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Lithium-ion batteries have been widely used as key carriers of electrochemical energy storage owing to their excellent performance. However, manufacturing defects or non-compliance with safety norms can easily trigger thermal runaway in lithium batteries, leading to safety accidents such as fires and explosions. This highlights the urgent need for advanced lithium battery fire suppression technology. This paper provides a comprehensive analysis of the fire characteristics and thermal runaway mechanisms of lithium batteries, based on a review of relevant literature. Additionally, it examines various fire extinguishing agents and strategies, highlighting the two primary requirements for managing and preventing thermal disasters associated with lithium batteries: fire suppression and cooling. For lithium battery fires, this study introduces and compares the fire extinguishing mechanisms, and the fire extinguishing and cooling efficiency of different types of extinguishing agents: gas-based, water-based, and solid agents. The comparison considers multiple perspectives, such as extinguishing and cooling capacity, insulation, toxicity, residue and cost. Notably, C6F12O exhibits excellent fire extinguishing capabilities, while water mist demonstrates superior cooling performance. To enhance fire extinguishing and cooling effects, new strategies and devices are analyzed, such as cooperative fire extinguishing, intermittent spray and fire extinguishing microcapsules. The study also summarizes the limitations of current extinguishing agents for suppressing lithium battery fires and the shortcomings of extinguishing strategies, offering several methods for improving the performance of extinguishing agents. Collaboration between early fire detection technology and fire suppression technology can achieve early warning and precise fire extinguishing effects. According to the specific characteristics of lithium battery fires, a comprehensive analysis from the perspectives of fire behavior, thermal behavior and system toxicity is essential. The development of an ideal extinguishing agent with high thermal conductivity, high insulation, clean, cost-effective and non-toxic byproducts can be achieved using additives or new formulations. By developing more efficient new extinguishing strategies, it is possible to effectively govern and prevent battery thermal disasters.

Abstract Effectively predicting the failure rate of train-controlled on-board equipment is of great significance for rationally allocating equipment spares, drawing up maintenance plans, and reducing the occurrence of failures. In order to tackle the problem of the intricate attributes and limited predictive precision of the sample data pertaining to the failure rate of on-board equipment, in this paper, a multivariate time series prediction model for the failure rate of train-controlled on-board equipment is based on the combination of multivariate variational modal decomposition (MVMD) graph neural network (GNN) and transformer. First, the original failure rate time series, air temperature, humidity and sand and dust time series are modally decomposed using MVMD, and the intrinsic modal functions (IMFs) of each series are obtained; then, the dynamic graph of the GNN network is defined according to the IMFs, and Transformer’s self-attention mechanism is utilised to capture the dynamic graph’s temporal and spatial dependencies. Finally, the failure rate is output through the feed-forward neural network prediction value. Experiments using the historical fault data of CTCS3-300T train-controlled on-board equipment are carried out to confirm the efficacy of the suggested method, and it is contrasted with other conventional machine learning techniques. The outcomes of the experiment show that compared with other train-controlled on-board equipment failure prediction models, the proposed method has a very good superiority, as evidenced by its mean absolute error (MAE) of 0.0489 and root mean square error (RMSE) of 0.0510, which is of certain reference value for equipment operation and maintenance.

This paper investigates the anti-windup synthesis problem for linear control systems subject to time-varying state delay and saturating actuators. To alleviate the redundant data transmission, the dynamic event-triggered mechanism is adopted. Moreover, to abate the inherent conservatism, novel delay-dependent sector conditions containing double integral terms are explored. Then, using augmented Lyapunov-Krasovskii functionals and several less conservative inequalities, delay-dependent anti-windup synthesis criteria are obtained in accordance with the feasibility of linear matrix inequalities. Subsequently, the optimization of the initial condition set is addressed. Finally, a simulation example illustrates the availability and technique advantages of the proposed results.

Objective and rationale: Children's clinical pain phenotypes are complex, and there is a lack of objective biological diagnostic markers and cognitive patterns. Detecting physiological signals through wearable devices simplifies disease diagnosis and holds the potential for remote medical applications. Method and results: This research established a pain recognition model based on AI skin potential (SP) signal analysis. A total of 237 subjects participated in this study, comprising 152 boys and 85 girls, ranging in age from 2 to 16 years old. Initially, we preprocessed SP signals and built datasets for pain and non-pain conditions, including 195 pain and 97 non-pain samples. Then, we applied wavelet transform (WT) to capture the time-frequency characteristics of the signals and extract energy features and created a feature set comprising 30 features and selected 10 most relevant ones using the “SelectKBest” function.We compared six algorithms, optimized their parameters, and evaluated the stability and fitting performance of each algorithm. The random forest (RF) algorithm emerged as the best, demonstrating significant performance in pain recognition with an accuracy of 80.3 % and a sensitivity of 92 %. The SP signals generated by children of different genders, ages, and needling positions during indwelling needle puncture were accurately recognized. Conclusion: We developed a comprehensive SP recognition model, innovatively employing WT for SP signal analysis. This time-frequency analysis method, by preserving low-frequency features, is particularly suitable for SP signals. By combining pain monitoring with SP signals and ML, subjective pain experiences are transformed into quantifiable data, achieving high accuracy and real-time measurement capabilities. These advantages provide valuable technical support for clinical pediatric pain management.

To evaluate four novel SARS-CoV-2 neutralizing antibody assay kits' application in neutralizing antibodies of population. Questionnaires from the voluntary participating researchers and selected the qualified questionnaires to analyse. For negative and positive coincidence rate, four novel SARS-Cov-2 neutralization antibody assay kits were tested. For within-run and between-run Precision verification study, four serum samples with two high and two low titer neutralizing antibodies were used to analyse. Based on the questionnaires, 175 qualified samples were divided into two groups. (1) negative neutralizing antibodies group: 31 samples had not been infected with the novel SARS-Cov-2 nor received the vaccine within the past one year; (2) positive neutralizing antibodies group: 144 samples were infected by COVID-19. There was 28 negative and 3 positive neutralizing antibodies of the individuals among the 31 negative samples which based on the questionnaires. The negative rates of 28 negative individules tested by GenScript, Vazyme and Hygeianey were 82.14 %, 60.71 % and 17.85 %, while the positive rates of the 147 positive samples were 93.87 %, 95.23 % and 100 %. The within-run coefficient of variations (C·V) of PBNAs, GenScript, Vazyme and Hygeianey were 11.49 %, 9.12 %, 7.97 % and 7.48 %, while the between-run coefficient of variations (C·V) were 21.37 %, 14.21 %, 12.29 % and 11.78 %. Due to the large within-run and between-run coefficient of variations, PBNAs was not suitable for large-scale promotion, while ELISAs could be leveraged for routine monitoring the titer of neutralizing antibodies against SARS-CoV-2.

Due to limited roof area, photovoltaic (PV) has gradually been installed on other facades of buildings. This research investigates the practical application of a lightweight PV curtain wall. We use EnergyPlus to build a base office building model of fit with a lightweight PV curtain wall. The performance of two typical lightweight PV curtain wall modules is evaluated in five sample Chinese cities of different climates. Simulations were carried out to determine the power generation of faux architectural material PV curtain wall modules (FAM PVCWMs) for the best cavity distance per facade in various cities. We discovered that, in Harbin, Beijing, and Shanghai, the capacity of PV curtain wall modules installed on the south facade is the best, while in Chengdu and Guangzhou, it is the west facade. We also analyzed the power generation and the impact on the indoor environment when installing semi-transparent PV curtain wall modules (ST PVCWMs). Compared with glass, the ST PVCWM’s power generation increased by at least 50%, while the glare index setpoint exceeded time reduced by at least 30.19%. Furthermore, when installed on the north facade of Chengdu and similar cities, it can ensure more than 50% of daylight indexed time and create a more favorable indoor environment.

This paper considers the local stabilization problem for a hyperchaotic finance system by using a time-delayed feedback controller based on discrete-time observations. The quadratic system theory is employed to represent the nonlinear finance system and a piecewise augmented discontinuous Lyapunov-Krasovskii functional is constructed to analyze the stability of the closed-loop system. By further incorporating some advanced integral inequalities, a stabilization criterion is proposed by means of the feasibility of a set of linear matrix inequalities under which the hyperchaotic finance system can be asymptotically stabilized for any initial condition satisfying certain constraint. As the by-product, a simplified criterion is also obtained for the case without time delay. Moreover, the optimization problems with respect to the domain of attraction are specially discussed, which are transformed into the minimization problems subject to linear matrix inequalities. Finally, numerical simulations are provided to illustrate the effectiveness of the derived results.

In aircraft panel assembly, the interference fit unevenly distributed along the axial direction of the rivet holes leads to an uneven stress–strain field around the rivet holes. The uneven stress–strain fields of single rivets, when accumulated through multiple rivets, result in overall bending and twisting deformation, severely impacting the assembly coordination quality of the panel. This study introduces a numerical model using a single row of multiple rivets to explore cumulative deformation during both sequential and changing order riveting. The results show that the deformation in sequential riveting is mainly bending-oriented towards the driven head side, with the maximum displacement exhibiting a fluctuating accumulation trend as the number of rivets increase. In contrast, a changing riveting order can lead to a reduction in deformation accumulation. To reveal the technological mechanism behind deformation accumulation during the riveting process, a model correlating to the residual stress field was established. It was indicated that the continuous increase in the maximum equivalent bending moment in the axial section is the primary factor leading to deformation accumulation. Based on this finding, a pre-bending suppression method aimed at reducing the local maximum equivalent bending moment was proposed. Numerical calculations and experimental results showed that the maximum displacement of the specimen was reduced by 73.27%, proving that this method can effectively suppress the cumulative increase in deformation.

The residual stress field induced by interference-fit riveting in aircraft panel structures significantly affects the fatigue performance around the rivet holes. Common residual stress analytical models often overlook the non-uniformity of interference between the rivet and the hole, which impacts the applicability of these models. Addressing this issue, an analytical model of residual stress around the rivet hole is proposed for a typical single-riveted structure based on the thick-walled cylinder theory and Lame’s equations, considering the non-uniform interference along the axis of the rivet hole. This novel model is then extended to multi-riveted structures in fuselage panels. Using vector synthesis, analytical models for single-row double-rivets and double-row quadruple-rivets configurations were derived. The established analytical models provide a three-dimensional characterization of the residual stress field in typical riveted structures. Finally, the accuracy of the model is verified through X-ray diffraction experiments and FEM simulation results.

In the study of remote sensing image classification, feature extraction and selection is an effective method to distinguish different classification targets. Constructing a high-quality spectral-spatial feature and feature combination has been a worthwhile topic for improving classification accuracy. In this context, this study constructed a spectral-spatial feature, namely the Pixel Neighbourhood Similarity (PNS) index. Meanwhile, the PNS index and 19 spectral, textural and terrain features were involved in the Correlation-based Feature Selection (CFS) algorithm for feature selection to generate a feature combination (PNS-CFS). To explore how PNS and PNS-CFS improve the classification accuracy of land types. The results show that: (1) The PNS index exhibited clear boundaries between different land types. The performance quality of PNS was relatively highest compared to other spectral-spatial features, namely the Vector Similarity (VS) index, the Change Vector Intensity (CVI) index and the Correlation (COR) index. (2) The Overall Accuracy (OA) of the PNS-CFS was 94.66% and 93.59% in study areas 1 and 2, respectively. These were 7.48% and 6.02% higher than the original image data (ORI) and 7.27% and 2.39% higher than the single-dimensional feature combination (SIN-CFS). Compared to the feature combinations of VS, CVI, and COR indices (VS-CFS, CVI-COM, COR-COM), PNS-CFS had the relatively highest performance and classification accuracy. The study demonstrated that the PNS index and PNS-CFS have a high potential for image classification.

Due to the extensive karst development zone in karst areas, the stability of the underground soil layer is poor. The support of foundation pits in this area will be affected by complex environmental factors, and if not handled properly, it will cause significant safety hazards and economic losses. In this paper, the three-dimensional finite element model of the complex foundation pit and adjacent foundation pit group was established with the help of Midas GTS NX numerical software, and numerical simulation was carried out for the whole foundation pit excavation and construction process, and the deformation results of the adjacent foundation pit support structure of the complex foundation pit group and the soil outside the foundation pit were obtained. The results show that the underground wall of the complex foundation pit shifted to the north under the action of buttresses, with a similar “cantilever” displacement pattern on the south side of the underground wall in the center, a “push-back displacement” on the north side of the underground wall in the center, and a “ventral” displacement pattern at the corners of the grounded wall and on the east and west sides. Combined with the field measurement data, the support piles in the internal support system have a “cantilever” displacement pattern under the condition of massive soil unloading in the vicinity of the foundation pit, while the supporting piles show a “parabolic” displacement pattern under the action of the corner internal support. The deformation characteristics of the foundation pit in the presence of adjacent pits are significantly different from the results based on empirical laws. The results of the above study will provide useful technical guidance for the safety of excavation support for foundation pit groups in complex environments and the reasonable control of the surrounding environment.

Few-shot relation extraction (FSRE) constitutes a critical task in natural language processing (NLP), involving learning relationship characteristics from limited instances to enable the accurate classification of new relations. The existing research primarily concentrates on using prototype networks for FSRE and enhancing their performance by incorporating external knowledge. However, these methods disregard the potential interactions among different prototype networks, and each prototype network can only learn and infer from its limited instances, which may limit the robustness and reliability of the prototype representations. To tackle the concerns outlined above, this paper introduces a novel prototype network called SACT (multi-head self-attention and contrastive-center loss), aimed at obtaining more comprehensive and precise interaction information from other prototype networks to bolster the reliability of the prototype network. Firstly, SACT employs a multi-head self-attention mechanism for capturing interaction information among different prototypes from traditional prototype networks, reducing the noise introduced by unknown categories with a small sample through information aggregation. Furthermore, SACT introduces a new loss function, the contrastive–center loss function, aimed at tightly clustering samples from a similar relationship category in the center of the feature space while dispersing samples from different relationship categories. Through extensive experiments on FSRE datasets, this paper demonstrates the outstanding performance of SACT, providing strong evidence for the effectiveness and practicality of SACT.

Abstract N6‐methyladenosine (m6A) RNA methylation is correlated with carcinogenesis and dynamically possessed through the m6A RNA methylation regulators. This paper aimed to explore 13 m6A RNA methylation regulators' role in gastrointestinal cancer (GIC) and determine the risk model and prognosis value of m6A RNA methylation regulators in GIC. We used several bioinformatics methods to identify the differential expression of m6A RNA methylation regulators in GIC, constructed a prognostic model, and carried out functional enrichment analysis. Eleven of 13 m6A RNA methylation regulators were differentially expressed in different clinicopathological characteristics of GIC, and m6A RNA methylation regulators were nearly associated with GIC. We constructed a risk model based on five m6A RNA methylation regulators (METTL3, FTO, YTHDF1, ZC3H13, and WTAP); the risk score is an independent prognosis biomarker. Moreover, the five m6A RNA methylation regulators can also forecast the 1‐, 3‐ and 5‐year overall survival through a nomogram. Furthermore, four hallmarks of oxidative phosphorylation, glycolysis, fatty acid metabolism, and cholesterol homoeostasis gene sets were significantly enriched in GIC. m6A RNA methylation regulators were related to the malignant clinicopathological characteristics of GIC and may be used for prognostic stratification and development of therapeutic strategies.

Shielded Metal Arc Welding (SMAW) is always used for the vertical welding of inner wall of LNG storage tanks in China, and there has been no successful case for the application of automatic welding technology to vertical welding so far. In this paper, an automatic Flux Cored Arc Welding (FCAW) technology with multi-layer, filament, short arc, circumferential swing and short pause on both sides of the groove was proposed through the automatic welding test of vertical welds of 9%Ni steel plates. Then, tests on mechanical properties were conducted for the welds made with this technology. The results show that the tensile, bending and low-temperature impact properties of the specimens can meet the requirements of relevant standards. In particular, the low-temperature Charpy impact energy of the heat affected area is 17.6% higher than that of SMAW. In addition, the vertical FCAW was applied to a 16×104 m3 LNG tank in a LNG receiving terminal of China, with the welding efficiency increased by 52% and the welding repair rate reduced by 2% in comparison to SMAW. In addition, the tank passed the hydrostatic test, kept stable in trial run, and was put into operation. So, the research results could provide some theoretical guidance and practical basis for the promotion of automatic welding for vertical welds of inner LNG storage tank in the future.

Abstract. Pulsed electric field (PEF) is a novel energy source by which high-voltage electric pulses are used to create irreversible electroporation. PEF is non-thermal and highly tissue-dependent in which specific targeting of the atrial myocardium is achieved and sparing of adjacent tissues is feasible, theoretically increasing the safety of the procedure, which could potentially break the trade-off between effective lesions and collateral damage and substantially improve risk-benefit ratios in atrial fibrillation (AF) ablation. Although recently published trials have shown a clear effect of AF ablation, large-scale clinical trials are lacking. Current clinical evidence has demonstrated significant efficacy in achieving durable pulmonary vein isolation without ablation-related adverse events. However, their putative benefits regarding efficacy, efficiency, and safety remain to be proven in randomized controlled trials.

This paper proposes a prey–predator model affected by fear effects and toxic substances. We used the Lipschitz condition to prove the uniqueness of the model solution and Laplace transform to prove the boundedness of the model solution. We used the fractional-order stability theorem to provide sufficient conditions for the local stability of equilibrium points, and selected fractional-order derivatives as parameters to perform Hopf bifurcation analysis on the system. Finally, the theoretical results are verified via numerical simulation. The results show that a value of α will affect the stability of the system and that the population size and the effect of toxic substances have a huge impact on the stability of the system.

Abstract Background Prolactinoma is a functional pituitary adenoma that secretes excessive prolactin. Dopamine agonists (DAs) such as bromocriptine (BRC) are the first-line treatment for prolactinomas, but the resistance rate is increasing year by year, creating a clinical challenge. Therefore, it is urgent to explore the molecular mechanism of bromocriptine resistance in prolactinomas. Activation of the P38 MAPK pathway affects multidrug resistance in tumours. Our previous studies have demonstrated that inhibiting MAPK14 can suppress the occurrence of prolactinoma, but the role of MAPK11/12/13/14 (p38 MAPK) signalling in dopamine agonist-resistant prolactinomas is still unclear. Methods A prolactinoma rat model was established to determine the effect of bromocriptine on MAPK11/12/13/14 signalling. DA-resistant GH3 cells and DA-sensitive MMQ cells were used, and the role of MAPK11/12/13/14 in bromocriptine-resistant prolactinomas was preliminarily verified by western blot, RT-qPCR, ELISA, flow cytometry and CCK-8 experiments. The effects of MAPK11 or MAPK14 on bromocriptine-resistant prolactinomas were further verified by siRNA transfection experiments. Results Bromocriptine was used to treat rat prolactinoma by upregulating DRD2 expression and downregulating the expression level of MAPK11/12/13/14 in vivo experiments. The in vitro experiments showed that GH3 cells are resistant to bromocriptine and that MMQ cells are sensitive to bromocriptine. Bromocriptine could significantly reduce the expression of MAPK12 and MAPK13 in GH3 cells and MMQ cells. Bromocriptine could significantly reduce the expression of MAPK11, MAPK14, NF-κB p65 and Bcl2 in MMQ but had no effect on MAPK11, MAPK14, NF-κB p65 and Bcl2 in GH3 cells. In addition, knockdown of MAPK11 and MAPK14 in GH3 cells by siRNA transfection reversed the resistance of GH3 cells to bromocriptine, and haloperidol (HAL) blocked the inhibitory effect of bromocriptine on MAPK14, MAPK11, and PRL in MMQ cells. Our findings show that MAPK11 and MAPK14 proteins are involved in bromocriptine resistance in prolactinomas. Conclusion Bromocriptine reduces the expression of MAPK11/12/13/14 in prolactinomas, and MAPK11 and MAPK14 are involved in bromocriptine resistance in prolactinomas by regulating apoptosis. Reducing the expression of MAPK11 or MAPK14 can reverse bromocriptine resistance in prolactinomas.

Primary liver cancer is the sixth most common cancer and the third leading cause of cancer mortality worldwide, hepatocellular carcinoma (HCC) is the most common type of liver cancer, accounting for 75%–85% of cases. The occurrence and progression of HCC involve multiple events. Pyroptosis is a gasdermins mediated programmed cell death and is intricately associated with cancerogenesis, including HCC. This review mainly concerns the recent research advances of the gasdermin family members in HCC. The biological roles and specific expression patterns of the family members are discussed, especially those that are involved in the regulatory pathways in the occurrence and progression of HCC. We provide the latest progress into the distinct molecular mechanisms of gasdermin family members involved in the occurrence and development of HCC.

ObjectiveWe investigate the characteristics of histological damage to myocardial cells in the ablation region and surrounding areas of the left ventricular epicardium in rabbits using our self-developed cardiac pulsed electric field (PEF) ablation instrument and ablation catheter.MethodsForty eight New Zealand rabbits underwent ablation on the left ventricular myocardium after open-heart exposure with a cardiac arrhythmia PEF ablation device and ablation catheter developed by the Medical Translation Laboratory of Pulsed Electric Field Technology in Zhejiang Province. The ablation parameters were set as biphasic electrical pulses; voltage, ±800 V; pulse width, 10 μs; interphase delay, 500 us. Six rabbits were included in the sham group and 42 other rabbits were randomly divided into immediately, 6-h, 1-, 3-day, 1-, 2-, and 4-week post-ablation groups, with six rabbits in each group. Creatine kinase- (CK)-MB isoenzyme (CK-MB), aspartate aminotransferase (AST), and lactate dehydrogenase (LDH) levels were measured before and at different time points after PEF ablation to analyze their dynamic evolution. Masson staining of tissue block sections of left ventricular myocardial ablation and adjacent tissue heart specimens was performed, and the occurrence of TUNEL apoptosis in myocardium tissue was analyzed.ResultsAll rabbits completed the PEF ablation procedure and the follow-up process. After PEF ablation, the levels of cardiac enzymes, including CK-MB, CK, and AST, increased significantly, peaking 1–3 days after the procedure. In particular, those of CK and CK-MB increased by 15–20 times but returned to the preoperative level after 2 weeks. Based on general observation, it was found that the myocardium in the ablation area was swollen immediately after PEF ablation. Masson staining analysis revealed that cardiomyocytes were broken and infiltrated by erythrocytes after 6 h. After 1 day, the cells started to experience atrophy and necrosis; after 3 days, fibrotic replacement of the necrotic area became obvious. Then, by 4 weeks, the myocardial cells were completely replaced by hyperplasia. Apoptosis occurred significantly at 6 h and peaked at 24 h post-ablation, demonstrating a 37.7-fold increase; apoptotic cell counts decreased significantly at 3 days post-ablation, and no significant apoptotic cardiomyocytes were seen after 1 week.ConclusionAfter PEF ablation, cardiomyocytes showed apoptotic process and dyed, at least partially, through a secondary necrosis, the ablation boundary was clear, the ablation area was replaced by structurally intact fibroblasts, no island myocardium tissue were seen, and the ablation area vessels and nerves were not affected.

Sparse unmixing plays a crucial role in the field of hyperspectral image unmixing technology, leveraging the availability of pre-existing endmember spectral libraries. In recent years, there has been a growing trend in incorporating spatial information from hyperspectral images into sparse unmixing models. There is a strong spatial correlation between pixels in hyperspectral images (that is, the spatial information is very rich), and many sparse unmixing algorithms take advantage of this to improve the sparse unmixing effect. Since hyperspectral images are susceptible to noise, the feature separability of ground objects is reduced, which makes most sparse unmixing methods and models face the risk of degradation or even failure. To address this challenge, a novel robust dual spatial weighted sparse unmixing algorithm (RDSWSU) has been proposed for hyperspectral image unmixing. This algorithm effectively utilizes the spatial information present in the hyperspectral images to mitigate the impact of noise during the unmixing process. For the proposed RDSWSU algorithm, which is based on ℓ1 sparse unmixing framework, a pre-calculated superpixel spatial weighting factor is used to smooth the noise, so as to maintain the original spatial structure of hyperspectral images. The RDSWSU algorithm, which builds upon the ℓ1 sparse unmixing framework, employs a pre-calculated spatial weighting factor at the superpixel level. This factor aids in noise smoothing and helps preserve the inherent spatial structure of hyperspectral images throughout the unmixing process. Additionally, another spatial weighting factor is utilized in the RDSWSU algorithm to capture the local smoothness of abundance maps at the sub-region level. This factor helps enhance the representation of piecewise smooth variations within different regions of the hyperspectral image. Specifically, the combination of these two spatial weighting factors in the RDSWSU algorithm results in an enhanced sparsity of the abundance matrix. The RDSWSU algorithm, which is a sparse unmixing model, offers an effective solution using the alternating direction method of multiplier (ADMM) with reduced requirements for tuning the regularization parameter. The proposed RDSWSU method outperforms other advanced sparse unmixing algorithms in terms of unmixing performance, as demonstrated by the experimental results on synthetic and real hyperspectral datasets.

This paper considers the $ H_\infty $ control problem for a hyperchaotic system with energy-bounded disturbance under the delayed feedback controller. Using the quadratic system theory, an augmented Lyapunov functional, some integral inequalities and rigorous mathematical derivations, a sufficient condition is first established by using linear matrix inequalities under which the closed-loop system can achieve some desirable performances including the boundedness, the $ H_\infty $ performance and the asymptotic stability. Subsequently, several convex optimization problems are formulated to obtain the optimal performance indices. Finally, numerical simulations are presented to illustrate the effectiveness of the obtained results.

Abstract Background Leguminous plants alter patterns of gene expression in response to symbiotic colonization and infection by their cognate rhizobial bacteria, but the extent of the transcriptomic response has rarely been examined below the species level. Here we describe the identification of 12 rhizobial biotypes of Ensifer meliloti, which form nitrogen-fixing nodules in the roots of alfalfa (Medicago sativa L.), followed by a comparative RNA-seq analysis of four alfalfa cultivars each inoculated with two E. meliloti strains varying in symbiotic performance and phylogenetic relatedness. Results Rhizobial biotypes were identified on the basis of their symbiotic performance, particularly shoot dry weight. Differentially expressed genes (DEGs) and metabolic pathways were determined by comparing the RNA-seq data with that of the uninoculated control plant. Significant differences were found between DEGs generated in each cultivar with the inoculation of two rhizobial strains in comparison (P

Abstract In this paper, we consider the local control problem for a chaotic finance system via the time-delayed feedback. Using the Lyapunov–Krasovskii stability theorem, the quadratic system theory, some integral inequalities, and rigorous mathematical analysis, we obtain a local stabilization condition by means of linear matrix inequalities. Then we discuss the estimate of the region of asymptotic stability and give the corresponding optimization problem. Also, we address the local control problem under the nondelayed feedback. Finally, we present numerical simulations to show the effectiveness of the proposed results.

Abstract This study aimed to establish and compare models of mammary gland hyperplasia (MGH) with hyperprolactinemia (HPRL) using two different methods. The models provide information on the relationship between mammary gland hyperplasia and associated hormones. Model A was constructed using intramuscular injections of estradiol benzoate injection (EBI), followed by progesterone (P), and then metoclopramide dihydrochloride (MDI). Model B was designed by administering MDI, follow by EBI, and then P intramuscularly. Model B showed higher MGH progression compared with model A. Notably, increase in estradiol (E2) was negatively correlated with prolactin (PRL) secretion. However, PRL levels in model B were significantly higher compared with the levels in model A. Estrogen (ER), prolactin receptor (PRLR), and progesterone receptor (PR) mRNA and protein expression levels in model B rats were positively correlated with changes in the corresponding hormone levels. However, E2, P, and PRL levels in model A showed no direct relationship with levels of the mRNAs of related hormones and protein expression levels. Our results suggest that model B is an appropriate model of MGH with HPRL that can be used to perform further studies about the interactions of the E2, P, and PRL hormones in this disorder.

Since the facts in the knowledge graph (KG) cannot be updated automatically over time, some facts have temporal conflicts. To discover and eliminate the temporal conflicts in the KG, this paper proposes a novel temporal conflict resolution method based on temporal KG embedding (named TeCre). Firstly, the predicate relation and timestamp information of time series are incorporated into the entity–relation embedding representation by leveraging the temporal KG embedding (KGE) method. Then, taking into account the chronological sequence of the evolution of the entity–relation representation over time, TeCre constrains the temporal relation in the KG according to the principles of time disjoint, time precedence, and time mutually exclusive constraints. Besides that, TeCre further considers the sequence vectorization of predicate relation to discover the temporal conflict facts in the KG. Finally, to eliminate the temporal conflict facts, TeCre deletes the tail entities of the temporal conflict facts, and employs the link prediction method to complete the missing tail entities according to the output of the score function based on the entity–relation embedding. Experimental results on four public datasets show that TeCre is significantly better than the state-of-the-art temporal KG conflict resolution model. The mean reciprocal ranking (MRR) and Hits@10 of TeCre are at least 5.46% and 3.2% higher than the baseline methods, respectively.

BackgroundProlactinoma is the most common type of pituitary tumors, and its resultant tumor occupying and hormone disturbance greatly damage the health of patients. In this study, we investigated a protein kinase-PDZ Binding Kinase (PBK)/T-LAK Cell-Originated Protein Kinase (TOPK) as a candidate protein regulating prolactin (PRL) secretion and tumor growth of prolactinomas.MethodsDownloaded prolactinoma transcriptome dataset from Gene Expression Omnibus (GEO) database, and screened differentially expressed genes (DEGs) between normal pituitary tissues and prolactinoma tissues. Then, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were performed, a protein-protein interaction (PPI) network was constructed and the hub genes were identified. After a literature search, TOPK was presumed as an candidate target regulating the prolactinoma. We found a specific inhibitor of TOPK to investigate its effects on the proliferation, migration, apoptosis and PRL secretion of pituitary tumor cells. Finally, the regulation of TOPK inhibitor on its downstream target-p38 Mitogen Activated Protein Kinase (p38 MAPK) was detected to explore the potential mechanism.ResultsA total of 361 DEGs were identified, and 20 hub genes were screened out. TOPK inhibitor HI-TOPK-032 could suppress the proliferation & migration and induce apoptosis of pituitary tumor cells in vitro, and reduce PRL secretion and tumor growth in vivo. HI-TOPK-032 also inhibited the phosphorylation level of the downstream target p38 MAPK, suggesting that TOPK inhibitors regulate the development of prolactinoma by mediating p38 MAPK.ConclusionOur study of identification and functional validation of TOPK suggests that this candidate can be a promising molecular target for prolactinoma treatment.

This paper focuses on a two-dimensional completely resonant beam equation with a quintic nonlinear term. This means studying utt+Δ2u+εfu=0,x∈T2,t∈ℝ, under periodic boundary conditions, where ε is a small positive parameter and fu is a real analytic odd function of the form fu=f5u5+∑i^≥3f2i^+1u2i∧+1,f5≠0. It is proved that the equation admits small-amplitude, Whitney smooth, linearly stable quasiperiodic solutions on the phase-flow invariant subspace ℤ†2=r=r1,r2,r1∈4ℤ−1,r2∈4ℤ. Firstly, the corresponding Hamiltonian system of the equation is transformed into an angle-dependent block-diagonal normal form by using symplectic transformation, which can be achieved by selecting the appropriate tangential position. Finally, the existence of a class of invariant tori is proved, which implies the existence of quasiperiodic solutions for most values of frequency vector by an abstract KAM (Kolmogorov-Arnold-Moser) theorem for infinite dimensional Hamiltonian systems.

Malt is the mature fruit of Hordeum vulgare L. after germination and drying and has been applied for treatment female abnormal galactorrhea. Previous studies have showed total alkaloids in malt have anti-HPRL effect. However, total alkaloids of malt change with the growth cycle, and the specified levels of total alkaloids in different bud length of malt have not been decided. To determine the definitive level of total alkaloids in different buds of malt and the most suitable bud length for clinical application by comparing effects on hyperprolactinemia rat. During the budding of malt, the content of total alkaloids first increased and then decreased, and it peaked at a bud length of 0.75 cm. Treated the HPRL model rats with different buds of malt, the PRL level was decreased, the number of PRLpositive cells and the mRNA expression level in the pituitary were significantly declined, and the number of dopamine D1 and D2 receptors in the hypothalamus was increased. The above changes were most significant in 0.75 cm bud. These results suggest that in terms of the content of effective substance and the effects on HPRL model rats, a malt bud length of 0.75 cm is optimal for clinical application.

Network is a powerful language to represent relational data. One way to understand network is to analyze groups of nodes which share same properties or functions. The task of discovering such groups is known as community detection. The community detection in real-life networks, the majority of which are weighted temporal text networks, is confronted with two main problems - how to model the weight of edges and how to exploit the temporal information. Existing works either ignore the edge weight or utilize it in graph measures like modularity, which lacks scalability. And currently the common-used method involving temporal information is to discretize the time, which leads to series of problems. We are thus motivated to present a new method to encode the edge weight and temporal information. A probabilistic generative model, named Custom Temporal Community Detection (CTCD) is introduced, which views the link between two nodes as a weighted edge with several time stamps. Our model utilizes network, semantic and temporal information simultaneously to extract temporal community affiliations for individual user, influence strength across communities and temporal interested topic in each community. An efficient inference method, which scales linearly, and corresponding parallel implementation are proposed to adapt to large datasets. Through the knowledge extracted by CTCD, we are able to spot the community shift of the individual user, to which little attention has been given, and employ it to track the development of the communities over time. Moreover, experiments on two large-scale weighted temporal text networks show that CTCD gains significant improvement over state-of-the-art methods on a series of tasks.

This paper aims to study the structure of the singular manifold of a fragment of protein backbone using the method of robot kinematics. By modeling chemical bonds as rigid links, and torsional motion around chemical bonds as revolute joints, a fragment of protein backbone turns into a kinematic linkage. The singular manifold of the forward kinematic map of this linkage contains important information about the structure of its inverse kinematics map, and the results also apply to the study of the conformation space of protein loops. By splitting the forward kinematic map into an orientation and position map, we derive equations of the singular manifolds for both maps and analyze their geometric and combinatorial structures. We discuss the application of singular manifolds in several problems of computational biology, including minimal parametrization of protein conformation space and conformation space sampling of protein loops.

Abstract This paper is concerned with the local asymptotic stability problem for a competitive Lotka–Volterra system with time-varying delays. By constructing the novel augmented Lyapunov–Krasovskii functionals and using the Wirtinger integral inequality, some alternative stability criteria are obtained by means of linear matrix inequalities. In particular, our constructed functionals contain some cross terms related to four different time delays. The proposed stability conditions in this paper are less conservative as compared with the most existing ones due to using some advanced techniques. Finally, a numerical example illustrates the effectiveness and improvement of the obtained results.

Objective: The purpose of this study is to evaluate the preliminary safety and effect of a pulsed electric field (PEF) ablation system. Methods: The pulmonary veins (PVs) and superior vena cava (SVC) were isolated with the pulsed field ablation (PFA) system, which included a PEF generator and an electrode. The effects of PFA were investigated in six porcines using a novel circular catheter with combined functions (mapping/ablation) designed to work with a cardiac mapping system. The PEF generator delivered a train of biphasic pulsed electric pulses with a high amplitude (800–2000 V) and short pulse duration. The voltage mapping, PVs and SVC potentials, ostial diameters, and phrenic nerve and esophagus viability data were collected 4 weeks later, after which the animals were subsequently euthanized for gross histopathology analysis. Results: PFA 100% isolated the PVs and SVC with four applications with a mean pulse number of 100–150 pulses, causing no muscle convulsion. PFA does not cause PV stenosis or phrenic nerve dysfunction. Histological analysis confirmed 100% transmurally without any venous stenoses or phrenic injuries. Pathology follow-up showed that PFA had selectively ablated cardiomyocytes but spared blood vessels, the esophagus, and phrenic nerves; after ablation, the myocardial tissue showed homogeneous fibrosis. Conclusion: The PFA system is safe and feasible in the preliminary porcine model, which can effectively isolate PVs and SVCs. Transmural tissue damage can be achieved without phrenic palsy or stenosis.

The quick and precise assessment of rice distribution by remote sensing technology is important for agricultural development. However, mountain rice is limited by the complex terrain, and its distribution is fragmented. Therefore, it is necessary to fully use the abundant spatial, temporal, and spectral information of remote sensing imagery. This study extracted 22 classification features from Sentinel-2 imagery (spectral features, texture features, terrain features, and a custom spectral-spatial feature). A feature selection method based on the optimal extraction period of features (OPFSM) was constructed, and a multitemporal feature combination (MC) was generated based on the separability of different vegetation types in different periods. Finally, the extraction accuracy of MC for mountain rice was explored using Random Forest (RF), CatBoost, and ExtraTrees (ET) machine learning algorithms. The results show that MC improved the overall accuracy (OA) by 3–6% when compared to the feature combinations in each rice growth stage, and by 7–14% when compared to the original images. MC based on the ET classifier (MC-ET) performed the best for rice extraction, with the OA of 86%, Kappa coefficient of 0.81, and F1 score of 0.95 for rice. The study demonstrated that OPFSM could be used as a reference for selecting multitemporal features, and the MC-ET classification scheme has high application potential for mountain rice extraction.

In this article, we investigate a sideways problem of the non-homogeneous time-fractional diffusion equation, which is highly ill-posed. Such a model is obtained from the classical non-homogeneous sideways heat equation by replacing the first-order time derivative by the Caputo fractional derivative. We achieve the result of conditional stability under an a priori assumption. Two regularization strategies, based on the truncation of high frequency components, are constructed for solving the inverse problem in the presence of noisy data, and the corresponding error estimates are proved.

The increasing peak-valley load difference, the increasing scale of external power and the priority of new energy consumption in Shanghai required the flexibility of peak load regulation. In this paper, the characteristics of Shanghai power load were analyzed systematically, and the optimization strategy and implementation scheme of Shanghai power supply structure were analyzed in detail from the perspective of enhancing the flexibility of peak load regulation. The results show that the installation of power supply in Shanghai in the future should focus on increasing gas turbine generating unit and improving peak load regulation performance. The strategy of replacing coal-fired generating units with gas turbine for Shanghai's gap capacity consists of the construction of gas turbine at the original site of retired coal machine and the planning construction of gas turbine power points in the city's outskirts. Shidongkou power plant had the construction condition of gas turbine at the original site of existing small coal-fired power plants. Jinshan, Qingpu, Jiading, and Pudong are suitable for the new gas turbine construction.

The inverse and ill-posed problem of determining a solute concentration for the two-dimensional nonhomogeneous fractional diffusion equation is investigated. This model is much worse than its homogeneous counterpart as the source term appears. We propose a modified kernel regularization technique for the stable numerical reconstruction of the solution. The convergence estimates under both a priori and a posteriori parameter choice rules are proven.

The optimal scheduling of multi-type combine harvesters is a crucial topic in improving the operating efficiency of combine harvesters. Due to the NP-hard property of this problem, developing appropriate optimization approaches is an intractable task. The multi-type combine harvesters scheduling problem considered in this paper deals with the question of how a given set of harvesting tasks should be assigned to each combine harvester, such that the total cost is comprehensively minimized. In this paper, a novel multi-type combine harvesters scheduling problem is first formulated as a constrained optimization problem. Then, a whale optimization algorithm (WOA) including an opposition-based learning search operator, adaptive convergence factor and heuristic mutation, namely, MWOA, is proposed and evaluated based on benchmark functions and comprehensive computational studies. Finally, the proposed intelligent approach is used to solve the multi-type combine harvesters scheduling problem. The experimental results prove the superiority of the MWOA in terms of solution quality and convergence speed both in the benchmark test and for solving the complex multi-type combine harvester scheduling problem.

Take Zhejiang Mobile as an example,the big data capability was demonstrated from three aspects:platform,data and technology.Combined with the enterprise practice and exploration,industry cases were shared in terms of credit score and site selection.

This paper is concerned with the finite-time $ H_\infty $ control problem for a chaotic finance system subject to energy-bounded external disturbance via delayed feedback controller. Based on an augmented Lyapunov-like functional, the Wirtinger-based inequality and the novel finite-time boundedness analysis approach, a delay-dependent sufficient condition is proposed in terms of linear matrix inequalities such that the closed-loop error system is finite-time bounded with a prescribed $ H_\infty $ performance level. By simple modification, the sufficient condition based on the standard $ H_\infty $ control design is also obtained. Finally, simulation results are given to demonstrate the effectiveness and advantages of our proposed results.

Abstract The nanosecond pulsed electric field (nsPEF) is investigated as an alternative plan for benign hepatic hydatid cyst. Altogether 72 C57B6 mice were included. Normal group (n = 12) had no parasite injection and the other 60 mice were used to induce hydatid cyst in liver by injecting protoscolices in portal vein. The liver hydatid cysts were exposed to nsPEF with different doses and then follow up. The standard surgery was performed as positive control. The hydatid cyst growth was monitored by ultrasound; the morphology was checked by gross anatomy and pathology was tested by H&E stain. In nsPEF-treated groups no hepatic failure nor bleeding were observed. As a comparison, in the surgery group, high post-treatment complications occurred (50%). Significant parasite growth inhibition was seen in high nsPEF dose group as compared with control group (P

A long-standing problem in biology, economics, and social sciences is to understand the conditions required for the emergence and maintenance of cooperation in evolving populations. This paper investigates how to promote the evolution of cooperation in the Prisoner’s Dilemma game (PDG). Differing from previous approaches, we not only propose a tag-based control (TBC) mechanism but also look at how the evolution of cooperation by TBC can be successfully promoted. The effect of TBC on the evolutionary process of cooperation shows that it can both reduce the payoff of defectors and inhibit defection; although when the cooperation rate is high, TBC will also reduce the payoff of cooperators unless the identified rate of the TBC is large enough. An optimal timing control (OTC) of switched replicator dynamics is designed to consider the control costs, the cooperation rate at terminal time, and the cooperator’s payoff. The results show that the switching control (SC) between an optimal identified rate control of the TBC and no TBC can properly not only maintain a high cooperation rate but also greatly enhance the payoff of the cooperators. Our results provide valuable insights for some clusters, for example, logistics parks and government, to regard the decision to promote cooperation.

The DEM generalization is the basis of multi-dimensional observation, the basis of expressing and analyzing the terrain. DEM is also the core of building the Multi-Scale Geographic Database. Thus, many researchers have studied both the theory and the method of DEM generalization. This paper proposed a new method of generalizing terrain, which extracts feature points based on the tree model construction which considering the nested relationship of watershed characteristics. The paper used the 5 m resolution DEM of the Jiuyuan gully watersheds in the Loess Plateau as the original data and extracted the feature points in every single watershed to reconstruct the DEM. The paper has achieved generalization from 1:10000 DEM to 1:50000 DEM by computing the best threshold. The best threshold is 0.06. In the last part of the paper, the height accuracy of the generalized DEM is analyzed by comparing it with some other classic methods, such as aggregation, resample, and VIP based on the original 1:50000 DEM. The outcome shows that the method performed well. The method can choose the best threshold according to the target generalization scale to decide the density of the feature points in the watershed. Meanwhile, this method can reserve the skeleton of the terrain, which can meet the needs of different levels of generalization. Additionally, through overlapped contour contrast, elevation statistical parameters and slope and aspect analysis, we found out that the W8D algorithm performed well and effectively in terrain representation.

This paper considers the robust stability analysis problem for a class of uncertain stochastic neural net- works with time-varying delay. Based on the Lyapunov functional method, and by resorting to the new technique for estimating the upper bound of the stochastic derivative of Lyapunov functionals, the novel asymptotic stability criteria are obatined in terms of Linear matrix inequalities (LMIs). Two numerical examples are presented to show the effectiveness and the less conservativeness of the proposed method. Keywords: robust stability, stochastic neural networks, time-varying delay, linear matrix inequalities.

This paper considers the delay-dependent exponential stability for discrete-time BAM neural networks with time-varying delays. By constructing the new Lyapunov functional, the improved delay-dependent exponential stability criterion is derived in terms of linear matrix inequality (LMI). Moreover, in order to reduce the conservativeness, some slack matrices are introduced in this paper. Two numerical examples are presented to show the effectiveness and less conservativeness of the proposed method.