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Abstract Addressing the rise in global temperatures and the associated increase in greenhouse gases, particularly carbon dioxide, is a critical challenge necessitating innovative approaches within the building sector, a significant contributor to worldwide carbon emissions. While previous studies have demonstrated the prefabrication’s potential in reducing emissions, comprehensive assessments using actual project data for buildings constructed entirely with modular methods in Hong Kong are lacking. This study bridges this gap by evaluating the modular integrated construction (MiC) method through an embodied carbon assessment of the Kai Tak Community Isolation Facility. Using comprehensive project data from China State Construction (HK) Limited, the research conducts a comparative analysis between the actual emissions of the MiC method and those of a hypothetical conventional construction approach. Quantitative analysis reveals that MiC achieves a 20.7% reduction in embodied carbon, primarily due to shortened construction timelines, decreased waste generation, and optimized material usage. This significant reduction suggests substantial potential for decreasing the construction industry’s carbon footprint. The study provides empirical evidence supporting the environmental benefits of MiC in Hong Kong construction industry, promoting its broader adoption of MiC as a strategy for achieving carbon reduction targets. The findings align with Hong Kong’s carbon neutrality goals and contribute to the global initiative to mitigate the effects of climate change.

Objective: Baicalin has antioxidative, antiviral, and anti-inflammatory properties. However, its ability to alleviate oxidative stress (OS) and DNA damage in liver cells exposed to aflatoxin B1 (AFB1), a highly hepatotoxic compound, remains uncertain. In this study, the protective effects of baicalin on AFB1-induced hepatocyte injury and the mechanisms underlying those effects were investigated. Methods: Stable cell lines expressing CYP3A4 were established using lentiviral vectors to assess oxidative stress levels by conducting assays to determine the content of reactive oxygen species (ROS), malondialdehyde (MDA), and superoxide dismutase (SOD). Additionally, DNA damage was evaluated by 8-hydroxy-2-deoxyguanosine (8-OHdG) and comet assays. Transcriptome sequencing, molecular docking, and in vitro experiments were conducted to determine the mechanisms underlying the effects of baicalin on AFB1-induced hepatocyte injury. In vivo, a rat model of hepatocyte injury induced by AFB1 was used to evaluate the effects of baicalin. Results: In vitro, baicalin significantly attenuated AFB1-induced injury caused due to OS, as determined by a decrease in ROS, MDA, and SOD levels. Baicalin also considerably decreased AFB1-induced DNA damage in hepatocytes. This protective effect of baicalin was found to be closely associated with the TP53-mediated ferroptosis pathway. To elaborate, baicalin physically interacts with P53, leading to the suppression of the expression of GPX4 and SLC7A11, which in turn inhibits ferroptosis. In vivo findings showed that baicalin decreased DNA damage and ferroptosis in AFB1-treated rat liver tissues, as determined by a decrease in the expression of γ-H2AX and an increase in GPX4 and SLC7A11 levels. Overexpression of TP53 weakened the protective effects of baicalin. Conclusions: Baicalin can alleviate AFB1-induced OS and DNA damage in liver cells via the TP53-mediated ferroptosis pathway. In this study, a theoretical foundation was established for the use of baicalin in protecting the liver from the toxic effects of AFB1.

Event type discovery in open domains aims to automate the induction of event types from completely unlabeled text data. Conventionally, small models utilize clustering techniques to address this task. Nonetheless, the fully unsupervised nature of these methods results in suboptimal performance by small models in this context. Recently, large language models (LLMs) have demonstrated excellent capabilities in contextual understanding, providing additional relevant information for specific task scenarios, albeit with challenges in precision and cost effectiveness. In this paper, we use LLM to guide the clustering of event texts and distill this process into a fine-tuning task for training smaller pre-trained language models. This approach enables effective event type discovery even in scenarios lacking annotated data. The study unfolds in three stages: in action acquisition, leveraging LLMs to extract type-relevant information from each event text, ensuring that the event representations are particular to task-specific details; in clustering refinement and dual-fine-tune, LLMs refine results from both task-agnostic and task-specific perspectives, with the refinement process designed as fine-tuning tasks under different viewpoints to adjust encoders; and in type generation, post-clustering, LLMs generate meaningful event type labels for each cluster. Experiments show that our method outperforms current state-of-the-art approaches and excels in event type discovery tasks even in completely open-domain with no labeled data.

Objective To examine the current prevalence and cost of paediatric off-label drug prescriptions in Gansu, China, and the potential influencing factors.Design The prevalence of off-label prescriptions in paediatrics was evaluated according to the National Medical Products Administration drug instructions in the China Pharmaceutical Reference (China Pharmaceutical Reference, MCDEX) database. The evidence of the prescription was determined by existing clinical practice guidelines and the Thomson Grade in the Micromedex 2021 compendium. We used logistic regression to investigate the characteristics that influence paediatric off-label drug use after single-factor regression analysis.Setting A multicentre cross-sectional study of outpatient paediatric prescriptions in 196 secondary and tertiary hospitals in Gansu Province, China, in March and September 2020.Results We retrieved 104 029 paediatric prescriptions, of which 39 480 (38.0%) contained off-label use. The most common diseases treated by off-label drugs were respiratory system diseases (n=15 831, 40.1%). A quarter of off-label prescriptions had adequate evidence basis (n=10 130, 25.6%). Unapproved indications were the most common type of off-label drug use (n=25 891, 65.6%). A total of 1177 different drugs were prescribed off-label, with multienzyme tablets being the most common drug (n=1790, 3.5%). The total cost of the prescribed off-label drugs was ¥106 116/day. Off-label prescriptions were less frequent in tertiary than in secondary hospitals. Topical preparations were more commonly prescribed off-label than other types of drugs. Senior-level clinicians prescribed drugs off-label more often than intermediate and junior clinicians.Conclusion Off-label drug use is widespread in paediatric practice in China. Three-quarters of the prescriptions may potentially include inappropriate medication use, resulting in a daily economic burden of about ¥81 000 in 2020 in Gansu Province with 25 million inhabitants. The management of off-label drug use in paediatrics in China needs improvement.

This paper presents Calderón-Zygmund estimates for the weak solutions of a class of nonuniformly elliptic equations in $ \mathbb{R}^n $, which are obtained through the use of the iteration-covering method. More precisely, a global Calderón-Zygmund type result $ \begin{equation*} |f|^{p_1}+a(x)|f|^{p_2}\in L^s(\mathbb{R}^n) \Rightarrow |Du|^{p_1}+a(x)|Du|^{p_2}\in L^s(\mathbb{R}^n)\quad {\rm for \; any} \; s>1 \end{equation*} $ is established for the weak solutions of $ \begin{equation*} -{\rm div}A(x, Du) = -{\rm div}F(x, f) \quad {\rm in} \; \mathbb{R}^n, \end{equation*} $ which are modeled on $ \begin{equation*} -{\rm div}(|Du|^{p_1-2}Du+a(x)|Du|^{p_2-2}Du) = -{\rm div}(|f|^{p_1-2}f+a(x)|f|^{p_2-2}f), \end{equation*} $ where $ 0\leq a(\cdot)\in C^{0, \alpha}(\mathbb{R}^n), \; \alpha\in (0, 1] $ and $ 1 < p_1 < p_2 < p_1+\frac{\alpha p_1}{n} $.

The future query of moving objects involves predicting their future positions based on their current locations and velocities to determine whether they will appear in a specified area. This technique is crucial for positioning and navigation, and its importance in our daily lives has become increasingly evident in recent years. Nonetheless, the growing volume of data renders traditional index structures for moving objects, such as the time-parameterized R-tree (TPR-tree), inefficient due to their substantial storage overhead and high query costs. Recent advancements in learned indexes have demonstrated a capacity to significantly reduce storage overhead and enhance query efficiency. However, most existing research primarily addresses static data, leaving a gap in the context of future queries for moving objects. We propose a novel future query technique for moving objects based on a learned index (FurMoLi for short). FurMoLi encompasses four key stages: firstly, a data partition through clustering based on velocity and position information; secondly, a dimensionality reduction mapping two-dimensional data to one dimension; thirdly, the construction of a learned index utilizing piecewise regression functions; and finally, the execution of a future range query and future KNN query leveraging the established learned index. The experimental results demonstrate that FurMoLi requires 4 orders of magnitude less storage overhead than TPR-tree and 5 orders of magnitude less than B+-tree for moving objects (Bx-tree). Additionally, the future range query time is reduced to just 41.6% of that for TPR-tree and 34.7% of that for Bx-tree. For future KNN queries, FurMoLi’s query time is only 70.1% of that for TPR-tree and 47.4% of that for Bx-tree.

Zero-shot event detection aims to involve the automatic discovery and classification of new events within unstructured text. Current zero-shot event detection methods have not considered addressing the problem more effectively from the perspective of improving event representations. In this paper, we propose dual-contrastive prompting (COPE) model for learning event representations to address zero-shot event detection, which leverages prompts to assist in generating event embeddings using a pretrained language model, and employs a contrastive fusion approach to capture complex interaction information between trigger representations and sentence embeddings to obtain enhanced event representations. Firstly, we introduce a sample generator to create ordered contrastive sample sequences with varying degrees of similarity for each event instance, aiding the model in better distinguishing different types of events. Secondly, we design two distinct prompts to obtain trigger representations and event sentence embeddings separately. Thirdly, we employ a contrastive fusion module, where trigger representations and event sentence embeddings interactively fuse in vector space to generate the final event representations. Experiments show that our model is more effective than the most advanced methods.

The existence of nontrivial solutions of the double phase problem with nonlinear boundary value condition is an important quasilinear problem: we use variational techniques and sum decomposition of a space $ W_0^{1, \xi}(\Omega) $ to prove the existence of infinitely many solutions of the problem considered. Moreover, our conditions are suitable and different from those considered previously.

Abstract Background The "Law on Doctors of the People's Republic of China," which was officially implemented on March 1, 2022, emphasizes the requirements for rational drug use and the necessity for appropriate management of off-label drug use. The safety and ethical considerations related to off-label drug use are different in children than in adults. There is so far no management guideline for pediatric off-label use of drugs in China, and the applicability of foreign guidelines is limited. Establishing a localized evidence-based management guideline for pediatric off-label use of drugs to support the national legislation and clinical practice is of critical importance. Methods We established a guideline working group, including experts from a broad range of disciplines and developed recommendations following the guidance of the World Health Organization Handbook and the Chinese Medical Association. The following themes were identified by questionnaires and expert interviews to be of great concern in the management of off-label drug use in children: general principles and characteristics of management of pediatric off-label drug use; establishment of expert committees; evidence evaluation; risk–benefit assessment; informed consent; monitoring and assessment of the risk; and monitoring and patient education. Two rounds of Delphi surveys were organized to determine the final recommendations of this guideline. We graded the recommendations based on the body of evidence, referring to the evaluation tool of the Evidence-based management (EBMgt) and the Oxford Center for Evidence-Based Medicine: Level of Evidence (March 2009). Results We developed the first guideline for the management of pediatric off-label use of drugs in China. Conclusions The guideline is to offer guidance for pediatricians, pharmacists, medical managers, policymakers, and primary care physicians on how to manage off-label drug use in pediatrics and to provide recommendations for Chinese healthcare policy in the future.

The advent of big data technologies makes a profound impact on various facets of our lives, which also presents an opportunity for Chinese audits. However, the heterogeneity of multi-source audit data, the intricacy of converting Chinese into SQL, and the inefficiency of data processing methods present significant obstacles to the growth of Chinese audits. In this article, we proposed BDMCA, a big data management system designed for Chinese audits. We developed a hybrid management architecture for handling Chinese audit big data, that can alleviate the heterogeneity of multi-mode data. Moreover, we defined an R-HBase spatio-temporal meta-structure for auditing purposes, which exhibits almost linear response time and excellent scalability. Compared to MD-HBase, R-HBase performs 4.5× and 3× better in range query and kNN query, respectively. In addition, we leveraged the slot value filling method to generate templates and build a multi-topic presentation learning model MRo-SQL. MRo-SQL outperforms the state-of-the-art X-SQL parsing model with improvements in logical-form accuracy of up to 5.2%, and execution accuracy of up to 5.9%.

Abstract Background Nursing home residents are at increased risk for hospital transfers resulting in emergency department visits, observation stays, and hospital admissions; transfers that can also result in adverse resident outcomes. Many nursing home to hospital transfers are potentially avoidable. Residents who experience repeat transfers are particularly vulnerable to adverse outcomes, yet characteristics of nursing home residents who experience repeat transfers are poorly understood. Understanding these characteristics more fully will help identify appropriate intervention efforts needed to reduce repeat transfers. Methods This is a mixed-methods study using hospital transfer data, collected between 2017 and 2019, from long-stay nursing home residents residing in 16 Midwestern nursing homes who transferred four or more times within a 12-month timeframe. Data were obtained from an acute care transfer tool used in the Missouri Quality Initiative containing closed- and open-ended questions regarding hospital transfers. The Missouri Quality Initiative was a Centers for Medicare and Medicaid demonstration project focused on reducing avoidable hospital transfers for long stay nursing home residents. The purpose of the analysis presented here is to describe characteristics of residents from that project who experienced repeat transfers including resident age, race, and code status. Clinical, resident/family, and organizational factors that influenced transfers were also described. Results Findings indicate that younger residents (less than 65 years of age), those who were full-code status, and those who were Black were statistically more likely to experience repeat transfers. Clinical complexity, resident/family requests to transfer, and lack of nursing home resources to manage complex clinical conditions underlie repeat transfers, many of which were considered potentially avoidable. Conclusions Improved nursing home resources are needed to manage complex conditions in the NH and to help residents and families set realistic goals of care and plan for end of life thus reducing potentially avoidable transfers.

To protect image privacy in real-time transmission, a fast chaotic image encryption algorithm using a novel divide and conquer diffusion strategy is proposed in this paper. Firstly, a fast pseudo-random sequence generator is constructed using 2D hyperchaotic systems. And by performing bitwise XOR operation between two integer sequences obtained from different systems, the final key stream used for diffusion will have better randomness and unpredictability. Secondly, to achieve divide and conquer diffusion, the plain image is divided into three parts, then a parallel CBC-based diffusion method can simultaneously act on upper and bottom parts (or left and right parts), which achieves a time complexity of $\boldsymbol {O}$ ( $W+H$ ). Moreover, within the diffusion process, the mechanism of information interaction on the central row or column further enhances avalanche effect to resist differential attack. Thirdly, the session key of the proposed algorithm is a mixture of the plain image’s hash value and a true random sequence, which not only improve plaintext sensitivity but also realize one-time pad. Finally, experimental results indicate the superiority of our algorithm to resist statistical, chosen-plaintext, entropy, and other common attacks. Furthermore, by comparison with previous works, our algorithm preforms much faster execution speed with only average of 0.08s to encrypt images of size $512\times 512$ , while it provide the same or even higher level of security. Therefore, the proposed algorithm can meet security and efficiency requirements of real-time communications of image data.

Motivated by the pursuit of both encryption efficiency and security, this paper presents a symmetric color image encryption algorithm using a novel cross–plane joint scrambling–diffusion method. To provide high–strength security, the color planes are firstly spliced horizontally and then vertically to be adequately encrypted by the joint scrambling–diffusion method. Therefore, minor changes in any pixel can cross every color plane that significantly influences the final cipher image. To achieve fast encryption speed, all employed chaotic systems have simple structures but robust properties to rapidly generate high–quality pseudorandom sequences. Additionally, the joint scrambling–diffusion method is vectorized to handle pixels in parallel for satisfactory execution efficiency. Security tests demonstrate the outstanding security performance of the proposed algorithm, with correlation coefficients and entropies of cipher images being close to 0 and greater than 7.99, respectively, and results of NPCR and UACI tests being similar to ideal cipher images, which can resist statistical, differential, exhaustive, and even chosen ciphertext/plaintext attacks. Moreover, because of the O(4W + 4H) time complexity of the novel scrambling–diffusion method, even a color image with a size of 1024 × 1024 only costs 0.26 s to be encrypted. Hence, the proposed algorithm can satisfy the efficiency and security requirements of color image encryption.

Using the local correlation of carrier images to embed secret data in MSBs is a popular scheme for reversible data hiding in encrypted images (RDHEI). However, most existing methods based on this scheme face challenges in achieving complete compression while ensuring security. In this study, an RDHEI method that uses a two-dimensional chaotic system and full bit-plane search (FBPS) is proposed. Specifically, the content owner provides cover images and employs a chaotic system to generate chaotic sequences for inter-block non-symmetrical permutation and intra-block diffusion. The special encryption method, combined with chaos, not only preserves the correlation of pixels within a block but also ensures an extremely high level of security. The FBPS technique is applied to detect all smooth bit planes, not limited to continuous MSBs. For data embedding, ‘0’ and ‘1’ are used to record all smooth and rough bit planes to achieve thorough compression. The results of the experiment show that our proposed method provides a high level of security and achieves 2.142 bit/pixel and 2.339 bit/pixel on the typical datasets BOSSbase (Break Our Steganographic System) and BOWS-2 (Break Our Watermarking System 2nd). Compared with the state-of-the-art methods, the embedding capacity has also been significantly improved.

Abstract Background Medicine purchasing in Chinese public hospitals is decided by the hospital Pharmacy Management Committee (PMC), that is complex, subjective and requires efficient approaches to ensure transparency and consistency for the factors being considered. This study aimed to use the Evidence and Value: Impact on Decision Making (EVIDEM) framework to assess medicine in these hospitals. In this study anti-diabetic drugs DPP-4 inhibitors, which work by inhibiting the activation of the Dipeptidyl Peptidase 4 (DPP-4) inhibitors, were appraised. Methods Following EVIDEM methodology (EVIDEM-10th), we convened an appraisal group and asked each individual to express their perspectives by assigning weights to each criterion. A systematic literature search for information of each criterion of five DPP-4 inhibitors was completed. Then the appraisal group scored for each criterion of the five DPP-4 inhibitors. The estimated value of the five DPP-4 inhibitors was obtained by Multi-Criteria Decision Analysis (MCDA) which combined individual weighting of each criterion with individual scoring for each intervention in each criterion. Results By assigning weights, the most important criterion was the quality of evidence (4.01±0.52), and that the comparative cost consequences-non-medical cost was the least important criterion (2.87±1.03). Criteria included disease severity, size of the affected population, comparative effectiveness, type of therapeutic/preventive benefit and cost of intervention, all of which were assigned the same weight of 3.58. After MCDA, the overall value orders for each DPP-4 inhibitor included Sitagliptin (0.45), Linagliptin (0.44), Vildagliptin (0.43), Alogliptin (0.42) and Saxagliptin (0.40). Conclusions Based on EVIDEM framework and MCDA, we found that overall value of five DPP-4 inhibitors was similar. It is feasible to use the EVIDEM framework and MCDA in purchasing medicine for Chinese public hospitals.

Abstract Hepatocellular carcinoma (HCC) is a common malignancy. CD8+ T cell-mediated immune response is critical for the inhibition of HCC progression. M2 macrophages participate in HCC progression. This study set out to investigate the effect of M2 macrophage-derived extracellular vesicles (EVs) on CD8+ T cell exhaustion in HCC. M2 macrophage-derived EVs were isolated and identified. The murine model of primary HCC was established through DEN/CCl4 induction, and model mice were injected with EVs. Peripheral blood mononuclear cells (PBMCs) were isolated from the mouse liver and CD8+ T cells were sorted. The expressions of immune checkpoint inhibitory receptors and effector cytokines on CD8+ T cells were detected, followed by the evaluation of CD8+ T cell proliferation and killing function. miR-21-5p expression in M2 macrophage-derived EVs was detected. The binding relationship between miR-21-5p and YOD1 was verified. The activation of the YAP/β-catenin pathway was detected. Consequently, M2 macrophage-derived EVs promoted CD8+ T cell exhaustion in HCC mice. miR-21-5p expression was upregulated in M2 macrophage-derived EVs, and EVs carried miR-21-5p into HCC tissues. miR-21-5p targeted YOD1. Inhibition of miR-21-5p or overexpression of YOD1 annulled the promoting effect of EVs on CD8+ T cell exhaustion. YOD1 inactivated the YAP/β-catenin pathway. In conclusion, M2 macrophage-derived EVs facilitated CD8+ T cell exhaustion via the miR-21-5p/YOD1/YAP/β-catenin axis. This study may confer novel insights into the immunotherapy of HCC.

(1) Background: Chinese news text is a popular form of media communication, which can be seen everywhere in China. Chinese news text classification is an important direction in natural language processing (NLP). How to use high-quality text classification technology to help humans to efficiently organize and manage the massive amount of web news is an urgent problem to be solved. It is noted that the existing deep learning methods rely on a large-scale tagged corpus for news text classification tasks and this model is poorly interpretable because the size is large. (2) Methods: To solve the above problems, this paper proposes a Chinese news text classification method based on key feature enhancement named KFE-CNN. It can effectively expand the semantic information of key features to enhance sample data and then combine the zero–one binary vector representation to transform text features into binary vectors and input them into CNN model for training and implementation, thus improving the interpretability of the model and effectively compressing the size of the model. (3) Results: The experimental results show that our method can significantly improve the overall performance of the model and the average accuracy and F1-score of the THUCNews subset of the public dataset reached 97.84% and 98%. (4) Conclusions: this fully proved the effectiveness of the KFE-CNN method for the Chinese news text classification task and it also fully demonstrates that key feature enhancement can improve classification performance.

To address the problems of low monitoring area coverage rate and the long moving distance of nodes in the process of coverage optimization in wireless sensor networks (WSNs), a multi-strategy improved sparrow search algorithm for coverage optimization in a WSN (IM-DTSSA) is proposed. Firstly, Delaunay triangulation is used to locate the uncovered areas in the network and optimize the initial population of the IM-DTSSA algorithm, which can improve the convergence speed and search accuracy of the algorithm. Secondly, the quality and quantity of the explorer population in the sparrow search algorithm are optimized by the non-dominated sorting algorithm, which can improve the global search capability of the algorithm. Finally, a two-sample learning strategy is used to improve the follower position update formula and to improve the ability of the algorithm to jump out of the local optimum. Simulation results show that the coverage rate of the IM-DTSSA algorithm is increased by 6.74%, 5.04% and 3.42% compared to the three other algorithms. The average moving distance of nodes is reduced by 7.93 m, 3.97 m, and 3.09 m, respectively. The results mean that the IM-DTSSA algorithm can effectively balance the coverage rate of the target area and the moving distance of nodes.

To protect image data privacy, a secure and fast image encryption algorithm using hyper-chaos based key generator and vector operation is proposed. Firstly, we introduce a novel post-process method to create a key matrix for significantly reducing the iterations of the hyperchaotic system from $W\,\,\times \,\,H$ /4 to $2W$ for an image size of $W\,\,\times \,\,H$ . And within the post-process, a random selector driven by the piecewise linear chaotic map ensures the good randomness and unpredictability of the generated key matrix. Secondly, the vector operation is employed to accelerate the cipher block chaining based diffusion process, so the plain image can be parallel encrypted row by row and column by column with high efficiency. Thirdly, we utilize a Logistic map to quickly produce an initial vector for the vectorized diffusion process. Finally, the proposed algorithm is evaluated by some common security and performance tests. Experimental results show that the cipher image can pass all tests of NIST SP 800–22 with $P$ -values $\gg ~0.01$ , its correlation coefficient between pixels is close to 0, and the entropy is greater than 7.999. Combined with other results of security tests, we can safely conclude that the proposed image encryption algorithm provides adequate protection against statistical, brute-force, chosen-plaintext, and other common types of attacks. In addition, the time complexity is in the order of ${O}$ ( $W+H$ ) and the average encryption time of 512 $\times $ 512 images is only 0.023s. The results indicate that our algorithm with high security and fast speed can meet the requirements of real-time confidential transmission of massive image data.

Abstract This paper is concerned with the existence of infinitely many positive solutions to a class of double phase problem. By variational methods and the theory of the Musielak–Orlicz–Sobolev space, we establish the existence of infinitely many positive solutions whose W 0 1 , H ( Ω ) $W_{0}^{1,H}(\varOmega )$ -norms and L ∞ $L^{\infty }$ -norms tend to zero under suitable hypotheses about nonlinearity.

In this paper, we construct a rating credibility model of red wine by the Analytic Hierarchy Process, achieve the classification of red grapes through the evaluation results of red wine and cluster analysis method and analyze the correlation of the physical and chemical indicators between red grapes and red wine. Thus, the paper demonstrates that aromatic substances play an important role in the quality of red wine, so we cannot evaluate the quality of wine only by the physical and chemical indicators of wine grapes and wine.

Abstract This paper is concerned with the following double-phase problem: { − div ( | ∇ u | p − 2 ∇ u + a ( x ) | ∇ u | q − 2 ∇ u ) = f ( x , u ) in Ω , u = 0 on ∂ Ω , $$ \textstyle\begin{cases} -\operatorname{div}( \vert \nabla u \vert ^{p-2}\nabla u+a(x) \vert \nabla u \vert ^{q-2}\nabla u)=f(x,u)& \text{in }\varOmega, \\ u=0 &\text{on }\partial\varOmega, \end{cases} $$ where N ≥ 2 $N\geq2$ and 1 < p < q < N $1< p< q< N$ . Assuming that the primitive of f ( x , u ) $f(x,u)$ is asymptotically q-linear as | u | → ∞ $|u|\rightarrow\infty$ and a weak version of Nehari-type monotonicity condition that the function u ↦ f ( x , u ) | u | q − 1 $u\mapsto\frac{f(x,u)}{|u|^{q-1}}$ is nondecreasing on ( − ∞ , 0 ) ∪ ( 0 , ∞ ) $(-\infty,0) \cup(0,\infty)$ for a.e. x ∈ Ω $x\in\varOmega$ , we prove the existence of one ground state sign-changing solution via the constraint variational method and quantitative deformation lemma for the equation. Our results improve and generalize some results obtained by Liu and Dai (J. Differ. Equ. 265(9):4311–4334, 2018).

Text2SQL can help non-professionals connect with databases by turning natural languages into SQL. Although previous researches about Text2SQL have provided some workable solutions, most of them extract values based on column representation. If there are multiple values in the query and these values belong to different columns, the previous approaches based on column representation cannot accurately extract values. In this work, we propose a new neural network architecture based on the pre-trained BERT, called M-SQL. The column-based value extraction is divided into two modules, value extraction and value-column matching. We evaluate M-SQL on a more complicated TableQA dataset, which comes from an AI competition. We rank first in this competition. Experimental results and competition ranking show that our proposed M-SQL achieves state-of-the-art results on TableQA.

Automatically parsing SQL queries from natural languages can help non-professionals access databases and improve the efficiency of information utilization. It is a long-term research issue and has recently received attention from the relevant communities. Although previous researches have provided some workable solutions, most of them only consider table schemas and natural language questions when parsing SQL queries, and do not use table contents. We observe that table contents can provide more helpful information for some user questions. In this paper, we propose a novel neural network approach, F-SQL, to focus on solving the problem of table content utilization. In particular, we employ the gate mechanism to fuse table schemas and table contents and get the more different representation about table schemas. We test this idea on the WikiSQL and TableQA datasets. Experimental results show that F-SQL achieves new state-of-the-art results on WikiSQL and TableQA.

In this paper, we consider the following class of the fractional p&q-Laplacian problem: (−Δ)psu+(−Δ)qsu+V(x)(|u|p−2u+|u|q−2u)+g(x)|u|r−2u=K(x)f(x,u)+h(u),x∈RN,V:RN→R+ is a potential function, and h:R→R is a perturbation term. We studied two cases: if f(x,u) is sublinear, by means of Clark’s theorem, which considers the symmetric condition about the functional, we get infinitely many solutions; if f(x,u) is superlinear, using the symmetric mountain-pass theorem, infinitely many solutions can be obtained.

The target of the multi-hop knowledge base question-answering task is to find answers of some factoid questions by reasoning across multiple knowledge triples in the knowledge base. Most of the existing methods for multi-hop knowledge base question answering based on a general knowledge graph ignore the semantic relationship between each hop. However, modeling the knowledge base as a directed hypergraph has the problems of sparse incidence matrices and asymmetric Laplacian matrices. To make up for the deficiency, we propose a directed hypergraph convolutional network modeled on hyperbolic space, which can better deal with the sparse structure, and effectively adapt to the problem of an asymmetric incidence matrix of directed hypergraphs modeled on a knowledge base. We propose an interpretable KBQA model based on the hyperbolic directed hypergraph convolutional neural network named HDH-GCN which can update relation semantic information hop-by-hop and pays attention to different relations at different hops. The model can improve the accuracy of the multi-hop knowledge base question-answering task, and has application value in text question answering, human–computer interactions and other fields. Extensive experiments on benchmarks—PQL, MetaQA—demonstrate the effectiveness and universality of our HDH-GCN model, leading to state-of-the-art performance.

In many fields, such as space astrophysics, plasma and vacuum electronics, there are many nonlinear strong coupling physical problems. In order to solve the problem of electron beam–wave interaction in cylindrical Traveling wave tube (TWT) with symmetrical structure, a multi particle simulation algorithm for beam circuit is designed. The algorithm allows aperiodic time input, nonuniform linearity and large space diagnosis for different particles. In this algorithm, the field of coupled slow-wave transmission line is simulated by finite difference method. Assuming that there is strong coupling between the beam and the circuit, the space center equation of transmission along the line is obtained, and the space charge field is modeled considering the space charge effect, which can easily be ignored. The Particle In Cell (PIC) method of frog leaping step scheme is adopted to evaluate the electric field of each particle center, determine the circuit and space charge field, and design the termination part to compensate for the loss in order to avoid self-excited agitation. Finally, a simple numerical simulation is carried out for the electromagnetic problem and the accuracy of the code is checked against the electromagnetic simulator CHPIC. Therefore, the algorithm can be used to solve the problem of beam–wave interactions in a fixed structure (cylindrical) in multiple fields and can accurately record the data in the process.

A latent Dirichlet allocation (LDA) model is a common method for mining the interest of microblog users. But the LDA model does not reflect the hierarchical and dynamic trend of microblog users' interest. As a result, this paper combines with the timeliness and interactivity of microblog, to judge the hierarchical orientation and dynamic interest trend orientation of users' interest. And based on the dynamic interest hierarchical orientation, the three-layers interest network (TIN-LDA) model is constructed to mine the interest of microblog users. In addition, this model expands interest attributes. Interest attributes include contents, contents marked with special symbols, forwarding contents, along with the authentication user name and authentication information. Bringing the interest attributes into users' interest analysis so as to improve the accuracy of mining microblog users' interest keywords and topics. Topic quality assessment and perplexity evaluation were used to verify the effectiveness of the TIN-LDA model in mining the interest of microblog users.

To address the problem of a poor security image encryption algorithm based on a single chaotic map, this paper proposes a cascade modulation chaotic system (CMCS) that can generate multiple chaotic maps. On this basis, a multi-image encryption algorithm with block-scrambling-diffusion is proposed using CMCS. The algorithm makes full use of the features of CMCS to achieve the effect of one encryption at a time for images. Firstly, the key-value associated with the plaintexts is generated using a secure hash algorithm-512 (SHA-512) operation and random sequence, and the three images are fully confused by the double scrambling mechanism. Secondly, the scrambled image is converted into a bit-level matrix, and the pixel values are evenly distributed using the bit-group diffusion. Finally, the non-sequence diffusion of hexadecimal addition and subtraction rules is used to improve the security of the encryption algorithm. Experimental results demonstrate that the encryption algorithm proposed in this paper has a good encryption effect and can resist various attacks.

Enzyme-induced carbonate precipitation (EICP) is a new biogeotechnical ground improvement technique that uses calcium carbonate (CaCO3) formed by biochemical processes to increase soil strength and stiffness. In this paper, crude urease extracted from soybeans was employed to catalyze the precipitation of CaCO3 in sand. To optimize the urease extraction efficiency, factors affecting the soybean crude urease extraction, including the powdered soybean particle size, concentration, soaking time, and soaking temperature, were addressed. This paper also provided further insight regarding the impact of the urease activity of soybean crude extract on the chemical conversion efficiency and the biocementation performance in EICP. The findings revealed that the powdered soybean concentration and the particle size were the two most important factors affecting the urease activity of the soybean crude extract. The enzyme activity utilized in the EICP process might further lead to different reactant efficiencies of urea-CaCl2 solution, and consequently, the improvement in the physical and mechanical properties of biocemented sand. Considering the chemical conversion efficiency and the biocementation performance, 60 g/L of powdered soybean was concluded as the preferred quantity for extracting the crude urease, with an enzyme activity of 6.62 mM urea min−1. Under this condition, a chemical conversion efficiency of approximately 95% for 0.5 M urea-0.5 M CaCl2 could be obtained in merely 12 h, and the unconfined compressive strength (UCS) of the EICP-treated sand exceeded 4 MPa with a CaCO3 content of ~8%. As a high-efficient cost-effective alternative to the purified enzyme for carbonate precipitation, the soybean crude urease showed great potential for ground improvement.

Vehicular edge computing is a new computing paradigm. By introducing edge computing into the Internet of Vehicles (IoV), service providers are able to serve users with low-latency services, as edge computing deploys resources (e.g., computation, storage, and bandwidth) at the side close to the IoV users. When mobile nodes are moving and generating structured tasks, they can connect with the roadside units (RSUs) and then choose a proper time and several suitable Mobile Edge Computing (MEC) servers to offload the tasks. However, how to offload tasks in sequence efficiently is challenging. In response to this problem, in this paper, we propose a time-optimized, multi-task-offloading model adopting the principles of Optimal Stopping Theory (OST) with the objective of maximizing the probability of offloading to the optimal servers. When the server utilization is close to uniformly distributed, we propose another OST-based model with the objective of minimizing the total offloading delay. The proposed models are experimentally compared and evaluated with related OST models using simulated data sets and real data sets, and sensitivity analysis is performed. The results show that the proposed offloading models can be efficiently implemented in the mobile nodes and significantly reduce the total expected processing time of the tasks.

The use of ionic liquids (ILs) for biomass pretreatment to produce cellulose-rich materials (CRMs) has been well proven. In this research, due to the wide range of applications and ease of using artificial intelligence procedures, on the basis of the algorithm of stochastic gradient boosting (SGB) decision tree, an artificial intelligence approach is proposed to estimate the properties of cellulose-rich materials (CRMs). That being the case, the dataset of the empirical output values was gathered and was randomly broken down into datasets for testing and training. These results show that the best forecasting tool for calculating the properties of CRMs is the developed model. Furthermore, the accuracy of the databank of the biodiesel target values has been examined. In contrast, the influences of model contributed variables on the output have been examined as a new issue. It reveals that the most influencing variable in determining the properties of CRMs is the cellulose enrichment factor. Therefore, this research provides an innovative and accurate tool for predicting the properties of CRMs and sensitivity investigation on effective parameters to help investigators developing the optimized process.

Vibration is a common phenomenon in various fields which can not only indicate the working condition of the installation, but also serve as an energy source if it is efficiently harvested. In this work, a robust silicone rubber strip-based triboelectric nanogenerator (SRS-TENG) for vibration energy harvesting and multi-functional self-powered sensing is proposed and systematically investigated. The SRS-TENG consists of a silicone rubber strip and two aluminum electrode layers supported by polylactic acid (PLA), and acts as a sustainable power source and vibration frequency, amplitude and acceleration sensor as well. The soft contact between the aluminum electrode and silicone rubber strip makes it robust and stable even after 14 days. It can be applied in ranges of vibration frequencies from 5 to 90 Hz, and amplitudes from 0.5 to 9 mm, which shows it has advantages in broadband vibration. Additionally, it can achieve lower startup limits due to its soft structure and being able to work in multi-mode. The output power density of the SRS-TENG can reach 94.95 W/m3, matching a resistance of 250 MΩ, and it can light up more than 100 LEDs and power a commercial temperature sensor after charging capacitors. In addition, the vibration amplitude can be successfully detected and displayed on a human–machine interface. Moreover, the frequency beyond a specific limit can be distinguished by the SRS-TENG as well. Therefore, the SRS-TENG can be utilized as an in situ power source for distributed sensor nodes and a multifunctional self-powered vibration sensor in many scenarios.

In this paper, we investigate the topological structure of the solution set to a fractional differential inclusion with delay defined on the half-line. We first prove that the solution set to the inclusion is an Rδ-set on compact intervals. Then, by means of the inverse limit method, we generalize our results to noncompact intervals. Moreover, under convex and nonconvex conditions, an Rδ-property solution set is obtained for some nonlocal problems, where the nonlocal function is set-valued. Further, we study the symmetry of the solution set under some conditions.

The aim of this paper is to establish the existence of solutions for singular double-phase problems depending on one parameter. This work improves and complements the existing ones in the literature. There seems to be no results on the existence of solutions for singular double-phase problems.

A new kind of temperature sensor based on a vacuum diode was proposed and numerically studied in this paper. This device operated under different electron emission mechanisms according to the electron density in the vacuum channel. The temperature determination ability of this device was only empowered when working in the electric-field-assisted thermionic emission regime (barrier-lowering effect). The simulated results indicated that the temperature-sensing range of this device was around 273 K–325 K with a supply current of 1 μA. To obtain a linear dependency of voltage on temperature, we designed a proportional-to-absolute-temperature (PTAT) circuit. The mathematic derivation of the PTAT voltage is presented in this study. The temperature-sensing sensitivity was calculated as 7.6 mV/K according to the measured I-U (current versus voltage) characteristic. The structure and principle of the device presented in this paper might provide an alternative method for the study of temperature sensors.

Abstract In this paper, using continued fraction, we provide a new quicker sequence convergent to Euler’s constant. We demonstrate the superiority of our new convergent sequences over DeTemple’s sequence, Mortici’s sequences, Vernescu’s sequence, and Lu’s sequence.

In this article, by means of a direct variational approach and the theory of the fractional differential space, we prove the existence of a nontrivial solution for superlinear fractional boundary value problems without Ambrosetti and Rabinowitz condition.

This article concerns the fractional Schrodinger type equations $$ (-\Delta)^\alpha u+V(x)u =f(x,u) \quad\text{in } \mathbb{R}^N, $$ where $N\geq 2$, $\alpha\in(0,1)$, $(-\Delta)^\alpha$ stands for the fractional Laplacian, $V$ is a positive continuous potential, $f\in C(\mathbb{R}^N\times\mathbb{R},\mathbb{R})$. We establish criteria that guarantee the existence of infinitely many solutions by using the genus properties in critical point theory.

As a vital measure to obtain valuable information and intelligence, web news are flooding all corners of the Internet anytime, anywhere. Traditionally, templates or hand-designed features are utilized to extract the content from web pages, but these models have higher time cost and lower extensibility. Recently, many scholars leverage DOM-tree-based or text-density-based models to extract the contents which have better extensibility and lower time cost, but most of them are hard to extract the content accurately and completely and are easy to introduce the noises. In this paper, we propose a title-based web content extracting model TWCEM to extract the contents of each web page, which leverage the title information to extract the web content. Compared with other extraction model, TWCEM can filter the noises effectively and locate the content positions more accurately. In this experiment, we evaluate the proposed model on real-life websites, and TWCEM achieves state-of-the-art results and outperforms its competitors on both extraction performance and time cost.

Classification in sparsely labeled networks is challenging to traditional neighborhood-based methods due to the lack of labeled neighbors. In this paper, we propose a novel behavior-based collective classification (BCC) method to improve the classification performance in sparsely labeled networks. In BCC, nodes' behavior features are extracted and used to build latent relationships between labeled nodes and unknown ones. Since mining the latent links does not rely on the direct connection of nodes, decrease of labeled neighbors will have minor effect on classification results. In addition, the BCC method can also be applied to the analysis of networks with heterophily as the homophily assumption is no longer required. Experiments on various public data sets reveal that the proposed method can obtain competing performance in comparison with the other state-of-the-art methods either when the network is labeled sparsely or when homophily is low in the network.

A matrix modelling method based on the production structure is proposed and a general exergy matrix model for the multi-energy integrated system is established, which provides a simple and effective tool for the contribution rate calculation of the external fuel exergy to the multi-energy integrated system. The proposed method is verified by a case study of a solar-aided 600 MW coal-fired power generation system. The influence of two fuel product definitions and different simplified systems on the calculation results is analyzed. The results show that the contribution rate of solar exergy is 4.24 % when the product is defined as the output exergy. But the solar exergy contribution rate is only 1.77 % when the product is defined as the exergy increment and output power. Inappropriate system simplification can also cause the contribution rate of external fuel to deviate from the original value.

In this paper, we are concerned with the singular elliptic problems driven by the double phase operator and and Dirichlet boundary conditions. In view of the variational approach, we establish the existence of at least one nontrivial solution and two distinct nontrivial solutions under some general assumptions on the nonlinearity f. Here we use Ricceri’s variational principle and Bonanno’s three critical points theorem in order to overcome the lack of compactness.

In this paper we study a non-homogeneous eigenvalue problem involving variable growth conditions and a sign-changing potential. We prove that any $\lambda>0$ sufficiently small is an eigenvalue of the nonhomogeneous eigenvalue problem \begin{equation*} \begin{cases} -{\rm div}(a(|\nabla u|) \nabla u)=\lambda V(x)|u|^{q(x)-2}u, & {\rm in}\; \Omega,\\ u=0,& {\rm on}\; \partial\Omega.\\ \end{cases} \end{equation*} The proofs of the main results are based on Ekeland's variational principle.

In this paper, we consider a nonlocal p(x)-Kirchhoff problem with a p+-superlinear subcritical Caratheodory reaction term, which does not satisfy the Ambrosetti–Rabinowitz condition. Under some certain assumptions, we prove the existence of nontrivial solutions and many solutions. Our results are an improvement and generalization of the corresponding results obtained by Hamdani et al. (2020).

In this paper, we consider the existence and uniqueness of solutions for a quasilinear elliptic equation with a variable exponent and a reaction term depending on the gradient. Based on the surjectivity result for pseudomonotone operators, we prove the existence of at least one weak solution of such a problem. Furthermore, we obtain the uniqueness of the solution for the above problem under some considerations. Our results generalize and improve the existing results.

Open domain relation prediction is an important task in triples extraction. When faced with the task of constructing large-scale knowledge graph systems, with the exception of structured data, it is necessary to automatically extract triples from a large amount of unstructured text to expand entities and relations. Although a large number of English open relation prediction methods have achieved good performance, the high-performance system for open domain Chinese triples extraction remains undeveloped due to the lack of large-scale Chinese annotation corpora and the difficulty of Chinese language processing. In this paper, we propose an integrated open domain Chinese triples hierarchical extraction method (CTHE) to solve this problem, considering the advantages of Bi-LSTM-CRF and Att-Bi-GRU models based on the pre-trained BERT encoding model. This method can recognize the named entities from Chinese sentences to establish entity pairs, and implement hierarchical extraction of specific and open relations based on the user-defined schema library and attention mechanism. The experimental results demonstrate the effectiveness of this method, which achieved stable performance on the test dataset, and better precision and F1-score in comparison with state-of-the-art Chinese open domain triples extraction methods. Furthermore, a large-scale annotated dataset for a Chinese named entity recognition (NER) task is established, which provides support for research on Chinese NER tasks.

With the rapid growth of patent applications, it has become an urgent problem to automatically classify the accepted patent application documents accurately and quickly. Most previous patent automatic classification studies are based on feature engineering and traditional machine learning methods like SVM, and some even rely on the knowledge of domain experts, hence they suffer from low accuracy problem and have poor generalization ability. In this paper, we propose a patent automatic classification method via the symmetric hierarchical convolution neural network (CNN) named PAC-HCNN. We use the title and abstract of the patent as the input data, and then apply the word embedding technique to segment and vectorize the input data. Then we design a symmetric hierarchical CNN framework to classify the patents based on the word embeddings, which is much more efficient than traditional RNN models dealing with texts, meanwhile keeping the history and future information of the input sequence. We also add gated linear units (GLUs) and residual connection to help realize the deep CNN. Additionally, we equip our model with a self attention mechanism to address the long-term dependency problem. Experiments are performed on large-scale datasets for Chinese short text patent classification. Experimental results prove our proposed model’s effectiveness, and it performs better than other state-of-the-art models significantly and consistently on both fine-grained and coarse-grained classification.

This article concerns the existence and multiplicity of solutions to a class of p(x)-Laplacian equations. We introduce a revised Ambrosetti-Rabinowitz condition, and show that the problem has a nontrivial solution and infinitely many solutions.

In the process of knowledge graph construction, entity linking is a pivotal step, which maps mentions in text to a knowledge base. Existing models only utilize individual information to represent their latent features and ignore the correlation between entities and their mentions. Besides, in the process of entity feature extraction, only partial latent features, i.e., context features, are leveraged to extract latent features, and the pivotal entity structural features are ignored. In this paper, we propose SA-ESF, which leverages the symmetrical Bi-LSTM neural network with the double attention mechanism to calculate the correlation between mentions and entities in two aspects: (1) entity embeddings and mention context features; (2) mention embeddings and entity description features; furthermore, the context features, structural features, and entity ID feature are integrated to represent entity embeddings jointly. Finally, we leverage (1) the similarity score between each mention and its candidate entities and (2) the prior probability to calculate the final ranking results. The experimental results on nine benchmark dataset validate the performance of SA-ESF where the average F1 score is up to 0.866.