bluuebunny/crossref_metadata_2025 huggingface dataset:
Testing the Effect of Sampling Effort on Inferring Phylogeographic History in Psolodesmus mandarinus (Calopterygidae, Odonata)
Authors: Wang Liang-Jong , Chou Yen-Wei , Huang Jen-Pan
Published: September 2022

Phylogeographic studies have revealed spatial genetic structure and inferred geographical processes that may have generated genetic diversity and divergence. These study results have implications not only on the processes that generate intraspecific and interspecific diversity but also on the essential integrals for defining evolutionary entities (e.g., species). However, the resulting phylogeographic inferences might be impacted by the sampling design, i.e., the number of individuals per population and the number of geographic populations studied. The effect of sampling bias on phylogeographic inferences remains poorly explored. With a comprehensive sampling design (including 186 samples from 56 localities), we studied the phylogeographic history of a Taiwanese endemic damselfly, Psolodesmus mandarinus, with a specific focus on testing the impact of the sampling design on phylogeographic inference. We found a significant difference in the genetic structure of eastern and western populations separated by the Central Mountain Range (CMR) of Taiwan. However, isolation by the CMR did not lead to reciprocally monophyletic geographic populations. We further showed that, when only a subset of individuals was randomly included in the study, monophyletic geographic populations were obtained. Furthermore, historical demographic expansion could become undetectable when only a subset of samples was used in the analyses. Our results demonstrate the impact of sampling design on phylogeographic inferences. Future studies need to be cautious when inferring the effect of isolation by a physical barrier.
Utility of Diagnostic Imaging in the Early Detection and Management of the Fournier Gangrene
Authors: Sumisławski Piotr , Kołecki Janusz , Piotrowska Martyna , Kotowski Maciej , Szemitko Marcin , Sieńko Jerzy
Published: September 2022

Fournier gangrene represents a urologic emergency. It is a rapidly progressing necrotizing fasciitis that comprises the perineal, perianal, and genital regions and has a high mortality rate. Diagnosis is usually made clinically, but radiological diagnostics, such as ultrasound (US), computed tomography (CT), or magnetic resonance imaging (MRI), can determine the extent of the disease in relation to pelvic structures. Early and accurate diagnosis precipitates the initiation of the effective treatment and, thus, affects the outcome of the therapy. The article reports an illustrative case study of a patient with Fournier gangrene, secondary to a perianal fistula and perianal abscess with a massive accumulation of fluid around the anus and testicles, requiring unilateral orchidectomy. Rapid radiological diagnosis via MRI enabled precise assessment of the degree of the disease, early surgical intervention, and a successful outcome.
Classical Hodgkin Lymphoma: A Joint Clinical and PET Model to Predict Poor Responders at Interim Assessment
Authors: Triumbari Elizabeth Katherine Anna , Morland David , Cuccaro Annarosa , Maiolo Elena , Hohaus Stefan , Annunziata Salvatore
Published: September 2022

(1) This study aimed to investigate whether baseline clinical and Positron Emission Tomography/Computed Tomography (bPET)-derived parameters could help predicting early response to the first two cycles of chemotherapy (Deauville Score at interim PET, DS at iPET) in patients with classical Hodgkin lymphoma (cHL) to identify poor responders (DS ≥ 4) who could benefit from first-line treatment intensification at an earlier time point. (2) cHL patients with a bPET and an iPET imaging study in our Centre’s records (2013–2019), no synchronous/metachronous tumors, no major surgical resection of disease prior to bPET, and treated with two cycles of ABVD chemotherapy before iPET were retrospectively included. Baseline International Prognostic Score for HL (IPS) parameters were collected. Each patient’s bPET total metabolic tumor volume (TMTV) and highest tumoral SUVmax were collected. ROC curves and Youden’s index were used to derive the optimal thresholds of TMTV and SUVmax with regard to the DS (≥4). Chi-square or Fisher’s exact test were used for the univariate analysis. A multivariate analysis was then performed using logistic regression. The type I error rate in the hypothesis testing was set to 5%. (3) A total of 146 patients were included. The optimal threshold to predict a DS ≥ 4 was >177 mL for TMTV and >14.7 for SUVmax (AUC of 0.65 and 0.58, respectively). The univariate analysis showed that only TMTV, SUVmax, advanced disease stage, and age were significantly associated with a DS ≥ 4. A multivariate model was finally derived from TMTV, SUVmax, and age, with an AUC of 0.77. (4) A multivariate model with bPET parameters and age at diagnosis was satisfactorily predictive of poor response at iPET after ABVD induction chemotherapy in cHL patients. More studies are needed to validate these results and further implement DS-predictive factors at baseline in order to prevent poor response and intensify therapeutic strategies a-priori when needed.
Single CT Appointment for Double Lung and Colorectal Cancer Screening: Is the Time Ripe?
Authors: Mascalchi Mario , Picozzi Giulia , Puliti Donella , Gorini Giuseppe , Mantellini Paola , Sali Lapo
Published: September 2022

Annual screening of lung cancer (LC) with chest low-dose computed tomography (CT) and screening of colorectal cancer (CRC) with CT colonography every 5 years are recommended by the United States Prevention Service Task Force. We review epidemiological and pathological data on LC and CRC, and the features of screening chest low-dose CT and CT colonography comprising execution, reading, radiation exposure and harm, and the cost effectiveness of the two CT screening interventions. The possibility of combining chest low-dose CT and CT colonography examinations for double LC and CRC screening in a single CT appointment is then addressed. We demonstrate how this approach appears feasible and is already reasonable as an opportunistic screening intervention in 50–75-year-old subjects with smoking history and average CRC risk. In addition to the crucial role Computer Assisted Diagnosis systems play in decreasing the test reading times and the need to educate radiologists in screening chest LDCT and CT colonography, in view of a single CT appointment for double screening, the following uncertainties need to be solved: (1) the schedule of the screening CT; (2) the effectiveness of iterative reconstruction and deep learning algorithms affording an ultra-low-dose CT acquisition technique and (3) management of incidental findings. Resolving these issues will imply new cost-effectiveness analyses for LC screening with chest low dose CT and for CRC screening with CT colonography and, especially, for the double LC and CRC screening with a single-appointment CT.
Choroidal Thickness in a Hyperopic Pediatric Population
Authors: Gerena Arévalo Vanessa Antonia , Ruiz-Moreno Jose Maria
Published: September 2022

Aims: To evaluate the choroidal thickness (CT) in hyperopic and emmetropic children using swept-source optical coherence tomography (SS-OCT). Methods: This was a prospective, cross-sectional comparative study. Macular choroidal thickness and axial length of 62 eyes from hyperopic pediatric patients were studied. CT was determined at nine different macular locations. The results were compared to 66 eyes of healthy pediatric patients. Results: Study groups were classified as a hyperopic group (SE ≥ 2D) and an emmetropic group (SE < 2D). The hyperopic group have shorter AL than the emmetropic group (p < 0.001). The mean CT is greater in the hyperopic group (p = 0.039), and there are no significant differences between CT and gender (p = 0.389). Study participants were also classified by age (2–5 years old and 6–18 years old), and we observe differences in CT, but these differences are only significant for the 6–18 years old group (p < 0.05). Conclusions: CT in hyperopic pediatric populations is statistically thicker than in healthy pediatric patients. AL and SE have statistically significant correlations with CT values, and those correlations are seen in children in the ocular slow-growing phase (6–18 years old), and not in the early years (2–5 years old).
New Faecal Calprotectin Assay by IDS: Validation and Comparison to DiaSorin Method
Authors: Castiglione Vincent , Berodes Maëlle , Lukas Pierre , Louis Edouard , Cavalier Etienne , Lutteri Laurence
Published: September 2022

Background: The faecal calprotectin (FC) measurement is used for inflammatory bowel disease (IBD) diagnosis and follow-up. The aim of this study was to validate for the first time the new IDS FC extraction device and immunoassay kit, and to compare it with the DiaSorin test in patients with and without IBD. Methods: First, the precision of the IDS assay and its stability were assessed. Then, 379 stool extracts were analysed with the IDS kit on iSYS and compared with a DiaSorin Liaison XL assay. Results: The intra- and inter-assay CVs did not exceed 5%. The stool samples were stable up to 4 weeks at −20 °C. Lot-to-lot comparison showed a good correlation (Lot1 = 1.06 × Lot2 + 0.60; p > 0.05). The Passing and Bablok regression showed no significant deviation from linearity between the two methods (IDS = 1.06 × DiaSorin − 0.6; p > 0.05; concordance correlation coefficient = 0.93). According to the recommended cut-offs, the IDS assay identified more IBD and irritable bowel syndrome patients than DiaSorin, which had more borderline results (16 vs. 20%, respectively). Conclusions: The IDS faecal calprotectin had good analytical validation parameters. Compared to the DiaSorin method, it showed comparable results, but slightly outperformed it in the identification of more IBD patients and active disease.
Endometrioid Carcinomas of the Ovaries and Endometrium Involving Endocervical Polyps: Comprehensive Clinicopathological Analyses
Authors: Sohn Jihee , Lee Yurimi , Kim Hyun-Soo
Published: September 2022

While synchronous ovarian and endometrial endometrioid carcinomas (ECs) have long been described in the literature, ovarian or endometrial EC involving concomitant endocervical polyp (ECP) has not yet been reported. This study aimed to investigate the histological types and prevalence of gynecological tumors co-existing with ECP and to comprehensively analyze the clinicopathological characteristics of ovarian and endometrial ECs involving ECPs. We searched for ECP cases associated with premalignant lesions or malignancies of the female genital tract occurring between March 2019 and February 2022. We then investigated the histological types and prevalence of gynecological tumors co-existing with ECP. In addition, we reviewed electronic medical records and pathology slides to collect the clinicopathological features of four patients with ovarian or endometrial EC involving ECP. We found 429 ECPs over the three-year study period. Of these, 68 (15.9%) were associated with premalignant or malignant lesions occurring in the uterine cervix, endometrium, and ovaries. Four of these cases, including two (0.5%) ovarian grade 3 ECs and two (0.5%) endometrial grade 1 ECs, involved ECPs. In the former cases (cases 1 and 2), ECs involving ECPs exhibited similar morphology and immunohistochemical staining results to those of advanced-stage ovarian EC. In the latter cases (cases 3 and 4), the histological and immunophenotypical features of EC involving ECP were identical to those of primary endometrial EC, despite the lack of tumor involvement in the myometrium, lower uterine segment, and cervical stroma as well as the absence of lymphovascular invasion and lymph node metastasis. In all cases, no evidence of benign endometriosis, endometrial hyperplasia without atypia, or atypical hyperplasia/endometrial intraepithelial neoplasm within ECP or the adjacent endocervical tissue was noted. Considering our results, the involvement of ECP by EC may have been caused by an implantation metastasis from the ovarian (cases 1 and 2) or endometrial (cases 3 and 4) EC. To the best of our knowledge, this is the first exploration of the synchronous occurrence of endometrial or ovarian EC and ECP involvement. Implantation metastasis via transtubal and trans-endometrial cavity migration may have been the pathogenic mechanism of ECP involvement.
Artificial Intelligence-Powered Whole-Slide Image Analyzer Reveals a Distinctive Distribution of Tumor-Infiltrating Lymphocytes in Neuroendocrine Neoplasms
Authors: Cho Hyung-Gyo , Cho Soo Ick , Choi Sangjoon , Jung Wonkyung , Shin Jiwon , Park Gahee , Moon Jimin , Ma Minuk , Song Heon , Mostafavi Mohammad , Kang Mingu , Pereira Sergio , Paeng Kyunghyun , Yoo Donggeun , Ock Chan-Young , Kim Seokhwi
Published: September 2022

Despite the importance of tumor-infiltrating lymphocytes (TIL) and PD-L1 expression to the immune checkpoint inhibitor (ICI) response, a comprehensive assessment of these biomarkers has not yet been conducted in neuroendocrine neoplasm (NEN). We collected 218 NENs from multiple organs, including 190 low/intermediate-grade NENs and 28 high-grade NENs. TIL distribution was derived from Lunit SCOPE IO, an artificial intelligence (AI)-powered hematoxylin and eosin (H&E) analyzer, as developed from 17,849 whole slide images. The proportion of intra-tumoral TIL-high cases was significantly higher in high-grade NEN (75.0% vs. 46.3%, p = 0.008). The proportion of PD-L1 combined positive score (CPS) ≥ 1 case was higher in high-grade NEN (85.7% vs. 33.2%, p < 0.001). The PD-L1 CPS ≥ 1 group showed higher intra-tumoral, stromal, and combined TIL densities, compared to the CPS < 1 group (7.13 vs. 2.95, p < 0.001; 200.9 vs. 120.5, p < 0.001; 86.7 vs. 56.1, p = 0.004). A significant correlation was observed between TIL density and PD-L1 CPS (r = 0.37, p < 0.001 for intra-tumoral TIL; r = 0.24, p = 0.002 for stromal TIL and combined TIL). AI-powered TIL analysis reveals that intra-tumoral TIL density is significantly higher in high-grade NEN, and PD-L1 CPS has a positive correlation with TIL densities, thus showing its value as predictive biomarkers for ICI response in NEN.
Communities’ Perception, Knowledge, and Practices Related to Human African Trypanosomiasis in the Democratic Republic of Congo
Authors: Kabanga Charlie , Valverde Mordt Olaf , Mbo Florent , Mbondo Medard , Olela Donatien , Etinkum Rinelle , Nkaji Dieudonne , Mukoso Bienvenu , Mananasi Lubanza
Published: September 2022

Background: The number of human African trypanosomiasis (HAT) cases in the Democratic Republic of Congo (DRC) has significantly reduced, thanks to more effective drugs and screening tools and regular mass screening. However, this potentially jeopardizes HAT control activities, especially community engagement. Methods: We used an ecological model framework to understand how various factors shape communities’ knowledge, perceptions, and behavior in this low endemicity context. Community members, frontline health providers, and policymakers were consulted using an ethnographic approach. Results: Communities in endemic areas are knowledgeable about causes, symptoms, and treatment of HAT, but this was more limited among young people. Few are aware of new HAT treatment or screening techniques. Participation in mass screening has declined due to many factors including fear and a lack of urgency, given the low numbers of cases. Delays in seeking medical care are due to confusion of HAT symptoms with those of other diseases and belief that HAT is caused by witchcraft. Conclusions: Community members see their role more in terms of vector control than participation in screening, referral, or accepting treatment. We propose recommendations for achieving sustainable community engagement, including development of an information and communication strategy and empowerment of communities to take greater ownership of HAT control activities.
Development of an Organisational Certification Process for Specific Category Drone Operations
Authors: Öz Emrehan , Heikkilä Eetu , Tiusanen Risto
Published: September 2022

To govern and ensure the safety of increasing drone operations, the European Aviation Safety Agency (EASA) has defined three risk-based categories of unmanned aircraft system (UAS) operations. A large part of commercial UAS operations fall into the Specific category, which includes operations with medium risk that require an authority approval. One possible way to gain approval for these operations is the light UAS operator certificate (LUC) awarded by a national aviation authority. It offers organisations the privilege to assess and approve operations without applying for authorisation from an authority. However, only general information regarding the LUC is available in the European regulatory publications. Thus, there is currently a lack of detailed guidelines and practical information about the process and requirements for acquiring LUC status. This paper describes the development of a LUC application process model following a design science research approach. The model supports organisations to assess their LUC capabilities, develop their organisation, create the documentation to fulfil relevant requirements, and to apply for the certificate. Finally, the results of a case study where the process model was applied are presented.
Some Information Measures Properties of the GOS-Concomitants from the FGM Family
Authors: Suter Florentina , Cernat Ioana , Drăgan Mihai
Published: September 2022

In this paper we recall, extend and compute some information measures for the concomitants of the generalized order statistics (GOS) from the Farlie–Gumbel–Morgenstern (FGM) family. We focus on two types of information measures: some related to Shannon entropy, and some related to Tsallis entropy. Among the information measures considered are residual and past entropies which are important in a reliability context.
A Clustering Scheme Based on the Binary Whale Optimization Algorithm in FANET
Authors: Yan Yonghang , Xia Xuewen , Zhang Lingli , Li Zhijia , Qin Chunbin
Published: September 2022

With the continuous development of Unmanned Aerial Vehicle (UAV) technology, UAVs are widely used in military and civilian fields. Multi-UAV networks are often referred to as flying ad hoc networks (FANET). Dividing multiple UAVs into clusters for management can reduce energy consumption, maximize network lifetime, and enhance network scalability to a certain extent, so UAV clustering is an important direction for UAV network applications. However, UAVs have the characteristics of limited energy resources and high mobility, which bring challenges to UAV cluster communication networking. Therefore, this paper proposes a clustering scheme for UAV clusters based on the binary whale optimization (BWOA) algorithm. First, the optimal number of clusters in the network is calculated based on the network bandwidth and node coverage constraints. Then, the cluster heads are selected based on the optimal number of clusters using the BWOA algorithm, and the clusters are divided based on the distance. Finally, the cluster maintenance strategy is set to achieve efficient maintenance of clusters. The experimental simulation results show that the scheme has better performance in terms of energy consumption and network lifetime compared with the BPSO and K-means-based schemes.
Re_Trans: Combined Retrieval and Transformer Model for Source Code Summarization
Authors: Zhang Chunyan , Zhou Qinglei , Qiao Meng , Tang Ke , Xu Lianqiu , Liu Fudong
Published: September 2022

Source code summarization (SCS) is a natural language description of source code functionality. It can help developers understand programs and maintain software efficiently. Retrieval-based methods generate SCS by reorganizing terms selected from source code or use SCS of similar code snippets. Generative methods generate SCS via attentional encoder–decoder architecture. However, a generative method can generate SCS for any code, but sometimes the accuracy is still far from expectation (due to the lack of numerous high-quality training sets). A retrieval-based method is considered to have a higher accurac, but usually fails to generate SCS for a source code in the absence of a similar candidate in the database. In order to effectively combine the advantages of retrieval-based methods and generative methods, we propose a new method: Re_Trans. For a given code, we first utilize the retrieval-based method to obtain its most similar code with regard to sematic and corresponding SCS (S_RM). Then, we input the given code and similar code into the trained discriminator. If the discriminator outputs onr, we take S_RM as the result; otherwise, we utilize the generate model, transformer, to generate the given code’ SCS. Particularly, we use AST-augmented (AbstractSyntax Tree) and code sequence-augmented information to make the source code semantic extraction more complete. Furthermore, we build a new SCS retrieval library through the public dataset. We evaluate our method on a dataset of 2.1 million Java code-comment pairs, and experimental results show improvement over the state-of-the-art (SOTA) benchmarks, which demonstrates the effectiveness and efficiency of our method.
Hypercontractive Inequalities for the Second Norm of Highly Concentrated Functions, and Mrs. Gerber’s-Type Inequalities for the Second Rényi Entropy
Authors: Levhari Niv , Samorodnitsky Alex
Published: September 2022

Let Tϵ, 0≤ϵ≤1/2, be the noise operator acting on functions on the boolean cube {0,1}n. Let f be a distribution on {0,1}n and let q>1. We prove tight Mrs. Gerber-type results for the second Rényi entropy of Tϵf which take into account the value of the qth Rényi entropy of f. For a general function f on {0,1}n we prove tight hypercontractive inequalities for the ℓ2 norm of Tϵf which take into account the ratio between ℓq and ℓ1 norms of f.
The Consensus Problem in Polities of Agents with Dissimilar Cognitive Architectures
Authors: Sowinski Damian Radosław , Carroll-Nellenback Jonathan , DeSilva Jeremy , Frank Adam , Ghoshal Gourab , Gleiser Marcelo
Published: September 2022

Agents interacting with their environments, machine or otherwise, arrive at decisions based on their incomplete access to data and their particular cognitive architecture, including data sampling frequency and memory storage limitations. In particular, the same data streams, sampled and stored differently, may cause agents to arrive at different conclusions and to take different actions. This phenomenon has a drastic impact on polities—populations of agents predicated on the sharing of information. We show that, even under ideal conditions, polities consisting of epistemic agents with heterogeneous cognitive architectures might not achieve consensus concerning what conclusions to draw from datastreams. Transfer entropy applied to a toy model of a polity is analyzed to showcase this effect when the dynamics of the environment is known. As an illustration where the dynamics is not known, we examine empirical data streams relevant to climate and show the consensus problem manifest.
The Impact of Teacher Quality on Student Motivation, Achievement, and Persistence in Science and Mathematics
Authors: Ekmekci Adem , Serrano Danya Marie
Published: September 2022

Science, technology, engineering and mathematics (STEM) fields occupy a significant role in human prosperity and advancement. This study explores the factors affecting student STEM outcomes. Traditionally, the associations of students’ own motivational or cognitive inputs to their STEM career outcomes have been investigated before. Similarly, association of teacher quality to student achievement outcomes have been made before. This paper presents a novel approach by introducing teacher quality as the contextual factor within the social cognitive career theoretical (SCCT) model using a comprehensive and robust model for teacher quality including teachers’ motivation, qualifications, and self-reported practices. This study examines the extent to which high school students’ mathematics and science teachers’ beliefs, professional background, and instructional practices relate to students’ motivation, achievement, and future career plans in STEM using a nationally representative, large dataset: High School Longitudinal Study 2009. The results indicate that science and mathematics teachers’ professional background, motivational beliefs, and self-reported instructional practices have significant impact on students’ motivation, persistence, and achievement outcomes in science and mathematics. No direct impact of teacher factors on STEM career plans are found; however, students motivational and achievement outcomes (impacted by teacher factors) do have significant impact on students’ career plans in STEM.
Online and Hybrid Teaching and Learning: Enhance Effective Student Engagement and Experience
Authors: Gamage Kelum A. A. , Gamage Achini , Dehideniya Shyama C. P.
Published: September 2022

The COVID-19 pandemic has had an unprecedented impact on the global higher education system, where many universities have adapted to online and hybrid teaching and learning. They continue with some activities on campus, particularly laboratory-based teaching, but some content is delivered remotely. Significant adjustment to traditional face-to-face student engagement activities is crucial for the success of online and hybrid teaching and learning. This paper investigates the student engagement and experience in these environments. Engaged students are more likely to reach their full potential academically, and this paper identifies the areas for enhancement to student engagement activities. A survey was conducted (in Sri Lanka) to identify students’ perceptions of engaging in activities during online and hybrid delivery. The results of the study illustrate a significant student engagement in learning whereas a pessimistic perception towards the transition to a completely online setting.
Teaching Students with Learning Difficulties or Disabilities: Regular Education Teachers’ Professional Development and Practices
Authors: Inês Helena , Pacheco José Augusto , Abelha Marta , Seabra Filipa
Published: September 2022

This article focuses on how curricular and pedagogical differentiation for students of special education is perceived by teachers of the second cycle of basic education in Portugal. The objectives of this research were (i) to inquire about the teachers’ perceptions regarding their training path and their training needs; (ii) to characterize the teaching practices of teachers, aimed at students of special education; and (iii) to learn the perceptions of these teachers regarding their curricular and pedagogical practices directed at students of special education—this called for an interpretative methodology. The data were collected through semi-structured interviews and subjected to content analysis. The results indicate a widespread concern with teachers’ current professional development and the management of diversity. The respondents’ perceptions, shown as favorable to change, are in line with personalized work, enhancing visible contributions to students’ personal and social development.
A Case Study of Prospective Teachers Engaged in Professional Noticing of their Students’ Mathematical Thinking
Authors: Selmer Sarah , Lindstrom Denise , Lampen Erna
Published: September 2022

In this study, prospective teachers engaged in professional noticing of their students’ mathematical thinking captured in pieces of written work. Researchers then worked to characterize the prospective teachers’ professional noticing using a lens of responsive teaching. Results indicate that prospective teacher decisions about how to respond to their students’ mathematical thinking fall on a responsiveness continuum, often shifting in responsiveness across pieces of student written work. The findings of this study provide guidance for teacher educators who work to develop K–12 educators’ responsive teaching practices.
Educating Young Consumers about Food Hygiene and Safety with SafeConsume: A Multi-Centre Mixed Methods Evaluation
Authors: Hann Magda , Allison Rosalie , Truninger Mónica , Junqueira Luís , Silva Alexandre , Touboul Lundgren Pia , Lacroix Hugues Virginie , Godard Marion , Fehér Ágnes , Csenki Eszter , Szakos Dávid , Kasza Gyula , Verlander Neville Q. , Chen Gina , Demirjian Alicia
Published: September 2022

The SafeConsume educational suite was designed to improve knowledge about undertaking safer food practices and avoiding food-borne illnesses among young people. The resources were designed to support educators and members of the community who teach young people (aged 11–18 years) and include lesson plans and supporting teacher training resources. To assess the efficacy and suitability of the resources, an evaluation of the central lesson, the ‘user journey’, was conducted within four European countries. The mixed-methods evaluation included the following elements: a pre- and post-scenario-based questionnaire, a satisfaction questionnaire, focus groups with students; and interviews with teachers. Data from the scenario-based questionnaires were analysed using a mixed effects normal linear regression model. Qualitative data were thematically analysed, and the main themes were discussed. A total of 171 students and 9 educators took part from schools based in Portugal, Hungary, France and England. The results indicated a significant improvement in students’ knowledge and understanding of appropriate food hygiene practices overall, although this varied among countries. The resources were found to be well-suited to help teachers deliver the lesson, being considered by teachers to be both informative and flexible. Minor alternations were suggested, including alterations to lesson delivery or breaking the lesson into smaller sections, and increasing the lesson’s interactivity.
Overview of Blockchain Based Electronic Healthcare Solutions and Security
Authors: Huh Jun-Ho
Published: September 2022

One of the major keywords in the current digital world is the blockchain, which plays a major part in all kinds of advanced service systems, offering more convenience and better efficiency/effectiveness by controlling system hardware intelligently in a way humans have never experienced [...]
Classification of Malware Families Based on Efficient-Net and 1D-CNN Fusion
Authors: Chong Xulei , Gao Yating , Zhang Ru , Liu Jianyi , Huang Xingjie , Zhao Jinmeng
Published: September 2022

A malware family classification method based on Efficient-Net and 1D-CNN fusion is proposed. Given the problem that some local information of malware itself as one-dimensional data will be lost when the malware is imaged, the malware is converted into an image and one-dimensional vector and then input into two neural networks. The network of two-dimensional convolution architecture is used to extract the texture features of malware, and the one-dimensional convolution is used to extract the features of local adjacent information, the deep characteristics of different networks are fused, and the two networks are modified at the same time during backpropagation. This method not only extracts the texture features of malware but also saves the features of the malware itself as one-dimensional data, which shows better performance for multiple datasets.
Collaborative Autonomy: Human–Robot Interaction to the Test of Intelligent Help
Authors: Cantucci Filippo , Falcone Rino
Published: September 2022

A big challenge in human–robot interaction (HRI) is the design of autonomous robots that collaborate effectively with humans, exposing behaviors similar to those exhibited by humans when they interact with each other. Indeed, robots are part of daily life in multiple environments (i.e., cultural heritage sites, hospitals, offices, touristic scenarios and so on). In these contexts, robots have to coexist and interact with a wide spectrum of users not necessarily able or willing to adapt their interaction level to the kind requested by a machine: the users need to deal with artificial systems whose behaviors must be adapted as much as possible to the goals/needs of the users themselves, or more in general, to their mental states (beliefs, goals, plans and so on). In this paper, we introduce a cognitive architecture for adaptive and transparent human–robot interaction. The architecture allows a social robot to dynamically adjust its level of collaborative autonomy by restricting or expanding a delegated task on the basis of several context factors such as the mental states attributed to the human users involved in the interaction. This collaboration has to be based on different cognitive capabilities of the robot, i.e., the ability to build a user’s profile, to have a Theory of Mind of the user in terms of mental states attribution, to build a complex model of the context, intended both as a set of physical constraints and constraints due to the presence of other agents, with their own mental states. Based on the defined cognitive architecture and on the model of task delegation theorized by Castelfranchi and Falcone, the robot’s behavior is explainable by considering the abilities to attribute specific mental states to the user, the context in which it operates and its attitudes in adapting the level of autonomy to the user’s mental states and the context itself. The architecture has been implemented by exploiting the well known agent-oriented programming framework Jason. We provide the results of an HRI pilot study in which we recruited 26 real participants that have interacted with the humanoid robot Nao, widely used in HRI scenarios. The robot played the role of a museum assistant with the main goal to provide the user the most suitable museum exhibition to visit.
FirecovNet: A Novel, Lightweight, and Fast Deep Learning-Based Network for Detecting COVID-19 Patients Using Chest X-rays
Authors: Hassanlou Leila , Meshgini Saeed , Afrouzian Reza , Farzamnia Ali , Moung Ervin Gubin
Published: September 2022

At the end of 2019, a new virus (SARS-CoV-2) called COVID-19 was reported in Wuhan, China, and spread rapidly worldwide. After two years later, several variants of this virus were created, infecting 608 million people and causing 6.51 million deaths. Due to the insufficient sensitivity of RT-PCR test kits, one of the main tools for detecting the virus, chest X-ray images are a popular tool for diagnosing the virus in patients with respiratory symptoms. Models based on deep learning are showing promising results in combating this pandemic. A novel convolutional neural network, FirecovNet, is suggested in this study that detects COVID-19 infection automatically based on raw chest X-ray images. With an architecture inspired by the integration of DarkNet and SqueezeNet networks, the proposed model has fewer parameters than state-of-the-art models and is trained using COVID-19, bacterial pneumonia, normal, lung opacity, and viral pneumonia images, which were collected from two public datasets and also are symmetric in the distribution in class. FirecovNet performance has been verified using the stratified 5-fold cross-validation method. A total of five classification tasks are performed, including four 4-class classifications, and one 5-class classification, and the accuracy of all tasks was at least 95.9%. For all classification tasks, the proposed network has demonstrated promising results in precision, sensitivity, and F1-score. Moreover, a comparison was made between the proposed network and eight deep transfer learning networks and in terms of accuracy, precision, sensitivity, F1-score, speed, and size of the saved model; FirecovNet was very promising. Therefore, FirecovNet can be useful as a tool for more accurate diagnosis of the COVID-19 virus, along with diagnostic tests, in situations where the number of specialist radiologists may be limited.
Implementation of Control Flow Checking—A New Perspective Adopting Model-Based Software Design
Authors: Amel Solouki Mohammadreza , Sini Jacopo , Violante Massimo
Published: September 2022

A common requirement of embedded software in charge of safety tasks is to guarantee the identification of random hardware failures (RHFs) that can affect digital components. RHFs are unavoidable. For this reason, the functional safety standard devoted to automotive applications requires embedded software designs able to detect and eventually mitigate them. For this purpose, various software-based error detection techniques have been proposed over the years, focusing mainly on detecting control flow errors. Many control flow checking (CFC) algorithms have been proposed to accomplish this task. However, applying these approaches can be difficult because their respective literature gives little guidance on their practical implementation in high-level programming languages, and they have to be implemented in low-level code, e.g., assembly. Moreover, the current trend in the automotive industry is to adopt the so-called model-based software design approach, where an executable algorithm model is automatically translated into C or C++ source code. This paper presents two novelties: firstly, the compliance of the experimental data on the capabilities of control flow checking (CFC) algorithms with the ISO 26262 automotive functional safety standard; secondly, by implementing the CFC algorithm in the application behavioral model, the off-the-shelves code generator seamlessly produces the hardened source code of the application. The assessment was performed using a novel fault injection environment targeting a RISC-V (RV32I) microcontroller.
Hybrid CLAHE-CNN Deep Neural Networks for Classifying Lung Diseases from X-ray Acquisitions
Authors: Hussein Fairouz , Mughaid Ala , AlZu’bi Shadi , El-Salhi Subhieh M. , Abuhaija Belal , Abualigah Laith , Gandomi Amir H.
Published: September 2022

Chest and lung diseases are among the most serious chronic diseases in the world, and they occur as a result of factors such as smoking, air pollution, or bacterial infection, which would expose the respiratory system and chest to serious disorders. Chest diseases lead to a natural weakness in the respiratory system, which requires the patient to take care and attention to alleviate this problem. Countries are interested in encouraging medical research and monitoring the spread of communicable diseases. Therefore, they advised researchers to perform studies to curb the diseases’ spread and urged researchers to devise methods for swiftly and readily detecting and distinguishing lung diseases. In this paper, we propose a hybrid architecture of contrast-limited adaptive histogram equalization (CLAHE) and deep convolutional network for the classification of lung diseases. We used X-ray images to create a convolutional neural network (CNN) for early identification and categorization of lung diseases. Initially, the proposed method implemented the support vector machine to classify the images with and without using CLAHE equalizer. The obtained results were compared with the CNN networks. Later, two different experiments were implemented with hybrid architecture of deep CNN networks and CLAHE as a preprocessing for image enhancement. The experimental results indicate that the suggested hybrid architecture outperforms traditional methods by roughly 20% in terms of accuracy.
Research on Upper Limb Action Intention Recognition Method Based on Fusion of Posture Information and Visual Information
Authors: Cui Jian-Wei , Du Han , Yan Bing-Yan , Wang Xuan-Jie
Published: September 2022

A prosthetic hand is one of the main ways to help patients with upper limb disabilities regain their daily living abilities. Prosthetic hand manipulation must be coordinated with the user’s action intention. Therefore, the key to the control of the prosthetic hand is to recognize the action intention of the upper limb. At present, there are still problems such as difficulty in decoding information and a low recognition rate of identifying action intention with EMG signals and EEG signals. While inertial sensors have the advantages of low cost and high accuracy and posture information can characterize the upper limb motion state, visual information has the advantages of high information and being able to detect the type of target objects, which can be complementarily fused with inertial sensors to further grasp the human motion requirements. Therefore, this paper proposes an upper limb action intention recognition method based on the fusion of posture information and visual information. The inertial sensor is used to collect the attitude angle data during the movement of the upper limb, and according to the similarity of the human upper limb structure to the linkage mechanism, a model of the upper limb of the human body is established using the positive kinematics theory of a mechanical arm to solve the upper limb end positions. The upper limb end positions were classified into three categories: torso front, upper body nearby, and the initial position, and a multilayer perceptron model was trained to learn the classification relationships. In addition, a miniature camera was installed on the hand to obtain visual image information during upper limb movement. The target objects are detected using the YOLOv5 deep learning method, and then, the target objects are classified into two categories: wearable items and non-wearable items. Finally, the upper limb intention is jointly decided by the upper limb motion state, target object type, and upper limb end position to achieve the control of the prosthetic hand. We applied the upper limb intention recognition method to the experimental system of a mechanical prosthetic hand and invited several volunteers to test it. The experimental results showed that the intention recognition success rate reached 92.4%, which verifies the feasibility and practicality of the upper limb action intention recognition method based on the fusion of posture information and visual information.
Explainable Artificial Intelligence for Intrusion Detection System
Authors: Patil Shruti , Varadarajan Vijayakumar , Mazhar Siddiqui Mohd , Sahibzada Abdulwodood , Ahmed Nihal , Sinha Onkar , Kumar Satish , Shaw Kailash , Kotecha Ketan
Published: September 2022

Intrusion detection systems are widely utilized in the cyber security field, to prevent and mitigate threats. Intrusion detection systems (IDS) help to keep threats and vulnerabilities out of computer networks. To develop effective intrusion detection systems, a range of machine learning methods are available. Machine learning ensemble methods have a well-proven track record when it comes to learning. Using ensemble methods of machine learning, this paper proposes an innovative intrusion detection system. To improve classification accuracy and eliminate false positives, features from the CICIDS-2017 dataset were chosen. This paper proposes an intrusion detection system using machine learning algorithms such as decision trees, random forests, and SVM (IDS). After training these models, an ensemble technique voting classifier was added and achieved an accuracy of 96.25%. Furthermore, the proposed model also incorporates the XAI algorithm LIME for better explainability and understanding of the black-box approach to reliable intrusion detection. Our experimental results confirmed that XAI LIME is more explanation-friendly and more responsive.
PUF-PSS: A Physically Secure Privacy-Preserving Scheme Using PUF for IoMT-Enabled TMIS
Authors: Yu Sungjin , Park Kisung
Published: September 2022

With the development of telecare medical information system (TMIS), doctors and patients are able to access useful medical services via 5G wireless communications without visiting the hospital in person. Unfortunately, TMIS should have the essential security properties, such as anonymity, mutual authentication, and privacy, since the patient’s data is transmitted via a public channel. Moreover, the sensing devices deployed in TMIS are resource-limited in terms of communication and computational costs. Thus, we design a physically secure privacy-preserving scheme using physical unclonable functions (PUF) in TMIS, called PUF-PSS to resolve the security requirements and efficiency of the existing related schemes. PUF-PSS prevents the security threats and also guarantees anonymity, key freshness, and authentication. We evaluate the security of PUF-PSS by performing formal and informal security analyses, including AVISPA implementation and ROR oracle model. We perform the test bed experiments utilizing well-known MIRACL based on a Raspberry PI 4 and compare the communication and computational costs of PUF-PSS with the previous schemes for TMIS. Consequently, PUF-PSS guarantees better efficiency and security than previous schemes and can be applied to TMIS environments.
Real-Time Non-Uniformity Correction without TEC for Microbolometer Array
Authors: Yeo Jun Dong , Woo DooHyung
Published: September 2022

This paper describes a new readout integrated circuit and non-uniformity correction (NUC) method that ensures that the bolometer array has low non-uniformity over a wide operating temperature range without a thermoelectric cooler (TEC). The proposed NUC minimizes the circuit and memory required for signal processing, making it suitable for compact and power-efficient portable infrared cameras. It corrects the aging phenomenon through start-up calibration and corrects non-uniformities without a TEC through calibration during operation mode. It minimizes the calibration process during operation mode and uses a pixel-level analog-to-digital converter to enable real-time NUC. A 0.18 μm standard CMOS process is applied to the proposed NUC. The frame rate for calibration during the operation mode is approximately 14.3 Hz. The proposed NUC demonstrates excellent uniformity with a non-uniformity of less than 0.12% over a wide operating temperature range (−20 to 50 °C).
Deep-Learning-Based Network for Lane Following in Autonomous Vehicles
Authors: Khanum Abida , Lee Chao-Yang , Yang Chu-Sing
Published: September 2022

The research field of autonomous self-driving vehicles has recently become increasingly popular. In addition, motion-planning technology is essential for autonomous vehicles because it mitigates the prevailing on-road obstacles. Herein, a deep-learning-network-based architecture that was integrated with VGG16 and the gated recurrent unit (GRU) was applied for lane-following on roads. The normalized input image was fed to the three-layer VGG16 output layer as a pattern and the GRU output layer as the last layer. Next, the processed data were fed to the two fully connected layers, with a dropout layer added in between each layer. Afterward, to evaluate the deep-learning-network-based model, the steering angle and speed from the control task were predicted as output parameters. Experiments were conducted using the a dataset from the Udacity simulator and a real dataset. The results show that the proposed framework remarkably predicted steering angles in different directions. Furthermore, the proposed approach achieved higher mean square errors of 0.0230 and 0.0936 and and inference times of 3–4 and 3 ms. We also implemented our proposed framework on the NVIDIA Jetson embedded platform (Jetson Nano 4 GB) and compared it with the GPU’s computational time. The results revealed that the embedded system took 45–46 s to execute a single epoch in order to predict the steering angle. The results show that the proposed framework generates fruitful and accurate motion planning for lane-following in autonomous driving.