bluuebunny/crossref_metadata_2025 huggingface dataset:
A Critical Comparison of Correlations for Rapid Estimation of Subgrade Stiffness in Pavement Design and Construction
Authors: Plati Christina , Tsakoumaki Maria
Published: March 2023

According to pavement design principles, the subgrade or soil layer serving as the foundation for pavement depends on the properties and stiffness of the soil material. The resilient modulus (MR) is the absolute measure of the bearing capacity of the subgrade for pavement design. However, due to the complexity of MR testing, indirect methods are chosen to determine MR. In this context, the CBR test is considered a practical tool for determining the strength of the subgrade, but the use of the correlations of MR-CBR has caused great controversy in the scientific community. Nevertheless, such correlations are widely used in practice for pavement design, and the question of their influence on design results is always raised. Therefore, the present study investigates the use and applicability of the MR-CBR correlations for the subgrade with respect to the design of flexible pavements, with the aim of optimizing the thickness and bearing capacity of the unbound base/sub-base. Based on the previous debate on the use of MR-CBR correlations, this study first investigates the main correlations between MR and the CBR index based on a thorough review of the literature. Using the properties of certain medium-grained soils used in practice and the corresponding values of MR, estimated by the various MR-CBR correlations reported in the international literature, a theoretical pavement design is then carried out on the basis of a sensitivity analysis. A major outcome of the sensitivity analysis is the identification of the most optimal correlation for estimating MR in pavement design, while the development of a global MR-CBR correlation applicable to most types of soil materials used in pavement construction remains an important topic for future research.
Synthesis and Luminescence Properties of Green-to-Red Color-Tunable Upconverting K2Gd(PO4)(WO4):Yb3+,Tb3+,Eu3+ Phosphors
Authors: Grigorjevaite Julija , Katelnikovas Arturas
Published: March 2023

Scientists are increasingly interested in new inorganic luminescence materials that could be excited with near-infrared (NIR) radiation. These materials can be used as luminescent thermometers, bio-imaging agents, anti-counterfeiting pigments, etc. In this manuscript, we report the synthesis and investigation of optical properties of two series of K2Gd(PO4)(WO4):20%Tb3+ (KGPW): the first, KGPW:20%Tb3+ doped with 1–20% Eu3+, and the second, KGPW:10%Yb3+,20%Tb3+ doped with 1–20% Eu3+. The phase-pure specimens were prepared using a solid-state synthesis method. Down-shifting and upconversion luminescence studies have been performed using 340 and 980 nm excitation, respectively. For upconversion emission luminescence, Yb3+ ions were used as sensitizers in the KGPW phosphors. In these phosphors, Yb3+ ions absorb the 980 nm radiation and transfer the energy to Tb3+ ions. At his point, Tb3+ ions either emit themselves or transfer part or all of their energy to Eu3+ ions. It was observed that the emission color of the synthesized phosphors could be successfully tuned from the green to red by varying the Tb/Eu concentration ratio regardless of the 340 or 980 nm excitation. Such color change proves that one luminescent material (KGPW) can provide three colors (i.e., green, orange, and red). Herein, the optical properties, such as reflection, down-shifting excitation and emission spectra, upconversion emission spectra, fluorescence lifetime, thermal quenching, color coordinates, and quantum efficiency, were studied using steady-state and kinetic spectroscopy.
Mechanical Property and Corrosion Behavior of Powder-Metallurgy-Processed 3D Graphene-Networks-Reinforced Al Matrix Composites
Authors: Zeng Meng , Chen Hongmei , Tao Xiaoma , Ouyang Yifang
Published: March 2023

Three-dimensional graphene networks (3DGN) have the potential to be used as a reinforcement for aluminum matrix composites due to their unique wrinkled structure and cost-effectiveness. In this work, the mechanical properties and corrosion resistance of 3DGN in Al matrix were systematically investigated. 3DGN/Al composites with weight ratios of 0, 0.075, 0.150, 0.225, and 0.300 3DGN were prepared by powder metallurgy following by ball mill and spark plasma sintering. Results revealed that the densification of 3DGN/Al composites slightly decreases with the increase of 3DGN content. Increased hardness without loss of ductility was recorded compared to the pure aluminum sample prepared under the same experimental conditions. 3DGN/Al composites exhibit higher corrosion currents density than that of pure aluminum, which shows that the addition of 3DGN reinforcement aggravates the corrosion of aluminum. This study can be used as a reference for future research on the effect of graphene on the various properties of graphene-reinforced aluminum matrix composites.
The Effect of Cr Additive on the Mechanical Properties of Ti-Al Intermetallics by First-Principles Calculations
Authors: Wang Hui , Su Fuyong , Wen Zhi
Published: March 2023

The structure, elastic properties and electronic structure of Ti-Al intermetallics including Ti3Al (space group P63/mmc), TiAl (space group I4/mmm) and TiAl3 (space group P4/mmm) are systematically studied by first-principles calculations. The results show that Ti-Al intermetallics can exist stably whether Cr replaces Ti or Al. The ductility of the alloy cannot be improved when Ti is replaced in Cr-doped TiAl and TiAl3. However, when it replaces Al, the alloy has better ductility. In Ti3Al, the ductility can be improved regardless of whether Cr replaces Ti or Al, and the effect is better when it replaces Al. The bond in Ti-Al intermetallics is mainly a Ti-Ti metal bond. The metal bond between Ti-Ti is strengthened and a solid metal bond is formed between Cr and Ti, inducing a better ductility of the material, after Cr replaces Al in Ti-Al intermetallics.
Geometrically Necessary Dislocation Analysis of Deformation Mechanism for Magnesium under Fatigue Loading at 0 °C
Authors: Li Qizhen
Published: March 2023

This study focused on the analysis of geometrically necessary dislocation (GND) densities for five selected fine-grained magnesium samples. Among the samples, three were tested under different fatigue-loading conditions at 0 °C, one experienced quasi-static tensile loading at 0 °C, and one represented the as-rolled state. The fatigue-tested samples were chosen according to the relationship between the maximum loading stress of a test and the material’s yield strength. This study provides new insights on the deformation mechanism of fine-grained magnesium at 0 °C. It is observed that the average GND densities were increased by 95~111% for the tested samples when compared with the as-rolled sample. It is especially interesting that there is a significant increase in the average GND density for the sample that experienced the fatigue loading with a low-maximum applied stress, and the maximum applied stress was lower than the material’s yield strength. This observation implies that the grain boundary mediated the dislocation-emission mechanism.
Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges
Authors: Farooq Muhammad Shoaib , Zulfiqar Aimen , Riaz Shamyla
Published: March 2023

Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many years, studies have been conducted to support the automatic diagnosis of epileptic seizures for clinicians’ ease. For that, several studies entail machine learning methods for early predicting epileptic seizures. Mainly, feature extraction methods have been used to extract the right features from the EEG data generated by the EEG machine. Then various machine learning classifiers are used for the classification process. This study provides a systematic literature review of the feature selection process and classification performance. This review was limited to finding the most used feature extraction methods and the classifiers used for accurate classification of normal to epileptic seizures. The existing literature was examined from well-known repositories such as MDPI, IEEE Xplore, Wiley, Elsevier, ACM, Springer link, and others. Furthermore, a taxonomy was created that recapitulates the state-of-the-art used solutions for this problem. We also studied the nature of different benchmark and unbiased datasets and gave a rigorous analysis of the working of classifiers. Finally, we concluded the research by presenting the gaps, challenges, and opportunities that can further help researchers predict epileptic seizures.
The Curious Case of the Choledochal Cyst—Revisiting the Todani Classification: Case Report and Review of the Literature
Authors: Miron Adrian , Popa Liliana Gabriela , Toma Elena Adelina , Calu Valentin , Parvuletu Radu Florin , Enciu Octavian
Published: March 2023

Choledochal cysts (CCs) are rare occurrences presenting as dilatations of biliary structures, which can present as single or multiple dilatations and can appear as both intra- and extrahepatic anomalies. The most widespread classification of CCs is the Todani classification, but there have been numerous reports of cysts that do not fall into any of the types described. We present such a case—a male patient 36 years of age who underwent preoperative CT, MRCP, and ERCP, which mistakenly indicated a type II Todani CC, and intraoperatively was found to be located at the confluence of the hepatic ducts and encompassed the origin of the common bile duct. Complete resection of the cyst and the proximal segment of the common bile duct was performed, and reconstruction was carried out by Roux-en-Y double-tutorized hepaticojejunostomy. Considering the risk of malignant transformation, the frequent preoperative misdiagnosis, as well as the technically challenging surgery required in such cases, we advocate for a revision of the classification and raise awareness of the need for guidelines regarding the proper short-term and long-term management of this disease to ensure adequate quality of life and disease-free survival for patients.
Replacing Endoscopy with Magnetic Resonance Enterography for Mucosal Activity Assessment in Terminal Ileal Crohn’s Disease: Are We There Yet?
Authors: Bohra Anuj , Vasudevan Abhinav , Kutaiba Numan , Van Langenberg Daniel Ross
Published: March 2023

Crohn’s disease (CD) is a chronic immune mediated disorder that most commonly affects the small bowel and/or the large bowel. Treatment targets in CD include mucosal healing assessed via ileocolonoscopy and transmural healing assessed through cross-sectional imaging modalities such as magnetic resonance enterography (MRE). More recently, histological healing in CD has emerged as a treatment target, though it is made cumbersome given its reliance on frequent endoscopic examinations. With expert guidelines now recommending regular objective assessments as part of a treat-to-target approach, accurate non-invasive assessment will become increasingly critical. MRE has an established role in the assessment of small bowel CD, with growing data supportive of its ability in detecting disease activity at mucosal and histological levels. This could therefore potentially reduce the need for serial endoscopic assessment. Thus, this review will assess the capacity of individual MRE parameters and MRE indices for detecting mucosal and histological small bowel CD activity. Furthermore, challenging scenarios, such as CD activity detection in post-operative clinical scenarios and abnormal findings in the context of a normal ileocolonoscopy, will be explored.
An Automated Method for Classifying Liver Lesions in Contrast-Enhanced Ultrasound Imaging Based on Deep Learning Algorithms
Authors: Mămuleanu Mădălin , Urhuț Cristiana , Săndulescu Larisa , Kamal Constantin , Pătrașcu Ana-Maria , Ionescu Alin , Șerbănescu Mircea-Sebastian , Streba Costin
Published: March 2023

Background: Contrast-enhanced ultrasound (CEUS) is an important imaging modality in the diagnosis of liver tumors. By using contrast agent, a more detailed image is obtained. Time-intensity curves (TIC) can be extracted using a specialized software, and then the signal can be analyzed for further investigations. Methods: The purpose of the study was to build an automated method for extracting TICs and classifying liver lesions in CEUS liver investigations. The cohort contained 50 anonymized video investigations from 49 patients. Besides the CEUS investigations, clinical data from the patients were provided. A method comprising three modules was proposed. The first module, a lesion segmentation deep learning (DL) model, handled the prediction of masks frame-by-frame (region of interest). The second module performed dilation on the mask, and after applying colormap to the image, it extracted the TIC and the parameters from the TIC (area under the curve, time to peak, mean transit time, and maximum intensity). The third module, a feed-forward neural network, predicted the final diagnosis. It was trained on the TIC parameters extracted by the second model, together with other data: gender, age, hepatitis history, and cirrhosis history. Results: For the feed-forward classifier, five classes were chosen: hepatocarcinoma, metastasis, other malignant lesions, hemangioma, and other benign lesions. Being a multiclass classifier, appropriate performance metrics were observed: categorical accuracy, F1 micro, F1 macro, and Matthews correlation coefficient. The results showed that due to class imbalance, in some cases, the classifier was not able to predict with high accuracy a specific lesion from the minority classes. However, on the majority classes, the classifier can predict the lesion type with high accuracy. Conclusions: The main goal of the study was to develop an automated method of classifying liver lesions in CEUS video investigations. Being modular, the system can be a useful tool for gastroenterologists or medical students: either as a second opinion system or a tool to automatically extract TICs.
Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images
Authors: Lv Baolong , Liu Feng , Li Yulin , Nie Jianhua , Gou Fangfang , Wu Jia
Published: March 2023

Bone malignant tumors are metastatic and aggressive. The manual screening of medical images is time-consuming and laborious, and computer technology is now being introduced to aid in diagnosis. Due to a large amount of noise and blurred lesion edges in osteosarcoma MRI images, high-precision segmentation methods require large computational resources and are difficult to use in developing countries with limited conditions. Therefore, this study proposes an artificial intelligence-aided diagnosis scheme by enhancing image edge features. First, a threshold screening filter (TSF) was used to pre-screen the MRI images to filter redundant data. Then, a fast NLM algorithm was introduced for denoising. Finally, a segmentation method with edge enhancement (TBNet) was designed to segment the pre-processed images by fusing Transformer based on the UNet network. TBNet is based on skip-free connected U-Net and includes a channel-edge cross-fusion transformer and a segmentation method with a combined loss function. This solution optimizes diagnostic efficiency and solves the segmentation problem of blurred edges, providing more help and reference for doctors to diagnose osteosarcoma. The results based on more than 4000 osteosarcoma MRI images show that our proposed method has a good segmentation effect and performance, with Dice Similarity Coefficient (DSC) reaching 0.949, and show that other evaluation indexes such as Intersection of Union (IOU) and recall are better than other methods.
Cross Dataset Analysis of Domain Shift in CXR Lung Region Detection
Authors: Xue Zhiyun , Yang Feng , Rajaraman Sivaramakrishnan , Zamzmi Ghada , Antani Sameer
Published: March 2023

Domain shift is one of the key challenges affecting reliability in medical imaging-based machine learning predictions. It is of significant importance to investigate this issue to gain insights into its characteristics toward determining controllable parameters to minimize its impact. In this paper, we report our efforts on studying and analyzing domain shift in lung region detection in chest radiographs. We used five chest X-ray datasets, collected from different sources, which have manual markings of lung boundaries in order to conduct extensive experiments toward this goal. We compared the characteristics of these datasets from three aspects: information obtained from metadata or an image header, image appearance, and features extracted from a pretrained model. We carried out experiments to evaluate and compare model performances within each dataset and across datasets in four scenarios using different combinations of datasets. We proposed a new feature visualization method to provide explanations for the applied object detection network on the obtained quantitative results. We also examined chest X-ray modality-specific initialization, catastrophic forgetting, and model repeatability. We believe the observations and discussions presented in this work could help to shed some light on the importance of the analysis of training data for medical imaging machine learning research, and could provide valuable guidance for domain shift analysis.
Performance and Agreement When Annotating Chest X-ray Text Reports—A Preliminary Step in the Development of a Deep Learning-Based Prioritization and Detection System
Authors: Li Dana , Pehrson Lea Marie , Bonnevie Rasmus , Fraccaro Marco , Thrane Jakob , Tøttrup Lea , Lauridsen Carsten Ammitzbøl , Butt Balaganeshan Sedrah , Jankovic Jelena , Andersen Tobias Thostrup , Mayar Alyas , Hansen Kristoffer Lindskov , Carlsen Jonathan Frederik , Darkner Sune , Nielsen Michael Bachmann
Published: March 2023

A chest X-ray report is a communicative tool and can be used as data for developing artificial intelligence-based decision support systems. For both, consistent understanding and labeling is important. Our aim was to investigate how readers would comprehend and annotate 200 chest X-ray reports. Reports written between 1 January 2015 and 11 March 2022 were selected based on search words. Annotators included three board-certified radiologists, two trained radiologists (physicians), two radiographers (radiological technicians), a non-radiological physician, and a medical student. Consensus labels by two or more of the experienced radiologists were considered “gold standard”. Matthew’s correlation coefficient (MCC) was calculated to assess annotation performance, and descriptive statistics were used to assess agreement between individual annotators and labels. The intermediate radiologist had the best correlation to “gold standard” (MCC 0.77). This was followed by the novice radiologist and medical student (MCC 0.71 for both), the novice radiographer (MCC 0.65), non-radiological physician (MCC 0.64), and experienced radiographer (MCC 0.57). Our findings showed that for developing an artificial intelligence-based support system, if trained radiologists are not available, annotations from non-radiological annotators with basic and general knowledge may be more aligned with radiologists compared to annotations from sub-specialized medical staff, if their sub-specialization is outside of diagnostic radiology.
Cell Count Differentials by Cytomorphology and Next-Generation Flow Cytometry in Bone Marrow Aspirate: An Evidence-Based Approach
Authors: Ríos-Tamayo Rafael , Sánchez María José , Gómez-Rojas Sandra , Rodríguez-Barranco Miguel , Segura Gloria Pérez , Redondo-Sánchez Daniel , CARREÑO-TARRAGONA Gonzalo , Nicolás Antonio Rodríguez , Ruiz-Cabello Francisco , Jiménez Pilar , Alonso Rafael , Lahuerta Juan José , Martínez-López Joaquín , Duarte Rafael F.
Published: March 2023

Despite a lack of evidence, a bone marrow aspirate differential of 500 cells is commonly used in the clinical setting. We aimed to test the performance of 200-cell counts for daily hematological workup. In total, 660 consecutive samples were analyzed recording differentials at 200 and 500 cells. Additionally, immunophenotype results and preanalytical issues were also evaluated. Clinical and statistical differences between both cutoffs and both methods were checked. An independent control group of 122 patients was included. All comparisons between both cutoffs and both methods for all relevant types of cells did not show statistically significant differences. No significant diagnostic discrepancies were demonstrated in the contingency table analysis. This is a real-life study, and some limitations may be pointed out, such as a different sample sizes according to the type of cell in the immunophenotype analysis, the lack of standardization of some preanalytical events, and the relatively small sample size of the control group. The comparisons of differentials by morphology on 200 and 500 cells, as well as by morphology (both cutoffs) and by immunophenotype, are equivalent from the clinical and statistical point of view. The preanalytical issues play a critical role in the assessment of bone marrow aspirate samples.
A Deep Learning Radiomics Nomogram to Predict Response to Neoadjuvant Chemotherapy for Locally Advanced Cervical Cancer: A Two-Center Study
Authors: Zhang Yajiao , Wu Chao , Xiao Zhibo , Lv Furong , Liu Yanbing
Published: March 2023

Purpose: This study aimed to establish a deep learning radiomics nomogram (DLRN) based on multiparametric MR images for predicting the response to neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC). Methods: Patients with LACC (FIGO stage IB-IIIB) who underwent preoperative NACT were enrolled from center 1 (220 cases) and center 2 (independent external validation dataset, 65 cases). Handcrafted and deep learning-based radiomics features were extracted from T2WI, DWI and contrast-enhanced (CE)-T1WI, and radiomics signatures were built based on the optimal features. Two types of radiomics signatures and clinical features were integrated into the DLRN for prediction. The AUC, calibration curve and decision curve analysis (DCA) were employed to illustrate the performance of these models and their clinical utility. In addition, disease-free survival (DFS) was assessed by Kaplan–Meier survival curves based on the DLRN. Results: The DLRN showed favorable predictive values in differentiating responders from nonresponders to NACT with AUCs of 0.963, 0.940 and 0.910 in the three datasets, with good calibration (all p > 0.05). Furthermore, the DLRN performed better than the clinical model and handcrafted radiomics signature in all datasets (all p < 0.05) and slightly higher than the DL-based radiomics signature in the internal validation dataset (p = 0.251). DCA indicated that the DLRN has potential in clinical applications. Furthermore, the DLRN was strongly correlated with the DFS of LACC patients (HR = 0.223; p = 0.004). Conclusion: The DLRN performed well in preoperatively predicting the therapeutic response in LACC and could provide valuable information for individualized treatment.
Organ-Specific Positron Emission Tomography Scanners for Breast Imaging: Comparison between the Performances of Prior and Novel Models
Authors: Satoh Yoko , Hanaoka Kohei , Ikegawa Chihiro , Imai Masamichi , Watanabe Shota , Morimoto-Ishikawa Daisuke , Onishi Hiroshi , Ito Toshikazu , Komoike Yoshifumi , Ishii Kazunari
Published: March 2023

The performances of photomultiplier tube (PMT)-based dedicated breast positron emission tomography (PET) and silicon photomultiplier tube (SiPM)-based time-of-flight (TOF) PET, which is applicable not only to breast imaging but also to head imaging, were compared using a phantom study. A cylindrical phantom containing four spheres (3–10 mm in diameter) filled with 18F-FDG at two signal-to-background ratios (SBRs), 4:1 and 8:1, was scanned. The phantom images, which were reconstructed using three-dimensional list-mode dynamic row-action maximum likelihood algorithm with various β-values and post-smoothing filters, were visually and quantitatively compared. Visual evaluation showed that the 3 mm sphere was more clearly visualized with higher β and smaller post-filters, while the background was noisier; SiPM-based TOF-PET was superior to PMT-based dbPET in sharpness, smoothness, and detectability, although the background was noisier at the SBR of 8:1. Quantitative evaluation revealed that the detection index (DI) and recovery coefficient (CRC) of SiPM-based TOF-PET images were higher than those of PMT-based PET images, despite a higher background coefficient of variation (CVBG). The two organ-specific PET systems showed that a 3 mm lesion in the breast could be visualized at the center of the detector, and there was less noise in the SiPM-based TOF-PET image.
Drone-Aided Delivery Methods, Challenge, and the Future: A Methodological Review
Authors: Li Xueping , Tupayachi Jose , Sharmin Aliza , Martinez Ferguson Madelaine
Published: March 2023

The use of drones for package delivery, commonly known as drone delivery or unmanned aerial vehicle (UAV) delivery, has gained significant attention from academia and industries. Compared to traditional delivery methods, it provides greater flexibility, improved accessibility, increased speed and efficiency, enhanced safety, and even some environmental benefits. With the increasing interest in this technology, it is crucial for researchers and practitioners to understand the current state of the art in drone delivery. This paper aims to review the current literature on drone delivery and identify research trends, challenges, and future research directions. Specifically, the relevant literature is identified and selected using a systematic literature review approach. We then categorize the literature according to the characteristics and objectives of the problems and thoroughly analyze them based on mathematical formulations and solution techniques. We summarize key challenges and limitations associated with drone delivery from technological, safety, societal, and environmental aspects. Finally, potential research directions are identified.
Existence for Nonlinear Fourth-Order Two-Point Boundary Value Problems
Authors: Agarwal Ravi , Mihaylova Gabriela , Kelevedjiev Petio
Published: March 2023

The present paper is devoted to the solvability of various two-point boundary value problems for the equation y(4)=f(t,y,y′,y″,y‴), where the nonlinearity f may be defined on a bounded set and is needed to be continuous on a suitable subset of its domain. The established existence results guarantee not just a solution to the considered boundary value problems but also guarantee the existence of monotone solutions with suitable signs and curvature. The obtained results rely on a basic existence theorem, which is a variant of a theorem due to A. Granas, R. Guenther and J. Lee. The a priori bounds necessary for the application of the basic theorem are provided by the barrier strip technique. The existence results are illustrated with examples.
Implicit Solutions of the Electrical Impedance Tomography Inverse Problem in the Continuous Domain with Deep Neural Networks
Authors: Strauss Thilo , Khan Taufiquar
Published: March 2023

Electrical impedance tomography (EIT) is a non-invasive imaging modality used for estimating the conductivity of an object Ω from boundary electrode measurements. In recent years, researchers achieved substantial progress in analytical and numerical methods for the EIT inverse problem. Despite the success, numerical instability is still a major hurdle due to many factors, including the discretization error of the problem. Furthermore, most algorithms with good performance are relatively time consuming and do not allow real-time applications. In our approach, the goal is to separate the unknown conductivity into two regions, namely the region of homogeneous background conductivity and the region of non-homogeneous conductivity. Therefore, we pose and solve the problem of shape reconstruction using machine learning. We propose a novel and simple jet intriguing neural network architecture capable of solving the EIT inverse problem. It addresses previous difficulties, including instability, and is easily adaptable to other ill-posed coefficient inverse problems. That is, the proposed model estimates the probability for a point of whether the conductivity belongs to the background region or to the non-homogeneous region on the continuous space Rd∩Ω with d∈{2,3}. The proposed model does not make assumptions about the forward model and allows for solving the inverse problem in real time. The proposed machine learning approach for shape reconstruction is also used to improve gradient-based methods for estimating the unknown conductivity. In this paper, we propose a piece-wise constant reconstruction method that is novel in the inverse problem setting but inspired by recent approaches from the 3D vision community. We also extend this method into a novel constrained reconstruction method. We present extensive numerical experiments to show the performance of the architecture and compare the proposed method with previous analytic algorithms, mainly the monotonicity-based shape reconstruction algorithm and iteratively regularized Gauss–Newton method.
Learners in the Metaverse: A Systematic Review on the Use of Roblox in Learning
Authors: Han Jining , Liu Geping , Gao Yuxin
Published: March 2023

The development of the Metaverse has drawn much attention in education. Roblox, as an important platform in the Metaverse, attracts millions of young users, which raises the question of how its effectiveness as a learning environment can be maximized. This study aims to synthesize the available evidence to provide an overview of the current research on learning in Roblox by exploring its benefits, challenges, and existing gaps. In line with PRISMA and assisted by LDA topic modeling, we included 40 studies that were analyzed to answer our questions. The research findings show that: (1) Roblox could be combined with social interactive learning or collaborative learning environments, provide a VR environment that supports learning, and be of benefit to programming in STEM education; (2) the use of Roblox in learning has the advantages of attracting a large number of student users, eliciting the positive attitudes of students, and promoting students’ cognitive and noncognitive learning abilities; and (3) there are also challenges such as cyberbullying, cybersecurity, lack of adequate teaching design, etc. Empirical studies on this topic have only begun to emerge, and more future research is needed into different pedagogical scenarios to explore the effects, factors, outcomes, designs, etc.
A Systematic Review of Literature on Emerging Technologies and Specific Learning Difficulties
Authors: Lozano-Álvarez María , Rodríguez-Cano Sonia , Delgado-Benito Vanesa , Mercado-Val Elvira
Published: March 2023

Educational research on emerging technologies, particularly virtual reality and augmented reality, is expanding at the moment. The purpose of this contribution is to conduct a systematic review to understand the impact of emerging technologies in the educational and social-health fields. The PRISMA 2020 methodology was used to respond to the objective and research questions, ensuring the fulfillment of the quality criteria in the elaboration of the systematic review. This systematic review analyses the possible benefits that new technologies, specifically virtual reality and augmented reality, can have on learning difficulties such as dyslexia and ADHD disorder, as well as their inclusion in the social sphere. In this study, articles from both the health and educational fields will be reviewed. The research focuses on highlighting the incipient benefits of these new technologies for the aforementioned learning difficulties in the school and health fields. The main conclusions drawn show that research on these emerging technologies is still scarce and incipient in the educational field, with the main results found in the healthcare field.
Attentional Interactive Encoder Network Focused on Aspect for Sentiment Classification
Authors: Yang Bin , Li Haoling , Teng Sikai , Sun Yuze , Xing Ying
Published: March 2023

Aspect-based sentiment analysis (ABSA) plays a significant role in the field of big data and aims to distinguish the sentiment polarity of specific aspects in given sentences; however, the previous works on ABSA had two limitations. They mainly considered semantic features, rather than syntactic dependency features, and paid too much attention to the context words, while ignoring the high-level interaction of multiple representations of aspects themselves. To cope with these limitations, we propose a new method based on the graph convolutional network (GCN) and the multi-head attention mechanism, called the attention interactive encoder network (AIEN). The GCN was used to obtain the syntactic information that has the greatest syntactic impact on the aspect based on the syntax dependency tree. The multi-head attention mechanism can not only obtain the context-aware information of the given aspects, but also the interaction information between multiple representations of the aspect itself. The high-level information generated by the interaction of multi-dimensional features can produce a stronger representation ability for the aspect. Our experiments with the proposed model on five benchmark datasets showed that our model outperformed other works significantly. The experimental results further demonstrated the feasibility and applicability of our proposed model in the ABSA task.
Low Complexity Speech Enhancement Network Based on Frame-Level Swin Transformer
Authors: Jiang Weiqi , Sun Chengli , Chen Feilong , Leng Yan , Guo Qiaosheng , Sun Jiayi , Peng Jiankun
Published: March 2023

In recent years, Transformer has shown great performance in speech enhancement by applying multi-head self-attention to capture long-term dependencies effectively. However, the computation of Transformer is quadratic with the input speech spectrograms, which makes it computationally expensive for practical use. In this paper, we propose a low complexity hierarchical frame-level Swin Transformer network (FLSTN) for speech enhancement. FLSTN takes several consecutive frames as a local window and restricts self-attention within it, reducing the complexity to linear with spectrogram size. A shifted window mechanism enhances information exchange between adjacent windows, so that window-based local attention becomes disguised global attention. The hierarchical structure allows FLSTN to learn speech features at different scales. Moreover, we designed the band merging layer and the band expanding layer for decreasing and increasing the spatial resolution of feature maps, respectively. We tested FLSTN on both 16 kHz wide-band speech and 48 kHz full-band speech. Experimental results demonstrate that FLSTN can handle speech with different bandwidths well. With very few multiply–accumulate operations (MACs), FLSTN not only has a significant advantage in computational complexity but also achieves comparable objective speech quality metrics with current state-of-the-art (SOTA) models.
Declarative Secure Placement of FaaS Orchestrations in the Cloud-Edge Continuum
Authors: Bocci Alessandro , Forti Stefano , Ferrari Gian-Luigi , Brogi Antonio
Published: March 2023

The decision-making related to the placement of applications made from orchestrated serverless functions onto Cloud-Edge infrastructures is a challenging problem as it must consider functional and non-functional requirements. In this article, we propose a novel declarative methodology to determine the placement of FaaS orchestration onto Cloud-Edge resources while satisfying all the requirements of the FaaS orchestrations and relying on information-flow analyses and padding techniques to prevent information leaks through side channels. A motivating example from Augmented Reality is used to showcase the open-source declarative prototype implementing our proposal. Besides, the prototype is assessed via simulation to evaluate execution times, placement success rates and energy consumption.
Use of Blockchain-Based Smart Contracts in Logistics and Supply Chains
Authors: Alqarni Mohammed Ali , Alkatheiri Mohammed Saeed , Chauhdary Sajjad Hussain , Saleem Sajid
Published: March 2023

Blockchain is a disrupting technology that has the capability to completely alter the design, activities, and product flows in logistics and supply chain networks. It provides assurance of openness, immutability, transparency, security, and neutrality for all supply chain agents and stakeholders. In this paper, we explore the improvements and tradeoffs introduced by using blockchains in logistics management in terms of the sustainability of society, the environment, and economic dimensions of the supply chain. Blockchain technology makes it much more difficult to counterfeit products by providing indisputable and immutable proof of the provenance of the raw materials, products, and sale to the end consumer. This can potentially enhance the trust of the consumer in the product and financially benefit the manufacturer through the protection of their intellectual property rights. This paper explores the benefits, applications, and issues related to the usage of blockchain and smart contracts for logistics and supply-chain management. We focus on the implementation, deployment, audit, and operational aspects of smart contracts in the blockchain applied to terrestrial, maritime, and aerial logistics networks. The paper also discusses opportunities and challenges that arise due to the use of smart contracts in these sectors.
Optimal GPS Acquisition Algorithm in Severe Ionospheric Scintillation Scene
Authors: Lin Mengying , Luo Yimei , Zhu Xuefen , Tu Gangyi , Lu Zhengpeng
Published: March 2023

The Global Positioning System (GPS) plays an important role in navigation and positioning services. When GPS signals propagate through a complex space environment, they are susceptible to interference of ionospheric scintillation. As one of the biggest interference sources on GPS navigation and positioning, ionospheric scintillation will lead to signal intensity decline and carrier phase fluctuation, making signal acquisition of the GPS receiver challenging. Thus, an acquisition algorithm based on differential coherent integration combining accumulation correlation and bit sign transition estimation is proposed. The coherent accumulation is applied to reduce computational loads and contribution by the Gaussian white noise in the signal. Moreover, the differential coherence integration is utilized to eliminate data blocks with bit transition, prolonging the coherence integration time and improving the data utilization rate. Experimental results show that under severe ionospheric scintillation condition, weak GPS signals can be acquired successfully after improving the acquisition algorithm, with the acquisition probability reaching 50% when the signal-to-interference ratio (SIR) drops to −34 dB. Comparing to the differential coherence integration, the complexity of the calculation reduces to only 21.75% effectively after the improvement. The execution time is less than half of the differential coherence integral.
Pre-Design of Multi-Band Planar Antennas by Artificial Neural Networks
Authors: Lahiani Mohamed Aziz , Raida Zbyněk , Veselý Jiří , Olivová Jana
Published: March 2023

In this communication, artificial neural networks are used to estimate the initial structure of a multiband planar antenna. The neural networks are trained on a set of selected normalized multiband antennas characterized by time-efficient modal analysis with limited accuracy. Using the Deep Learning Toolbox in Matlab, several types of neural networks have been created and trained on the sample planar multiband antennas. In the neural network learning process, suitable network types were selected for the design of these antennas. The trained networks, depending on the desired operating bands, will select the appropriate antenna geometry. This is further optimized using Newton’s method in HFSS. The use of the neural pre-design concept speeds up and simplifies the design of multiband planar antennas. The findings presented in this paper will be used to refine and accelerate the design of planar multiband antennas.
Research on a Non-Invasive Hemoglobin Measurement System Based on Four-Wavelength Photoplethysmography
Authors: Chen Zhencheng , Qin Huishan , Ge Wenjun , Li Shiyong , Liang Yongbo
Published: March 2023

Hemoglobin is an essential parameter in human blood. This paper proposes a non-invasive hemoglobin concentration measurement method based on the characteristic parameters of four-wavelength photoplethysmography (PPG) signals combined with machine learning. The DCM08 sensor and NRF52840 chip form a data acquisition system to collect 58 human fingertip photoelectric volumetric pulse wave signals. The 160 four-wavelength PPG signal feature parameters were constructed and extracted. The feature parameters were screened by combining three feature selection methods: reliefF, Chi-square score, and information gain. The top 10, 20, and 30 features screened were used as input to evaluate the prediction performance of different feature sets for hemoglobin. The prediction models used were XGBoost, support vector machines, and logistic regression. The results showed that the optimal performance of the 30 feature sets screened using the Chi-square test was achieved by the XGBoost model with a coefficient of determination (R2) of 0.997, root mean square error (RMSE) of 0.762 g/L, and mean absolute error (MAE) of 0.325 g/L. The study showed that the four-wavelength-based PPG signal feature parameters with the XGBoost algorithm could effectively achieve non-invasive detection of hemoglobin, providing a new measurement method in clinical practice.
Multi-Attention-Based Semantic Segmentation Network for Land Cover Remote Sensing Images
Authors: Jia Jintong , Song Jiarui , Kong Qingqiang , Yang Huan , Teng Yunhe , Song Xuan
Published: March 2023

Semantic segmentation is a key technology for remote sensing image analysis widely used in land cover classification, natural disaster monitoring, and other fields. Unlike traditional image segmentation, there are various targets in remote sensing images, with a large feature difference between the targets. As a result, segmentation is more difficult, and the existing models retain low accuracy and inaccurate edge segmentation when used in remote sensing images. This paper proposes a multi-attention-based semantic segmentation network for remote sensing images in order to address these problems. Specifically, we choose UNet as the baseline model, using a coordinate attention-based residual network in the encoder to improve the extraction capability of the backbone network for fine-grained features. We use a content-aware reorganization module in the decoder to replace the traditional upsampling operator to improve the network information extraction capability, and, in addition, we propose a fused attention module for feature map fusion after upsampling, aiming to solve the multi-scale problem. We evaluate our proposed model on the WHDLD dataset and our self-labeled Lu County dataset. The model achieved an mIOU of 63.27% and 72.83%, and an mPA of 74.86% and 84.72%, respectively. Through comparison and confusion matrix analysis, our model outperformed commonly used benchmark models on both datasets.
Eco-Driving Cruise Control for 4WIMD-EVs Based on Receding Horizon Reinforcement Learning
Authors: Zhang Zhe , Ding Haitao , Guo Konghui , Zhang Niaona
Published: March 2023

Aiming to improve the distance per charge of four in-wheel independent motor-drive electric vehicles in intelligent transportation systems, a hierarchical energy management strategy that weighs their computational efficiency and optimization performance is proposed. According to the information of an intelligent transportation system, a method combining reinforcement learning with receding horizon optimization is proposed at the upper level, which solves the cruising velocity for eco-driving in a long predictive horizon based on the online construction of a velocity planning problem. At the lower level, a multi-objective optimal torque allocation method that considers energy saving and safety is proposed, where an analytical solution based on the state feedback control was obtained with the vehicle following the optimal speed of the upper level and tracking the centerline of the target path. The energy management strategy proposed in this study effectively reduces the complexity of the intelligent energy-saving control system of the vehicle and achieves a fast solution to the whole vehicle energy optimization problem, integrating macro-traffic information while considering both power and safety. Finally, an intelligent, connected hardware-in-the-loop (HIL) simulation platform is built to verify the method formulated in this study. The simulation results demonstrate that the proposed method reduces energy consumption by 12.98% compared with the conventional constant-speed cruising strategy. In addition, the computational time is significantly reduced.
MWSR-YLCA: Improved YOLOv7 Embedded with Attention Mechanism for Nasopharyngeal Carcinoma Detection from MR Images
Authors: Wu Huixin , Zhao Xin , Han Guanghui , Li Haojiang , Kong Yuhao , Li Jiahui
Published: March 2023

Nasopharyngeal carcinoma (NPC) is a malignant tumor, and early diagnosis and timely treatment are important for NPC patients. Accurate and reliable detection of NPC lesions in magnetic resonance (MR) images is very helpful for the disease diagnosis. However, recent deep learning methods need to be improved for NPC detection in MR images. Because NPC tumors are invasive and usually small in size, it is difficult to distinguish NPC tumors from the closely connected surrounding tissues in a huge and complex background. In this paper, we propose an automatic detection method, named MWSR-YLCA, to accurately detect NPC lesions in MR images. Specifically, we design two modules, the multi-window settings resampling (MWSR) module and an improved YOLOv7 embedded with a coordinate attention mechanism (YLCA) module, to detect NPC lesions more accurately. First, the MWSR generates a pseudo-color version of MR images based on a multi-window resampling method, which preserves richer information. Subsequently, the YLCA detects the NPC lesion areas more accurately by constructing a novel network based on an improved YOLOv7 framework embedded with the coordinate attention mechanism. The proposed method was validated on an MR image set of 800 NPC patients and obtained 80.1% mAP detection performance with only 4694 data samples. The experimental results show that the proposed MWSR-YLCA method can perform high-accuracy detection of NPC lesions and has superior performance.
DA-FPN: Deformable Convolution and Feature Alignment for Object Detection
Authors: Fu Xiang , Yuan Zemin , Yu Tingjian , Ge Yun
Published: March 2023

This study sought to address the problem of the insufficient extraction of shallow object information and boundary information when using traditional FPN structures in current object detection algorithms, which degrades object detection accuracy. In this paper, a new FPN structure model, DA-FPN, is proposed. DA-FPN replaces the 1 × 1 convolution used in the conventional FPN structure for lateral connection with a 3 × 3 deformable convolution and adds a feature alignment module after the 2x downsampling operation used for lateral connection. This design allows the detection framework to extract more accurate information about the boundary of the object, particularly the boundary information of small objects. A bottom-up module was also added to incorporate the shallow information of the object more accurately into the high-level feature map, and a feature alignment module was added to the bottom-up module, thereby improving object detection accuracy. The experimental results show that DA-FPN can improve the accuracy of the single-stage object detection algorithms FoveaBox and GFL by 1.7% and 2.4%, respectively, on the MS-COCO dataset. This model was also found to improve the two-stage object detection algorithm SABL by 2.4% and offer higher small object detection accuracy and better robustness.