bluuebunny/crossref_metadata_2025 huggingface dataset:
Lead-Lag Relationship between the Price-to-Rent Ratio and the Macroeconomy: An Empirical Study of the Residential Market of Hong Kong
Authors: Lo Daniel , Yau Yung , McCord Michael , Haran Martin
Published: September 2022

The price-to-rent (PtR) ratio is one of the most commonly used indicators to assess housing market conditions by policy makers and real estate practitioners. It is often employed as an economic barometer to detect whether a housing bubble exists and determine whether the property market has become unaffordable relative to historical trends. Despite a plethora of research studies on the PtR ratio in the housing literature, relatively little is known about its long-term dynamics with macroeconomic and financial determinants. By utilising time series data on the Hong Kong residential property market, this study examines the cointegration and causal relationships between a wide spectrum of macroeconomic indicators and the PtR ratios of housing segments of different tiers which comprise different socioeconomic groups of homebuyers and investors. The results point towards market compartmentalisation, in the sense that the PtR ratios of the housing submarkets respond to changes in macroeconomic fundamentals in a differential manner. For instance, the PtR ratios of housing segments with a greater proportion of owner-occupiers are statistically less y correlated with investment-related macroeconomic attributes, such as foreign direct investment and equity market performance. On the other hand, the pricing of large-sized housing units in prime locations, generally favoured by investors from mainland China, are found to be Granger-caused by the exchange rate of the Chinese Yuan to the Hong Kong dollar.
Construction of a Sustainability-Based Building Attribute Conservation Assessment Model in Historic Areas
Authors: Xia Sisi , Liu Bomin , Wang Haobing
Published: September 2022

As important locations that carry the spirit and culture of the city, the conservation and regeneration of historic areas should be a sustainable process. However, in the current Chinese urban historical and cultural heritage conservation system, there is a certain degree of discontinuity in the management of the two-level division between historic districts and heritage protection units, resulting in a lack of control over the renewal of other buildings in the district. The fragmented conservation of historic districts eventually leads to the loss of the historical culture and urban spirit they are supposed to carry, which is not in line with the requirements of sustainable conservation. This study aims to combine sustainability theories with the essential properties of buildings to construct an assessment model to address this issue. Through integrating the discussion on the nature of building and the sustainability assessment dimensions, three aspects of common concern can be identified as economic, cultural, and social. As the ontology for transmitting and preserving culture for future generations, building should take on the three major responsibilities of presenting and inheriting regional culture, improving regional conservation and developing economic functions. Therefore, the three dimensions of sustainability-based building attributes are “Economy of construction, Cultural of form, and Social of benefit”. A sustainable assessment model is constructed based on the three dimensions, which can help to dynamically monitor changes in building attributes during implementation. Finally, a case study was analyzed using the proposed model, and the feasibility and applicability of the assessment model were proved. The model provides effective guidance for comparative evaluation before and after the implementation of protecting plans, and corrects deviations to sustainable conservation goals in a timely manner.
Numerical Study of Structural Performance and Wind Flow Dynamic Behavior for PPVC Steel Modular Construction (MSC) under Various Extreme Wind Loads
Authors: Elsayed Mohamed , A. Mutalib Azrul , Elsayed Khaled
Published: September 2022

PPVC modular construction building has become one of the most recent construction technologies in the civil engineering sector and has piqued researchers’ interest. Few published studies consider the overall structural response to extreme wind load. As a result, there is a lack of appropriate design for PPVC modular systems under extreme wind standards. However, the existing literature has not yet studied the wind flow dynamic behaviors of PPVC modular steel construction (MSC) systems subjected to extreme wind loads. This paper, therefore, presents a numerical investigation into the structural performance and wind flow dynamic behavior of innovative PPVC modular steel construction (MSC) systems under extreme wind loads. The numerical technique varied in comparison with previous studies. The results showed that the suggested novel (MSC1) modular system is applicable to prevention of extreme wind action up to cyclone 2nd degree, the high story drift resistance compared with previous research, high stiffness performance, and overall strain energy. Additionally, the actual wind velocity surrounding (MSC2) was 31.5% higher compared to the Saffir–Simpson wind speed scale, and the 1.5 wind speed safety factor was suggested.
Crack Self-Healing of Cement Mortar Containing Ureolytic Bacteria Immobilized in Artificial Functional Carrier under Different Exposure Environments
Authors: Luo Mian , Liu Ye , Li Xu , Dai Junjie
Published: September 2022

The ureolytic bacteria and nutrients were immobilized in the artificial functional carrier (AFC) and the self-healing cement mortar, based on the AFC-encapsulated bacteria, was prepared for this paper. The crack self-healing effect of mortars with and without bacteria under different exposure environments (standard curing, dry–wet cycle curing, and water curing) was investigated by the visual observation of surface and internal cracks, water permeability tests, and mechanical performance recovery. In addition, the internal healing products of the cracks were observed using the metallographic microscope. The results show that the mortar specimens containing ureolytic bacteria immobilized in artificial functional carrier have a higher crack area repair ratio, and better water tightness regain and recovery ratio of flexural strength compared with the control mortars under the same exposure environment. The self-healing effect of mortar cracks with and without bacteria is obviously affected by the exposure environments. The self-healing effect of the cracks are the best when the mortar specimens are cured in water, followed by dry–wet cycle curing, and the self-healing effect of the cracks is the worst in standard curing, indicating that the presence of water is necessary for crack self-healing. The mortar specimens with bacteria generate more repair products in the surface and interior of the cracks to greatly improve the self-repair ability of the specimens, which promotes the recovery of water tightness and mechanical performance.
Efficacy of the Immobilized Kocuria flava Lipase on Fe3O4/Cellulose Nanocomposite for Biodiesel Production from Cooking Oil Wastes
Authors: Najjar Azhar A. , Hassan Elhagag A. , Zabermawi Nidal M. , Almasaudi Saad B. , Moulay Mohammed , Harakeh Steve , Abd El-Aal Mohamed
Published: September 2022

The increasing global demand for petroleum oils has led to a significant increase in their cost and has led to the search for renewable alternative waste resources for biodiesel synthesis and production using novel environmentally sound and acceptable methods. In the current study, Kocuria flava lipase was immobilized on Fe3O4/cellulose nanocomposite; and used as a biocatalyst for the conversion of cooking oil wastes into biodiesel through the transesterification/esterification process. The characterization of Fe3O4/cellulose nanocomposite revealed several functional groups including carboxyl (C=O) and epoxy (C-O-C) groups that act as multipoint covalent binding sites between the lipase and the Fe3O4/cellulose nanocomposite and consequently increasing lipase immobility and stability. The immobilized lipase showed a high thermo-stability as it retained about 70% of its activity at 80 °C after 30 min. The kinetics of immobilized lipase revealed that the Km and Vmax values were 0.02 mM and 32.47 U/mg protein, respectively. Moreover, the immobilized lipase showed high stability and reusability for transesterification/esterification reactions for up to four cycles with a slight decline in the enzyme activity. Furthermore, the produced biodiesel characteristics were compatible with the standards, indicating that the biodiesel obtained is doable and may be utilized in our daily life as a diesel fuel.
Green Chemo-Enzymatic Protocols for the Synthesis of Enantiopure β-Blockers (S)-Esmolol and (S)-Penbutolol
Authors: Troøyen Susanne Hansen , Bocquin Lucas , Tennfjord Anna Lifen , Klungseth Kristoffer , Jacobsen Elisabeth Egholm
Published: September 2022

The β-blocker (S)-esmolol, has been synthesized in 97% enantiomeric excess and 26% total yield in a four-step synthesis, with a transesterification step of the racemic chlorohydrin methyl 3-(4-(3-chloro-2-hydroxypropoxy)phenyl)propanoate, catalysed by lipase B from Candida antarctica from Syncozymes, Shanghai, China. The β-blocker (S)-penbutolol, has been synthesized in 99% enantiomeric excess and in 22% total yield. The transesterification step of the racemic chlorohydrin 1-chloro-3-(2-cyclopentylphenoxy)propan-2-ol was catalyzed by the same lipase as used for the esmolol building block. We have used different bases for the deprotonation step of the starting phenols, and vinyl butanoate as the acyl donor in the transesterification reactions. The reaction times for the kinetic resolution steps catalysed by the lipase varied from 23 to 48 h, and were run at 30–38 °C. Specific rotation values confirmed the absolute configuration of the enantiopure drugs, however, an earlier report of the specific rotation value of (S)-esmolol is not consistent with our measured specific rotation values, and we here claim that our data are correct. Compared to the previously reported syntheses of these two enantiopure drugs, we have replaced toluene or dichloromethane with acetonitrile, and replaced the flammable acetyl chloride with lithium chloride. We have also reduced the amount of epichlorohydrin and bases, and identified dimeric byproducts in order to obtain higher yields.
An Epitope-Specific LGI1-Autoantibody Enhances Neuronal Excitability by Modulating Kv1.1 Channel
Authors: Extrémet Johanna , El Far Oussama , Ankri Norbert , Irani Sarosh R. , Debanne Dominique , Russier Michaël
Published: September 2022

Leucine-rich Glioma-Inactivated protein 1 (LGI1) is expressed in the central nervous system and its genetic loss of function is associated with epileptic disorders. Additionally, patients with LGI1-directed autoantibodies have frequent focal seizures as a key feature of their disease. LGI1 is composed of a Leucine-Rich Repeat (LRR) and an Epitempin (EPTP) domain. These domains are reported to interact with different members of the transsynaptic complex formed by LGI1 at excitatory synapses, including presynaptic Kv1 potassium channels. Patient-derived recombinant monoclonal antibodies (mAbs) are ideal reagents to study whether domain-specific LGI1-autoantibodies induce epileptiform activities in neurons and their downstream mechanisms. We measured the intrinsic excitability of CA3 pyramidal neurons in organotypic cultures from rat hippocampus treated with either an LRR- or an EPTP-reactive patient-derived mAb, or with IgG from control patients. We found an increase in intrinsic excitability correlated with a reduction of the sensitivity to a selective Kv1.1-channel blocker in neurons treated with the LRR mAb, but not in neurons treated with the EPTP mAb. Our findings suggest LRR mAbs are able to modulate neuronal excitability that could account for epileptiform activity observed in patients.
Influence of the Postmortem/Storage Time of Human Corneas on the Properties of Cultured Limbal Epithelial Cells
Authors: Le-Bel Gaëtan , Desjardins Pascale , Gross Christelle , Cortez Ghio Sergio , Couture Camille , Germain Lucie , Guérin Sylvain L.
Published: September 2022

Besides being a powerful model to study the mechanisms of corneal wound healing, tissue-engineered human corneas (hTECs) are sparking interest as suitable substitutes for grafting purposes. To ensure the histological and physiological integrity of hTECs, the primary cultures generated from human cornea (identified as human limbal epithelial cells (hLECs) that are used to produce them must be of the highest possible quality. The goal of the present study consisted in evaluating the impact of the postmortem/storage time (PM/ST) on their properties in culture. hLECs were isolated from the entire cornea comprising the limbus and central cornea. When grown as monolayers, short PM/ST hLECs displayed increased daily doublings and generated more colonies per seeded cells than long PM/ST hLECs. Moreover, hLECs with a short PM/ST exhibited a markedly faster wound closure kinetic both in scratch wound assays and hTECs. Collectively, these results suggest that short PM/ST hLECs have a greater number of highly proliferative stem cells, exhibit a faster and more efficient wound healing response in vitro, and produce hTECs of a higher quality, making them the best candidates to produce biomaterial substitutes for clinical studies.
Omics Analysis of Chemoresistant Triple Negative Breast Cancer Cells Reveals Novel Metabolic Vulnerabilities
Authors: Kordias Dimitris , Kostara Christina E. , Papadaki Styliani , Verigos John , Bairaktari Eleni , Magklara Angeliki
Published: September 2022

The emergence of drug resistance in cancer poses the greatest hurdle for successful therapeutic results and is associated with most cancer deaths. In triple negative breast cancer (TNBC), due to the lack of specific therapeutic targets, systemic chemotherapy is at the forefront of treatments, but it only benefits a fraction of patients because of the development of resistance. Cancer cells may possess an innate resistance to chemotherapeutic agents or develop new mechanisms of acquired resistance after long-term drug exposure. Such mechanisms involve an interplay between genetic, epigenetic and metabolic alterations that enable cancer cells to evade therapy. In this work, we generated and characterized a chemoresistant TNBC cell line to be used for the investigation of mechanisms that drive resistance to paclitaxel. Transcriptomic analysis highlighted the important role of metabolic-associated pathways in the resistant cells, prompting us to employ 1H-NMR to explore the metabolome and lipidome of these cells. We identified and described herein numerous metabolites and lipids that were significantly altered in the resistant cells. Integrated analysis of our omics data revealed MSMO1, an intermediate enzyme of cholesterol biosynthesis, as a novel mediator of chemoresistance in TNBC. Overall, our data provide a critical insight into the metabolic adaptations that accompany acquired resistance in TNBC and pinpoint potential new targets.
Traditional vs. Modern Data Paths: A Comprehensive Survey
Authors: Barghash Ahmad , Hammad Lina , Gharaibeh Ammar
Published: September 2022

Recently, many new network paths have been introduced while old paths are still in use. The trade-offs remain vague and should be further addressed. Since last decade, the Internet is playing a major role in people’s lives, and the demand on the Internet in all fields has increased rapidly. In order to get a fast and secure connection to the Internet, the networks providing the service should get faster and more reliable. Many network data paths have been proposed in order to achieve the previous objectives since the 1970s. It started with the Transmission Control Protocol (TCP) and the User Datagram Protocol (UDP) and later followed by several more modern paths including Quick UDP Internet Connections (QUIC), remote direct memory access (RDMA), and the Data Plane Development Kit (DPDK). This raised the question on which data path should be adopted and based on which features. In this work, we try to answer this question using different perspectives such as the protocol techniques, latency and congestion control, head of line blocking, the achieved throughput, middleboxes consideration, loss recovery mechanisms, developer productivity, host resources utilization and targeted application.
On Predicting Soccer Outcomes in the Greek League Using Machine Learning
Authors: Malamatinos Marios-Christos , Vrochidou Eleni , Papakostas George A.
Published: September 2022

The global expansion of the sports betting industry has brought the prediction of outcomes of sport events into the foreground of scientific research. In this work, soccer outcome prediction methods are evaluated, focusing on the Greek Super League. Data analysis, including data cleaning, Sequential Forward Selection (SFS), feature engineering methods and data augmentation is conducted. The most important features are used to train five machine learning models: k-Nearest Neighbor (k-NN), LogitBoost (LB), Support Vector Machine (SVM), Random Forest (RF) and CatBoost (CB). For comparative reasons, the best model is also tested on the English Premier League and the Dutch Eredivisie, exploiting data statistics from six seasons from 2014 to 2020. Convolutional neural networks (CNN) and transfer learning are also tested by encoding tabular data to images, using 10-fold cross-validation, after applying grid and randomized hyperparameter tuning: DenseNet201, InceptionV3, MobileNetV2 and ResNet101V2. This is the first time the Greek Super League is investigated in depth, providing important features and comparative performance between several machine and deep learning models, as well as between other leagues. Experimental results in all cases demonstrate that the most accurate prediction model is the CB, reporting 67.73% accuracy, while the Greek Super League is the most predictable league.
Changes in Apparent Diffusion Coefficient (ADC) in Serial Weekly MRI during Radiotherapy in Patients with Head and Neck Cancer: Results from the PREDICT-HN Study
Authors: Ng Sweet Ping , Cardenas Carlos E. , Bahig Houda , Elgohari Baher , Wang Jihong , Johnson Jason M. , Moreno Amy C. , Shah Shalin J. , Garden Adam S. , Phan Jack , Gunn G. Brandon , Frank Steven J. , Ding Yao , Na Lumine , Yuan Ying , Urbauer Diana , Mohamed Abdallah S. R. , Rosenthal David I. , Morrison William H. , MacManus Michael P. , Fuller Clifton D.
Published: September 2022

Background: The PREDICT-HN study aimed to systematically assess the kinetics of imaging MR biomarkers during head and neck radiotherapy. Methods: Patients with intact squamous cell carcinoma of the head and neck were enrolled. Pre-, during, and post-treatment MRI were obtained. Serial GTV and ADC measurements were recorded. The correlation between each feature and the GTV was calculated using Spearman’s correlation coefficient. The linear mixed model was used to evaluate the change in GTV over time. Results: A total of 41 patients completed the study. The majority (76%) had oropharyngeal cancer. A total of 36 patients had intact primary tumours that can be assessed on MRI, and 31 patients had nodal disease with 46 nodes assessed. Median primary GTV (GTVp) size was 14.1cc. The rate of GTVp shrinkage was highest between pre-treatment and week 4. Patients with T3-T4 tumours had a 3.8-fold decrease in GTVp compared to T1-T2 tumours. The ADC values correlated with residual GTVp. The median nodal volume (GTVn) was 12.4cc. No clinical features were found to correlate with GTVn reduction. The overall change in ADC for GTVn from pre-treatment was significant for 35th–95th percentiles in weeks 1–4 (p < 0.001). Conclusion: A discrepancy in the trajectory of ADC between primary and nodal sites suggested that they exhibit different treatment responses and should be analysed separately in future studies.
A Cationic Contrast Agent in X-ray Imaging of Articular Cartilage: Pre-Clinical Evaluation of Diffusion and Attenuation Properties
Authors: Fantoni Simone , Gabucci Ilenia , Cardarelli Paolo , Paternò Gianfranco , Taibi Angelo , Cristofori Virginia , Trapella Claudio , Bazzani Armando , Assenza Marta , Zanna Bonacorsi Alice , Conti Daniele , Baruffaldi Fabio
Published: September 2022

The aim of this study was the preliminary assessment of a new cationic contrast agent, the CA4+, via the analysis of spatial distribution in cartilage of ex vivo bovine samples, at micrometer and millimeter scale. Osteochondral plugs (n = 18) extracted from bovine stifle joints (n = 2) were immersed in CA4+ solution up to 26 h. Planar images were acquired at different time points, using a microCT apparatus. The CA4+ distribution in cartilage and saturation time were evaluated. Tibial plates from bovine stifle joints (n = 3) were imaged with CT, before and after 24 h-CA4+ bath immersion, at different concentrations. Afterward, potential CA4+ washout from cartilage was investigated. From microCT acquisitions, the CA4+ distribution differentiated into three distinct layers inside the cartilage, reflecting the spatial distribution of proteoglycans. After 24 h of diffusion, the iodine concentration reached in cartilage was approximately seven times that of the CA4+ bath. The resulting saturation time was 1.9 ± 0.9 h and 2.6 ± 2.9 h for femoral and tibial samples, respectively. Analysis of clinical CT acquisitions confirmed overall contrast enhancement of cartilage after 24 h immersion, observed for each CA4+ concentration. Distinct contrast enhancement was reached in different cartilage regions, depending on tissue’s local features. Incomplete but remarkable washout of cartilage was observed. CA4+ significantly improved cartilage visualization and its qualitative analysis.
Prognostic Significance of the N-Terminal Pro-B-Type Natriuretic Peptide in Lung Transplant Candidates on the Waiting List
Authors: Izhakian Shimon , Frajman Assaf , Freidkin Lev , Shtraichman Osnat , Rosengarten Dror , Pertzov Barak , Barac Yaron D. , Kramer Mordechai Reuven
Published: September 2022

We investigated the prognostic significance of N-terminal pro-B-type natriuretic peptide (NT-proBNP) in lung transplant candidates, in a retrospective single-center study. Data regarding various baseline characteristics and all-cause mortality were collected for 205 lung transplant candidates placed on waitlist for transplantation from November 2017 to December 2019. Associations of NT-proBNP levels with baseline characteristics and mortality were analyzed. Results showed NT-proBNP values correlated positively with age, forced vital capacity, mean pulmonary artery pressure (MPAP), and pulmonary capillary wedge pressure; and negatively with diffusing lung capacity for carbon monoxide and cardiac index. The optimal cut-off of NT-proBNP for predicting MPAP levels > 35 mmHg was 251 pg/mL; with 58.1% sensitivity, 85.7% specificity, 45.0% positive predictive value, and 91.0% negative predictive value. During a median follow-up period of 2.2 years, 97 patients underwent lung transplantation, 42 died waiting for donation, and 66 were alive and still waiting for transplantations. On multivariate analysis, higher NT-proBNP levels were strongly associated with increased mortality among waitlisted lung transplant candidates (HR 1.49, 95% CI 1.10–2.03, p = 0.01). In conclusion NT-proBNP can predict mortality among waitlisted lung transplant candidates. Lower levels of NT-proBNP can preclude severe pulmonary artery hypertension. Assessment of NT-proBNP may improve risk stratification among lung transplant candidates.
An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset
Authors: Alam Talha Mahboob , Shaukat Kamran , Khan Waseem Ahmad , Hameed Ibrahim A. , Almuqren Latifah Abd. , Raza Muhammad Ahsan , Aslam Memoona , Luo Suhuai
Published: September 2022

Efficient skin cancer detection using images is a challenging task in the healthcare domain. In today’s medical practices, skin cancer detection is a time-consuming procedure that may lead to a patient’s death in later stages. The diagnosis of skin cancer at an earlier stage is crucial for the success rate of complete cure. The efficient detection of skin cancer is a challenging task. Therefore, the numbers of skilful dermatologists around the globe are not enough to deal with today’s healthcare. The huge difference between data from various healthcare sector classes leads to data imbalance problems. Due to data imbalance issues, deep learning models are often trained on one class more than others. This study proposes a novel deep learning-based skin cancer detector using an imbalanced dataset. Data augmentation was used to balance various skin cancer classes to overcome the data imbalance. The Skin Cancer MNIST: HAM10000 dataset was employed, which consists of seven classes of skin lesions. Deep learning models are widely used in disease diagnosis through images. Deep learning-based models (AlexNet, InceptionV3, and RegNetY-320) were employed to classify skin cancer. The proposed framework was also tuned with various combinations of hyperparameters. The results show that RegNetY-320 outperformed InceptionV3 and AlexNet in terms of the accuracy, F1-score, and receiver operating characteristic (ROC) curve both on the imbalanced and balanced datasets. The performance of the proposed framework was better than that of conventional methods. The accuracy, F1-score, and ROC curve value obtained with the proposed framework were 91%, 88.1%, and 0.95, which were significantly better than those of the state-of-the-art method, which achieved 85%, 69.3%, and 0.90, respectively. Our proposed framework may assist in disease identification, which could save lives, reduce unnecessary biopsies, and reduce costs for patients, dermatologists, and healthcare professionals.
A Simple and Rapid Low-Cost Procedure for Detection of Vancomycin-Resistance Genes in Enterococci Reveals an Outbreak of Vancomycin-Variable Enterococcus faecium
Authors: Abdullah Hozan Muhammed , Marbjerg Lis Høy , Andersen Lise , Hoegh Silje Vermedal , Kemp Michael
Published: September 2022

The detection of resistance to vancomycin in enterococci cultured from patients is important for the treatment of individual patients and for the prevention of hospital transmission. Phenotypic antimicrobial resistance tests may fail to detect potential vancomycin-resistant enterococci. We have developed and tested a PCR based procedure for routine screening for vancomycin-resistance genes in clinical samples with enterococci. Primary cultures from diagnostic samples reported with growth of Enterococcus faecium or E. facalis were tested for vanA and vanB genes by real-time PCR without the isolation of specific bacteria. Up to ten samples were pooled and tested in each real-time PCR reaction, with subsequent individual testing of cultures from positive pools. In a one-month test period in 2017 vanA gene was detected in one out of 340 urine samples with vancomycin-susceptible enterococci reported from diagnostic culture. A second test period in 2018 included 357 urine samples, and vanA gene was detected in samples from eight patients. Subsequently, all urine samples reported with growth of E. faecium during a period of one year were tested. Fifty-eight individuals were identified with enterococci, carrying the vanA gene not previously detected. Routine molecular testing of primary culture material from patient samples may improve the detection of hospitalized patients carrying E. faecium with resistance genes to vancomycin.
Repeated Daily Use of Dual-Light Antibacterial Photodynamic Therapy in Periodontal Disease—A Case Report
Authors: Trujiilo Katherina , Räisänen Ismo T. , Sorsa Timo , Pätilä Tommi
Published: September 2022

Good oral hygiene at home is the foundation for optimal treatment response and long-term periodontal disease control. Antibacterial photodynamic therapy (aPDT) provides a very potent adjunctive treatment for plaque control. However, the literature regarding repeated aPDT use is sparse. aPDT has been a modality applied mainly in the dental office environment, and when applied once a year or every few months, the results have been usually disappointing. Recently, LED development has brought aPDT for repeated and practical use at home. We present the very positive results and clinical outcome of daily applied dual-light aPDT-technology treatment in conjunction with mechanical cleaning of a 78-year-old male patient with severe periodontal disease (Stage IV and Grade B).
Relationship between Beliefs of Teachers about and Their Use of Explicit Instruction When Fostering Students’ Scientific Inquiry Competencies
Authors: Petermann Verena , Vorholzer Andreas
Published: September 2022

An efficient approach to fostering students’ scientific inquiry (SI) competencies (e.g., planning investigations) is to combine student engagement in inquiry activities with explicit instruction that addresses corresponding concepts (e.g., the control-of-variables strategy). Despite its effectiveness, explicit instruction on SI-related concepts seems to be rarely employed in science classrooms. As a vital component of professional competence, teachers’ beliefs are a potential cause for the rare use of explicit instruction. The aim of this study is to investigate the relationship between teachers’ beliefs about the usefulness of explicit instruction as well as their own abilities and teacher performance. In a mixed method approach, the beliefs of N = 16 teachers were captured with a questionnaire, while their teaching practice was approximated through a combination of a lesson planning task and a semi-structured interview. Analyses of response patterns, a qualitative content analysis of the planned lessons, and correlation analyses were used to investigate the relationship between beliefs and performance. The findings suggest that beliefs about the usefulness of explicit instruction for fostering SI competencies may be a necessary but not a sufficient condition for its implementation. Furthermore, the results suggest the importance of assessing and investigating teachers’ beliefs on a goal-specific level.
Analyzing the Influence of Belbin’s Roles on the Quality of Collaborative Learning for the Study of Business Fundamentals
Authors: Flores Ureba Sandra , Simón de Blas Clara , Borrás-Gené Oriol , Macías-Guillén Almudena
Published: September 2022

This work uses Belbin’s balanced group theory to analyze whether the formation of teams improved the performance of 21 groups formed by the 149 students of an Introduction to Business class in a collaborative learning environment in Higher Education. The analysis is based on a comparison of two types of groups: balanced groups (according to Belbin’s role theory) and those that are not so balanced. The analysis techniques used to determine any significant differences in student grade scores were the student average comparison and the Chi homogeneity tests. The relevance of the different roles that students can acquire when forming work groups was derived from multiple regression in the exams and practices punctuations, and the relevance of each student role was determined by discriminant analysis. The results indicate that balanced groups facilitate greater homogeneity in group grades, improving the performance of the group overall.
Using Systems Maps to Visualize Chemistry Processes: Practitioner and Student Insights
Authors: Schultz Madeleine , Chan Drew , Eaton Andrew C. , Ferguson Joseph P. , Houghton Rebecca , Ramdzan Adlin , Taylor Oliver , Vu Hanh H. , Delaney Seamus
Published: September 2022

Discussing socio-scientific issues in a secondary chemistry classroom poses a challenge to traditional classroom practice because students and teachers need to think more broadly about chemical processes. Allowing students to create open-ended maps to generate and represent their understanding of socio-scientific issues while also learning chemistry theory can develop Systems Thinking capacity in students. This manuscript presents three vignettes of the classroom use of mapping exercises within separate action research studies, involving diverse school types, curricula, chemistry topics and student groups. The mapping exercises were effective to engage students in the development of Systems Thinking and were readily integrated into different curricula. Sequential student-generated maps for chemical processes illustrate increasing sophistication in their Systems Thinking approaches.
Optimal Bias Condition of Dummy WL for Sub-Block GIDL Erase Operation in 3D NAND Flash Memory
Authors: Kim Beomsu , Kang Myounggon
Published: September 2022

In this study, we have analyzed the optimal bias condition of dummy WL for the sub-block gate induced drain leakage (GIDL) erase operation in 16-layer 3D NAND flash memory. Three-dimensional NAND flash memory performs an erase operation in units of pages. Increasing the number of stacks increases the number of cells that are erased at one time, which can lead to undesirable durability degradation. In this case, the sub-block erase operation can reduce the burden on the cell by up to half, due to the erase operation. The distribution of the hole density (hDensity) and the potential, according to VDummy, was analyzed when block1 and block2 were erased by setting WL0:WL7 to block1, WL9:WL15 to block2, and WL8 to dummy WL. For the simulation results, block1 showed an optimal distribution of hDensity and potential in the order of 20 V, floating, and 0 V. In block2, the optimal distribution of hDensity was shown in the order of 20 V, floating, and 0 V, with the optimal distribution of the potential in the order of floating and 0 V.
Detection of Diabetic Retinopathy in Retinal Fundus Images Using CNN Classification Models
Authors: Asia Al-Omaisi , Zhu Cheng-Zhang , Althubiti Sara A. , Al-Alimi Dalal , Xiao Ya-Long , Ouyang Ping-Bo , Al-Qaness Mohammed A. A.
Published: September 2022

Diabetes is a widespread disease in the world and can lead to diabetic retinopathy, macular edema, and other obvious microvascular complications in the retina of the human eye. This study attempts to detect diabetic retinopathy (DR), which has been the main reason behind the blindness of people in the last decade. Timely or early treatment is necessary to prevent some DR complications and control blood glucose. DR is very difficult to detect in time-consuming manual diagnosis because of its diversity and complexity. This work utilizes a deep learning application, a convolutional neural network (CNN), in fundus photography to distinguish the stages of DR. The images dataset in this study is obtained from Xiangya No. 2 Hospital Ophthalmology (XHO), Changsha, China, which is very large, little and the labels are unbalanced. Thus, this study first solves the problem of the existing dataset by proposing a method that uses preprocessing, regularization, and augmentation steps to increase and prepare the image dataset of XHO for training and improve performance. Then, it takes the advantages of the power of CNN with different residual neural network (ResNet) structures, namely, ResNet-101, ResNet-50, and VggNet-16, to detect DR on XHO datasets. ResNet-101 achieved the maximum level of accuracy, 0.9888, with a training loss of 0.3499 and a testing loss of 0.9882. ResNet-101 is then assessed on 1787 photos from the HRF, STARE, DIARETDB0, and XHO databases, achieving an average accuracy of 0.97, which is greater than prior efforts. Results prove that the CNN model (ResNet-101) has better accuracy than ResNet-50 and VggNet-16 in DR image classification.
A Novel Fuzzy Logic-Based Scheme for Malicious Node Eviction in a Vehicular Ad Hoc Network
Authors: Igried Bashar , Alsarhan Ayoub , Al-Khawaldeh Igried , AL-Qerem Ahmad , Aldweesh Amjad
Published: September 2022

Securing communication in vehicular ad hoc networks (VANETs) is hampered by numerous constraints, making it more difficult. First, traditional security schemes cannot be directly applied in VANET because they consider fixed topology. Second, VANET enables dynamic spectrum access where nodes constantly change frequencies due to their high degree of mobility, resulting in severe consequences on network performance. Third, an effective security scheme in VANET needs local and continual knowledge of nodes. Last, the presence of malicious nodes and their misbehaving activities impair the safety of the drivers since they might alter the content of the sent safety alerts. With these constraints in mind, this paper presents a unique security strategy that utilizes node behaviour during message exchange as a security metric to address these issues. Through the message alert exchange phase, node behaviour is measured through the fuzzy logic framework to generate a rank for each node called trust level (BL), which describes the node’s reliability in exchanging safety messages correctly. Moreover, all messages in VANET are encrypted using the existing cryptography techniques. The proposed scheme is developed to enhance communication security in VANET, minimize the effects of malicious nodes, and improve resource utilization in VANET. Evaluation of the proposed scheme shows that it improves the performance of VANET in terms of end-to-end delay, packet delivery ratio, and packet loss ratio. According to the results, our scheme improves throughput by up to 23% and reduces end-to-end delay by up to 60%.
Hemerocallis citrina Baroni Maturity Detection Method Integrating Lightweight Neural Network and Dual Attention Mechanism
Authors: Zhang Liang , Wu Ligang , Liu Yaqing
Published: September 2022

North of Shanxi, Datong Yunzhou District is the base for the cultivation of Hemerocallis citrina Baroni, which is the main production and marketing product driving the local economy. Hemerocallis citrina Baroni and other crops’ picking rules are different: the picking cycle is shorter, the frequency is higher, and the picking conditions are harsh. Therefore, in order to reduce the difficulty and workload of picking Hemerocallis citrina Baroni, this paper proposes the GGSC YOLOv5 algorithm, a Hemerocallis citrina Baroni maturity detection method integrating a lightweight neural network and dual attention mechanism, based on a deep learning algorithm. First, Ghost Conv is used to decrease the model complexity and reduce the network layers, number of parameters, and Flops. Subsequently, combining the Ghost Bottleneck micro residual module to reduce the GPU utilization and compress the model size, feature extraction is achieved in a lightweight way. At last, the dual attention mechanism of Squeeze-and-Excitation (SE) and the Convolutional Block Attention Module (CBAM) is introduced to change the tendency of feature extraction and improve detection precision. The experimental results show that the improved GGSC YOLOv5 algorithm reduced the number of parameters and Flops by 63.58% and 68.95%, respectively, and reduced the number of network layers by about 33.12% in terms of model structure. In the case of hardware consumption, GPU utilization is reduced by 44.69%, and the model size was compressed by 63.43%. The detection precision is up to 84.9%, which is an improvement of about 2.55%, and the real-time detection speed increased from 64.16 FPS to 96.96 FPS, an improvement of about 51.13%.
CNN-Based Feature Fusion Motor Fault Diagnosis
Authors: Qian Long , Li Binbin , Chen Lijuan
Published: September 2022

Artificial intelligence fields have been using deep learning in recent years. Due to its powerful data mining capabilities, deep learning has a wide-ranging impact on the diagnosis of motor faults. A method for diagnosing motor faults based on the multi-feature fusion of convolutional neural network (CNN) is presented in this paper. As far as the method is concerned, CNN is used as the basic framework, and the CNN model has been improved. First, the collected vibration and current signals are preprocessed. Second, segmented multi-time window synchronous input is performed on the processed data. In addition, a multi-scale feature extraction process and time series fusion of vibration and current signals subject to synchronous input in the same time window can be performed, which ultimately enables the identification of motor faults with a high degree of accuracy. In order to verify the validity of the proposed fault diagnosis model, an experimental platform for fault simulation was built for the motor, and vibration and current signals of different motor states were collected and verified by experimentation. According to the results of the experiment, the method can effectively combine motor vibration and current signal fault features, and thus motor fault diagnosis can be improved. In comparison with a single signal input, a multi-signal input provides greater accuracy and stability. As compared to other multi-signal feature fusion methods, such a deep learning model is able to extract fault features in a more comprehensive manner, which helps to improve the accuracy of motor fault diagnosis.
Phenolic Extraction of Moringa oleifera Leaves in DES: Characterization of the Extracts and Their Application in Methylcellulose Films for Food Packaging
Authors: Braham Fazia , Amaral Luísa M. P. F. , Biernacki Krzysztof , Carvalho Daniel O. , Guido Luis F. , Magalhães Júlia M. C. S. , Zaidi Farid , Souza Hiléia K. S. , Gonçalves Maria P.
Published: September 2022

In this work, a qualitative study of the phenolic content of Moringa oleifera leaves (MO), extracted with deep eutectic solvents (DES) based on choline chloride (ChCl) with lactic acid (LA) or glycerol (GLY), was performed by high-resolution mass spectrometry (HPLC-DAD-ESI-MSn). The two solvents (DES-LA and DES-GLY) extract similar classes of phenolics, and ten compounds were identified. The antioxidant profile was also studied (TPC, TFC, DPPH, FRAP, ORAC, and ABTS). Both solvents show an efficient extraction of phenolic compounds and high antioxidant capacity was verified for the extracts. However, the DES-Gly have a higher capacity for polyphenolic extraction (TPC led to 38.409 ± 0.095 mg GAE.g−1 and 2.259 ± 0.023 mg QE.g−1 for TFC). Films based on methylcellulose (MC) containing different amounts of DES or MO extracts, acting as plasticizers, were developed and characterized regarding their mechanical, optical, water vapor permeability, and microstructural properties. All films are uniform, clear, and transparent with smooth, homogeneous surfaces. It was found that the presence of more than 10% of MO extract and/or DES provided more flexible films (Eb for MC 2%_DES 20% achieved 4.330 ± 0.27 %, and 8.15 ± 0.39 % for MC 2%_MO 20%) with less mechanical and barrier resistance. The ultimate objective of this study was to provide information that could assist in the development of antimicrobial active methylcellulose films for sliced wheat bread packaging.
Optimized Dynamic Monitoring and Quality Management System for Post-Harvest Matsutake of Different Preservation Packaging in Cold Chain
Authors: Yang Zihan , Xu Jinchao , Yang Lin , Zhang Xiaoshuan
Published: September 2022

The quality of Tibetan matsutake drops during cold chain transportation. To extend the shelf life and improve the market value, this study analyzed the matsutake logistics process, and optimized the dynamic monitoring and quality management systems for post-harvest matsutake with different preservation packaging in the cold chain. This system monitored the micro-environmental parameters of the cold chain in real time, and it identified the best preservation method by analyzing the quality change characteristics of the matsutake with different preservation packaging. It was concluded that the matsutake were best preserved under the conditions of modified atmosphere packaging. The data analysis on the collected data verified the performance of the system. Relevant personnel were invited to participate in the system performance analysis and offer optimization suggestions to improve the applicability of the established monitoring system. The optimized model could provide a more effective theoretical reference for the dynamic monitoring and quality management of the system.
Mapping Climate Parameters over the Territory of Botswana Using GMT and Gridded Surface Data from TerraClimate
Authors: Lemenkova Polina
Published: September 2022

This articles presents a new series of maps showing the climate and environmental variability of Botswana. Situated in southern Africa, Botswana has an arid to semi-arid climate, which significantly varies in its different regions: Kalahari Desert, Makgadikgadi Pan and Okavango Delta. While desert regions are prone to droughts and periods of extreme heat during the summer months, other regions experience heavy downpours, as well as episodic and unpredictable rains that affect agricultural activities. Such climatic variations affect social and economic aspects of life in Botswana. This study aimed to visualise the non-linear correlations between the topography and climate setting at the country’s scale. Variables included T °C min, T °C max, precipitation, soil moisture, evapotranspiration (PET and AET), downward surface shortwave radiation, vapour pressure and vapour pressure deficit (VPD), wind speed and Palmer Drought Severity Index (PDSI). The dataset was taken from the TerraClimate source and GEBCO for topographic mapping. The mapping approach included the use of Generic Mapping Tools (GMT), a console-based scripting toolset, which enables the use of a scripting method of automated mapping. Several GMT modules were used to derive a set of climate parameters for Botswana. The data were supplemented with the adjusted cartographic elements and inspected by the Geospatial Data Abstraction Library (GDAL). The PDSI in Botswana in 2018 shows stepwise variation with seven areas of drought: (1) −3.7 to −2.2. (extreme); (2) −2.2 to −0.8 (strong, southern Kalahari); (3) −0.8 to 0.7 (significant, central Kalahari; (4) 0.7 to 2.1 (moderate); (5) 2.1 to 3.5 (lesser); (6) 3.5 to 4.9 (low); (7) 4.9 to 6.4 (least). The VPD has a general trend towards the south-western region (Kalahari Desert, up to 3.3), while it is lower in the north-eastern region of Botswana (up to 1.4). Other values vary respectively, as demonstrated in the presented 12 maps of climate and environmental inventory in Botswana.
CT Lymphography Using Lipiodol® for Sentinel Lymph Node Biopsy in Early-Stage Oral Cancer
Authors: Mahieu Rutger , Donders Dominique N. V. , Dankbaar Jan Willem , de Bree Remco , de Keizer Bart
Published: September 2022

This study evaluated sentinel lymph node (SLN) identification with CT lymphography (CTL) following peritumoral administration of Lipiodol® relative to conventional 99mTc-nanocolloid lymphoscintigraphy (including SPECT/CT) in 10 early-stage oral cancer patients undergoing SLN biopsy. Patients first underwent early dynamic and static scintigraphy after peritumoral administration of 99mTc-nanocolloid. Subsequently, Lipiodol® was administered at the same injection sites, followed by fluoroscopy and CT acquisition. Finally, late scintigraphy and SPECT/CT were conducted, enabling the fusion of late CTL and SPECT imaging. The next day, designated SLNs were harvested, radiographically examined for Lipiodol® uptake and histopathologically assessed. Corresponding images of CT, 99mTc-nanocolloid lymphoscintigraphy and SPECT/late CTL fusion were evaluated. 99mTc-nanocolloid lymphoscintigraphy identified 21 SLNs, of which 7 were identified with CTL (33%). CTL identified no additional SLNs and failed to identify any SLNs in four patients (40%). Out of six histopathologically positive SLNs, two were identified by CTL (33%). Radiographic examination confirmed Lipiodol® uptake in seven harvested SLNs (24%), of which five were depicted by CTL. CTL using Lipiodol® reached a sensitivity of 50% and a negative predictive value (NPV) of 75% (median follow-up: 12.3 months). These results suggest that CTL using Lipiodol® is not a reliable technique for SLN mapping in early-stage oral cancer.