Reviewer Locator模块在科技期刊中的应用实践与发展前景——以《中国肺癌杂志》为例

科技期刊作为科技传播的重要力量,其对重要科研成果的发布速度影响着科学技术的传播效力,而编辑工作效率直接决定稿件发表周期,提高编辑工作效率是科技期刊发展的推进器。为此,从同行评议审稿人储备和遴选方面入手,重点阐述如何准确高效地多维度遴选优质审稿人,归纳并探析Reviewer Locator模块在科技期刊办刊工作中的实际应用经验,助力出版业实现质量更好、效率更高的发展。

Microseismic source location using deep learning:A coal mine case study in China

Microseismic source location is crucial for the early warning of rockburst risks.However,the conventional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy.Intelligent techniques,such as the full convolutional neural network(FCNN),can capture spatial information but struggle with complex microseismic sequence.Combining the FCNN with the long shortterm memory(LSTM)network enables better time-series signal classification by integrating multiscale information and is therefore suitable for waveform location.The LSTM-FCNN model does not require extensive data preprocessing and it simplifies the microseismic source location through feature extraction.In this study,we utilized the LSTM-FCNN as a regression learning model to locate the seismic focus.Initially,the method of short-time-average/long-time-average(STA/LTA)arrival time picking was employed to augment spatiotemporal information.Subsequently,oversampling the on-site data was performed to address the issue of data imbalance,and finally,the performance of LSTM-FCNN was tested.Meanwhile,we compared the LSTM-FCNN model with previous deep-learning models.Our results demonstrated remarkable location capabilities with a mean absolute error(MAE)of only 7.16 m.The model can realize swift training and high accuracy,thereby significantly improving risk warning of rockbursts.

A Secure Device Management Scheme with Audio-Based Location Distinction in IoT

Identifying a device and detecting a change in its position is critical for secure devices management in the Internet of Things(IoT).In this paper,a device management system is proposed to track the devices by using audio-based location distinction techniques.In the proposed scheme,traditional cryptographic techniques,such as symmetric encryption algorithm,RSA-based signcryption scheme,and audio-based secure transmission,are utilized to provide authentication,non-repudiation,and confidentiality in the information interaction of the management system.Moreover,an audio-based location distinction method is designed to detect the position change of the devices.Specifically,the audio frequency response(AFR)of several frequency points is utilized as a device signature.The device signature has the features as follows.(1)Hardware Signature:different pairs of speaker and microphone have different signatures;(2)Distance Signature:in the same direction,the signatures are different at different distances;and(3)Direction Signature:at the same distance,the signatures are different in different directions.Based on the features above,amovement detection algorithmfor device identification and location distinction is designed.Moreover,a secure communication protocol is also proposed by using traditional cryptographic techniques to provide integrity,authentication,and non-repudiation in the process of information interaction between devices,Access Points(APs),and Severs.Extensive experiments are conducted to evaluate the performance of the proposed method.The experimental results show that the proposedmethod has a good performance in accuracy and energy consumption.

Using Improved Particle Swarm Optimization Algorithm for Location Problem of Drone Logistics Hub

Drone logistics is a novel method of distribution that will become prevalent.The advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption,resulting in cost savings for the company’s transportation operations.Logistics firms must discern the ideal location for establishing a logistics hub,which is challenging due to the simplicity of existing models and the intricate delivery factors.To simulate the drone logistics environment,this study presents a new mathematical model.The model not only retains the aspects of the current models,but also considers the degree of transportation difficulty from the logistics hub to the village,the capacity of drones for transportation,and the distribution of logistics hub locations.Moreover,this paper proposes an improved particle swarm optimization(PSO)algorithm which is a diversity-based hybrid PSO(DHPSO)algorithm to solve this model.In DHPSO,the Gaussian random walk can enhance global search in the model space,while the bubble-net attacking strategy can speed convergence.Besides,Archimedes spiral strategy is employed to overcome the local optima trap in the model and improve the exploitation of the algorithm.DHPSO maintains a balance between exploration and exploitation while better defining the distribution of logistics hub locations Numerical experiments show that the newly proposed model always achieves better locations than the current model.Comparing DHPSO with other state-of-the-art intelligent algorithms,the efficiency of the scheme can be improved by 42.58%.This means that logistics companies can reduce distribution costs and consumers can enjoy a more enjoyable shopping experience by using DHPSO’s location selection.All the results show the location of the drone logistics hub is solved by DHPSO effectively.

Dynamic Location Method for Shallow Ocean Bottom Nodes Using the Levenberg-Marquart Algorithm

Ocean bottom node(OBN)data acquisition is the main development direction of marine seismic exploration;it is widely promoted,especially in shallow sea environments.However,the OBN receivers may move several times because they are easily affected by tides,currents,and other factors in the shallow sea environment during long-term acquisition.If uncorrected,then the imaging quality of subsequent processing will be affected.The conventional secondary positioning does not consider the case of multiple movements of the receivers,and the accuracy of secondary positioning is insufficient.The first arrival wave of OBN seismic data in shallow ocean mainly comprises refracted waves.In this study,a nonlinear model is established in accordance with the propagation mechanism of a refracted wave and its relationship with the time interval curve to realize the accurate location of multiple receiver movements.In addition,the Levenberg-Marquart algorithm is used to reduce the influence of the first arrival pickup error and to automatically detect the receiver movements,identifying the accurate dynamic relocation of the receivers.The simulation and field data show that the proposed method can realize the dynamic location of multiple receiver movements,thereby improving the accuracy of seismic imaging and achieving high practical value.

Earthquake relocation using a 3D velocity model and implications on seismogenic faults in the Beijing-Tianjin-Hebei region

To enhance the understanding of the geometry and characteristics of seismogenic faults in the Beijing-Tianjin-Hebei region,we relocated 14805 out of 16063 earthquakes(113°E-120°E,36°N-43°N)that occurred between January 2008 and December 2020 using the double-difference tomography method.Based on the spatial variation in seismicity after relocation,the Beijing-Tianjin-Hebei region can be divided into three seismic zones:Xingtai-Wen'an,Zhangbei-Ninghexi,and Tangshan.(1)The Xingtai-Wen'an Seismic Zone has a northeastsouthwest strike.The depth profile of earthquakes perpendicular to the strike reveals three northeast-striking,southeast-dipping,high-angle deep faults(>10 km depth),including one below the shallow(<10 km depth)listric,northwest-dipping Xinghe fault in the Xingtai region.Two additional deep faults in the Wen'an region are suggested to be associated with the 2006 M 5.1 Wen'an Earthquake and the 1967 M 6.3 Dacheng earthquake;(2)The Zhangbei-Ninghexi Seismic Zone is oriented north-northwest.Multiple northeast-striking faults(10-20 km depth),inferred from the earthquake-intensive zones,exist beneath the shallow(<10 km depth)Xiandian Fault,Xiaotangshan Fault,Huailai-Zhuolu Basin North Fault,Yangyuan Basin Fault and Yanggao Basin North Fault;(3)In the Tangshan Seismic Zone,earthquakes are mainly concentrated near the northeast-striking Tangshan-Guye Fault,Lulong Fault,and northwest-striking Luanxian-Laoting Fault.An inferred north-south-oriented blind fault is present to the north of the Tangshan-Guye Fault.The 1976 M 7.8 Tangshan earthquake occurred at the junction of a shallow northwest-dipping fault and a deep southeast-dipping fault.This study emphasizes that earthquakes in the region are primarily associated with deep blind faults.Some deep blind faults have different geometries compared to shallow faults,suggesting a complex fault system in the region.Overall,this research provides valuable insights into the seismogenic faults in the Beijing–Tianjin–Hebei region.Further studies and monitoring of these faults are essential for earthquake mitigation efforts in this region.

Location Prediction from Social Media Contents using Location Aware Attention LSTM Network

Location prediction in social media,a growing research field,employs machine learning to identify users' locations from their online activities.This technology,useful in targeted advertising and urban planning,relies on natural language processing to analyze social media content and understand the temporal dynamics and structures of social networks.A key application is predicting a Twitter user's location from their tweets,which can be challenging due to the short and unstructured nature of tweet text.To address this challenge,the research introduces a novel machine learning model called the location-aware attention LSTM(LAA-LSTM).This hybrid model combines a Long Short-Term Memory(LSTM) network with an attention mechanism.The LSTM is trained on a dataset of tweets,and the attention network focuses on extracting features related to latitude and longitude,which are crucial for pinpointing the location of a user's tweet.The result analysis shows approx.10% improvement in accuracy over other existing machine learning approaches.

An envelope-based machine learning workflow for locating earthquakes in the southern Sichuan Basin

The development of machine learning technology enables more robust real-time earthquake monitoring through automated implementations. However, the application of machine learning to earthquake location problems faces challenges in regions with limited available training data. To address the issues of sparse event distribution and inaccurate ground truth in historical seismic datasets, we expand the training dataset by using a large number of synthetic envelopes that closely resemble real data and build an earthquake location model named ENVloc. We propose an envelope-based machine learning workflow for simultaneously determining earthquake location and origin time. The method eliminates the need for phase picking and avoids the accumulation of location errors resulting from inaccurate picking results. In practical application, ENVloc is applied to several data intercepted at different starting points. We take the starting point of the time window corresponding to the highest prediction probability value as the origin time and save the predicted result as the earthquake location. We apply ENVloc to observed data acquired in the southern Sichuan Basin, China, between September 2018 and March 2019. The results show that the average difference with the catalog in latitude, longitude, depth, and origin time is 0.02°,0.02°, 2 km, and 1.25 s, respectively. These suggest that our envelope-based method provides an efficient and robust way to locate earthquakes without phase picking, and can be used in earthquake monitoring in near-real time.

A Multi-Objective Optimization for Locating Maintenance Stations and Operator Dispatching of Corrective Maintenance

In this study,we introduce a novel multi-objective optimization model tailored for modern manufacturing,aiming to mitigate the cost impacts of operational disruptions through optimized corrective maintenance.Central to our approach is the strategic placement of maintenance stations and the efficient allocation of personnel,addressing a crucial gap in the integration of maintenance personnel dispatching and station selection.Our model uniquely combines the spatial distribution of machinery with the expertise of operators to achieve a harmonious balance between maintenance efficiency and cost-effectiveness.The core of our methodology is the NSGA Ⅲ+Dispatch,an advanced adaptation of the Non-Dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ),meticulously designed for the selection of maintenance stations and effective operator dispatching.This method integrates a comprehensive coding process,crossover operator,and mutation operator to efficiently manage multiple objectives.Rigorous empirical testing,including a detailed analysis from a taiwan region electronic equipment manufacturer,validated the effectiveness of our approach across various scenarios of machine failure frequencies and operator configurations.The findings reveal that the proposed model significantly outperforms current practices by reducing response times by up to 23%in low-frequency and 28.23%in high-frequency machine failure scenarios,leading to notable improvements in efficiency and cost reduction.Additionally,it demonstrates significant improvements in oper-ational efficiency,particularly in selective high-frequency failure contexts,while ensuring substantial manpower cost savings without compromising on operational effectiveness.This research significantly advances maintenance strategies in production environments,providing the manufacturing industry with practical,optimized solutions for diverse machine malfunction situations.Furthermore,the methodologies and principles developed in this study have potential applications in various other sectors,including healthcare,transportation,and energy,where maintenance efficiency and resource optimization are equally critical.

Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm

The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.

Multi-Branch Fault Line Location Method Based on Time Difference Matrix Fitting

The distribution network exhibits complex structural characteristics,which makes fault localization a challenging task.Especially when a branch of the multi-branch distribution network fails,the traditional multi-branch fault location algorithm makes it difficult to meet the demands of high-precision fault localization in the multi-branch distribution network system.In this paper,the multi-branch mainline is decomposed into single branch lines,transforming the complex multi-branch fault location problem into a double-ended fault location problem.Based on the different transmission characteristics of the fault-traveling wave in fault lines and non-fault lines,the endpoint reference time difference matrix S and the fault time difference matrix G were established.The time variation rule of the fault-traveling wave arriving at each endpoint before and after a fault was comprehensively utilized.To realize the fault segment location,the least square method was introduced.It was used to find the first-order fitting relation that satisfies the matching relationship between the corresponding row vector and the first-order function in the two matrices,to realize the fault segment location.Then,the time difference matrix is used to determine the traveling wave velocity,which,combined with the double-ended traveling wave location,enables accurate fault location.

Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.

球帽和Locator附着体种植覆盖义齿的临床疗效评价

目的:评价球帽附着体和Locator附着体应用于种植覆盖义齿的临床效果。方法:纳入无牙颌患者67例,共植入150颗Straumann或Bego种植体,完成球帽式或Locator式种植覆盖义齿修复。定期随访复查,包括修复后生物并发症和机械并发症情况和患者满意度调查。采用SPSS 17.0软件统计分析。结果:完成随访58例,球帽组35例,Locator组26例,球帽改Locator组7例。生物并发症比较显示,球帽组的牙龈增生发生率明显高于Locator组(P<0.05);球帽组和Locator组的机械并发症发生率无显著差异(P>0.05),但Locator组各类机械并发症的发生次数少于球帽组,球帽组平均修理频次为1.9次,Locator组为0.9次;Locator组患者对咀嚼和固位的满意度高于球帽组,不同性别的患者满意度无差异,满意度与患者的年龄和随访时间无相关性。结论:Locator式种植覆盖义齿相比球帽式种植覆盖义齿,临床效果具有一定优势。

Locator附着体种植覆盖义齿在肿瘤切除术后牙列缺失修复中的应用

目的：口腔颌面部肿瘤切除手术及其他治疗对颌骨造成的创伤可能会对口腔重建修复和功能恢复造成一定的困难。本文旨在介绍口腔颌面部肿瘤术后患者采用Locator附着体支持式种植覆盖义齿修复牙列缺失的应用方法，探讨解决无牙颌导致义齿固位不良的修复新方法。方法：对6例颌面部肿瘤切除术后牙列缺失患者，在颌骨内植入种植体，3~4个月后进行Locator附着体支持的种植覆盖义齿修复，并对其进行定期复查及种植覆盖总义齿满意度情况调查。结果：种植术后，通过1~5年的临床观察，所有种植体均无松动、脱落，Locator附着体支持式种植覆盖义齿固位良好。种植体植入后及负载后X线片对比，显示种植体-骨结合良好，种植体颈部无明显骨吸收。患者戴用Locator基台种植覆盖义齿，义齿的稳定性有明显改善，咀嚼效率提高，使用效果满意。结论：对口腔颌面部肿瘤切除术后的患者，Locator附着体种植覆盖义齿修复提高了义齿的固位力和稳定性，从而改善了患者的咀嚼效率。另外，其摘带、清洁及维护较为方便，这些都大大增强了患者的生活质量及使用信心。但是，Locator附着体种植覆盖义齿仍无法弥补因肿瘤手术导致的面部外形的改变。

二或四枚种植体支持的Locator覆盖义齿修复五年回顾

目的:比较下颌牙列缺失患者分别采用2枚或者4枚种植体支持的Locator覆盖义齿,修复5年后的临床效果。方法:选取采用2枚种植体(A组)和4枚种植体(B组)支持Locator覆盖义齿患者分别为20例、15例。评估2组患者满意度(整体满意度、咀嚼能力、说话适合性、固位和稳定性、舒适度),并检测咀嚼效率和边缘骨吸收值。结果:两组病人在整体满意度、咀嚼能力、说话适合性、固位和稳定性、舒适度及咀嚼效率、边缘骨吸收方面均无显著性差异(P>0.05)。结论:Locator种植覆盖义齿无论是采用2枚还是4枚种植体,5年后均能获得很好的临床效果,患者满意度高,但对其远期临床效果还有待观察。对于有可能出现义齿旋转的患者建议种植4枚种植体。

NOVEL APPROACH TO LOCATOR LAYOUT OPTIMIZATION BASED ON GENETIC ALGORITHM

Proper fixture design is crucial to obtain the better product quality according to the design specification during the workpiece fabrication. Locator layout planning is one of the most important tasks in the fixture design process. However, the design of a fixture relies heavily on the designerts expertise and experience up to now. Therefore, a new approach to loeator layout determination for workpieces with arbitrary complex surfaces is pro- posed for the first time. Firstly, based on the fuzzy judgment method, the proper locating reference and locator - numbers are determined with consideration of surface type, surface area and position tolerance. Secondly, the lo- cator positions are optimized by genetic algorithm（GA）. Finally, a typical example shows that the approach is su- perior to the experiential method and can improve positioning accuracy effectively.

杆卡、球帽和locator附着体种植覆盖义齿的临床对比研究

目的：比较杆卡、球帽、locator附着体种植覆盖义齿修复1．3年后的临床效果。方法：对38名无牙颌患者进行覆盖义齿修复，其中杆卡、球帽、locator附着体种植覆盖义齿分别为11、18和9例。在义齿完成后0．5、1年及每年复查1次。对各组种植体周围粘膜状况和边缘骨吸收（MBL）进行评估，同时检验患者满意度。结果：杆卡、球帽、locator附着体最近一次随访的种植体边缘骨吸收为（1．3±1．0）ram、（1.2±0．8）mm、（1．4±1．0）mm，3种方式修复后MBL比较无统计学意义（P〉0．05）。与原有全口义齿比较，患者满意度均有明显提升（P〈0．05）。三种义齿间种植体周围粘膜状况、患者满意度亦无统计学差异。结论：杆卡、球帽、locator附着体种植覆盖义齿均能明显改善无牙颌患者的咀嚼功能。出于经济学考虑，我们建议选用2枚种植体支持的球帽或locator附着体覆盖义齿，临床上角度偏差较大的种植体可以优先考虑locator附着体覆盖义齿修复。

LOCATOR附着体用于种植覆盖义齿的短期效果观察

目的评价LOCATOR附着体在种植覆盖义齿的短期临床应用效果。方法种植覆盖义齿修复下牙列缺失患者11例,全部采用LOCATOR附着体系统,制作覆盖义齿11件;单颌种植BEGO种植系统4枚,共44枚种植体。修复后平均追踪18个月,应用满意度的问卷调查,分析种植覆盖总义齿的合理性和稳定性;摄X线片检查种植体周围骨结合情况。结果 11例患者的种植覆盖义齿均可正常使用,患者对义齿的固位和咀嚼功能均满意,无不满意。结论 4枚LOCATOR附着体固位的种植覆盖义齿的操作简单,固位与稳定良好,患者满意,临床效果好。

Locator附着体、球帽附着体在种植体覆盖义齿修复中的应用及对咀嚼功能、美学效果的影响比较

目的:比较Locator附着体、球帽附着体在种植体覆盖义齿修复中的应用及对咀嚼功能、美学效果的影响。方法:选择2017年1月-2019年12月笔者医院就诊的100例下颌无牙颌患者为研究对象,种植体覆盖义齿修复中采用Locator附着体的50例患者为A组、采用球帽附着体的50例患者为B组,比较两组治疗后6个月时种植体周围软组织健康状况、咀嚼功能、美学效果及并发症发生情况,其中周围软组织健康状况指标包括边缘骨吸收值(BML)、菌斑指数(PLI)、牙周袋探诊深度(PPD),牙龈指数(GI),咀嚼功能指标包括咀嚼次数、咀嚼效果及语音清晰度,美学修复效果包括修复体完整、边缘适宜、染色A级率及患者对面部形态的满意率。结果:治疗后6个月,两组种植体周围软组织健康状况均良好,组间BML、PLI、PPD、GI比较差异均无统计学意义(P>0.05);A组咀嚼次数、咀嚼效率显著高于B组,语音清晰度显著高于B组,组间比较差异均有统计学意义(P<0.05);A、B组的修复体完整A级率比较,差异无统计学意义(P>0.05),A组边缘适宜A级率、染色A级率及患者对面部形态满意率显著高于B组,组间比较差异均有统计学意义(P<0.05);A、B组并发症发生率分别为4.0%、16.0%,A组显著低于B组,差异有统计学意义(P<0.05)。结论:Locator附着体在种植体覆盖义齿修复中的应用效果更佳,在保证种植体周围软组织健康状况的同时,能更好地提高咀嚼功能、美学效果,并且并发症更少,是安全、有效的临床方案。

老年人应用locator附着体-种植覆盖义齿的护理分析

目的:探讨应用locator附着体-种植覆盖义齿的修复老年无牙颌患者的临床护理特点。方法:选择22例老年无牙颌患者进行术前沟通和心理护理;术后对应用locator附着体-种植覆盖义齿的患者进行护理和口腔健康教育指导,并进行满意度调查。结果:22例患者的种植体成功植入颌骨内,locator附着体-种植覆盖义齿固位良好,患者满意度高。结论:正确细心的护理对保证应用locator附着体-种植覆盖义齿修复的成功起到重要作用。