bluuebunny/crossref_metadata_2025 huggingface dataset:
Multiparametric Ultrasound of Cervical Lymph Node Metastases in Head and Neck Cancer for Planning Non-Surgical Therapy
Authors: Künzel Julian , Brandenstein Moritz , Zeman Florian , Symeou Luisa , Platz Batista da Silva Natascha , Jung Ernst Michael
Published: August 2022

Background: We aimed to evaluate multiparametric ultrasound, to achieve a better understanding of the baseline characteristics of suspected cervical lymph node metastases in head and neck cancer before induction chemotherapy or chemoradiation. Methods: From February 2020 to April 2021, our complete ultrasound examination protocol was carried out on clinically evident malignant lymph nodes of histologically proven HNSCC in the pre-therapeutic setting. Results: A total of 13 patients were eligible for analysis. Using elastography, irregular clear hardening in areas in the center of the lymph node could be detected in all cases. Elastographic Q-analysis showed a significantly softer cortex compared to the center and surrounding tissue. The time–intensity curve analysis showed high values for the area under the curve and a short time-to-peak (fast wash-in) in all cases compared to the surrounding tissue. A parametric evaluation of contrast enhanced the ultrasound in the early arterial phase and showed an irregular enhancement from the margin in almost all investigated lymph nodes. These results show that the implementation of comprehensive, multiparametric ultrasound is suitable for classifying suspected lymph node metastasis more precisely than conventional ultrasound alone in the pre-therapeutic setting of HNSCC. Thus, these parameters may be used for improvements in the re-staging after chemoradiation or neoadjuvant therapy monitoring, respectively.
Network Analysis for Better Understanding the Complex Psycho-Biological Mechanisms behind Fibromyalgia Syndrome
Authors: Valera-Calero Juan Antonio , Arendt-Nielsen Lars , Cigarán-Méndez Margarita , Fernández-de-las-Peñas César , Varol Umut
Published: August 2022

The aim of this study was to assess potential associations between sensory, cognitive, health-related, and physical variables in women with fibromyalgia syndrome (FMS) using a network analysis for better understanding the complexity of psycho-biological mechanisms. Demographic, clinical, pressure pain threshold (PPT), health-related, physical, and psychological/cognitive variables were collected in 126 women with FMS. A network analysis was conducted to quantify the adjusted correlations between the modeled variables and to assess the centrality indices (i.e., the degree of connection with other symptoms in the network and the importance in the system modeled as a network. This model showed several local associations between the variables. Multiple positive correlations between PPTs were observed, being the strongest weight between PPTs over the knee and tibialis anterior (ρ: 0.28). Catastrophism was associated with higher hypervigilance (ρ: 0.23) and lower health-related EuroQol-5D (ρ: −0.24). The most central variables were PPT over the tibialis anterior (the highest strength centrality), hand grip (the highest harmonic centrality) and Time Up and Go (the highest betweenness centrality). This study, applying network analysis to understand the complex mechanisms of women with FMS, supports a model where sensory-related, psychological/cognitive, health-related, and physical variables are connected. Implications of the current findings, e.g., developing treatments targeting these mechanisms, are discussed.
Cardiovascular Magnetic Resonance Imaging in the Early Detection of Cardiotoxicity Induced by Cancer Therapies
Authors: Wei Xiaoting , Lin Ling , Zhang Guizhi , Zhou Xuhui
Published: August 2022

The significant progress in cancer treatment, including chemotherapy, immunotherapy, radiotherapy, and combination therapies, has led to higher long-term survival rates in cancer patients, while the cardiotoxicity caused by cancer treatment has become increasingly prominent. Cardiovascular magnetic resonance (CMR) is a non-invasive comprehensive imaging modality that provides not only anatomical information, but also tissue characteristics and cardiometabolic and energetic assessment, leading to its increased use in the early identification of cardiotoxicity, and is of major importance in improving the survival rate of cancer patients. This review focused on CMR techniques, including myocardial strain analysis, T1 mapping, T2 mapping, and extracellular volume fraction (ECV) calculation in the detection of early myocardial injury induced by cancer therapies. We summarized the existing studies and ongoing clinical trials using CMR for the assessment of subclinical ventricular dysfunction and myocardial changes at the tissue level. The main focus was to explore the potential of clinical and preclinical CMR techniques for continuous non-invasive monitoring of myocardial toxicity associated with cancer therapy.
Multimodal Study of PRPH2 Gene-Related Retinal Phenotypes
Authors: Antonelli Giulio , Parravano Mariacristina , Barbano Lucilla , Costanzo Eliana , Bertelli Matteo , Medori Maria Chiara , Parisi Vincenzo , Ziccardi Lucia
Published: August 2022

PRPH2 gene mutations are frequently found in inherited retinal dystrophies (IRD) and are associated with a wide spectrum of clinical phenotypes. We studied 28 subjects affected by IRD carrying pathogenic PRPH2 mutations, belonging to 11 unrelated families. Functional tests (best-corrected visual acuity measurement, chromatic test, visual field, full-field, 30 Hz flicker, and multifocal electroretinogram), morphological retino-choroidal imaging (optical coherence tomography, optical coherence tomography angiography, and fundus autofluorescence), and clinical data were collected and analyzed. Common primary complaints, with onset in their 40s, were visual acuity reduction and abnormal dark adaptation. Visual acuity ranged from light perception to 20/20 Snellen. Visual field peripheral constriction and central scotoma were found. Chromatic sense was reduced in one third of patients. Electrophysiological tests were abnormal in most of the patients. Choroidal neovascular lesions were detected in five patients. Three novel PRPH2 variants were found in four different families. Based on the present multimodal study, we identified seven distinct PRPH2 phenotypes in 11 unrelated families carrying either different mutations or the same mutation, both within the same family or among them. Fundus autofluorescence modality turned out to be the most adequate imaging method for early recognition of this dystrophy, and the optical coherence tomography angiography was highly informative to promptly detect choroidal neovascularization, even in the presence of the extensive chorioretinal atrophy phenotype.
The Role of Lung Ultrasound in SARS-CoV-19 Pneumonia Management
Authors: Lugarà Marina , Tamburrini Stefania , Coppola Maria Gabriella , Oliva Gabriella , Fiorini Valeria , Catalano Marco , Carbone Roberto , Saturnino Pietro Paolo , Rosano Nicola , Pesce Antonella , Galiero Raffaele , Ferrara Roberta , Iannuzzi Michele , Vincenzo D’Agostino , Negro Alberto , Somma Francesco , Fasano Fabrizio , Perrella Alessandro , Vitiello Giuseppe , Sasso Ferdinando Carlo , Soldati Gino , Rinaldi Luca
Published: August 2022

Purpose: We aimed to assess the role of lung ultrasound (LUS) in the diagnosis and prognosis of SARS-CoV-2 pneumonia, by comparing it with High Resolution Computed Tomography (HRCT). Patients and methods: All consecutive patients with laboratory-confirmed SARS-CoV-2 infection and hospitalized in COVID Centers were enrolled. LUS and HRCT were carried out on all patients by expert operators within 48–72 h of admission. A four-level scoring system computed in 12 regions of the chest was used to categorize the ultrasound imaging, from 0 (absence of visible alterations with ultrasound) to 3 (large consolidation and cobbled pleural line). Likewise, a semi-quantitative scoring system was used for HRCT to estimate pulmonary involvement, from 0 (no involvement) to 5 (>75% involvement for each lobe). The total CT score was the sum of the individual lobar scores and ranged from 0 to 25. LUS scans were evaluated according to a dedicated scoring system. CT scans were assessed for typical findings of COVID-19 pneumonia (bilateral, multi-lobar lung infiltration, posterior peripheral ground glass opacities). Oxygen requirement and mortality were also recorded. Results: Ninety-nine patients were included in the study (male 68.7%, median age 71). 40.4% of patients required a Venturi mask and 25.3% required non-invasive ventilation (C-PAP/Bi-level). The overall mortality rate was 21.2% (median hospitalization 30 days). The median ultrasound thoracic score was 28 (IQR 20–36). For the CT evaluation, the mean score was 12.63 (SD 5.72), with most of the patients having LUS scores of 2 (59.6%). The bivariate correlation analysis displayed statistically significant and high positive correlations between both the CT and composite LUS scores and ventilation, lactates, COVID-19 phenotype, tachycardia, dyspnea, and mortality. Moreover, the most relevant and clinically important inverse proportionality in terms of P/F, i.e., a decrease in P/F levels, was indicative of higher LUS/CT scores. Inverse proportionality P/F levels and LUS and TC scores were evaluated by univariate analysis, with a P/F–TC score correlation coefficient of −0.762, p < 0.001, and a P/F–LUS score correlation coefficient of −0.689, p < 0.001. Conclusions: LUS and HRCT show a synergistic role in the diagnosis and disease severity evaluation of COVID-19.
Comparison of a Blood Self-Collection System with Routine Phlebotomy for SARS-CoV-2 Antibody Testing
Authors: Wixted Douglas , Neighbors Coralei E. , Pieper Carl F. , Wu Angie , Kingsbury Carla , Register Heidi , Petzold Elizabeth , Newby L. Kristin , Woods Christopher W.
Published: August 2022

The Coronavirus Disease 2019 (COVID-19) pandemic forced researchers to reconsider in-person assessments due to transmission risk. We conducted a pilot study to evaluate the feasibility of using the Tasso-SST (Tasso, Inc, Seattle, Washington) device for blood self-collection for use in SARS-CoV-2 antibody testing in an ongoing COVID-19 prevalence and immunity research study. 100 participants were recruited between January and March 2021 from a previously identified sub-cohort of the Cabarrus County COVID-19 Prevalence and Immunity (C3PI) Study who were under-going bimonthly COVID-19 antibody testing. Participants were given a Tasso-SST kit and asked to self-collect blood during a scheduled visit where trained laboratory personnel performed routine phlebotomy. All participants completed an after-visit survey about their experience. Overall, 70.0% of participants were able to collect an adequate sample for testing using the device. Among those with an adequate sample, there was a high concordance in results between the Tasso-SST and phlebotomy blood collection methods (Cohen’s kappa coefficient = 0.88, Interclass correlation coefficient 0.98 [0.97, 0.99], p < 0.0001). The device received a high-level (90.0%) of acceptance among all participants. Overall, the Tasso-SST could prove to be a valuable tool for seroprevalence testing. However, future studies in larger, diverse populations over longer periods may provide a better understanding of device usability and acceptance among older participants and those with comorbidities in various use scenarios.
3D AQI Mapping Data Assessment of Low-Altitude Drone Real-Time Air Pollution Monitoring
Authors: Duangsuwan Sarun , Prapruetdee Phoowadon , Subongkod Mallika , Klubsuwan Katanyoo
Published: August 2022

Air pollution primarily originates from substances that are directly emitted from natural or anthropogenic processes, such as carbon monoxide (CO) gas emitted in vehicle exhaust or sulfur dioxide (SO2) released from factories. However, a major air pollution problem is particulate matter (PM), which is an adverse effect of wildfires and open burning. Application tools for air pollution monitoring in risk areas using real-time monitoring with drones have emerged. A new air quality index (AQI) for monitoring and display, such as three-dimensional (3D) mapping based on data assessment, is essential for timely environmental surveying. The objective of this paper is to present a 3D AQI mapping data assessment using a hybrid model based on a machine-learning method for drone real-time air pollution monitoring (Dr-TAPM). Dr-TAPM was designed by equipping drones with multi-environmental sensors for carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), particulate matter (PM2.5,10), and sulfur dioxide (SO2), with data pre- and post-processing with the hybrid model. The hybrid model for data assessment was proposed using backpropagation neural network (BPNN) and convolutional neural network (CNN) algorithms. Experimentally, we considered a case study detecting smoke emissions from an open burning scenario. As a result, PM2.5,10 and CO were detected as air pollutants from open burning. 3D AQI map locations were shown and the validation learning rates were apparent, as the accuracy of predicted AQI data assessment was 98%.
Ozonized Oleic Acid as a New Viticultural Treatment? Study of the Effect of LIQUENSO® Oxygenate on the Carpoplane Microbial Community and Wine Microorganisms Combining Metabarcoding and In Vitro Assays
Authors: Stahl Lea Franziska , Edo Manon , Nonnenmacher Timon , Reif Daniela , Rex Friederike , Wegmann-Herr Pascal , Kortekamp Andreas , Fischer-Schuch Jochen , Thines Eckhard , Scharfenberger-Schmeer Maren
Published: August 2022

In this study, an amplicon metagenomic approach was used to determine the effect of repeated treatments with ozonized oleic acid on the microbial community of grapevine carpoplane. Differences in community composition of treated vineyards were compared to non-treated and conventionally treated samples regarding the prokaryotic and eukaryotic microbiome at two developmental stages (BBCH 83, BBCH 87). The results showed effects both on occurrence and on abundance of microorganisms and the community assembly. Wine-relevant genera such as Acetobacter and members of the former genus Lactobacillus could be identified as part of the natural microbiota. The impact of the new viticultural treatment on these organisms was assessed in liquid culture-based microtiter assays. Therefore, we investigated an array of two acetic acid bacteria (AAB), four lactic acid bacteria (LAB) and nine saccharomyces and non-saccharomyces yeasts. Brettanomyces bruxellensis, Saccharomyces cerevisiae, Pediococcus sp. and Acetobacter aceti revealed the highest sensitivities against ozonized oleic acid (LIQUENSO® Oxygenat). Culture growth of these organisms was significantly reduced at an ozonide concentration of 0.25% (v/v), which corresponded to a quarter of the concentration used in the vineyard. The metabarcoding approach in combination with complementary in vitro assays allow new insights into treatment effects on the community and species scale.
Digital Financial Inclusion, Digital Financial Services Tax and Financial Inclusion in the Fourth Industrial Revolution Era in Africa
Authors: Mpofu Favourate Y. , Mhlanga David
Published: August 2022

The digital economy has risen dramatically in the global environment, and many developing countries, including African countries, have seen a spike in digital activity over recent years. The digital economy’s growth has resulted in an increase in digital financial services (DFS) in Africa and other developing regions. Since many African countries are under pressure to raise domestic revenue, taxing the digital economy has become a viable option. As a result, this study attempted to respond to the following questions: first, what is the link between DFS growth and digital inclusion in African countries? Second, what justifies the imposition of DFS taxes in Africa? Third, what are the potential consequences of DFS taxes in African countries? Using secondary data from the literature review and document analysis, a systematic technique for assessing or evaluating printed and electronic documents, and computer-based and internet-transmitted material, the study discovered that digital financial inclusion is driving financial inclusion on the African continent. The study also found that, despite several negative consequences associated with the growth of the digital economy, most African economic activities are informal and are being aided by various digital financial services. Therefore, it is equally crucial that when adopting digital finance taxes, care is taken to avoid excluding low-income earners from the financial sector and to take note of the usage, affordability, and distortive implications of taxation.
Is EU Fiscal Governance Effective? A Case Study for the Period 1999–2019
Authors: Liargovas Panagiotis , Pilichos Vasilis
Published: August 2022

This paper examines the factors that influence the effectiveness of fiscal governance in the EU through a panel of 19 Eurozone countries for the period 1999–2019 using an OLS method. The results show the positive effects of economic growth, inflation and the change in the general government balance on the fiscal forecast error. Furthermore, the fiscal forecast error is negatively affected by the level of public debt and by elections. Fiscal transparency is integrated into the analysis through independent financial institutions, which positively influence the general government balance forecast error. Finally, Economic Adjustment Programs have a positive effect on the fiscal forecast error, thus improving the efficiency of fiscal governance. This paper suggests that independent budgetary institutions, such as fiscal councils, and the delegation of further responsibilities to them increase countries’ sustainability of public finances.
To Touch or Not to Touch: Evaluating Student Laboratory Outcomes of Hands-On versus Visual Examination of Prosected Cadavers
Authors: Dennis Jennifer F. , Creamer Bradley A.
Published: August 2022

We utilized the physical manipulation of cadaveric donors by students to evaluate learning outcomes in two undergraduate anatomy courses. Students (n = 176) were assigned as those whom ‘manipulated’ (n = 100) or ‘did not manipulate’ (n = 76) donors, and data were compared to performance on laboratory assessments. A Fisher’s exact test was conducted within individual and combined course populations to assess if the relationship between physical touch and receiving a passing grade is more than expected by chance. BIO 201 ‘manipulating’ students received a ‘C’ or better (n = 58) vs. 6 D, F, or Withdraw grades (DFW) on lab practicals as compared to those who did not manipulate (n = 33 DFW vs. 18 C or better) (p < 0.0001). BIO 221 ‘manipulating’ students (n = 34) received a ‘C’ or better vs. DFW grades (n = 2), while ‘non-manipulating’ students were able to earn outcomes of ‘C’ or better (n = 13 vs. 12 DFW). However, students involved in donor manipulation were more likely to receive a ‘C or better’ (p = 0.0002). Analysis of the data as a single cohort indicates non-manipulating students are 7.24 times more likely to earn a ‘DFW’ on lab practicals, demonstrating that students are better prepared for laboratory assessments when encouraged to manipulate anatomical structures.
Advancing Engineering Students’ Technical Writing Skills by Implementing Team-Based Learning Instructional Modules in an Existing Laboratory Curriculum
Authors: Wu Shenghua , Zha Shenghua , Estis Julie , Li Xiaojun
Published: August 2022

Technical writing is a crucial skill for engineering students’ effective written communication, which shapes and affects their professional careers. This study designs team-based learning (TBL) instructional modules that are integrated into an existing engineering laboratory curriculum and evaluates their feasibility and effectiveness in improving engineering students’ technical writing skills. The data were collected from structured survey questions distributed to student participants and from the instructor’s independent evaluation of students’ lab reports. The effect of assigning a team leader on students’ writing performances was also evaluated. Our results indicated that students who have received TBL modules perceive a greater improvement in their technical writing skills than those who did not. A strong correlation was found between students’ perception and the instructor’s evaluation of the students’ technical writing skills. Assigning a team leader in TBL instructional modules has not played a significant role in improving engineering students’ technical writing skills. The same happens with gender. Overall, utilization of TBL instructional modules can advance engineering students’ technical writing skills.
Comparative Study of Corrosion Inhibition Efficacy of Alkaloid Extract of Artemesia vulgaris and Solanum tuberosum in Mild Steel Samples in 1 M Sulphuric Acid
Authors: Parajuli Davilal , Sharma Srijana , Oli Hari , Bohara Dilip , Bhattarai Deval , Tiwari Arjun , Yadav Amar
Published: August 2022

Two different types of alkaloids are successfully extracted from two plants Artemisia vulgaris (AV) and Solanum tuberosum (ST) in the laboratory and used as corrosion inhibitors for mild steel samples. The corrosion inhibition potential of these alkaloids is determined by weight loss and potentiodynamic polarization measurement methods. Based on the weight loss measurement study of a sample immersed for 6 h in 1000 ppm inhibitor solution of AV and ST alkaloids, the corrosion inhibition efficiency is found to be 92.58% and 90.79%, respectively. The potentiodynamic polarization measurement shows 88.06% and 83.22% corrosion inhibition efficiency for AV and ST alkaloids, respectively, for the sample immersed for 1 h in 1000 ppm inhibitor solution. These promising efficiency and suitable immersion time effect can lead to the development of good green inhibitors.
Clustering-Based Decision Tree for Vehicle Routing Spatio-Temporal Selection
Authors: Liu Yixiao , Zhang Lei , Zhou Yixuan , Xu Qin , Fu Wen , Shen Tao
Published: August 2022

The algorithm of the clustering-based decision tree, which is a methodology of multimodal fusion, has made many achievements in many fields. However, it is not common in the field of transportation, especially in the application of automobile navigation. Meanwhile, the concept of Spatio-temporal data is now widely used. Therefore, we proposed a vehicle routing Spatio-temporal selection system based on a clustering-based decision tree. By screening and clustering Spatio-temporal data, which is a collection of individual point data based on historical driving data, we can identify the routes and many other features. Through the decision tree modeling of the state information of Spatio-temporal data, which includes the features of the historical data and route selection, we can obtain an optimal result, that is, the route selection made by the system. Moreover, all the above calculations and operations are done on the edge, which is different from the vast majority of current cloud computing vehicle navigation. We have also experimented with our system using real vehicle data. The experiments show that it can output path decision results for a given situation, which takes little time and is the same as the approximated case of networked navigation. The experiments yielded satisfactory results. Our system could save a lot of cloud computing power, which might change the current navigation systems.
An Area-Optimized and Power-Efficient CBC-PRESENT and HMAC-PHOTON
Authors: Ngo Chi Trung , Eshraghian Jason K. , Hong Jong-Phil
Published: August 2022

This paper introduces an area-optimized and power-efficient implementation of the Cipher Block Chaining (CBC) mode for an ultra-lightweight block cipher, PRESENT, and the Keyed-Hash Message Authentication Code (HMAC)-expanded PHOTON by using a feedback path for a single block in the scheme. The proposed scheme is designed, taped out, and integrated as a System-on-a-Chip (SoC) in a 65-nm CMOS process. An experimental analysis and comparison between a conventional implementation of CBC-PRESENT/HMAC-PHOTON with the proposed feedback basis is performed. The proposed CBC-PRESENT/HMAC-PHOTON has 128-bit plaintext/text and a 128-bit secret key, which have a gate count of 5683/20,698 and low power consumption of 1.03/2.62 mW with a throughput of 182.9/14.9 Mbps at the maximum clock frequency of 100 MHz, respectively. The overall improvement in area and power dissipation is 13/50.34% and 14.87/75.28% when compared to a conventional design.
ASPDC: Accelerated SPDC Regularized Empirical Risk Minimization for Ill-Conditioned Problems in Large-Scale Machine Learning
Authors: Liang Haobang , Cai Hao , Wu Hejun , Shang Fanhua , Cheng James , Li Xiying
Published: August 2022

This paper aims to improve the response speed of SPDC (stochastic primal–dual coordinate ascent) in large-scale machine learning, as the complexity of per-iteration of SPDC is not satisfactory. We propose an accelerated stochastic primal–dual coordinate ascent called ASPDC and its further accelerated variant, ASPDC-i. Our proposed ASPDC methods achieve a good balance between low per-iteration computation complexity and fast convergence speed, even when the condition number becomes very large. The large condition number causes ill-conditioned problems, which usually requires many more iterations before convergence and longer per-iteration times in data training for machine learning. We performed experiments on various machine learning problems. The experimental results demonstrate that ASPDC and ASPDC-i converge faster than their counterparts, and enjoy low per-iteration complexity as well.
A Systematic Review of Machine-Vision-Based Leather Surface Defect Inspection
Authors: Chen Zhiqiang , Deng Jiehang , Zhu Qiuqin , Wang Hailun , Chen Yi
Published: August 2022

Machine-vision-based surface defect inspection is one of the key technologies to realize intelligent manufacturing. This paper provides a systematic review on leather surface defect inspections based on machine vision. Leather products are regarded as the most traded products all over the world. Automatic detection, location, and recognition of leather surface defects are very important for the intelligent manufacturing of leather products, and are challenging but noteworthy tasks. This work investigates a large amount of literature related to leather surface defect inspection. In addition, we also investigate and evaluate the performance of some edge detectors and threshold detectors for leather defect detection, and the identification accuracy of the classical machine learning method SVM for leather surface defect identification. A detailed and methodical review of leather surface defect inspection with image analysis and machine learning is presented. Main challenges and future development trends are discussed for leather surface defect inspection, which can be used as a source of guidelines for designing and developing new solutions in this field.
Utilizing Spatio Temporal Gait Pattern and Quadratic SVM for Gait Recognition
Authors: Masood Hajra , Farooq Humera
Published: August 2022

This study aimed to develop a vision-based gait recognition system for person identification. Gait is the soft biometric trait recognizable from low-resolution surveillance videos, where the face and other hard biometrics are not even extractable. The gait is a cycle pattern of human body locomotion that consists of two sequential phases: swing and stance. The gait features of the complete gait cycle, referred to as gait signature, can be used for person identification. The proposed work utilizes gait dynamics for gait feature extraction. For this purpose, the spatio temporal power spectral gait features are utilized for gait dynamics captured through sub-pixel motion estimation, and they are less affected by the subject’s appearance. The spatio temporal power spectral gait features are utilized for a quadratic support vector machine classifier for gait recognition aiming for person identification. Spatio temporal power spectral preserves the spatiotemporal gait features and is adaptable for a quadratic support vector machine classifier-based gait recognition across different views and appearances. We have evaluated the gait features and support vector machine classifier-based gait recognition on a locally collected gait dataset that captures the effect of view variance in high scene depth videos. The proposed gait recognition technique achieves significant accuracy across all appearances and views.
MobileUNetV3—A Combined UNet and MobileNetV3 Architecture for Spinal Cord Gray Matter Segmentation
Authors: Alsenan Alhanouf , Ben Youssef Belgacem , Alhichri Haikel
Published: August 2022

The inspection of gray matter (GM) tissue of the human spinal cord is a valuable tool for the diagnosis of a wide range of neurological disorders. Thus, the detection and segmentation of GM regions in magnetic resonance images (MRIs) is an important task when studying the spinal cord and its related medical conditions. This work proposes a new method for the segmentation of GM tissue in spinal cord MRIs based on deep convolutional neural network (CNN) techniques. Our proposed method, called MobileUNetV3, has a UNet-like architecture, with the MobileNetV3 model being used as a pre-trained encoder. MobileNetV3 is light-weight and yields high accuracy compared with many other CNN architectures of similar size. It is composed of a series of blocks, which produce feature maps optimized using residual connections and squeeze-and-excitation modules. We carefully added a set of upsampling layers and skip connections to MobileNetV3 in order to build an effective UNet-like model for image segmentation. To illustrate the capabilities of the proposed method, we tested it on the spinal cord gray matter segmentation challenge dataset and compared it to a number of recent state-of-the-art methods. We obtained results that outperformed seven methods with respect to five evaluation metrics comprising the dice similarity coefficient (0.87), Jaccard index (0.78), sensitivity (87.20%), specificity (99.90%), and precision (87.96%). Based on these highly competitive results, MobileUNetV3 is an effective deep-learning model for the segmentation of GM MRIs in the spinal cord.
Toward Understanding Most of the Context in Document-Level Neural Machine Translation
Authors: Choi Gyu-Hyeon , Shin Jong-Hun , Lee Yo-Han , Kim Young-Kil
Published: August 2022

Considerable research has been conducted to obtain translations that reflect contextual information in documents and simultaneous interpretations. Most of the existing studies use concatenation data which merge previous and current sentences for training translation models. Although this corpus improves the performance of the model, ignoring the contextual correlation between the sentences can disturb translation performance. In this study, we introduce a simple and effective method to capture the contextual correlation of the sentence at the document level of the current sentence, thereby learning an effective contextual representation. In addition, the proposed model structure is applied to a separate residual connection network to minimize the loss of the beneficial influence of incorporating the context. The experimental results show that our methods improve the translation performance in comparison with the state-of-the-art baseline of the Transformer in various translation tasks and two benchmark machine translation tasks.
Multi-Agent Deep Reinforcement Learning-Based Partial Task Offloading and Resource Allocation in Edge Computing Environment
Authors: Ke Hongchang , Wang Hui , Sun Hongbin
Published: August 2022

In the dense data communication environment of 5G wireless networks, with the dramatic increase in the amount of request computation tasks generated by intelligent wireless mobile nodes, its computation ability cannot meet the requirements of low latency and high reliability. Mobile edge computing (MEC) can utilize its servers with mighty computation power and closer to tackle the computation tasks offloaded by the wireless node (WN). The physical location of the MEC server is closer to WN, thereby meeting the requirements of low latency and high reliability. In this paper, we implement an MEC framework with multiple WNs and multiple MEC servers, which consider the randomness and divisibility of arrival request tasks from WN, the time-varying channel state between WN and MEC server, and different priorities of tasks. In the proposed MEC system, we present a decentralized multi-agent deep reinforcement learning-based partial task offloading and resource allocation algorithm (DeMADRL) to minimize the long-term weighted cost including delay cost and bandwidth cost. DeMADRL is a model-free scheme based on Double Deep Q-Learning (DDQN) and can obtain the optimal computation offloading and bandwidth allocation decision-making policy by training the neural networks. The comprehensive simulation results show that the proposed DeMADRL optimization scheme has a nice convergence and outperforms the other three baseline algorithms.
A Hierarchical Random Graph Efficient Sampling Algorithm Based on Improved MCMC Algorithm
Authors: Tie Zhixin , Zhu Dingkai , Hong Shunhe , Xu Hui
Published: August 2022

A hierarchical random graph (HRG) model combined with a maximum likelihood approach and a Markov Chain Monte Carlo algorithm can not only be used to quantitatively describe the hierarchical organization of many real networks, but also can predict missing connections in partly known networks with high accuracy. However, the computational cost is very large when hierarchical random graphs are sampled by the Markov Chain Monte Carlo algorithm (MCMC), so that the hierarchical random graphs, which can describe the characteristics of network structure, cannot be found in a reasonable time range. This seriously limits the practicability of the model. In order to overcome this defect, an improved MCMC algorithm called two-state transitions MCMC (TST-MCMC) for efficiently sampling hierarchical random graphs is proposed in this paper. On the Markov chain composed of all possible hierarchical random graphs, TST-MCMC can generate two candidate state variables during state transition and introduce a competition mechanism to filter out the worse of the two candidate state variables. In addition, the detailed balance of Markov chain can be ensured by using Metropolis–Hastings rule. By using this method, not only can the convergence speed of Markov chain be improved, but the convergence interval of Markov chain can be narrowed as well. Three example networks are employed to verify the performance of the proposed algorithm. Experimental results show that our algorithm is more feasible and more effective than the compared schemes.
High-Level Design Optimizations for Implementing Data Stream Sketch Frequency Estimators on FPGAs
Authors: Ebrahim Ali
Published: August 2022

This paper presents simple yet effective optimizations for implementing data stream frequency estimation sketch kernels using High-Level Synthesis (HLS). The paper addresses design issues common to sketches utilizing large portions of the embedded RAM resources in a Field Programmable Gate Array (FPGA). First, a solution based on Load-Store Queue (LSQ) architecture is proposed for resolving the memory dependencies associated with the hash tables in a frequency estimation sketch. Second, performance fine-tuning through high-level pragmas is explored to achieve the best possible throughput. Finally, a technique based on pre-processing the data stream in a small cache memory prior to updating the sketch is evaluated to reduce the dynamic power consumption. Using an Intel HLS compiler, a proposed optimized hardware version of the popular Count-Min sketch utilizing 80% of the embedded RAM in an Intel Arria 10 FPGA, achieved more than 3x the throughput of an unoptimized baseline implementation. Furthermore, the sketch update rate is significantly reduced when the input stream is skewed. This, in turn, minimizes the effect of high throughput on dynamic power consumption. Compared to FPGA sketches in the published literature, the presented sketch is the most well-rounded sketch in terms of features and versatility. In terms of throughput, the presented sketch is on a par with the fastest sketches fine-tuned at the Register Transfer Level (RTL).
Recent Progress in Fabrication and Physical Properties of 2D TMDC-Based Multilayered Vertical Heterostructures
Authors: Lv Qiuran , Chen Fei , Xia Yuan , Su Weitao
Published: August 2022

Two-dimensional (2D) vertical heterojunctions (HSs), which are usually fabricated by vertically stacking two layers of transition metal dichalcogenide (TMDC), have been intensively researched during the past years. However, it is still an enormous challenge to achieve controllable preparation of the TMDC trilayer or multilayered van der Waals (vdWs) HSs, which have important effects on physical properties and device performance. In this review, we will introduce fundamental features and various fabrication methods of diverse TMDC-based multilayered vdWs HSs. This review focuses on four fabrication methods of TMDC-based multilayered vdWs HSs, such as exfoliation, chemical vapor deposition (CVD), metal-organic chemical vapor deposition (MOCVD), and pulsed laser deposition (PLD). The latest progress in vdWs HS-related novel physical phenomena are summarized, including interlayer excitons, long photocarrier lifetimes, upconversion photoluminescence, and improved photoelectrochemical catalysis. At last, current challenges and prospects in this research field are provided.
Investigation of a Novel Consequent-Pole Flux-Intensifying Memory Machine
Authors: Tu Rui , Yang Hui , Lin Heyun , Zhan Hanlin , Wu Di , Yu Minghu , Chen Liang , Chen Wenjie
Published: August 2022

This paper mainly focuses on the investigation and analysis of a novel consequent-pole flux-intensifying memory machine (CP-FIMM). The proposed CP-FIMM exhibits the advantages of a satisfactory flux-regulation range, reduction of the required magnetizing current magnitude, as well as similar torque with much less PM utilization compared to its conventional counterpart. By designing the q-axis flux barriers, the flux-intensifying structure can be realized to enhance the demagnetization withstand capability of the CP-FIMM. The machine topology and operating principle are described. Moreover, the equivalent magnetic circuit model is developed to highlight the performance improvement of the proposed CP-FIMM. Finally, the electromagnetic performance of the proposed CP-FIMM is compared with that of a benchmark conventional FIMM by 2-D and 3-D finite element analysis.
The Characteristics of the After-Combustion in a Commercial CFBC Boiler Using the Solid Waste Fuel
Authors: Shin Jong-Seon , Shun Dowon , Cho Churl-Hee , Choi Yujin , Bae Dal-Hee
Published: August 2022

A CFBC (Circulating Fluidized Bed Combustor) boiler for combusted SRF (Solid Refused Fuel) is designed for solid waste combustion and power generation. The boiler consumes about 200 tons/day of SRF and generates 60 ton/h of steam or 10 MWe in electricity. The boiler is designed to burn pelletized waste fuel made of municipal solid waste collected from a town with a population of 400,000. Heat and mass balance calculations over the combustor and at each boiler section were performed and compared between the designed and measured data to analyze the boiler’s performance. After-combustion, the most significant phenomenon in low-density waste-derived fuel combustion in a CFBC boiler was monitored. The heat and mass balance were the most appropriate tools to analyze the boiler performance. The flow rate of spray water at the de-superheater was a reliable indicator to quantify the after-combustion. The design modification of the boiler unit for after-combustion control in the existing boiler was based on the quantification of spray water. The load distribution of the de-superheater decreases from 1.76% to 0.87% in 89% MCR before the installation of the evaporator and 82* % MCR load distribution of each boiler part after installation. The result was effective for the control of after-combustion in the existing boiler.
An Early Fault Diagnosis Method for Ball Bearings of Electric Vehicles Based on Integrated Subband Averaging and Enhanced Kurtogram Method
Authors: Kim Woojoong , Lee Munsu , Park Sang-Jun , Jang Sung-Hyun , Kang Byeong-Su , Kim Namjin , Hong Young-Sun
Published: August 2022

Faults of mechanical transmission systems generally occur in the rotating bearing part at high speeds, which causes problems such as performance degradation of transmission, generation of noise or vibration, and additional damage to connected adjacent systems. In this way, faults cause adverse effects to the entire system, such as deterioration and damage. The early detection and correction of bearing problems allows for improved system safety and the reduction of maintenance costs, resulting in efficient system operation. As a result, a variety of methods have been developed by many researchers in order to diagnose bearing mechanical defects, and one of the most representative methods is applying various signal processing techniques to vibration data. Wavelet packet transform (WPT) and kurtogram were used in this study to identify the frequency band that contained the fault component, and the enhanced kurtogram technique was used to analyze the fault. A technique for minimizing the effect of intermittent abnormal peak components caused by noise and external influences has been presented using sub-band averaging to detect early fault frequency component detection and fault development. Using the technique proposed in this study, the state of the bearing based on the degree of fault was evaluated quantitatively, and it was demonstrated experimentally that the bearing fault frequency could be detected at an early stage by the filtered data. In a situation where it is difficult to accept all the detailed design specifications and operating conditions of the complex mechanical systems at industrial sites, determining the degree of fault with simple time-series data and detecting fault components at an early stage is a practical analysis technique for fault diagnosis in the industrial field using various rotating bodies.
Energy and Exergy-Based Screening of Various Refrigerants, Hydrocarbons and Siloxanes for the Optimization of Biomass Boiler–Organic Rankine Cycle (BB–ORC) Heat and Power Cogeneration Plants
Authors: Douvartzides Savvas , Tsiolikas Aristidis , Charisiou Nikolaos , Souliotis Manolis , Karayannis Vayos , Taousanidis Nikolaos
Published: August 2022

The cogeneration of power and heat was investigated for Biomass Boiler–Organic Rankine Cycle (BB–ORC) plants with the characteristics of typical units, such as the 1 MWel Turboden ORC 10 CHP. The thermodynamic analysis of the ORC unit was undertaken considering forty-two (42) dry and isentropic candidate pure working fluids. Only subcritical Rankine cycles were considered, and the pinch point temperature differences for the evaporation and condensation heat exchangers were kept constant at 10 °C in all cases. The study provides an original and unique screening of almost all pure working fluids that are considered appropriate in the literature under the same operation and optimization conditions and compiles them into a single reference. In its conclusions, the study provides useful fluid selection and design guidelines, which may be easily followed depending on the optimization objective of the ORC designer or operator. In general, hydrocarbons are found to lie in the optimum middle range of the fluid spectrum, between the siloxanes that maximize the production of mechanical power and the refrigerants that maximize the production of heat. Specific hydrocarbon fluids, such as cyclopentane, heptane, hexane, benzene, and toluene, are found as rational options for maximum mechanical efficiency when operating with practically feasible condensation pressures between 10 and 200 kPa. At condensation pressures below 10 kPa, ethylbenzene, o-xylene, m-xylene, p-xylene, and nonane are also found to be feasible options. Finally, cyclopentane, hexane, and MM (hexamethyldisiloxane) are selected as the most appropriate options for cogeneration plants aiming simultaneously at high mechanical power and maximum temperature water production.
Energy Landscape and Renewable Energy Resources in Nigeria: A Review
Authors: Eweka Ebuwa Elisabeth , Lopez-Arroyo Enrique , Medupin Christian Oluwaremilekun , Oladipo Abiola , Campos Luiza Cintra
Published: August 2022

Renewable energy resource utilisation is becoming more prevalent in society and can be a sustainable method to meet rising energy demand and consumption. The International Renewable Energy Agency reports that presently, approximately 33% of total electricity output around the world is from renewable energy sources. However, in Nigeria, the World Bank reported this figure as 18.2% in 2015. It is important to understand how Nigeria can raise this average as the world moves towards less use of fossil fuels. Therefore, the aim of this work was to provide a review of the established literature, which can then be used to support the development of renewable energy systems in Nigeria. The methodology employed for the study of the literature involved a systematic review method, including identification, screening, eligibility and inclusion criterium steps. Understanding the national energy landscape is essential to support potential projects, and consequently, energy supply, demand and consumption in Nigeria are observed. The review examined renewable energy sources in Nigeria with a focus on solar and biomass/bioenergy. The levelised cost of electricity from renewable technologies was analysed, and the paper identified and evaluated past projects centered on solar and biomass, presenting three main case studies. The results indicate that solar and biomass have the most potential for energy generation in Nigeria, with 4–6.5 kWh/m2 of solar irradiation available per day and approximately 2.3 × 109 GJ bioenergy potential per year. However, solar PV is currently of most interest in Nigeria due to the lower cost of on-grid solar PV electricity, which can comfortably compete with fossil fuels. Nevertheless, costs still need to be lowered to compete fully. Finally, the findings also indicate that there is an omission in the literature surrounding the existing renewable projects and technologies in Nigeria.
Net-Zero Action Recommendations for Scope 3 Emission Mitigation Using Life Cycle Assessment
Authors: Sadhukhan Jhuma
Published: August 2022

Greenhouse gas emissions anywhere across the value chain cause the global temperature to rise. A responsible net-zero strategy is reducing and removing direct and indirect greenhouse gas emissions. The current net-zero actions aim to offset rather than reduce or remove life cycle greenhouse gas emissions (GHG). Unless the demands/consumptions are reduced, net-zero actions will merely be a burden-shifting practice. Scope 3 emissions are considered in the life cycle assessment (LCA) of goods and services and account for direct and indirect emissions with imported goods and services. Scope 3 emission tariff seems an effective way to shift consumption patterns to carbon-neutral options. This article explores tools and systems for ‘just transition’ using three buckets of scientific questions: (1) Technical: which GHG to remove, when, where, and by what mechanism; (2) Social-Policy: how to share GHG obligations between stakeholders to deliver the UN SDGs; (3) Data: how to create robust, trusted, and transparent data for reporting, accounting, and actions. Building on the analyses, this study recommends thirteen scientific evidence-based net-zero actions.