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Abstract This paper aims to construct a green environmental protection system by advancing database energy-saving techniques and optimizing the energy-saving mechanism against the backdrop of blockchain integration. The protocol classification of wireless sensor networks is examined within the context of the rapid growth of information technology. The analysis draws upon the database storage and sharing model and recent research examples that connect blockchain and database technology. Additionally, the paper investigates the blockchain data structure and storage path by analyzing the spatial–temporal correlation properties of blockchain data. The findings demonstrate that increasing the number of network nodes in the database system within a reasonable range can reduce the overall execution time. Additionally, as time progresses, the query traffic of the system database continues to increase. The research has practical implications in the domains of databases and the Internet of Things.

Summary: Objectives: Intraoperative frozen section (FS) analysis is pivotal in guiding surgical interventions for early-stage lung adenocarcinoma. However, the challenge arises when distinguishing between Minimally Invasive Adenocarcinoma (MIA) and Invasive Adenocarcinoma (IAC) poses diagnostic difficulties. This study investigates the prognosis and clinicopathological characteristics of patients encountering this diagnostic challenge. Methods: We conducted a retrospective analysis of 7082 intraoperative FSs from early-stage lung adenocarcinoma cases. The cases with pulmonary nodules within 3 cm and diagnosed as indeterminate FSs were included. We analyzed baseline data, computed tomography (CT) findings, and pathological characteristics. Prognostic data were obtained from patients with confirmed IAC diagnoses through final pathological examination. Results: Out of 7082 FSs, 551 cases presented challenges in distinguishing between MIA and IAC. Upon final pathological examination, 233 cases were identified as IAC, while 314 were classified as MIA. The median invasive pathological size in the IAC group was larger than that in the MIA group (0.6 cm vs 0.3 cm). 131 cases (56.2 %) with IAC underwent lobectomy, while 102 cases (43.8 %) underwent sub-lobar resection.Among the MIA cases, 220 cases (69.8 %) underwent sub-lobar resection, while 95 cases (30.2 %) underwent lobectomy. No recurrence and disease specific death was observed during the follow-up period, regardless of surgical strategy. Conclusions: Indeterminate intraoperative FSs, posing diagnostic challenges in distinguishing between MIA and IAC. Sub-lobar resection presented the same long term survival benefit compared with the lobectomy for indeterminate lung adenocarcinoma within 3 cm during intraoperative FSs.

Abstract Background Evidence has shown that oxidative stress induced by high glucose microenvironment in placenta of gestational diabetes mellitus (GDM) is indispensable to the progression of this condition. Adipokine chemerin was linked with GDM, yet the roles of chemerin in placental oxidative stress and its underlying effects on GDM in vivo remain elusive. Methods We firstly analyzed the disparities of oxidative stress levels in placenta between GDM and normoglycaemic pregnant women, and then added recombinant active chemerin to the high-glucose treated human trophoblastic cells to investigate effects of chemerin on reactive oxygen species (ROS), total antioxidant capacity (T-AOC) and intake of glucose. Finally, a GDM animal model induced by high-fat diet (HFD) was established and the impacts of chemerin on oxidative stress of placenta and fetal growth of GDM were explored. Results Analysis of human samples showed that the extent of lipid peroxidation in placenta was significantly elevated in GDM patients compared with their normoglycaemic counterparts. In the high glucose cell model, active chemerin lessened the content of ROS, heightened the index of T-AOC and stimulated glucose uptake in a concentration-dependent manner. Importantly, we successfully constructed a GDM mouse model through HFD. The treatment of chemerin was found to alleviate the high blood glucose levels in these HFD-fed pregnant mice and attenuate the excessive growth of their offspring. Our data also revealed that chemerin might counteract placental oxidative stress in HFD mice by improving the activity of superoxide dismutase. Conclusions The present study further elucidated the molecular biology of chemerin, which plays a pivotal role in ameliorating oxidative stress and hyperglycemia, resulting in improved fetal overgrowth in GDM. Graphical abstract

Abstract Currently, collaborative distribution models have not reached the optimal state of carbon emissions. The cost of additional low-carbon expenditures and the problem of carbon data verification have led to the lack of motivation for reducing emissions among collaborative distribution enterprises. Therefore, how to incentivize them to adopt the low-carbon model is crucial for achieving low-carbon goal. By relying on a government-led digital platform, this paper designs a dual low-carbon incentive strategy to encourage enterprise-alliance to adopt a low-carbon distribution model. In this paper, we first construct an evolutionary game model of the government, enterprise-alliance and end-users; then we explore the conditions of the three-party equilibrium evolution strategy by solving the model and analyzing the stability; and finally, we conduct simulation validation and results analysis with the help of MATLAB. In summary, we found that government punishment is more effective at regulating enterprise-alliance than reward. End-users’ behavior is affected by the costs they need to bear, and they no longer support enterprise-alliance to carry out collaborative low-carbon distribution above a certain threshold.

Abstract Background Currently, there is still a lack of valuable neuroimaging markers to assess the clinical severity of stroke patients with small artery occlusion (SAO). Quantitative susceptibility mapping (QSM) is a quantitative processing method for neuroradiological diagnostics. Gray matter (GM) volume changes in stroke patients are also proved to be associated with neurological deficits. This study aims to explore the predictive value of QSM and GM volume in neurological deficits of patients with SAO. Methods As neurological deficits, the National Institutes of Health Stroke Scale (NIHSS) was used. Sixty‐six SAO participants within 24 h of first onset were enrolled and divided into mild and moderate groups based on NIHSS. QSM values of infarct area and GM volume were calculated from magnetic resonance imaging (MRI) data. Two‐sample t‐tests were used to compare differences in QSM value and GM volume between the two groups, and the diagnostic efficacy of the combination of QSM value and GM volume was evaluated. Results The results revealed both the QSM value and GM volume within the infarct area of the moderate group were lower compared to the mild group. Moderate group exhibited lower GM volume in some specific gyrus compared with mild group in the case of voxel‐wise GM volume on whole‐brain voxel level. The support vector machine (SVM) classifier's analysis showed a high power for the combination of QSM value, GM volume within the infarct area, and voxel‐wise GM volume. Conclusion Our research first reported the combination of QSM value, GM volume within the infarct area, and voxel‐wise GM volume could be used to predict neurological impairment of patients with SAO, which provides new insights for further understanding the SAO stroke.

Introduction: Icaritin (ICT), a promising anti-hepatocellular carcinoma (HCC) prenylated flavonoid, is hindered from being applied due to its low water solubility and high lipophilicity in poorly differentiated HCC which is associated with upregulation of CD44 isoforms. Thus, hyaluronic acid (HA), a natural polysaccharide with high binding ability to CD44 receptors, was used to formulate a modified liposome as a novel targeted ICT-delivery system for HCC treatment.Methods: The ICT-Liposomes (Lip-ICT) with and without HA were prepared by a combined method of thin-film dispersion and post-insertion. The particle size, polydispersity (PDI), zeta potential, encapsulation efficacy (%EE), drug loading content (%DLC), and in vitro drug release profiles were investigated for physicochemical properties, whereas MTT assay was used to assess cytotoxic effects on HCC cells, HepG2, and Huh7 cells. Tumor bearing nude mice were used to evaluate the inhibitory effect of HA-Lip-ICT and Lip-ICT in vivo.Results: Lip-ICT and HA-Lip-ICT had an average particle size of 171.2 ± 1.2 nm and 208.0 ± 3.2 nm, with a zeta potential of −13.9 ± 0.83 and −24.8 ± 0.36, respectively. The PDI resulted from Lip-ICT and HA-Lip-ICT was 0.28 ± 0.02 and 0.26 ± 0.02, respectively. HA-Lip-ICT demonstrated higher in vitro drug release when pH was dropped from 7.4 to 5.5, The 12-h release rate of ICT from liposomes increased from 30% at pH7.4 to more than 60% at pH5.5. HA-Lip-ICT displayed higher toxicity than Lip-ICT in both HCC cells, especially Huh7with an IC50 of 34.15 ± 2.11 μM. The in vivo tissue distribution and anti-tumor experiments carried on tumor bearing nude mice indicated that HA-Lip- ICT exhibited higher tumor accumulation and achieved a tumor growth inhibition rate of 63.4%.Discussion: The nano-sized Lip-ICT was able to prolong the drug release time and showed long-term killing HCC cells ability. Following conjugation with HA, HA-Lip-ICT exhibited higher cytotoxicity, stronger tumor targeting, and tumor suppression abilities than Lip-ICT attributed to HA-CD44 ligand-receptor interaction, increasing the potential of ICT to treat HCC.

Query execution time prediction is essential for database query optimization tasks, such as query scheduling, progress monitoring, and resource allocation. In the query execution time prediction tasks, the query plan is often used as the modeling object of a prediction model. Although the learning-based prediction models have been proposed to capture plan features, there are two limitations need to be considered more. First, the parent–child dependencies between plan operators can be captured, but the operator’s branch independence cannot be distinguished. Second, each operator’s output row is its following operator input, but the data iterate transfer operations between operators are ignored. In this study, we propose a graph query execution time prediction model containing a plan module, a query module, a plan-query module, and a prediction module to improve prediction effectiveness. Specifically, the plan module is used to capture the data iterate transfer operations and distinguish independent of branch operators; the query module is used to learn features of query terms that have an influence on the composition of operators; the plan-query interaction module is used to learn the logical correlations of plan and query. The experiment on datasets proves the effectiveness of the operator iterate-aware and query-plan interaction method in our proposed graph query execution prediction model.

Abstract Background Paclobutrazol is widely used in the agricultural field. This study investigated the effects of seed priming with different concentrations of paclobutrazol on seedling quality, 2-acetyl-1-pyrroline (2-AP, a key aroma component of fragrant rice) biosynthesis, and related physiological and biochemical indicators in fragrant rice seedlings. Results The experiment is being conducted at the College of Agriculture, South China Agricultural University. In the experiment, three concentrations of paclobutrazol (Pac 1: 20 mg·L−1; Pac 2: 40 mg·L−1; Pac 3: 80 mg·L−1) were used to initiate the treatment of fragrant rice seeds, while water treatment was used as a control (CK). The results showed that compared with CK, paclobutrazol treatment reduced plant height, increased stem diameter, and increased fresh and dry weight of aromatic rice seedlings. Moreover, paclobutrazol treatment also increased the seedlings’ photosynthetic pigment content and net photosynthetic rate. Conclusions This study demonstrates that paclobutrazol primarily increases the content of proline by reducing the content of glutamate and down-regulating the expression of P5CS2, thereby promoting the conversion of proline to the aromatic substance 2-AP. Under the appropriate concentration of paclobutrazol (40 mg·L−1~80 mg·L−1), the seedling quality, stress resistance, and aroma of fragrant rice can be improved.

Abstract This study explores the analysis and modeling of energy consumption in the context of database workloads, aiming to develop an eco-friendly database management system (DBMS). It leverages vibration energy harvesting systems with self-sustaining wireless vibration sensors (WVSs) in combination with the least square support vector machine algorithm to establish an energy consumption model (ECM) for relational database workloads. Through experiments, the performance of self-sustaining WVS in providing power is validated, and the accuracy of the proposed ECM during the execution of Structured Query Language (SQL) statements is evaluated. The findings demonstrate that this approach can reliably predict the energy consumption of database workloads, with a maximum prediction error rate of 10% during SQL statement execution. Furthermore, the ECM developed for relational databases closely approximates actual energy consumption for query operations, with errors ranging from 1 to 4%. In most cases, the predictions are conservative, falling below the actual values. This finding underscores the high predictive accuracy of the ECM in anticipating relational database workloads and their associated energy consumption. Additionally, this paper delves into prediction accuracy under different types of operations and reveals that ECM excels in single-block read operations, outperforming multi-block read operations. ECM exhibits substantial accuracy in predicting energy consumption for SQL statements in sequential and random read modes, especially in specialized database management system environments, where the error rate for the sequential read model is lower. In comparison to alternative models, the proposed ECM offers superior precision. Furthermore, a noticeable correlation between model error and the volume of data processed by SQL statements is observed. In summary, the relational database ECM introduced in this paper provides accurate predictions of workload and database energy consumption, offering a theoretical foundation and practical guidance for the development of eco-friendly DBMS.

Focused on the problem that the planned routes cannot meet the personalized demand of different users in route planning of personalized self-driving tour, a keyword-aware optimal route planning method based on different user interests was proposed.Firstly, the road network information preprocessing model was set up and the road network information query graph was built by the road network information preprocessing algorithm.Secondly, the inverted index algorithm was proposed to prune the road network information query graph according to the personalized requirements from users, which improved the execution efficiency of keyword-aware optimal route planning method and reduced the memory cost of large-scale data processing effectively.Finally, the keyword-aware optimal route planning algorithm was proposed to realize personalized recommendation according to user interest by bidirectional parallel extension.The experimental results show that the method not only realizes the route planning to meet the individual needs of users but also improves the execution efficiency of the method through pruning and bidirectional parallel extension.

Background and objective The biological and molecular characteristics of spread through air spaces (STAS), a newly recognized invasive mode of lung cancer, remain controversial. The aim of this study was to investigate the clinicopathological features and molecular characteristics of STAS in patients with pulmonary adenocarcinoma. Methods A total of 694 resected invasive non-mucinous lung adenocarcinomas diagnosed by clinicopathology from July 2019 to March 2021 in the First Affiliated Hospital of Guangzhou Medical University were collected, and the relationship between STAS and clinicopathological factors was analyzed. The state of protein expression of anaplastic lymphoma kinase (ALK) was detected by immunohistochemical method. Epidermal growth factor receptor (EGFR) was detected by amplification refractory mutation system-polymerase chain reaction (ARMS-PCR). ROS proto-oncogene 1-receptor (ROS1) was detected by reverse transcription-PCR (RT-PCR). Results A total of 344 STAS positive cases and 350 STAS negative cases were collected. By univariate analysis, STAS positivity was statistically associated with tumor maximum diameter (P

Query execution time estimation is an essential task for databases, accurate estimation results can help administrators to manage and monitor systems. This study proposes an interaction-aware and dependency-aware query execution time estimation approach that utilizes graph neural networks to capture dependence and interaction relationships. We divide graph query execution time estimation tasks into three stages: workload generation and running, graph-based feature modeling and representation, training and estimation. Specifically, we generate query workloads and run them to collect the database and plan information when queries are executed. Then, the collected plan and database components are modeled into vertexes, the interaction and dependency between them are modeled into edges of graph-based feature representation. We develop an estimation model based on graph neural networks, in which the vertex embedding network is proposed to deal with the vertex heterogeneity, and the message passing network is proposed to aggregate the local representation into the global representation to obtain an embedding that can represent the higher-order feature information of the whole graph, and the estimation network is proposed to estimate execution times. The experiment results on datasets show that our estimation approach can improve estimation quality and outperform other estimation approaches in terms of estimation accuracy.

Abstract Outlier detection is an important topic in machine learning and has been used in a wide range of applications. Outliers are objects that are few in number and deviate from the majority of objects. As a result of these two properties, we show that outliers are susceptible to a mechanism called fluctuation. This article proposes a method called fluctuation-based outlier detection (FBOD) that achieves a low linear time complexity and detects outliers purely based on the concept of fluctuation without employing any distance, density or isolation measure. Fundamentally different from all existing methods. FBOD first converts the Euclidean structure datasets into graphs by using random links, then propagates the feature value according to the connection of the graph. Finally, by comparing the difference between the fluctuation of an object and its neighbors, FBOD determines the object with a larger difference as an outlier. The results of experiments comparing FBOD with eight state-of-the-art algorithms on eight real-worlds tabular datasets and three video datasets show that FBOD outperforms its competitors in the majority of cases and that FBOD has only 5% of the execution time of the fastest algorithm. The experiment codes are available at: https://github.com/FluctuationOD/Fluctuation-based-Outlier-Detection .

Abstract Background In plants, cytokinin is activated into trans-zeatin to fight abiotic stresses. However, the mechanism of the effect of trans-zeatin on 2-acetyl-1-pyrroline (2-AP) biosynthesis in fragrant rice has yet to be studied. The present study was conducted to explore the effects of exogenous trans-zeatin on enzymes activities, genes expression, and precursors involved in 2-AP biosynthesis and 2-AP contents as well as the seedling quality of a fragrant rice cultivar viz., Meixiangzhan2. Four concentrations of trans-zeatin solutions at 20, 40, and 80 μmol L− 1 (ZT1, ZT2, and ZT3) were sprayed onto rice seedlings. Results Compared to the control, trans-zeatin treatments showed significantly higher 2-AP contents of fragrant rice seedlings. Increased plant height and stem width were observed due to trans-zeatin treatments. The trans-zeatin application increased 1-pyrroline, methylglyoxal, proline, and P5C contents, enhanced P5CS and OAT activities, and reduced glutamic acid contents. In addition, expressions of ProDH, P5CS2, and DAO4 were comparatively higher under trans-zeatin treatments than CK in fragrant rice seedlings. Conclusions Overall, up-regulation of P5C, 1-pyrroline, and proline and down-regulation of glutamic acid under appropriate trans-zeatin concentrations (20 and 40 μmol L− 1) resulted in enhanced 2-AP biosynthesis in fragrant rice seedlings and 20–40 μmol L− 1 was considered as the suggested concentrations of trans-zeatin application in fragrant rice seedling.

Cardinality estimation is critical for optimizing database queries, and accurate results are essential for a good query plan. Traditional models use statistical principles but struggle with complex data associations. Learning-based methods solve these problems but need to improve accuracy and reduce parameter size, and adapt to multi-table training. Therefore, we propose the Convolutional Masked Autoencoder for Distribution Estimation(ConvMADE) model, which uses the Re-parameterization Convolution(RepConv) structure, which enhances the ability of the model to obtain data features, thereby improving the accuracy of cardinality estimation. At the same time, the DepthWise Multilayer Perceptron (DWMP) structure is added to reduce the number of model parameters, and each table is explicitly trained to improve the ability to capture multi-table data features. We compare the ConvMADE model with traditional and learning-based methods on the DMV and IMDB datasets. The results show that the performance of the ConvMADE model in both single-table and multi-table models is superior to other models, and the parameter amount of the ConvMADE model is much lower than that of the baseline model. The single table can be as low as 18% of the baseline model, the multi-table can be as low as 81%, and the multi-table average q-error is 27.2% lower than the baseline model.

Cardinality estimation is critical for database management systems (DBMSs) to execute query optimization tasks, which can guide the query optimizer in choosing the best execution plan. However, traditional cardinality estimation methods cannot provide accurate estimates because they cannot accurately capture the correlation between multiple tables. Several recent studies have revealed that learning-based cardinality estimation methods can address the shortcomings of traditional methods and provide more accurate estimates. However, the learning-based cardinality estimation methods still have large errors when an SQL query involves multiple tables or is very complex. To address this problem, we propose a sampling-based tree long short-term memory (TreeLSTM) neural network to model queries. The proposed model addresses the weakness of traditional methods when no sampled tuples match the predicates and considers the join relationship between multiple tables and the conjunction and disjunction operations between predicates. We construct subexpressions as trees using operator types between predicates and improve the performance and accuracy of cardinality estimation by capturing the join-crossing correlations between tables and the order dependencies between predicates. In addition, we construct a new loss function to overcome the drawback that Q-error cannot distinguish between large and small cardinalities. Extensive experimental results from real-world datasets show that our proposed model improves the estimation quality and outperforms traditional cardinality estimation methods and the other compared deep learning methods in three evaluation metrics: Q-error, MAE, and SMAPE.

The two-dimensional (2D) MXene (Ti3C2Tx) nanomaterial, a novel transition metal carbon/nitride 2D layered material, is receiving tremendous attention in the fields of energy, catalysis, and biomedicine. Membrane materials constructed on MXene-based have also been gradually attracting more and more attention in recent years. Herein, we review the preparation and physicochemical properties of 2D MXene nanomaterials, and construction and modification of MXene-based membranes. The advantages and disadvantages of 2D MXene membranes have been identified as well. Moreover, the application and potential used of 2D MXene membranes in water treatment process have been summarized, such as for oil/water separation, removal of small molecules and desalination process. Finally, the mechanism of MXene membranes for water treatment, and their future perspectives are presented for addressing the problems in swelling resistance and pollution resistance of 2D membranes in the water treatment field. Therefore, this review provides a reference for the design and application of high performance MXene-based membrane.

ABSTRACT Symbiont recognition is essential in many symbiotic relationships, especially for horizontally transferred symbionts. Therefore, how to find the right partner is a crucial challenge in these symbiotic relationships. Previous studies have demonstrated that both animals and plants have evolved various mechanisms to recognize their symbionts. However, studies about the mechanistic basis of establishing protist-bacterium symbioses are scarce. This study investigated this question using a social amoeba Dictyostelium discoideum and their Burkholderia symbionts. We found no evidence that D. discoideum hosts could distinguish different Burkholderia extracellularly in chemotaxis assays. Instead, symbiont-induced phagosome biogenesis contributed to the formation of social amoeba symbiosis, and D. discoideum hosts have a higher phagosome pH when carrying symbiotic Burkholderia than nonsymbiotic Burkholderia. In conclusion, the establishment of social amoeba symbiosis is not linked with extracellular discrimination but related to symbiont-induced phagosome biogenesis, which provides new insights into the mechanisms of endosymbiosis formation between protists and their symbionts. IMPORTANCE Protists are single-celled, extremely diverse eukaryotic microbes. Like animals and plants, they live with bacterial symbionts and have complex relationships. In protist-bacterium symbiosis, while some symbionts are strictly vertically transmitted, others need to reestablish and acquire symbionts from the environment frequently. However, the mechanistic basis of establishing protist-bacterium symbioses is mostly unclear. This study uses a novel amoeba-symbiont system to show that the establishment of this symbiosis is not linked with extracellular discrimination. Instead, symbiont-induced phagosome biogenesis contributes to the formation of social amoeba-bacterium symbiosis. This study increases our understanding of the mechanistic basis of establishing protist-bacterium symbioses.

Cloud Data Computing (CDC) is conducive to precise energy-saving management of user data centers based on the real-time energy consumption monitoring of Information Technology equipment. This work aims to obtain the most suitable energy-saving strategies to achieve safe, intelligent, and visualized energy management. First, the theory of Convolutional Neural Network (CNN) is discussed. Besides, an intelligent energy-saving model based on CNN is designed to ameliorate the variable energy consumption, load, and power consumption of the CDC data center. Then, the core idea of the policy gradient (PG) algorithm is introduced. In addition, a CDC task scheduling model is designed based on the PG algorithm, aiming at the uncertainty and volatility of the CDC scheduling tasks. Finally, the performance of different neural network models in the training process is analyzed from the perspective of total energy consumption and load optimization of the CDC center. At the same time, simulation is performed on the CDC task scheduling model based on the PG algorithm to analyze the task scheduling demand. The results demonstrate that the energy consumption of the CNN algorithm in the CDC energy-saving model is better than that of the Elman algorithm and the ecoCloud algorithm. Besides, the CNN algorithm reduces the number of virtual machine migrations in the CDC energy-saving model by 9.30% compared with the Elman algorithm. The Deep Deterministic Policy Gradient (DDPG) algorithm performs the best in task scheduling of the cloud data center, and the average response time of the DDPG algorithm is 141. In contrast, the Deep Q Network algorithm performs poorly. This paper proves that Deep Reinforcement Learning (DRL) and neural networks can reduce the energy consumption of CDC and improve the completion time of CDC tasks, offering a research reference for CDC resource scheduling.

Infant failure analyzing is an effective approach to improve production quality continuously. The root causes of infant failure have always been a puzzle to manufacturers. To satisfy the increasing demand for the fuzzy root cause analysis of product infant failure in the era of big data, a novel root cause identification approach based on the associated tree and fuzzy data envelopment analysis (DEA) is presented for product infant failure. First, to decrease fuzziness with regard to the mechanism of infant failure, the associated tree is adapted to guide the analysis process for possible root causes based on axiomatic domain mapping. Second, considering the fuzzy mechanism and massive data, the fuzzy DEA technique is adopted to cluster all the potential factors of functional parameters, physical parameters, and process parameters from big data regarding product life cycle. Third, the ranking method of decision-making unit efficiency in fuzzy DEA is used to model and rank the weight of each node in the established associated tree of infant failure. Finally, a case study of root cause identification for a typical infant failure of the vibration and noise of a washing machine is presented to demonstrate the feasibility and validity of the proposed method.

With database management systems becoming complex, predicting the execution time of graph queries before they are executed is one of the challenges for query scheduling, workload management, resource allocation, and progress monitoring. Through the comparison of query performance prediction methods, existing research works have solved such problems in traditional SQL queries, but they cannot be directly applied in Cypher queries on the Neo4j database. Additionally, most query performance prediction methods focus on measuring the relationship between correlation coefficients and retrieval performance. Inspired by machine-learning methods and graph query optimization technologies, we used the RBF neural network as a prediction model to train and predict the execution time of Cypher queries. Meanwhile, the corresponding query pattern features, graph data features, and query plan features were fused together and then used to train our prediction models. Furthermore, we also deployed a monitor node and designed a Cypher query benchmark for the database clusters to obtain the query plan information and native data store. The experimental results of four benchmarks showed that the average mean relative error of the RBF model reached 16.5% in the Northwind dataset, 12% in the FIFA2021 dataset, and 16.25% in the CORD-19 dataset. This experiment proves the effectiveness of our proposed approach on three real-world datasets.

Investigation of joint torque constraint compliance is of significance for robot manipulators especially working in complex environments. A lot of which is attributed to that, on the one hand, it is beneficial to the improvement of both safety and reliability of the mission execution. On the other hand, the energy consumption required by the robot to complete the desired mission can be reduced. Most existing schemes do not take the joint torque limit and other inherent physical structure limits in a manipulator into account at the same time. In addition, many unavoidable uncertainties such as the external environmental disturbance and/or electromagnetism interferences in the circuit system may influence the accuracy and effectiveness of the task execution for a robot. In this study, we cast light on the acceleration level control of redundant robot manipulators considering both four physical constraint limits and interference rejection. A robust unified quadratic-programming-based hybrid control scheme is proposed, where the joint torque constraints are converted as two inequality constraints based on the robots’ dynamics equation. A recurrent-neural-network-based controller is designed for solving the control variable. Numerical experiments performing in PUMA 560 manipulator and planer manipulator illustrate that a rational torque distribution is obtained among the joints and the considered physical structural vectors are all restricted to the respective constraint range. In addition, even disturbed by the noise, the manipulator still successfully tracks the desired trajectory under the proposed control scheme.

C20H22N2O6Mn, orthorhombic, Pccn (no. 56), a = 14.1678(16) Å, b = 14.3469(18) Å, c = 19.335(2) Å, V = 3930.1(8) Å3, Z = 8, Rgt(F) = 0.0504, wRref(F2) = 0.1441, T = 293(2) K.

C30H44N2O4Sn2, monoclinic, P21/c, a = 8.8810(8) Å, b = 16.8999(13) Å, c = 10.5705(10) Å, β = 92.0460(10)°, V = 1585.5(2) Å3, Z = 2, Rgt(F) = 0.0660, wRref(F2) = 0.1528, T = 298(2) K.

C10H6N4Cu2O4Br, orthorhombic, Pnma (no. 62), a = 6.7680(10) Å, b = 23.693(2) Å, c = 7.7301(11) Å, V = 1239.5(3) Å3, Z = 4, Rgt(F) = 0.0473, wRref(F2) = 0.1293, T = 298(2) K.

Clustering is widely used as an unsupervised learning algorithm. However, it is often necessary to manually enter the number of clusters, and the number of clusters has a great impact on the clustering effect. At present, researchers propose some algorithms to determine the number of clusters, but the results are not very good for determining the number of clusters of data sets with complex and scattered shapes. To solve these problems, this paper proposes using the Gaussian Kernel density estimation function to determine the maximum number of clusters, use the change of center point score to get the candidate set of center points, and further use the change of the minimum distance between center points to get the number of clusters. The experiment shows the validity and practicability of the proposed algorithm.

We report a case of 59‐year‐old female with solitary bilateral renal metastases after surgery of stage IA primary lung adenocarcinoma who underwent next‐generation sequencing (NGS) of both lesions. The patient received right upper lobectomy and lymph node dissection, which revealed primary invasive lung adenocarcinoma (pT1cN0M0, stage IA3). Two years following this, positron emission tomography–computed tomography (PET/CT) revealed multiple masses in both kidneys without other distant metastases, and ultrasonography‐guided puncture biopsy indicated the presence of metastatic lung adenocarcinoma. The NGS of both the primary and metastatic lesions revealed the co‐alteration of epidermal growth factor receptor (EGFR), RB transcriptional corepressor 1 (RB1), and mitogen‐activated protein kinase kinase 1 (MAP3K1), which is potentially associated with the risk of renal metastasis in early postoperative non‐small cell lung cancer. [ABSTRACT FROM AUTHOR]