Network Configuration Entity Extraction Method Based on Transformer with Multi-Head Attention Mechanism

Nowadays,ensuring thequality of networkserviceshas become increasingly vital.Experts are turning toknowledge graph technology,with a significant emphasis on entity extraction in the identification of device configurations.This research paper presents a novel entity extraction method that leverages a combination of active learning and attention mechanisms.Initially,an improved active learning approach is employed to select the most valuable unlabeled samples,which are subsequently submitted for expert labeling.This approach successfully addresses the problems of isolated points and sample redundancy within the network configuration sample set.Then the labeled samples are utilized to train the model for network configuration entity extraction.Furthermore,the multi-head self-attention of the transformer model is enhanced by introducing the Adaptive Weighting method based on the Laplace mixture distribution.This enhancement enables the transformer model to dynamically adapt its focus to words in various positions,displaying exceptional adaptability to abnormal data and further elevating the accuracy of the proposed model.Through comparisons with Random Sampling(RANDOM),Maximum Normalized Log-Probability(MNLP),Least Confidence(LC),Token Entrop(TE),and Entropy Query by Bagging(EQB),the proposed method,Entropy Query by Bagging and Maximum Influence Active Learning(EQBMIAL),achieves comparable performance with only 40% of the samples on both datasets,while other algorithms require 50% of the samples.Furthermore,the entity extraction algorithm with the Adaptive Weighted Multi-head Attention mechanism(AW-MHA)is compared with BILSTM-CRF,Mutil_Attention-Bilstm-Crf,Deep_Neural_Model_NER and BERT_Transformer,achieving precision rates of 75.98% and 98.32% on the two datasets,respectively.Statistical tests demonstrate the statistical significance and effectiveness of the proposed algorithms in this paper.

Efficacy of static magnetic field for pain of adjuvant arthritis rats

Rats suffering from adjuvant arthritis (AA) were used to examine the effect of a static magnetic field (SMF) upon pain relief. Rats were divided into SMF- treated AA rats, non-SMF treated AA rats and control rats. Following SMF stimulation, we measured blood flow volume in the paw and then reactive speed response to thermal stimulation. The AA groups exhibited significantly lower blood volume and reactivity to thermal stimulation compared to the control group. Compared to non-SMF, SMF exhibited increased blood flow volume in both the tail and paw, along with an increased reactive speed response to thermal stimulation. Our findings suggest that an improved of blood flow and reactive speed response, induced by SMF, appears to be effective for the relief of pain induced by chronic inflammation.

含有木糖醇和氟化物的牙膏对釉质龋的再矿化作用

目的用定量光导荧光系统(Quantitative Light-induced Fluorescence,QLF)研究含氟化物和木糖醇牙膏对人牙釉质早期龋的再矿化作用。材料和方法28个人牙釉质标本随机分成4组,每组7个。先将每个标本泡在脱矿液中制备约2mm×3mm的表层下脱矿病变;然后将各组标本分别用不同的牙膏浆处理:每天2次,每次3分钟,25℃,持续14天。各组牙膏样品(以二氧化硅为基质)如下:①不含氟和木糖醇的空白对照;②含500ppm氟离子;③含500ppm氟离子和5%木糖醇;④含500ppm氟离子的市售牙膏。用QLF检测各组标本的荧光损失量(F(%))和病变面积(mm2),计算ΔQ(%,mm2)。结果用含500ppm氟离子和5%木糖醇牙膏处理的人牙釉质龋标本,表层下病变面积缩小最多、ΔQ降低最大,且与其他牙膏处理组有显著的差异(P<0.05)。结论加入木糖醇的含氟牙膏可能有利于增进对釉质脱矿的再矿化作用。

A Change in Bathing Style May Improve Mental Fitness

Frequent bathtub bathing (BB) improves the mental health of middle-aged and older Japanese in-dividuals. This study investigated the chronic mental health effects of BB, maintaining warmth using an insulating sheet and sleeping bag after bathtub bathing (BBW), and bathtub bathing with herbal extracts (BBH) in healthy young adults. The study involved healthy young adults who habitually showered, as opposed to bathing. In the first experiment, 18 participants were randomly assigned to either the BB or BBW groups for 14 consecutive nights. After a 2-week washout period, the participants were asked to switch their bathing styles (a cross-over design). In the second experiment, 20 participants were randomly assigned to the BB or BBH group. The herbal extracts for the BBH group contained angelicae radix, aurantii nobilis pericarpium, chamomile, and zingiberis rhizoma. After a 2-week washout period, these participants also switched to the other bathing style. The participants’ mental conditions pre- and post-intervention were assessed using the Profile of Mood States-Brief Form questionnaire, Japanese version, and were statistically analyzed. The participants’ Anger-Hostility score converged to an average (50 points) in the post-BBW and post-BBH participants, and there were no significant differences in BB. The Confusion change rate was significantly different in the first experiment (BB versus BBW). The Depression-Dejection and Fatigue change rates were significantly different in the second experiment (BB versus BBH). Our findings suggest that changing bathing style from showering to BBW or BBH improves the POMS Anger-Hostility scores of healthy young adults.

UNCOVERING BENEFITS AND RISKS OF INTEGRATED PRODUCT SERVICE OFFERINGS-USING A CASE OF TECHNOLOGY ENCAPSULATION

The objective of this article is to uncover benefits and risks of Integrated Product Service Offering （IPSO） in a systematic manner. To do so, it adopts an explorative longitudinal in-depth case study （development of an IPSO based on a new technology） and adds insights to the existing literature. The article first proposes a theoretical and generic framework termed the PCP （Provider - Customer - Product） triangle with associated information flow and uncertainty. Second, various types of benefits and risks are presented based on the framework. Among others, the benefit of keeping IPR （Intellectual Property Rights） with the provider and the risk of regulation change are new findings from the case study. In addition, the case study reveals that IPSO is regarded as a positive contributor to innovation. Applying the framework and classification of benefits and risks as norms to other cases has yet to be done for verification. However, the framework contributes scientifically to a better understanding of the benefits and risks of IPSO. In addition, this framework is advantageous with its easiness to understand, which contributes practically to the dissemination of IPS0 insight to industry.

Investigation and Application on the Vertical Vibration Models of the Seated Human Body

Investigation of the vertical vibration characteristics of the seated human body is beneficial for the design and development of vehicle ride comfort.In this study,we first established models of the seated human body with two,three and four degrees of freedom(DOF).Then,the vibration characteristics of 30 volunteers were tested under standard conditions with a vibration test rig to obtain data for the apparent mass,driving point mechanical impedance,and seat-to-head transfer function.Based on the experimental data,the parameters of these models are identified and the results show that the four-DOF model can simulate the vertical vibration characteristics of the seated human body more comprehensively.Then,different seated human body models were applied to optimize the damping of shock absorber.The results show that the optimized damping with the four-DOF Chinese seated human body model is 27%more than that with rigid mass and 7%less than that with ISO 5982:2001 seated human body model.

Listwise approaches based on feature ranking discovery

Listwise approaches are an important class of learning to rank, which utilizes automatic learning techniques to discover useful information. Most previous research on listwise approaches has focused on optimizing ranking models using weights and has used imprecisely labeled training data; optimizing ranking models using features was largely ignored thus the continuous performance improvement of these approaches was hindered. To address the limitations of previous listwise work, we propose a quasi-KNN model to discover the ranking of features and employ rank addition rule to calculate the weight of combination. On the basis of this, we propose three listwise algorithms, FeatureRank, BL-FeatureRank, and DiffRank. The experimental results show that our proposed algorithms can be applied to a strict ordered ranking training set and gain better performance than state-of-the-art listwise algorithms.