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ABSTRACTChina is vigorously implementing rural revitalization strategy, emphasizing the significance of human resources in the sustainable development of rural areas and agriculture. As China’s economy is entering a new normal stage, the growth rate of rural labourers’ income in cities is slowing down, and the gap between urban and rural net incomes is gradually closing. The labourers who have migrated are gradually showing the willingness to return to their hometowns for development. This study constructs a theoretical framework for the studying of rural labourers’ willingness to return to their hometowns. It finds that the perceived value of rural labour positively influences their willingness to return. Further perceived extrinsic–intrinsic benefits, perceived extrinsic costs, and their perceived values are all significantly and positively correlated. Moreover, formal support has a positive moderating effect between the perceived value of rural labour and willingness to return. The perceived value of rural labour increases with higher perceived benefits and lower perceived costs; the stronger the perceived benefits and perceived value of labour, the greater the willingness to return.

Abstract Rain gardens play a key role in urban non-point source pollution control. The drainage type affects the infiltration processes of runoff pollutants. The soil properties and microbial community structures were studied to reveal the stability of the ecosystem in rain gardens with different drainage types under long-term operation. The results showed that the soil water content and total organic carbon in the drained rain gardens were always higher than that of the infiltrated ones. With the increase in running time, the contents of heavy metals in rain gardens showed significant accumulation phenomena, especially the contents of Zn and Pb in drained rain gardens were higher than that in infiltrated ones. The accumulation of pollutants resulted in lower microbial diversity in drained rain gardens than in infiltrated rain gardens, but the microbial community structures were the same in all rain gardens. The effects of drainage type on microbial community evolution were not significant, only the accumulation of heavy metals led to changes in the abundance of dominant microorganisms. There were differences in the soil environment of rain gardens with different drainage types. The long-term operation of rain gardens led to fluctuations in the soil ecosystem, while the internal micro-ecosystems of the drained rain gardens were in unstable states.

Abstract The potential for toxic elements to contaminate soil has been extensively studied. Therefore, the development of cost-effective methods and materials to prevent toxic element residues in the soil from entering the food chain is of great significance. Industrial and agricultural wastes such as wood vinegar (WV), sodium humate (NaHA) and biochar (BC) were used as raw materials in this study. HA was obtained by acidizing NaHA with WV and then loaded onto BC, which successfully prepared a highly efficient modification agent for nickel-contaminated soil, namely biochar-humic acid material (BC-HA). The characteristics and parameters of BC-HA were obtained by FTIR, SEM, EDS, BET and XPS. The chemisorption of Ni(II) ions by BC-HA conforms to the quasi-second-order kinetic model. Ni(II) ions are distributed on the heterogeneous surface of BC-HA by multimolecular layer adsorption, which accords with the Freundlich isotherm model. WV promotes better binding of HA and BC by introducing more active sites, thus increasing the adsorption capacity of Ni(II) ions on BC-HA. Ni(II) ions in soil are anchored to BC-HA by physical and chemical adsorption, electrostatic interaction, ion exchange and synergy.

Foundation models play an increasingly pivotal role in the field of deep neural networks. Given that deep neural networks are widely used in real-world systems and are generally susceptible to adversarial attacks, securing foundation models becomes a key research issue. However, research on adversarial attacks against the Segment Anything Model (SAM), a visual foundation model, is still in its infancy. In this paper, we propose the prompt batch attack (PBA), which can effectively attack SAM, making it unable to capture valid objects or even generate fake shards. Extensive experiments were conducted to compare the adversarial attack performance among optimizing without prompts, optimizing all prompts, and optimizing batches of prompts as in PBA. Numerical results on multiple datasets show that the cross-prompt attack success rate (ASR∗) of the PBA method is 17.83% higher on average, and the attack success rate (ASR) is 20.84% higher. It is proven that PBA possesses the best attack capability as well as the highest cross-prompt transferability. Additionally, we introduce a metric to evaluate the cross-prompt transferability of adversarial attacks, effectively fostering research on cross-prompt attacks. Our work unveils the pivotal role of the batched prompts technique in cross-prompt adversarial attacks, marking an early and intriguing exploration into this area against SAM.

Abstract Moisture removal and water recovery from the air are vital for regulating indoor humidity and mitigating water scarcity. Most atmospheric water harvesters (AWH) focus primarily on increasing the moisture capture rate, but for it to be economical and sustainable, it is essential to consider the energy required to recover and harvest the captured water. Here, a mechanically flexible, biphilic sorption‐based AWH made of green, environmentally friendly material is presented. It consists of a hygroscopic chitosan polymer embedded within a flexible, hydrophobic silica xerogel that can harvest 86.3 g water/g chitosan at 97% relative humidity and 25°C reaching saturation after 30 days (i.e. 2.88 g water/g chitosan/day). Roughly 88% of the sorbed moisture was recovered by mechanical squeezing (ca. 0.020 MPa) within 150 s. Repeated water harvesting experiments and uniaxial compression tests demonstrate that chitosan‐silica xerogel is durable for long‐term operations, providing a fast, reliable, and sustainable moisture removal and water harvesting tool.

DNS root servers are located at the top of the domain name system and are the cornerstone of the Internet. Currently, root servers deploy numerous root instances using anycast technology. Introducing root instances can improve the parsing performance of root servers and the user access experience. However, we found that some root instances do not show optimal performance, and users cannot access the closest root instance when accessing the root instance or even cross-border access. This paper deploys three types of operator detection points in 31 provincial-level administrative regions in mainland China. Each detection point requests the NS record of the top-level domain name from the root instance server introduced in China to obtain the access data of the root instance server hit by domestic users. At the same time, we propose two methods to determine whether users have achieved the optimal choice of root instance, including the method based on the shortest AS path and the method based on geographical distance. In these two methods, we analyze the optimal selection of root instances for each root server. Finally, we analyze the cross-border access of users and find that China Telecom users are more likely to access the root instance across borders.

Abstract Existing soft ionic conductors fall into two distinct categories: liquid‐rich ionic conductors containing large amounts of liquid electrolytes (≈70–90 wt.% water for hydrogels and ≈20–80 wt.% ionic liquids for ionogels), and liquid‐free ionic conductors that do not contain liquid components (e.g., ionic conductive elastomers). However, they are often plagued by dehydration, leakage of toxic ionic liquids, and air aging. Here, using hydrophobic polymer networks and hydrophilic salt hydrates, ionic conductive elastomers (s‐ICEs for short) containing only a tiny amount of bound water (≈1–5 wt.% are synthesized). Thanks to the small embedded water content, the s‐ICEs are advantageous over liquid‐rich ionic conductors in terms of enhanced mechanical/electrical stabilities and safety; they also outperform previously reported liquid‐free ionic conductors by avoiding air‐aging issues. The s‐ICEs introduced here also show excellent stretchability, good elasticity, high fracture resistance, desirable optical transparency and ionic conductivity, which are comparable to those of state‐of‐the‐art liquid‐rich and liquid‐free ionic conductors. With all the above advantages, the s‐ICE represents an ideal material for practical applications of soft ionotronics in ambient conditions.

Objectives: The aim of the present study was to develop a machine learning model to predict the risk of molar incisor hypomineralization (MIH) and to identify factors associated with MIH in an endemic fluorosis region in central China.Methods: A cross-sectional study was conducted with 1,568 schoolchildren from selected regions. The clinical examination included an investigation of MIH based on the European Academy of Paediatric Dentistry (EAPD) criteria. In this study, supervised machine learning (e.g., logistic regression) and correlation analysis (e.g., Spearman correlation analysis) were used for classification and prediction.Results: The overall prevalence of MIH was 13.7%. The nomograph showed that non-dental fluorosis (DF) had a considerable influence on the early occurrence of MIH and that this influence became weaker as DF severity increased. We examined the association between MIH and DF and found that DF had a protective correlation with MIH; the protective effect became stronger as DF severity increased. Furthermore, children with defective enamel were more likely to experience caries, and dental caries were positively correlated with MIH (OR = 1.843; 95% CI: 1.260–2.694). However, gender, oral hygiene, and exposure to poor-quality shallow underground water did not increase the likelihood of developing MIH.Conclusions: DF should be considered a protective factor within the multifactorial etiology of MIH.

Twelve new grayanoids (1−12) along with five known compounds were isolated from flowers of Rhododendron molle. Their structures were fully characterized using a combination of spectroscopic analyses, computational calculations, and single crystal X-ray diffraction. Rhomollone A (1) possesses an unprecedented 5/6/6/5 tetra-cyclic ring system (B-nor grayanane) incorporating a cyclopentene-1,3-dione scaffold. Rhodomollein XLIII (2) is a dimeric grayanoid, containing a novel 14-membered heterocyclic ring with a C2 symmetry axis. The antinociceptive activities of compounds 3, 4, 6, 7, and 12−17 were evaluated by an acetic acid-induced writhing test. Among them, compounds 3, 7, 12, 15 and 16 displayed significant antinociceptive activities at a dose of 20 mg/kg with inhibition rates ranging from 41.9% to 91.6%. Compounds 6 and 13 inhibited 46.0% and 39.4% of the acetic acid-induced writhes at a dose of 2 mg/kg, while compound 17 inhibited 34.3% of the writhes at a dose of 0.4 mg/kg.

Detecting and controlling illegal websites (gambling and pornography sites) through illegal domain names has been an unsolved problem. Therefore, how to mine and discover potential illegal domain names in advance has become a current research hotspot. This paper studies a method of generating illegal domain names based on the character similarity of domain name structure. Firstly, the K-means algorithm classified illegal domain names with similar structures. Then, put the classified clusters into the adversarial generative network for training. Finally, through a specific result verification method, the experiment shows that the average concentration of the generation algorithm is 23.82%, the effective concentration is 63.54%, and the expansion rate is 7.5. By comparing the results with the enumeration algorithm, the generation algorithm has greatly improved in terms of generation efficiency and accuracy.

Bioretention system is one of the most used green stormwater infrastructures (GSI), and its media is a key factor in reducing runoff water volume and purifying water quality. Many studies have investigated media improvement to enhance the pollutant removal capacity. However, the long-term cumulative effect and microbial effect of pollutants in the modified-media bioretention system is less known. This study investigated the cumulative effect of pollutants and their influence on microbial characteristics in conventional and modified media bioretention system. The addition of modifiers increased the background content of pollutants in the media, and the accumulation of pollutants in planting soil (PS) and bioretention soil mixing + water treatment residuals (BSM+WTR) was relatively higher after the simulated rainfall experiment. The accumulation of pollutants led to a decrease in dehydrogenase activity, and an increase in urease and invertase activities. Ten dominant bacterial species at the phylum level were found in all bioretention systems. The relative abundances of the bacteria with good viability under low nutritional conditions decreased, while the species which could live in the pollutant-rich environment increased. The accumulation of pollutants in the bioretention system led to the extinction of some functional microorganisms. The better the effects of modified media on pollutant removal showed, the more obvious effect on the media micro-ecosystem was. To ensure the long-term efficient and stable operation of the modified-media bioretention system, we recommend balancing the pollutant removal efficiency and cumulative effect in modified-media bioretention systems.

DNS plays an important role on the Internet. The addressing of most applications depends on the proper operation of DNS. The root servers and the top-level domain servers are relied upon by many domains on the Internet, and their security affects the whole Internet. As a result, more attention has been paid to the security of servers at these two levels. However, the security of second-level domains and their servers also needs to be brought to the forefront. This paper focuses on showing the complex resolving dependencies and identifying influential name servers for second-level domains. We start by detecting domain name resolution paths and building up a name dependency graph. Then we construct domain name resolution networks of different numbers and sizes, which are connected by a certain number of domain name resolution graphs. On this basis, the network is analyzed from the perspective of complex network analysis, and a multi-indicators node importance evaluation method based on partial order is proposed to identify the influential name servers of the network. Once these name servers are not properly configured and fail or are compromised by DDoS attacks, it will cause resolution failure for a wide range of domain names.

Assessing the structural vulnerability of online social networks has been one of the most engaging topics recently, which is quite essential and beneficial to holding the network connectivity and facilitating information flow, but most of the existing vulnerability assessment measures and the corresponding solutions fail to accurately reveal the global damage done to the network. In order to accurately measure the vulnerability of networks, an invulnerability index based on the concept of improved tenacity is proposed in the present study. Compared with existing measurements, the new method does not measure a single property performance, such as giant component size or the number of components after destruction, but pays special attention to the potential equilibrium between the removal cost and the removal effect. Extensive experiments on real-world social networks demonstrate the accuracy and effectiveness of the proposed method. Moreover, compared with results of attacks based on the different centrality indices, we found an individual node’s prominence in a network is inherently related to the structural properties of network. In high centralized networks, the nodes with higher eigenvector are more important than the others in maintaining stability and connectivity. But in low centralized networks, the nodes with higher betweenness are more powerful than the others. In addition, the experimental results indicate that low centralized networks can tolerate high intentional attacks and has a better adaptability to attacks than high centralized networks.

Peri-implantitis is characterized by inflammatory cell infiltration and hyperactivation of the osteoclasts surrounding dental implants which can result in bone resorption and ultimately implant failure. Therefore, coordinating the activity of inflammatory response and bone-resorbing osteoclasts is crucial for the prevention of peri-implantitis. Asperuloside (ASP), an iridoid glycoside, has significant anti-inflammatory activities, suggesting the great potential in attenuating peri-implantitis bone resorption. A ligature-induced peri-implantitis model in the maxilla of rats was established, and the effects of ASP on preventing peri-implantitis were evaluated after four weeks of ligation using micro-CT and histological staining. RT-PCR, western blotting, tartrate-resistant acid phosphatase (TRAP), and immunofluorescent staining were conducted on osteoclasts to confirm the mechanisms of ASP on osteoclastogenesis. The results show that ASP could lead to attenuation of alveolar bone resorption in peri-implantitis by inhibiting osteoclast formation and decreasing pro-inflammatory cytokine levels in vivo. Furthermore, ASP could inhibit osteoclastogenesis by downregulating expression levels of transcription factors nuclear factor of activated T-cell (NFATc1) via restraining the activations of nuclear factor kappa beta (NF-κB) and the phosphorylation of extracellular signal-related kinase 1/2 (ERK1/2). In conclusion, ASP could significantly attenuate bone resorption in peri-implantitis via inhibition of osteoclastogenesis by suppressing NF-κB and ERK1/2 signaling pathways activations.

Trams have increasingly deployed object detectors to perceive running conditions, and deep learning networks have been widely adopted by those detectors. Growing neural networks have incurred severe attacks such as adversarial example attacks, imposing threats to tram safety. Only if adversarial attacks are studied thoroughly, researchers can come up with better defence methods against them. However, most existing methods of generating adversarial examples have been devoted to classification, and none of them target tram environment perception systems. In this paper, we propose an improved projected gradient descent (PGD) algorithm and an improved Carlini and Wagner (C&W) algorithm to generate adversarial examples against Faster R-CNN object detectors. Experiments verify that both algorithms can successfully conduct nontargeted and targeted white-box digital attacks when trams are running. We also compare the performance of the two methods, including attack effects, similarity to clean images, and the generating time. The results show that both algorithms can generate adversarial examples within 220 seconds, a much shorter time, without decrease of the success rate.

X.509 certificates play an important role in encrypting the transmission of data on both sides under HTTPS. With the popularization of X.509 certificates, more and more criminals leverage certificates to prevent their communications from being exposed by malicious traffic analysis tools. Phishing sites and malware are good examples. Those X.509 certificates found in phishing sites or malware are called malicious X.509 certificates. This paper applies different machine learning models, including classical machine learning models, ensemble learning models, and deep learning models, to distinguish between malicious certificates and benign certificates with Verification for Extraction (VFE). The VFE is a system we design and implement for obtaining plentiful characteristics of certificates. The result shows that ensemble learning models are the most stable and efficient models with an average accuracy of 95.9%, which outperforms many previous works. In addition, we obtain an SVM-based detection model with an accuracy of 98.2%, which is the highest accuracy. The outcome indicates the VFE is capable of capturing essential and crucial characteristics of malicious X.509 certificates.

Staphylococcus aureus (S. aureus)-induced acute lung injury (ALI) is a serious disease that has a high risk of death among infants and teenagers. Acetylharpagide, a natural compound of Ajuga decumbens Thunb. (family Labiatae), has been found to have anti-tumor, anti-inflammatory and anti-viral effects. This study investigates the therapeutic effects of acetylharpagide on S. aureus-induced ALI in mice. Here, we found that acetylharpagide alleviated S. aureus-induced lung pathological morphology damage, protected the pulmonary blood-gas barrier and improved the survival of S. aureus-infected mice. Furthermore, S. aureus-induced myeloperoxidase (MPO) activity of lung homogenate and pro-inflammatory factors in bronchoalveolar lavage (BAL) fluid were suppressed by acetylharpagide. Mechanically, acetylharpagide inhibited the interaction between polyubiquitinated receptor interacting protein 1 (RIP1) and NF-κB essential modulator (NEMO), thereby suppressing NF-κB activity. In summary, these results show that acetylharpagide protects mice from S. aureus-induced ALI by suppressing the NF-κB signaling pathway. Acetylharpagide is expected to become a potential treatment for S. aureus-induced ALI.

Network anomaly detection plays a vital role in safeguarding network security. However, the existing network anomaly detection task is typically based on the one-class zero-positive scenario. This approach is susceptible to overfitting during the training process due to discrepancies in data distribution between the training set and the test set. This phenomenon is known as prediction drift. Additionally, the rarity of anomaly data, often masked by normal data, further complicates network anomaly detection. To address these challenges, we propose the PUNet network, which ingeniously combines the strengths of traditional machine learning and deep learning techniques for anomaly detection. Specifically, PUNet employs a reconstruction-based autoencoder to pre-train normal data, enabling the network to capture potential features and correlations within the data. Subsequently, PUNet integrates a sampling algorithm to construct a pseudo-label candidate set among the outliers based on the reconstruction loss of the samples. This approach effectively mitigates the prediction drift problem by incorporating abnormal samples. Furthermore, PUNet utilizes the CatBoost classifier for anomaly detection to tackle potential data imbalance issues within the candidate set. Extensive experimental evaluations demonstrate that PUNet effectively resolves the prediction drift and data imbalance problems, significantly outperforming competing methods. [ABSTRACT FROM AUTHOR]

The secure and normal operation of distributed networks is crucial for accurate parameter estimation. However, distributed networks are frequently susceptible to Byzantine attacks. Considering real-life scenarios, this paper investigates a probability Byzantine (PB) attack, utilizing a Bernoulli distribution to simulate the attack probability. Historically, additional detection mechanisms are used to mitigate such attacks, leading to increased energy consumption and burdens on distributed nodes, consequently diminishing operational efficiency. Differing from these approaches, an adaptive updating distributed estimation algorithm is proposed to mitigate the impact of PB attacks. In the proposed algorithm, a penalty strategy is initially incorporated during data updates to weaken the influence of the attack. Subsequently, an adaptive fusion weight is employed during data fusion to merge the estimations. Additionally, the reason why this penalty term weakens the attack has been analyzed, and the performance of the proposed algorithm is validated through simulation experiments. [ABSTRACT FROM AUTHOR]