiration from the structural resemblance between a natural product N-(3-carboxypropyl)-2-acetylpyrrole and phenylbutyric acid, a pioneer HDAC inhibitor evaluated in clinical trials, we embarked on the design and synthesis of a novel array of HDAC inhibitors containing an N-linked 2-acetylpyrrole cap by utilizing the pharmacophore fusion strategy. Among them, compound 20 exhibited potential inhibitory activity on HDAC1, and demonstrated notable potency against RPMI-8226 cells with an IC50 value of 2.89 ± 0.43 μM, which was better than chidamide (IC50 = 10.23 ± 1.02 μM). Western blot analysis and Annexin V-FTIC/propidium iodide (PI) staining showed that 20 could enhance the acetylation of histone H3, as well as remarkably induce apoptosis of RPMI-8226 cancer cells. The docking study highlighted the presence of a hydrogen bond between the carbonyl oxygen of the 2-acetylpyrrole cap group and Phe198 of the HDAC1 enzyme in 20, emphasizing the crucial role of introducing this natural product-inspired cap group. Molecular dynamics simulations showed that the docked complex had good conformational stability. The ADME parameters calculation showed that 20 possesses remarkable theoretical drug-likeness properties. Taken together, these results suggested that 20 is worthy of further exploration as a potential HDAC-targeted anticancer drug candidate.

Accurately predicting carbon dioxide (CO2) emissions is crucial for environmental protection. Currently, there are two main issues with predicting CO2 emissions: (1) existing CO2 emission prediction models mainly rely on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) models, which can only model unidirectional temporal features, resulting in insufficient accuracy: (2) existing research on CO2 emissions mainly focuses on designing predictive models, without paying attention to model optimization, resulting in models being unable to achieve their optimal performance. To address these issues, this paper proposes a framework for predicting CO2 emissions, called CGAOA-AttBiGRU. In this framework, Attentional-Bidirectional Gate Recurrent Unit (AttBiGRU) is a prediction model that uses BiGRU units to extract bidirectional temporal features from the data, and adopts an attention mechanism to adaptively weight the bidirectional temporal features, thereby improving prediction accuracy. CGAOA is an improved Arithmetic Optimization Algorithm (AOA) used to optimize the five key hyperparameters of the AttBiGRU. We first validated the optimization performance of the improved CGAOA algorithm on 24 benchmark functions. Then, CGAOA was used to optimize AttBiGRU and compared with 12 optimization algorithms. The results indicate that the AttBiGRU optimized by CGAOA has the best predictive performance.

Neuroendocrine prostate cancer (NEPC) is a highly aggressive cancer that is resistant to hormone therapy and characterized by poor prognosis, as well as limited therapeutic options. Since the natural product lycobetaine was reported to exhibit good antitumor activities against various types of cancers, we initially simplified the scaffold of lycobetaine to obtain the active compound 1, an isoquinoline derivative with an aryl moiety substitution at the 4-position, which showed apparent antiproliferative activities against NPEC cell line LASCPC-01 in vitro. Subsequently, we carried out structural optimization and systematic structure–activity relationship (SAR) studies on compound 1, leading to the discovery of compound 46, which demonstrated potent inhibitory activities against the LASCPC-01 cell line with an IC50 value of 0.47 μM. Moreover, compound 46 displayed remarkable selectivity over prostate cancer cell line PC-3 with a selectivity index greater than 190-fold. Further cell-based mechanism studies revealed that compound 46 and lycobetaine can effectively induce G1 cell cycle arrest and apoptosis dose dependently. However, lycobetaine inhibited the expression of neuroendocrine markers, while compound 46 slightly upregulated these proteins. This suggested that compound 46 might exert its antitumor activities through a different mechanism than lycobetaine, warranting further study.

Acrylamide (AA) can be formed during the thermal processing of carbohydrate-rich foods. Deoxynivalenol (DON), a mycotoxin produced by Fusarium spp., contaminates many cereal-based products. In addition to potential co-exposure through a mixed diet, co-occurrence of AA and DON in thermally processed cereal-based products is also likely, posing the question of combinatory toxicological effects. In the present study, the effects of AA (0.001–3 mM) and DON (0.1–30 µM) on the cytotoxicity, gene transcription, and expression of major cytochrome P450 (CYP) enzymes were investigated in differentiated human hepatic HepaRG cells. In the chosen ratios of AA–DON (10:1; 100:1), cytotoxicity was clearly driven by DON and no overadditive effects were observed. Using quantitative real-time PCR, about twofold enhanced transcript levels of CYP1A1 were observed at low DON concentrations (0.3 and 1 µM), reflected by an increase in CYP1A activity in the EROD assay. In contrast, CYP2E1 and CYP3A4 gene transcription decreased in a concentration-dependent manner after incubation with DON (0.01–0.3 µM). Nevertheless, confocal microscopy showed comparably constant protein levels. The present study provided no indication of an induction of CYP2E1 as a critical step in AA bioactivation by co-occurrence with DON. Taken together, the combination of AA and DON showed no clear physiologically relevant interaction in HepaRG cells.

Northern Goshawk Optimization (NGO) is an efficient optimization algorithm, but it has the drawbacks of easily falling into local optima and slow convergence. Aiming at these drawbacks, an improved NGO algorithm named the Multi-Strategy Improved Northern Goshawk Optimization (MSINGO) algorithm was proposed by adding the cubic mapping strategy, a novel weighted stochastic difference mutation strategy, and weighted sine and cosine optimization strategy to the original NGO. To verify the performance of MSINGO, a set of comparative experiments were performed with five highly cited and six recently proposed metaheuristic algorithms on the CEC2017 test functions. Comparative experimental results show that in the vast majority of cases, MSINGO’s exploitation ability, exploration ability, local optimal avoidance ability, and scalability are superior to those of competitive algorithms. Finally, six real world engineering problems demonstrated the merits and potential of MSINGO.

The prediction of total ionospheric electron content (TEC) is of great significance for space weather monitoring and wireless communication. Recently, deep learning models have become increasingly popular in TEC prediction. However, these deep learning models usually contain a large number of hyperparameters. Finding the optimal hyperparameters (also known as hyperparameter optimization) is currently a great challenge, directly affecting the predictive performance of the deep learning models. The Beluga Whale Optimization (BWO) algorithm is a swarm intelligence optimization algorithm that can be used to optimize hyperparameters of deep learning models. However, it is easy to fall into local minima. This paper analyzed the drawbacks of BWO and proposed an improved BWO algorithm, named FAMBWO (Firefly Assisted Multi-strategy Beluga Whale Optimization). Our proposed FAMBWO was compared with 11 state-of-the-art swarm intelligence optimization algorithms on 30 benchmark functions, and the results showed that our improved algorithm had faster convergence speed and better solutions on almost all benchmark functions. Then we proposed an automated machine learning framework FAMBWO-MA-BiLSTM for TEC prediction, where MA-BiLSTM is for TEC prediction and FAMBWO for hyperparameters optimization. We compared it with grid search, random search, Bayesian optimization algorithm and beluga whale optimization algorithm. Results showed that the MA-BiLSTM model optimized by FAMBWO is significantly better than the MA-BiLSTM model optimized by grid search, random search, Bayesian optimization algorithm, and BWO.

Three new polyketides, including three ester derivatives (1, 3, and 5) and a new natural product, which was a benzoquinone derivative, embelin A (4), together with nine known ones (2 and 6–13), were isolated from the mangrove-derived fungus Penicillium sp. SCSIO 41411. Their structures were determined by detailed NMR and MS spectroscopic analyses. The X-ray single-crystal diffraction analysis of 4 was described for the first time. Compound 9 displayed obvious inhibition against PDE4 with an inhibitory ratio of 40.78% at 10 μM. Compound 12 showed DPPH radical scavenging activity, with an EC50 of 16.21 µg/mL, compared to the positive control (ascorbic acid, EC50, 11.22 µg/mL). Furthermore, compound 4 exhibited cytotoxicity against PC-3 and LNCaP with IC50 values of 18.69 and 31.62 µM, respectively.

In recent years, forest fires have been occurring frequently around the globe, affected by extreme weather and dry climate, causing serious economic losses and environmental pollution. In this context, timely detection of forest fire smoke is crucial for realizing real-time early warning of fires. However, fire and smoke from forest fires can spread to cover large areas and may affect distant areas. In this paper, a lightweight joint forest fire and smoke detection network, SmokeFireNet, is proposed, which employs ShuffleNetV2 as the backbone for efficient feature extraction, effectively addressing the computational efficiency challenges of traditional methods. To integrate multi-scale information and enhance the semantic feature extraction capability, a feature pyramid network (FPN) and path aggregation network (PAN) are introduced in this paper. In addition, the FPN network is optimized by a lightweight DySample upsampling operator. The model also incorporates efficient channel attention (ECA), which can pay more attention to the detection of forest fires and smoke regions while suppressing irrelevant features. Finally, by embedding the receptive field block (RFB), the model further improves its ability to understand contextual information and capture detailed features of fire and smoke, thus improving the overall detection accuracy. The experimental results show that SmokeFireNet is better than other mainstream target detection algorithms in terms of average APall of 86.2%, FPS of 114, and GFLOPs of 8.4, and provides effective technical support for forest fire prevention work in terms of average precision, frame rate, and computational complexity. In the future, the SmokeFireNet model is expected to play a greater role in the field of forest fire prevention and make a greater contribution to the protection of forest resources and the ecological environment.

Karst ecosystems have become complex social–ecological systems (SESs) as a result of the interventions of large-scale ecological restoration programs, and the ecosystem services (ESs) that provide regional well-being can, to some extent, be described as social–ecological system services (S–ESs). Understanding the relationships among multiple S–ESs and exploring their drivers are essential for effective ecological management in karst areas, especially in regions differently affected by ecological engineering programs. Taking South China Karst (SCK) as a study area, we first identified two regions as comparative boundaries, namely significant engineering impact regions (SEERs) and non-significant ecological engineering impact regions (NEERs). Then we used ES assessment models, Spearman correlation, and optimal parameter geographic detector to identify the supply capacity, trade-offs/synergies, and their drivers of six types of S–ESs in SEERs and NEERs. The findings included: (1) SEERs were predominantly concentrated in the central and southern SCK regions, accounting for 33.98% of the total SCK area, with the most concentrated distribution observed in Guizhou and Guangxi. (2) Within the entire SCK, six S–ESs maintained a relatively stable spatial distribution pattern over time, with the most pronounced increase in soil conservation and a slight decrease in water retention, and the S–ES hotspots were more concentrated within the SEERs. (3) Most S–ES pairs within SEERs were optimized synergistically, with lower trade-off intensity and higher synergy intensity compared to NEERs. (4) S–ES pairs were affected by the interactions between the natural and socio-economic factors, with land use changes playing a crucial role, and natural factors were difficult to predict but cannot be ignored. Based on the results, we propose different SES sustainable development suggestions, with a view to providing theoretical support for the optimization of SES functions and the consolidating of integrated ecological construction.

Tea plants are a perennial crop with significant economic value. Chlorophyll, a key factor in tea leaf color and photosynthetic efficiency, is affected by the photoperiod and usually exhibits diurnal and seasonal variations. In this study, high-throughput transcriptomic analysis was used to study the chlorophyll metabolism, under different photoperiods, of tea plants. We conducted a time-series sampling under a skeleton photoperiod (6L6D) and continuous light conditions (24 L), measuring the chlorophyll and carotenoid content at a photoperiod interval of 3 h (24 h). Transcriptome sequencing was performed at six time points across two light cycles, followed by bioinformatics analysis to identify and annotate the differentially expressed genes (DEGs) involved in chlorophyll metabolism. The results revealed distinct expression patterns of key genes in the chlorophyll biosynthetic pathway. The expression levels of CHLE (magnesium-protoporphyrin IX monomethyl ester cyclase gene), CHLP (geranylgeranyl reductase gene), CLH (chlorophyllase gene), and POR (cytochrome P450 oxidoreductase gene), encoding enzymes in chlorophyll synthesis, were increased under continuous light conditions (24 L). At 6L6D, the expression levels of CHLP1.1, POR1.1, and POR1.2 showed an oscillating trend. The expression levels of CHLP1.2 and CLH1.1 showed the same trend, they both decreased under light treatment and increased under dark treatment. Our findings provide potential insights into the molecular basis of how photoperiods regulate chlorophyll metabolism in tea plants.

Osteoporosis (OP) is a common metabolic bone disease characterized by low bone mass, decreased bone mineral density, and degradation of bone tissue microarchitecture. However, our understanding of the mechanisms of bone remodeling and factors affecting bone mass remains incomplete. Sirtuin1 (SIRT1) is a nicotinamide adenine dinucleotide-dependent deacetylase that regulates a variety of cellular metabolisms, including inflammation, tumorigenesis, and bone metabolism. Recent studies have emphasized the important role of SIRT1 in bone homeostasis. This article reviews the role of SIRT1 in bone metabolism and OP and also discusses therapeutic strategies and future research directions for targeting SIRT1.

Digital inclusive finance (DIF) is a strategic tool that fosters the green transformation of the industrial economy. Based on the data from the 11 provinces and municipalities in the Yangtze River Economic Belt of China between 2012 and 2021, This paper utilizes the Tobit, intermediary effect, and threshold effect models to empirically study the impact of DIF on the industrial land carbon emission intensity (ILCEI). This paper reaches the following conclusions: (1) The ILCEI in the region revealed a downward trend during the study period. There are substantial differences in carbon ILCEI; higher upstream and lower downstream. The average ILCEI in the lower reach provinces is 0.5829 ton/m2 during the research period, while that in the upper reach region is 1.0104 ton/m2. (2) DIF has a significantly inhibitory effect on the ILCEI; this effect has nonlinear characteristics. The impact of DIF on ILCEI exhibits a marginally diminishing trend as the industrial land economic agglomeration degree improves. (3) Regarding the transmission mechanism, the level of industrial R&D investment plays a primary intermediary role in the impact of DIF on ILCEI. (4) Concerning control variables, foreign investment dependence and trade contribute significantly to inhibiting ILCEI. Lastly, this paper proposes a series of measures to promote DIF to fully utilize the emission reduction effect. The research outcomes have substantial implications for the sustainable development of industrial land.

Social capital plays a crucial role in enhancing community resilience during flood disasters. This study investigates the influence of social capital on community resilience in Nanjing, China. Social capital is composed of five aspects: cohesion, collective efficacy, sense of belonging, trust and reciprocity and informal social control. Factor analysis and multiple regression analysis are employed to analyze the dimensions of social capital and its impact on community resilience. Our results demonstrate that social cohesion and collective efficacy are the most representative factors of social capital. Reformed housing communities typically have higher cohesion than those in commercial and affordable housing. Affordable housing communities in flood-prone areas have higher collective efficacy but lower trust and reciprocity. Commercial housing communities have higher informal social control but have great internal differences in collective efficacy. We strongly urge government decision makers to enhance flood resilience by fostering social capital within local communities.

The annual seasonal influenza vaccination rate among high-risk healthcare workers (HCWs) has fallen below expectations, underscoring the importance of exploring the impact of perception on vaccination behavior. An online survey, grounded in the Health Belief Model (HBM), was administered to high-risk healthcare workers at West China Hospital. The data analysis encompassed descriptive statistics, logistic regression for univariate analysis, and path regression for multivariate analysis. A total of 1845 healthcare workers completed the survey, with an acceptance rate of 83.90% (95% CI, 82.20–85.60%). Path analysis revealed significant correlations between vaccination acceptance and perceived susceptibility (β = 0.142), perceived benefits (β = 0.129), perceived barriers (β = 0.075), exposure to vaccination advertisements (β = 0.115), and knowledge about seasonal influenza (β = 0.051). Vaccination education efforts should prioritize elucidating the risks associated with the disease and emphasizing the benefits of vaccination. Furthermore, leveraging advertising proves to be an effective strategy for promotion.

The accurate measurement of cable force plays an important role in the structural form, structural behavior, and safety evaluation of large-span cable-stayed bridges. A fast cable-force estimation method is proposed based on the alignment recognition method and smartphone-captured video. This method can realize real-time, non-contact, and non-destructive force measurements. Videos of bridge cables were collected using a portable smartphone, and an alignment recognition method was employed to obtain the lateral displacement along the cable, followed by numerical differentiation of the lateral displacement of the cable to acquire the acceleration time history. Based on the fundamental dynamic equation of beam elements, a unified explicit formula for cable-force calculation considering service characteristics was established in the frequency method. Finally, the method was applied to the cable-force measurement during the operation and maintenance stage of the Gengcun Dou Bridge. The measured cable forces were compared with the values obtained from the on-site monitoring system. The results show that the proposed method can accurately identify the acceleration time history at different positions of the cable, and the corresponding frequency components are essentially consistent. The average relative deviation of the measured cable forces from the reference method is within 3%. The proposed cable-force measurement method is simple as regards equipment, convenient for operation, and high in efficiency, providing a new method for the measurement of cable forces in large-span cable-stayed bridges, which can offer reliable measured cable-force data for structural analysis during bridge operation and the maintenance stage.

Pulsed lasers alter the optical properties of semiconductors and affect the photoelectric function of the photodetectors significantly, resulting in transient changes known as bleaching. Bleaching has a profound impact on the control and interference of photodetector applications. Experiments using pump–probe techniques have made significant contributions to understanding ultrafast carrier dynamics. However, there are few theoretical studies to the best of our knowledge. Here, carrier dynamic models for semiconductors and photodetectors are established, respectively, employing the rectified carrier drift-diffusion model. The pulsed laser bleaching effect on seven types of semiconductors and photodetectors from visible to long-wave infrared is demonstrated. Additionally, a continuous bleaching method is provided, and the finite-difference time-domain (FDTD) method is used to solve carrier dynamic theory models. Laser parameters for continuous bleaching of semiconductors and photodetectors are calculated. The proposed bleaching model and achieved laser parameters for continuous bleaching are essential for several applications using semiconductor devices, such as infrared detection, biological imaging, and sensing.

The booming growth of the internet of things has brought about widespread deployment of devices and massive amounts of sensing data to be processed. Federated learning (FL)-empowered mobile edge computing, known for pushing artificial intelligence to the network edge while preserving data privacy in learning cooperation, is a promising way to unleash the potential information of the data. However, FL’s multi-server collaborative operating architecture inevitably results in communication energy consumption between edge servers, which poses great challenges to servers with constrained energy budgets, especially wireless communication servers that rely on battery power. The device-to-device (D2D) communication mode developed for FL turns high-cost and long-distance server interactions into energy-efficient proximity delivery and multi-level aggregations, effectively alleviating the server energy constraints. A few studies have been devoted to D2D-enabled FL management, but most of them have neglected to investigate server computing power for FL operation, and they have all ignored the impact of dataset characteristics on model training, thus failing to fully exploit the data processing capabilities of energy-constrained edge servers. To fill this gap, in this paper we propose a D2D-assisted FL mechanism for energy-constrained edge computing, which jointly incorporates computing power allocation and dataset correlation into FL scheduling. In view of the variable impacts of computational power on model accuracy at different training stages, we design a partite graph-based FL scheme with adaptive D2D pairing and aperiodic variable local iterations of heterogeneous edge servers. Moreover, we leverage graph learning to exploit the performance gain of the dataset correlation between the edge servers in the model aggregation process, and we propose a graph-and-deep reinforcement learning-based D2D server pairing algorithm, which effectively reduces FL model error. The numerical results demonstrate that our designed FL schemes have great advantages in improving FL training accuracy under edge servers’ energy constraints.

Photoacoustic imaging integrates the strengths of optics and ultrasound, offering high resolution, depth penetration, and multimodal imaging capabilities. Practical considerations with instrumentation and geometry limit the number of available acoustic sensors and their “view” of the imaging target, which result in image reconstruction artifacts degrading image quality. To address this problem, YOLOv8-Pix2Pix is proposed as a hybrid artifact-removal algorithm, which is advantageous in comprehensively eliminating various types of artifacts and effectively restoring image details compared to existing algorithms. The proposed algorithm demonstrates superior performance in artifact removal and segmentation of photoacoustic images of brain tumors. For the purpose of further expanding its application fields and aligning with actual clinical needs, an experimental system for photoacoustic detection is designed in this paper to be verified. The experimental results show that the processed images are better than the pre-processed images in terms of reconstruction metrics PSNR and SSIM, and also the segmentation performance is significantly improved, which provides an effective solution for the further development of photoacoustic imaging technology.

A new imaging technique called photoacoustic imaging (PAI) combines the advantages of ultrasound imaging and optical absorption to provide structural and functional details of tissues. It has broad application prospects in the accurate diagnosis and treatment monitoring of brain tumours. However, the existing photoacoustic image classification algorithms cannot effectively distinguish benign tumours from malignant tumours. To address this problem, the YoLov8-MedSAM model is proposed in this research to provide precise and adaptable brain tumour identification and detection segmentation. Additionally, it employs convolutional neural networks (CNNs) to classify and identify tumours in order to distinguish between benign and malignant variations in PAI. The experimental results show that the method proposed in this study not only effectively detects and segments brain tumours of various shapes and sizes but also increases the accuracy of brain tumour classification to 97.02%. The method provides richer and more valuable diagnostic information to the clinic and effectively optimizes the diagnosis and treatment strategy of brain tumours.

Photoacoustic imaging (PAI) is an emerging imaging technique that offers real-time, non-invasive, and radiation-free measurements of optical tissue properties. However, image quality degradation due to factors such as non-ideal signal detection hampers its clinical applicability. To address this challenge, this paper proposes an algorithm for super-resolution reconstruction and segmentation based on deep learning. The proposed enhanced deep super-resolution minimalistic network (EDSR-M) not only mitigates the shortcomings of the original algorithm regarding computational complexity and parameter count but also employs residual learning and attention mechanisms to extract image features and enhance image details, thereby achieving high-quality reconstruction of PAI. DeepLabV3+ is used to segment the images before and after reconstruction to verify the network reconstruction performance. The experimental results demonstrate average improvements of 19.76% in peak-signal-to-noise ratio (PSNR) and 4.80% in structural similarity index (SSIM) for the reconstructed images compared to those of their pre-reconstructed counterparts. Additionally, mean accuracy, mean intersection and union ratio (IoU), and mean boundary F1 score (BFScore) for segmentation showed enhancements of 8.27%, 6.20%, and 6.28%, respectively. The proposed algorithm enhances the effect and texture features of PAI and makes the overall structure of the image restoration more complete.

One of the primary challenges in cable-stayed bridges is to assess the service performance of stay cables in response to applied loads and ensure that they meet safety requirements. This paper proposes a new strategy to analyze the time-varying reliability of the ultimate load-carrying capacity of stay cables under resistance and stress uncertainty conditions. Initially, we employ the frequency-squeezing processing (FSP) technique within the vibration frequency method (VFM) to enhance the accuracy and effectiveness of cable force identification through field measurement. Subsequently, we thoroughly discuss and establish the statistical characteristics and probabilistic models of stress, including both slow-varying trend and fast-varying trend components, as well as resistance considering the strengthening deterioration effect. The slow-varying trends of the cable forces are extracted using the moving average method (MAM), and both the extracted slow variation and the fast-varying trend components are analyzed in detail. Finally, we introduce a Gaussian process-based surrogate model to assess the time-varying structural reliability by analyzing the associated limit-state function for the ultimate load capacity of the stay cables. In this study, the proposed strategy is applied to quantify the ultimate load-carrying reliability of a stay cable under the uncertainty of the coupled action of corrosion and fatigue. Compared with conventional reliability analysis, the failure probability interval estimation shows the uncertainty boundaries and provides specific years of reliability failure, which can serve as an important reference for bridge maintenance and strengthening.

Diabetic retinopathy (DR) is one of the most severe complications of diabetes mellitus and potentially leads to significant visual impairment and blindness. The complex mechanisms involved in the pathological changes in DR make it challenging to achieve satisfactory outcomes with existing treatments. Diets conducive to glycemic control have been shown to improve outcomes in diabetic patients, thus positioning dietary interventions as promising avenues for DR treatment. Investigations have demonstrated that natural products (NPs) may effectively manage DR. Many types of natural compounds, including saponins, phenols, terpenoids, flavonoids, saccharides, alkaloids, and vitamins, have been shown to exert anti-inflammatory, antioxidant, anti-neovascular, and antiapoptotic effects in vivo and in vitro. Nevertheless, the clinical application of NPs still faces challenges, such as suboptimal specificity, poor bioavailability, and a risk of toxicity. Prospective clinical studies are imperative to validate the therapeutic potential of NPs in delaying or preventing DR.

Zinc ions (Zn2+) play a key role in maintaining and regulating protein structures and functions. To better understand the intracellular Zn2+ homeostasis and signaling role, various fluorescent sensors have been developed that allow the monitoring of Zn2+ concentrations and bioimaging in live cells in real time. This review highlights the recent development of organic fluorescent probes for the detection and imaging of intracellular Zn2+, including the design and construction of the probes, fluorescent response mechanisms, and their applications to intracellular Zn2+ detection and imaging on-site. Finally, the current challenges and prospects are discussed.

The study used whole-genome sequencing (WGS) and bioinformatics analysis for the genomic characterization of 60 isolates of Listeria monocytogenes obtained from the beef production chain (cattle farms, abattoirs, and retail outlets) in Gauteng province, South Africa. The sequence types (STs), clonal complexes (CCs), and the lineages of the isolates were determined using in silico multilocus sequence typing (MLST). We used BLAST-based analyses to identify virulence and antimicrobial genes, plasmids, proviruses/prophages, and the CRISPR-Cas system. The study investigated any association of the detected genes to the origin in the beef production chain of the L. monocytogenes isolates. Overall, in 60 isolates of Listeria monocytogenes, there were seven STs, six CCs, forty-four putative virulence factors, two resistance genes, one plasmid with AMR genes, and three with conjugative genes, one CRISPR gene, and all 60 isolates were positive for proviruses/prophages. Among the seven STs detected, ST204 (46.7%) and ST2 (21.7%) were the most prominent, with ST frequency varying significantly (p < 0.001). The predominant CC detected were CC2 (21.7%) and CC204 (46.7%) in lineages I and II, respectively. Of the 44 virulence factors detected, 26 (across Listeria Pathogenicity Islands, LIPIs) were present in all the isolates. The difference in the detection frequency varied significantly (p < 0.001). The two AMR genes (fosX and vga(G)) detected were present in all 60 (100%) isolates of L. monocytogenes. The only plasmid, NF033156, was present in three (5%) isolates. A CRISPR-Cas system was detected in six (10%), and all the isolates carried proviruses/prophages. The source and sample type significantly affected the frequencies of STs and virulence factors in the isolates of L. monocytogenes. The presence of fosX and vga(G) genes in all L. monocytogenes isolates obtained from the three industries of the beef production chain can potentially cause therapeutic implications. Our study, which characterized L. monocytogenes recovered from the three levels in the beef production chain, is the first time genomics was performed on this type of data set in the country, and this provides insights into the health implications of Listeria.

This study aimed to improve the safety and economy of cement production and to investigate the gasification performance and tar properties of wheat straw in a small electrically heated bubbling fluidized bed by varying three factors, namely, gasification reaction conditions, fuel quality and type, and the natural environment, so as to promote the application of the low-temperature gasification of biomass in the cement industry. The gasification experiment was carried out at temperatures of 550–700 °C, air equivalence ratios of 0.1–0.2, moisture contents of 5.25–24%, blended rubber ratios of 0–100%, and furnace vacuums of 0–0.03 within the parameter ranges, and the component analyses of the produced gases and tars were carried out by gas chromatography (GC) and gas chromatography–mass spectrometry (GC-MS). The experimental findings revealed that the optimal operating conditions for gasification were attained at a temperature of 650 °C, an equivalence ratio of 0.15, a moisture content of 5.25%, a rubber blending ratio of 0, and a vacuum degree of 0. Under these conditions, the concentrations of combustible components (H2, CH4, and CO) in the produced gas were 4.01%, 4.60%, and 21.05%, respectively. The carbon conversion rate was 62.40%, with the cold gas efficiency of 39.37%. The lower heating value of the produced gas was 5.915 MJ/Nm3, accompanied by a tar yield of 118.15 g/Nm3 and lower heating value of 3.385 MJ/Nm3.

Spodoptera frugiperda (J.E. Smith) is a highly significant agricultural pest that poses a threat to crop production worldwide. It is polyphagous, has a strong migratory ability, and is difficult to control, which makes it a threat not only to crops but also to woody plants. However, research on its adaptability to woody plants is limited. This study compares the feeding adaptations (survival rate, pupation time, pupation rate, weight, length, and daily food consumption), protective enzyme activities, and feeding preferences of S. frugiperda on leaves of six economically important tree species (Areca catechu L., Aquilaria sinensis (Lour.) Spreng, Cocos nucifera L., Camellia oleifera Abel, Dalbergia odorifera T. Chen, and Hevea brasiliensis (Willd. ex A. Juss.) Müll. Arg.), with Zea mays L. used as a control treatment. The results indicate that S. frugiperda did not survive when fed with A. catechu, C. oleifera, and D. odorifera. A. catechu had similar survival rates (83.33%), pupation (86.67%), weight, and length data compared to the maize control. C. nucifera had lower survival rates (46.67%) but no significant differences in pupation (76.67%), weight, and length data of S. frugiperda. H. brasiliensis exhibited significantly lower survival (50.00%) and pupation rates (46.67%) compared to maize. However, there was no significant difference in weight and length data, and its preference index was higher among the six tree species treatments. Therefore, A. sinensis is a vulnerable tree with a high risk, while H. brasiliensis and C. nucifera showed varying degrees of susceptibility. A. catechu, C. oleifera, and D. odorifera were found to be unsuitable hosts for S. frugiperda. In conclusion, this study extensively explores the feeding effects of S. frugiperda on six economically important tree species. It provides insights into the feeding preferences of the pest, thereby informing the potential threat posed by S. frugiperda to economically important trees. It helps to prevent further damage from S. frugiperda and provides a reference for agriculture and forestry to develop effective joint prevention and control measures.

Functional foods have potential health benefits for humans. Lotus seeds (LS) as functional foods have excellent antioxidant activities. However, the differences in chemical composition of different LS cultivars may affect their antioxidant activities. This study comprehensively analyzed the differences among five LS cultivars based on metabolomics and further revealed the effects of metabolites on antioxidant activities by correlation analysis. A total of 125 metabolites were identified in LS using UPLC-Q/TOF-MS. Then, 15 metabolites were screened as differential metabolites of different LS cultivars by chemometrics. The antioxidant activities of LS were evaluated by DPPH•, FRAP, and ABTS•+ assays. The antioxidant activities varied among different LS cultivars, with the cultivar Taikong 66 showing the highest antioxidant activities. The correlation analysis among metabolites and antioxidant activities highlighted the important contribution of phenolics and alkaloids to the antioxidant activities of LS. Particularly, 11 metabolites such as p-coumaric acid showed significant positive correlation with antioxidant activities. Notably, 6 differential metabolites screened in different LS cultivars showed significant effects on antioxidant activities. These results revealed the important effects of phytochemicals on the antioxidant activities of different LS cultivars. This study provided evidence for the health benefits of different LS cultivars.

Human–robot collaboration stands as one of the research frontiers in industrial applications due to the possibility for human operators to be supported by robots in carrying out their tasks in a shared workspace. However, advances in this field can be slowed down by the lack of standards regarding the safety and ergonomics of such applications. This article aims at reducing this gap by presenting an adaptation of the standard ISO 11228-3 for the ergonomic evaluation of hand-guiding applications through the OCRA index. This innovative methodology is innovatively applied to a drilling application in which a human operator hand-guides a collaborative robotic system consisting of a Franka Emika Panda robot, a force/torque sensor and an IMU suit to track the motion of the operator’s body. The SaRAH app, a MATLAB 2020a-based software tool developed on purpose, implements the ergonomic assessment procedure, allowing the proper redesign of the working shift (offline mode) or providing the worker suggestions to improve his/her behavior (online mode) so as to reduce the ergonomic risk.

The decipherment of ancient Chinese scripts, such as oracle bone and bronze inscriptions, holds immense significance for understanding ancient Chinese history, culture, and civilization. Despite substantial progress in recognizing oracle bone script, research on the overall recognition of ancient Chinese characters remains somewhat lacking. To tackle this issue, we pioneered the construction of a large-scale image dataset comprising 9233 distinct ancient Chinese characters sourced from images obtained through archaeological excavations. We propose the first model for recognizing the common ancient Chinese characters. This model consists of four stages with Linear Embedding and Swin-Transformer blocks, each supplemented by a CoT Block to enhance local feature extraction. We also advocate for an enhancement strategy, which involves two steps: firstly, conducting adaptive data enhancement on the original data, and secondly, randomly resampling the data. The experimental results, with a top-one accuracy of 87.25% and a top-five accuracy of 95.81%, demonstrate that our proposed method achieves remarkable performance. Furthermore, through the visualizing of model attention, it can be observed that the proposed model, trained on a large number of images, is able to capture the morphological characteristics of ancient Chinese characters to a certain extent.

With the rapid development of economic globalization and green manufacturing, traditional flexible job shop scheduling has evolved into the low-carbon heterogeneous distributed flexible job shop scheduling problem (LHDFJSP). Additionally, modern smart manufacturing processes encounter complex and diverse contingencies, necessitating the ability to address dynamic events in real-world production activities. To date, there are limited studies that comprehensively address the intricate factors associated with the LHDFJSP, including workshop heterogeneity, job insertions and transfers, and considerations of low-carbon objectives. This paper establishes a multi-objective mathematical model with the goal of minimizing the total weighted tardiness and total energy consumption. To effectively solve this problem, diverse composite scheduling rules are formulated, alongside the application of a deep reinforcement learning (DRL) framework, i.e., Rainbow deep-Q network (Rainbow DQN), to learn the optimal scheduling strategy at each decision point in a dynamic environment. To verify the effectiveness of the proposed method, this paper extends the standard dataset to adapt to the LHDFJSP. Evaluation results confirm the generalization and robustness of the presented Rainbow DQN-based method.

Food-derived peptides have been extensively studied for their benefits in humans. Hen eggs, characterized by high protein and digestibility, are an excellent source of food-derived bioactive peptides. This review summarizes the preparation methods, purification, and identification of hen egg-derived peptides (HEPs). The preparation methods mainly include enzymatic hydrolysis, microbial fermentation, and chemical synthesis. Genetic engineering is an emerging trend of HEP preparation. Then, we summarize the biological activities of HEPs, such as antioxidant activities, enzyme inhibitory activity, and antibacterial activity, of which the enzyme inhibitory activity is comprehensively summarized for the first time. The structure–activity relationship and underlying mechanism of the HEPs are further elucidated. Finally, the applications, future challenges, and opportunities of HEPs were mainly discussed in the food and non-food sectors. We focus on the potential applications of HEPs in intestinal health and assembly delivery and provide a reference for the further utilization and commercial development of HEPs.

Flexible thermoelectric generators (FTEGs), which can overcome the energy supply limitations of wearable devices, have received considerable attention. However, the use of toxic Te-based materials and fracture-prone electrodes constrains the application of FTEGs. In this study, a novel Ag2Se and Poly (3,4-ethylene dioxythiophene): poly (styrene sulfonate) (PEDOT:PSS)/multi-walled carbon nanotube (MWCNT) FTEG with a high output performance and good flexibility is developed. The thermoelectric columns formulated in the work are environmentally friendly and reliable. The key enabler of this work is the use of embedded EGaIn electrodes, which increase the temperature difference collected by the thermoelectric column, thereby improving the FTEG output performance. Additionally, the embedded EGaIn electrodes could be directly printed on polydimethylsiloxane (PDMS) molds without wax paper, which simplifies the preparation process of FTEGs and enhances the fabrication efficiency. The FTEG with embedded electrodes exhibits the highest output power density of 25.83 μW/cm2 and the highest output power of 10.95 μW at ΔT = 15 K. The latter is 31.6% higher than that of silver-based FTEGs and 2.5% higher than that of covered EGaIn-based FTEGs. Moreover, the prepared FTEG has an excellent flexibility (>1500 bends) and output power stability (>30 days). At high humidity and high temperature, the prepared FTEG maintains good performance. These results demonstrate that the prepared FTEGs can be used as a stable and environmentally friendly energy supply for wearable devices.

The Lower Cambrian Niutitang Formation in the Northern Guizhou harbors abundant organic-rich mud shale, constituting the most significant marine shale gas reservoir in Guizhou. In this article, the reservoir characteristics of Lower Cambrian Niutitang Formation in Northern Guizhou are analyzed in terms of lithology, mineralogy, organic geochemistry, pore structure, gas content and continuous thickness of shale, and the exploration potential of shale gas in this area is evaluated. The results indicate that the content of brittle minerals in the shale of well QX1 is 65.29% to 95.22% (average of 82.10%). The total organic carbon (TOC) content ranges from 2.06% to 12.10% (average of 5.64%). The organic matter maturity (Ro) within the range of 2.29–2.67%, and the kerogen type is identified as type I. The shale samples from the Niutitang Formation have high TOC content, suitable thermal maturity, and a favorable kerogen type, suggesting good gas generation potential. The results of scanning electron microscopy (SEM) show that intergranular pores, intragranular pores and microfractures are developed in the shale of well QX1, which can provide sufficient storage space for shale gas. The shale exhibits a continuous thickness of 105.66 m in the QX1 well, comprising a gas-bearing interval of 32.89 m at the top (with an effective continuous thickness of 18 m) and a hydrocarbon source rock layer of 75.78 m at the bottom. In comparison with other shale gas regions, Niutitang Formation shale in Northern Guizhou exhibits characteristics such as favorable gas generation conditions, greater storage conditions, excellent gas-bearing, strong frackability, and substantial continuous thickness, it has greater potential for shale gas exploration.

MADS-box is a key transcription factor regulating the transition to flowering and flower development. Lagerstroemia indica ‘Xiang Yun’ is a new cultivar of crape myrtle characterized by its non-fruiting nature. To study the molecular mechanism underlying the non-fruiting characteristics of ‘Xiang Yun’, 82 MADS-box genes were identified from the genome of L. indica. The physicochemical properties of these genes were examined using bioinformatics methods, and their expression as well as endogenous hormone levels at various stages of flower development were analyzed. The results showed that LiMADS genes were primarily classified into two types: type I and type II, with the majority being type II that contained an abundance of cis-acting elements in their promoters. By screening nine core proteins by predicted protein interactions and performing qRT-PCR analysis as well as in combination with transcriptome data, we found that the expression levels of most MADS genes involved in flower development were significantly lower in ‘Xiang Yun’ than in the wild type ‘Hong Ye’. Hormonal analysis indicated that ‘Xiang Yun’ had higher levels of iP, IPR, TZR, and zeatin during its early stages of flower development than ‘Hong Ye’, whereas the MeJA content was substantially lower at the late stage of flower development of ‘Hong Ye’. Finally, correlation analysis showed that JA, IAA, SA, and TZR were positively correlated with the expression levels of most type II genes. Based on these analyses, a working model for the non-fruiting ‘Xiang Yun’ was proposed. During the course of flower development, plant hormone response pathways may affect the expression of MADS genes, resulting in their low expression in flower development, which led to the abnormal development of the stamen and embryo sac and ultimately affected the fruiting process of ‘Xiang Yun’.

As an effective soil amendment, biochars require a comprehensive ecological evaluation before they can be widely used in agriculture because endogenous contaminants, such as environmentally persistent free radicals (EPFRs), certainly pose an ecological risk to soil invertebrates. In this study, Caenorhabditis elegans (C. elegans) was used as a model organism to investigate the neurotoxicity of two rice straw biochars pyrolyzed at 500 and 700 °C. After 24 h exposure to unwashed biochar, washed biochar, and leaching fluids (supernatants), the neurobehavioral parameters of C. elegans were determined in a liquid toxicity test. The results showed that the washed 700 °C biochar particles significantly impaired locomotion and prolonged the defecation interval at a biochar concentration of 4 g·well−1, while the unwashed biochar and supernatants caused no apparent impairment. Supporting this, electron paramagnetic resonance (EPR) results showed that the intensity of EPFRs in unwashed 700 °C biochar was stronger than that of the corresponding washed particles. This indicates that, in the liquid test, the EPR signal alone is not indicative of particle toxicity. The accessibility and activity of the EPFRs should be considered. Dissolved organic matter (DOM) was observed in the leaching fluids. The neurotoxic activity of the washed biochar was alleviated after the re-addition of leaching fluids to the washed biochar, suggesting that the dissolved organic materials modulate the reactivity of the EPFRs in the liquid phase. This study suggests that the leaching process may increase the risk of biochar when used in the field environment.

Biomacromolecular probes have been extensively employed in the detection of metal ions for their prominent biocompatibility, water solubility, high selectivity, and easy modification of fluorescent groups. In this study, a fluorescent probe FP was constructed. The probe FP exhibited high specificity recognition for Cu2+. With the combination of Cu2+, the probe was subjected to fluorescence quenching. The research suggested that the probe FP carried out the highly sensitive detection of Cu2+ with detection limits of 1.7 nM. The fluorescence quenching of fluorescamine was induced by Cu2+ perhaps due to the PET (photoinduced electron transfer) mechanism. The FP-Cu2+ complex shows weak fluorescence, which is likely due to the PET quenching effect from Cu2+ to fluorescamine fluorophore. Moreover, the probe FP can be employed for imaging Cu2+ in living cells. The new fluorescent probe developed in this study shows the advantages of good biocompatibility and low cytotoxicity. It can be adopted for the targeted detection of Cu2+ in cells, and it has promising applications in the mechanism research and diagnosis of Cu2+-associated diseases.

Surface plasmon resonance (SPR) is a powerful tool for determining molecular interactions quantitatively. SPR imaging (SPRi) further improves the throughput of SPR technology and provides the spatially resolved capability for observing the molecular interaction dynamics in detail. SPRi is becoming more and more popular in biological and chemical sensing and imaging. However, SPRi suffers from low spatial resolution due to the imperfect optical components and delocalized features of propagating surface plasmonic waves along the surface. Diverse kinds of approaches have been developed to improve the spatial resolution of SPRi, which have enormously impelled the development of the methodology and further extended its possible applications. In this minireview, we introduce the mechanisms for building a high-spatial-resolution SPRi system and present its experimental schemes from prism-coupled SPRi and SPR microscopy (SPRM) to surface plasmonic scattering microscopy (SPSM); summarize its exciting applications, including molecular interaction analysis, molecular imaging and profiling, tracking of single entities, and analysis of single cells; and discuss its challenges in recent decade as well as the promising future.

In the intelligent transformation of spraying operations, the investigation into the robotic spraying process holds significant importance. The spraying process, however, falls within the realm of experience-driven technology, characterized by high complexity, diverse parameters, and coupling effects. Moreover, the quality of manual spraying processes relies entirely on manual experience. Thus, the crux of the intelligent transformation of spraying robots lies in establishing a mapping model between the spraying process and the resultant spraying quality. To address the challenge of intelligently transforming empirical spraying processes and achieving the mapping from the spraying process to spraying quality, an algorithm employing an enhanced extreme learning machine-based neural network is proposed for predicting spraying process parameters with respect to the evaluation index of spraying quality. In this approach, an algorithmic model based on the Extreme Learning Machine (ELM) neural network is initially constructed utilizing five spraying process parameters: spraying speed, spraying height, spraying width pressure, atomization pressure, and oil spraying pressure. Two spraying quality evaluation indexes, namely average film thickness at the center point and surface roughness, are also incorporated. Subsequently, the prediction neural network is optimized using the K-means improved predator optimization algorithm (KHPO) to enhance the model’s prediction accuracy. This optimization step aims to improve the efficiency of the model in predicting spraying quality based on the specified process parameters. Finally, data collection and model validation for the spraying quality prediction algorithm are conducted using a designed robotic automated waterborne paint spraying experimental system. The experimental results demonstrate a significant reduction in the prediction error of the KHPO-ELM neural network model for the average film thickness center point, showcasing a decrease of 61.95% in comparison to the traditional ELM neural network and 50.81% in comparison to the BP neural network. Likewise, the improved neural network model yields a 2.31% decrease in surface roughness prediction error compared to the traditional ELM neural network and a substantial 54.0% reduction compared to the BP neural network. Consequently, the KHPO-ELM neural network, incorporating the prediction algorithm, effectively facilitates the prediction of multi-spraying process parameters for the center point of average film thickness and surface roughness in automated robot spraying. Notably, the prediction algorithm exhibits a commendable level of accuracy in these predictions.

The circadian clock refers to the formation of a certain rule in the long-term evolution of an organism, which is an invisible ‘clock’ in the body of an organism. As one of the largest TF families in higher plants, the MYB transcription factor is involved in plant growth and development. MYB is also inextricably correlated with the circadian rhythm. In this study, the transcriptome data of the tea plant ‘Baiyeyihao’ were measured at a photoperiod interval of 4 h (24 h). A total of 25,306 unigenes were obtained, including 14,615 unigenes that were annotated across 20 functional categories within the GO classification. Additionally, 10,443 single-gene clusters were annotated to 11 sublevels of metabolic pathways using KEGG. Based on the results of gene annotation and differential gene transcript analysis, 22 genes encoding MYB transcription factors were identified. The G10 group in the phylogenetic tree had 13 members, of which 5 were related to the circadian rhythm, accounting for 39%. The G1, G2, G8, G9, G15, G16, G18, G19, G20, G21 and G23 groups had no members associated with the circadian rhythm. Among the 22 differentially expressed MYB transcription factors, 3 members of LHY, RVE1 and RVE8 were core circadian rhythm genes belonging to the G10, G12 and G10 groups, respectively. Real-time fluorescence quantitative PCR was used to detect and validate the expression of the gene transcripts encoding MYB transcription factors associated with the circadian rhythm.

In contemporary society, a notable trend of diminishing family sizes has led to an increasing number of elderly individuals living in solitude, often facing the end of life alone. This phenomenon underscores a critical challenge: addressing the pervasive loneliness experienced by many seniors. In response to this pressing issue, the concept of “cross-generational co-living” emerges as a potential solution. By exploring and implementing cross-generational co-living models, this research contributes to the development of more inclusive, supportive, and adaptable environments. The investigation involved an extensive field study and comprehensive data analysis of twenty-four instances of cross-generational co-living spaces in China and Italy. This analysis utilized space syntax as a fundamental theoretical framework, incorporating convex graphical topological relationship extraction and visibility graph analysis models. The outcomes of the study indicate that the configuration of cross-generational co-living spaces include spatial form, type, location, and the proportion of areas. Spaces arranged in a cluster form are most effective in promoting mutual communication. Spatial types and locations characterized by elevated integration values demonstrate a heightened potential for cross-generational communication. Space possessing a higher integration value typically correlates with a reduced ratio of area discreteness. These findings are instrumental in understanding how cultural and societal variances shape the design and utilization of cross-generational co-living spaces. Consequently, this study provides valuable guidelines for improving environments that are essential for advancing the principles of age-friendly design, which aims to enhance the quality of life for the elderly and foster a more harmonious and interconnected society across all generations.

This review covers the volatile organic compounds (VOCs) emitted by the bodies and secretions of introduced mammalian predators in New Zealand forests, with a specific focus on mice, rats, ferrets, stoats, and possums. The primary aim is to compile information on these VOCs and assess the potential of exploiting these VOCs as unique biomarkers for predator detection in the forest. While a universal VOC has yet to be identified, the review discusses various VOCs associated with predators and their potential as unique biomarkers for detecting the presence of predators, including 2-heptanone, 4-heptanone, 2-octanone, and benzaldehyde. Furthermore, a brief overview of different VOC detection techniques is provided, connecting the selection of biosensing techniques with the detection of volatile biomarkers. Further research and advancement in the biosensing field hold substantial potential to enhance biomarker detection technologies and elevate predator management strategies within New Zealand forests.

In recent years, polyurethane has drawn great attention because of its many advantages in physical and chemical performance. In this work, firstly, polyurethane was impregnated in a non-woven fabric (NWF). Then, polyurethane-impregnated NWF was coagulated utilizing a wet phase inversion. Finally, after alkali treatment, microfiber non-woven fabrics with a porous polyurethane matrix (PNWF) were fabricated and used as substrates. SnIn4S8 (SIS) prepared by a microwave-assisted method was used as a photocatalyst and a novel SIS/PNWF substrate with multiple uses and highly efficient catalytic degradation ability under visible light was successfully fabricated. The surface morphology, chemical and crystal structures, optical performance, and wettability of SIS/PNWF substrates were observed. Subsequently, the photocatalytic performance of SIS/PNWF substrates was investigated by the decomposition of rhodamine B (RhB) under visible light irradiation. Compared with SIS/PNWF-2% (2%, the weight ratio of SIS and PNWF, same below), SIS/PNWF-5% as well as SIS/PNWF-15%, SIS/PNWF-10% substrates exhibited superior photocatalytic efficiency of 97% in 2 h. This may be due to the superior photocatalytic performance of SIS and the inherent hierarchical porous structure of PNWF substrates. Additionally, the hydrophobicity of SIS/PNWF substrates can enable them to float on the solution and further be applied on an open-water surface. Furthermore, tensile strength and recycle experiments demonstrated that SIS/PNWF substrates possessed superior mechanical strength and excellent recycle stability. This work provides a facile and efficient pathway to prepare SIS/PNWF substrates for the degradation of organic pollutants with enhanced catalytic efficiency.

Twenty-six cities in the Yangtze River Delta urban agglomeration were taken as the research object, and this study comprehensively evaluated urbanization quality and ecological resilience from 2005 to 2020. On this basis, the spatiotemporal evolution characteristics and main influencing factors of the coupling relationship between urbanization and ecological resilience were systematically explored using a coupling coordination model and panel Tobit regression model. The results can be summarized as follows: (1) from 2005 to 2020, the quality of urbanization in the Yangtze River Delta urban agglomeration continued to grow, the level of ecological resilience grew slowly and fluctuated, and the development among municipalities tended to be balanced. (2) The overall coupling coordination degree of urbanization and ecological resilience showed a continuous increasing trend, and the coupling coordination type changed from basic coupling coordination to good coupling coordination. The number of cities with lagging urbanization quality decreased significantly; spatially, the gap in the coupling coordination degree between municipalities narrowed, and the cities with good coupling gradually clustered. (3) The results of the panel Tobit regression showed that the differences in the spatial evolution of coupling coordination mainly resulted from the interaction of drivers such as real utilized foreign capital, per capita GDP, carbon emission intensity, the proportion of science and technology expenditure to fiscal expenditure, the ratio of per capita disposable income of urban and rural residents, fixed asset investment in municipal utility construction, and the index of ecological land area ratio. In the future, the coupling coordination degree of urbanization and ecological resilience should be improved based on the type of coupling coordination according to local conditions, and the seven influencing factors should be carefully examined to accelerate the high-quality integrated development of the Yangtze River Delta.

Benefitting from lightweight, high strength, long life, and green recyclability, continuous fiber reinforced thermoplastic composite (CFTPC) pipes have attracted extensive interest, especially in the on-orbit additive manufacturing of structural components. However, the preparation of CFTPC pipes remains challenging due to the on-orbit limited space and high processing temperature of thermoplastic resin. Here, we report an effective approach for high performance carbon fiber/polyether-ether-ketone (CF/PEEK) thin-walled pipes via bidirectional reinforcement using the pultrusion-winding technique. The continuous fabrication of thin-walled pipes can be achieved, but the limitation by the size of core mold is also broken. The compressive and shear performance of CF/PEEK pipes with different layer designs have been studied based on experiments and simulations. With the increase in axial prepreg tape layer, the resultant CF/PEEK pipes exhibit greatly improved axial compression strength. The finite element analysis indicates that the maximum axial stress is decreased due to the axial enhancement. The flexural strength is greatly proved with pultrusion–winding cycles. The simulation confirms that the circumferential strain is effectively reduced. The high performance of bidirectional reinforced CF/PEEK pipes and the facile controllability of this approach highlight their suitability for utilization in on-orbit manufacturing of large-scale structures.