Semiochemical Diversity in Practice: Antiattractant Semiochemicals Reduce Bark Beetle Attacks on Standing Trees—A First Meta-Analysis by Fredrik Schlyter

“…Heterogeneity resulted from different designs and beetle population levels (as year of study). …”
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High-Speed Target Identification System Based on the Plume’s Spectral Distribution by Wenjie Lang, Guoguang Chen, Xiaoli Tian, Changfan Xin

“…In order to recognize the target of high speed quickly and accurately, an identification system was designed based on analysis of the distribution characteristics of the plume spectrum. …”
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Nanomaterials for targeted therapy of kidney diseases: Strategies and advances by Zhiwen Wang, Chun Zhang

“…Rationally designing and modifying nanomaterials based on the anatomical structure and pathophysiological environment of the kidney to achieve highly specific kidney-targeted nanomaterials or nanodrug delivery systems is both feasible and promising. …”
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DomeVR: Immersive virtual reality for primates and rodents. by Katharine A Shapcott, Marvin Weigand, Mina Glukhova, Martha N Havenith, Marieke L Schölvinck

“…As such, they have been employed across different species, and by a diverse range of research groups. Unfortunately, designing and implementing behavioral tasks in such environments often proves complicated. …”
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Typification of three Linnaean names in the genus Euphorbia (Euphorbiaceae) by P. Pablo Ferrer-Gallego

“…The lectotype of E. paralias is designated in this paper from a specimen preserved at S-LINN according to the ICN. …”
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<i>Foederis Arca</i>—The Ark of the Covenant, a Biblical Symbol of the Virgin Mary by José María Salvador-González

“…This article attempts to document why the Virgin Mary is symbolically designated by the biblical figure “Ark of the Covenant” (<i>Foederis Arca</i>), as reflected in one of the invocations of the Litany of Loreto (<i>Litaniae Lauretanae</i>). …”
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Real Time Implementation of Incremental Fuzzy Logic Controller for Gas Pipeline Corrosion Control by Gopalakrishnan Jayapalan, Ganga Agnihotri, D. M. Deshpande

“…The proposed fuzzy incremental controller is designed using a very simple control rule base and the most natural and unbiased membership functions. …”
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Collusion node detection method based on fuzzy evaluation density clustering in Internet of vehicles by Haibo ZHANG, Dabin WANG, Ruyan WANG, Dongyu WANG

“…In order to solve the security problems caused by collusion of malicious vehicles in the open Internet of vehicles environment, a collusion node detection method based on fuzzy evaluation density clustering was designed.The method realized vehicle reputation update through subjective recommendation trust, objective data trust and historical reputation.Two rounds malicious node detection method was designed to detect malicious vehicles, in the first round, fuzzy comprehensive evaluation was used to screen out a single malicious vehicle node, and in the second round, the conspiracy malicious vehicle nodes were searched by the improved density clustering method according to a single malicious vehicle node to ensure the sustainable development of network environment security.Experimental results show that the proposed method has a high recognition rate for malicious vehicles, and when the proportion of malicious vehicles reaches 30%, the detection accuracy can still remain above 90%.At the same time, under the proportion of different malicious vehicles, the detection recall rate and detection F value still remain higher value, indicating that the proposed method has a high stability.For the change of total vehicle nodes, the performance evaluation index shows a small fluctuation and remain within the range from 80% to 100%.…”
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Construire un problème : un premier pas vers l'investigation en sciences. Analyse d’une formation d’enseignants de primaire en contexte français et colombien by Luz Helena Martínez Barrera, Cécile de Hosson, Nicolas Décamp

“…Consequently, helping teachers in the task of designing a problem appears as an essential step for inquiry-based science education to be implemented. …”
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Convolutional neural network model over encrypted data based on functional encryption by Chen WANG, Jiarun LI, Jian XU

“…Currently, homomorphic encryption, secure multi-party computation, and other encryption schemes are used to protect the privacy of sensitive data in outsourced convolutional neural network (CNN) models.However, the computational and communication overhead caused by the above schemes would reduce system efficiency.Based on the low cost of functional encryption, a new convolutional neural network model over encrypted data was constructed using functional encryption.Firstly, two algorithms based on functional encryption were designed, including inner product functional encryption and basic operation functional encryption algorithms to implement basic operations such as inner product, multiplication, and subtraction over encrypted data, reducing computational and communication costs.Secondly, a secure convolutional computation protocol and a secure loss optimization protocol were designed for each of these basic operations, which achieved ciphertext forward propagation in the convolutional layer and ciphertext backward propagation in the output layer.Finally, a secure training and classification method for the model was provided by the above secure protocols in a module-composable way, which could simultaneously protect the confidentiality of user data as well as data labels.Theoretical analysis and experimental results indicate that the proposed model can achieve CNN training and classification over encrypted data while ensuring accuracy and security.…”
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Human Motion Gesture Recognition Based on Computer Vision by Rui Ma, Zhendong Zhang, Enqing Chen

“…The generation and identification part of the network is designed to encode the first hierarchy (parent) and the second hierarchy (child) and show the spatial relationship of human body parts. …”
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Adaptive control of uncertain non-linear fractional-order systems in the presence of actuator failures and input constraints by Masood Bagherzadeh

“…Purpose: This paper studies the problem of adaptive actuator failure compensation in non-linear fractional-order systems with strict feedback forms and provides a theoretical framework for designing control systems in the presence of parametric uncertainties, input constraints, and actuator failures to ensure stability and output tracking.Methodology: An adaptive controller is designed to compensate for actuator failures and control input constraints. …”
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Real and Pseudoaccommodation in Accommodative Lenses by Ioannis G. Pallikaris, Georgios A. Kontadakis, Dimitra M. Portaliou

“…In the attempt to manage presbyopia, different intraocular lens designs have been proposed such as monofocal IOLs with monovision or multifocal IOLs. …”
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Synchronization in Finite Time of Fuzzy Neural Networks with Hybrid Delays and Uncertain Nonlinear Perturbations by Shuyue Zhao, Kelin Li, Weiyi Hu

“…By applying the famous finite-time stability theory, combining differential inequality techniques, and the analysis approach, several new algebraic sufficient criteria are obtained to realize finite-time synchronization between the drive system and the response system by designing a state feedback controller and an adaptive controller. …”
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A Decision-Making Model for Self-Driving Vehicles Based on Overtaking Frequency by Mengyuan Huang, Shiwu Li, Mengzhu Guo, Lihong Han

“…In addition, the reward function and multiple driving scenarios are designed so that the most efficient driving strategy at the current moment can be determined by optimal search under the condition of ensuring safety. …”
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Seismic energy dissipation capacity of confined concrete columns with infilled-AAC bricks subjected to quasi-static cyclic loading by Bambang Sabariman, Tavio, Slamet Widodo

“…All test columns in the study failed in flexural modes as designed (no shear failures occurred).…”

A multi-source threat intelligence confidence value evaluation method based on machine learning by Hansheng LIU, Hongyu TANG, Mingxia BO, Jianfeng NIU, Tianbo LI, Lingxiao LI

“…During the collection process of multi-source threat intelligence,it is very hard for the intelligence center to make a scientific decision to massive intelligence because the data value density is low,the intelligence repeatabil-ity is high,and the ineffective time is very short,etc.Based on those problems,a new multi-source threat intelligence confidence value evaluation method was put forward based on machine learning.First of all,according to the STIX intelligence standard format,a multi-source intelligence data standardization process was designed.Secondly,ac-cording to the characteristic of data,14 characteristics were extracted from four dimensions of publishing time,source,intelligence content and blacklist matching degree to be the basis of determining the intelligence reliability.After getting the feature encoding,an intelligence confidence value evaluation model was designed based on deep neural network algorithm and Softmax classifier.Backward propagation algorithm was also used to minimize recon-struction error.Last but not least,according to the 2 000 open source marked sample data,k-ford cross-validation method was used to evaluate the model and get an average of 91.37% macro-P rate and 84.89% macro-R rate.It was a good reference for multi-source threat intelligence confidence evaluation.…”
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Contributions of Matrix Metalloproteinases to Neural Plasticity, Habituation, Associative Learning and Drug Addiction by John W. Wright, Joseph W. Harding

“…., neural plasticity) by degrading cell adhesion molecules (CAMs) designed to provide stability to those extracellular matrix (ECM) proteins that form scaffolding supporting neurons and glia. …”
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Construction of advanced persistent threat attack detection model based on provenance graph and attention mechanism by Yuancheng LI, Hao LUO, Xinyu WANG, Jiexuan YUAN

“…In response to the difficulty of existing attack detection methods in dealing with advanced persistent threat (APT) with longer durations, complex and covert attack methods, a model for APT attack detection based on attention mechanisms and provenance graphs was proposed.Firstly, provenance graphs that described system behavior based on system audit logs were constructed.Then, an optimization algorithm was designed to reduce the scale of provenance graphs without sacrificing key semantics.Afterward, a deep neural network (DNN) was utilized to convert the original attack sequence into a semantically enhanced feature vector sequence.Finally, an APT attack detection model named DAGCN was designed.An attention mechanism was applied to the traceback graph sequence.By allocating different weights to different positions in the input sequence and performing weight calculations, sequence feature information of sustained attacks could be extracted over a longer period of time, which effectively identified malicious nodes and reconstructs the attack process.The proposed model outperforms existing models in terms of recognition accuracy and other metrics.Experimental results on public APT attack datasets show that, compared with existing APT attack detection models, the accuracy of the proposed model in APT attack detection reaches 93.18%.…”
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Double adversarial attack against license plate recognition system by Xianyi CHEN, Jun GU, Kai YAN, Dong JIANG, Linfeng XU, Zhangjie FU

“…Recent studies have revealed that deep neural networks (DNN) used in artificial intelligence systems are highly vulnerable to adversarial sample-based attacks.To address this issue, a dual adversarial attack method was proposed for license plate recognition (LPR) systems in a DNN-based scenario.It was demonstrated that an adversarial patch added to the pattern location of the license plate can render the target detection subsystem of the LPR system unable to detect the license plate class.Additionally, the natural rust and stains were simulated by adding irregular single-connected area random points to the license plate image, which results in the misrecognition of the license plate number.or the license plate research, different shapes of adversarial patches and different colors of adversarial patches are designed as a way to generate adversarial license plates and migrate them to the physical world.Experimental results show that the designed adversarial samples are undetectable by the human eye and can deceive the license plate recognition system, such as EasyPR.The success rate of the attack in the physical world can reach 99%.The study sheds light on the vulnerability of deep learning and the adversarial attack of LPR, and offers a positive contribution toward improving the robustness of license plate recognition models.…”
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