bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting Bacteremia among Septic Patients Based on ED Information by Machine Learning Methods: A Comparative Study
Authors: Goh Vivian , Chou Yu-Jung , Lee Ching-Chi , Ma Mi-Chia , Wang William Yu Chung , Lin Chih-Hao , Hsieh Chih-Chia
Published: October 2022

Introduction: Bacteremia is a common but life-threatening infectious disease. However, a well-defined rule to assess patient risk of bacteremia and the urgency of blood culture is lacking. The aim of this study is to establish a predictive model for bacteremia in septic patients using available big data in the emergency department (ED) through logistic regression and other machine learning (ML) methods. Material and Methods: We conducted a retrospective cohort study at the ED of National Cheng Kung University Hospital in Taiwan from January 2015 to December 2019. ED adults (≥18 years old) with systemic inflammatory response syndrome and receiving blood cultures during the ED stay were included. Models I and II were established based on logistic regression, both of which were derived from support vector machine (SVM) and random forest (RF). Net reclassification index was used to determine which model was superior. Results: During the study period, 437,969 patients visited the study ED, and 40,395 patients were enrolled. Patients diagnosed with bacteremia accounted for 7.7% of the cohort. The area under the receiver operating curve (AUROC) in models I and II was 0.729 (95% CI, 0.718–0.740) and 0.731 (95% CI, 0.721–0.742), with Akaike information criterion (AIC) of 16,840 and 16,803, respectively. The performance of model II was superior to that of model I. The AUROC values of models III and IV in the validation dataset were 0.730 (95% CI, 0.713–0.747) and 0.705 (0.688–0.722), respectively. There is no statistical evidence to support that the performance of the model created with logistic regression is superior to those created by SVM and RF. Discussion: The advantage of the SVM or RF model is that the prediction model is more elastic and not limited to a linear relationship. The advantage of the LR model is that it is easy to explain the influence of the independent variable on the response variable. These models could help medical staff identify high-risk patients and prevent unnecessary antibiotic use. The performance of SVM and RF was not inferior to that of logistic regression. Conclusions: We established models that provide discrimination in predicting bacteremia among patients with sepsis. The reported results could inspire researchers to adopt ML in their development of prediction algorithms.
Unraveling the Puzzle: Oocyte Maturation Abnormalities (OMAS)
Authors: Hatirnaz Safak , Hatirnaz Ebru , Çelik Samettin , Çalışkan Canan Soyer , Tinelli Andrea , Malvasi Antonio , Sparic Radmila , Baldini Domenico , Stark Michael , Dahan Michael H.
Published: October 2022

Oocyte maturation abnormalities (OMAS) are a poorly understood area of reproductive medicine. Much remains to be understood about how OMAS occur. However, current knowledge has provided some insight into the mechanistic and genetic origins of this syndrome. In this study, current classifications of OMAS syndromes are discussed and areas of inadequacy are highlighted. We explain why empty follicle syndrome, dysmorphic oocytes, some types of premature ovarian insufficiency and resistant ovary syndrome can cause OMAS. We discuss live births in different types of OMAS and when subjects can be offered treatment with autologous oocytes. As such, we present this review of the mechanism and understanding of OMAS to better lead the clinician in understanding this difficult-to-treat diagnosis.
Expiratory Technique versus Tracheal Suction to Obtain Good-Quality Sputum from Patients with Suspected Lower Respiratory Tract Infection: A Randomized Controlled Trial
Authors: Cartuliares Mariana B. , Rosenvinge Flemming S. , Mogensen Christian B. , Skovsted Thor A. , Andersen Steen L. , Pedersen Andreas K. , Skjøt-Arkil Helene
Published: October 2022

Microbiological diagnostics of good-quality sputum samples are fundamental for infection control and targeted treatment of lower respiratory tract infections (LRTI). This study aims to compare the expiratory technique and tracheal suction on the quality of sputa from adults acutely hospitalized with suspected LRTI. We performed an open-label, randomized controlled trial. Patients were randomized to sputum sampling by tracheal suction (standard care) or the expiratory technique. The primary outcome was quality of sputum evaluated by microscopy and was analysed in the intention-to-treat population. The secondary outcomes were adverse events and patients experience. In total, 280 patients were assigned to tracheal suction (n = 141, 50.4%) or the expiratory technique (n = 139, 49.6%). Sputum samples were collected from 122 (86.5%) patients with tracheal suction and 67 (48.2%) patients with expiratory technique. Good-quality sputa were obtained more often with tracheal suction than with expiratory technique (odds ratio 1.83 [95% CI 1.05 to 3.19]; p = 0.035). There was no statistical difference in adverse events (IRR 1.21 [95% CI, 0.94 to 1.66]; p = 0.136), but patient experience was better in the expiratory technique group (p < 0.0001). In conclusion, tracheal suction should be considered a routine procedure in emergency departments for patients with suspected LRTI.
Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD
Authors: Machetanz Lena , Huber David , Lau Steffen , Kirchebner Johannes
Published: October 2022

Today’s extensive availability of medical data enables the development of predictive models, but this requires suitable statistical methods, such as machine learning (ML). Especially in forensic psychiatry, a complex and cost-intensive field with risk assessments and predictions of treatment outcomes as central tasks, there is a need for such predictive tools, for example, to anticipate complex treatment courses and to be able to offer appropriate therapy on an individualized basis. This study aimed to develop a first basic model for the anticipation of adverse treatment courses based on prior compulsory admission and/or conviction as simple and easily objectifiable parameters in offender patients with a schizophrenia spectrum disorder (SSD). With a balanced accuracy of 67% and an AUC of 0.72, gradient boosting proved to be the optimal ML algorithm. Antisocial behavior, physical violence against staff, rule breaking, hyperactivity, delusions of grandeur, fewer feelings of guilt, the need for compulsory isolation, cannabis abuse/dependence, a higher dose of antipsychotics (measured by the olanzapine half-life) and an unfavorable legal prognosis emerged as the ten most influential variables out of a dataset with 209 parameters. Our findings could demonstrate an example of the use of ML in the development of an easy-to-use predictive model based on few objectifiable factors.
L-Tetrolet Pattern-Based Sleep Stage Classification Model Using Balanced EEG Datasets
Authors: Barua Prabal Datta , Tuncer Ilknur , Aydemir Emrah , Faust Oliver , Chakraborty Subrata , Subbhuraam Vinithasree , Tuncer Turker , Dogan Sengul , Acharya U. Rajendra
Published: October 2022

Background: Sleep stage classification is a crucial process for the diagnosis of sleep or sleep-related diseases. Currently, this process is based on manual electroencephalogram (EEG) analysis, which is resource-intensive and error-prone. Various machine learning models have been recommended to standardize and automate the analysis process to address these problems. Materials and methods: The well-known cyclic alternating pattern (CAP) sleep dataset is used to train and test an L-tetrolet pattern-based sleep stage classification model in this research. By using this dataset, the following three cases are created, and they are: Insomnia, Normal, and Fused cases. For each of these cases, the machine learning model is tasked with identifying six sleep stages. The model is structured in terms of feature generation, feature selection, and classification. Feature generation is established with a new L-tetrolet (Tetris letter) function and multiple pooling decomposition for level creation. We fuse ReliefF and iterative neighborhood component analysis (INCA) feature selection using a threshold value. The hybrid and iterative feature selectors are named threshold selection-based ReliefF and INCA (TSRFINCA). The selected features are classified using a cubic support vector machine. Results: The presented L-tetrolet pattern and TSRFINCA-based sleep stage classification model yield 95.43%, 91.05%, and 92.31% accuracies for Insomnia, Normal dataset, and Fused cases, respectively. Conclusion: The recommended L-tetrolet pattern and TSRFINCA-based model push the envelope of current knowledge engineering by accurately classifying sleep stages even in the presence of sleep disorders.
“3D Counterpart Analysis”: A Novel Method for Enlow’s Counterpart Analysis on CBCT
Authors: D’Attilio Michele , Peluso Antonino , Falone Giulia , Pipitone Rossana , Moscagiuri Francesco , Caroccia Francesco
Published: October 2022

The aim of this study was to propose a novel 3D Enlow’s counterpart analysis traced on cone-beam computed tomography (CBCT) images. Eighteen CBCT images of skeletal Class I (ANB = 2° ± 2°) subjects (12 males and 6 females, aged from 9 to 19 years) with no history of previous orthodontic treatment were selected. For each subject, a 2D Enlow’s counterpart analysis was performed on lateral cephalograms extracted from the CBCT images. The following structures were identified: mandibular ramus, middle cranial floor, maxillary skeletal arch, mandibular skeletal arch, maxillary dento-alveolar arch, mandibular dento-alveolar arch. The differences between each part and its relative counterpart obtained from the 2D analysis were than compared with those obtained from a 3D analysis traced on the CBCT images. A Student’s t-test did not show any statistical significant difference between the 2D and 3D measurements. The landmarks proposed by this study identified the cranio-facial structures on the 3D images in a way that could be superimposed on those described by Enlow in his analysis performed on 2D lateral cephalograms.
Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome Data
Authors: Imangaliyev Sultan , Schlötterer Jörg , Meyer Folker , Seifert Christin
Published: October 2022

Most of the microbiome studies suggest that using ensemble models such as Random Forest results in best predictive power. In this study, we empirically evaluate a more powerful ensemble learning algorithm, multi-view stacked generalization, on pediatric inflammatory bowel disease and adult colorectal cancer patients’ cohorts. We aim to check whether stacking would lead to better results compared to using a single best machine learning algorithm. Stacking achieves the best test set Average Precision (AP) on inflammatory bowel disease dataset reaching AP = 0.69, outperforming both the best base classifier (AP = 0.61) and the baseline meta learner built on top of base classifiers (AP = 0.63). On colorectal cancer dataset, the stacked classifier also outperforms (AP = 0.81) both the best base classifier (AP = 0.79) and the baseline meta learner (AP = 0.75). Stacking achieves best predictive performance on test set outperforming the best classifiers on both patient cohorts. Application of the stacking solves the issue of choosing the most appropriate machine learning algorithm by automating the model selection procedure. Clinical application of such a model is not limited to diagnosis task only, but it also can be extended to biomarker selection thanks to feature selection procedure.
Comparison of Spectral-Domain OCT versus Swept-Source OCT for the Detection of Deep Optic Disc Drusen
Authors: Rothenbuehler Simon P. , Malmqvist Lasse , Belmouhand Mohamed , Bjerager Jakob , Maloca Peter M. , Larsen Michael , Hamann Steffen
Published: October 2022

Deep optic disc drusen (ODD) are located below Bruch’s membrane opening (BMO) and may go undetected due to the challenges in imaging them. The purpose of this study is a head-to-head comparison of currently widely used imaging technologies: swept-source optical coherence tomography (SS-OCT; DRI OCT-1 Triton, Topcon) and enhanced depth imaging spectral-domain optical coherence tomography (EDI SD-OCT; Spectralis OCT, Heidelberg Engineering) for the detection of deep ODD and associated imaging features. The eyes included in this study had undergone high-resolution imaging via both EDI SD-OCT and SS-OCT volume scans, which showed at least one deep ODD or a hyperreflective line (HL). Grading was performed by three graders in a masked fashion. The study findings are based on 46 B-scan stacks of 23 eyes including a total of 7981 scans. For scan images with ODD located above or below the level of BMO, no significant difference was found between the two modalities compared in this study. However, for HLs and other features, EDI SD-OCT scan images had better visualization and less artifacts. Although SS-OCT offers deep tissue visualization, it did not appear to offer any advantage in ODD detection over a dense volume scan via EDI SD-OCT with B-scan averaging.
Predictors of Hepatocellular Carcinoma Early Recurrence in Patients Treated with Surgical Resection or Ablation Treatment: A Single-Center Experience
Authors: Giuffrè Mauro , Zuliani Enrico , Visintin Alessia , Tarchi Paola , Martingano Paola , Pizzolato Riccardo , Bonazza Deborah , Masutti Flora , Moretti Rita , Crocè Lory Saveria
Published: October 2022

Introduction: Hepatocellular carcinoma (HCC) is the sixth most diagnosed malignancy and the fourth leading cause of cancer-related death worldwide, with poor overall survival despite available curative treatments. One of the most crucial factors influencing survival in HCC is recurrence. The current study aims to determine factors associated with early recurrence of HCC in patients with BCLC Stage 0 or Stage A treated with surgical resection or local ablation. Materials and Methods: We retrospectively enrolled 58 consecutive patients diagnosed with HCC within BCLC Stage 0 or Stage A and treated either by surgical resection or local ablation with maximum nodule diameter < 50 mm. In the first year of follow-up after treatment, imaging was performed regularly one month after treatment and then every three months. Each case was discussed collectively by the Liver Multidisciplinary Group to decide diagnosis, treatment, follow-up, and disease recurrence. Variables resulting in statistically significant difference were then studied by Cox regression analysis; univariately and then multivariately based on forward stepwise Cox regression. Results are represented in hazard ratio (H.R.) with 95% confidence interval (C.I.). Results: There was no statistically significant difference in recurrence rates (34.8 vs. 45.7%, log-rank test, p = 0.274) between patients undergoing surgical resection and local ablation, respectively. Early recurrence was associated with male gender (HR 2.5, 95% C.I. 1.9–3.1), nodule diameter > 20 mm (HR 4.5, 95% C.I. 3.9–5.1), platelet count < 125 × 103 cell/mm3 (HR 1.6, 95% C.I. 1.2–1.9), platelet-lymphocyte ratio < 95 (HR 2.1, 95% C.I. 1.7–2.6), lymphocyte-monocyte ratio < 2.5 (HR 1.9, 95% C.I. 1.4–2.5), and neutrophil-lymphocyte ratio > 2 (HR 2.7, 95% C.I. 2.2–3.3). Discussion and Conclusions: Our results are in line with the current literature. Male gender and tumor nodule dimension are the main risk factors associated with early HCC recurrence. Platelet count and other combined scores can be used as predictive tools for early HCC recurrence, although more studies are needed to define cut-offs.
Atypical Viral Infections in Gastroenterology
Authors: Crnčević Neira , Rifatbegović Zijah , Hukić Mirsada , Deumić Sara , Pramenković Emina , Selimagić Amir , Gavrankapetanović Ismet , Avdić Monia
Published: October 2022

Enteric viruses are commonly found obligate parasites in the gastrointestinal (GI) tract. These viruses usually follow a fecal-oral route of transmission and are characterized by their extraordinary stability as well as resistance in high-stress environments. Most of them cause similar symptoms including vomiting, diarrhea, and abdominal pain. In order to come in contract with mucosal surfaces, these viruses need to pass the three main lines of defense: mucus layer, innate immune defenses, and adaptive immune defenses. The following atypical gastrointestinal infections are discussed: SARS-CoV2, hantavirus, herpes simplex virus I, cytomegalovirus, and calicivirus. Dysbiosis represents any modification to the makeup of resident commensal communities from those found in healthy individuals and can cause a patient to become more susceptible to bacterial and viral infections. The interaction between bacteria, viruses, and host physiology is still not completely understood. However, with growing research on viral infections, dysbiosis, and new methods of detection, we are getting closer to understanding the nature of these viruses, their typical and atypical characteristics, long-term effects, and mechanisms of action in different organ systems.
Automatic Tuning and Turbulence Mitigation for Fixed-Wing UAV with Segmented Control Surfaces
Authors: Sattar Abdul , Wang Liuping , Hoshu Ayaz Ahmed , Ansari Shahzeb , Karar Haider-e , Mohamed Abdulghani
Published: October 2022

Unlike bigger aircraft, the small fixed-wing unmanned aerial vehicles face significant stability challenges in a turbulent environment. To improve the flight performance, a fixed-wing UAV with segmented aileron control surfaces has been designed and deployed. A total of four ailerons are attached to the main wing and grouped into inner and outer aileron pairs. The controllers are automatically tuned by utilizing the frequency response data obtained via the frequency sampling filter and the relay with embedded integrator experiments. The hardware validation experiments are performed in the normal and turbulent flight environments under three configurations: inner aileron pair only, outer aileron pair only and collective actuation of all the aileron pairs. The error-threshold-based control is introduced to handle collective actuation of aileron pairs. The experiments have manifested that the collective usage of all aileron segments improves the roll attitude stability by a margin of 38.69% to 43.51% when compared to the independent actuation of aileron pairs in a turbulent atmosphere.
Effect of Channel Roughness on Micro-Droplet Distribution in Internal Minimum Quantity Lubrication
Authors: Craig Michael , Raval Jay , Tai Bruce , Patterson Albert , Hung Wayne
Published: October 2022

This research studied the effect of channel roughness on micro-droplet distributions in internal minimum quantity lubrication for effective machining. Mixtures of different oils and air were flown though internal channels with simulated different roughness: as fabricated, partially threaded, and fully threaded. The airborne droplets were collected, analyzed, and compared with simulated results by computational fluid dynamics. For low-viscous lubricant, the rough channel surface helped to break large droplets in the boundary layer into smaller droplets and reintroduce them into the main downstream flow. The opposite trend was found for the higher viscous lubricant. The study also performed chemical etching to roughen selected surfaces of carbide cutting tools. The synergy of hand and ultrasonic agitation successfully roughened a carbide surface within twelve minutes. Scanning electron microscopy examination showed deep etching that removed all grinding marks on a WC–Co cutting tool surface.
Baseline Electroencephalogram and Its Evolution after Activation of Dopaminergic System by Apomorphine in Middle-Aged 5XFAD Transgenic Mice, a Model of Alzheimer’s Disease
Authors: Vorobyov Vasily , Deev Alexander , Oganesyan Zoya , Sengpiel Frank , Ustyugov Aleksey A.
Published: October 2022

Aging and Alzheimer’s disease (AD) are characterized by common pathological features associated with alterations in neuronal connections. These inevitably affect the functioning of specific brain areas and their interrelations, leading to questions about neuronal plasticity and the compensatory mechanisms associated with dopaminergic (DA) mediation. In this study on twelve-month-old freely moving 5XFAD-transgenic mice, serving as a model of AD, and their wild-type (WT) littermates, we analyze electroencephalograms (EEGs) from the motor cortex (MC), putamen (Pt) and the DA-producing ventral tegmental area (VTA) and substantia nigra (SN). Baseline EEGs in the transgenic mice were characterized by delta 2 activity enhancements in VTA and alpha attenuation in VTA and SN. In contrast to WT mice, which lack differences in EEG from these brain areas, 5XFAD mice showed theta–alpha attenuation and delta 2 and beta 2 enhancements in EEG from both VTA and SN vs. MC. In 5XFAD mice, a DA mimetic, apomorphine, lowered (vs. saline) the theta oscillations in Pt, VTA and SN and enhanced alpha in MC, Pt, VTA and beta 1 in all brain areas. These results and those obtained earlier in younger (six-month-old) mice suggest that the age-related characteristics of cerebral adaptive mechanisms affected by AD might be associated with modification of dopaminergic mediation in the mechanisms of intracerebral dynamic interrelations between different brain areas.
A Supervised Link Prediction Method Using Optimized Vertex Collocation Profile
Authors: Wang Peng , Wu Chenxiao , Huang Teng , Chen Yizhang
Published: October 2022

Classical link prediction methods mainly utilize vertex information and topological structure to predict missing links in networks. However, accessing vertex information in real-world networks, such as social networks, is still challenging. Moreover, link prediction methods based on topological structure are usually heuristic, and mainly consider common neighbors, vertex degrees and paths, which cannot fully represent the topology context. In recent years, network embedding models have shown efficiency for link prediction, but they lack interpretability. To address these issues, this paper proposes a novel link prediction method based on an optimized vertex collocation profile (OVCP). First, the 7-subgraph topology was proposed to represent the topology context of vertexes. Second, any 7-subgraph can be converted into a unique address by OVCP, and then we obtained the interpretable feature vectors of vertexes. Third, the classification model with OVCP features was used to predict links, and the overlapping community detection algorithm was employed to divide a network into multiple small communities, which can greatly reduce the complexity of our method. Experimental results demonstrate that the proposed method can achieve a promising performance compared with traditional link prediction methods, and has better interpretability than network-embedding-based methods.
The Self-Information Weighting-Based Node Importance Ranking Method for Graph Data
Authors: Liu Shihu , Gao Haiyan
Published: October 2022

Due to their wide application in many disciplines, how to make an efficient ranking for nodes, especially for nodes in graph data, has aroused lots of attention. To overcome the shortcoming that most traditional ranking methods only consider the mutual influence between nodes but ignore the influence of edges, this paper proposes a self-information weighting-based method to rank all nodes in graph data. In the first place, the graph data are weighted by regarding the self-information of edges in terms of node degree. On this base, the information entropy of nodes is constructed to measure the importance of each node and in which case all nodes can be ranked. To verify the effectiveness of this proposed ranking method, we compare it with six existing methods on nine real-world datasets. The experimental results show that our method performs well on all of these nine datasets, especially for datasets with more nodes.
Development of a Framework to Assess Challenges to Virtual Education in an Emergency Remote Teaching Environment: A Developing Country Student Perspective—The Case of Peru
Authors: Mu Enrique , Florek-Paszkowska Anna , Pereyra-Rojas Milagros
Published: October 2022

The COVID-19 pandemic forced most countries’ higher-education systems to shift to distance learning, which has been called either “Corona Teaching” or, more formally, “Emergency Remote Teaching (ERT).” Students were suddenly faced with a new class format delivery and the many challenges of virtual education. The present study aims to identify and measure the challenges in three stages: (1) a qualitative method approach was used to gather the opinions of 50 students that were then analyzed and coded to identify their perceived major challenges; (2) a survey was completed by 165 students to prioritize the relative importance of the previously identified challenges using the AHP as the weighting approach; (3) an assessment framework was developed, using statistical techniques to measure the extent of the challenges for specific stakeholders based on survey responses. The main challenges students face are inadequate physical facilities at home, difficulties with the learning platforms, and financial concerns. These results are applicable beyond the present research context. For the first time, an ERT assessment framework of the challenges was developed using composite indicators derived from students’ opinions and perspectives. This ERT framework allows for the exploration of a community of students’ vulnerability to the challenges within the context of an emergency remote environment.
Construction of a Participatory Model of School Accompaniment to Improve School Inclusion
Authors: Moliner Odet , Lozano María , Sanahuja Aida
Published: October 2022

This article presents research that examines how the processes of school change and improvement are promoted in those Spanish schools that are developing educational projects based on an inclusive, participatory and democratic model. The aim is to investigate the key elements involved in the processes of accompaniment of those schools that initiate processes of improvement and change from an inclusive approach, as well as the relationships established among them. Methodologically, this is a phenomenological study developed in two consecutive phases: in the first phase, the key elements are identified from the perspective of 24 inclusive education researchers participating in four focus groups and in the second phase, the study delves into their characteristic features and the relationship between them from the perspective of 19 professionals and researchers through 9 heterogeneous thematic focus groups. The analysis provides an integrated view of the actors involved: teachers, school management team, education authorities and researchers. The results show and define 8 key factors and present a model, collaboratively constructed, that links these key elements to develop more inclusive schools and helps to problematise them in order to build knowledge about what inclusive education means in a situated way.
The STEM Crisis and Teacher Practice: Exploring Responses to the Competing Discursive Arrangements of Education in the Sciences in a Catholic School Setting
Authors: Leonard Simon N. , O’Keeffe Lisa , White Bruce , O’Leary Melanie , Sloan Karen
Published: October 2022

STEM has become a pervasive part of global education reform. The STEM discourse positions the purpose of scientific education as being to prepare young people for work in a hyper-competitive 21st century knowledge economy, pushing aside alternative approaches focussed on interrogating social, moral and political issues in context. This narrative does not always sit comfortably with the holistic ambitions of many state and faith-based education systems. In this paper we will argue that these tensions emerge from deeper conflicts in the cultural-discursive arrangements around education in the advanced democratic states through an exploration of the response to a STEM curriculum project in a Catholic education system. The exploration is based on a phenomenographic analysis of reflective interviews conducted with participating teachers. We conclude that while the teachers are aware of the tensions, they may benefit from access to a language for discussing the various pressures on learning design and meaning making.
Did the COVID-19 Pandemic Lockdown Harm Pre-Schoolers Learning in Portugal? Yes, but with Variations Depending on Socio-Economic Status
Authors: Bem-Haja Pedro , Nossa Paulo , Pereira Diogo Simões , Silva Carlos F.
Published: October 2022

The literature has shown that the COVID-19 pandemic has indelibly affected student performance. However, this deterioration is not the same for all students, with students of a lower socio-economic status (SES) being the most affected. The present study aims to understand if the pandemic lockdown in the last year of pre-school impacted the learning skills considered crucial for the transition to primary school, and whether this impact was moderated by SES or a quiet place to study (QPS). A total of 11,158 students belonging to 318 Portuguese schools underwent an assessment protocol composed of writing skills, maths, and motor-control tasks. A pandemic effect was observed for writing skills, especially during the first lockdown. Said effects were found to be potentiated by SES. Regarding maths, the fall in skills was only observed to be significant for less economically advantaged children. Motor tasks suffered; however, this was without any significant effect for SES or QPS. Thus, a detrimental effect of the pandemic lockdown was found on pre-school skills, particularly pre-literary abilities, and especially during the first lockdown. SES appeared to potentiate some inequalities. In other words, skills differences between individuals with higher and lower SES increased during the pandemic, particularly in the first lockdown, due to novelty, unpredictability, and the need for quick adaptation.
Exploring the Alignment between Digital Strategies and Educational Practices in Higher Education Infrastructures
Authors: Øvrelid Egil
Published: October 2022

Higher education is a key pillar in constructing new knowledge economies for the 21st century, and the digitalization of higher education is a central focus area for national authorities. Visionary discourses from authorities state that the decision-making authority for digital strategies should be centralized to the domain of management. Digitalization is, however, driven by key features of modern technology and may also lead to the transformation of traditional educational methods as well as educational practices. Since the university contains several disciplines, different strategies can be used when products or processes within the disciplines are digitalized. It is important to consider in the ways that different disciplines can proceed to digitalize their educational practices. Based on these interests, our research question is as follows: how do digital strategies in higher education emerge, and how do they align with the educational context? Through a qualitative case study with interviews, participation in workshops, and document analyses, we investigated two digitalization efforts in the fields of medicine and law. We found that the two classical disciplines’ strategic approaches differed substantially. Based on the findings, our main contribution is a digitalization model with two archetypes, namely digital transformation strategy and digital innovation strategy. The model highlight the main object of the respective strategies, but also the institutional reaction to digitalization efforts. An implication from our study is the demonstration of how specific faculties adapt digital strategies to educational practices. This may sometimes lead to the transformation of educational practices, while other times more incremental moderate changes may be implemented. From a practical point of view, policymakers, politicians, educational management, and professionals need knowledge and expertise about the implications of digital strategies for educational practices. Our contribution, we propose, strengthens the understanding of strategies within digital infrastructures in higher education.
Collaborative Road Damage Classification and Recognition Based on Edge Computing
Authors: Dang Xiaochao , Shang Xu , Hao Zhanjun , Su Lin
Published: October 2022

Road damage brings serious threats and inconvenience to traffic safety travel. Road damage detection and recognition can assist in eliminating the potential safety hazards in time and reduce traffic accidents. The majority of the existing road damage detection methods require significant computing resources and are difficult to deploy on resource-constrained edge devices. Therefore, the road surface data collected during the driving process of the vehicle are usually transmitted to the cloud service for analysis. However, during the driving process of the vehicle, due to problems, such as network coverage, connection, and response, it is difficult to meet the needs of real-time detection and identification of road damage. Therefore, this paper proposes a road damage classification and identification method based on edge computing. This method adds edge services. First, deep learning models are deployed on edge and cloud servers; then, a standardized entropy is set by information entropy to find the appropriate threshold as well as the best point of edge and cloud that work together to ensure high accuracy and fast response of road damage identification; finally, the cloud uses the data uploaded by the edge to assist the edge in updating the edge model. In comparison with the two cases of uploading data to the cloud server for analysis and uploading data to the edge server for analysis, the results show that the accuracy of the method is 16.21% higher than the method only executed at the edge end, and the average recognition time is 38.82% lower than the method only executed at the cloud end. While ensuring a certain accuracy, it also improves the efficiency of classification and recognition, and can meet the needs of fast and accurate road damage classification and recognition.
IRS-Based UAV-Assisted Low-Altitude Passive Relaying: SER Performance Analysis of Optimal Deployment
Authors: Li Tianhe , Mao Minghe , Xu Mengjie , He Yang , Feng Ye , Shi Rui
Published: October 2022

This paper presents a method for analyzing the symbol error rate (SER) of a UAV cellular data network based on an IRS. This method can change the distances between the BS, IRS, UAV, and the elevation of the UAV to observe their impacts on the SER; then, a two-dimensional curve diagram of the parameters and the SER is drawn to determine the deployment position of the UAV. Finally, by flexibly changing the maximum angle of the UAV’s transmitted signal and the height of the UAV, the coverage area of the UAV’s cellular data network can be obtained through three-dimensional imaging, function fitting, and the extreme value of the condition function. As a relay of a cellular data network, a UAV can be flexibly deployed at any effective position at a low altitude to compensate for the limited coverage of the base station and the poor quality of the user’s received signal. In particular, an IRS deployed on the surface of a building can generate an equivalent line-of-sight channel to reflect the signal from the base station to the UAV. Using an IRS to change the signal phase and amplitude with the flexible deployment of UAVs can give priority to solving the problems of cellular data network coverage and signal quality in urban scenarios.
Distributed-Ledger-Based Blockchain Technology for Reliable Electronic Voting System with Statistical Analysis
Authors: Ch Rupa , Kumari D Jaya , Gadekallu Thippa Reddy , Iwendi Celestine
Published: October 2022

In today’s society, voting is crucial to choosing the representatives of the people. The current voting process is filled with a vast array of disputes and manipulations. The leader must be selected in a precise manner without any malpractices. In addition, the people and authorities are not happy with the election results and label them unpredictable. We offer a better solution to the current problems, such as tampering, non-residents voting outside of the polling place, quick results analysis, quick counting, and reduced use of staff and funds during the electoral franchise process. In this offer, blockchain technology is used to create the distributed application (dApp) framework that will be used for the proposed e-voting system. Additionally, it offers unique characteristics such as immutability, transparency, privacy, and reception freedom that reduce crimes involving the processing of sensitive data in the electoral process. Ganache, MetaMask, and specified dagger hashing algorithm are used to develop the dApp. A key strength of this paper is the statistical analysis of transactions on the blockchain. Moreover, it also provides security to voters’ identity and leads to immediate acceptable counting results with more accuracy.
Efficient Microwave Filter Design by a Surrogate-Model-Assisted Decomposition-Based Multi-Objective Evolutionary Algorithm
Authors: Wei Yongfeng , Qi Guangfei , Wang Yanxing , Yan Ningchaoran , Zhang Yongliang , Feng Linping
Published: October 2022

As a crucial frequency selection device in modern communication systems, the microwave filter plays an increasingly prominent role. There is a great demand for the multi-objective design of microwave filters. The filter’s performance affects the quality of the whole communication system directly. However, traditional multi-objective electromagnetic (EM) optimization design demands repetitive EM simulations to adjust the physical parameters of the microwave filters. Accordingly, using electromagnetic simulation directly to design optimization is quite expensive. Given this situation, this paper applies a novel surrogate model based on one-dimensional convolutional autoencoders (1D-CAE) into the multi-objective algorithm evolutionarily based on decomposition (MOEA/D) for the first time. This approach uses MOEA/D as the multi-objective optimizer, and a novel low-complexity surrogate model based on one-dimensional convolutional autoencoders (1D-CAE) is constructed to predict the expensive EM simulation results. The surrogate model based on 1D-CAE is used to generate the results of scalar subproblems of MOEA/D, which greatly improves the design efficiency. Compared with the traditional design methods based on an EM solver, this method not only effectively optimizes multiple design objectives but also completes the design of microwave filters in a shorter time. The proposed method is verified using the design of a sixth-order ceramic filter and a seventh-order metal cavity filter.
Selection of Relevant Geometric Features Using Filter-Based Algorithms for Point Cloud Semantic Segmentation
Authors: Atik Muhammed Enes , Duran Zaide
Published: October 2022

Semantic segmentation of mobile LiDAR point clouds is an essential task in many fields such as road network management, mapping, urban planning, and 3D High Definition (HD) city maps for autonomous vehicles. This study presents an approach to improve the evaluation metrics of deep-learning-based point cloud semantic segmentation using 3D geometric features and filter-based feature selection. Information gain (IG), Chi-square (Chi2), and ReliefF algorithms are used to select relevant features. RandLA-Net and Superpoint Grapgh (SPG), the current and effective deep learning networks, were preferred for applying semantic segmentation. RandLA-Net and SPG were fed by adding geometric features in addition to 3D coordinates (x, y, z) directly without any change in the structure of the point clouds. Experiments were carried out on three challenging mobile LiDAR datasets: Toronto3D, SZTAKI-CityMLS, and Paris. As a result of the study, it was demonstrated that the selection of relevant features improved accuracy in all datasets. For RandLA-Net, mean Intersection-over-Union (mIoU) was 70.1% with the features selected with Chi2 in the Toronto3D dataset, 84.1% mIoU was obtained with the features selected with the IG in the SZTAKI-CityMLS dataset, and 55.2% mIoU with the features selected with the IG and ReliefF in the Paris dataset. For SPG, 69.8% mIoU was obtained with Chi2 in the Toronto3D dataset, 77.5% mIoU was obtained with IG in SZTAKI-CityMLS, and 59.0% mIoU was obtained with IG and ReliefF in Paris.
Product Authentication Technology Integrating Blockchain and Traceability Structure
Authors: Gao Xiao , Zhang Wenyin , Zhao Bin , Zhang Jiqun , Wang Jiuru , Gao Yilong
Published: October 2022

To tackle the problems of questioning the authenticity of products faced in e-commerce live streaming and the low data integrity in the process of product transactions, the concept of traceability and a product authentication scheme that integrates blockchain technology and traceability are proposed. The proposed scheme deploys the product authentication strategy on the Ethereum blockchain in the form of a smart contract and calls the product authentication method on the traceability body by executing the smart contract to realize the authentication of product quality. A combination of on-chain and off-chain is used to store private information to guarantee information integrity. Security analysis results show that the proposed scheme can make the risk of information leakage lower and data security higher under the premise of achieving product certification. A system simulation experiment shows that the solution can meet product certification requirements and have a favorable impact on the live commerce industry.
Time-Varying Reflectivity Modulation on Inverse Synthetic Aperture Radar Image Using Active Frequency Selective Surface
Authors: Xu Yong , Sui Ran , Wang Junjie , Feng Dejun
Published: October 2022

Interrupted-sampling repeater modulation (ISRM) can generate multiple virtual target images by sampling and forwarding radar signals to confront the inverse synthetic aperture radar (ISAR). However, the repeating delay is unavoidable and the system is complex. Moreover, numerous works mainly discuss the ISAR image deception based on periodic modulation, and the modulation effect is relatively limited. Active frequency selective surface (AFSS) can perform intermittent amplitude modulation to the reflected signal by varying target reflectivity, thus, it can achieve the function of ISRM. In this paper, the possibility of AFSS applied in the multiple virtual target generation is discussed, and an ISAR image-modulation method is proposed. The radar signal is periodically modulated by varying target reflectivity, which is accomplished by the AFSS-loaded PIN diodes. On this basis, the AFSS modulating frequency is randomly varied in a slow time domain. When the modulation signal is processed by the ISAR system, the produced target image is unfocused and cannot be recognized by radar. Simulation results are utilized to demonstrate the effectiveness of the proposed method.
Cooperative Following of Multiple Autonomous Robots Based on Consensus Estimation
Authors: Kong Guojie , Cai Jie , Gong Jianwei , Tian Zheming , Huang Lu , Yang Yuan
Published: October 2022

When performing a specific task, a Multi-Agent System (MAS) not only needs to coordinate the whole formation but also needs to coordinate the dynamic relationship among all the agents, which means judging and adjusting their positions in the formation according to their location, velocity, surrounding obstacles and other information to accomplish specific tasks. This paper devises an integral separation feedback method for a single-agent control with a developed robot motion model; then, an enhanced strategy incorporating the dynamic information of the leader robot is proposed for further improvement. On this basis, a method of combining second-order formation control with path planning is proposed for multiple-agents following control, which uses the system dynamic of one agent and the Laplacian matrix to generate the consensus protocol. Due to a second-order consensus, the agents exchange information according to a pre-specified communication digraph and keep in a certain following formation. Moreover, an improved path planning method using an artificial potential field is developed to guide the MAS to reach the destination and avoid collisions. The effectiveness of the proposed approach is verified with simulation results in different scenarios.
Tightly Coupled Ultra-Wideband Phased-Array Implemented by Three-Dimensional Inkjet Printing Technique
Authors: Han Lei , Wang Gang , Zhang Lin , Jiang Weixu , Zhao Pengbing , Tang Wei , Dang Tao , Zheng Hongxing
Published: October 2022

In order to enhance the gains from antennas suitable for airplane-mounted platforms, a tightly coupled antenna array is investigated in this paper. Specifically, a three-dimensional (3-D) inkjet printing technique is used to implement the conformal characteristics needed for the array. Both the radiators and substrate of the antenna array have been fabricated by combining the fused deposition modeling and microdroplet injection molding technologies, based on an existing 3-D printer. Here, through a unique combination of 3-D and 2-D inkjet printing of dielectric material and metallic ink, respectively, we demonstrate a monolithically integrated design for a nonplanar antenna for the first time. The antenna measurements herein show the complete characterization of this new process in terms of minimum feature size and achievable conductivities. This antenna configuration offers a high-gain performance with a low-cost and rapid fabrication technique by using 3-D printing techniques. To check our design, the voltage standing wave ratio and radiation patterns were tested after adding the newly designed feed structure. The results show that the design process is very efficient. Both the antenna element and the array demonstrate positive properties and are in very good agreement with the specially mounted platform.
Combining the Broadband Coverage and Speed to Improve Fiscal System Efficiency in the Eastern European Union Countries
Authors: Doran Marius Dalian , Puiu Silvia , Berceanu Dorel , Țăran Alexandra Mădălina , Para Iulia , Popescu Jenica
Published: October 2022

Current challenges triggered by the limited interactions between people and institutions during the pandemic crisis have emphasized the need to strengthen the digitization process of all public services. In this sense, we considered it opportune to carry out research in order to establish the impact of the technological infrastructure, in terms of coverage and download speed, on the efficiency of the fiscal policy expressed by the volume of income from taxes and fees. Therefore, we propose a robust regression model tested with S-estimator that allows for outliers in the dataset. The results indicate that an improvement in download speed has a significant positive effect on the level of tax collection and that a better broadband coverage improves the amount of revenues from taxes and contributions. In the analyzed countries, the technological infrastructure is developing, and the decision-makers should make efforts to reach the targets proposed in the Digital Agenda for Europe in terms of broadband coverage and speed.