bluuebunny/crossref_metadata_2025 huggingface dataset:
HCC or Something Else? Frequency of Various Benign and Malignant Etiologies in Cirrhotic Patients with Newly Detected Focal Liver Lesions in Relation to Different Clinical and Sonographic Parameters
Authors: Alhyari Amjad , Görg Christian , Alakhras Raed , Dietrich Christoph Frank , Trenker Corrina , Safai Zadeh Ehsan
Published: August 2022

Background and Aims: To investigate the frequency of different benign and malignant focal liver lesions (FLLs) in relation to clinical and sonographic features among patients with liver cirrhosis (LC) and newly detected FLLs. Methods: This study was a retrospective analysis of 225 cirrhotic patients with newly detected FLLs who underwent hepatic ultrasound (US) examinations at our university hospital from 2011 to 2022. The diagnosis of FLLs was based on histology and/or consensus radiological criteria, in accordance with the current diagnostic guidelines. The FLLs were classified into benign (bFLLs) or malignant (mFLLs) lesions and the latter group was subclassified into HCC and non-HCC mFLLs. The frequency, clinical parameters, and sonographic features of the different groups were examined and compared. Results: Of the 225 FLLs, 154 (68.4%) were mFLLs and 71 (31.6%) bFLLs. HCC was the most frequent subcategory of FLLs (132; 58.7%). There were (22; 9.8%) non-HCC mFLLs with 11 (4.9%) metastases and 11 (4.9%) non-HCC primary liver tumors. Regenerative nodules (RNs) were the most frequent form of bFLLs (25; 11.1%), followed by simple cysts (22; 9.8%) and hemangiomas (14; 6.2%). The other bFLLs (10; 14.1%) were fat deposition/sparing (5), hematomas (2), abscesses (2), and echinococcal cysts (1). The distribution of bFLLs and HCC and non-HCC mFLLs varied significantly according to the clinical scenarios. HCC mFLLs were more frequent in males (p = 0.001), in those with no history of active non-hepatic primary malignant disease (NHPMD) (p < 0.001), in those with a hepatitis B or C etiology of LC (p = 0.002), when located in the right lobe (p = 0.008), and when portal vein thrombosis was present (p = 0.03). Conclusion: In cirrhotic patients with newly detected FLLs, the non-HCC etiology was more frequently diagnosed in lesions that were located in the left lobe, in females, and in patients with a history of active NHPMD. Thus, the lower frequency of HCC in the abovementioned groups demonstrated that a cautious implementation of the current consensus radiological criteria would be required for these groups, particularly in patients with an active NHPMD, given the fact that the consensus criteria were not validated in these populations. A more active diagnostic approach may ultimately be needed for these patients. Large prospective studies are needed to validate these findings.
Image Embeddings Extracted from CNNs Outperform Other Transfer Learning Approaches in Classification of Chest Radiographs
Authors: Gozzi Noemi , Giacomello Edoardo , Sollini Martina , Kirienko Margarita , Ammirabile Angela , Lanzi Pierluca , Loiacono Daniele , Chiti Arturo
Published: August 2022

To identify the best transfer learning approach for the identification of the most frequent abnormalities on chest radiographs (CXRs), we used embeddings extracted from pretrained convolutional neural networks (CNNs). An explainable AI (XAI) model was applied to interpret black-box model predictions and assess its performance. Seven CNNs were trained on CheXpert. Three transfer learning approaches were thereafter applied to a local dataset. The classification results were ensembled using simple and entropy-weighted averaging. We applied Grad-CAM (an XAI model) to produce a saliency map. Grad-CAM maps were compared to manually extracted regions of interest, and the training time was recorded. The best transfer learning model was that which used image embeddings and random forest with simple averaging, with an average AUC of 0.856. Grad-CAM maps showed that the models focused on specific features of each CXR. CNNs pretrained on a large public dataset of medical images can be exploited as feature extractors for tasks of interest. The extracted image embeddings contain relevant information that can be used to train an additional classifier with satisfactory performance on an independent dataset, demonstrating it to be the optimal transfer learning strategy and overcoming the need for large private datasets, extensive computational resources, and long training times.
Older Age and High α-Fetoprotein Predict Higher Risk of Hepatocellular Carcinoma in Chronic Hepatitis-B-Related Cirrhotic Patients Receiving Long-Term Nucleos(t)ide Analogue Therapy
Authors: Liu Yuh-Ying , Lin Chih-Lang , Weng Cheng-Hao , Chang Pei-Hung , Chien Cheng-Hung , Huang Kuang-Chen , Hua Man-Chin , Hu Ching-Chih
Published: August 2022

Background: Nucleos(t)ide analogues (NUCs) were proved to reduce hepatocellular carcinoma (HCC) development in chronic hepatitis B (CHB) patients, but data were limited on their efficacy in cirrhotic CHB patients. Methods: A total of 447 cirrhotic CHB patients treated with tenofovir/entecavir were retrospectively analyzed and divided into HCC (n = 48) and non-HCC (n = 399) groups. The median follow-up period was 62.1 months. Results: A total of 48 patients (10.7%) developed HCC during surveillance. The annual incidence rate of HCC was 2.04 per 100 person-years. The cumulative incidence of HCC was 0.9%, 9.8%, and 22.1% at 1, 5, and 10 years, respectively. Significant predictors for HCC identified using a multiple Cox regression analysis were age ≥50 years (hazard ratio (HR): 2.34) and α-fetoprotein (AFP) ≥8 ng/mL (HR: 2.05). The incidence rate of HCC was 8.67-fold higher in patients with age ≥50 years and AFP ≥8 ng/mL (3.14 per 100 person-years) than those with age <50 years and AFP <8 ng/mL (0.36 per 100 person-years). Conclusions: Cirrhotic CHB patients with age <50 years and AFP <8 ng/mL had the lowest annual incidence of HCC. However, those with age ≥50 years or/and AFP ≥8 ng/mL had a significantly higher risk for HCC development and warrant a careful surveillance schedule.
An Electroanalytical Flexible Biosensor Based on Reduced Graphene Oxide-DNA Hybrids for the Early Detection of Human Papillomavirus-16
Authors: Rawat Reema , Roy Souradeep , Goswami Tapas , Mathur Ashish
Published: August 2022

Human Papilloma Virus 16 (HPV 16) is the well-known causative species responsible for triggering cervical cancer. When left undiagnosed and untreated, this disease leads to life-threatening events among the female populace, especially in developing nations where healthcare resources are already being stretched to their limits. Considering various drawbacks of conventional techniques for diagnosing this highly malignant cancer, it becomes imperative to develop miniaturized biosensing platforms which can aid in early detection of cervical cancer for enhanced patient outcomes. The current study reports on the development of an electrochemical biosensor based on reduced graphene oxide (rGO)/DNA hybrid modified flexible carbon screen-printed electrode (CSPE) for the detection of HPV 16. The carbon-coated SPEs were initially coated with rGO followed by probe DNA (PDNA) immobilization. The nanostructure characterization was performed using UV-Vis spectroscopy, Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy and X-ray diffraction (XRD) techniques. Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) were employed to study the electrochemical characterization of the nano-biohybrid sensor surface. The optimization studies and analytical performance were assessed using differential pulse voltammetry (DPV), eventually exhibiting a limit of detection (LoD) ~2 pM. The developed sensor was found to be selective solely to HPV 16 target DNA and exhibited a shelf life of 1 month. The performance of the developed flexible sensor further exhibited a promising response in spiked serum samples, which validates its application in future point-of-care scenarios.
Chest CT Severity Score and Systemic Inflammatory Biomarkers as Predictors of the Need for Invasive Mechanical Ventilation and of COVID-19 Patients’ Mortality
Authors: Halmaciu Ioana , Arbănași Emil Marian , Kaller Réka , Mureșan Adrian Vasile , Arbănași Eliza Mihaela , Bacalbasa Nicolae , Suciu Bogdan Andrei , Cojocaru Ioana Iulia , Runcan Andreea Ioana , Grosu Florin , Vunvulea Vlad , Russu Eliza
Published: August 2022

Background: Numerous tools, including inflammatory biomarkers and lung injury severity scores, have been evaluated as predictors of disease progression and the requirement for intensive therapy in COVID-19 patients. This study aims to verify the predictive role of inflammatory biomarkers [monocyte to lymphocyte ratio (MLR), neutrophil to lymphocyte ratio (NLR), systemic inflammatory index (SII), Systemic Inflammation Response Index (SIRI), Aggregate Index of Systemic Inflammation (AISI), and interleukin-6 (IL-6)] and the total system score (TSS) in the need for invasive mechanical ventilation (IMV) and mortality in COVID-19 patients. Methods: The present study was designed as an observational, analytical, retrospective cohort study and included all patients over 18 years of age with a diagnosis of COVID-19 pneumonia, confirmed through real time-polymerase chain reaction (RT-PCR) and radiological chest CT findings admitted to County Emergency Clinical Hospital of Targu-Mureș, Romania, and Modular Intensive Care Unit of UMFST “George Emil Palade” of Targu Mures, Romania between January 2021 and December 2021. Results: Non-Survivors patients were associated with higher age (p = 0.01), higher incidence of cardiac disease [atrial fibrillation (AF) p = 0.0008; chronic heart failure (CHF) p = 0.01], chronic kidney disease (CKD; p = 0.02), unvaccinated status (p = 0.001), and higher pulmonary parenchyma involvement (p < 0.0001). Multivariate analysis showed a high baseline value for MLR, NLR, SII, SIRI, AISI, IL-6, and TSS independent predictor of adverse outcomes for all recruited patients. Moreover, the presence of AF, CHF, CKD, and dyslipidemia were independent predictors of mortality. Furthermore, AF and dyslipidemia were independent predictors of IMV need. Conclusions: According to our findings, higher MLR, NLR, SII, SIRI, AISI, IL-6, and TSS values at admission strongly predict IMV requirement and mortality. Moreover, patients above 70 with AF, dyslipidemia, and unvaccinated status highly predicted IMV need and fatality. Likewise, CHF and CKD were independent predictors of increased mortality.
Hybrid Funnel Technique: A Novel Approach for Implant Site Preparation: A Pilot Study
Authors: Canullo Luigi , Iacono Roberta , Pires Godoy Eduardo , Punzo Andrea , Cavicchia Alessio , Gianfreda Francesco , Bollero Patrizio
Published: August 2022

(1) Background: Different techniques and tools have been developed for implant site preparation. In this clinical scenario, Hybrid Funnel Technique (HFT), a novel osteotomy procedure, has been proposed. (2) Aim: The aim of this retrospective observational study was to consider the different responses to compression of the histological bony compartments (cancellus and cortical). HFT involves the use of multiple drills for the cortical layer preparation and of an osteotome for the osteocompaction of the cancellous bone. (3) Materials and Methods: Following computer-supported implant planning and guided surgery, 10 osteotomies with HFT were performed and 10 implants with the same length and diameter were placed in seven healthy and no daily smoking patients. Periapical X-ray and intraoral photographs were performed at baseline and after 12 months of follow-up to evaluate marginal bone level (MBL) changes and aesthetic results obtained from implant prosthetic rehabilitation. (4) Results: At 1 year of follow-up, 100% of the implants were successfully integrated, MBL change mean value was 0.17 mm ± 0.21. No differences in terms of MBL were noted between thin and thick biotypes. Pink esthetic score (PES) and white esthetic score (WES), assessed one year after definitive restoration placement, were 7.5 ± 2.3 and 8.5 ± 1.1, respectively. (5) Conclusions: Based on the findings of this preliminary clinical study, HFT has led to stability of peri-implant tissues and could represent a reliable technique for surgical preparation of the implant site.
Effects of Nb Additions and Heat Treatments on the Microstructure, Hardness and Wear Resistance of CuNiCrSiCoTiNbx High-Entropy Alloys
Authors: Avila-Salgado Denis Ariel , Juárez-Hernández Arturo , Lara Banda María , Bedolla-Jacuinde Arnoldo , Guerra Francisco V.
Published: August 2022

In this research, a set of CuNiCrSiCoTi (H-0Nb), CuNiCrSiCoTiNb0.5 (H-0.5Nb) and CuNiCrSiCoTiNb1 (H-1Nb) high-entropy alloys (HEAs) were melted in a vacuum induction furnace. The effects of Nb additions on the microstructure, hardness, and wear behavior of these HEAs (compared with a CuBe commercial alloy) in the as-cast (AC) condition, and after solution (SHT) and aging (AT) heat treatments, were investigated using X-ray diffraction, optical microscopy, and electron microscopy. A ball-on-disc configuration tribometer was used to study wear behavior. XRD and SEM results showed that an increase in Nb additions and modification by heat treatment (HT) favored the formation of BCC and FCC crystal structures (CS), dendritic regions, and the precipitation of phases that promoted microstructure refinement during solidification. Increases in hardness of HEA systems were recorded after heat treatment and Nb additions. Maximum hardness values were recorded for the H-1Nb alloy with measured increases from 107.53 HRB (AC) to 112.98 HRB, and from 1104 HV to 1230 HV (aged for 60 min). However, the increase in hardness caused by Nb additions did not contribute to wear resistance response. This can be attributed to a high distribution of precipitated phases rich in high-hardness NiSiTi and CrSi. Finally, the H-0Nb alloy exhibited the best wear resistance behavior in the aged condition of 30 min, with a material loss of 0.92 mm3.
Brain Tumor Segmentation Based on Bendlet Transform and Improved Chan-Vese Model
Authors: Meng Kexin , Cattani Piercarlo , Villecco Francesco
Published: August 2022

Automated segmentation of brain tumors is a difficult procedure due to the variability and blurred boundary of the lesions. In this study, we propose an automated model based on Bendlet transform and improved Chan-Vese (CV) model for brain tumor segmentation. Since the Bendlet system is based on the principle of sparse approximation, Bendlet transform is applied to describe the images and map images to the feature space and, thereby, first obtain the feature set. This can help in effectively exploring the mapping relationship between brain lesions and normal tissues, and achieving multi-scale and multi-directional registration. Secondly, the SSIM region detection method is proposed to preliminarily locate the tumor region from three aspects of brightness, structure, and contrast. Finally, the CV model is solved by the Hermite-Shannon-Cosine wavelet homotopy method, and the boundary of the tumor region is more accurately delineated by the wavelet transform coefficient. We randomly selected some cross-sectional images to verify the effectiveness of the proposed algorithm and compared with CV, Ostu, K-FCM, and region growing segmentation methods. The experimental results showed that the proposed algorithm had higher segmentation accuracy and better stability.
Analysis of Energy Loss Characteristics of Vertical Axial Flow Pump Based on Entropy Production Method under Partial Conditions
Authors: Yang Fan , Chang Pengcheng , Cai Yiping , Lin Zhikang , Tang Fangping , Lv Yuting
Published: August 2022

The energy loss of the vertical axial flow pump device increases due to the unstable internal flow, which reduces the efficiency of the pump device and increases its energy consumption of the pump device. The research results of the flow loss characteristics of the total internal conduit are still unclear. Therefore, to show the internal energy loss mechanism of the axial flow pump, this paper used the entropy production method to calculate the energy loss of the total conduit of the pump device to clarify the internal energy loss mechanism of the pump device. The results show that the energy loss of the impeller is the largest under various flow conditions, accounting for more than 40% of the total energy loss of the pump device. The variation trend of the volume average entropy production and the energy loss is similar under various flow coefficients (KQ). The volume average entropy production rate (EPR) and the energy loss decrease first and then increase with the increase of flow, the minimum volume average entropy production is 378,000 W/m3 at KQ = 0.52, and the area average EPR of the impeller increases gradually with the increase of flow. Under various flow coefficient KQ, the energy loss of campaniform inlet conduit is the smallest, accounting for less than 1% of the total energy loss. Its maximum value is 63.58 W. The energy loss of the guide vane and elbow increases with the increase of flow coefficient KQ, and the maximum ratio of energy loss to the total energy loss of the pump device is 29% and 21%, respectively, at small flow condition KQ = 0.38. The energy loss of straight outlet conduit reduces first and then increases with the increase of flow coefficient KQ. When flow coefficient KQ = 0.62, it accounts for 27% of the total energy loss of the pump device, but its area average entropy production rate (EPR) and volume average entropy production rate (EPR) are small. The main entropy production loss in the pump device is dominated by entropy production by turbulent dissipation (EPTD), and the proportion of entropy production by direct dissipation (EPDD) is the smallest.
A Brief Parent-Coaching Package for Tiered Language Interventions
Authors: Zhu Lin , Shepley Collin , Grisham Jennifer , Lane Justin D.
Published: August 2022

This study utilized a brief coaching package to train parents to support their children’s language development in home environments. Two parents of dual language learners were trained to use naturalistic language strategies that ranged in complexity. Parents participated in individual training sessions targeting three strategies: narration, imitation, and environmental arrangement and responding. A multiple baseline design across behaviors replicated across parent–child dyads was used to evaluate the effectiveness of the brief coaching package. Therapeutically accelerating data in a baseline condition affected interpretation of results for one dyad, while the package demonstrated effectiveness for the other dyad. Concomitant increases in children’s use of English language during sessions was also observed. Multi-level models were used to estimate the moderating effect of parent engagement in naturalistic language strategies on children’s vocal initiations. Implications for home-based service providers are presented.
A Modified LZW Algorithm Based on a Character String Parallel Search in Cluster-Based Telemetry Data Compression
Authors: He Yigen , Shi Xuesen , Wang Yongqing
Published: August 2022

The volume of telemetry data is gradually increasing, both because of the increasingly larger number of parameters involved, and the use of higher sampling frequencies. Efficient data compression schemes are therefore needed in space telemetry systems to improve transmission efficiency and reduce the burden of required spacecraft resources, in particular regarding their transmitter power. In our primary study, a D-CLU algorithm was proposed to perform lossless compression for telemetry data, and achieve better performance. However, a limitation of this algorithm is that the compression time may become longer when the clustering head (CH) and outlier (which are compressed by LZW algorithm) numbers increase. To reduce compression delay, this paper proposed a modified character string (MCS) parallel search strategy for LZW algorithm (denoted by MCS-based LZW). The proposed MCS-based LZW algorithm designs coding principle, dictionary update rule and search strategy according to the character string matching results. Example verification and simulation results show that the proposed algorithm can effectively decrease the dictionary search times, and thus reduce the compression time.
Research on Signal Modulation Classification under Low SNR Based on ResNext Network
Authors: Zou Binghang , Yan Hanzhi , Wang Faquan , Zhou Yucheng , Zeng Xiaodong
Published: August 2022

To address the shortcomings of existing methods such as low recognition accuracy and poor anti-interference performance under low signal-to-noise ratios, this paper proposes the RFSE-ResNeXt (Residual-fusion squeeze–excitation aggregated residual for networks, RFSE-ResNeXt) network. In this paper, we improve the residual structure of the network based on the ResNeXt network and then introduce the compressed excitation structure to improve the generalization ability of the network. The improvement of the residual structure of the network leads to a good improvement in the overall recognition accuracy of the network; meanwhile, the compressed excitation structure improves the confusion phenomenon when the network faces complex signals with low signal-to-noise ratios. The experimental results show that the proposed network improves the recognition accuracy by 4% on average at a very low SNR of -10dB and reduces the misclassification of AM-DSB into CPFSK by about 27%.
Slotted Antenna Array with Enhanced Radiation Characteristics for 5G 28 GHz Communications
Authors: Ibrahim Ahmed A. , Zahra Hijab , Dardeer Osama M. , Hussain Niamat , Abbas Syed Muzahir , Abdelghany Mahmoud A.
Published: August 2022

This paper presents a 1 × 4 linear antenna array working at 28 GHz for 5G communication systems. The proposed array employs four rectangular slotted antenna elements fed by a 1 × 4 T-power divider. An artificial magnetic conductor (AMC) layer is placed below the array for increasing the radiation intensity and improving overall array gain. The measured impedance bandwidth of the proposed array with (|S11| < −10 dB) is extended from 25.36 to 26.03 GHz (with a bandwidth of 0.67 GHz) and from 26.75 to 28.81 GHz (with a bandwidth of 2.06 GHz). The proposed array design exhibits a measured gain value that varies between 11.8 dBi and 13.1 dBi within the operating bands and reaches 13.1 dBi at 28 GHz. The proposed array achieves a radiation efficiency of 83.05%, and a front-to-back ratio ranging between 15 and 20 dB across the operating frequency band. The array is fabricated and tested with good matching between the simulated and tested outcomes. The improved performance of the array makes it a suitable candidate for 5G new radio (NR) communications.
Color Point Defect Detection Method Based on Color Salient Features
Authors: Wang Zhixi , Xie Wenqiang , Chen Huaixin , Liu Biyuan , Shuai Lingyu
Published: August 2022

Display color point defect detection is an important link in the display quality inspection process. To improve the detection accuracy of color point defects, a color point defect detection method based on color salient features is proposed. Color point defects that conform to the perception of the human vision are used as the key point for detection. First, the human visual perception constraint coefficient is used to correct the RGB three-channel image to obtain the color-channel-transformed image. Then, the local contrast method is used to extract the point features of the color channel, which achieves point defect enhancement, noise and background suppression. Finally, the mean and standard deviation of the defect feature maps of R, G, and B channels are calculated. The maximum mean and standard deviation are selected as thresholds using the maximum fusion criterion to perform binarization segmentation of the defect feature maps of R, G, and B channels. An OR operation was performed on the segmented images and the point defect segmentation results were combined. The experimental results show that the average detection accuracy and recall of the algorithm is higher than 94%, which is a significant improvement compared with mainstream detection methods and meets the needs of industrial production.
Temporal Context Modeling Network with Local-Global Complementary Architecture for Temporal Proposal Generation
Authors: Yuan Yunfeng , Yang Wenzhu , Luo Zifei , Gou Ruru
Published: August 2022

Temporal Action Proposal Generation (TAPG) is a promising but challenging task with a wide range of practical applications. Although state-of-the-art methods have made significant progress in TAPG, most ignore the impact of the temporal scales of action and lack the exploitation of effective boundary contexts. In this paper, we propose a simple but effective unified framework named Temporal Context Modeling Network (TCMNet) that generates temporal action proposals. TCMNet innovatively uses convolutional filters with different dilation rates to address the temporal scale issue. Specifically, TCMNet contains a BaseNet with dilated convolutions (DBNet), an Action Completeness Module (ACM), and a Temporal Boundary Generator (TBG). The DBNet aims to model temporal information. It handles input video features through different dilated convolutional layers and outputs a feature sequence as the input of ACM and TBG. The ACM aims to evaluate the confidence scores of densely distributed proposals. The TBG is designed to enrich the boundary context of an action instance. The TBG can generate action boundaries with high precision and high recall through a local–global complementary structure. We conduct comprehensive evaluations on two challenging video benchmarks: ActivityNet-1.3 and THUMOS14. Extensive experiments demonstrate the effectiveness of the proposed TCMNet on tasks of temporal action proposal generation and temporal action detection.
Grape Maturity Detection and Visual Pre-Positioning Based on Improved YOLOv4
Authors: Qiu Chang , Tian Guangzhao , Zhao Jiawei , Liu Qin , Xie Shangjie , Zheng Kui
Published: August 2022

To guide grape picking robots to recognize and classify the grapes with different maturity quickly and accurately in the complex environment of the orchard, and to obtain the spatial position information of the grape clusters, an algorithm of grape maturity detection and visual pre-positioning based on improved YOLOv4 is proposed in this study. The detection algorithm uses Mobilenetv3 as the backbone feature extraction network, uses deep separable convolution instead of ordinary convolution, and uses the h-swish function instead of the swish function to reduce the number of model parameters and improve the detection speed of the model. At the same time, the SENet attention mechanism is added to the model to improve the detection accuracy, and finally the SM-YOLOv4 algorithm based on improved YOLOv4 is constructed. The experimental results of maturity detection showed that the overall average accuracy of the trained SM-YOLOv4 target detection algorithm under the verification set reached 93.52%, and the average detection time was 10.82 ms. Obtaining the spatial position of grape clusters is a grape cluster pre-positioning method based on binocular stereo vision. In the pre-positioning experiment, the maximum error was 32 mm, the mean error was 27 mm, and the mean error ratio was 3.89%. Compared with YOLOv5, YOLOv4-Tiny, Faster_R-CNN, and other target detection algorithms, which have greater advantages in accuracy and speed, have good robustness and real-time performance in the actual orchard complex environment, and can simultaneously meet the requirements of grape fruit maturity recognition accuracy and detection speed, as well as the visual pre-positioning requirements of grape picking robots in the orchard complex environment. It can reliably indicate the growth stage of grapes, so as to complete the picking of grapes at the best time, and it can guide the robot to move to the picking position, which is a prerequisite for the precise picking of grapes in the complex environment of the orchard.
High-Efficiency Power Optimization Based on Reconfigurable Intelligent Surface for Nonlinear SWIPT System
Authors: Zheng Hongxia , Li Xingquan , He  Chunlong , Yang Yatao
Published: August 2022

This paper presents an investigation of the transmitting power consumption of a base station (BS) in a simultaneous wireless information and power transfer (SWIPT) system enhanced by a reconfigurable intelligent surface (RIS). The aim is to optimize the total transmitting power consumption when sending information signals and energy from the BS to ground sensors. To this end, the transmitting power consumption of the BS is optimized by satisfying the sensor’s minimum signal-to-interference-plus-noise ratio (SINR), the phase shift constraints of the RIS, and each sensor’s power-splitting (PS) ratio. In order to decouple the optimization variables, we use the technique of block coordinate descent (BCD) to transform the total problem into subproblems. In the second subproblem, the unit modulus constraints are approximated using the successive convex approximation (SCA) method, allowing the optimal solutions to be obtained by solving subproblems in an iterative manner. Our numerical simulation results show that transmitting power consumption can be significantly decreased by adding RIS to an SPWIT system, even in nonlinear harvest models of real application scenarios.
Classification of Parkinson’s Disease Patients—A Deep Learning Strategy
Authors: Carvajal-Castaño Helber Andrés , Pérez-Toro Paula Andrea , Orozco-Arroyave Juan Rafael
Published: August 2022

(1) Background and objectives: Parkinson’s disease (PD) is one of the most prevalent neurodegenerative diseases whose typical symptoms include bradykinesia, abnormal gait and posture, shortened strides, and other movement disorders. In this study, we present a novel framework to evaluate PD gait patterns using state of the art deep learning algorithms. A comparative analysis with three different approaches is presented and evaluated upon three groups of subjects: PD patients, Young Healthy Controls (YHC), and Elderly Healthy Controls (EHC). (2) Methods: The three approaches used in the study include: (i) The energy content of the gait signals in the frequency domain is captured with spectrograms that are used to feed a CNN model, (ii) Temporal information is incorporated by creating GRU networks, (iii) Temporal and spectral information is simultaneously captured by creating a new architecture based on CNNs and GRUs. (3) Results: Accuracies of up to 83.7% and 92.7% are found for the classification between PD vs. EHC and PD vs. YHC, respectively. According to our observations, the proposed approach based on the combination of temporal and spectral information, yields better results than others reported in the state of the art. (4) Conclusions: The results obtained in this study suggest that the combination of temporal and spectral information is more accurate than individual approaches used to classify and evaluate gait patterns in PD patients. To the best of our knowledge, this is the first study in gait analysis where temporal and spectral information is combined in an architecture of deep learning.
A Low-Power Area-Efficient Precision Scalable Multiplier with an Input Vector Systolic Structure
Authors: Tang Xiqin , Li Yang , Lin Chenxiao , Shang Delong
Published: August 2022

In this paper, a small-area low-power 64-bit integer multiplier is presented, which is suitable for portable devices or wireless applications. To save the area cost and power consumption, an input vector systolic (IVS) structure is proposed based on four 16-bit radix-8 Booth multipliers and a data input scheme is proposed to reduce the number of signal transitions. This structure is similar to a systolic array in matrix multiply units of a Convolutional Neural Network (CNN), but it reduces the number of processing elements by 3/4 concerning the same vector systolic accelerator in reference. The comparison results prove that the IVS multiplier reduces at least 61.9% of the area and 45.18% of the power over its counterparts. To increase the hardware resource utilization, a Transverse Carry Array (TCA) structure for Partial Products Accumulation (PPA) was designed by replacing the 32-bit adders with 3/17-bit adders in the 16-bit multipliers. The experiment results show that the optimization could lead to at least a 6.32% and 13.65% reduction in power consumption and area cost, respectively, compared to the standard 16-bit radix-8 Booth multiplier. In the end, the precise scale of the proposed IVS multiplier is discussed. Benefiting from the modular design, the IVS multiplier can be configured to support sixteen different kinds of multiplications at a step of 16 bits [16b, 32b, 48b, 64b] × [16b, 32b, 48b, 64b].
Hardware/Software Co-Design of a Circle Detection System Based on Evolutionary Computing
Authors: Rojas-Muñoz Luis Felipe , Rostro-González Horacio , García-Capulín Carlos Hugo , Sánchez-Solano Santiago
Published: August 2022

In recent years, the strategy of co-designing Hardware/Software (HW/SW) systems has been widely adopted to exploit the synergy between both approaches thanks to technological advances that have led to more powerful devices providing an increasingly better cost–benefit trade-off. This paper presents an HW/SW system for the detection of multiple circles in digital images based on a genetic algorithm. It is implemented on an Ultra96-v2 development board, which contains a Xilinx Zynq UltraScale+ MPSoC device and supports a Linux operating system that facilitates application development. The design is powered by developing an interactive computing environment by means of the Jupyter Notebook platform, in which different programming languages coexist. The specific advantages of each of these languages have been used to describe the hardware component that accelerates the evolutionary computation for circle detection (VHDL), to execute SW-HW interaction functions, as well as the pre- and post-processing of the images (ANSI-C) and to code, evaluate, and document the system execution process (Python). As a result, a computationally efficient application was obtained, with high accuracy in the detection of circles in synthetic and real images, and with a high degree of reconfigurability that provides the user with the necessary tools to incorporate it in a specific area of interest.
Augmented Reality-Based English Language Learning: Importance and State of the Art
Authors: Wedyan Mohammad , Falah Jannat , Elshaweesh Omar , Alfalah Salsabeel F. M. , Alazab Moutaz
Published: August 2022

Augmented reality is increasingly used in the educational domain. However, little is known concerning the actual importance of AR for learning English skills. The weakness of the English language among English as a foreign Language (EFL) students is widespread in different educational institutions. Accordingly, this paper aims at exploring the importance of AR for learning English skills from the perspectives of English language teachers and educators. Mixed qualitative methods were used. To achieve the objective of this study, 12 interviews were conducted with English teachers concerning the topic under investigation. Second, a systematic literature review (SLR) that demonstrates the advantages, the limitation, and the approach of AR for learning English was performed. This study is different from other studies in using two methods and conducting comprehensive research on the importance of AR in improving English language skills in general. Thus, the study concluded that AR improves language skills and academic achievements. It also reduces students’ anxiety levels, improves students’ creativity, and increases students’ collaboration and engagement. Moreover, the students have positive attitudes towards using AR for learning the English language. The findings present important implications for the integration and development of AR for learning.
A Comparative Study of Consensus Mechanisms in Blockchain for IoT Networks
Authors: Auhl Zachary , Chilamkurti Naveen , Alhadad Rabei , Heyne Will
Published: August 2022

The consensus mechanism is a core component of Blockchain technology, allowing thousands of nodes to agree on a single and consistent view of the Blockchain. A carefully selected consensus mechanism can provide attributes such as fault tolerance and immutability to an application. The Internet of Things (IoT) is a use case that can take advantage of these unique Blockchain properties. IoT devices are commonly implemented in sensitive domains such as health, smart cities, and supply chains. Resilience and data integrity are important for these domains, as failures and malicious data tampering could be detrimental to the systems that rely on these IoT devices. Additionally, Blockchains are well suited for decentralised networks and networks with high churn rates. A difficulty involved with applying Blockchain technology to the IoT is the lack of computational resources. This means that traditional consensus mechanisms like Proof of Work (PoW) are unsuitable. In this paper, we will compare several popular consensus mechanisms using a set of criteria, with the aim of understanding which consensus mechanisms are suitable for deployment in the IoT, and what trade-offs are required. We show that there are opportunities for both PoW and PoS to be implemented in the IoT, with purpose-made IoT consensus mechanisms like PoSCS and Microchain. Our analysis shows that Microchain and PoSCS have characteristics that are well suited for IoT consensus.
Experiment and Application of Wax Deposition in Dabei Deep Condensate Gas Wells with High Pressure
Authors: Cao Lihu , Sun Jinsheng , Liu Jianyi , Liu Jiquan
Published: August 2022

The Dabei deep high-pressure condensate gas field occupies the paramount position in the Tarim Oilfield in China, the exploration and developments of which have been progressing. Since the initial development, the wax deposition and plugging in the wellbore and gathering pipeline have been the most bothering issues, resulting in the reduction or even shutdown of condensate gas well production. Therefore, the wax appearance temperature of Dabei condensate oil was studied using the capillary viscometer, differential scanning calorimetry (DSC), and polarizing microscope observation. The wax content was tested by using the DSC and crystallization separation test method. Finally, the wax appearance temperatures of degassed condensate oil and equilibrium condensate oil under different pressures were tested. Experimental results show that the wax appearance temperature measured by polarizing microscope observation was higher than that measured by the DSC and capillary viscometer, the lag of which can be recorded as the cloud point. The wax appearance temperature measured by polarizing microscope observation is of high accuracy. Secondly, the DSC method is not sufficient for measuring wax precipitation at low temperatures, showing a lower wax content than the crystallization separation test method. Thus, the wax content of Dabei condensate oil can be better measured by using the crystallization separation test method. Additionally, the wax precipitation law of equilibrium condensate oil is opposite to that of degassed condensate oil. The wax appearance temperature of equilibrium condensate oil increases as the pressure decreases. The results of wax appearance temperature of equilibrium condensate oil provide a useful and quick index to judge the potential risk of wax precipitation in the Tarim Oilfield, which can provide an efficient strategy for the development of waxy condensate gas reservoirs and the optimization of wax prevention and treatment technology.
Micro-Displacement and Storage Mechanism of CO2 in Tight Sandstone Reservoirs Based on CT Scanning
Authors: Yue Ping , Liu Feng , Yang Kai , Han Chunshuo , Ren Chao , Zhou Jiangtang , Wang Xiukun , Fang Quantang , Li Xinxin , Dou Liangbin
Published: August 2022

Tight sandstone reservoirs are ideal locations for CO2 storage. To evaluate the oil displacement efficiency and storage potential of CO2 in the tight sandstone reservoir in the Huang 3 area of the Changqing Oilfield, four kinds of displacement experiments were conducted on core samples from the Chang 8 Formation in the Huang 3 area. These experiments were performed using micro-displacement equipment, digital core technology, and an online CT scanning system; the different oil displacement processes were recorded as three-dimensional images. The results show that the CO2 flooding alternated with water scheme can improve crude oil recovery the most. Comparing the cores before and after the displacement shows that the amount of crude oil in pores with larger sizes decreases more. The remaining oil is mainly in thin films or is dispersed and star-shaped, indicating that the crude oil in the medium and large pores is swept and recovered. The CO2 displacement efficiency is 41.67~55.08%, and the CO2 storage rate is 38.16~46.89%. The proportion of remaining oil in the throat of the small and medium-sized pores is still high, which is the key to oil recovery in the later stages.
Photocatalytic Evolution of Hydrogen Peroxide: A Minireview
Authors: Karamoschos Nikolaos , Tasis Dimitrios
Published: August 2022

Hydrogen peroxide (H2O2) has demonstrated applicability in a wide range of applications, spanning from a bleaching agent in the pulp industry, environmental remediation, and fuel cell technology. Industrial scale synthesis, either by the anthraquinone method or catalytic oxidation of hydrogen gas, has serious drawbacks which are related with energy demanding and multi-step processes. An alternative green strategy involves the photocatalytic synthesis of H2O2. All that is needed is the renewable energy of the sun, a semiconducting species absorbing in the visible region, water, and oxygen. In this minireview, we describe the evolution of research milestones that have been achieved within the recent decades regarding the development of functional photocatalytic systems. In the early studies, back in the 1980’s, TiO2-based systems were mostly investigated. However, due to the large band gap of titania (3.2 eV), alternative semiconductors were studied which strongly absorb in the visible region. Thus, a variety of semiconductor families have been investigated, such as doped titania systems, other metal oxides, metal sulfides, organic semiconductors, metal-organic frameworks, carbon nitride systems, etc. In parallel, the development of functional dopants onto the surface of the main semiconductor has lead to both the inhibition of electron-hole recombination and H2O2 degradation. The current minireview collectively provides the studies of the higher H2O2 production rates and offer some suggestions for the near future.
HYSCORE Spectroscopy to Resolve Electron–Nuclear Structure of Vanadyl Porphyrins in Asphaltenes from the Athabasca Oil Sands In Situ Conditions
Authors: Sadovnikova Margarita A. , Murzakhanov Fadis F. , Mamin Georgy V. , Gafurov Marat R.
Published: August 2022

The purpose of this work is to analyze the electron–nuclear interactions of the vanadyl-porphyrin (VP) complexes in oil asphaltenes. Asphaltenes from the Athabasca oil sands were studied by HYperfine Sublevel CORrelation Spectroscopy (HYSCORE) electron paramagnetic resonance (EPR). It makes it possible to resolve and interpret complex hyperfine spectra of intrinsic VP with strong and weak hyperfine interactions between the electron magnetic moment and various nuclear spins (1H, 14N, 51V). The main parameters of spin-Hamiltonian for the VP spin system are determined. The axially symmetric structure of the VP complexes is revealed, and the local nuclear environment of the paramagnetic center is investigated. The results can be used for the study of asphaltene electron–nuclear structure and asphaltene aggregates with the aim of elucidating asphaltenes’ transformation(s) under the influence of external treatment.
Phenyl Vinylsulfonate, a Novel Electrolyte Additive to Improve Electrochemical Performance of Lithium-Ion Batteries
Authors: Mosallanejad Behrooz , Javanbakht Mehran , Shariatinia Zahra , Akrami Mohammad
Published: August 2022

Irreversible capacity fading, originating from the formation of the solid electrolyte interphase (SEI), is a common challenge encountered in lithium-ion batteries (LIBs) containing an electrolyte based on ethylene carbonate (EC). In this research, phenyl vinyl sulfonate (PVS) is examined as a novel electrolyte additive to mitigate this issue and subsequently enhance the cyclic stability of LIBs. As evidenced by density functional theory (DFT) calculations, PVS has a higher reduction potential than that of EC, which is in accordance with the cyclic voltammetry (CV) measurements. Accordingly, the PVS-containing electrolyte demonstrated a reduction peak at ~1.9 V, which was higher than that of the electrolyte without an additive (at ~1.7 V). In contrast to the SEI derived from the reference electrolyte, the one built-in PVS-containing electrolyte was capable of completely inhibiting the electrolyte reduction. In terms of the Raman spectroscopy and electrochemical impedance spectroscopy (EIS) analysis, SEI formation as the result of PVS reduction can lead to less structural disorder in the graphite electrode; the battery with the additive showed less interfacial and charge transfer resistance. The Li/graphite cell with 1 wt % of PVS delivered capacity retention much higher than that of its counterpart without the additive after 35 cycles at 1 C.
Thévenin’s Battery Model Parameter Estimation Based on Simulink
Authors: Barletta Giulio , DiPrima Piera , Papurello Davide
Published: August 2022

Lithium-ion batteries (LIB) proved over time to be one of the best choices among rechargeable batteries. Their small size, high energy density, long life, and low maintenance need make them a prominent candidate for the role of the most widespread energy storage system. They have the potential to monopolize the green technology sector. An accurate definition of the parameters defining the behaviour of the battery in different operating conditions is thus essential, as their knowledge proves crucial in certain fields such as those that involve electric vehicles. This paper proposes the estimation of the values of the parameters of the Thévenin equivalent circuit of a LIB commercial cell. Experimental data obtained through constant current charge/discharge cycles are analysed through a Simulink model, and results are obtained as a function of the state of charge (SOC) for a fixed and controlled temperature value. The results achieved with the proposed model can monitor the salient parameters of the equivalent circuit with an error between 7 and 10%.
A Multi-Region CFD Model for Aircraft Ground Deicing by Dispersed Liquid Spray
Authors: Ernez Sami , Morency François
Published: August 2022

The aircraft ground deicing (AGD) process is a mandatory step before taking off in a cold climate. The development of CFD (computational fluid dynamics) tools to simulate AGD could help the industry reduce its costs and limit pollution. Previous works have modelled some parts of the AGD process. Building on these previous works, this paper presents a three-dimensional (3D) CFD algorithm to simulate the process in full scale. The algorithm comprises a multi-region model where a Lagrangian method solves the spray particle equations, and an enthalpy–porosity approach with an Eulerian method simulates the ice melting. The multi-region approach is verified in this paper through a spray-tip penetration (STP) test. The STP predicted using the multi-region model had 99% agreement with the STP predicted using a Lagrangian method. Therefore, the multi-region technique correctly modeled the particle momentum between the two regions. This paper also presents a numerical calibration of the permeability coefficient for the extended enthalpy–porosity technique in the context of AGD. The numerical calibration of the permeability coefficient will enable future parametric studies of the AGD process.