bluuebunny/crossref_metadata_2025 huggingface dataset:
Treatment of impacted or retained second molars with the miniscrew-supported pole technique: a prospective follow-up study
Authors: Lorente Carmen , Lorente Pedro , Perez-Vela Maria , Esquinas Cristina , Lorente Teresa
Published: October 2022

Background

Eruption disturbances of permanent molars are uncommon; however, it is important to treat them as soon as they are diagnosed. The main objective was to analyze the effectiveness of the “miniscrew-supported pole technique,” a surgically assisted orthodontic procedure to force the eruption of impacted/retained second molars (M2s) when there are indicators of complex molar inclusion. An observational prospective study was carried out during a 2-year period. Sociodemographic, clinical and low-dose scanner variables were taken at baseline (T0). Follow-up variables (T1) were the time between surgery and tooth eruption, radiographic measurements, debonding of buttons, failure rate of miniscrews and success rate of eruption.

Results

A total of 21 patients (mean age of 13.9 years) with 24 retained/impacted M2s were recruited; 13 molars were maxillary (54.2%) and 11 (45.8%) were mandibular. Six (25%) were impacted molars and 18 (75%) primarily retained. At T0, molar angulation was mesial in six molars (25%), distal in five molars (20.8%) and 13 molars were vertically positioned (54.2%). Infraocclusion degree was moderate in four (16.7%) molars and severe in 20 (83.3%). Only three (12.5%) third molars were removed due to lack of space. All M2s managed to erupt, achieving a success rate of 100%; however, two molars of the same patient did not achieve occlusion. The period of eruption after surgery was 126.8 (117.3) days. Anatomical radicular alteration was the only variable independently related to a longer time of treatment (p = 0.027).

Conclusions

The pole technique, using one mesial miniscrew and simple orthodontic mechanics, applies forces that succeed in erupting complicated retained/impacted M2s in a short period of time and with a low failure rate.
The composition of cell-based therapies obtained from point-of-care devices/systems which mechanically dissociate lipoaspirate: a scoping review of the literature
Authors: Liu Perry , Gurung Binay , Afzal Irrum , Santin Matteo , Sochart David H. , Field Richard E. , Kader Deiary F. , Asopa Vipin
Published: October 2022

Purpose

Cell-based therapies using lipoaspirate are gaining popularity in orthopaedics due to their hypothesised regenerative potential. Several ‘point-of-care’ lipoaspirate-processing devices/systems have become available to isolate cells for therapeutic use, with published evidence reporting their clinical relevance. However, few studies have analysed the composition of their ‘minimally-manipulated’ cellular products in parallel, information that is vital to understand the mechanisms by which these therapies may be efficacious. This scoping review aimed to identify devices/systems using mechanical-only processing of lipoaspirate, the constituents of their cell-based therapies and where available, clinical outcomes.

Methods

PRISMA extension for scoping reviews guidelines were followed. MEDLINE, Embase and PubMed databases were systematically searched to identify relevant articles until 21st April 2022. Information relating to cellular composition and clinical outcomes for devices/systems was extracted. Further information was also obtained by individually searching the devices/systems in the PubMed database, Google search engine and contacting manufacturers.

Results

2895 studies were screened and a total of 15 articles (11 = Level 5 evidence) fulfilled the inclusion criteria. 13 unique devices/systems were identified from included studies. All the studies reported cell concentration (cell number regardless of phenotype per millilitre of lipoaspirate) for their devices/systems (range 0.005–21 × 106). Ten reported cell viability (the measure of live cells- range 60–98%), 11 performed immuno-phenotypic analysis of the cell-subtypes and four investigated clinical outcomes of their cellular products. Only two studies reported all four of these parameters.

Conclusion

When focussing on cell concentration, cell viability and MSC immuno-phenotypic analysis alone, the most effective manual devices/systems were ones using filtration and cutting/mincing. However, it was unclear whether high performance in these categories would translate to improved clinical outcomes. Due to the lack of standardisation and heterogeneity of the data, it was also not possible to draw any reliable conclusions and determine the role of these devices/systems in clinical practice at present.

Level of Evidence

Level V Therapeutic.
Global variation in isolated posterior cruciate ligament reconstruction
Authors: Knapik Derrick M. , Gopinatth Varun , Jackson Garrett R. , Chahla Jorge , Smith Matthew V. , Matava Matthew J. , Brophy Robert H.
Published: October 2022

Purpose

In the setting of persistent instability or failed non-operative management, surgical reconstruction is commonly recommended for isolated posterior cruciate ligament (PCL) tears. The purpose of this study was to systematically review published studies to evaluate regional variation in the epidemiology of and surgical approaches to primary, isolated PCL reconstruction.

Methods

A systematic review was performed in June 2022 to identify studies examining operative techniques during primary, isolated PCL reconstruction. Collected variables consisted of reconstruction technique, graft type, graft source, tibial reconstruction technique, femoral and tibial drilling and fixation methods, and whether the remnant PCL was preserved or debrided. Studies were classified into four global regions: Asia, Europe, North America, and South America.

Results

Forty-five studies, consisting of 1461 total patients, were identified. Most of the included studies were from Asia (69%, n = 31/45). Single bundle reconstruction was more commonly reported in studies out of Asia, Europe, and North America. Hamstring autografts were utilized in 51.7% (n = 611/1181) of patients from Asia and 60.8% (n = 124/204) of patients from Europe. Trans-tibial drilling and outside-in femoral drilling were commonly reported in all global regions. The PCL remnant was generally debrided, while remnant preservation was commonly reported in studies from Asia.

Conclusion

Surgical treatment of isolated PCL injuries varies by region, with the majority of published studies coming from Asia. Single-bundle reconstruction with hamstring autograft through a trans-tibial approach is the most commonly reported technique in the literature, with males reported to undergo isolated reconstruction more often than females.

Level of Evidence

Systematic review, Level IV.
Influence of experience on dental implant placement: an in vitro comparison of freehand, static guided and dynamic navigation approaches
Authors: Wang Xiaotong , Shaheen Eman , Shujaat Sohaib , Meeus Jan , Legrand Paul , Lahoud Pierre , do Nascimento Gerhardt Maurício , Politis Constantinus , Jacobs Reinhilde
Published: October 2022

Purpose

This study aimed to investigate the performance of novice versus experienced practitioners for placing dental implant using freehand, static guided and dynamic navigation approaches.

Methods

A total of 72 implants were placed in 36 simulation models. Three experienced and three novice practitioners were recruited for performing the osteotomy and implant insertion with freehand, surgical guide (pilot-drill guidance) and navigation (X-Guide, X-Nav technologies) approaches. Each practitioner inserted 4 implants per approach randomly with a 1-week gap to avoid memory bias (4 insertion sites × 3 approaches × 6 practitioners = 72 implants). The performance of practitioners was assessed by comparing actual implant deviation to the planned position, time required for implant placement and questionnaire-based self-confidence evaluation of practitioners on a scale of 1–30.

Results

The navigation approach significantly improved angular deviation compared with freehand (P < 0.001) and surgical guide (P < 0.001) irrespective of the experience. Surgical time with navigation was significantly longer compared to the freehand approach (P < 0.001), where experienced practitioners performed significantly faster compared to novice practitioners (P < 0.001). Overall, self-confidence was higher in favor of novice practitioners with both guided approaches. In addition, the confidence of novice practitioners (median score = 26) was comparable to that of experienced practitioners (median score = 27) for placing implants with the navigation approach.

Conclusions

Dynamic navigation system could act as a viable tool for dental implant placement. Unlike freehand and static-guided approaches, novice practitioners showed comparable accuracy and self-confidence to that of experienced practitioners with the navigation approach.

Graphical Abstract
Compared to what? Effects of social and temporal comparison standards of feedback in an e-learning context
Authors: Janson Marc P. , Siebert Jan , Dickhäuser Oliver
Published: October 2022

Performance evaluation is based on comparison standards. Results can either be contrasted to former results (temporal comparison) or results of others (social comparison). Existing literature analyzed potential effects of teachers’ stable preferences for comparison standards on students’ learning outcomes. The present experiments investigated effects of learners’ own preferences for comparison standards on learning persistence and performance. Based on research and findings on person-environment-fit, we postulated a fit hypothesis for learners’ preferences for comparison standards and framed feedback on learning persistence and performance. We tested our hypotheses in two separate experiments (N = 203 and N = 132) using different manipulations of framed feedback (temporal vs. social) in an e-learning environment, thus establishing high ecological validity and allowing objective data to be collected. We found first evidence for beneficial effects of receiving framed feedback towards own preferences on learning persistence and performance in our experiments. We tested fluency as a possible underlying psychological mechanism in our second experiment and observed a larger fit effect on learning persistence under disfluency. The results are discussed regarding a new theoretical perspective on the concept of preferences for comparison standards as well as opportunities for adaptive e-learning.
In vitro antibacterial effect of probiotics against Carbapenamase-producing multidrug-resistant Klebsiella pneumoniae clinical isolates, Cairo, Egypt
Authors: Abdelhalim Mona Mohiedden , Saafan Ghada Samy , El-Sayed Hoda Samir , Ghaith Doaa Mohammad
Published: October 2022

Background

Searching for a non-antibiotic therapeutic option such as probiotics is gaining momentum nowadays. We aimed to evaluate the in vitro antibacterial ability of cell-free supernatant (CFS) of selected Lactobacillus strains (with probiotic properties) against clinical isolates of OXA-48-producing multidrug-resistant (MDR) Klebsiella pneumoniae separately and in combination with cefoperazone antibiotic.

Methods

Over a period of 8 months, a cross-sectional experimental study involving 590 Klebsiella pneumoniae isolates was done. Our study took place at The Specialized Pediatric Teaching Hospital of Cairo University. Of the 590 Klebsiella pneumoniae isolates collected from blood cultures, pus, endotracheal aspirates, and pleural fluid, only 50 unrepeated clinical isolates of MDR Klebsiella pneumoniae-producing OXA-48-like detected by CHROMID® OXA-48 (bioMérieux, France) were selected for our study. After determining the minimal inhibitory concentration of CFS of ten Lactobacillus strains and cefoperazone each, the synergistic effect of both was tested.

Results

Among ten tested Lactobacillus spp., a significant increase in the mean value of inhibition zone diameter with CFS of L. helveticus (14.32 mm) and L. rhamnosus (13.3 mm) was detected separately. On the contrary, an antagonistic activity against all tested isolates was detected upon combination of Lactobacilli with cefoperazone (512 μg/ml). The mean value of inhibition zone diameter of L. helveticus CFS+ cefoperazone was (11.0 mm) and for L. rhamnosus CFS+ cefoperazone was (10.88 mm) (p value <0.001).

Conclusion

The antimicrobial efficiency of using CFS of Lactobacillus species separately indicates that these therapies may be a substitute treatment strategy against MDR Klebsiella pneumoniae.
Adaptations of evidence-based trauma-focused interventions for children and adolescents: a systematic review
Authors: Lange Brittany C. L. , Nelson Ashley , Lang Jason M. , Stirman Shannon Wiltsey
Published: October 2022

Background

Rates of potentially traumatic events (PTEs) and other forms of adversity among children are high globally, resulting in the development of a number of evidence-based interventions (EBIs) to address the adverse outcomes stemming from these experiences. Though EBIs are intended to be delivered according to set parameters, these EBIs are frequently adapted. However, little is known about existing adaptations of EBIs for children who experienced PTEs or other adversities. As such, this review aimed to determine: (1) why existing EBIs designed to address PTEs and other adversities experienced by children are adapted, (2) what processes are used to determine what elements should be adapted, and (3) what components of the intervention are adapted.

Methods

Nine academic databases and publicly available search engines were used to identify academic and grey literature. Initial screening, full-text review, data extraction, and quality determinations were completed by two members of the research team. Data were synthesized narratively for each adapted EBI by research question.

Results

Forty-two studies examining the adaptations of nine different EBIs were located, with Trauma-Focused Cognitive Behavioral Therapy and Cognitive Behavioral Intervention for Trauma in Schools being the most commonly adapted EBIs. Most frequently, EBIs were adapted to improve fit with a new population and to address cultural factors. Most commonly, researchers in combination with others made decisions about adapting interventions, though frequently who was involved in these decisions was not described. Common content adaptations included the addition of intervention elements and the tailoring/tweaking/refining of intervention materials. Common contextual adaptations included changes to the intended population, changes to the channel of treatment delivery, and changes to who administered the intervention.

Conclusions

Most published studies of EBI adaptions have been developed to improve fit and address cultural factors, but little research is available about adaptations made by clinicians in day-to-day practice. Efforts should be made to evaluate the various types of adaptations and especially whether adaptations improve access to services or improve child outcomes in order to ensure that all children exposed to trauma can access effective treatment.

Trial registration

The protocol for this systematic review was published with PROSPERO (CRD42020149536).
Building resilience through improving groundwater management for sustainable agricultural intensification in African Sahel
Authors: Ciampi L. , Plumpton H. J. , Osbahr H. , Cornforth R. J. , Petty Celia
Published: October 2022

Background

This paper examines the role of improved groundwater access and management in providing opportunities for sustainable agricultural intensification and building the resilience of community farmers in Southern Burkina Faso. The findings contribute to current debates about pathways of commercialisation and adaptation in the African Sahel, especially those seeking to find responses to managing the impacts of climate change and delivering on the Sustainable Development Goals.

Methods

This paper presents data that has been thematically analysed based on the Framework for Sustainable Intensification developed by Pretty and Bharucha (Ann Bot 114:1571–1596, 2014). The data used includes 144 Vulnerability Baseline Assessments which were conducted at the start of the project with the four target Burkinabe communities (Kado, Poa, Tomo, and Zhilivele), and 33 monitoring interviews and vulnerability assessments from the Burkinabe communities of Poa and Tomo to track progress and behaviour change resulting from the BRAVE project interventions.

Results

The data analysis showed that Burkinabe communities are already making some use of groundwater to support their agricultural livelihoods; most do this through accessing groundwater from shallow wells. It was also shown that there were improvements in the four main themes Identified by the Sustainable Intensification Framework. These included improved information sharing through increased peer-to-peer learning and improvements in confidence levels; improved social cohesion through reduction in community conflict over water resource management; asset improvements shown by tangible improvement of yields; and increased awareness exemplified by behaviour change.

Conclusions

Through using the Sustainable Intensification framework, this paper argues that such an approach improves essential aspects of resilience building such as information sharing, improved local governance and increased social capital and income. We argue that such changes provide essential pathways to reducing vulnerability and increasing resilience of at-risk communities in the Sahel, but that national policy alignment and investment is essential for long term change and sustainability.
The place of “Southwestern” Khanty among the Khanty dialects: Testimony of metadata and lexicon
Authors: Fejes László
Published: October 2022

Southwestern Khanty is a dialect documented only in the 2008 PhD dissertation by Olga Vaysman. This paper attempts to place Southwestern Khanty among other Khanty dialects. Metadata available on Southwestern Khanty are presented and interpreted: based on these, it remains obscure where Southwestern Khanty is spoken. Additionally, data on the lexicon of Southwestern Khanty are compared with the data of the largest dialectal and etymological dictionary of Khanty. Comparison shows that Southwestern Khanty, despite its name, is closest to the northernmost dialect, Obdorsk Khanty.

Kokkuvõte. László Fejes: “Edelahandi” paiknemine handi murrete hulgas: tõendid metaandmetest ja sõnavarast.Edelahandi murre on dokumenteeritud ainult Olga Vaysmani 2008. aasta doktoritöös. See artikkel püüab asetada edelahandi teiste handi murrete hulka. Esitatakse ja tõlgendatakse edelahandi murde kohta saadaolevaid metaandmeid, mille põhjal jääb segaseks, kus edelahandi murret kõneldakse. Lisaks võrreldakse edelahandi leksikoni andmeid handi suurima murde- ja etümoloogilise sõnaraamatu andmetega. Võrdlus näitab, et edelahandi murre on erinevalt nimetusest kõige lähemal põhjapoolseimale dialektile, obdorski murdele.
An Improved Real-Coded Genetic Algorithm for Synthesizing a Massive Planar Array
Authors:   Electrical Engineering Department, Faculty of Engineering Technology, Al-Balqa Applied University, 15008 Amman 11134 Jordan, Al-Qaisi Aws
Published: October 2022

The design of the Bi-State Antenna Massive Planar Array contributes to the mitigation of the energy consumption using the genetic algorithm under the constraints of minimizing the sidelobe level (SLL) and controlling the changing of the first null beamwidth (FNBW). Usually, Planar arrays are employed in communication applications based on battery usage such as portable radars. This paper optimizes a Uniform Rectangular Array (URA) with 1600 identical antenna elements using a Real-Coded Genetic Algorithm (RCGA). The optimization process is performed because the optimum set of the current excitation weight of radiating elements is found in the form of the ON-OFF state to conserve the consumed power. Hence, the highest performance of the Array Factor (AF) and the desired Beamwidth is selected. The main contribution presented in the paper is the ability to optimize a large number of array elements using the RCGA algorithm by dividing the array into a subset of arrays. The simulated results are performed to verify the effectiveness of the genetic thinned URA. The equivalent of 24.4% of the energy consumption is saved by selecting the antenna elements that could be scrambled with high effectiveness. In this paper, the results were obtained using MATLAB CAD Ver. 2018a as a platform.
Energy Efficient Routing Protocol for IoT Networks Using Ns2 Simulation
Authors:   Department of Information technologies, Altinbas University, Istanbul, Turkey, Ahmed Ali Saadoon , Kurnaz Sefer
Published: October 2022

Limited power, poor memory, and low computing capabilities make the Internet of Things (IoT) devices resource restricted. Extending the network lifetime is one of the critical objectives in IoT. When it comes to preserving energy in a smart city, routing is one of the most critical factors. Designing an energy-efficient routing protocol boosts energy utilization to a significant extent. Networks with Reduced Power and Loss, the Energy Aware Routing Protocol for the IoT, decreases network traffic and extends network lifespan. As a result, routing variables such as load, remaining energy, and predicted transmission count identify the optimum parent for data transfer. The data traffic is dispersed throughout the network because it considers the load routing measure during route construction. As a result, it enhances network lifetime and achieves a high packet delivery ratio.
Performance Analysis of Typical Routing Protocols for Cognitive Radio Ad Hoc Networks
Authors:   Hung Yen University of Technology and Education, Hung Yen 160000, Viet Nam, Dang Anh V. , Nguyen Tan D. , Nguyen Ban T. , Nguyen Quy M.
Published: October 2022

Cognitive Radio Ad Hoc Networks (CRAHNs) have powerful growth potential, a solution to solve the scarce spectrum resources problem. Due characteristics can change configuration parameters during transmission, thereby opening up the possibility to use the dynamic spectrum. This leads to the complexity of the design of routing protocols. In this paper, we present the routing protocol is proposed to apply for Cognitive Radio Ad hoc Networks. After explaining the principle of operation, we analyze and evaluate the performance of the protocol on NS2. Simulation results show that the protocol End-to-End with throughput and latency is best improved. Finally, we discuss the limitations of recently routing protocols and propose some further research directions.
An Improved Whale optimization Algorithm for Cross layer Neural Connection Network of MANET
Authors:   Department of Computer Science and Engineering, SCSVMV University, Enathur, Kanchipuram, 631561, India, Gayatri V. , Kumaran M. Senthil
Published: October 2022

The connecting of numerous remote mobile nodes is known as a mobile ad hoc network. These networks are dynamic and self-contained, allowing them to move about freely. It is referred to as a structure-less network since it lacks a central controller. MANET(Mobile ad hoc network) is one of the most recent developing technologies to gain popularity. This research presents a improved Whale Optimization is enabled for the best feasible solution of the CNCN.The improved whale optimization uses a probability function to determine the best communication path in the network. According to a comparative examination of research, Improved WOA gives significant performance. A two-layer Neural Connection Network model with a cross-layer structure. Physical layer and data link layer. Then in the Physical Layer the load balancing as well as the packet specification is happensso we go for optimization technique. In Data Link Layer the Packet with huge amount of network path is enabled and the packets are delivered with the help of the Connector. The improved whale optimization is enabled in order to achieve the highest level of overall performance such as Waiting time, reliability, failure probability, throughput, and Instantaneous Throughput.
Studying Routing Issues in Vanets Using Ns-3 and SUMO
Authors:   Electrical Engineering Department, Faculty of Engineering, The British University in Egypt, Cairo, Egypt, Abdellatif Mohammad M. , Aly Omar
Published: October 2022

Vehicular Ad-hoc Networks VANETs are normally sparse, highly dense, and highly mobile with many different and ever-changing topologies. These characteristics impose a challenge on finding a routing algorithm that fits the requirements of such network. The aim of this work is to study the performance issues of VANETs under different scenarios using realistic mobility models. In this paper, a comparative study is done among Ad-hoc On- Demand Distance Vector (AODV), Optimized Link State Routing (OLSR) and position-based routing protocols, namely Greedy Perimeter stateless routing (GPSR), and Max duration Min angle GPSR (MMGPSR). The comparison is done using key quality of service QoS metrics such as average routing goodput, end-to-end delay, MacPhy overhead, and packet delivery ratio PDR. The study is conducted using Network Simulator 3 (NS3) and SUMO.
Comparative Review of Routing Protocols in MANET for Future Research in Disaster Management
Authors:   Department of Computer Science, FSKTM, University Tun Hussein Onn Malaysia (UTHM), Malaysia, Soomro Abdul Majid , Fudzee Mohd Farhan Bin Md. , Hussain Muzammil , Saim Hafiz Muhammad , Zaman Gohar , Atta-ur-Rahman Atta-ur-Rahman , AlUbaidan Haya , Nabil Majed
Published: October 2022

A mobile ad hoc network (MANET) is a non-centralized, self-organized, and self-managed wireless ad hoc network that is used in a specific circumstance. The changing nature of the network's architecture and nodes necessitates a routing protocol to ensure efficient source-to-destination communication. MANET manipulates different sectors of life such as rescue and emergency operations, real-time information, interpersonal communication, network portioning, and catastrophe management due to its ad hoc and non-identical uses. Routing and maintenance are critical parts of effective time management and considered research study for MANET communication. This study focuses on the routing and maintenance process of MANET that has dynamic change in topology and nodes to deliver data rapidly. The motivational component of MANET for the research is route discovery, and several techniques have been proposed for it. Each has higher expectations than the other. This comparative analysis reveals issues for route discovery and maintenance of MANET communication, such as whether one is better in different network conditions than the others. This study examines the literature on MANET route discovery and maintenance strategies. As a result, a comparison of several routing protocol techniques is made. To achieve improvement, these methods include some suggestions for performance improvement using routing protocols especially in disaster management. This study presents directions for future experimentation, which will be pursued to identify the most advantageous collection of tactics for meeting the needs of various applications.
Intelligent Reflecting Surface (IRS) assisted mmWave Wireless Communication Systems: A Survey
Authors:   LBS Institute of Technology for Women, APJ Abdul Kalam Technological University, Kerala, India, S. Nandan , Rahiman M. Abdul
Published: October 2022

Intelligent Reflecting Surface (IRS) is a promising technology for enhancing the coverage of millimeter wave (mmWave) based future wireless communication systems. Severe path loss and high directivity make mmWave transmission vulnerable to blockage. To overcome these issues, Intelligent Reflecting Surface (IRS) is introduced as a revolutionizing technology to provide better communication with the help of a virtual line-of-sight (LOS) link to bypass obstacles between transceivers through intelligent reflections. Significant performance improvement is achieved by smartly reconfiguring the wireless propagation environment using low-cost passive reflecting elements integrated on a planar surface. In this paper, we provide a survey of the IRS technology, its applications in mmWave wireless communication scenario, advantages over existing technologies, key design challenges, hardware architecture and the practical constraints while deployment. Furthermore, we highlight the relevant directions in future work.
Deep Learning Algorithm Models for Spam Identification on Cellular Short Message Service
Authors:   Telecommunication Engineering, Institut Teknologi Telkom Purwokerto, Indonesia, Hikmaturokhman Alfin , Nafi’ah Hanin , Larasati Solichah , Wahyudin Ade , Ariprawira Galih , Pramono Subuh
Published: October 2022

Nowadays, the types and products of cellular telecommunications services are very diverse, especially with the upcoming of 5G technology, which makes telecommunications service products such as voice, video, and text messages rely on data packages. Even though the digital era is rapidly growing, the Short Messaging Service (SMS) is still relevant and used as a telecommunication service despite so many sophisticated instant messaging services that rely on the internet. Smartphone users especially in Indonesia are often terrorized by spam messages with pretentious content. Moreover, the SMS came from an unknown number and contained a message or link to a fraudulent site. This study develops a Deep Learning model to predict whether a short text message (SMS) is important or spam. This research domain belongs to Natural Language Processing (NLP) for text processing. The models used are Dense Network, Long Short Term Memory (LSTM), and Bi-directional Long Short Term Memory (Bi-LSTM). Based on the evaluation of the Dense Network model, it produces a loss of 14.22% and an accuracy of 95.63%. The evaluation of the LSTM model is 19.89% loss and 94.76% accuracy. Finally, the evaluation of the Bi-LSTM model is 19.88% loss and 94.75% accuracy.
KOOPMAN OPERATOR BASED FAULT DIAGNOSTIC METHODS FOR MECHANICAL SYSTEMS
Authors: NICHIFOR ALEXANDRU , QU YONGZHI
Published: October 2022

Traditionally, dynamical systems can be simulated with physics-based model when the design parameters and material property are pre-known. However, when a system is deployed in field and has suffered potential degradation, a physics-based model might be infeasible to obtain. Moreover, the non-linearity and unknown coupling between the system and contacting constraints are often hard to determine accurately. The analysis of those systems becomes practically problematic. In this paper, the Koopman operator is used to learn and represent a dynamic system in a data driven manner. This paper proposes two methods of using the Koopman operator to extract and classify critical parameters of a non-linear dynamic mechanical system for fault diagnosis. The first method proposes a model to extract key features from a dynamic system and feed the features to a neural network to classify the existence of a fault. The second method uses parameters derived from the Koopman operator to create a prediction model with healthy data. This prediction model is then used to predict future system dynamics for a measured time evolution and compare that with direct measurements when future dynamics become available. Both methods are then tested via an experimental case study and the results are discussed.
UNSUPERVISED CONDITION MONITORING OF STRUCTURES USING VMD AND ISOLATION FOREST
Authors: MOUSAVI MOHSEN , GANDOMI AMIR H.
Published: October 2022

In this paper, an unsupervised condition monitoring of civil infrastructures under environmental and operational variations is proposed. To this end, a couple of the lowest natural frequency signals of the structure recorded over a long period of time are studied for damage. Most of the existing techniques are supervised and require baseline information from the healthy state of the structure. In this paper, however, we explore the possibility of performing unsupervised condition monitoring using Isolation Forest (IF) as a well-known unsupervised machine learning approach. We show that a preprocessing of the frequency signals using the Variational Mode Decomposition (VMD) algorithm plays a key role in the success of the IF algorithm in the unsupervised condition monitoring of structures. To investigate the performance of the proposed method, the benchmark problem of the Z24 bridge is studied in this paper. The results show that the proposed methodology stays successful in most of the cases but at a period of very cold temperature where a nonlinear relationship between the frequencies and temperature presents.
HIGH-SPEED RAIL INSPECTION EXPLOITING AN ANOMALY DETECTION DATA PROCESSING APPROACH
Authors: WU YUNING , ZHU XUAN , BAILLARGEON JAY
Published: October 2022

Rail internal defects such as detail fracture and transverse fissure are among the leading causes of track-related railway accidents. Therefore, it is critical to develop effective rail defect inspection systems and data processing methods to prevent catastrophic accidents and derailments. This study developed an anomaly detection framework using deep autoencoder (DAE) for rail defect detection. And the team evaluated its performance based on data collected by a prototype passive acoustic rail inspection system. Autoencoder is a semi-supervised learning algorithm that identifies observations in a dataset that deviate significantly from the remaining data. First, the team performed data cleaning and transfer function reconstruction using a dataset collected at the Federal Railroad Administration’s Transportation Technology Center in Pueblo, Colorado. Then, handcrafted or knowledge-driven features were extracted from the transfer functions and fed into a statistical outlier analysis as the benchmark. Also, reconstructed transfer functions at clean rail segments were directly used as the input to train and validate the DAE algorithm. The results demonstrated the effectiveness of DAE for structural discontinuity detection and showed promise for rail flaw detection.
A NEW APPROACH FOR ANALYZING SAFETY AND PERFORMANCE FACTORS IN CIVIL INFRASTRUCTURES USING CORRELATION NETWORKS AND POPULATION ANALYSIS
Authors: CHETTI PRASAD , ALI HESHAM , GHERSI DARIO , GANDHI ROBIN , RICKS BRIAN , NAJJAR LOTFOLLAH
Published: October 2022

Public safety and economic growth are some of the key factors in motivating governments to keep their civil infrastructures, in particular bridges, safe and sound. However, the American Society for Civil Engineers gave a C+ grade for U.S. bridges in 2017. It has been observed that many parameters associated with bridges, such as geographical locations, designs, materials used, and traffic patterns, play key roles in determining the safety and deterioration rates of bridges. However, there is still a lack of studies that analyze the exact impact of all relevant parameters. The motivation of this study is to propose a new data-driven model that employs the concept of population analysis in assessing the impact of each potential parameter and extracting critical information associated with civil infrastructures and their deterioration patterns. We use a correlation network model to analyze and visualize the big data associated with more than 600,000 bridges in the national bridge inventory database. Graph theoretic analysis is applied to the correlation networks to find elements or clusters of interest. A sub-set of 268 bridges across the US of the same age are considered for this case study and the Markov clustering algorithm is used to obtain the clusters from the correlation network. Enrichment analysis is applied to the clusters to identify the significantly enriched input parameters. Preliminary results reveal several facts, including that prestressed concrete bridges in the Southeast region perform better than steel bridges in the Midwestern region. The obtained results are supported by previous research and further validated by the exploratory factor analysis when dividing the clusters into two groups. The proposed network model provides a new data-driven methodology for evaluating the safety and performance of structures. It provides domain experts with valuable information on how to efficiently allocate time and funds for inspecting existing bridges and how to identify key bridge parameters suitable for designing and constructing new bridges in various geographical areas.
NONLINEAR REDUCED ORDER MODELLING OF SOIL STRUCTURE INTERACTION EFFECTS VIA LSTM AND AUTOENCODER NEURAL NETWORKS
Authors: SIMPSON THOMAS , DERVILIS NIKOLAOS , COUTURIER PHILIPPE , MALJAARS NICO , CHATZI ELENI
Published: October 2022

In the field of structural health monitoring (SHM), inverse problems which require repeated analyses are common. With the increase in the use of nonlinear models, the development of nonlinear reduced order modelling techniques is of paramount interest. Of considerable research interest, is the use of flexible and scalable machine learning methods which can learn to approximate the behaviour of nonlinear dynamic systems using input and output data. One such nonlinear system of interest, in the context of wind turbine structures, is the soil structure interaction (SSI) problem. Soil demonstrates strongly nonlinear behaviour with regards to its restoring force and has been shown to considerably influence the dynamic response of wind turbine structures. In this work, we demonstrate the application of a recently developed nonlinear reduced order modelling method, which leverages Autoencoder and LSTM neural networks, to a nonlinear soil structure interaction problem of a wind turbine monopile subject to realistic loading at the seabed level. The accuracy and efficiency of the methodology is compared to full order simulations carried out using Abaqus. The ROM was shown to have good fidelity and a considerable reduction in computational time for the system considered.
DEBONDING ANALYSIS OF COMPOSITE MATERIAL USING ULTRASONIC WAVE-BASED NDT METHODS
Authors: BALASUBRAMANIAM, KALEESWARAN , WANDOWSKI TOMASZ , MALINOWSKI PAWEL
Published: October 2022

The article investigates multiple debonding in a glass fibre reinforced polymer structure (GFRPS) using nondestructive testing (NDT) based on ultrasonic guided waves (UGW) propagation. The piezoelectric transducers (PZT) attached to the material excite the UGW and the registered time signals are analyzed. The debonding are in various depths of the GFRPS. It was assessed using NDT-based tools. The presence of debonding and wave scattering based on the depth and location in GFRPS is studied. This is followed by using signal processing methods to visualize and analyze the different characteristics of the ultrasonic waves before and after the debonding. Thus an experimental-based approach to identify the debonding inside the GFRPS and its influence is studied.
SPATIAL FILTERING TECHNIQUE-BASED ENHANCEMENT OF THE RECONSTRUCTION ALGORITHM FOR THE PROBABILISTIC INSPECTION OF DAMAGE (RAPID)
Authors: LOMAZZI LUCA , GONZALEZ-JIMENEZ ALVARO , CADINI FRANCESCO , MANES ANDREA , GIGLIO MARCO
Published: October 2022

A common active structural health monitoring (SHM) solution for thin-walled structures consists of processing ultrasonic guided waves signals, which are excited and sensed by means of a network of piezoelectric devices installed on the structure, with the purpose of providing a tomographic reconstruction-based damage probability map of the structure. A promising reconstruction algorithm typically employed within this framework is the Reconstruction Algorithm for the Probabilistic Inspection of Damage (RAPID) algorithm, which has been shown to provide satisfactory results in terms of damage detection and localisation. However, this algorithm comes with some disadvantages and minor issues, such as artefacts creation in case an unevenly distributed sensors layout is installed on the structure, which may significantly worsen the damage diagnosis performance of the monitoring framework. In this paper, an enhancement of the original RAPID algorithm is presented, which exploits spatial filtering techniques to reduce possible artificially created artefacts, thus allowing installing on structures any network of sensors without reducing the diagnostic performances. The improved damage localisation accuracy obtained using the proposed algorithm is proven by means of a case study involving a numerical model of a realistic composite panel with an unevenly distributed network of sensors.
USING LIDAR TO IDENTIFY PLANAR MEASUREMENT REGIONS IN ULTRASONIC INSPECTIONS OF COMPLEX STRUCTURES
Authors: CUMMINGS IAN T. , REINISCH ELENA C. , JACOBSON ERICA M. , FRASER DAVID H. , WACHTOR ADAM J. , FLYNN ERIC B.
Published: October 2022

Acoustic Steady State Excitation Spatial Spectroscopy (ASSESS) is an ultrasonic inspection technique that was developed to rapidly evaluate large structures and identify regions of damage. An ultrasonic transducer affixed to the structure emits a single tone, and a scanning laser Doppler vibrometer (LDV) records the structure’s steady-state surface velocity response. Previous work has shown how local wavenumber can be estimated from the complex steady-state velocity response. This process has proved successful in detecting corrosion defects, delaminations, and regions of varying thickness. This work introduces a new processing method that utilizes a LiDAR generated point cloud representation of the scan region to identify and extract large planar sections from the measurement without causing distortion in the final wavenumber estimates. This new method uses the RANSAC algorithm to robustly extract planar sections and maps the complex steady-state response data onto a uniform grid on the detected planes. This is done in order to facilitate the use of an existing wavenumber estimation technique. We present wavefield and wavenumber results generated by applying this algorithm on a real-world dataset from a large area scan in an industrial structure with steel walls containing stringers, columns, and regions with different thicknesses.
INSTANCE-SEGMENTATION-BASED DENSE ON-SITE ROCK FRAGMENT RECOGNITION DURING REAL-WORLD TUNNEL EXCAVATION
Authors: YANG XU , WEIDONG QIAO , HUI LI
Published: October 2022

Timely recognition of rock fragments and their morphological sizes can help adjust excavation parameters during tunnel boring machine (TBM) tunneling. Traditional manual inspection highly relies on experiences and subjective judgments of human operators and conducting sieving tests is not real-time and energy-consuming. Rock fragments in real-world images are often observed against a dark background, distributed with high size diversity, complicatedly distributed, and blocked by each other. To solve these problems, this study proposes a novel instance segmentation-based method for on-site rock fragments recognition. The proposed instance segmentation model includes two subnetworks: object detection and semantic segmentation. The results show that 88% of rock fragments can be recognized, and the average recall and average IoU values reach 0.85 and 0.75 on the 15 test images, respectively. Besides, both small and large rock fragments can be recognized well. The predicted size distributions of the major and minor axis lengths of the rock fragments fit well with the ground-truth ones statistically. In conclusion, this study can provide both visual recognition and statistical results for the size distribution of on-site rock fragments.
MACHINE LEARNING OF ULTRASONIC DATA FOR EXPANSION PREDICTION OF CONCRETE WITH ALKALI-SILICA REACTION
Authors: SUN HONGBIN , ZHU JINYING , RAMUHALLI PRADEEP
Published: October 2022

Ultrasonic nondestructive testing is a promising method for performing damage assessments on concrete subjected to alkali-silica reactions (ASRs). Previous research incorporated only some ultrasonic wave parameters, and the other information from the ultrasonic signals was discarded. In this work, 13 features, including wave velocity and wavelet features, were extracted from the ultrasonic signals. A curve-fitting method was used to fit a polynomial relationship between the wave velocity and expansion of one concrete sample subjected to ASR to predict the expansion of another concrete sample subjected to ASR. Support vector regression (SVR), a machine learning model, was trained using all 13 features derived from the ultrasonic data obtained from the ASR samples. The SVR was then tested using the datasets from the ASR-2D sample. The performance showed that the curve-fitting method and the SVR had poor prediction results on the expansion of the ASR-2D sample. With feature selection, the performance of the SVR model using six selected features was significantly improved.
INCORPORATING PRE-EXISTING CRACKS IN STRUCTURAL ASSESSMENT OF RC STRUCTURES
Authors: YU KE , SHU JIANGPENG , ZANDI KAMYAB
Published: October 2022

Cracks in reinforced concrete (RC) structures can be detrimental as they grow beyond the limits. Cracks should be included in structural assessment methods to ensure the durability and load capacity of existing structures. However, conventional models used in the assessment of existing structures do not reflect the real cracking condition which implies that advanced assessment methods are required. In this study, pre-existing cracks were introduced into finite element analysis to identify the ductility, failure characteristics, and ultimate capacity of cracked structures. A beam specimen taken from the edge beams of an existing bridge had been subjected to a four-point bending test, and the results are used in this study for validation purposes. The specimens showed varying levels of cracking due to loading as well as reinforcement corrosion during the service life. Five different analyses were carried out to account for the effect of loading cracks and corrosion cracks based on two crack modeling approaches, namely weakened element approach and weakened bond-slip relation approach. The results showed that the failure of the beams was caused by anchorage failure. The differences in the load capacity predicated by different models are discussed. It was observed that incorporating pre-existing cracks by using weakened elements and weakened bond-slip relation approaches can be a practical method to model and assess cracked RC beams.
AN IMAGE-BASED CONCRETE CRACK DETECTION METHOD USING CONVOLUTIONAL NEURAL NETWORKS
Authors: LUO XI , GUO JIA , ZANDI KAMYAB
Published: October 2022

This paper proposes a CNN-based crack detection method that can recognize and extract cracks from photos of concrete structures. The algorithm consists of two subsequent procedures, classification, and segmentation, achieved by two convolutional neural networks respectively. First, full images are divided into patches and classified as positive and negative. Then, those sub-images classified as positive are further processed by the image segmentation procedure to obtain the pixel level geometry of the cracks. For the classification part, the performance of transfer learning models based on pre-trained VGG16, Inception V3, MobileNet and DenseNet169 is compared with different classifier. Finally, the CNN based on MobileNet was trained with 30,000 training images and reached 97% testing accuracy and 0.96 F1 score on testing image. For the segmentation part, different neural networks based on the elegant U-net architecture are built and tested. The models are trained with 3840 crack images and annotated ground truth and compared quantitatively and qualitatively. The model with the best performance reached 88% sensitivity on test data set. The combination of the classification and segmentation neural networks achieves an image-based crack detection method with high efficiency and accuracy. The algorithm can process any full image size as input. Compared with most machine learning based crack detection algorithms using sub-image classification, a relatively larger patch size is used in this paper and in this way the classification is more robust and accurate. On the other hand, the negative areas in the full image will not be concerned in the segmentation procedure and this fact not only saves a lot of computational power but also significantly increases the accuracy compared to the segmentation performed on full images.