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。

Overview of the identification of traffic accident-prone locations driven by big data

Effective identification of traffic accident-prone points can reduce accident risks and eliminate safety hazards.This paper first systematically compares the research in Chinese and foreign literature,and proposes three types of identification indicators,namely absolute,relative and comprehensive,according to different reference standards.According to the evaluation indicators and modelling methods,the current status of research and problems in identification theory and methods are systematically summarised in terms of mathematical statistics,cluster analysis,machine learning and conflict technology.The study shows that the foreign literature focuses on the innovation of data and indicators and changes from accident point safety management to road network safety management,while the research in Chinese literature focuses on the integration of multiple identification methods and theoretical innovation.Driven by big data,the identification of traffic accident-prone points has been further developed at the meso-micro scale.Morphological image processing methods are widely used,combined with GIS platforms,to accurately mine the spatial attributes and correlations of accidents.Also,considering the spatial and temporal distribution of accidents,the identification results are also transformed from regions to specific road sections and points to achieve more accurate identification.

A Computational Analysis of the Influence of Anastomosis Angle on Stenosis-Prone Locations during Radio-Cephalic Arteriovenous Fistula Maturation

In dialysis treatment, the radio-cephalic arteriovenous fistula (RCAVF) is a commonly used fistula, yet its low maturation rate remains a challenge. To enhance surgical outcomes, the relationship between stenosis-prone locations and RCAVF anastomosis angle is studied during maturation by developing two sets of RCAVF models for early (non-mature) and mature RCAVFs at five anastomosis angles. The impact of hemodynamics and wall shear stress (WSS) is examined to determine optimal anastomotic angles. Results indicate that acute angles produce more physiological WSS distributions and fewer disturbed regions, with early stenosis-prone regions located near the anastomosis that shift to the bending venous segment during remodeling. A pilot study comparing clinical and numerical results is conducted for validation.

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.

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.

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.

Quantitative data about active tectonics and possible locations of strong earthquakes in the future in the northwestern Beijing

Deterministic, probabilistic and composite-grading methods are used to get the possible locations of strong earth-quakes in the future in Norwest Beijing and its vicinity based on the quantitative data and their accuracy about active tectonics in the research area and by ordering, some questions in the results are also discussed. It shows that the most dangerous fault segments for strong earthquakes in the future include: segments B and A of the southern boundary fault of the Yangyuan basin, the southern boundary fault of the Xuanhua basin, the east segment of the southern Huaian fault and the east segment of the northern YanggaoTianzhen fault. The most dangerous area is YangyuanShenjing basin, the second one is TianzhenHuaianXuanhua basin and the third dangerous areas are WanquanZhangjiakou and northeast of Yuxian to southwest of Fanshan.

The Application of GGE Biplot Analysis for Evaluating Test Locations and Mega-Environment Investigation of Cotton Regional Trials

In the process to the marketing of cultivars, identification of superior test locations within multi-environment variety trial schemes is of critical relevance. It is relevant to breeding organizations as well as to governmental organizations in charge of cultivar registration. Where competition among breeding companies exists, effective and fair multi-environment variety trials are of utmost importance to motivate investment in breeding. The objective of this study was to use genotype main effect plus genotype by environment interaction（GGE） biplot analysis to evaluate test locations in terms of discrimination ability, representativeness and desirability, and to investigate the presence of multiple mega-environments in cotton production in the Yangtze River Valley（YaRV）, China. Four traits（cotton lint yield, fiber length, lint breaking tenacity, micronaire） and two composite selection indices were considered. It was found that the assumption of a single mega-environment in the YaRV for cotton production does not hold. The YaRV consists of three cotton mega-environments： a main one represented by 11 locations and two minor ones represented by two test locations each. This demands that the strategy of cotton variety registration or recommendation must be adjusted. GGE biplot analysis has also led to the identification of test location superior for cotton variety evaluation. Although test location desirable for selecting different traits varied greatly, Jinzhou, Hubei Province, China, was found to be desirable for selecting for all traits considered while Jianyang, Sichuan Province, China, was found to be desirable for none.

Colorectal endoscopic submucosal dissection in special locations

Colorectal endoscopic submucosal dissection(ESD) is considered one of the most challenging endoscopic procedures for novice endoscopists. When compared with the stomach, the colon and rectum have a narrower tubular lumen, greater angulation at the flexures, and a thinner muscle layer. These factors make endoscopic control and maneuverability difficult. ESD of the colorectum was considered more difficult than gastric and esophageal ESD. However, with learning from the experts, practicing, and selecting an appropriate technique,most of colorectal ESD could be performed successfully. Nevertheless, some colorectal locations are extremely specialized either from unique anatomy or given unstable scope position. Accordingly, the objective of this review was to provide endoscopists with an overview of the techniques and outcomes associated with ESD at these special colorectal locations. ESD at the discussed special locations of the ileo-colo-rectum was found to be feasible, and outcomes were comparable to those of ESD performed in non-special locations of the ileocolo-rectum. Practice for skill improvement and awareness of the unique characteristics of each special location is the key to performing successful ESD.

Northward Shift in Landfall Locations of Tropical Cyclones over the Western North Pacific during the Last Four Decades

This study analyzes landfall locations of tropical cyclones(TCs)over the western North Pacific during 1979–2018.Results demonstrate that the landfall locations of TCs over this region have shifted northward during the last four decades,primarily due to the shift of landfalling TC tracks,with the decreasing/increasing proportion of westward/northward TC tracks.In particular,the northward shift of the landfalling TCs was not related to their formation locations,which have not markedly changed,whereas"no-landed"TCs have significantly shifted northward.TC movement was significantly and positively correlated to the zonal component of the steering flow,while the correlation between TC movement and the meridional component of the steering flow was relatively unobvious.The westward steering flow in the tropical central Pacific that occurred around the formation and early development of the westward TCs was significantly weakened,which was unfavorable for their westward movement,thereby,causing the higher proportions of northward moving tracks.This weakened westward flow was related to the northward shift of the subtropical high ridge,which was caused by significant weakening of the southern part of the subtropical high.The vertical wind shear,sea surface temperature,and convective available potential energy also showed that the northern region of the western North Pacific became more favorable for TC development,whereas the upper divergence,low-layer relative vorticity,and accumulated water vapor content were not obviously related to the northward shift of TCs.

Calculate Target Position of Object in 3-Dimensional Area Based on the Perceived Locations Using EOG Signals

EOG is a biosignal which occurs during eye activities such as eye movement and blink. EOG has a linear relationship with gaze distance. Detection object position in 3-dimensional area using gaze motion was proposed in this research. To calculate the distance of gaze motion in pixel unit, affine transform method was developed. The homogeneous matrix from five geometry processes: transformation-1, rotation, transformation-2, shear, and dilatation was determined. To give tracking ability in 3-dimensional area, two cameras were attached each in front of and top side of object. The cameras were accessed by voluntary blink. The EOG characteristic of blink eye was determined based on the absolute ratio between positive peak and negative peak which was greater than 1. Every blink toggled the active camera. The position of object was given by the perceived locations from the two cameras. Every movement in pixel coordinate was converted into centimeter unit. Then, the perceived location was used to calculate to the base coordinate. The result shows that the blink method successfully accessed the camera. Both of the cameras could show the location of object from their side. Calculating the gaze distance using affine transform also gave a satisfied result. Using this method controlling a machine in 3-dimensional area by EOG could be developed.

Determining effective criteria for the selection of MDF industry locations in Mazandaran Province: Application of AHP

Selecting the optimum location with attention to conditions and restrictions is one of the most importantfactors in establishing a manufacturing plant. Identification of effective criteria is an important stage in the selection for the location of industrial units. In this study, an analytic hierarchy process （AHP） was applied to select the most effective criteria for the location of MDF （medium density fiberboard） industry in Mazandaran Province, Iran. A consideration in ttaining this goal is that Mazandaran is favored over other provinces because of its resources of raw lignocellulosic ma- erial required for wood and paper industries. The results indicate that the criterion of ＂materials and products＂ and the ub-criterion of ＂reliability of supply of raw material＂ are the most important factors.

Radio map updated method based on subscriber locations in indoor WLAN localization

With the rapid development of wireless local area network （WLAN） technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online calibration effort to overcome signal time-varying. A novel fingerprint positioning algorithm, known as the adaptive radio map with updated method based on hidden Markov model （HMM）, is proposed. It is shown that by using a collection of user traces that can be cheaply obtained, the proposed algorithm can take advantage of these data to update the labeled calibration data to further improve the position estimation accuracy. This algorithm is a combination of machine learning, information gain theory and fingerprinting. By collecting data and testing the algorithm in a realistic indoor WLAN environment, the experiment results indicate that, compared with the widely used K nearest neighbor algorithm, the proposed algorithm can improve the positioning accuracy while greatly reduce the calibration effort.

Associations between Lesion Locations and Stroke Recurrence in Survivors of First-ever Ischemic Stroke:A Prospective Cohort Study

Summary:Several studies have indicated that stroke survivors with multiple lesions or with larger lesion volumes have a higher risk of stroke recurrence.However,the relationship between lesion locations and stroke recurrence is unclear.We conducted a prospective cohort study of first-ever ischemic stroke survivors who were consecutively enrolled from January 2010 to December 2015.Stroke recurrence was assessed every 3 months after post-discharge via telephone interviews by trained interviewers.Lesion locations were obtained from hospital-based MRI or CT scans and classified using two classification systems that were based on cerebral hemisphere or vascular territory and brain anatomical structures.Flexible parametric survival models using the proportional hazards scale(PH model)were used to analyze the time-to-event data.Among 633 survivors,63.51%(n-402)had anterior circulation ischemia(ACI),and morc than half of all ACIs occurred in the subcortex.After a median follow-up of 2.5 years,117(18.48%)survivors developed a recurrent stroke.The results of the multivariate PH model showed that survivors with non-brain lesions were at higher risk of recurrence than those with right-side lesions(HR,2.79;95%CI,1.53,5.08;P-0.001).There was no increase in risk among survivors with left-side lesions(HR,0.97;95%CI,0.53,1.75;P=0.914)or both-side lesions(HR,1.24;95%CI,0.75,2.07;P-0.401)compared to those with right-side lesions.Additionally,there were no associations between stroke ecurrence and lesion locations that were classified based on vascular territory and brain anatomical structures.It was concluded that first-ever ischemic stroke survivors with non-brain lesion had higher recurrence risk than those with right-side lesion,although no significant associations were found when the lesion locations were classified by vascular territory and brain anatomical structures.

The clinical efficacy of percutaneous ethanol-lipiodol injection(PEI)combined with high-intensity focused ultrasound(HIFU)for small hepatocellularcarcinoma in special or high-risk locations

Objective The objective of this study was to explore the short-term effects and postoperative complications of ultrasound-guided percutaneous ethanol-lipiodol injection(PEI)combined with high-intensity focused ultrasound(HIFU)for the treatment of small hepatocellular carcinoma in a special or high-risk location.Methods Forty patients with small liver cancer in a special or high-risk location were randomly divided into two groups:20 patients were treated with PEI combined with HIFU(P+H group),and 20 patients were treated with HIFU alone(H group).There were no significant differences in average age,liver function,tumor location,tumor number,or tumor size between the two groups(P>0.05).Results Significant differences were observed in ablation parameters between the two groups(P<0.05).Under the same power,ablation rates of the P+H group were significantly higher than those in the H group,and postoperative complications in the P+H group were significantly lower than those in the H group(P<0.05).Conclusion The combination of PEI and HIFU has better clinical value than HIFU alone for small-cell liver cancer in special or high-risk locations.