bluuebunny/crossref_metadata_2025 huggingface dataset:
Resilient City: Characterization, Challenges and Outlooks
Authors: Wu Chengwei , Cenci Jeremy , Wang Wei , Zhang Jiazhen
Published: April 2022

The increasingly severe environmental pollution and the Earth’s ecological crisis make the concept of resilient cities (RCs) a hot topic in urban research. We ran a bibliometric analysis to analyze the research progress, areas, hotspots, and strategies pertaining to RCs. The core collection came from the Web of Science (WoS) database as the data source to explore 4462 literature works on RCs. The results revealed that development time series analysis is divided into three stages. Changes in the number of publications are linked to natural disasters, the ecological environment, and science policy. The top five issuing journals accounted for 24.15% of the total sample. Country cooperation mainly is concentrated in countries with good economic development trends, such as the United States, China, and the United Kingdom. There were 63 core authors. The most published research institution was the Chinese Academy of Sciences. The RC research hotspots included the definition of resilience and evolution, the study of resilience as an analytical framework for urban issues, and resilience assessment indicators. This paper shows that RCs should strengthen multi-country cooperation and interdisciplinary integration and should focus on comprehensive research on basic theories, evaluation systems, and action mechanisms to reference future research on RCs further.
Machine-Learning-Based Clinical Biomarker Using Cell-Free DNA for Hepatocellular Carcinoma (HCC)
Authors: Lee Taehee , Rawding Piper A. , Bu Jiyoon , Hyun Sunghee , Rou Woosun , Jeon Hongjae , Kim Seokhyun , Lee Byungseok , Kubiatowicz Luke J. , Kim Dawon , Hong Seungpyo , Eun Hyuksoo
Published: April 2022

(1) Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Although various serum enzymes have been utilized for the diagnosis and prognosis of HCC, the currently available biomarkers lack the sensitivity needed to detect HCC at early stages and accurately predict treatment responses. (2) Methods: We utilized our highly sensitive cell-free DNA (cfDNA) detection system, in combination with a machine learning algorithm, to provide a platform for improved diagnosis and prognosis of HCC. (3) Results: cfDNA, specifically alpha-fetoprotein (AFP) expression in captured cfDNA, demonstrated the highest accuracy for diagnosing malignancies among the serum/plasma biomarkers used in this study, including AFP, aspartate aminotransferase, alanine aminotransferase, albumin, alkaline phosphatase, and bilirubin. The diagnostic/prognostic capability of cfDNA was further improved by establishing a cfDNA score (cfDHCC), which integrated the total plasma cfDNA levels and cfAFP-DNA expression into a single score using machine learning algorithms. (4) Conclusion: The cfDHCC score demonstrated significantly improved accuracy in determining the pathological features of HCC and predicting patients’ survival outcomes compared to the other biomarkers. The results presented herein reveal that our cfDNA capture/analysis platform is a promising approach to effectively utilize cfDNA as a biomarker for the diagnosis and prognosis of HCC.
KRAS Mutations Impact Clinical Outcome in Metastatic Non-Small Cell Lung Cancer
Authors: Eklund Ella A. , Wiel Clotilde , Fagman Henrik , Akyürek Levent M. , Raghavan Sukanya , Nyman Jan , Hallqvist Andreas , Sayin Volkan I.
Published: April 2022

There is an urgent need to identify new predictive biomarkers for treatment response to both platinum doublet chemotherapy (PT) and immune checkpoint blockade (ICB). Here, we evaluated whether treatment outcome could be affected by KRAS mutational status in patients with metastatic (Stage IV) non-small cell lung cancer (NSCLC). All consecutive patients molecularly assessed and diagnosed between 2016–2018 with Stage IV NSCLC in the region of West Sweden were included in this multi-center retrospective study. The primary study outcome was overall survival (OS). Out of 580 Stage IV NSCLC patients, 35.5% harbored an activating mutation in the KRAS gene (KRASMUT). Compared to KRAS wild-type (KRASWT), KRASMUT was a negative factor for OS (p = 0.014). On multivariate analysis, KRASMUT persisted as a negative factor for OS (HR 1.478, 95% CI 1.207–1.709, p < 0.001). When treated with first-line platinum doublet (n = 195), KRASMUT was a negative factor for survival (p = 0.018), with median OS of 9 months vs. KRASWT at 11 months. On multivariate analysis, KRASMUT persisted as a negative factor for OS (HR 1.564, 95% CI 1.124–2.177, p = 0.008). KRASMUT patients with high PD-L1 expression (PD-L1high) had better OS than PD-L1highKRASWT patients (p = 0.036). In response to first-line ICB, KRASMUT patients had a significantly (p = 0.006) better outcome than KRASWT patients, with a median OS of 23 vs. 6 months. On multivariable Cox analysis, KRASMUT status was an independent prognostic factor for better OS (HR 0.349, 95% CI 0.148–0.822, p = 0.016). kRAS mutations are associated with better response to treatment with immune checkpoint blockade and worse response to platinum doublet chemotherapy as well as shorter general OS in Stage IV NSCLC.
Genomic Profile in a Non-Seminoma Testicular Germ-Cell Tumor Cohort Reveals a Potential Biomarker of Sensitivity to Platinum-Based Therapy
Authors: González-Barrios Rodrigo , Alcaraz Nicolás , Montalvo-Casimiro Michel , Cervera Alejandra , Arriaga-Canon Cristian , Munguia-Garza Paulina , Hinojosa-Ugarte Diego , Sobrevilla-Moreno Nora , Torres-Arciga Karla , Mendoza-Perez Julia , Diaz-Chavez José , Cortes-González Carlo Cesar , Castro-Hernández Clementina , Martínez-Cedillo Jorge , Scavuzzo Ana , Pérez-Montiel Delia , Jiménez-Ríos Miguel A. , Herrera Luis A.
Published: April 2022

Despite having a favorable response to platinum-based chemotherapies, ~15% of Testicular Germ-Cell Tumor (TGCT) patients are platinum-resistant. Mortality rates among Latin American countries have remained constant over time, which makes the study of this population of particular interest. To gain insight into this phenomenon, we conducted whole-exome sequencing, microarray-based comparative genomic hybridization, and copy number analysis of 32 tumors from a Mexican cohort, of which 18 were platinum-sensitive and 14 were platinum-resistant. We incorporated analyses of mutational burden, driver mutations, and SNV and CNV signatures. DNA breakpoints in genes were also investigated and might represent an interesting research opportunity. We observed that sensitivity to chemotherapy does not seem to be explained by any of the mutations detected. Instead, we uncovered CNVs, particularly amplifications on segment 2q11.1 as a novel variant with chemosensitivity biomarker potential. Our data shed light into understanding platinum resistance in a Latin-origin population.
Efficient Small Extracellular Vesicles (EV) Isolation Method and Evaluation of EV-Associated DNA Role in Cell–Cell Communication in Cancer
Authors: Chetty Venkatesh Kumar , Ghanam Jamal , Anchan Srishti , Reinhardt Katarina , Brenzel Alexandra , Gelléri Márton , Cremer Christoph , Grueso-Navarro Elena , Schneider Markus , von Neuhoff Nils , Reinhardt Dirk , Jablonska Jadwiga , Nazarenko Irina , Thakur Basant Kumar
Published: April 2022

Small extracellular vesicles (sEVs) play essential roles in intercellular signaling both in normal and pathophysiological conditions. Comprehensive studies of dsDNA associated with sEVs are hampered by a lack of methods, allowing efficient separation of sEVs from free-circulating DNA and apoptotic bodies. In this work, using controlled culture conditions, we enriched the reproducible separation of sEVs from free-circulated components by combining tangential flow filtration, size-exclusion chromatography, and ultrafiltration (TSU). EV-enriched fractions (F2 and F3) obtained using TSU also contained more dsDNA derived from the host genome and mitochondria, predominantly localized inside the vesicles. Three-dimensional reconstruction of high-resolution imaging showed that the recipient cell membrane barrier restricts a portion of EV-DNA. Simultaneously, the remaining EV-DNA overcomes it and enters the cytoplasm and nucleus. In the cytoplasm, EV-DNA associates with dsDNA-inflammatory sensors (cGAS/STING) and endosomal proteins (Rab5/Rab7). Relevant to cancer, we found that EV-DNA isolated from leukemia cell lines communicates with mesenchymal stromal cells (MSCs), a critical component in the BM microenvironment. Furthermore, we illustrated the arrangement of sEVs and EV-DNA at a single vesicle level using super-resolution microscopy. Altogether, employing TSU isolation, we demonstrated EV-DNA distribution and a tool to evaluate the exact EV-DNA role of cell–cell communication in cancer.
Convolutional Neural Networks to Detect Vestibular Schwannomas on Single MRI Slices: A Feasibility Study
Authors: Koechli Carole , Vu Erwin , Sager Philipp , Näf Lukas , Fischer Tim , Putora Paul M. , Ehret Felix , Fürweger Christoph , Schröder Christina , Förster Robert , Zwahlen Daniel R. , Muacevic Alexander , Windisch Paul
Published: April 2022

In this study. we aimed to detect vestibular schwannomas (VSs) in individual magnetic resonance imaging (MRI) slices by using a 2D-CNN. A pretrained CNN (ResNet-34) was retrained and internally validated using contrast-enhanced T1-weighted (T1c) MRI slices from one institution. In a second step, the model was externally validated using T1c- and T1-weighted (T1) slices from a different institution. As a substitute, bisected slices were used with and without tumors originating from whole transversal slices that contained part of the unilateral VS. The model predictions were assessed based on the categorical accuracy and confusion matrices. A total of 539, 94, and 74 patients were included for training, internal validation, and external T1c validation, respectively. This resulted in an accuracy of 0.949 (95% CI 0.935–0.963) for the internal validation and 0.912 (95% CI 0.866–0.958) for the external T1c validation. We suggest that 2D-CNNs might be a promising alternative to 2.5-/3D-CNNs for certain tasks thanks to the decreased demand for computational power and the fact that there is no need for segmentations. However, further research is needed on the difference between 2D-CNNs and more complex architectures.
A Review on Green Synthesis of Nanoparticles and Their Diverse Biomedical and Environmental Applications
Authors: Samuel Melvin S. , Ravikumar Madhumita , John J. Ashwini , Selvarajan Ethiraj , Patel Himanshu , Chander P. Sharath , Soundarya J. , Vuppala Srikanth , Balaji Ramachandran , Chandrasekar Narendhar
Published: April 2022

In recent times, metal oxide nanoparticles (NPs) have been regarded as having important commercial utility. However, the potential toxicity of these nanomaterials has also been a crucial research concern. In this regard, an important solution for ensuring lower toxicity levels and thereby facilitating an unhindered application in human consumer products is the green synthesis of these particles. Although a naïve approach, the biological synthesis of metal oxide NPs using microorganisms and plant extracts opens up immense prospects for the production of biocompatible and cost-effective particles with potential applications in the healthcare sector. An important area that calls for attention is cancer therapy and the intervention of nanotechnology to improve existing therapeutic practices. Metal oxide NPs have been identified as therapeutic agents with an extended half-life and therapeutic index and have also been reported to have lesser immunogenic properties. Currently, biosynthesized metal oxide NPs are the subject of considerable research and analysis for the early detection and treatment of tumors, but their performance in clinical experiments is yet to be determined. The present review provides a comprehensive account of recent research on the biosynthesis of metal oxide NPs, including mechanistic insights into biological production machinery, the latest reports on biogenesis, the properties of biosynthesized NPs, and directions for further improvement. In particular, scientific reports on the properties and applications of nanoparticles of the oxides of titanium, cerium, selenium, zinc, iron, and copper have been highlighted. This review discusses the significance of the green synthesis of metal oxide nanoparticles, with respect to therapeutically based pharmaceutical applications as well as energy and environmental applications, using various novel approaches including one-minute sonochemical synthesis that are capable of responding to various stimuli such as radiation, heat, and pH. This study will provide new insight into novel methods that are cost-effective and pollution free, assisted by the biodegradation of biomass.
Tribological Properties of the 40Cr/GCr15 Tribo-Pair under Unidirectional Rotary and Reciprocating Dry Sliding
Authors: Cao Jialiang , Teng Huan , Wang Wurong , Wei Xicheng , Zhao Hongshan
Published: April 2022

The unidirectional rotary and reciprocating sliding experiments of the 40Cr pin/GCr15 disc tribo-pair were carried out on the MFT-5000 Rtec friction and wear tester under the same test conditions with a sliding speed of 0.2 m/s and a load of 150 N. Compared with reciprocating sliding, the tribo-pair in rotary sliding exhibits a stabler friction coefficient and better wear resistance. By analyzing the wear surface morphologies of the two pins, the main wear mechanism was found to be adhesive wear. For the tribo-layer of pin under reciprocating sliding, the surface microstructure plastically converges and forms a ridge from both sides to the middle, while a vortex structure is generated in the tribo-layer of pin under rotating sliding. The metamorphic structure and mircohardness of tribo-layer caused by the sliding forms are the key factors affecting the tribological properties.
Preparation of a Heterogeneous Catalyst CuO-Fe2O3/CTS-ATP and Degradation of Methylene Blue and Ciprofloxacin
Authors: Zhang Ting , Li Wenhui , Guo Qiyang , Wang Yi , Li Chunlei
Published: April 2022

A heterogeneous particle catalyst (CuO-Fe2O3/CTS-ATP) was synthesized via injection molding and ultrasonic immersion method, which is fast and effective. The particle catalyst applied attapulgite (ATP) wrapped by chitosan (CTS) as support, which was loaded dual metal oxides CuO and Fe2O3 as active components. After a series of characterizations of catalysts, it was found that CuO and Fe2O3 were successfully and evenly loaded on the surface of the CTS-ATP support. The catalyst was used to degrade methylene blue (MB) and ciprofloxacin (CIP), and the experimental results showed that the degradation ratios of MB and CIP can reach 99.29% and 86.2%, respectively, in the optimal conditions. The degradation mechanism of as-prepared catalyst was analyzed according to its synthesis process and ∙OH production, and the double-cycle catalytic mechanism was proposed. The intermediate products of MB and CIP degradation were also identified by HPLC-MS, and the possible degradation pathways were put forward.
Dynamic Behavior of Kinetic Projectile Impact on Honeycomb Sandwich Panels and Multi-Layer Plates
Authors: Yue Shuai , Bai Yushuai , Du Zhonghua , Zou Huaiwu , Shi Wenhui , Zheng Guang
Published: April 2022

In order to study the dynamic response associated with the impact of a kinetic projectile on the internal structure of an artificial satellite, we propose a kinetic projectile configuration with non-metallic material wrapping and multiple damage elements. The artificial satellite is simplified as a honeycomb sandwich panel and multi-layer plates. We carried out a ground damage test and finite element dynamic simulation, and we determined the lateral effect and penetration performance of the projectile. Then, we studied the dynamic behavior of the projectile penetrating the honeycomb sandwich panel using a theoretical model. We found that its ballistic limit velocity was 150 m/s, and the deformation of the opening had little relationship with the projectile velocity. Finally, we studied the dynamic response of the kinetic projectile impacting the multi-layer plates under various launch parameters. We found that the launch velocity required to meet the damage requirements was within 325 ± 25 m/s. Projectiles with a higher initial velocity had a stronger ability to penetrate the plates, but initial velocities higher than 325 m/s led to a reduced damage area. The kinetic projectile could adapt to incident angles less than 5° when damaging the plates. With the increasing incident angle, the penetration ability was reduced, and the damage area increased.
Enhancement of Protein Crystallization Using Nano-Sized Metal–Organic Framework
Authors: Zhang Xianfang , Xu Zhengtao , Zhou Jiahai , Xing Xiwen , Li Long
Published: April 2022

Protein crystallization plays a fundamental role in structural biology and chemistry, drug discovery, and crystallography itself. Determining how to improve the crystal growth is necessary and vital during the whole process. According to the recently published data, crystallizing proteins on nanoporous surfaces (i.e., metal–organic framework, abbreviated as MOF) is faster and demands less protein. However, dispersing micro-sized MOF materials uniformly is still a challenge and limiting process in protein crystallization. Here, we investigate the uniformity of micro-sized MOF under the treatment of the high-pressure homogenizer. At various pressures, the MOF is split into particles of different sizes, including the uniform and stable nano-sized MOF. Crystallization experiments demonstrated its enhancement in protein crystallization, and the number of crystals is significantly increased in the presence of nano-sized MOF. This work explores the use of nano-sized MOF solids to crystallize proteins of limited availability (i.e., insufficient for conventional methods) or of a hard-to-crystallize nature.
Non-Mutated Nucleophosmin 1 Is Recognized by the CD8+ T Lymphocytes of an AML Patient after the Transplantation of Hematopoietic Stem Cells from an HLA-Haploidentical Donor
Authors: Nemeckova Sarka , Alexova-Zurkova Kamila , Hainz Petr , Krystofova Jitka , Mackova Jana , Roubalova Katerina , Stastna-Markova Marketa , Vrana Milena , Vydra Jan
Published: April 2022

Nucleophosmin (NPM1, B23) is a multifunctional phosphoprotein expressed in all tissues. The protein is mainly localized in nucleoli. In hematological malignancies, NPM1 belongs to commonly altered genes. Its mutation, always heterozygous, leads to the re-localization of the NPM1 protein from the nucleolus to the cytoplasm (NPM1c+). NPM1c+ is found in 30% of acute myeloid leukemia (AML). Our study showed that an AML patient, whose leukemia cells carried the NPM1c+ mutation and who was the recipient of allogeneic HSCT from a haploidentical donor, raised a robust allorestricted CD8+ T cell response directed against the NPM1wt protein. Favourably, the response against NPM1wt was not accompanied by side effects such as GvHD. Moreover, the induction of a high NPM1wt-specific response coincided with the decrease in NPM1c+ transcripts detected, implying a beneficial graft versus leukemia effect. On the basis of these results, we suppose that TCRs from allorestricted NPM1wt-specific T cells are worth studying in other recipients of grafts from haploidentical donors as a possible tool for TCR gene therapy.
Aposymbiotic Specimen of the Photosynthetic Sea Slug Elysia crispata
Authors: Cartaxana Paulo , Lopes Diana , Martinez Begoña , Martins Patrícia , Cruz Sónia
Published: April 2022

Elysia crispata is a sacoglossan sea slug that retains intracellular, functional chloroplasts stolen from their macroalgal food sources. Elysia crispata juveniles start feeding on the algae following metamorphosis, engulfing chloroplasts and turning green. In laboratory-reared animals, we report one juvenile “albino” specimen unable to retain chloroplasts. Within 6 weeks post-metamorphosis, the aposymbiotic sea slug was significantly smaller than its chloroplast-bearing siblings. This evidence highlights that chloroplast acquisition is required for the normal development of E. crispata.
A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network
Authors: Srivinay  , Manujakshi B. , Kabadi Mohan , Naik Nagaraj
Published: April 2022

Stock prices are volatile due to different factors that are involved in the stock market, such as geopolitical tension, company earnings, and commodity prices, affecting stock price. Sometimes stock prices react to domestic uncertainty such as reserve bank policy, government policy, inflation, and global market uncertainty. The volatility estimation of stock is one of the challenging tasks for traders. Accurate prediction of stock price helps investors to reduce the risk in portfolio or investment. Stock prices are nonlinear. To deal with nonlinearity in data, we propose a hybrid stock prediction model using the prediction rule ensembles (PRE) technique and deep neural network (DNN). First, stock technical indicators are considered to identify the uptrend in stock prices. We considered moving average technical indicators: moving average 20 days, moving average 50 days, and moving average 200 days. Second, using the PRE technique-computed different rules for stock prediction, we selected the rules with the lowest root mean square error (RMSE) score. Third, the three-layer DNN is considered for stock prediction. We have fine-tuned the hyperparameters of DNN, such as the number of layers, learning rate, neurons, and number of epochs in the model. Fourth, the average results of the PRE and DNN prediction model are combined. The hybrid stock prediction model results are computed using the mean absolute error (MAE) and RMSE metric. The performance of the hybrid stock prediction model is better than the single prediction model, namely DNN and ANN, with a 5% to 7% improvement in RMSE score. The Indian stock price data are considered for the work.
A Descriptive Review of the Impact of Patient Motion in Early Childhood Resting-State Functional Magnetic Resonance Imaging
Authors: Schabdach Jenna , Ceschin Rafael , Schmithorst Vanessa , Tisdall M. Dylan , Alexander-Bloch Aaron , Panigrahy Ashok
Published: April 2022

Resting-state functional magnetic images (rs-fMRIs) can be used to map and delineate the brain activity occurring while the patient is in a task-free state. These resting-state activity networks can be informative when diagnosing various neurodevelopmental diseases, but only if the images are high quality. The quality of an rs-fMRI rapidly degrades when the patient moves during the scan. Herein, we describe how patient motion impacts an rs-fMRI on multiple levels. We begin with how the electromagnetic field and pulses of an MR scanner interact with a patient’s physiology, how movement affects the net signal acquired by the scanner, and how motion can be quantified from rs-fMRI. We then present methods for preventing motion through educational and behavioral interventions appropriate for different age groups, techniques for prospectively monitoring and correcting motion during the acquisition process, and pipelines for mitigating the effects of motion in existing scans.
A Generic View Planning System Based on Formal Expression of Perception Tasks
Authors: Kong Yanzi , Zhu Feng , Sun Haibo , Lin Zhiyuan , Wang Qun
Published: April 2022

View planning (VP) is a technique that guides the adjustment of the sensor’s postures in multi-view perception tasks. It converts the perception process into active perception, which improves the intelligence and reduces the resource consumption of the robot. We propose a generic VP system for multiple kinds of visual perception. The VP system is built on the basis of the formal description of the visual task, and the next best view is calculated by the system. When dealing with a given visual task, we can simply update its description as the input of the VP system, and obtain the defined best view in real time. Formal description of the perception task includes the task’s status, the objects’ prior information library, the visual representation status and the optimization goal. The task’s status and the visual representation status are updated when data are received at a new view. If the task’s status has not reached its goal, candidate views are sorted based on the updated visual representation status, and the next best view that can minimize the entropy of the model space is chosen as the output of the VP system. Experiments of view planning for 3D recognition and reconstruction tasks are conducted, and the result shows that our algorithm has good performance on different tasks.
Thresholding Approach for Low-Rank Correlation Matrix Based on MM Algorithm
Authors: Tanioka Kensuke , Furotani Yuki , Hiwa Satoru
Published: April 2022

Background: Low-rank approximation is used to interpret the features of a correlation matrix using visualization tools; however, a low-rank approximation may result in an estimation that is far from zero, even if the corresponding original value is zero. In such a case, the results lead to a misinterpretation. Methods: To overcome this, we propose a novel approach to estimate a sparse low-rank correlation matrix based on threshold values. We introduce a new cross-validation function to tune the corresponding threshold values. To calculate the value of a function, the MM algorithm is used to estimate the sparse low-rank correlation matrix, and a grid search was performed to select the threshold values. Results: Through numerical simulation, we found that the false positive rate (FPR), interpretability, and average relative error of the proposed method were superior to those of the tandem approach. For the application of microarray gene expression, the FPRs of the proposed approach with d=2,3 and 5 were 0.128, 0.139, and 0.197, respectively, while the FPR of the tandem approach was 0.285. Conclusions: We propose a novel approach to estimate sparse low-rank correlation matrices. The advantage of the proposed method is that it provides results that are interpretable using a heatmap, thereby avoiding result misinterpretations. We demonstrated the superiority of the proposed method through both numerical simulations and real examples.
Experimental Validation of Entropy-Driven Swarm Exploration under Sparsity Constraints with Sparse Bayesian Learning
Authors: Manss Christoph , Kuehner Isabel , Shutin Dmitriy
Published: April 2022

Increasing the autonomy of multi-agent systems or swarms for exploration missions requires tools for efficient information gathering. This work studies this problem from theoretical and experimental perspectives and evaluates an exploration system for multiple ground robots that cooperatively explore a stationary spatial process. For the distributed model, two conceptually different distribution paradigms are considered. The exploration is based on fusing distributively gathered information using Sparse Bayesian Learning (SBL), which permits representing the spatial process in a compressed manner and thus reduces the model complexity and communication load required for the exploration. An entropy-based exploration criterion is formulated to guide the agents. This criterion uses an estimation of a covariance matrix of the model parameters, which is then quantitatively characterized using a D-optimality criterion. The new sampling locations for the agents are then selected to minimize this criterion. To this end, a distributed optimization of the D-optimality criterion is derived. The proposed entropy-driven exploration is then presented from a system perspective and validated in laboratory experiments with two ground robots. The experiments show that SBL together with the distributed entropy-driven exploration is real-time capable and leads to a better performance with respect to time and accuracy compared with similar state-of-the-art algorithms.
A Vibration Fault Signal Identification Method via SEST
Authors: Li Xuemei , Wang Chunyang , Liu Xuelian , Xiao Bo , Wang Zishuo
Published: April 2022

(1) Background: with the development of intelligent transportation, effectively collecting and identifying the working state of vehicles is conducive to the analysis and processing of vehicle information by internet of vehicles, so as to reduce the occurrence of traffic accidents. Aiming at the problem of low identification accuracy of the mechanical vibration fault signal, a signal identification method based on time-frequency detection is introduced; (2) Methods: this paper constructs a parameter model of the synchroextracting S transform on the basis of the poor time-frequency concentration of the original S transform; (3) Results: in the case of SNR = −5~+30 dB, compared with other transformations, the Rényi entropy value of SEST is the smallest, and the Rényi entropy value is 0.5246 when SNR = +22 dB; (4) Conclusions: through simulation comparison and analysis, the excellent time-frequency concentration and anti-noise characteristics of the SEST are highlighted, and the rotor vibration fault signals such as rotor misalignment, unbalance and bearing wear are identified by SEST.
Novel Radiation-Hardened High-Speed DFF Design Based on Redundant Filter and Typical Application Analysis
Authors: Jiang Shuang , Liu Shibin , Zheng Hongchao , Wang Liang , Li Tongde
Published: April 2022

A cell-level radiation hardening by design (RHBD) method based on commercial processes of single event transient (SET) and single event upset (SEU) is proposed in this paper, in which new radiation-hardened D-type flip-flops (DFFs) are designed. An application-specific integrated circuit (ASIC) of a million gates level is developed based on DFFs, and SEU and single event functional interruption (SEFI) heavy-ion radiation tests are carried out. The experimental results show that the new DFF SEU ability is increased by 63 times compared with the DICE-designed DFF, and is three orders of magnitude higher than the redundantly designed DFF. The SEFI ability of the ASIC designed by the new DFF is 2.6 times higher than the circuit hardened by the TMR design.
Ultra-Wideband Graphene-Based Micro-Sized Circular Patch-Shaped Yagi-like MIMO Antenna for Terahertz Wireless Communication
Authors: Alharbi Abdullah G. , Sorathiya Vishal
Published: April 2022

In this work, an ultra-wideband graphene-based micro-sized circular patch-shaped Yagi-like MIMO antenna was investigated over 1–30 THz of the frequency spectrum. The proposed antenna structure was designed over a polyimide substrate with 620 × 800 µm2. This antenna was radiated over three bands over 1–30 THz. The maximum bandwidth achieved 10.96 THz, with −26 dB of the return loss isolation. These three bands were also mathematically analyzed using various MIMO antenna parameters to match the MIMO antenna criteria. This antenna provided all the accepted results as per the limits set by these antenna parameters. The effect of the different port excitation on the change of directivity of the overall structure is also reported. The MIMO antenna parameters such as TARC (Total Active Reflection Coefficient), ECC (Envelope Correlation Coefficient), MEG (Mean Effective Gain), CCL (Channel Capacity Loss), and DG (Directivity Gain) were investigated for the proposed structure. These values were also identified to check the compatibility and challenges associated with short-distance communication. The suggested THz MIMO antenna provides the operating bands of 1–10 THz and 15–30 THz, with good isolation values. An average of 7 dB gain was observed in the 2 × 2 antenna structure. The newly developed MIMO antenna in the THz frequency range may be used for high-speed short-distance and terahertz communications.
A Flexible Lightweight Triboelectric Nanogenerator for Protector and Scoring System in Taekwondo Competition Monitoring
Authors: Sun Fengxin , Zhu Yongsheng , Jia Changjun , Ouyang Bowen , Zhao Tianming , Li Caixia , Ba Ning , Li Xinxing , Chen Song , Che Tongtong , Mao Yupeng
Published: April 2022

Nowadays, the applications of the triboelectric nanogenerator in sensing and monitoring sports experience a blooming prosperity. Here, we report a flexible and lightweight triboelectric nanogenerator (FL-TENG) made of hydrogel electrodes, polytetrafluoroethylene (PTFE), PDMS, and polyurethane (PU). Based on the triboelectric effect, the FL-TENG can work as a self-powered sensor attaching to taekwondo protective gear, which can be used to monitor athletes’ competition performance and improve the fairness of the competition. In addition, the FL-TENG can drive micro-wireless devices for wireless transmitting sports data during the competition in real time. This kind of sustainable green self-powered sensor provides a new path for the field of sports competition monitoring.
Bathtub ECG as a Potential Alternative to Light Stress Test in Daily Life
Authors: Li Tianhui , Chen Wenxi
Published: April 2022

The exercise stress test (EST) is a common procedure to evaluate cardiovascular functions. However, the EST is not suitable for daily use, is sometimes risky, and even accompanies fatal incidents of myocardial infarction, arrhythmia, and sudden death during the test. The aim of this study was to evaluate heart rate variability (HRV) behaviors in the EST and during bathing, and to explore if daily bathing can serve as a potential alternative means of performing the EST. Electrocardiogram (ECG) signals were collected from 10 healthy subjects during the EST and bathing test (BT). The EST follows the modified Bruce protocol. ECG collection in the BT was conducted at five water temperatures ranging from 37 to 41 degrees Celsius (°C); each BT lasted 15 min. Twenty-three HRV features were used to group different bathing conditions corresponding to the EST stages using the Voronoi diagram method in terms of HRV behaviors. In all equivalent EST stages of BTs at the five water temperatures, the low stage, medium stage, and high stage account for 17.86%, 52.86%, and 29.29%, respectively. The results show that higher water temperatures and longer bathing durations in BT correspond to higher stages in the EST. The BT at the most severe condition of 41 °C and 15 min corresponds to a high EST stage in terms of HRV behavior. The results suggest that daily bathing can serve not only for healthcare monitoring but also as a reference for an at-home alternative to the EST.
Interference-Aware Schedulability Analysis and Task Allocation for Multicore Hard Real-Time Systems
Authors: Aceituno José María , Guasque Ana , Balbastre Patricia , Simó José , Crespo Alfons
Published: April 2022

There has been a trend towards using multicore platforms for real-time embedded systems due to their high computing performance. In the scheduling of a multicore hard real-time system, there are interference delays due to contention of shared hardware resources. The main sources of interference are memory, cache memory, and the shared memory bus. These interferences are a great source of unpredictability and they are not always taken into account. Recent papers have proposed task models and schedulability algorithms to account for this interference delay. The aim of this paper is to provide a schedulability analysis for a task model that incorporates interference delay, for both fixed and dynamic priorities. We assume an implicit deadline task model. We rely on a task model where this interference is integrated in a general way, without depending on a specific type of hardware resource. There are similar approaches, but they consider fixed priorities. An allocation algorithm to minimise this interference (Imin) is also proposed and compared with existing allocators. The results show how Imin has the best rates in terms of percentages of schedulability and increased utilisation. In addition, Imin presents good results in terms of solution times.
Building and Using Multiple Stacks of Models for the Classification of Learners and Custom Recommending of Quizzes
Authors: Mihăescu Marian Cristian , Popescu Paul Ştefan , Mocanu Mihai Lucian
Published: April 2022

Recommending quizzes in e-Learning systems always represents a challenging task, as the quality of recommendations may have a high impact on the student’s progress. We propose a data analysis workflow based on building multiple stacks of models that use information from former students’ taken quizzes. The current implementation uses the RandomForest algorithm for building the models on a real-world dataset that has been obtained in a controlled environment. As preprocessing techniques, we have used normalization and discretization such that training data have been used for classification and regression tasks. At run-time, the models are queried for classifying the student and inferring an optimal quiz that is personalized for the student. We have evaluated the accuracy parametrized on the previous number of quizzes and found that a possible optimal timeframe for each class of students should be used and may provide more helpful quizzes.
Mechanical Harvesting of Castor Bean (Ricinus communis L.) with a Combine Harvester Equipped with Two Different Headers: A Comparison of Working Performance
Authors: Stefanoni Walter , Latterini Francesco , Malkogiannidis Valantis , Salpiggidis Vlasis , Alexopoulou Efthymia , Pari Luigi
Published: April 2022

Castor bean (Ricinus communis L.) is a promising industrial crop suitable for cultivation in marginal conditions in the Mediterranean area, but the mechanical harvesting of the seeds is still usually performed manually. In this manuscript, the authors present a preliminary test to assess the effectiveness of equipping a combine harvester with a sunflower header to mechanically harvest castor beans. Machinery performance, seed loss from impact (ISL) and cleaning systems (CSL), and seed cleaning were evaluated and compared with the results obtained from the same combine harvester equipped with a cereal header. According to the results, no statistically significant difference in CSL was found. Values ranged from 162. 41 kg dry matter (DM) ha−1 in the cereal header to 145.56 kg DM ha−1 in the sunflower header, corresponding, respectively, to 8% w/w and 7% w/w of the potential seed yield (PSY). Using the sunflower header significantly lowered ISL (158.16 kg DM ha−1, i.e., 8% w/w of PSY) in comparison with the cereal header (282.02 kg DM ha−1, i.e., 14% w/w of PSY). This suggests more gentle cutting and conveying capability of the sunflower header to harvest the plants without losing capsules. On the other hand, the use of different headers did not significantly affect the cleaning of the seeds which averaged at 20% of the total seeds collected in both cases. In conclusion, the study highlights that a conventional combine harvester equipped with a sunflower header could be the first step towards the development of a fully mechanized harvest phase in castor beans which triggers lower seed loss and does not negatively affect the cleaning capacity of the combine harvester. Further studies are also encouraged to confirm these findings in other hybrids.
Green Intellectual Capital as a Support for Corporate Environmental Development—Polish Company Experience
Authors: Bombiak Edyta
Published: April 2022

Green intellectual capital (GIC) is a distinctive intangible asset that may assist organizations in pursuing sustainable development strategies. In Polish organizations, interest in this new concept is low. Analysis of the literature showed a lack of research regarding the implementation of GIC practices or their impacts on the sustainable development of organizations in Polish enterprises. In order to fill the detected research gap, the study covered 150 randomly selected Polish enterprises. The purpose of the research was to determine the impact of activities fostering GIC on the environmental development of companies in Poland and to identify major practices supporting GIC development. In addition, the author attempted to establish a correlation between the impact of individual practices oriented at GIC formation and their practical implementations in the analyzed enterprises. The first stage of the analysis focused on identification of activities leading to the accumulation of GIC implemented in Polish organizations. The second stage involved an assessment of the level of impact of actions contributing to GIC formation on the environmental development of the studied enterprises. During the third stage, the author investigated the relationship between the impact of individual practices oriented at GIC formation and their practical implementation in the analyzed organizations. The study demonstrated that actions supporting GIC formation have an uneven impact on corporate environmental development. Among the key factors identified by the author were environmental attitudes of employees in the working environment (such as paper and energy saving), environmental knowledge, and the implementation of innovative environmental projects. Furthermore, the author established a correlation between the impact assessments of activities leading to GIC accumulation and their practical implementations. The research demonstrated that activities assessed by respondents as more important are more often implemented in practice. The findings of the research may stimulate interest in GIC development and extend the scope of application of GIC-fostering practices over organizations operating in the energy sector.
Thermoelectric Generator as the Waste Heat Recovery Unit of Proton Exchange Membrane Fuel Cell: A Numerical Study
Authors: Pourrahmani Hossein , Shakeri Hamed , Van herle Jan
Published: April 2022

The proton exchange membrane fuel cell (PEMFC) is a prominent environmentally friendly alternative candidate to internal combustion engines in automotive applications. The recovery of the waste heat of light-duty diesel engines has been investigated recently, which is similarly relevant for PEMFCs. Thermoelectric generators (TEG) applied on the stack’s walls have been already proposed and tested as a cooling method for small scale applications of the PEMFC. For the medium scale usages of the PEMFC stack, TEG technology may be further used to recover heat lost through the cooling water required for stack thermal management, which was the focus of the present study. Using an agglomerate model for the PEMFC and a computational fluid dynamic (CFD) thermal model for the TEG heat exchanger unit, the operation and performance of the PEMFC stack and heat recovery unit were simulated, respectively. After validation, results indicated that the transferred heat from the PEMFC to the cooling channel increased the temperature of the coolant from room temperature to 330.5 K at the current density of 0.8 A/cm2. CFD analysis revealed that 37.7 W of the heated wasted by the PEMFC stack could be recovered by the currently available TEG material and geometry.
Source for Autonomous Power Supply System Based on Flow Battery
Authors: Kuzmin Ivan , Loskutov Alexey , Osetrov Evgeny , Kurkin Andrey
Published: April 2022

The article deals with the urgent task of creating a technological and production basis for the development and serial production of energy storage systems with flow batteries and uninterruptible power systems based on them. Flow batteries are a highly efficient solution for long-term energy storage in critical and alternative energy facilities. The main advantage of the flow batteries is the ability to create a system with the required power and capacity without redundant parameters due to the fact that the characteristics of the system are regulated by independent blocks, as in a fuel cell. Among flow batteries, vanadium redox flow batteries (VRFB) are of particular interest, as they have a long service life. The main elements of a flow battery are the stack, which determines the power of the battery and its efficiency, and the electrolyte, which determines the energy capacity of the battery and its service life. A stand for testing the operating modes of the flow battery stack has been developed. A 5 kW flow battery operating on an electrolyte with the addition of hydrochloric acid, which is a stabilizer in new generation electrolytes, has been tested.
Voltage Readjustment Methodology According to Pressure and Temperature Applied to a High Temperature PEM Fuel Cell
Authors: Baudy Mathieu , Rondeau Olivier , Jaafar Amine , Turpin Christophe , Abbou Sofyane , Grignon Mélanie
Published: April 2022

The operating conditions can have uncontrolled effects on the voltage of a High-Temperature Proton Exchange Membrane Fuel Cell (HT-PEMFC). For instance, the HT-PEMFC can be used at ambient pressure, i.e., without having a back pressure regulator. In this case, the variation in the atmospheric pressure directly affects pressures inside the fuel cell, which induces voltage variation. Moreover, in transient phases, several coupled phenomena can have an uncontrolled effect on the voltage. For example, following a change in the current operating point, thermal conditions in the fuel cell can vary, and the temperature stabilization then leads to a voltage variation. This article introduces a readjustment method for the fuel cell voltage to compensate for the effects of the pressure and temperature variations that are undergone and to decouple their effects. This methodology is based on the realization of a design of experiments to characterize the voltage sensitivity to pressure ([1; 1.5 bar]) and temperature ([120; 180 °C]) between 0.2 and 1 A/cm2 of an Advent PBI MEA (formerly BASF Celtec®-P 1100 W). The data obtained allowed identifying an empirical model that takes into account the aging caused by the experiment. Finally, the methodology is criticized before proposing an alternative method.
A New Short Term Electrical Load Forecasting by Type-2 Fuzzy Neural Networks
Authors: Tian Man-Wen , Alattas Khalid , El-Sousy Fayez , Alanazi Abdullah , Mohammadzadeh Ardashir , Tavoosi Jafar , Mobayen Saleh , Skruch Paweł
Published: April 2022

In this study, we present a new approach for load forecasting (LF) using a recurrent fuzzy neural network (RFNN) for Kermanshah City. Imagine if there is a need for electricity in a region in the coming years, we will have to build a power plant or reinforce transmission lines, so this will be resolved if accurate forecasts are made at the right time. Furthermore, suppose that by building distributed generation plants, and predicting future consumption, we can conclude that production will be more than consumption, so we will seek to export energy to other countries and make decisions on this. In this paper, a novel combination of neural networks (NNs) and type-2 fuzzy systems (T2FSs) is used for load forecasting. Adding feedback to the fuzzy neural network can also benefit from past moments. This feedback structure is called a recurrent fuzzy neural network. In this paper, Kermanshah urban electrical load data is used. The simulation results prove the efficiency of this method for forecasting the electrical load. We found that we can accurately predict the electrical load of the city for the next day with 98% accuracy. The accuracy index is the evaluation of mean absolute percentage error (MAPE). The main contributions are: (1) Introducing a new fuzzy neural network. (2) Improving and increasing the accuracy of forecasting using the proposed fuzzy neural network. (3) Taking data from a specific area (Kermanshah City) and forecasting the electrical load for that area. (4) The ability to enter new data without calculations from the beginning.