bluuebunny/crossref_metadata_2025 huggingface dataset:
Knowledge, Attitudes and Professional Behavior of Silver Diamine Fluoride among Dental Personnel: A Systematic Review
Authors: Mohammed Intan Elliayana , Shariff Nursharhani , Mohd Hanim Muhammad Faiz , Mohd Yusof Mohd Yusmiaidil Putera , Md Sabri Budi Aslinie , Md Bohari Nor Faezah , Venkiteswaran Annapurny
Published: December 2022

Apart from the major drawback of black staining once lesion is arrested, few studies have indicated that dental personnel’s perception and attitude towards silver diamine fluoride (SDF) influences its usage. This report aims to provide a systematic review presenting dental personnel’s knowledge, attitudes, and perceptions, (KAPs) regarding SDF. A search of multiple electronic literature databases and a manual search were performed. The review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement. A comprehensive search yielded 719 publications and 14 cross-sectional studies matching the inclusion criteria. Awareness of SDF among respondents ranged from 29.6% to 87.8%, whereas awareness of its use in dentistry ranged from 14.4% to 94.5%. Regarding attitude towards SDF, the mean score ranged from 3.39 to 14.35. An increase in knowledge of and attitude towards SDF is significantly associated with the usage of SDF and may increase the usage of SDF. This review provides vital information on dental personnel’s KAPs regarding SDF. It is anticipated that the adaptation of SDF usage will be more widespread in the future, especially among children. Findings from the review may assist intervention programs to change misperceptions and to enhance awareness regarding SDF among dental personnel.
Brain-Directed Care: Why Neuroscience Principles Direct PICU Management beyond the ABCs
Authors: Long Debbie A. , Waak Michaela , Doherty Nicola N. , Dow Belinda L.
Published: December 2022

Major advances in pediatric intensive care (PICU) have led to increased child survival. However, the long-term outcomes among these children following PICU discharge are a concern. Most children admitted to PICU are under five years of age, and the stressors of critical illness and necessary interventions can affect their ability to meet crucial developmental milestones. Understanding the neuroscience of brain development and vulnerability can inform PICU clinicians of new ways to enhance and support the care of these most vulnerable children and families. This review paper first explores the evidence-based neuroscience principles of brain development and vulnerability and the impact of illness and care on children’s brains and ultimately wellbeing. Implications for clinical practice and training are further discussed to help optimize brain health in children who are experiencing and surviving a critical illness or injury.
Screening, Diagnosis, and Treatment of Pediatric Ocular Diseases
Authors: Lam Matthew , Suh Donny
Published: December 2022

Vision is an important aspect of a child’s quality of life and intellectual, social, and emotional development. Disruptions to vision during infancy and early childhood can cause lifelong vision impairment or blindness. However, early identification and treatment of eye disease can prevent loss of sight and its consequent long-term effects. Therefore, screening guidelines exist to guide physicians in detecting the most common threats to sight in the different stages of infancy and childhood. This review describes common causes of pediatric vision impairment, the recommended screening guidelines for diagnosing them, and current treatment modalities.
COVID-19 Related Information on Pediatric Dental Care including the Use of Teledentistry: A Narrative Review
Authors: Di Spirito Federica , Amato Alessandra , Di Palo Maria Pia , Ferraro Giuseppe A. , Baroni Adone , Serpico Rosario , Contaldo Maria
Published: December 2022

In addition to the direct impact of the SARS-CoV-2 infection, the COVID-19 pandemic reports multiple effects on people’s health and psycho-physical well-being. In the dental field, oral hygiene sessions, regular dental check-ups, and aerosol-generating procedures were commonly postponed, thus resulting in repercussions on oral health also favored by the changing eating and oral hygiene habits during the multiple lockdowns. Furthermore, dental settings and practices were generally perceived as at high risk for SARS-CoV-2 transmission, thus unsafe, and by general and pediatric dentists themselves. Last, the consequences of stress related to deprivation of social life and playful activities should not be underestimated in children, exposing them to the deleterious effects of bad oral habits, with repercussions on the balanced growth and development of the stomatognathic system. The present work intends to analyze the medium-term and long-term impact of COVID-19 on pediatric oral and dental care provision, reviewing pediatric dentistry practice and oral and dental needs of pedodontics patients during the first peak and the various waves of the COVID-19 pandemic, and lessons learned.
Factors Affecting Body Image Distortion in Adolescents
Authors: Jung Eun-Ha , Jun Mi-Kyoung
Published: December 2022

Body image distortion is an important problem in physical and psychological health in adolescents. This study examined the factors affecting body image distortion in Korean adolescents. A Rao–Scott χ2 test and a complex samples logistic regression was conducted using the statistics from the 17th (2021) Korea Youth Risk Behavior Web-based Survey. The study sample included 41,124 middle and high school students. There was a difference in the presence or absence of body image distortion according to the subject’s gender, school grade, school achievement, and economic status (p < 0.001). Approximately 49.5% of subjects with body image distortion had tried to lose weight, but there were few cases where they attempted to lose weight through physical activities, such as moderate physical activity (22.8%), vigorous physical activity (23.3%), and muscle physical activity (23.9%) (p < 0.001). The group with body image distortion was 1.77 times more likely to sit for more than four hours a day on average (p < 0.001). To prevent various factors influencing the distortion of the adolescent’s body image, the development of a systematic intervention program for forming no distortion of adolescents’ body image is required.
The Importance of Monitoring and Factors That May Influence Leg Length Difference in Developmental Dysplasia of the Hip
Authors: Merchant Rajiv M. , Tolk Jaap J. , Ayub Anouska A. , Eastwood Deborah M. , Hashemi-Nejad Aresh
Published: December 2022

In unilateral Developmental Dysplasia of the Hip (DDH), avascular necrosis (AVN), femoral or pelvic osteotomy, and residual dysplasia causing subluxation of the proximal femur may influence Leg Length Discrepancy (LLD). This can lead to gait compensation, pelvic obliquity, and spinal curvature. The aim of this study is to determine the prevalence of LLD, establish which limb segment contributes to the discrepancy, describe how AVN influences LLD, and ascertain variables that may influence the need for LLD corrective procedures. Methodology: This study assessed long-leg radiographs at skeletal maturity. Radiographs were assessed for the articulo-trochanteric distance (ATD) and femoral and tibial length. AVN was classified according to Kalamchi–MacEwen. Results: 109 patients were included. The affected/DDH leg was longer in 72/109 (66%) patients. The length difference was mainly in the subtrochanteric segment of the femur. AVN negatively influenced leg length. Older (≥three years) patients with multiple procedures were more likely to have AVN. LLD interventions were performed in 30 (27.5%) patients. AVN grade or type of DDH surgery did not influence the odds of needing a procedure to correct LLD. Conclusions: Procedures to correct LLD were performed irrespective of previous DDH surgery or AVN grades. In most patients, the affected/DDH leg was longer, mainly in the subtrochanteric segment of the femur, largely influenced by femoral osteotomy in patients with multiple operative procedures for DDH. We recommend careful monitoring of LLD in DDH.
Grading of Intraventricular Hemorrhage and Neurodevelopment in Preterm <29 Weeks’ GA in Canada
Authors: Shah Vibhuti , Musrap Natasha , Maharaj Krishanta , Afifi Jehier , El-Naggar Walid , Kelly Edmond , Mukerji Amit , Shah Prakesh , Vincer Michael
Published: December 2022

Objective: The aim of this study was to evaluate the neurodevelopmental outcome at 18–24 months in surviving preterm infants with grades I–IV intraventricular hemorrhages (IVHs) compared to those with no IVH. Study Design: We included preterm survivors <29 weeks’ GA admitted to the Canadian Neonatal Network’s NICUs from April 2009 to September 2011 with follow-up data at 18–24 months in a retrospective cohort study. The neonates were grouped based on the severity of the IVH detected on a cranial ultrasound scan and recorded in the database: no IVH; subependymal hemorrhage or IVH without ventricular dilation (grades I–II); IVH with ventricular dilation (grade III); and persistent parenchymal echogenicity/lucency (grade IV). The primary outcomes of neurodevelopmental impairment (NDI), significant neurodevelopmental impairment (sNDI), and the effect modification by other short-term neonatal morbidities were assessed. Using multivariable regression analysis, the adjusted ORs (AOR) and 95% of the CIs were calculated. Results: 2327 infants were included. The odds of NDI were higher in infants with grades III and IV IVHs (AOR 2.58, 95% CI 1.56, 4.28 and AOR 2.61, 95% CI 1.80, 3.80, respectively) compared to those without IVH. Infants with an IVH grade ≤II had similar outcomes for NDI (AOR 1.08, 95% CI 0.86, 1.35) compared to those without an IVH, but the odds of sNDI were higher (AOR 1.58, 95% CI 1.16, 2.17). Conclusions: There were increased odds of sNDI in infants with grades I–II IVHs, and an increased risk of adverse NDI in infants with grades ≥III IVHs is corroborated with the current literature.
Online Bottleneck Matching Problem with Two Heterogeneous Sensors in a Metric Space
Authors: Xiao Man , Yang Yaru , Li Weidong
Published: December 2022

In this paper, we consider the online matching problem with two heterogeneous sensors s1 and s2 in a metric space (X,d). If a request r is assigned to sensor s1, the service cost of r is the distance d(r,s1). Otherwise, r is assigned to sensor s2, and the service cost of r is d(r,s2)w, where w≥1 is the weight of sensor s2. The goal is to minimize the maximum matching cost, we design an optimal online algorithm with a competitive ratio of 1+w+1w for 1≤w≤1.839, and an optimal online algorithm with a competitive ratio of w+1+w2+6w+12 for w>1.839.
Advancement of All-Trans Retinoic Acid Delivery Systems in Dermatological Application
Authors: Omar Sharifah Shakirah Syed , Hadi Hazrina
Published: December 2022

Dermatological conditions, such as acne, photoaging, psoriasis, and wounds, have been treated topically with all-trans retinoic acid (ATRA) for many years owing to its anti-inflammatory activity, comedolytic effect, and ability to increase collagen production. However, common side effects of ATRA known as the retinoid reaction can occur. These side effects are countered by ATRA encapsulation in solid lipid nanoparticles (SLN), nanostructured lipid carriers (NLCs), and liposomes. Liposomes used to encapsulate ATRA include niosomes, ethosomes, and transfersomes. Side effects involving inflammatory reactions, such as irritation, redness, and erythema, were diminished using these approaches. The use of such carriers enhanced the efficacy of ATRA by enhancing its permeation into skin. These formulations have been compared in terms of improving the activity of ATRA and the ability to relieve the side effects. Further research into different delivery systems for ATRA using various formulations will improve the future of topical ATRA delivery.
Effect of Solvent Composition on Solubility, Thermodynamics, Metastable Zone Width (MSZW) and Crystal Habit of L-Ascorbic Acid
Authors: Yadav Jyoti , Dumitrescu Dan G. , Kendall Thomas , Guguta Carmen , Patel Swati A.
Published: December 2022

This work investigates the effect of different solvent systems on solubility, thermodynamics, metastable zone width (MSZW), and crystal habit of ascorbic acid, in order to help optimize its crystallization process. The solubility curves and metastable zone (MSZ) limits were determined experimentally using the polythermal method in pure solvents: water and alcohols (methanol/ethanol/isopropanol), as well as water-alcohol binary solvent systems. The solubility decreases with increasing alcohol composition for all solvent systems. The solubility data were well correlated using the Jouyban–Acree model as a function of two variables: temperature and solvent composition. The dissolution enthalpy (ΔHdiss), dissolution entropy (ΔSdiss), and Gibbs free energy (ΔGdiss) were determined using Van’t Hoff and Jouyban–Acree models. The thermodynamic properties increase with increasing alcohol composition. The lowest and highest values of enthalpy were obtained for water (20.52 kJ mol −1) and isopropanol (35.33 kJ mol−1), respectively. Pure alcohols as solvents widen the metastable zone width, indicating high supersaturation required for the nucleation. Crystal images captured during cooling crystallization in water confirm the cubic crystal habit formation, whereas increasing alcohol composition in the solvent system promotes preferential growth along one crystallographic axis, leading to elongated prism-shaped crystals in methanol and ethanol and needle-shaped crystals in isopropanol.
Hydrogen-Bonded Chain of Rings Motif in N-(4-Methoxyphenyl)piperazin-1-ium Salts with Benzoate Anions: Supramolecular Assemblies and Their Energy Frameworks
Authors: Priyanka Prabhakar , Jayanna Bidarur K. , Divakara Thayamma R. , Suresha Gejjalagere P. , Vinaya  , Basavaraju Yeriyur B. , Yathirajan Hemmige S. , Parkin Sean R. , Chęcińska Lilianna
Published: December 2022

The crystal structures of three salts, namely N-(4-methoxyphenyl)piperazin-1-ium ethoxybenzoate monohydrate (I), N-(4-methoxyphenyl)piperazin-1-ium methoxybenzoate monohydrate (II) and N-(4-methoxyphenyl)piperazin-1-ium hydroxybenzoate monohydrate (III), have been determined and compared. In each of them, the ionic components and the water molecules are linked by a combination of N—H···O and O—H···O hydrogen bonds to form infinite chains of edge-fused centrosymmetric rings running parallel to the [100] direction. The C—H···O, C—H···π(arene) interactions and O—H···O in (III) are responsible for the further propagation of the aforementioned chains into di-periodic layers or tri-periodic networks. From an energetic point of view, all structures are primarily di-periodic; the very strong ionic interactions determine the periodicity. For comparison purposes, quantum chemical calculations were performed to show the difference between the ionic and neutral components. The energy of the hydrogen-bonded ring motifs was also estimated.
Sources of Free and Added Sugars and Their Nutritional Impact in Diabetic Patients
Authors: Fernandes Tatiana , Faria Ana , Loureiro Helena
Published: December 2022

A high consumption of sugar leads to an increase in caloric intake, which in turn will lead to a higher risk of developing health issues. Foods contain both naturally occurring sugars and added sugars. The World Health Organization recommends that the daily intake of free sugars be below 10% of the total daily energy intake. Food performs a key role in maintaining an adequate glycaemic control in people with diabetes. However, there is a low compliance to dietary recommendations, namely in the amount of sugar intake. This review article aims to assess and compare the intake of various types of sugars in the general population and among individuals with and without a diabetes diagnosis, identify the food sources that contribute to the intake of free and added sugars, and understand their impact on health. Studies performed on the general population found that the consumption of sugar was high, and that children and teens are more likely to exceed the recommended amounts. It was found that diabetics consume less total and added sugar than non-diabetics, as well as a less sugary drinks. Guidelines and public health policy measures aimed at limiting the intake of free and added sugars are needed in order to minimize the consumption of foods high in empty calories.
Automatic Detection and Classification of Cardiovascular Disorders Using Phonocardiogram and Convolutional Vision Transformers
Authors: Abbas Qaisar , Hussain Ayyaz , Baig Abdul Rauf
Published: December 2022

The major cause of death worldwide is due to cardiovascular disorders (CVDs). For a proper diagnosis of CVD disease, an inexpensive solution based on phonocardiogram (PCG) signals is proposed. (1) Background: Currently, a few deep learning (DL)-based CVD systems have been developed to recognize different stages of CVD. However, the accuracy of these systems is not up-to-the-mark, and the methods require high computational power and huge training datasets. (2) Methods: To address these issues, we developed a novel attention-based technique (CVT-Trans) on a convolutional vision transformer to recognize and categorize PCG signals into five classes. The continuous wavelet transform-based spectrogram (CWTS) strategy was used to extract representative features from PCG data. Following that, a new CVT-Trans architecture was created to categorize the CWTS signals into five groups. (3) Results: The dataset derived from our investigation indicated that the CVT-Trans system had an overall average accuracy ACC of 100%, SE of 99.00%, SP of 99.5%, and F1-score of 98%, based on 10-fold cross validation. (4) Conclusions: The CVD-Trans technique outperformed many state-of-the-art methods. The robustness of the constructed model was confirmed by 10-fold cross-validation. Cardiologists can use this CVT-Trans system to help patients with the diagnosis of heart valve problems.
Clinical Evaluation of the ButterfLife Device for Simultaneous Multiparameter Telemonitoring in Hospital and Home Settings
Authors: Salton Francesco , Kette Stefano , Confalonieri Paola , Fonda Sergio , Lerda Selene , Hughes Michael , Confalonieri Marco , Ruaro Barbara
Published: December 2022

We conducted a two-phase study to test the reliability and usability of an all-in-one artificial intelligence-based device (ButterfLife), which allows simultaneous monitoring of five vital signs. The first phase of the study aimed to test the agreement between measurements performed with ButterfLife vs. standard of care (SoC) in 42 hospitalized patients affected by acute respiratory failure. In this setting, the greatest discordance between ButterfLife and SoC was in respiratory rate (mean difference −4.69 bpm). Significantly close correlations were observed for all parameters except diastolic blood pressure and oxygen saturation (Spearman’s Rho −0.18 mmHg; p = 0.33 and 0.20%; p = 0.24, respectively). The second phase of the study was conducted on eight poly-comorbid patients using ButterfLife at home, to evaluate the number of clinical conditions detected, as well as the patients’ compliance and satisfaction. The average proportion of performed tests compared with the scheduled number was 67.4%, and no patients reported difficulties with use. Seven conditions requiring medical attention were identified, with a sensitivity of 100% and specificity of 88.9%. The median patient satisfaction was 9.5/10. In conclusion, ButterfLife proved to be a reliable and easy-to-use device, capable of simultaneously assessing five vital signs in both hospital and home settings.
MAGNet: A Camouflaged Object Detection Network Simulating the Observation Effect of a Magnifier
Authors: Jiang Xinhao , Cai Wei , Zhang Zhili , Jiang Bo , Yang Zhiyong , Wang Xin
Published: December 2022

In recent years, protecting important objects by simulating animal camouflage has been widely employed in many fields. Therefore, camouflaged object detection (COD) technology has emerged. COD is more difficult to achieve than traditional object detection techniques due to the high degree of fusion of objects camouflaged with the background. In this paper, we strive to more accurately and efficiently identify camouflaged objects. Inspired by the use of magnifiers to search for hidden objects in pictures, we propose a COD network that simulates the observation effect of a magnifier called the MAGnifier Network (MAGNet). Specifically, our MAGNet contains two parallel modules: the ergodic magnification module (EMM) and the attention focus module (AFM). The EMM is designed to mimic the process of a magnifier enlarging an image, and AFM is used to simulate the observation process in which human attention is highly focused on a particular region. The two sets of output camouflaged object maps were merged to simulate the observation of an object by a magnifier. In addition, a weighted key point area perception loss function, which is more applicable to COD, was designed based on two modules to give greater attention to the camouflaged object. Extensive experiments demonstrate that compared with 19 cutting-edge detection models, MAGNet can achieve the best comprehensive effect on eight evaluation metrics in the public COD dataset. Additionally, compared to other COD methods, MAGNet has lower computational complexity and faster segmentation. We also validated the model’s generalization ability on a military camouflaged object dataset constructed in-house. Finally, we experimentally explored some extended applications of COD.
Complex Knowledge Base Question Answering for Intelligent Bridge Management Based on Multi-Task Learning and Cross-Task Constraints
Authors: Yang Xiaoxia , Yang Jianxi , Li Ren , Li Hao , Zhang Hongyi , Zhang Yue
Published: December 2022

In the process of bridge management, large amounts of domain information are accumulated, such as basic attributes, structural defects, technical conditions, etc. However, the valuable information is not fully utilized, resulting in insufficient knowledge service in the field of bridge management. To tackle these problems, this paper proposes a complex knowledge base question answering (C-KBQA) framework for intelligent bridge management based on multi-task learning (MTL) and cross-task constraints (CTC). First, with C-KBQA as the main task, part-of-speech (POS) tagging, topic entity extraction (TEE), and question classification (QC) as auxiliary tasks, an MTL framework is built by sharing encoders and parameters, thereby effectively avoiding the error propagation problem of the pipeline model. Second, cross-task semantic constraints are provided for different subtasks via POS embeddings, entity embeddings, and question-type embeddings. Finally, using template matching, relevant query statements are generated and interaction with the knowledge base is established. The experimental results show that the proposed model outperforms compared mainstream models in terms of TEE and QC on bridge management datasets, and its performance in C-KBQA is outstanding.
Gender Inequality in European Football: Evidence from Competitive Balance and Competitive Intensity in the UEFA Men’s and Women’s Champions League
Authors: François Aurélien , Scelles Nicolas , Valenti Maurizio
Published: December 2022

Competitiveness of sporting contests is key to attract fan interest. However, limited research compared levels of competitiveness in men’s and women’s sports. This study focuses on the evolution of intra-match competitive balance (IMCB) and competitive intensity (IMCI) in the UEFA Men’s and Women’s Champions League (UMCL and UWCL). Data were initially collected for 3299 games over 2001–2019 (2314 in UMCL; 985 in UWCL) to analyse the evolution within and between each tournament. In addition, 989 matches played in UMCL over 1955–1973 were added to compare both competitions in their early stages. Results show a deterioration in IMCB and IMCI between 2001–2009 and 2009–2019 for the UMCL. Conversely, the UWCL benefitted from an increase in IMCB but not in IMCI, except for the final. The UWCL is still less competitive than the UMCL. This result holds true even when comparing the early stages of both competitions, i.e., replacing 2001–2019 by 1955–1973 for the UMCL. However, the UWCL has become closer to the UMCL in terms of IMCB and IMCI over time, a result linked to the development of women’s football. From a theoretical perspective, this article advances knowledge of IMCB and IMCI, appropriate for competitions with knockout stages. It underlines the role of the pool of players as an explanatory factor for the gap in competitiveness between European men’s and women’s football.
Reflections on an Academic Leadership Approach to Implementing Digital Education in Higher Education
Authors: Visintini Gloria
Published: December 2022

The aim of this paper is to reflect upon personal experiences with a Foucauldian-inspired academic leadership approach to implementing digital education at the University of Bristol. Higher Education Institutions across the UK often invest in digital infrastructure and central support teams (technical and educational) and expect these investments alone to be the main vehicles to achieve digital education. While clearly, having technologies and support play a key role in digital education, according to my experience the implementation of digital education is complex and requires focused and scholarly leadership to drive it. This is because I argue that digital education can be considered a ‘discourse’, in the Foucauldian sense, of our era whose implementation involves considerable social change. Through my work as the academic person first responsible for digital education in the School of Modern Languages and then across the Faculty of Arts at the University of Bristol, I will unpack how having someone with the appropriate practical and theoretical expertise leading the digital education agenda brought positive impacts before and during the pandemic. Drawing on reflections from years of academic leadership, I will consider questions such as: What practices can lead the digital education discourse to advance in HE settings? And, which agents in HE can develop these practices? This theoretical-oriented discussion grounded on personal experience can be useful for institutions making decisions about how to take digital education forward. Indeed, as we emerge from the pandemic, universities are in the process of choosing whether to go back to a pre-COVID time characterised by a power struggle between different educational discourses, which often meant that the digital education discourse remained marginal, or whether to embrace and invest more in digital education and associated academic leadership.
Predictors of the Intention of Learning in Higher Education: Motivation, Self-Handicapping, Executive Function, Parents’ Education and School Achievement
Authors: Józsa Gabriella , Oo Tun Zaw , Amukune Stephen , Józsa Krisztián
Published: December 2022

Learning motivation, self-handicapping, executive function, and school achievement are interrelated variables affecting students’ intension of learning in higher education. However, many educators are unaware of the ability of these predictive variables and fail to encourage students’ further education. Therefore, with the aim of helping educators, this study was conducted to predict students’ intention of learning in higher education based on their learning motivation, self-handicapping, executive function, school achievement, and mothers’ education. We sampled Hungarian 12th graders (N = 1330) who participated in this study while collecting the data online during class hours. Questionnaires were used to measure learning motivation, self-handicapping, and executive function. Findings showed that higher learning motivation, lower self-handicapping, stronger working memory, and mother’s education could increase students’ intention of learning in higher education. Beyond our expectations, higher school achievement (GPA) was not a significant predictor. Comparing different theoretical perspectives, we deduced reliable predictors underlying Hungarian students’ intention of learning in higher education. Therefore, this study is of great importance for educators to pick out the real support for their students’ intention of learning in higher education by following our research findings.
Starting the Educational Reform from the Beginning: Croatian Pre-Service Chemistry Teachers’ Level of Pedagogical Content Knowledge on Particle Theory
Authors: Sojat Lana , Markic Silvija
Published: December 2022

Changes in the educational system need strong support from and connection to teachers. For this, knowledge about teachers is needed. A key factor in changing the chemistry classroom reality are teachers—in-service and pre-service. Chemistry Education Research is a new domain in Croatia. Since the transfer of the data from various undertaken studies on teachers’ knowledge is not easy based on differences in educational systems, research in the Croatian context is needed. Particle Theory is one of the main topics and the basis of chemistry teaching and learning. This study is based on open-ended questions following the frame of pedagogical content knowledge (PCK) and Big Ideas about Particle Theory by Loughran. Data analysis follows the pattern of graduating the PCK of the participants at the novice, intermediate or advanced level. The results show that pre-service chemistry teachers’ PCK is either novice or intermediate on the national level, while the results for particular universities show some differences in chemistry pre-service teachers’ PCK. Thus, the study can be an excellent starting point for chemistry teacher educators in university for further development of pre-service teachers’ PCK.
Kindergarten through Grade 3 Outcomes Associated with Participation in High-Quality Early Care and Education: A RCT Follow-Up Study
Authors: Horm Diane M. , Jeon Shinyoung , Clavijo Moira V. , Acton Melissa
Published: December 2022

An accepted conclusion is that children at risk for educational failure who participate in high-quality early care and education (ECE) enter kindergarten “more ready”, possessing skills comparable to their more advantaged peers. There is less consensus about longer-term outcomes with some studies finding continuation of early gains, while others report “fade out” by elementary school. This study investigated child outcomes, kindergarten through Grade 3, of 75 children randomly assigned as infants to either participate or not in an enhanced Early Head Start/Head Start program. It was hypothesized that the children who experienced this high-quality ECE would perform better than their control group peers across a range of measures. From kindergarten to Grade 3, children in the treatment group demonstrated higher skills in letter and word identification, vocabulary, oral comprehension, and math than control group children after controlling for child/family characteristics and classroom quality. Results for executive functioning were mixed with children in the treatment group showing higher skills on one of the two measures of executive function. No group differences were found for social-emotional skills. This study contributes to the scant literature of longitudinal studies spanning infancy through to Grade 3. In addition to the findings of a general pattern of continuation of positive child outcomes in early academic skills associated with earlier high-quality ECE attendance, this study also contributes information about the potential size of impacts of contemporary ECE programs starting in infancy.
DNNs Based Computation Offloading for LEO Satellite Edge Computing
Authors: Wu Jian , Jia Min , Zhang Liang , Guo Qing
Published: December 2022

Huge low earth orbit (LEO) satellite networks can achieve global coverage with low latency. In addition, mobile edge computing (MEC) servers can be mounted on LEO satellites to provide computing offloading services for users in remote areas. A multi-user multi-task system model is modeled and the problem of user’s offloading decisions and bandwidth allocation is formulated as a mixed integer programming problem to minimize the system utility function expressed as the weighted sum of the system energy consumption and delay. However, it cannot be effectively solved by general optimizations. Thus, a deep learning-based offloading algorithm for LEO satellite edge computing networks is proposed to generate offloading decisions through multiple parallel deep neural networks (DNNs) and store the newly generated optimal offloading decisions in memory to improve all DNNs to obtain near-optimal offloading decisions. Moreover, the optimal bandwidth allocation scheme of the system is theoretically derived for the user’s bandwidth allocation problem. The simulation results show that the proposed algorithm can achieve a good convergence effect within a small number of training steps, and obtain the optimal system utility function values compared with the comparative algorithms under different system parameters, and the time cost of the system and DNNs is very satisfactory.
Triple Coprime Vector Array for DOA and Polarization Estimation: A Perspective of Mutual Coupling Isolation
Authors: Yang Meng , Yuan Qi , Lai Xin , Zhu Beizuo , Zhang Xiaofei
Published: December 2022

Traditional polarization-sensitive sensors involve a triplet of spatially collocated, orthogonally oriented, and diversely polarized electric dipoles. However, this kind of sensor has the drawback of severe mutual coupling among the three dipoles due to the characteristic of collocation, as well as low radiation efficiency because of the short length of the dipoles. Based on this problem, in this study we designed a new array structure called a ‘triple coprime array (TCA)’, equipped with long electric dipoles to obtain higher radiation efficiency. In this structure, the dipoles within different subarrays have orthogonal polarization modes, leading to mutual coupling isolation. The dipole interval of the subarrays is enlarged by means of a pairwise coprime relationship, which further weakens the mutual coupling effect and extends the array aperture. Simultaneously, a stable direction-of-arrival (DOA) and polarization estimation method is proposed. DOA information is accurately refined from the three subarrays without ambiguity problems, with the triple coprime characteristic improving the estimation results. Subsequently, polarization estimates can be obtained using the reconstructed model matrix and the least squares method. Numerous theoretical analyses were conducted and extensive simulation results verified the advantages of the TCA structure in mutual coupling, along with the superiority of the proposed joint DOA and polarization estimation algorithm in terms of estimation accuracy.
A Classification Method for Airborne Full-Waveform LiDAR Systems Based on a Gramian Angular Field and Convolution Neural Networks
Authors: Hu Bin , Zhao Yiqiang , He Jiaji , Liu Qiang , Chen Rui
Published: December 2022

The data processing of airborne full-waveform light detection and ranging (LiDAR) systems has become a research hotspot in the LiDAR field in recent years. However, the accuracy and reliability of full-waveform classification remain a challenge. The manual features and deep learning techniques in the existing methods cannot fully utilize the temporal features and spatial information in the full waveform. On the premise of preserving temporal dependencies, we convert them into Gramian angular summation field (GASF) images using the polar coordinate method. By introducing spatial attention modules into the neural network, we emphasize the importance of the location of texture information in GASF images. Finally, we use open source and simulated data to evaluate the impact of using different network architectures and transformation methods. Compared with the performance of the state-of-art method, our proposed method can achieve higher precision and F1 scores. The results suggest that transforming the full waveform into GASF images and introducing a spatial attention module outperformed other classification methods.
Recent Advances and Future Prospects of Using AI Solutions for Security, Fault Tolerance, and QoS Challenges in WSNs
Authors: Osamy Walid , Khedr Ahmed M. , Salim Ahmed , El-Sawy Ahmed A. , Alreshoodi Mohammed , Alsukayti Ibrahim
Published: December 2022

The increasing relevance and significant acceptance of Wireless Sensor Network (WSN) solutions have aided the creation of smart environments in a multitude of sectors, including the Internet of Things, and offer ubiquitous practical applications. We examine current research trends in WSN using Artificial Intelligence (AI) technologies and the potential application of these methods for WSN improvement in this study. We emphasize the security, fault detection and tolerance, and quality of service (QoS) concerns in WSN, and provide a detailed review of current research that used different AI technologies to satisfy particular WSN objectives from 2010 to 2022. Specifically, this study’s purpose is to give a current review that compares various AI methodologies in order to provide insights for tackling existing WSN difficulties. Furthermore, there has been minimal existing related work concentrating employing AI approaches to solve security, fault detection and tolerance, and quality of service (QoS) concerns associated to WSN, and our goal is to fill the gap in existing studies. The application of AI solutions for WSN is the goal of this work, and we explore all parts of it in order to meet different WSN challenges such as security, fault detection and tolerance, and QoS. This will lead to an increased understanding of current AI applications in the areas of security, fault detection and tolerance, and QoS. Secondly, we present a comprehensive study and analysis of various AI schemes utilized in WSNs, which will aid the researchers in recognizing the most widely used techniques and the merits of employing various AI solutions to tackle WSN-related challenges. Finally, a list of open research issues has been provided, together with considerable bibliographic information, which provides useful recent research trends on the topics and encourages new research directions and possibilities.
Trust-Based Beacon Node Localization Algorithm for Underwater Networks by Exploiting Nature Inspired Meta-Heuristic Strategies
Authors: Draz Umar , Chaudary Muhammad Hasanain , Ali Tariq , Sohail Abid , Irfan Muhammad , Nowakowski Grzegorz
Published: December 2022

Conventional underwater technologies were not able to provide authentication and proper visualization of unexplored ocean areas to accommodate a wide range of applications. The aforesaid technologies face several challenges including decentralization, beacon node localization (for identification of nodes), authentication of Internet of Underwater Things (IoUTs) objects and unreliable beacon node communication between purpose oriented IoT-enabled networks. Recently, new technologies such as blockchain (BC) and the IoUTs have been used to reduce the issues but there are still some research gaps; for example, unreliable beacon messages for node acquisition have significant impacts on node identification and localization and many constrained node resources, etc. Further, the uncertainty of acoustic communication and the environment itself become problems when designing a trust-based framework for the IoUTs. In this research, a trust-based hybrid BC-enabled beacon node localization (THBNL) framework is proposed to employ a secure strategy for beacon node localization (BNL) to mine the underwater localized nodes via the hybrid blockchain enabled beacon node localization (HB2NL) algorithm. This framework helps to merge two disciplines; it is hybrid because it follows the nature and bio inspired meta heuristics algorithms for scheduling the beacon nodes. The performance of the proposed approach is also evaluated for different factors such as node losses, packet delivery ratios, residual and energy consumption and waiting time analysis, etc. These findings show that the work done so far has been successful in achieving the required goals while remaining within the system parameters.
Game-Theory-Based Multimode Routing Protocol for Internet of Things
Authors: Khan Shafiullah , Umar Muhammad Muneer , Jin Chunhua , Xiao Shaozhang , Iqbal Zeeshan , Alnazzawi Noha
Published: December 2022

Various routing protocols have been proposed for ad hoc networks such as the Internet of Things (IoT). Most of the routing protocols introduced for IoT are specific to applications and networks. In the current literature, it is essential to configure all the network nodes with a single proposed protocol. Moreover, it is also possible for a single IoT network to consist of different kinds of nodes. Two or more IoT networks can also be connected to create a bigger heterogeneous network. Such networks may need various routing protocols with some gateway nodes installed. The role of gateway nodes should not be limited to the interconnection of different nodes. In this paper, a multi-mode hybrid routing mechanism is proposed that can be installed on all or a limited number of nodes in a heterogenous IoT network. The nodes configured with the proposed protocols are termed smart nodes. These nodes can be used to connect multiple IoT networks into one. Furthermore, a game-theory-based model is proposed that is used for intercommunication among the smart nodes to gain optimal efficiency. Various performance matrices are assessed under different network scenarios. The simulation results show that the proposed mechanism outperforms in broader heterogeneous IoT networks with diverse nodes.
Ship Detection in Spaceborne SAR Images under Radio Interference Environment Based on CFAR
Authors: Ma Bengteng , Yang Huizhang , Yang Jian
Published: December 2022

Spaceborne synthetic aperture radar (SAR) can be easily interfered with by narrowband radio frequency interference (RFI) from ground radiation sources, causing significant degradation of image quality. In the application of SAR ship detection, the radio interference will raise the detection threshold of a constant false alarm rate (CFAR) detector, and consequently results in the degradation of detection performance. In order to solve this problem, we propose a ship-detection method for SAR under a narrowband RFI environment. The proposed method is mainly divided into five steps: (1) transform the input SAR image with narrowband RFI into 2-D frequency domain by fast Fourier transform (FFT); (2) use CFAR detector to detect RFI in 2-D frequency domain; (3) suppress RFI data points using adaptively weighting in the 2-D frequency domain; (4) transform the RFI suppressed 2-D spectrum into the image domain via inverse FFT; (5) apply CFAR detector for ship detection. Simulation and real data experiments show that the proposed method can effectively detect ships from SAR images with ocean background even if there exists serious RFI.
A Hardware Trojan-Detection Technique Based on Suspicious Circuit Block Partition
Authors: Mao Jiajie , Jiang Xiaowen , Liu Dehong , Chen Jianjun , Huang Kai
Published: December 2022

To ensure that a hardware Trojan remains hidden in a circuit, it is usually necessary to ensure that the trigger signal has a low testability, which has been widely recognized and proven. The most advanced testability-based detection methods are rather slow for large circuits, and the false-positive rate is not as low as that for small circuits. In this paper, a hardware Trojan, through the low testability of the trigger signal and its position characteristics in the circuit, was detected, which greatly improves the detection speed while maintaining a lower false positive rate when being applied to large circuits. First, the Sandia Controllability/Observability Analysis Program (SCOAP) was applied to obtain the 0–1 controllability of the signals in the netlist. Secondly, the controllability value was calculated by the differential amplification model, in order to facilitate K-means clustering to get better results. Then, we calculate the shortest path between each suspicious signal to get the connection between each suspicious signal. Finally, we divide the suspicious signals into several suspicious circuit blocks to screen the real trigger signal. As a result, the false-negative rate of 0% and the highest false-positive rate of 5.02% were obtained on the Trust-Hub benchmarks.
A Transformer-Based DeepFake-Detection Method for Facial Organs
Authors: Xue Ziyu , Liu Qingtong , Shi Haichao , Zou Ruoyu , Jiang Xiuhua
Published: December 2022

Nowadays, deepfake detection on subtle-expression manipulation, facial-detail modification, and smeared images has become a research hotspot. Existing deepfake-detection methods on the whole face are coarse-grained, where the details are missing due to the negligible manipulated size of the image. To address the problems, we propose to build a transformer model for a deepfake-detection method by organ, to obtain the deepfake features. We reduce the detection weight of defaced or unclear organs to prioritize the detection of clear and intact organs. Meanwhile, to simulate the real-world environment, we build a Facial Organ Forgery Detection Test Dataset (FOFDTD), which includes the images of mask face, sunglasses face, and undecorated face collected from the network. Experimental results on four benchmarks, i.e., FF++, DFD, DFDC-P, Celeb-DF, and for FOFDTD datasets, demonstrated the effectiveness of our proposed method.
The Dielectric Properties of Worker Bee Homogenate in a High Frequency Electric Field
Authors: Szychta Leszek , Jankowski-Mihułowicz Piotr , Szychta Elżbieta , Olszewski Krzysztof , Putynkowski Grzegorz , Barczak Tadeusz , Wasilewski Piotr
Published: December 2022

Biological tissues, including insect tissues, are among lossy dielectric materials. The permittivity properties of these materials are described by loss factor ε″ and loss tangent tgδ. The dielectric properties of the worker honeybee body homogenate are tested in the range of high frequencies from 1 MHz to 6 GHz. The homogenate is produced by mixing whole worker honeybees and tested with an epsilometer from Compass Technology and a Copper Mountain Technologies vector circuit analyser VNA. Due to their consistency, the homogenate samples are placed inside polyurethane sachets. The measured permittivity relates to two components of a sample: homogenate and polyurethane. For five samples, two extremes were specified for the permittivity, loss factor ε″, and the loss tangent tgδ, for the frequency range 20 ÷ 80 MHz and 3 GHz. Four techniques of testing permittivity in biological tissues were used to determine the dielectric properties of the homogenate. A calculation model was developed featuring a minimum measurement error of the loss factor ε″ and the loss tangent tgδ. The power absorbed per unit volume is described for the whole frequency range.
Deep Learning for Molecular Thermodynamics
Authors: Malik Hassaan , Chaudhry Muhammad Umar , Jasinski Michal
Published: December 2022

The methods used in chemical engineering are strongly reliant on having a solid grasp of the thermodynamic features of complex systems. It is difficult to define the behavior of ions and molecules in complex systems and to make reliable predictions about the thermodynamic features of complex systems across a wide range. Deep learning (DL), which can provide explanations for intricate interactions that are beyond the scope of traditional mathematical functions, would appear to be an effective solution to this problem. In this brief Perspective, we provide an overview of DL and review several of its possible applications within the realm of chemical engineering. DL approaches to anticipate the molecular thermodynamic characteristics of a broad range of systems based on the data that are already available are also described, with numerous cases serving as illustrations.