bluuebunny/crossref_metadata_2025 huggingface dataset:
Gaming and Resilience: Teaching by Playing Together—Online Educational Competition at School during the Pandemic
Authors: Piangiamore Giovanna Lucia , Maramai Alessandra
Published: November 2022

Educational and training initiatives for natural hazard reduction, climate change, and environmental sustainability are increasingly common. We describe educational games in which the protagonist, a fictional character girl, saves herself and others from natural disasters. This girl faces risk situations, from earthquake to flood to environmental challenges, and for each of these she is the protagonist of educational quizzes and of an escape room. These games were designed and played online during the COVID-19 pandemic, to introduce an engaging activity, reducing the difficulties of both students and teachers in distance learning. Simultaneous challenges between several classes were played during special scientific events, with a total of more than 8000 students flanked by about 500 teachers, always with very positive feedback. We pooled our knowledge to embrace innovation in gamification at school. Our games aim at increasing the response capacity of future more resilient citizens to protect themselves and others, adapting to natural risks, and to spread good practices in support of the civil protection. Each online race between classes from primary and middle school (ISCDE 2) is based on cooperative learning and followed by an important debriefing moment of reflective learning, guided by researchers to deeply analyze scientific topics.
Time Series Prediction of Dam Deformation Using a Hybrid STL–CNN–GRU Model Based on Sparrow Search Algorithm Optimization
Authors: Lin Chuan , Weng Kailiang , Lin Youlong , Zhang Ting , He Qiang , Su Yan
Published: November 2022

During its long service life, an arch dam affected by a combination of factors exhibits a typical time-varying characteristic in terms of its structure and material properties, and the deformation in the dam structure can directly and reliably reflect the health and service status of dams. Therefore, an accurate deformation prediction is an important part of dam safety monitoring. However, due to multiple factors, dam deformation data often tend to be highly volatile, and most existing deformation estimation techniques employ a single algorithm, which may not effectively capture the potential change process. A hybrid model for dam deformation prediction has been proposed to overcome this problem. First, dam deformation data are decomposed into three components by seasonal and trend decomposition using loess. Second, a convolutional neural network–gated recurrent unit (GRU) hybrid model, which optimizes hyperparameters using the sparrow search algorithm, is used to capture the nonlinear relationships that exist in each component. Finally, the final prediction result of dam deformation is the comprehensive output of multiple submodules. The deformation monitoring data (period: 2009–2019) of a parabolic variable-thickness double-curved arch dam located in China are considered as the survey target. The test results indicate that the proposed model is suitable for short-term and long-term prediction and outperforms other models in terms of higher robustness to abnormal sequences than other conventional models (R² differs by 5.50% and 7.87%, respectively, in short-term and long-term predictions for different measurement points, while other models differ by 9.78% to reach 15.71%, respectively). Among the models studied, the GRU shows better robustness to abnormal series than the LSTM with good prediction accuracy, fewer parameters, and a simpler structure. Hence, the GRU can be employed for dam deformation prediction in practical engineering.
Occurrence and Degradation of Free and Conjugated Estrogens in a River Receiving Feedlot Animal Discharge
Authors: Hung Hao-Shen , Yeh Kuei-Jyum C. , Hsieh Chi-Ying , Chen Ting-Chien
Published: November 2022

This study analyzed concentrations of 17β-estradiol (E2), estrone (E1), estriol (E3), 17α-ethynylestradiol (EE2), diethylstilbestrol (DES), 17β-estradiol-3-sulfate (E2-3S), estrone-3-sulfate (E1-3S), 17β-estradiol-3-glucuronide (E2-3G), and estrone-3-glucuronide (E1-3G) in river water, received from intensive feedlot operations wastewater in WuLo Creek, Taiwan. Moreover, the estrogen degradation in situ was analyzed. The average concentrations were 54.15 ± 31.42, 9.71 ± 6.42 and 3.55 ± 2.41 ng/L for E1, E2 and E3, respectively. The concentrations and order were similar to the polluted river and higher than most rivers’ concentrations. The conjugated estrogen concentrations ranged from ND to 13.2 ng/L (E1-3S), ND to 10.4 ng/L (E2-3S), ND to 10.0 ng/L (E1-3G), and ND to 3.6 ng/L (E2-3G), and the detection rates were 76%, 71%, 56%, and 15%, respectively. In the present study, the high detection rates of conjugate estrogen were more elevated than the water receiving STP effluent, suggesting that the source was the river water close to the animal wastewater discharge. In the degradation test, the DES concentrations slightly declined after 24 h, but E1-3G did not significantly change over time (p > 0.05). The degradation of free estrogen occurred during the first 12 h period, and residual concentration was not further decreased after 24 h. In the environment, E1 had higher concentrations than E2 and E3, suggesting that E1 was more resistant to degradation than E2 and E3 at low concentrations. However, the degradation test in the present study suggested that E1 rapidly degraded at high ambient concentrations due to the high degradation constant.
A Nested U-Shaped Residual Codec Network for Strip Steel Defect Detection
Authors: Guo Huaping , Zhan Shanggui , Zhang Li , Zhu Wenbo , Sun Yange , Wang Jing
Published: November 2022

Strip steel is an important raw material for the related industries, such as aerospace, shipbuilding, and pipelines, and any quality defects in the strip steel would lead to huge economic losses. However, it is still a challenge task to effectively detect the defects from the background of the strip steel due to its complex variations, including variable flaws, chaotic background, and noise invasion. This paper proposes a novel strip steel defect detection method based on a U-shaped residual network, including an encoder and a decoder. The encoder is a fully convolutional neural network in which attention mechanisms are embedded to adequately extract multi-scale defect features and ro ignore irrelevant background regions. The decoder is a U-shaped residual network to capture more contextual data from different scales, without significantly increasing the computational cost due to the pooling operations used in the U-shaped network. Furthermore, a residual refinement module is designed immediately after the decoder to further optimize the coarse defect map. Experimental results show that the proposed method can effectively segment surface defect objects from irrelevant background noise and is superior to other advanced methods with clear boundaries.
Surface Displacement Measurements of Artworks: New Data Processing for Speckle Pattern Interferometry
Authors: Auber--Le Saux Jessica , Detalle Vincent , Bai Xueshi , Andrianakis Michalis , Wilkie-Chancellier Nicolas , Tornari Vivi
Published: November 2022

Curators have developed preventive conservation strategies and usually try to control the temperature (T) and relative humidity (RH) variations in the museum rooms to stabilise the artworks. The control systems chosen by museums depend on the size and age of the building, the financial means and the strategies that can be adapted. However, there is a lack of methods that can monitor mechanical changes or chemical reactions of objects in real-time or regularly. It would therefore ideally be preferable to monitor each of them to alert them to preserve them. For this purpose, a non-destructive, non-contact, full-field technique, Digital Holographic Speckle Pattern Interferometry (DHSPI), has already been developed and allows direct tracking of changes on the surface of artworks. This technique is based on phase-shifting speckle interferometry and gives the deformation of the surface below the level of the micro-meter of the analysed object. In order to monitor the deformation continuously, a large number of images are acquired by DHSPI and have to be processed. The existing process consists of removing noise from the interferogram, unwrapping this image, and deriving and displaying a 2D or 3D deformation map. In order to improve the time and accuracy of processing the imaging data, a simpler and faster processing method is developed. Using Matlab®, a denoising methodology for the interference pattern generated during data acquisition is created, based on a stationary wavelet transform. The unwrapped image is calculated using the CPULSI (Calibrated Phase Unwrapping based on Least-Squares and Iterations) algorithm as it gives the fastest results among the tested methods. The unwrapped phase is then transformed into surface displacement. This process performs these steps for each interferogram automatically. It allows access to 2D or 3D deformation maps.
Analysis and Identification of Bioactive Compounds of Cannabinoids in Silico for Inhibition of SARS-CoV-2 and SARS-CoV
Authors: Chen Chenxiao , Liang Hao , Deng Yanchun , Yang Xiushi , Li Xiaoming , Hou Chunsheng
Published: November 2022

Despite the approval of multiple vaccinations in different countries, the majority of the world’s population remains unvaccinated due to discrepancies in vaccine distribution and limited production capacity. The SARS-CoV-2 RBD-ACE2 complex (receptor binding domain that binds to ACE2) could be a suitable target for the development of a vaccine or an inhibitor. Various natural products have been used against SARS-CoV-2. Here, we docked 42 active cannabinoids to the active site of the SARS-CoV-2 and SARS-CoV complex of RBD-ACE2. To ensure the flexibility and stability of the complex produced after docking, the top three ligand molecules with the best overall binding energies were further analyzed through molecular dynamic simulation (MDS). Then, we used the webserver Swissadme program and binding free energy to calculate and estimate the MMPBSA and ADME characteristics. Our results showed that luteolin, CBGVA, and CBNA were the top three molecules that interact with the SARS-CoV-2 RBD-ACE2 complex, while luteolin, stigmasterol, and CBNA had the strongest contact with that SARS-CoV. Our findings show that luteolin may be a potential inhibitor of infections caused by coronavirus-like pathogens such as COVID-19, although further in vivo and in vitro research is required.
A Few-Shot Learning-Based EEG and Stage Transition Sequence Generator for Improving Sleep Staging Performance
Authors: You Yuyang , Guo Xiaoyu , Zhong Xuyang , Yang Zhihong
Published: November 2022

In this study, generative adversarial networks named SleepGAN are proposed to expand the training set for automatic sleep stage classification tasks by generating both electroencephalogram (EEG) epochs and sequence relationships of sleep stages. In order to reach high accuracy, most existing classification methods require substantial amounts of training data, but obtaining such quantities of real EEG epochs is expensive and time-consuming. We introduce few-shot learning, which is a method of training a GAN using a very small set of training data. This paper presents progressive Wasserstein divergence generative adversarial networks (GANs) and a relational memory generator to generate EEG epochs and stage transition sequences, respectively. For the evaluation of our generated data, we use single-channel EEGs from the public dataset Sleep-EDF. The addition of our augmented data and sequence to the training set was shown to improve the performance of the classification model. The accuracy of the model increased by approximately 1% after incorporating generated EEG epochs. Adding both the augmented data and sequence to the training set resulted in a further increase of 3%, from the original accuracy of 79.40% to 83.06%. The result proves that SleepGAN is a set of GANs capable of generating realistic EEG epochs and transition sequences under the condition of insufficient training data and can be used to enlarge the training dataset and improve the performance of sleep stage classification models in clinical practice.
A Genome-Wide Association Study Reveals a BDNF-Centered Molecular Network Associated with Alcohol Dependence and Related Clinical Measures
Authors: Levchenko Anastasia , Malov Sergey , Antonik Alexey , Protsvetkina Anastasia , Rybakova Kseniya V. , Kanapin Alexander , Yakovlev Alexey N. , Nenasteva Anna Y. , Nikolishin Anton E. , Cherkasov Nikolay , Chuprova Natalia A. , Blagonravova Anna S. , Sergeeva Angelica V. , Zhilyaeva Tatyana V. , Denisenko Maria K. , Gainetdinov Raul R. , Kibitov Alexander O. , Krupitsky Evgeny M.
Published: November 2022

At least 50% of factors predisposing to alcohol dependence (AD) are genetic and women affected with this disorder present with more psychiatric comorbidities, probably indicating different genetic factors involved. We aimed to run a genome-wide association study (GWAS) followed by a bioinformatic functional annotation of associated genomic regions in patients with AD and eight related clinical measures. A genome-wide significant association of rs220677 with AD (p-value = 1.33 × 10−8 calculated with the Yates-corrected χ2 test under the assumption of dominant inheritance) was discovered in female patients. Associations of AD and related clinical measures with seven other single nucleotide polymorphisms listed in previous GWASs of psychiatric and addiction traits were differently replicated in male and female patients. The bioinformatic analysis showed that regulatory elements in the eight associated linkage disequilibrium blocks define the expression of 80 protein-coding genes. Nearly 68% of these and of 120 previously published coding genes associated with alcohol phenotypes directly interact in a single network, where BDNF is the most significant hub gene. This study indicates that several genes behind the pathogenesis of AD are different in male and female patients, but implicated molecular mechanisms are functionally connected. The study also reveals a central role of BDNF in the pathogenesis of AD.
Molecular-Targeted Therapy for Tumor-Agnostic Mutations in Acute Myeloid Leukemia
Authors: Arai Hironori , Minami Yosuke , Chi SungGi , Utsu Yoshikazu , Masuda Shinichi , Aotsuka Nobuyuki
Published: November 2022

Comprehensive genomic profiling examinations (CGPs) have recently been developed, and a variety of tumor-agnostic mutations have been detected, leading to the development of new molecular-targetable therapies across solid tumors. In addition, the elucidation of hereditary tumors, such as breast and ovarian cancer, has pioneered a new age marked by the development of new treatments and lifetime management strategies required for patients with potential or presented hereditary cancers. In acute myeloid leukemia (AML), however, few tumor-agnostic or hereditary mutations have been the focus of investigation, with associated molecular-targeted therapies remaining poorly developed. We focused on representative tumor-agnostic mutations such as the TP53, KIT, KRAS, BRCA1, ATM, JAK2, NTRK3, FGFR3 and EGFR genes, referring to a CGP study conducted in Japan, and we considered the possibility of developing molecular-targeted therapies for AML with tumor-agnostic mutations. We summarized the frequency, the prognosis, the structure and the function of these mutations as well as the current treatment strategies in solid tumors, revealed the genetical relationships between solid tumors and AML and developed tumor-agnostic molecular-targeted therapies and lifetime management strategies in AML.
Pathophysiology and Outcomes of Endothelium Function in Coronary Microvascular Diseases: A Systematic Review of Randomized Controlled Trials and Multicenter Study
Authors: Avtaar Singh Sanjeet Singh , Nappi Francesco
Published: November 2022

Background: Coronary macrovascular disease is a concept that has been well-studied within the literature and has long been the subject of debates surrounding coronary artery bypass grafting (CABG) vs. Percutaneous Coronary Intervention (PCI). ISCHEMIA trial reported no statistical difference in the primary clinical endpoint between initial invasive management and initial conservative management, while in the ORBITA trial PCI did not improve angina frequency score significantly more than placebo, albeit PCI resulted in more patient-reported freedom from angina than placebo. However, these results did not prove the superiority of the PCI against OMT, therefore do not indicate the benefit of PCI vs. the OMT. Please rephrase the sentence. We reviewed the role of different factors responsible for endothelial dysfunction from recent randomized clinical trials (RCTs) and multicentre studies. Methods: A detailed search strategy was performed using a dataset that has previously been published. Data of pooled analysis include research articles (human and animal models), CABG, and PCI randomized controlled trials (RCTs). Details of the search strategy and the methods used for data pooling have been published previously and registered with Open-Source Framework. Results: The roles of nitric oxide (NO), endothelium-derived contracting factors (EDCFs), and vasodilator prostaglandins (e.g., prostacyclin), as well as endothelium-dependent hyperpolarization (EDH) factors, are crucial for the maintenance of vasomotor tone within the coronary vasculature. These homeostatic mechanisms are affected by sheer forces and other several factors that are currently being studied, such as vaping. The role of intracoronary testing is crucial when determining the effects of therapeutic medications with further studies on the horizon. Conclusion: The true impact of coronary microvascular dysfunction (CMD) is perhaps underappreciated, which supports the role of medical therapy in determining outcomes. Ongoing trials are underway to further investigate the role of therapeutic agents in secondary prevention.
Assessing Soil Organic Carbon Stocks and Particle-Size Fractions across Cropping Systems in the Kiti Sub-Watershed in Central Benin
Authors: Ahogle Arcadius Martinien Agassin , Alladassi Felix Kouelo , Akplo Tobi Moriaque , Azontonde Hessou Anastase , Houngnandan Pascal
Published: November 2022

Soil organic carbon storage in agricultural soil constitutes a crucial potential for sustainable agricultural productivity and climate change mitigation. This paper aimed at assessing soil organic carbon stock and its distribution in three particle size fractions across five cropping systems located in Kiti sub-watershed in Benin. Soil samples were collected using a grid sampling method on four soil depth layers: 0–10, 10–20, 20–30 and 30–40 cm in five cropping systems maize–cotton relay cropping (MCRC), yam–maize intercropping (YMI), teak plantation (TP), 5-year fallow (5YF) and above 10-year fallow (Ab10YF) from July to August 2017. Soil organic carbon stock (C stock) was estimated for the different soil layers and particle-size fractionation of soil organic matter was performed considering three fractions. The fractions coarse particulate organic matter (cPOM: 250–2000 µm), fine particulate organic matter (fPOM: 53–250 µm) and non-particulate organic matter (NOM: <53 µm) were separated from two soil depth layers: 0–10 and 10–20 cm. The results showed that fallow lands Ab10YF and 5YF exhibited the highest C stock, 22.20 and 17.74 Mg C·ha−1, while cultivated land under tillage MCRC depicted the lowest, C stock 11.48 Mg C·ha−1. The three organic carbon fractions showed a significant variation across the cropping systems with the NOM fraction holding the largest contribution to total soil organic carbon for all the cropping systems, ranging between 3.40 and 7.99 g/kg. The cPOM and fPOM were the most influenced by cropping systems with the highest concentration observed in Ab10YF and 5YF. The findings provide insights for upscaling farm management practices towards sustainable agricultural systems with substantial potential for carbon sequestration and climate change mitigation.
A Review of Biomass-Derived Heterogeneous Catalysts for Biodiesel Production
Authors: Chandra Kishore Somasundaram , Perumal Suguna , Atchudan Raji , Sundramoorthy Ashok K. , Alagan Muthulakshmi , Sangaraju Sambasivam , Lee Yong Rok
Published: November 2022

The scientific community is being forced to consider alternative renewable fuels such as biodiesel as a result of the sharp increases in the price of petroleum and the increased demand for petroleum-derived products. Transesterification is a technique used to create biodiesel where a variety of edible oils, non-edible oils, and animal fats are used. For this, either a homogeneous or heterogeneous catalyst is utilized. An appropriate catalyst is chosen based on the quantity of free fatty acid content in the oil. The main distinction between homogeneous and heterogeneous catalysts is that compared to the heterogeneous catalyst, the homogeneous catalyst is not affected by the quantity of free fatty acids in the oil. Early methods of producing biodiesel relied on homogeneous catalysts, which have drawbacks such as high flammability, toxicity, corrosion, byproducts such as soap and glycerol, and high wastewater output. The majority of these issues are solved by heterogeneous catalysts. Recent innovations use novel heterogeneous catalysts that are obtained from biomass and biowaste resources. Numerous researchers have documented the use of biomass-derived heterogeneous catalysts in the production of high-quality, pure biodiesel as a potentially greener manufacturing method. The catalysts were significantly altered through conventional physical processes that were both cost- and energy-effective. The present review is intended to analyze catalysts from biowaste for making biodiesel at a minimal cost. The most recent methods for creating diverse kinds of catalysts—including acidic, basic, bifunctional, and nanocatalysts—from various chemicals and biomass are highlighted in this review. Additionally, the effects of various catalyst preparation methods on biodiesel yield are thoroughly explored.
Stemness Activity Underlying Whole Brain Regeneration in a Basal Chordate
Authors: Gordon Tal , Zaquin Tal , Kowarsky Mark Alec , Voskoboynik Yotam , Hendin Noam , Wurtzel Omri , Caicci Federico , Manni Lucia , Voskoboynik Ayelet , Shenkar Noa
Published: November 2022

Understanding how neurons regenerate following injury remains a central challenge in regenerative medicine. Adult mammals have a very limited ability to regenerate new neurons in the central nervous system (CNS). In contrast, the basal chordate Polycarpa mytiligera can regenerate its entire CNS within seven days of complete removal. Transcriptome sequencing, cellular labeling, and proliferation in vivo essays revealed that CNS regeneration is mediated by a newly formed neural progeny and the activation of neurodevelopmental pathways that are associated with enhanced stem-cell activity. Analyzing the expression of 239 activated pathways enabled a quantitative understanding of gene-set enrichment patterns at key regeneration stages. The molecular and cellular mechanisms controlling the regenerative ability that this study reveals can be used to develop innovative approaches to enhancing neurogenesis in closely-related chordate species, including humans.
Dihydromyricetin Attenuates Depressive-like Behaviors in Mice by Inhibiting the AGE-RAGE Signaling Pathway
Authors: Huang Jun , Chen Bin , Wang Hao , Hu Sheng , Yu Xudong , Reilly James , He Zhiming , You Yong , Shu Xinhua
Published: November 2022

Depression is a complex mental disorder, affecting approximately 280 million individuals globally. The pathobiology of depression is not fully understood, and the development of new treatments is urgently needed. Dihydromyricetin (DHM) is a natural flavanone, mainly distributed in Ampelopsis grossedentata. DHM has demonstrated a protective role against cardiovascular disease, diabetes, liver disease, cancer, kidney injury and neurodegenerative disorders. In the present study, we examined the protective effect of DHM against depression in a chronic depression mouse model induced by corticosterone (CORT). Animals exposed to CORT displayed depressive-like behaviors; DHM treatment reversed these behaviors. Network pharmacology analyses showed that DHM’s function against depression involved a wide range of targets and signaling pathways, among which the inflammation-linked targets and signaling pathways were critical. Western blotting showed that CORT-treated animals had significantly increased levels of the advanced glycation end product (AGE) and receptor of AGE (RAGE) in the hippocampus, implicating activation of the AGE-RAGE signaling pathway. Furthermore, enzyme-linked immunosorbent assay (ELISA) detected a marked increase in the production of proinflammatory cytokines, interleukin-1 beta (IL-1β), IL-6 and tumor necrosis factor-alpha (TNFα) in the hippocampus of CORT-treated mice. DHM administration significantly counteracted these CORT-induced changes. These findings suggest that protection against depression by DHM is mediated by suppression of neuroinflammation, predominantly via the AGE-RAGE signaling pathway.
Reduced SKP2 Expression Adversely Impacts Genome Stability and Promotes Cellular Transformation in Colonic Epithelial Cells
Authors: Neudorf Nicole M. , Thompson Laura L. , Lichtensztejn Zelda , Razi Tooba , McManus Kirk J.
Published: November 2022

Despite the high morbidity and mortality rates associated with colorectal cancer (CRC), the underlying molecular mechanisms driving CRC development remain largely uncharacterized. Chromosome instability (CIN), or ongoing changes in chromosome complements, occurs in ~85% of CRCs and is a proposed driver of cancer development, as the genomic changes imparted by CIN enable the acquisition of karyotypes that are favorable for cellular transformation and the classic hallmarks of cancer. Despite these associations, the aberrant genes and proteins driving CIN remain elusive. SKP2 encodes an F-box protein, a variable subunit of the SKP1-CUL1-F-box (SCF) complex that selectively targets proteins for polyubiquitylation and degradation. Recent data have identified the core SCF complex components (SKP1, CUL1, and RBX1) as CIN genes; however, the impact reduced SKP2 expression has on CIN, cellular transformation, and oncogenesis remains unknown. Using both short- small interfering RNA (siRNA) and long-term (CRISPR/Cas9) approaches, we demonstrate that diminished SKP2 expression induces CIN in both malignant and non-malignant colonic epithelial cell contexts. Moreover, temporal assays reveal that reduced SKP2 expression promotes cellular transformation, as demonstrated by enhanced anchorage-independent growth. Collectively, these data identify SKP2 as a novel CIN gene in clinically relevant models and highlight its potential pathogenic role in CRC development.
Triage Grading and Correct Diagnosis Are Critical for the Emergency Treatment of Anaphylaxis
Authors: Dondi Arianna , Calamelli Elisabetta , Scarpini Sara , Candela Egidio , Biserni Giovanni Battista , Ghizzi Chiara , Lombardi Francesca , Salvago Paola , Serra Laura , Corsini Ilaria , Lanari Marcello
Published: November 2022

Introduction: Anaphylaxis is one of the most frequent and misdiagnosed emergencies in the pediatric emergency department (PED). We aimed to assess which factors play a major role for a correct diagnosis and an appropriate therapy. Methods: We reviewed the records of children discharged with a diagnosis of anaphylaxis or an allergic reaction over 11 years from 3 hospitals in the Bologna city area. Results: One hundred and sixteen cases matched the criteria (0.03% of the total admittances) and were divided according to the patients’ symptoms at arrival: active acute patients [AP], n = 50, or non-acute patients ([NAP], n = 66). At the patients’ discharge, anaphylaxis was diagnosed in 39 patients (33.6%). Some features seemed to favor a correct diagnosis: active symptoms at arrival (AP vs. NAP, p < 0.01), high-priority triage code (p < 0.01), and upper airway involvement (p < 0.01). Only 14 patients (12.1%), all in the AP group, received epinephrine, that was more likely administered to patients recognized to have anaphylaxis (p < 0.01) and with cardiovascular, respiratory, or persistent gastrointestinal symptoms (p < 0.02), as confirmed by logistic regression analysis. Conclusions: Anaphylaxis is still under-recognized and under-treated. Correct triage coding and a proper diagnosis seem to foster an appropriate treatment. Physicians often prefer third-line interventions. Specific training for nurses and physicians might improve the management of this disease.
Effect of Adipose-Derived Mesenchymal Stem Cells (ADMSCs) Application in Achilles-Tendon Injury in an Animal Model
Authors: Arnaud-Franco Ángel , Lara-Arias Jorge , Marino-Martínez Iván A. , Cienfuegos-Jiménez Oscar , Barbosa-Quintana Álvaro , Peña-Martínez Víctor M.
Published: November 2022

Background: Achilles-tendon rupture prevails as a common tendon pathology. Adipose-derived mesenchymal stem cells (ADMSCs) are multipotent stem cells derived from adipose tissue with attractive regeneration properties; thus, their application in tendinopathies could be beneficial. Methods: Male rabbit ADMSCs were obtained from the falciform ligament according to previously established methods. After tenotomy and suture of the Achilles tendon, 1 × 106 flow-cytometry-characterized male ADMSCs were injected in four female New Zealand white rabbits in the experimental group (ADMSC group), whereas four rabbits were left untreated (lesion group). Confirmation of ADMSC presence in the injured site after 12 weeks was performed with quantitative sex-determining region Y (SRY)-gene RT-PCR. At Week 12, histochemical analysis was performed to evaluate tissue regeneration along with quantitative RT-PCR of collagen I and collagen III mRNA. Results: Presence of male ADMSCs was confirmed at Week 12. No statistically significant differences were found in the histochemical analysis; however, statistically significant differences between ADMSC and lesion group expression of collagen I and collagen III were evidenced, with 36.6% and 24.1% GAPDH-normalized mean expression, respectively, for collagen I (p < 0.05) and 26.3% and 11.9% GAPDH-normalized mean expression, respectively, for collagen III (p < 0.05). The expression ratio between the ADMSC and lesion group was 1.5 and 2.2 for collagen I and collagen III, respectively. Conclusion: Our results make an important contribution to the understanding and effect of ADMSCs in Achilles-tendon rupture.
Data-Driven Machine Learning Approach for Predicting the Higher Heating Value of Different Biomass Classes
Authors: Afolabi Inioluwa Christianah , Epelle Emmanuel I. , Gunes Burcu , Güleç Fatih , Okolie Jude A.
Published: November 2022

Higher heating values (HHV) is a very useful parameter for assessing the design and large-scale operation of biomass-driven energy systems. HHV is conventionally measured experimentally with an adiabatic oxygen bomb calorimeter. This procedure is often time-consuming and expensive. Furthermore, limited access to the required facilities is the main bottleneck for researchers. Empirical linear and nonlinear models have initially been proposed to address these concerns. However, most of the models showed discrepancies with experimental results. Data-driven machine learning (ML) methods have also been adopted for HHV predictions due to their suitability for nonlinear problems. However, most ML correlations are based on proximate or ultimate analysis. In addition, the models are only applicable to either the originator biomass or one specific type. To address these shortcomings, a total of 227 biomass datasets based on four classes of biomass, including agricultural residue, industrial waste, energy crop, and woody biomass, were employed to develop and verify three different ML models, namely artificial neural network (ANN), decision tree (DT) and random forest (RF). The model incorporates proximate and ultimate analysis data and biomass as input features. RF model is identified as the most reliable because of its lowest mean absolute error (MAE) of 1.01 and mean squared error (MSE) of 1.87. The study findings can be used to predict HHV accurately without performing experiments.
Differences in Sleep Offset Timing between Weekdays and Weekends in 79,161 Adult Participants in the UK Biobank
Authors: Kelly Rachael M. , McDermott John H. , Coogan Andrew N.
Published: November 2022

Variability in the timing of daily sleep is increasingly recognized as an important factor in sleep and general physical health. One potential driver of such daily variations in sleep timing is different work and social obligations during the “working week” and weekends. To investigate the nature of weekday/weekend differences in the timing of sleep offset, we examined actigraphy records of 79,161 adult participants in the UK Biobank who wore an actiwatch for 1 week. The time of sleep offset was found to be on average 36 min later on weekends than on weekdays, and when this difference was expressed as an absolute value (i.e., irrespective of sleep offset being either later or earlier on weekends), it was 63 min. Younger age, more socioeconomic disadvantage, currently being in employment, being a smoker, being male, being of non-white ethnicity and later chronotype were associated with greater differences in sleep offset between weekdays and weekend days. Greater differences in sleep offset timing were associated with age-independent small differences in cardiometabolic health indicators of BMI and diastolic blood pressure, but not HbA1c or systolic blood pressure. In a subset of participants with Type 2 Diabetes Mellitus, weekday/weekend sleep offset differences were associated weakly with BMI, systolic blood pressure and physical activity. Overall, this study demonstrates potentially substantive differences in sleep offset timings between weekdays and weekends in a large sample of UK adults, and that such differences may have public health implications.
Behavior Prediction Based Trust Evaluation for Adaptive Consensus of Quadrotors
Authors: Xie Yichen , Li Yuzhu , Dong Wei
Published: November 2022

Without proper treatment, a malfunctional quadrotor may bring severe consequences, e.g., becoming out of control, to the whole swarm. To tackle this problem, we develop a trust evaluations based consensus protocol. Specifically, each quadrotor in the swarm communicates with its connected neighbors, exchanging behavior predictions. By comparing the predicted and the actual behaviors of its neighbor regarding a pre-defined tolerance, each quadrotor assigns trust values to determine potentially legitimate or malfunctional companions. On this basis, an online adaptive controller adjusts each weight in the protocol corresponding to the trust evaluations designed before. We prove that, within proper tolerance, it is almost sure that the legitimate quadrotors can identify the malfunctional quadrotors through trust evaluations and ameliorate their effects on the whole system. Almost surely, the legitimate quadrotors can converge to their center in a finite time. We verify our method through MATLAB and GAZEBO. In particular, with our proposed method, the swarm system discussed in this paper is able to reach position and velocity consensus in the presence of malfunctional quadrotors.
Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics
Authors: Mazher Moona , Qayyum Abdul , Puig Domenec , Abdel-Nasser Mohamed
Published: November 2022

To completely comprehend neurodevelopment in healthy and congenitally abnormal fetuses, quantitative analysis of the human fetal brain is essential. This analysis requires the use of automatic multi-tissue fetal brain segmentation techniques. This paper proposes an end-to-end automatic yet effective method for a multi-tissue fetal brain segmentation model called IRMMNET. It includes a inception residual encoder block (EB) and a dense spatial attention (DSAM) block, which facilitate the extraction of multi-scale fetal-brain-tissue-relevant information from multi-view MRI images, enhance the feature reuse, and substantially reduce the number of parameters of the segmentation model. Additionally, we propose three methods for predicting gestational age (GA)—GA prediction by using a 3D autoencoder, GA prediction using radiomics features, and GA prediction using the IRMMNET segmentation model’s encoder. Our experiments were performed on a dataset of 80 pathological and non-pathological magnetic resonance fetal brain volume reconstructions across a range of gestational ages (20 to 33 weeks) that were manually segmented into seven different tissue categories. The results showed that the proposed fetal brain segmentation model achieved a Dice score of 0.791±0.18, outperforming the state-of-the-art methods. The radiomics-based GA prediction methods achieved the best results (RMSE: 1.42). We also demonstrated the generalization capabilities of the proposed methods for tasks such as head and neck tumor segmentation and the prediction of patients’ survival days.
Plant Identification in the 21st Century—What Possibilities Do Modern Identification Keys Offer for Biology Lessons?
Authors: Finger Alexander , Groß Jorge , Zabel Jörg
Published: November 2022

The loss of biodiversity and the accompanying “plant blindness” are major problems for mankind. Biology classes in Germany deal with this topic with the aim of enabling students to identify plants in their surroundings. Here, the process of plant identification plays a key role. To render the process of plant identification, more student-oriented, new digital approaches are being developed. Thus, teachers are now being confronted with digital tools for plant identification without having exact knowledge of their added value. This intervention study was therefore conducted in order to determine the effects on learning by means of a paper-based dichotomous identification key (Eikes Baumschule) and a digital identification app (ID-Logics). The results show that both tools have individual media-related differences that should be considered when designing learning strategies: With the previously reduced, paper-based tool, students can identify plants more quickly and often more correctly. However, the digital app has advantages in terms of enjoyment and learning about individual characteristics of plants. The study shows the challenges and opportunities associated with the (digital) medium. Furthermore, it sheds light on the process of species determination and reveals further fields of research in science education.
Integration of EV in the Grid Management: The Grid Behavior in Case of Simultaneous EV Charging-Discharging with the PV Solar Energy Injection
Authors: Rwamurangwa Evode , Gonzalez Juan Diaz , Butare Albert
Published: November 2022

The actual research in terms of energy focuses drastically on the use of green energy resources. Hydropower systems have been the most known green sources for years. However, the hydropower systems, which are seasonal and most exploited, do not cover the speed of increasing daily demand. The injection of solar power could be a supporting alternative, but it is only in daylight, weather dependent and intermittent. Therefore, a storage system is required. The batteries are the quick recourse. Not only the energy sector, but also the transport systems are not left behind; they are striving to turn green. Therefore, they are turning to electric vehicles (EVs) and electric moto-bicycles (EMBs). On the other hand, this option tends to be a sharply increasing demand that can be a burden to the grid, i.e., the increase in the EVs and EMBs implies increases in power demand, grid components and pressure on the grid. Fortunately, the EVs use batteries to store energy for their use. Therefore, the EVs are the power storage system, they become part of the power management system and they can save the power surplus. With the injection of PV solar power, there is no need for an extra storage system, as the EVs are charged from the grid and store the solar energy that can be used later after sunset. The bi-directional off-board charger is a solution as it allows the grid to charge the vehicle (G2V) and the vehicle to send power back to grid (V2G). The inclusion of EVs in power management introduces the concept of vehicle-to-vehicle (V2V) when one EV can charge another, and the vehicle-to-load (V2X) where the EV can supply power to EMBs or any load. The V2G, G2V, V2X, the inclusion on solar energy to the grid and the behavior of the grid in that scenario will be illustrated in this paper.
A Gain-Enhanced Low Hardware Complexity Charge-Domain Read-Out Integrated Circuit Using a Sampled Charge Redistribution Technique
Authors: Jo Sung-Hun
Published: November 2022

A gain-enhanced low hardware complexity charge-domain read-out integrated circuit is implemented. By adopting a sampled charge redistribution technique, low hardware complexity is achieved, which in turn saves 10% of the die area and provides 33% gain enhancement compared to the conventional topology. In particular, a charge-domain discrete-time filter with inherent reconfigurability is a key building block, which can also act as an anti-aliasing filter before the analog-to-digital converter. The measurement results show good agreement with the intended frequency response. The proposed filter is implemented using a 0.11 μm CMOS process and occupies 0.15 mm2.
Memristive Circuit Design of Nonassociative Learning under Different Emotional Stimuli
Authors: Sun Junwei , Zhao Linhao , Wen Shiping , Wang Yanfeng
Published: November 2022

Most memristor-based circuits only consider the mechanism of nonassociative learning, and the effect of emotion on nonassociative learning is ignored. In this paper, a memristive circuit that can realize nonassociative learning under different emotional stimuli is designed. The designed circuit consists of stimulus judgment module, habituation module, sensitization module, emotion module. When different stimuli are applied, habituation or sensitisation is formed based on the intensity and nature of the stimuli. In addition, the influence of emotion on nonassociative is considered. Different emotional stimuli will affect the speed of habituation formation and strong negative stimuli will lead to sensitization. The simulation results on PSPICE show that the circuit can simulate the above complex biological mechanism. The memristive circuit of nonassociative learning under different emotional stimuli provides some references for brain-like systems.
Modeling and Measuring Thermodynamic and Transport Thermophysical Properties: A Review
Authors: D’Alessandro Giampaolo , Potenza Michele , Corasaniti Sandra , Sfarra Stefano , Coppa Paolo , Bovesecchi Gianluigi , de Monte Filippo
Published: November 2022

The present review describes the up-to-date state of the evaluation of thermophysical properties (TP) of materials with three different procedures: modeling (also including inverse problems), measurements and analytical methods (e.g., through computing from other properties). Methods to measure specific heat and thermal conductivity are described in detail. Thermal diffusivity and thermal effusivity are a combination of the previously cited properties, but also for these properties, specific measurement and calculation methods are reported. Experiments can be carried out in steady-state, transient, and pulse regimes. For modeling, special focus is given to the inverse methods and parameter estimation procedures, because through them it is possible to evaluate the thermophysical property, assuring the best practices and supplying the measurement uncertainty. It is also cited when the most common data processing algorithms are used, e.g., the Gauss–Newton and Levenberg–Marquardt least squares minimization algorithms, and how it is possible to retrieve values of TP from other data. Optimization criteria for designing the experiments are also mentioned.
Experimental and Simulation Investigation of Performance of Scaled Model for a Rotor of a Savonius Wind Turbine
Authors: Al-Gburi Kumail Abdulkareem Hadi , Al-quraishi Balasem Abdulameer Jabbar , Ismail Alnaimi Firas Basim , Tan Ee Sann , Al-Safi Ali Hussein Shamman
Published: November 2022

Renewable energy sources are preferred for many power generation applications. Energy from the wind is one of the fastest-expanding kinds of sustainable energy, and it is essential in preventing potential energy issues in the foreseeable future. One pertinent issue is the many geometrical alterations that the scientific community has suggested to enhance rotor performance features. Hence, to address the challenge of developing a model that resolves these problems, the purpose of this investigation was to determine how well a scaled-down version of a Savonius turbine performed in terms of power output using a wind tunnel. Subsequently, the effect of the blockage ratio produced in the wind tunnel during the chamber test on the scaled model was evaluated. This study discusses the influences of various modified configurations on the turbine blades’ torque and power coefficients (Cp) at various tip speed ratios (TSRs) using three-dimensional (3D) unsteady computational fluid dynamics. The findings showed that the scaled model successfully achieved tunnel blockage corrections, and the experimental results obtained can be used in order to estimate how the same turbine would perform in real conditions. Furthermore, numerically, the new models achieved improvements in Cp of 19.5%, 16.8%, and 12.2%, respectively, for the flow-guiding channel (FGC at Ⴔ = 30°), wavy area at tip and end (WTE), and wavy area on the convex blade (WCB) models in comparison to the benchmark S-ORM model and under identical wind speed conditions. This investigation can provide guidance for improvements of the aerodynamic characteristics of Savonius wind turbines.
Optimization of Miller Cycle, EGR, and VNT on Performance and NOx Emission of a Diesel Engine for Range Extender at High Altitude
Authors: Wang Weichao , Wang Guiyong , Wang Zhengjiang , Lei Jilin , Huang Junwei , Nie Xuexuan , Shen Lizhong
Published: November 2022

Due to the increasing sales of extended-range hybrid vehicles and the increasingly stringent emission regulations for light vehicles in China, the performance and emission of diesel engines for range extenders in the plateau region have attracted increasing attention. In order to obtain the superior performance of diesel engines for range extenders operating at high altitudes, a multi-objective optimization of the optimal economic operating point of the diesel engine was performed at an altitude of 1960 m. A diesel engine system model with MC-EGR-VNT (MEV) technology was developed using GT-Power based on the data of the engine bench to analyze the effects of the Miller cycle (MC), exhaust gas recirculation (EGR), and variable nozzle turbine (VNT) technologies on the power, economy, and emission performance of high-speed diesel engines. The response surface method (RSM) design was carried out with the Miller cycle rate (MCR), EGR value opening, VNT nozzle opening as variable factors and torque, brake-specific fuel consumption (BFSC), nitrogen oxide (NOx) emission as optimization objectives based on Box Behnken Design (BBD). The optimization results showed that the torque and BFSC remained almost constant, and NOx emission decreased by 59.5% compared with the original machine. The proposed multi-objective optimization method could make the diesel engine with a MEV system achieve a good comprehensive performance.