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2076-3417
Updated Wednesday, 19 January 2022

2022 ◽  
Vol 12 (2) ◽  
pp. 841
Author(s):  
Maria Filomena Macedo ◽  
Ana Zélia Miller ◽  
Ana Catarina Pinheiro ◽  
António Portugal

This Special Issue of the Applied Sciences, entitled “Application of Biology to Cultural Heritage” aimed to cover all the latest outstanding progress of biological and biochemical methods developed and applied to cultural heritage [...]


2022 ◽  
Vol 12 (2) ◽  
pp. 859
Author(s):  
Giulia Boccacci ◽  
Francesca Frasca ◽  
Chiara Bertolin ◽  
Anna Maria Siani

Among non-destructive testing (NDT) techniques applied to structural health monitoring in existing timber structures, ranging from visual inspection to more sophisticated analysis, acoustic emission (AE) is currently seldomly used to detect mechanical stresses in wooden building assets. This paper presents the results from a systematic literature review on AE NDT applied to monitor micro and macro fracture events in softwood, specifically Scots pine. This survey particularly investigates its application with respect to the tree rings density and grain angle inspection, as influencing factors well correlated with physical and mechanical characteristics of wood. The literature review was performed in a three-step process defined by the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) flow diagram, leading to the selection of 31 documents from different abstract and citation databases (Scopus, Web of Science and Google Scholar). The outcomes have highlighted how laboratory experiments, including several types of tests (tensile, cutting, compressive, etc.), were conducted in most cases, while a very limited number of studies investigated on in situ monitoring. In addition, theoretical approaches were often explored in parallel with the experimental one. It emerges that—for tree ring density studies—a multi-technique approach, which may include microscopic observations, could be more informative. Indeed, although not widely investigated, high/low tree ring density and grain angle were found as influencing factors on the AE parameters detected by the sensors, during condition and structural health monitoring experiments.


2022 ◽  
Vol 12 (2) ◽  
pp. 835
Author(s):  
Wojciech Zgłobicki

The modification of the chemical composition of environment components, including the concentration of heavy metals, is one of the consequences of the development of human societies [...]


2022 ◽  
Vol 12 (2) ◽  
pp. 839
Author(s):  
Wangdo Kim ◽  
Emir A. Vela

The first peak of the external knee abduction moment (KAM) is often used as a surrogate measure of the medial compartment loading and has been correlated with pain and progression of knee osteoarthritis (OA). As a result, reducing the KAM is often the target of conservative interventions. OA should be considered as a “Whole Person” disease, including ecological psychosocial aspects. Scientists have developed gait alteration strategies to reduce the KAM. They attempted to force into a new position any particular part without reference to the pattern of the whole. We propose an alternative approach: in the vicinity of a special configuration of the knee, some or all of the components of the knee become overloaded. This study has shown that when six lines $1′,$2′,$3′,$4′,$5′,$6′ are so situated that forces acting along them equilibrate when applied to one degree of freedom, 1° F knee, a certain determinant vanishes. We wish to define the six lines as the knee complex in involution by virtue of some constraint upon the knee.


2022 ◽  
Vol 12 (2) ◽  
pp. 834
Author(s):  
Zhuang Li ◽  
Xincheng Tian ◽  
Xin Liu ◽  
Yan Liu ◽  
Xiaorui Shi

Aiming to address the currently low accuracy of domestic industrial defect detection, this paper proposes a Two-Stage Industrial Defect Detection Framework based on Improved-YOLOv5 and Optimized-Inception-ResnetV2, which completes positioning and classification tasks through two specific models. In order to make the first-stage recognition more effective at locating insignificant small defects with high similarity on the steel surface, we improve YOLOv5 from the backbone network, the feature scales of the feature fusion layer, and the multiscale detection layer. In order to enable second-stage recognition to better extract defect features and achieve accurate classification, we embed the convolutional block attention module (CBAM) attention mechanism module into the Inception-ResnetV2 model, then optimize the network architecture and loss function of the accurate model. Based on the Pascal Visual Object Classes 2007 (VOC2007) dataset, the public dataset NEU-DET, and the optimized dataset Enriched-NEU-DET, we conducted multiple sets of comparative experiments on the Improved-YOLOv5 and Inception-ResnetV2. The testing results show that the improvement is obvious. In order to verify the superiority and adaptability of the two-stage framework, we first test based on the Enriched-NEU-DET dataset, and further use AUBO-i5 robot, Intel RealSense D435 camera, and other industrial steel equipment to build actual industrial scenes. In experiments, a two-stage framework achieves the best performance of 83.3% mean average precision (mAP), evaluated on the Enriched-NEU-DET dataset, and 91.0% on our built industrial defect environment.


2022 ◽  
Vol 12 (2) ◽  
pp. 852
Author(s):  
Jesús Díaz-Verdejo ◽  
Javier Muñoz-Calle ◽  
Antonio Estepa Alonso ◽  
Rafael Estepa Alonso ◽  
Germán Madinabeitia

Signature-based Intrusion Detection Systems (SIDS) play a crucial role within the arsenal of security components of most organizations. They can find traces of known attacks in the network traffic or host events for which patterns or signatures have been pre-established. SIDS include standard packages of detection rulesets, but only those rules suited to the operational environment should be activated for optimal performance. However, some organizations might skip this tuning process and instead activate default off-the-shelf rulesets without understanding its implications and trade-offs. In this work, we help gain insight into the consequences of using predefined rulesets in the performance of SIDS. We experimentally explore the performance of three SIDS in the context of web attacks. In particular, we gauge the detection rate obtained with predefined subsets of rules for Snort, ModSecurity and Nemesida using seven attack datasets. We also determine the precision and rate of alert generated by each detector in a real-life case using a large trace from a public webserver. Results show that the maximum detection rate achieved by the SIDS under test is insufficient to protect systems effectively and is lower than expected for known attacks. Our results also indicate that the choice of predefined settings activated on each detector strongly influences its detection capability and false alarm rate. Snort and ModSecurity scored either a very poor detection rate (activating the less-sensitive predefined ruleset) or a very poor precision (activating the full ruleset). We also found that using various SIDS for a cooperative decision can improve the precision or the detection rate, but not both. Consequently, it is necessary to reflect upon the role of these open-source SIDS with default configurations as core elements for protection in the context of web attacks. Finally, we provide an efficient method for systematically determining which rules deactivate from a ruleset to significantly reduce the false alarm rate for a target operational environment. We tested our approach using Snort’s ruleset in our real-life trace, increasing the precision from 0.015 to 1 in less than 16 h of work.


2022 ◽  
Vol 12 (2) ◽  
pp. 819
Author(s):  
Lena A. Hofmann ◽  
Steffen Lau ◽  
Johannes Kirchebner

Linear statistical methods may not be suited to the understanding of psychiatric phenomena such as aggression due to their complexity and multifactorial origins. Here, the application of machine learning (ML) algorithms offers the possibility of analyzing a large number of influencing factors and their interactions. This study aimed to explore inpatient aggression in offender patients with schizophrenia spectrum disorders (SSDs) using a suitable ML model on a dataset of 370 patients. With a balanced accuracy of 77.6% and an AUC of 0.87, support vector machines (SVM) outperformed all the other ML algorithms. Negative behavior toward other patients, the breaking of ward rules, the PANSS score at admission as well as poor impulse control and impulsivity emerged as the most predictive variables in distinguishing aggressive from non-aggressive patients. The present study serves as an example of the practical use of ML in forensic psychiatric research regarding the complex interplay between the factors contributing to aggressive behavior in SSD. Through its application, it could be shown that mental illness and the antisocial behavior associated with it outweighed other predictors. The fact that SSD is also highly associated with antisocial behavior emphasizes the importance of early detection and sufficient treatment.


2022 ◽  
Vol 12 (2) ◽  
pp. 823
Author(s):  
Md. Rafiqul Islam ◽  
Mehrdad Shahmohammadi Beni ◽  
Shigeki Ito ◽  
Shinichi Gotoh ◽  
Taiga Yamaya ◽  
...  

Proton range monitoring and verification is important to enhance the effectiveness of treatment by ensuring that the correct dose is delivered to the correct location. Upon proton irradiation, different positron emitting radioisotopes are produced by the inelastic nuclear interactions of protons with the target elements. Recently, it was reported that the 16O(p,2p2n)13N reaction has a relatively low threshold energy, and it could be potentially used for proton range verification. In the present work, we have proposed an analysis scheme (i.e., algorithm) for the extraction and three-dimensional visualization of positron emitting radioisotopes. The proposed step-by-step analysis scheme was tested using our own experimentally obtained dynamic data from a positron emission mammography (PEM) system (our developed PEMGRAPH system). The experimental irradiation was performed using an azimuthally varying field (AVF) cyclotron with a 80 MeV monoenergetic pencil-like beam. The 3D visualization showed promising results for proton-induced radioisotope distribution. The proposed scheme and developed tools would be useful for the extraction and 3D visualization of positron emitting radioisotopes and in turn for proton range monitoring and verification.


2022 ◽  
Vol 12 (2) ◽  
pp. 821
Author(s):  
Sarosh Ahmad ◽  
Umer Ijaz ◽  
Salman Naseer ◽  
Adnan Ghaffar ◽  
Muhammad Awais Qasim ◽  
...  

A type of telecommunication technology called an ultra-wideband (UWB) is used to provide a typical solution for short-range wireless communication due to large bandwidth and low power consumption in transmission and reception. Printed monopole antennas are considered as a preferred platform for implementing this technology because of its alluring characteristics such as light weight, low cost, ease of fabrication, integration capability with other systems, etc. Therefore, a compact-sized ultra-wideband (UWB) printed monopole antenna with improved gain and efficiency is presented in this article. Computer simulation technology microwave studio (CSTMWS) software is used to build and analyze the proposed antenna design technique. This broadband printed monopole antenna contains a jug-shaped radiator fed by a coplanar waveguide (CPW) technique. The designed UWB antenna is fabricated on a low-cost FR-4 substrate with relative permittivity of 4.3, loss tangent of 0.025, and a standard height of 1.6 mm, sized at 25 mm × 22 mm × 1.6 mm, suitable for wireless communication system. The designed UWB antenna works with maximum gain (peak gain of 4.1 dB) across the whole UWB spectrum of 3–11 GHz. The results are simulated, measured, and debated in detail. Different parametric studies based on numerical simulations are involved to arrive at the optimal design through monitoring the effects of adding cuts on the performance of the proposed antennas. Therefore, these parametric studies are optimized to achieve maximum antenna bandwidth with relatively best gain. The proposed patch antenna shape is like a jug with a handle that offers greater bandwidth, good gain, higher efficiency, and compact size.


2022 ◽  
Vol 12 (2) ◽  
pp. 828
Author(s):  
Tebogo Bokaba ◽  
Wesley Doorsamy ◽  
Babu Sena Paul

Road traffic accidents (RTAs) are a major cause of injuries and fatalities worldwide. In recent years, there has been a growing global interest in analysing RTAs, specifically concerned with analysing and modelling accident data to better understand and assess the causes and effects of accidents. This study analysed the performance of widely used machine learning classifiers using a real-life RTA dataset from Gauteng, South Africa. The study aimed to assess prediction model designs for RTAs to assist transport authorities and policymakers. It considered classifiers such as naïve Bayes, logistic regression, k-nearest neighbour, AdaBoost, support vector machine, random forest, and five missing data methods. These classifiers were evaluated using five evaluation metrics: accuracy, root-mean-square error, precision, recall, and receiver operating characteristic curves. Furthermore, the assessment involved parameter adjustment and incorporated dimensionality reduction techniques. The empirical results and analyses show that the RF classifier, combined with multiple imputations by chained equations, yielded the best performance when compared with the other combinations.


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