bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of the Web Data Access Object (WebDAO) Design Pattern on Productivity
Authors: Jánki Zoltán Richárd , Bilicki Vilmos
Published: July 2023

In contemporary software development, it is crucial to adhere to design patterns because well-organized and readily maintainable source code facilitates bug fixes and the development of new features. A carefully selected set of design patterns can have a significant impact on the productivity of software development. Data Access Object (DAO) is a frequently used design pattern that provides an abstraction layer between the application and the database and is present in the back-end. As serverless development arises, more and more applications are using the DAO design pattern, but it has been moved to the front-end. We refer to this pattern as WebDAO. It is evident that the DAO pattern improves development productivity, but it has never been demonstrated for WebDAO. Here, we evaluated the open source Angular projects to determine whether they use WebDAO. For automatic evaluation, we trained a Natural Language Processing (NLP) model that can recognize the WebDAO design pattern with 92% accuracy. On the basis of the results, we analyzed the entire history of the projects and presented how the WebDAO design pattern impacts productivity, taking into account the number of commits, changes, and issues.
The Generation of Articulatory Animations Based on Keypoint Detection and Motion Transfer Combined with Image Style Transfer
Authors: Ling Xufeng , Zhu Yu , Liu Wei , Liang Jingxin , Yang Jie
Published: July 2023

Knowing the correct positioning of the tongue and mouth for pronunciation is crucial for learning English pronunciation correctly. Articulatory animation is an effective way to address the above task and helpful to English learners. However, articulatory animations are all traditionally hand-drawn. Different situations require varying animation styles, so a comprehensive redraw of all the articulatory animations is necessary. To address this issue, we developed a method for the automatic generation of articulatory animations using a deep learning system. Our method leverages an automatic keypoint-based detection network, a motion transfer network, and a style transfer network to generate a series of articulatory animations that adhere to the desired style. By inputting a target-style articulation image, our system is capable of producing animations with the desired characteristics. We created a dataset of articulation images and animations from public sources, including the International Phonetic Association (IPA), to establish our articulation image animation dataset. We performed preprocessing on the articulation images by segmenting them into distinct areas each corresponding to a specific articulatory part, such as the tongue, upper jaw, lower jaw, soft palate, and vocal cords. We trained a deep neural network model capable of automatically detecting the keypoints in typical articulation images. Also, we trained a generative adversarial network (GAN) model that can generate end-to-end animation of different styles automatically from the characteristics of keypoints and the learned image style. To train a relatively robust model, we used four different style videos: one magnetic resonance imaging (MRI) articulatory video and three hand-drawn videos. For further applications, we combined the consonant and vowel animations together to generate a syllable animation and the animation of a word consisting of many syllables. Experiments show that this system can auto-generate articulatory animations according to input phonetic symbols and should be helpful to people for English articulation correction.
Convolutional Neural Networks: A Survey
Authors: Krichen Moez
Published: July 2023

Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing (NLP), and even in the field of genomics, where they have been utilized to classify DNA sequences. This paper provides a comprehensive overview of CNNs and their applications in image recognition tasks. It first introduces the fundamentals of CNNs, including the layers of CNNs, convolution operation (Conv_Op), Feat_Maps, activation functions (Activ_Func), and training methods. It then discusses several popular CNN architectures such as LeNet, AlexNet, VGG, ResNet, and InceptionNet, and compares their performance. It also examines when to use CNNs, their advantages and limitations, and provides recommendations for developers and data scientists, including preprocessing the data, choosing appropriate hyperparameters (Hyper_Param), and evaluating model performance. It further explores the existing platforms and libraries for CNNs such as TensorFlow, Keras, PyTorch, Caffe, and MXNet, and compares their features and functionalities. Moreover, it estimates the cost of using CNNs and discusses potential cost-saving strategies. Finally, it reviews recent developments in CNNs, including attention mechanisms, capsule networks, transfer learning, adversarial training, quantization and compression, and enhancing the reliability and efficiency of CNNs through formal methods. The paper is concluded by summarizing the key takeaways and discussing the future directions of CNN research and development.
Automated Diagnosis of Prostate Cancer Using mpMRI Images: A Deep Learning Approach for Clinical Decision Support
Authors: Gavade Anil B. , Nerli Rajendra , Kanwal Neel , Gavade Priyanka A. , Pol Shridhar Sunilkumar , Rizvi Syed Tahir Hussain
Published: July 2023

Prostate cancer (PCa) is a significant health concern for men worldwide, where early detection and effective diagnosis can be crucial for successful treatment. Multiparametric magnetic resonance imaging (mpMRI) has evolved into a significant imaging modality in this regard, which provides detailed images of the anatomy and tissue characteristics of the prostate gland. However, interpreting mpMRI images can be challenging for humans due to the wide range of appearances and features of PCa, which can be subtle and difficult to distinguish from normal prostate tissue. Deep learning (DL) approaches can be beneficial in this regard by automatically differentiating relevant features and providing an automated diagnosis of PCa. DL models can assist the existing clinical decision support system by saving a physician’s time in localizing regions of interest (ROIs) and help in providing better patient care. In this paper, contemporary DL models are used to create a pipeline for the segmentation and classification of mpMRI images. Our DL approach follows two steps: a U-Net architecture for segmenting ROI in the first stage and a long short-term memory (LSTM) network for classifying the ROI as either cancerous or non-cancerous. We trained our DL models on the I2CVB (Initiative for Collaborative Computer Vision Benchmarking) dataset and conducted a thorough comparison with our experimental setup. Our proposed DL approach, with simpler architectures and training strategy using a single dataset, outperforms existing techniques in the literature. Results demonstrate that the proposed approach can detect PCa disease with high precision and also has a high potential to improve clinical assessment.
Isocyanide π-Hole Interactions Supported by Aurophilic Forces
Authors: Smirnov Andrey S. , Kinzhalov Mikhail A. , Gomila Rosa M. , Frontera Antonio , Bokach Nadezhda A. , Kukushkin Vadim Yu.
Published: July 2023

Treatment of the [AuCl(tetrahydrothiophene)] complex with 4-chloro-2-iodo-1-isocyanobenzene furnished the gold(I) compound [AuCl(CNC6H3-4-Cl-2-I)] (1). In the crystal structure of 1, the linear C–Au–Cl group is subject to the solid-state head-to-tail pairing, which is determined by the aurophilic Au⋯Au and the rare π-holeCN⋯Cl interactions. These two types of structure-determining interactions are complementary to each other, and the system of Au⋯Au and CCN⋯Cl contacts accomplishes a 2D extended ladder-type architecture. In addition, the terminal I-atoms are involved in the three-center halogen bonding. Density functional theory calculations, employing a set of computational tools, verified the role of Au⋯Au and π-holeCN⋯Cl noncovalent bonds in the spectrum of noncovalent forces.
Safety and Effectiveness of Chemotherapy in Elderly Biliary Tract Cancer Patients
Authors: Okamoto Takeshi , Takeda Tsuyoshi , Sasaki Takashi , Hamada Tsuyoshi , Mie Takafumi , Ishitsuka Takahiro , Yamada Manabu , Nakagawa Hiroki , Hirai Tatsuki , Furukawa Takaaki , Kasuga Akiyoshi , Ozaka Masato , Sasahira Naoki
Published: July 2023

The safety and effectiveness of chemotherapy in elderly patients with biliary tract cancer (BTC) remain unclear. Therefore, we retrospectively reviewed patients who underwent chemotherapy for locally advanced, metastatic, or recurrent BTC at our institution from January 2016 to December 2021. Of the 283 included patients, 91 (32.5%) were aged 75 years or older when initiating chemotherapy. Elderly patients were more likely than non-elderly patients to receive monotherapy with gemcitabine or S-1 (58.7% vs. 9.4%, p < 0.001) and were less likely to experience grade 3–4 toxicities (55.4% vs. 70.2%, p = 0.015). The rates of termination due to intolerance (6.5% vs. 5.8%, p = 0.800) and transition to second-line chemotherapy (39.1% vs. 40.3%, p = 0.849) were similar between groups. In the overall cohort, age was not an independent predictor of overall survival (OS). Within the elderly cohort, there were no differences in severe adverse events between patients receiving monotherapy and combination therapy (50.0% vs. 63.2%, p = 0.211). Median OS was longer in the combination therapy group (10.4 vs. 14.1 months; p = 0.010); however, choice of monotherapy was not an independent predictor of overall survival. Monotherapy appears to be a viable alternative in selected elderly BTC patients.
Successional Changes in Vegetation Communities near Mine Pits
Authors: Gura Dmitry , Semenycheva Irina
Published: July 2023

This study investigates demutational changes in plant communities of phosphorite mines between 2005 and 2021. A total of 23 plant species were initially found in the surveyed areas in 2005, while 68 species were identified in spring 2021. The plant communities were categorized into five different groups, with the older communities (No. I and II) exhibiting richer vegetation and greater diversity. Community No. III comprised various cereals, legumes, orchid plants, and mosses. Dominant species in community No. I included Betula pubescens, Carpinus betulus, Quercus robur, and Picea obovata, while community No. II featured Juniperus communis, Larix sibirica, Pinus sylvestris, and Abies sibirica. Erosion was observed in community No. V on slopes of different ages. The study also found changes in physicochemical indices, such as increased organic matter concentration and decreased pH value (4.9). The basal respiration rate of the pit soil was low (around 0.4 mg CO2/m2/h), and carbonate content ranged from 0.06 to 0.9%. This study presents novel data on the dynamics of species composition, plant community structure, and physicochemical soil parameters in human-altered habitats, thereby contributing to the expansion of our knowledge of vegetation recovery and evolution in such environments.
ECG-Gated CCTA in the Assessment of Post-Procedural Complications
Authors: Liguori Carlo , Lassandro Giulia , Ferrandino Giovanni , Picchi Stefano Giusto , Tamburrini Stefania , Toro Gabriella , Tamburro Fabio , Masala Salvatore , Scaglione Mariano
Published: July 2023

Introduction: The aim of our study was to assess the role of ECG-gated coronary CT angiography (CCTA) in the diagnosis, imaging follow-up, and treatment guidance in post-procedural/surgical interventions in the heart and thoracic aorta (PTCA, TAVI, PMK/ICD placement, CABGs). Materials and Methods: We retrospectively evaluated 294 ECG-gated CCTA studies performed in our center from January 2020 to January 2023. CCTA studies were acquired to detect/exclude possible complications related to the endovascular or surgical procedure. Results: There were 27 cases (9.2%) of post-procedural complications. Patients enrolled in the study were 18 males and 9 females (male/female ratio: 2), with age ranging from 47 to 86 years (mean age, 68.3 years). Among percutaneous coronary intervention (PCI) complications, coronary intimal dissection with ascending aorta involvement was found to be the most frequent complication after PTCA (22.2%). Vascular wall pseudoaneurysm formation (11.1%) and coronary stent misalignment or displacement (14.8%) were complications less frequently encountered after PTCA. Right atrial or ventricular perforation with associated hemopericardium were the most common complications (18.5%) after pacemaker implantation. Complications encountered after aortic valve interventions were loosening and dislocation of the prosthesis associated with aortic root pseudoaneurysm (7.4%), para-valvular leak (11.1%), and hemopericardium (7.4%). In one patient who underwent transcatheter repair of patent foramen ovale (3.7%), CTTA detected the dislocation of the Amplatzer septal occluder. Conclusions: ECG-gated CCTA is a fundamental diagnostic tool for the detection of post-procedural endovascular/surgical complications to enable optimal patient management. Radiologists must be familiar with the use of cardiac synchronization in the course of CT and must be aware of all possible complications that can occur in the context of acute settings or routine follow-up studies.
Diagnostic Techniques to Increase the Safety of Phakic Intraocular Lenses
Authors: Naujokaitis Tadas , Auffarth Gerd U. , Łabuz Grzegorz , Kessler Lucy Joanne , Khoramnia Ramin
Published: July 2023

Preoperative and postoperative diagnostics play an important role in ensuring the safety of patients with phakic intraocular lenses (pIOLs). The risk of endothelial cell loss can be addressed by regularly measuring the endothelial cell density using specular microscopy and considering the endothelial cell loss rate and the endothelial reserve in accordance with the patient’s age when deciding whether to explant a pIOL. The anterior chamber morphometrics, including the anterior chamber depth and the distance between the pIOL and the endothelium, measured using Scheimpflug tomography and anterior segment optical coherence tomography (AS-OCT), can help to assess the risk of the endothelial cell loss. In patients undergoing posterior chamber pIOL implantation, accurate prediction of the vault and its postoperative measurements using AS-OCT or Scheimpflug tomography are important when assessing the risk of anterior subcapsular cataract and secondary glaucoma. Novel approaches based on ultrasound biomicroscopy and AS-OCT have been proposed to increase the vault prediction accuracy and to identify eyes in which prediction errors are more likely. Careful patient selection and regular postoperative follow-up visits can reduce the complication risk and enable early intervention if a complication occurs.
A Coarse-to-Fine Fusion Network for Small Liver Tumor Detection and Segmentation: A Real-World Study
Authors: Wu Shu , Yu Hang , Li Cuiping , Zheng Rencheng , Xia Xueqin , Wang Chengyan , Wang He
Published: July 2023

Liver tumor semantic segmentation is a crucial task in medical image analysis that requires multiple MRI modalities. This paper proposes a novel coarse-to-fine fusion segmentation approach to detect and segment small liver tumors of various sizes. To enhance the segmentation accuracy of small liver tumors, the method incorporates a detection module and a CSR (convolution-SE-residual) module, which includes a convolution block, an SE (squeeze and excitation) module, and a residual module for fine segmentation. The proposed method demonstrates superior performance compared to conventional single-stage end-to-end networks. A private liver MRI dataset comprising 218 patients with a total of 3605 tumors, including 3273 tumors smaller than 3.0 cm, were collected for the proposed method. There are five types of liver tumors identified in this dataset: hepatocellular carcinoma (HCC); metastases of the liver; cholangiocarcinoma (ICC); hepatic cyst; and liver hemangioma. The results indicate that the proposed method outperforms the single segmentation networks 3D UNet and nnU-Net as well as the fusion networks of 3D UNet and nnU-Net with nnDetection. The proposed architecture was evaluated on a test set of 44 images, with an average Dice similarity coefficient (DSC) and recall of 86.9% and 86.7%, respectively, which is a 1% improvement compared to the comparison method. More importantly, compared to existing methods, our proposed approach demonstrates state-of-the-art performance in segmenting small objects with sizes smaller than 10 mm, achieving a Dice score of 85.3% and a malignancy detection rate of 87.5%.
Heart-Rate-to-Blood-Pressure Ratios Correlate with Malignant Brain Edema and One-Month Death in Large Hemispheric Infarction: A Cohort Study
Authors: Song Xindi , Wang Yanan , Guo Wen , Liu Meng , Deng Yilun , Ye Kaili , Liu Ming
Published: July 2023

Introduction: Large hemispheric infarction (LHI) can lead to fatal complications such as malignant brain edema (MBE). We aimed to investigate the correlation between heart-rate-to-blood-pressure ratios and MBE or one-month death after LHI. Methods: We prospectively included LHI patients from a registered cohort. Hourly heart-rate-to-blood-pressure ratios were recorded as a variation of the traditional shock index (SI), SIs and SId (systolic and diastolic pressures, respectively), and calculated for mean and variability (standard deviation) in 24 h and two 12 h epochs (1–12 h and 13–24 h) after onset of symptoms. MBE was defined as neurological deterioration symptoms with imaging evidence of brain swelling. We employed a generalized estimating equation to compare the trend in longitudinal collected SIs and SId between patients with and without MBE. We used multivariate logistic regression to investigate the correlation between SIs, SId and outcomes. Results: Of the included 162 LHI patients, 28.4% (46/162) developed MBE and 25.3% (40/158) died within one month. SIs and SId increased over baseline in all patients, with a similar ascending profile during the first 12 h epoch and a more intensive increase in the MBE group during the second 12 h epoch (p < 0.05). During the overall 24 h, patients with greater SId variability had a significantly increased MBE risk after adjustment (OR 3.72, 95%CI 1.38–10.04). Additionally, during the second 12 h epoch (13–24 h after symptom onset), patients developing MBE had a significantly higher SId level (OR 1.18, 95%CI 1.00–1.39) and greater SId variability (OR 3.16, 95%CI 1.35–7.40). Higher SId and greater SId variability within 24 h independently correlated with one-month death (all p < 0.05). Within the second 12 h epoch, higher SIs, higher SId and greater SId variability independently correlated with one-month death (all p < 0.05). No significant correlation was observed in the first 12 h epoch. Conclusions: Higher and more fluctuated heart-rate-to-blood-pressure ratios independently correlated with MBE development and one-month death in LHI patients, especially during the second 12 h (13–24 h) epoch after onset.
Cardiac Biomarkers and Their Role in Identifying Increased Risk of Cardiovascular Complications in COVID-19 Patients
Authors: Yaluri Nagendra , Stančáková Yaluri Alena , Žeňuch Pavol , Žeňuchová Zuzana , Tóth Štefan , Kalanin Peter
Published: July 2023

Cardiovascular disease (CVD) is a global health concern, causing significant morbidity and mortality. Both lifestyle and genetics influence the development of CVD. It is often diagnosed late, when the treatment options are limited. Early diagnosis of CVD with help of biomarkers is necessary to prevent adverse outcomes. SARS-CoV-2 infection can cause cardiovascular complications even in patients with no prior history of CVD. This review highlights cardiovascular biomarkers, including novel ones, and their applications as diagnostic and prognostic markers of cardiovascular complications related to SARS-CoV-2 infection. Patients with severe SARS-CoV-2 infection were shown to have elevated levels of cardiac biomarkers, namely N-terminal pro-brain natriuretic peptide (NT-pro-BNP), creatine kinase-myocardial band (CK-MB), and troponins, indicating acute myocardial damage. These biomarkers were also associated with higher mortality rates and therefore should be used throughout COVID-19 patient care to identify high-risk patients promptly to optimize their outcomes. Additionally, microRNAs (miRNAs) are also considered as potential biomarkers and predictors of cardiac and vascular damage in SARS-CoV-2 infection. Identifying molecular pathways contributing to cardiovascular manifestations in COVID-19 is essential for development of early biomarkers, identification of new therapeutic targets, and better prediction and management of cardiovascular outcomes.
Morphological Variations and Anomalies of the Sella Turcica on Lateral Cephalograms of Cleft-Palate-Only (CPO) Patients
Authors: Zawiślak Alicja , Jankowska Agnieszka , Grocholewicz Katarzyna , Janiszewska-Olszowska Joanna
Published: July 2023

Sella turcica abnormalities were reported in malocclusions and clefts. No studies were found on sella turcica abnormalities in CPO patients. This study aimed to compare the prevalence of sella turcica abnormalities on cephalometric radiographs in CPO versus non-cleft orthodontic patients. Cephalograms of CPO patients (n = 89) and controls (n = 89) were analyzed for normal sella turcicae and sella turcica abnormalities. Then, cephalometric analysis was performed using specialized software. Statistical analysis was performed using the Rv.4.1.1 package. No variation in or anomaly of the sella turcica was more frequent in CPO compared to non-cleft individuals. Patients with hypertrophic posterior clinoid process had higher interincisal and 1+:Nasion-A angles. Subjects with hypertrophic posterior clinoid process and double contour of the floor had higher Sella-Nasion-A, Sella-Nasion-B and Sella-Nasion-Pogonion and lower ANB. A pyramidal shape of the dorsum sellae was more prevalent in males, as was double contour of the floor in females. Subjects with an oblique anterior wall had lower SNB, GntgoAr and NLA. Subjects with a normal sella had higher SNPg, ML-NSL and 1+:NAmm. A normal sella was more prevalent in younger patients. CPO is not associated with sella turcica abnormalities compared to non-cleft orthodontic patients.
Radiomics and Hybrid Models Based on Machine Learning to Predict Levodopa-Induced Dyskinesia of Parkinson’s Disease in the First 6 Years of Levodopa Treatment
Authors: Luo Yang , Chen Huiqin , Gui Mingzhen
Published: July 2023

Background: Current research on the prediction of movement complications associated with levodopa therapy in Parkinson’s disease (PD) is limited. levodopa-induced dyskinesia (LID) is a movement complication that seriously affects the life quality of PD patients. One-third of PD patients develop LID within 1 to 6 years of levodopa treatment. This study aimed to construct models based on radiomics and machine learning to predict early LID in PD. Methods: We extracted radiomics features from the T1-weighted MRI obtained in the baseline of 49 PD control and 54 PD with LID in the first 6 years of levodopa therapy. Six brain regions related to the onset of PD were segmented as regions of interest (ROIs). The least absolute shrinkage and selection operator (LASSO) was used for feature selection. Using the machine learning methods of support vector machine (SVM), random forest (RF), and AdaBoost, we constructed radiomics models and hybrid models. The hybrid models combined the radiomics features and the Unified Parkinson’s Disease Rating Scale part III (UPDRS III) total score. The five-fold cross-validation was performed and repeated 20 times to validate the stability of the classifiers. We used sensitivity, specificity, accuracy, receiver operating characteristic (ROC) curves, and area under the ROC curve (AUC) for model validation. Results: We selected 33 out of 6138 radiomics features. In the testing set of the radiomics model, the AUC values of the SVM, RF, and AdaBoost classifiers were 0.905, 0.808, and 0.778, respectively, and the accuracies were 0.839, 0.742, and 0.710. The hybrid models had better prediction performance. In the testing set, the AUC values of SVM, RF, and AdaBoost classifiers were 0.958, 0.861, and 0.832, respectively, and the accuracies were 0.903, 0.806, and 0.774. Conclusions: Our results indicate that T1-weighted MRI is valuable in predicting early LID in PD. This work demonstrates that the combination of radiomics features and clinical features has good potential and value for identifying early LID in PD.
Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review
Authors: Sivari Esra , Senirkentli Guler Burcu , Bostanci Erkan , Guzel Mehmet Serdar , Acici Koray , Asuroglu Tunc
Published: July 2023

Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019–May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detection, and segmentation tasks. According to the results, the most commonly used task type was classification (n = 51), the most commonly used dental image material was panoramic radiographs (n = 55), and the most frequently used performance metric was sensitivity/recall/true positive rate (n = 87) and accuracy (n = 69). Dataset sizes ranged from 60 to 12,179 images. Although deep learning algorithms are used as individual or at least individualized architectures, standardized architectures such as pre-trained CNNs, Faster R-CNN, YOLO, and U-Net have been used in most studies. Few studies have used the explainable AI method (n = 22) and applied tests comparing human and artificial intelligence (n = 21). Deep learning is promising for better diagnosis and treatment planning in dentistry based on the high-performance results reported by the studies. For all that, their safety should be demonstrated using a more reproducible and comparable methodology, including tests with information about their clinical applicability, by defining a standard set of tests and performance metrics.
Improving the Accuracy of Continuous Blood Glucose Measurement Using Personalized Calibration and Machine Learning
Authors: Kumari Ranjita , Anand Pradeep Kumar , Shin Jitae
Published: July 2023

Despite tremendous developments in continuous blood glucose measurement (CBGM) sensors, they are still not accurate for all patients with diabetes. As glucose concentration in the blood is <1% of the total blood volume, it is challenging to accurately measure glucose levels in the interstitial fluid using CBGM sensors due to within-patient and between-patient variations. To address this issue, we developed a novel data-driven approach to accurately predict CBGM values using personalized calibration and machine learning. First, we scientifically divided measured blood glucose into smaller groups, namely, hypoglycemia (<80 mg/dL), nondiabetic (81–115 mg/dL), prediabetes (116–150 mg/dL), diabetes (151–181 mg/dL), severe diabetes (181–250 mg/dL), and critical diabetes (>250 mg/dL). Second, we separately trained each group using different machine learning models based on patients’ personalized parameters, such as physical activity, posture, heart rate, breath rate, skin temperature, and food intake. Lastly, we used multilayer perceptron (MLP) for the D1NAMO dataset (training to test ratio: 70:30) and grid search for hyperparameter optimization to predict accurate blood glucose concentrations. We successfully applied our proposed approach in nine patients with type 1 diabetes and observed that the mean absolute relative difference (MARD) decreased from 17.8% to 8.3%.
Area-Detector Computed Tomography for Pulmonary Functional Imaging
Authors: Ohno Yoshiharu , Ozawa Yoshiyuki , Nagata Hiroyuki , Bando Shuji , Cong Shang , Takahashi Tomoki , Oshima Yuka , Hamabuchi Nayu , Matsuyama Takahiro , Ueda Takahiro , Yoshikawa Takeshi , Takenaka Daisuke , Toyama Hiroshi
Published: July 2023

An area-detector CT (ADCT) has a 320-detector row and can obtain isotropic volume data without helical scanning within an area of nearly 160 mm. The actual-perfusion CT data within this area can, thus, be obtained by means of continuous dynamic scanning for the qualitative or quantitative evaluation of regional perfusion within nodules, lymph nodes, or tumors. Moreover, this system can obtain CT data with not only helical but also step-and-shoot or wide-volume scanning for body CT imaging. ADCT also has the potential to use dual-energy CT and subtraction CT to enable contrast-enhanced visualization by means of not only iodine but also xenon or krypton for functional evaluations. Therefore, systems using ADCT may be able to function as a pulmonary functional imaging tool. This review is intended to help the reader understand, with study results published during the last a few decades, the basic or clinical evidence about (1) newly applied reconstruction methods for radiation dose reduction for functional ADCT, (2) morphology-based pulmonary functional imaging, (3) pulmonary perfusion evaluation, (4) ventilation assessment, and (5) biomechanical evaluation.
Diagnostic Performance of Contrast-Enhanced Digital Mammography versus Conventional Imaging in Women with Dense Breasts
Authors: Moffa Giuliana , Galati Francesca , Maroncelli Roberto , Rizzo Veronica , Cicciarelli Federica , Pasculli Marcella , Pediconi Federica
Published: July 2023

The aim of this prospective study was to compare the diagnostic performance of contrast-enhanced mammography (CEM) versus digital mammography (DM) combined with breast ultrasound (BUS) in women with dense breasts. Between March 2021 and February 2022, patients eligible for CEM with the breast composition category ACR BI-RADS c–d at DM and an abnormal finding (BI-RADS 3-4-5) at DM and/or BUS were considered. During CEM, a nonionic iodinated contrast agent (Iohexol 350 mg I/mL, 1.5 mL/kg) was power-injected intravenously. Images were evaluated independently by two breast radiologists. Findings classified as BI-RADS 1–3 were considered benign, while BI-RADS 4–5 were considered malignant. In case of discrepancies, the higher category was considered for DM+BUS. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated, using histology/≥12-month follow-up as gold standards. In total, 51 patients with 65 breast lesions were included. 59 (90.7%) abnormal findings were detected at DM+BUS, and 65 (100%) at CEM. The inter-reader agreement was excellent (Cohen’s k = 0.87 for DM+BUS and 0.97 for CEM). CEM showed a 93.5% sensitivity (vs. 90.3% for DM+BUS), a 79.4–82.4% specificity (vs. 32.4–35.5% for DM+BUS) (McNemar p = 0.006), a 80.6–82.9% PPV (vs. 54.9–56.0% for DM+BUS), a 93.1–93.3% NPV (vs. 78.6–80.0% for DM+BUS), and a 86.1–87.7% accuracy (vs. 60.0–61.5% for DM+BUS). The AUC was higher for CEM than for DM+BUS (0.865 vs. 0.613 for Reader 1, and 0.880 vs. 0.628, for Reader 2) (p < 0.001). In conclusion, CEM had a better diagnostic performance than DM and BUS alone and combined together in patients with dense breasts.
Characterizing the Spatio-Temporal Variations of Urban Growth with Multifractal Spectra
Authors: Fu Meng , Chen Yanguang
Published: July 2023

Urban morphology exhibits fractal characteristics, which can be described by multifractal scaling. Multifractal parameters under positive moment orders primarily capture information about central areas characterized by relatively stable growth, while those under negative moment orders mainly reflect information about marginal areas that experience more active growth. However, effectively utilizing multifractal spectra to uncover the spatio-temporal variations of urban growth remains a challenge. To addresses this issue, this paper proposes a multifractal measurement by combining theoretical principles and empirical analysis. To capture the difference between growth stability in central areas and growth activity in marginal areas, an index based on generalized correlation dimension Dq is defined. This index takes the growth rate of Dq at extreme negative moment order as the numerator and that at extreme positive moment order as the denominator. During the stable stage of urban growth, the index demonstrates a consistent pattern over time, while during the active stage, the index may exhibit abnormal fluctuations or even jumps. This indicates that the index can reveal spatio-temporal information about urban evolution that cannot be directly observed through multifractal spectra alone. By integrating this index with multifractal spectra, we can more comprehensively characterize the evolutionary characteristics of urban spatial structure.
Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning
Authors: Liu Qingli , Sun Jiaxu , Wang Peiling
Published: July 2023

In order to solve the problem wherein too many base station antennas are deployed in a massive multiple-input–multiple-output system, resulting in high overhead for downlink channel state information feedback, this paper proposes an uplink-assisted channel feedback method based on deep learning. The method applies the reciprocity of the uplink and downlink, uses uplink channel state information in the base station to help users give feedback on unknown downlink information, and compresses and restores the channel state information. First, an encoder–decoder structure is established. The encoder reduces the network depth and uses multi-resolution convolution to increase the accuracy of channel state information extraction while reducing the number of computations relating to user equipment. Afterward, the channel state information is compressed to reduce feedback overhead in the channel. At the decoder, with the help of the reciprocity of the uplink and downlink, the feature extraction of the uplink’s magnitudes is carried out, and the downlink channel state information is integrated into a channel state information feature matrix, which is restored to its original size. The simulation results show that compared with CSINet, CRNet, CLNet, and DCRNet, indoor reconstruction precision was improved by an average of 16.4%, and outside reconstruction accuracy was improved by an average of 21.2% under all compressions.
Hybrid Threshold Denoising Framework Using Singular Value Decomposition for Side-Channel Analysis Preprocessing
Authors: Wang Yuanzhen , Zhang Hongxin , Fang Xing , Cui Xiaotong , Ning Wenxu , Wang Danzhi , Fan Fan , Shu Lei
Published: July 2023

The traces used in side-channel analysis are essential to breaking the key of encryption and the signal quality greatly affects the correct rate of key guessing. Therefore, the preprocessing of side-channel traces plays an important role in side-channel analysis. The process of side-channel leakage signal acquisition is usually affected by internal circuit noise, external environmental noise, and other factors, so the collected signal is often mixed with strong noise. In order to extract the feature information of side-channel signals from very low signal-to-noise ratio traces, a hybrid threshold denoising framework using singular value decomposition is proposed for side-channel analysis preprocessing. This framework is based on singular value decomposition and introduces low-rank matrix approximation theory to improve the rank selection methods of singular value decomposition. This paper combines the hard threshold method of truncated singular value decomposition with the soft threshold method of singular value shrinkage damping and proposes a hybrid threshold denoising framework using singular value decomposition for the data preprocessing step of side-channel analysis as a general preprocessing method for non-profiled side-channel analysis. The data used in the experimental evaluation are from the raw traces of the public database of DPA contest V2 and AES_HD. The success rate curve of non-profiled side-channel analysis further confirms the effectiveness of the proposed framework. Moreover, the signal-to-noise ratio of traces is significantly improved after preprocessing, and the correlation with the correct key is also significantly enhanced. Experimental results on DPA v2 and AES_HD show that the proposed noise reduction framework can be effectively applied to the side-channel analysis preprocessing step, and can successfully improve the signal-to-noise ratio of the traces and the attack efficiency.
Proposal for Trapped-Ion Quantum Memristor
Authors: Stremoukhov Sergey , Forsh Pavel , Khabarova Ksenia , Kolachevsky Nikolay
Published: July 2023

A quantum memristor combines the memristive dynamics with the quantum behavior of the system. We analyze the idea of a quantum memristor based on ultracold ions trapped in a Paul trap. Corresponding input and output memristor signals are the ion electronic levels populations. We show that under certain conditions the output/input dependence is a hysteresis curve similar to classical memristive devices. This behavior becomes possible due to the partial decoherence provided by the feedback loop, which action depends on previous state of the system (memory). The feedback loop also introduces nonlinearity in the system. Ion-based quantum memristor possesses several advantages comparing to other platforms—photonic and superconducting circuits—due to the presence of a large number of electronic levels with different lifetimes as well as strong Coulomb coupling between ions in the trap. The implementation of the proposed ion-based quantum memristor will be a significant contribution to the novel direction of “quantum neural networks”.
Croatian Regional Export Value-Added Chains
Authors: Mance Davor , Debelić Borna , Vilke Siniša
Published: July 2023

This paper examines Croatian Regional Value Chains (RVCs). We use the value chain approach to study supply chains, examining data specifically for Croatia and its proximate trading partners in the European Union, as well as some other European countries. The value added in Croatian exports is statistically related to the value added in the exports of Croatian trading partners. We argue that Croatian RVCs are organized around regional production centers, with proximity to production capacity, raw materials, and customers as driving factors, and thus most of the value added originates in these RVCs. This paper emphasizes the advantages of representing trade through value added over traditional gross trade statistics and highlights the robustness of RVCs in Croatia to global shocks and transportation reliability issues. We conclude that despite its small absolute size, the Croatian economy is dynamically well-integrated into the EU RVCs, which is crucial for increasing business efficiency and competitiveness, notwithstanding the constraints of relatively low returns to scale. Overall, this study contributes to a better understanding of RVCs in Croatia and their potential benefits for Croatia in the global production organization.
An Interdisciplinary Learning Community of Education and Psychology Majors
Authors: Yates Shelby , Hord Casey
Published: July 2023

The researchers conducted a qualitative case study to describe the experiences (over the course of a semester) of an interdisciplinary team of three special education and three psychology undergraduates who participated in a relational learning community and a graduate student who designed and facilitated this learning community. An Associate Professor and special education researcher oversaw and co-facilitated the project. The design of the learning community promoted the building of rapport and trust among the group members and the progress of the group toward a common goal of incorporating principles from psychology to develop teaching strategies for students who are struggling in math and experiencing severe math anxiety. Gathering more frequent and individualized feedback would have helped the learning community facilitator make some key adjustments earlier in the project, but the incorporation of rapport building activities that supported trust and collaboration among the group was supportive of group progress toward a common goal. We learned key lessons about how to design and implement a learning community that can be applied to the field of education, interdisciplinary collaboration, and other contexts.
An Examination of the Structural Validity of Instruments Assessing PE Teachers’ Beliefs, Intentions, and Self-Efficacy towards Teaching Physically Active Classes
Authors: Brown Trent D.
Published: July 2023

Schools and, in particular, health and physical education (HPE) classes, has the potential to engage children and adolescents in health-enhancing physical activity. HPE teachers may enable or constrain this behaviour through appropriate classroom strategies and pedagogies that enhance not only student learning but also their engagement in physical activity. Such practices are a result of a teacher’s curricula beliefs, self-efficacy, and intention to teach physical activity as part of their HPE classes. The purpose of this study is to investigate the structural validity of six instruments designed to assess teachers’ beliefs, self-efficacy, and intentions regarding physical activity in physical education using the Rasch Measurement Model (RMM). Data suggest that three out of six instruments demonstrated multidimensional characteristics (curriculum beliefs, self-efficacy, and subjective norm). Model fit data were in the ranges of 0.5 to 1.61 for infit and −2.58 to 3.20 for outfit data. Differential item functioning was only present on one item in the curriculum beliefs instrument. Person reliability was >0.55 and item reliability was >0.73. Qualitative interpretation of Wright maps demonstrated a very good spread of items. Overall, each instrument demonstrated appropriate structural validity when weighed up against all of the components of the RMM.
Inspiring, Supporting, and Propelling Urban Educators: Understanding the Effectiveness of a University-Based Induction Support Program
Authors: Many Joyce E. , Tanguay Carla L. , Bhatnagar Ruchi , Belden Jocelyn , Griffin Tilifayea , Hagan Claudia , Pettaway Candice
Published: July 2023

This research focuses on understanding the effectiveness of a university-based induction support program (ISP) instituted to support the graduates of an urban university who completed their preparation during the COVID-19 pandemic. We framed the evaluation of our ISP as participatory action research (PAR) and chose a critical theoretical perspective of adult learning and development as our theoretical lens because of the close alignment with this perspective to our college’s conceptual framework on social justice and equity. Primary data sources consisted of individual interviews with 15 key informants identified by the ISP research team. Data analyses occurred through a recursive and generative process moving between open coding using Nvivo and reflection on the literature related to critical adult learning theory and research on effective induction and coaching models. Findings included (a) the ISP as a liberating space to engage with other educators, (b) the ISP’s role as a university-based program for urban educators, (c) the ISP program’s impact on stakeholders’ professional identity, and (d) the ISP and the concept of criticality. The study also underscored the advantages of using PAR designs for program evaluation and/or accreditation inquiries focusing on continuous improvement.
Improving Massive Open Online Courses to Reduce the Inequalities Created by Colonialism
Authors: Morgan Hani
Published: July 2023

Many nations that were once colonized continue to suffer from the economic effects of the colonial period. People in countries with high levels of poverty may benefit from taking massive open online courses (MOOCs) because these courses are broadcast for free or for considerably less than the cost of enrolling in traditional classes. However, these courses have been criticized for maintaining the inequalities created by colonialism. This study focuses on exploring whether MOOCs create inequalities toward people living in the Global South. It addresses how language, access to technology, and economic insecurity may make these courses less beneficial for people from low-income families than for those from more privileged backgrounds. It begins with a discussion of how colonialism impacted many nations in the world. Although many nations became free of colonial rule, colonialism led to economic instability, much of which persists to the present day. The findings indicate that MOOCs contribute to inequalities in several ways. One of these ways is by not providing enough support to help people from low-income families complete these courses. Another relates to the cost associated with having a strong internet connection and the other resources needed to submit work on time. The findings offer ideas on improving MOOCs. These ideas include offering MOOCs in the native languages of people living in the Global South and avoiding offering these courses according to the xMOOC model.
Top Achievers in Mathematics in the End of Upper Secondary School
Authors: Niemi Laura , Metsämuuronen Jari , Hannula Markku S. , Laine Anu
Published: July 2023

Important questions regarding mathematical giftedness are how and when it is possible to identify. To be identified as gifted, the student must have natural potential but also an appropriate mix of motivation, support, and challenges. This study is based on longitudinal data following students from 3rd grade in primary school to the end of upper secondary school between 2005 and 2015. We focus on top achievers (<2% of age cohort) of the national mathematics final exam at the end of upper secondary school. We investigate how accurately top achievers at the end of secondary school can be identified in 3rd, 6th, and 9th grades using national tests. We identify mathematical tasks that predict future top achievement and analyze how attitudes, gender, and parental background factors relate to high proficiency. Most top achievers had already been identified by 3rd grade and almost all of them by 9th grade. However, recognizing future top achievers was not very accurate, as they were indistinguishable from many students whose performance did not reach the same level over time. The best predictor for future top achievement was a student’s ability to solve non-routine and atypical tasks in early school years.