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Multimodal knowledge graph link prediction aims to improve the accuracy and efficiency of link prediction tasks for multimodal data. However, for complex multimodal information and sparse training data, it is usually difficult to achieve interpretability and high accuracy simultaneously for most methods. To address this difficulty, a new model is developed in this paper, namely Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modeling (IMKGA-SM). First, a multi-modal fine-grained fusion method is proposed, and Vgg16 and Optical Character Recognition (OCR) techniques are adopted to effectively extract text information from images and images. Then, the knowledge graph link prediction task is modelled as an offline reinforcement learning Markov decision model, which is then abstracted into a unified sequence framework. An interactive perception-based reward expectation mechanism and a special causal masking mechanism are designed, which "converts" the query into an inference path. Then, an autoregressive dynamic gradient adjustment mechanism is proposed to alleviate the insufficient problem of multimodal optimization. Finally, two datasets are adopted for experiments, and the popular SOTA baselines are used for comparison. The results show that the developed IMKGA-SM achieves much better performance than SOTA baselines on multimodal link prediction datasets of different sizes., Comment: 12pages,10 figures

Background: CXCR (CXC Chemokine Receptor) is a complex of the immune-associated protein involved in tumor activation, invasion, migration, and angiogenesis through various chemical signals in the tumor microenvironment (TME). However, significant prognostic characteristics of CXCR members and their impact on the occurrence and progression of glioma have not yet been fully elucidated.Methods: In this research, we used Oncomine, TCGA, GTEx, and CGGA databases to analyze the transcription and survival data of glioma patients. Afterward, the influence of CXCR members on the TME was explored using comprehensive bioinformatics analysis.Results: The mRNA expression levels of CXCR1/2/3/4/7 were significantly up-regulated in glioma than in normal samples, whereas the mRNA expression level of CXCR5 was decreased. We then summarized the genetic alteration landscape of CXCR and identified two molecular subtypes based on CXCR expression patterns in glioma. The characteristics of CXCRs were also investigated, including the clinicopathological parameters, TME cell infiltration, and prognosis of patients with glioma. After Lasso and multivariable Cox regression, a CR-Score for predicting overall survival (OS) was constructed and the predictive performance of the signature was validated. The high-risk group was a significantly poorer prognostic group than the low-risk group as judged by the CR-Score (TCGA cohort, p < 0.001, CGGA cohort, p < 0.001). Moreover, the CR-Score was significantly correlated to the tumor-immune infiltration and cancer stem cell (CSC) index. A risk scale-based nomogram incorporating clinical factors for individual risk estimation was established thereby.Conclusion: These findings may pave the way for enhancing our understanding of CXCR modification patterns and developing better immune therapeutic approaches for glioma.

BackgroundAs a serious public health problem, dementia has placed a heavy burden on society and families. Evidence suggests that the use of music therapy as a non-pharmacological intervention has certain advantages with respect to reducing the behavioral and psychological symptoms of dementia (BPSD) and improving the cognition and mental status of dementia patients. However, research trends and hotspots regarding music therapy intervention for dementia analysis have not been systematically studied via bibliometric analysis.MethodsWe searched the Web of Science Core Collection (WoSCC) for texts published between January 1, 2010, and October 31, 2021, and visualized country, institution, journal, keyword co-occurrence, keyword emergence and keyword clustering.ResultsA total of 217 articles from the WoSCC database were analyzed. In this research field, the annual number of publications has generally shown a slowly increasing trend, and the United States has the most publications and the most frequent cooperation among countries. University College London (UCL) has the most extensive influence among research institutions. Among articles, those published in the JOURNAL OF ALZHEIMER'S DISEASE were the most numerous, with 20 such articles being published, accounting for 9.22% (20/217) of the total. Comprehensive analysis of five clusters via biclustering shows that the research hotspots in this field during the past 11 years have mainly focused on the autobiographical memory, cognitive function, mental state and BPSD of dementia patients.ConclusionThis study conducted a bibliometric and visual analysis of relevant studies concerning music therapy intervention for dementia patients. Psychological problems faced by dementia patients and the topics of quality of life, individualized music therapy, the mental state of caregivers and other related topics may be important research directions in the future. Therefore, the question of how to develop standardized research protocols and identify unified efficacy evaluation indicators should be a focus of and difficulty for future research.

Primers for Sanger sequencing. (DOCX 14 kb)

A compact, low cost, high speed, non-destructive testing NIR (near infrared) spectrometer optical system based on MOEMS grating device is developed. The MOEMS grating works as the prismatic element and wavelength scanning element in our optical system. The MOEMS grating enables the design of compact grating spectrometers capable of acquiring full spectra using a single detector element. This MOEMS grating is driven by electromagnetic force and integrated with angle sensor which used to monitored deflection angle while the grating working. Comparing with the traditional spectral system, there is a new structure with a single detector and worked at high frequency. With the characteristics of MOEMS grating, the structure of the spectrometer system is proposed. After calculating the parameters of the optical path, ZEMAX optical software is used to simulate the system. According the ZEMAX output file of the 3D model, the prototype is designed by SolidWorks rapidly, fabricated. Designed for a wavelength range between 800 nm and 1500 nm, the spectrometer optical system features a spectral resolution of 16 nm with the volume of 97 mm × 81.7 mm × 81 mm. For the purpose of reduce modulated effect of sinusoidal rotation, spectral intensity of the different wavelength should be compensated by software method in the further. The system satisfies the demand of NIR micro-spectrometer with a single detector.

The design of the multi-function wireless monitoring system based on Android is designed in this paper. The system consists of superior-machine and lower-machine. The lower-machine takes ATmega16 as the core processor, and RCV420 is the signal processor for measuring and sampling the 4–20mA current signal. The superior-machine is connected with lower-machine to receive and send data by WIFI module and acquire eight environmental real-time variables. The superior-machine is based on Android Development Environment and Javascript which uses Client to obtain real-time variables and gets with function such as curve display, data saving and warning prompt. The system can obtain an value of scale 2048 points by setting the ID number. The field experiment and application shows that this system has good performance in remote monitoring of the multi-point environmental values and industrial production with high accuracy and real-time requirement.

In this paper, an approximate constrained optimal guidance law is proposed for Mars entry vehicles guidance. Firstly, the original guidance of Mars entry vehicle is transformed into a fixed-time optimal tracking control problem, which depends on the solution of the Hamilton-Jacobi-Bellman (HJB) equation. Considering the case the control input is constrained, a generalized non-quadratic performance index is defined. In general, the HJB equation is a nonlinear partial differential equation that is difficult or even impossible to be solved analytically. To overcome the difficulty, neural network (NN) is used to solve the HJB equation approximately. Subsequently, the Monte-Carlo integration method and Latin Hypercube Sampling (LHS) are introduced to compute the integrals on multi-dimensional domains. Finally, the Monte-Carlo simulation results on the Mars entry vehicle demonstrate the effectiveness of the proposed method.

In this paper, two Q-learning (QL) methods are proposed and their convergence theories are established for addressing the model-free optimal control problem of general nonlinear continuous-time systems. By introducing the Q-function for continuous-time systems, policy iteration based QL (PIQL) and value iteration based QL (VIQL) algorithms are proposed for learning the optimal control policy from real system data rather than using mathematical system model. It is proved that both PIQL and VIQL methods generate a nonincreasing Q-function sequence, which converges to the optimal Q-function. For implementation of the QL algorithms, the method of weighted residuals is applied to derived the parameters update rule. The developed PIQL and VIQL algorithms are essentially off-policy reinforcement learning approachs, where the system data can be collected arbitrary and thus the exploration ability is increased. With the data collected from the real system, the QL methods learn the optimal control policy offline, and then the convergent control policy will be employed to real system. The effectiveness of the developed QL algorithms are verified through computer simulation., Comment: Submitted for Review

The $H_\infty$ control design problem is considered for nonlinear systems with unknown internal system model. It is known that the nonlinear $ H_\infty $ control problem can be transformed into solving the so-called Hamilton-Jacobi-Isaacs (HJI) equation, which is a nonlinear partial differential equation that is generally impossible to be solved analytically. Even worse, model-based approaches cannot be used for approximately solving HJI equation, when the accurate system model is unavailable or costly to obtain in practice. To overcome these difficulties, an off-policy reinforcement leaning (RL) method is introduced to learn the solution of HJI equation from real system data instead of mathematical system model, and its convergence is proved. In the off-policy RL method, the system data can be generated with arbitrary policies rather than the evaluating policy, which is extremely important and promising for practical systems. For implementation purpose, a neural network (NN) based actor-critic structure is employed and a least-square NN weight update algorithm is derived based on the method of weighted residuals. Finally, the developed NN-based off-policy RL method is tested on a linear F16 aircraft plant, and further applied to a rotational/translational actuator system., Comment: Accepted by IEEE Transactions on Cybernetics. IEEE Transactions on Cybernetics, Online Available, 2014

This paper addresses the model-free nonlinear optimal problem with generalized cost functional, and a data-based reinforcement learning technique is developed. It is known that the nonlinear optimal control problem relies on the solution of the Hamilton-Jacobi-Bellman (HJB) equation, which is a nonlinear partial differential equation that is generally impossible to be solved analytically. Even worse, most of practical systems are too complicated to establish their accurate mathematical model. To overcome these difficulties, we propose a data-based approximate policy iteration (API) method by using real system data rather than system model. Firstly, a model-free policy iteration algorithm is derived for constrained optimal control problem and its convergence is proved, which can learn the solution of HJB equation and optimal control policy without requiring any knowledge of system mathematical model. The implementation of the algorithm is based on the thought of actor-critic structure, where actor and critic neural networks (NNs) are employed to approximate the control policy and cost function, respectively. To update the weights of actor and critic NNs, a least-square approach is developed based on the method of weighted residuals. The whole data-based API method includes two parts, where the first part is implemented online to collect real system information, and the second part is conducting offline policy iteration to learn the solution of HJB equation and the control policy. Then, the data-based API algorithm is simplified for solving unconstrained optimal control problem of nonlinear and linear systems. Finally, we test the efficiency of the data-based API control design method on a simple nonlinear system, and further apply it to a rotational/translational actuator system. The simulation results demonstrate the effectiveness of the proposed method., Comment: 22 pages, 21 figures, submitted for Peer Review

Refining heterogeneity of subsidiary can make the group implementing classified performance management under the framework of performance management, evaluating performance of the heterogeneous subsidiary correctly, enhancing objectivity and comparability of heterogeneous subsidiary performance. This paper proposes the four innate heterogeneity of subsidiary: industry span, geographical dispersion, heterogeneity of the establishment period, the type of ownership structure, gives their measurement, and sums up their impction to the performance of enterprise group.

There is asymmetric information between the headquarter and its subsidiaries. In order to maximize their utilities, subsidiaries incline to submit fake performance information, headquarter doesn't participate subsidiaries' business operation, so it can't identify these information. In order to control subsidiaries' behavior of manipulation, this paper sets up a “principal-agent” model, and then we obtain the optimal supervision mechanism and incentives contracts, which let subsidiaries submit truth performance information. Deductive mechanism show that, through improve the validity of supervision, the intensity of punishment, headquarter can reduce the probability of supervision and manipulation. When subsidiaries obtain specific share of profit, it can make them work hard, submit truth performance information and maximize utilities of headquarter.

Background Animal acariasis is one of the important veterinary skin diseases. Chemical drugs have been widely used to treat and control this kind of disease. But many chemicals control could increase resistance in target species, toxicity and environmental hazards. We found that the 9-oxo-10, 11-dehydroageraphorone (euptox A) extracted from E. adenophorum has strong toxicity against P. cuniculi in vitro, but the in vivo acaricidal actions of euptox A have yet to be investigated. Results A 14-day experiment was performed using rabbits that were naturally infested with P. cuniculi on a farm. Rabbits were randomly divided into five groups; animals in groups A, B and C were treated in each ear topically with 4.0 ml of 2.0 and 1.0 g/L (w/v) euptox A, respectively. Animals in groups D and E were treated with ivermectin (by injection; positive controls) and glycerol with water only (by embrocation; negative controls), respectively. Each rabbit was treated twice with separate treatments on days 0 and 7. Rabbits were observed daily and detailed examinations were performed on days 0, 7 and 14, to inspect the presence or absence of mites and scabs/crusts. Seven days after the initial treatment, the mean clinical scores (presence of scabs/crusts) decreased from 3.48, 3.37, 3.43 and 3.45 to 0.37, 0.42, 0.78 and 0.38 in the ears of animals in groups A, B , C and D, respectively, which were similar to the observations recorded in the positive control rabbits. However, the clinical score for negative control rabbits did not increase significantly (P > 0.05) during the experiment, and this changed from 3.32 to 3.37 in the ears, and there were no significant differences in clinical efficacy between left and right ears. After two treatments (0 and 7 d), the rabbits in groups A, B, C and D had recovered completely 14 days after the last treatment and no recurrences of infection were observed. Conclusions These results indicate that euptox A was potent compounds for the effective control of animal P. cuniculi in vivo.