bluuebunny/crossref_metadata_2025 huggingface dataset:
Optical flow estimation from event-based cameras and spiking neural networks
Authors: Cuadrado Javier , Rançon Ulysse , Cottereau Benoit R. , Barranco Francisco , Masquelier Timothée
Published: May 2023

Event-based cameras are raising interest within the computer vision community. These sensors operate with asynchronous pixels, emitting events, or “spikes”, when the luminance change at a given pixel since the last event surpasses a certain threshold. Thanks to their inherent qualities, such as their low power consumption, low latency, and high dynamic range, they seem particularly tailored to applications with challenging temporal constraints and safety requirements. Event-based sensors are an excellent fit for Spiking Neural Networks (SNNs), since the coupling of an asynchronous sensor with neuromorphic hardware can yield real-time systems with minimal power requirements. In this work, we seek to develop one such system, using both event sensor data from the DSEC dataset and spiking neural networks to estimate optical flow for driving scenarios. We propose a U-Net-like SNN which, after supervised training, is able to make dense optical flow estimations. To do so, we encourage both minimal norm for the error vector and minimal angle between ground-truth and predicted flow, training our model with back-propagation using a surrogate gradient. In addition, the use of 3d convolutions allows us to capture the dynamic nature of the data by increasing the temporal receptive fields. Upsampling after each decoding stage ensures that each decoder's output contributes to the final estimation. Thanks to separable convolutions, we have been able to develop a light model (when compared to competitors) that can nonetheless yield reasonably accurate optical flow estimates.
Chemical suppression of harmaline-induced body tremor yields recovery of pairwise neuronal coherence in cerebellar nuclei neurons
Authors: Baumel Yuval , Yamin Hagar Grazya , Cohen Dana
Published: May 2023

Neuronal oscillations occur in health and disease; however, their characteristics can differ across conditions. During voluntary movement in freely moving rats, cerebellar nuclei (CN) neurons display intermittent but coherent oscillations in the theta frequency band (4–12 Hz). However, in the rat harmaline model of essential tremor, a disorder attributed to cerebellar malfunction, CN neurons display aberrant oscillations concomitantly with the emergence of body tremor. To identify the oscillation features that may underlie the emergence of body tremor, we analyzed neuronal activity recorded chronically from the rat CN under three conditions: in freely behaving animals, in harmaline-treated animals, and during chemical suppression of the harmaline-induced body tremor. Suppression of body tremor did not restore single neuron firing characteristics such as firing rate, the global and local coefficients of variation, the likelihood of a neuron to fire in bursts or their tendency to oscillate at a variety of dominant frequencies. Similarly, the fraction of simultaneously recorded neuronal pairs oscillating at a similar dominant frequency (<1 Hz deviation) and the mean frequency deviation within pairs remained similar to the harmaline condition. Moreover, the likelihood that pairs of CN neurons would co-oscillate was not only significantly lower than that measured in freely moving animals, but was significantly worse than chance. By contrast, the chemical suppression of body tremor fully restored pairwise neuronal coherence; that is, unlike in the harmaline condition, pairs of neurons that oscillated at the same time and frequency displayed high coherence, as in the controls. We suggest that oscillation coherence in CN neurons is essential for the execution of smooth movement and its loss likely underlies the emergence of body tremor.
The influence of conceptual concreteness on the reading acquisition and integration of novel words into semantic memory via thematic relations
Authors: Ding Jinfeng , Liang Panpan , Guo Xinyu , Yang Yufang
Published: May 2023

Plenty of studies have been conducted to reveal neurocognitive underpinnings of conceptual representation. Compared with that of concrete concepts, the neurocognitive correlates of abstract concepts remain elusive. The current study aimed to investigate the influence of conceptual concreteness on the reading acquisition and integration of novel words into semantic memory. We constructed two-sentence contexts in which two-character pseudowords were embedded as novel words. Participants read the contexts to infer the meaning of novel words which were either concrete or abstract, and then performed a lexical decision task and a cued-recall memory task. In lexical decision task, primed by the learned novel words, their corresponding concepts, thematically related or unrelated words as well as unlearned pseudowords were judged whether they were words or not. In memory task, participants were presented with the novel words and asked to write down their meaning. The contextual reading and memory test can demonstrate the modulation of conceptual concreteness on novel word learning and the lexical decision task can reveal whether concrete and abstract novel words are integrated into semantic memory similarly or not. During contextual reading, abstract novel words presented for the first time elicited a larger N400 than concrete ones. In memory task, the meaning of concrete novel words was recollected better than abstract novel words. These results indicate that abstract novel words are more difficult to acquire during contextual reading, and to retain afterwards. For lexical decision task behavioral and ERPs were graded, with the longest reaction time, the lowest accuracy and the largest N400s for the unrelated words, then the thematically related words and finally the corresponding concepts of the novel words, regardless of conceptual concreteness. The results suggest that both concrete and abstract novel words can be integrated into semantic memory via thematic relations. These findings are discussed in terms of differential representational framework which posits that concrete words connect with each other via semantic similarities, and abstract ones via thematic relations.
What we talk about when we talk about COVID-19 vaccination campaign impact: a narrative review
Authors: Hastenreiter Filho Horácio N. , Peres Igor T. , Maddalena Lucas G. , Baião Fernanda A. , Ranzani Otavio T. , Hamacher Silvio , Maçaira Paula M. , Bozza Fernando A.
Published: May 2023

Background

The lack of precise definitions and terminological consensus about the impact studies of COVID-19 vaccination leads to confusing statements from the scientific community about what a vaccination impact study is.

Objective

The present work presents a narrative review, describing and discussing COVID-19 vaccination impact studies, mapping their relevant characteristics, such as study design, approaches and outcome variables, while analyzing their similarities, distinctions, and main insights.

Methods

The articles screening, regarding title, abstract, and full-text reading, included papers addressing perspectives about the impact of vaccines on population outcomes. The screening process included articles published before June 10, 2022, based on the initial papers’ relevance to this study’s research topics. The main inclusion criteria were data analyses and study designs based on statistical modelling or comparison of pre- and post-vaccination population.

Results

The review included 18 studies evaluating the vaccine impact in a total of 48 countries, including 32 high-income countries (United States, Israel, and 30 Western European countries) and 16 low- and middle-income countries (Brazil, Colombia, and 14 Eastern European countries). We summarize the main characteristics of the vaccination impact studies analyzed in this narrative review.

Conclusion

Although all studies claim to address the impact of a vaccination program, they differ significantly in their objectives since they adopt different definitions of impact, methodologies, and outcome variables. These and other differences are related to distinct data sources, designs, analysis methods, models, and approaches.
Effects of Ozone Addition on Multi-Wave Modes of Hydrogen–Air Rotating Detonations
Authors: Wang Yang , Tian Cheng , Yang Pengfei
Published: May 2023

Ozone addition presents a promising approach for optimizing and regulating both combustion and ignition mechanisms. In Rotating Detonation Engines (RDEs), investigating the impact of ozone addition is particularly important due to the fact of their unique operating conditions and potential for improved efficiency. This study explores the influence of ozone concentration, total temperature, and equivalent ratio on the combustion characteristics of a hydrogen–air mixture infused with ozone. Utilizing the mixture as a propellant, the combustion chamber of a continuous rotating detonation engine is replicated through an array of injection ports, with numerical simulations conducted to analyze the detonation wave combustion mode. Our results show that an increase in total temperature leads to an increase in the number of detonation waves. Incorporating a minor quantity of ozone can facilitate the ignition process for the detonation wave. Increasing the ozone content can result in the conversion from a single-wave to dual-wave or multi-wave mode, providing a more stable combustion interface. A low ozone concentration acts as an auxiliary ignition agent and can significantly shorten the induction time. As the total temperature increases, the detonation propagation velocity and the peak heat release rate both decrease concurrently, which leads to a decline in the exit total pressure and an augmentation in the specific impulse. Employing ozone exerts a minimal impact on the detonation propagation and the overall propulsion performance. The requirement for ozone-assisted initiation differs noticeably between rich and lean combustion.
A Novel Embedding Model for Knowledge Graph Entity Alignment Based on Graph Neural Networks
Authors: Li Hongchan , Han Zhaoyang , Zhu Haodong , Qian Yuchao
Published: May 2023

The objective of the entity alignment (EA) task is to identify entities with identical semantics across distinct knowledge graphs (KGs) situated in the real world, which has garnered extensive recognition in both academic and industrial circles. Within this paper, a pioneering entity alignment framework named PCE-HGTRA is proposed. This framework integrates the relation and property information from varying KGs, along with the heterogeneity information present within the KGs. Firstly, by learning embeddings, this framework captures the similarity that exists between entities present across diverse KGs. Additionally, property triplets in KGs are used to generate property character-level embeddings, facilitating the transfer of entity embeddings from two distinct KGs onto an identical space. Secondly, the framework strengthens the property character-level embeddings using the transitivity rule to increase the count of entity properties. Then, in order to effectively capture the heterogeneous features in the entity neighborhoods, a heterogeneous graph transformer with relation awareness is designed to model the heterogeneous relations in KGs in the framework. Finally, comparative experimental results on four widely recognized real-world datasets demonstrate that PCE-HGTRA performs exceptionally well. In fact, its Hits@1 performance exceeds the best baseline by 7.94%, outperforming seven other state-of-the-art methods.
A Momentum Contrastive Learning Framework for Low-Data Wafer Defect Classification in Semiconductor Manufacturing
Authors: Wang Yi , Ni Dong , Huang Zhenyu
Published: May 2023

Wafer bin maps (WBMs) are essential test data in semiconductor manufacturing. WBM defect classification can provide critical information for the improvement of manufacturing processes and yield. Although deep-learning-based automatic defect classification models have demonstrated promising results in recent years, they require a substantial amount of labeled data for training, and manual labeling is time-consuming. Such limitations impede the practical application of existing algorithms. This study introduces a low-data defect classification algorithm based on contrastive learning. By employing momentum contrastive learning, the network extracts effective representations from large-scale unlabeled WBMs. Subsequently, a prototypical network is utilized for fine-tuning with only a minimal amount of labeled data to achieve low-data classification. Experimental results reveal that the momentum contrastive learning method improves the defect classification performance by learning feature representation from large-scale unlabeled data. The proposed method attains satisfactory classification accuracy using a limited amount of labeled data and surpasses other comparative methods in performance. This approach allows for the exploitation of information derived from large-scale unlabeled data, significantly reducing the reliance on labeled data.
Dynamic Modeling and Analysis of a Driving Passenger Vehicle
Authors: Yun Seen , Lee Jeonga , Jang Woojae , Kim Daeji , Choi Minseok , Chung Jintai
Published: May 2023

This study presents a dynamic model of a passenger vehicle and analyzes its dynamic characteristics and responses. A dynamic vehicle model with seven degrees of freedom was established to analyze the behavior of a driving vehicle. The vehicle model had three degrees of freedom for the sprung mass’s motion and four degrees for the unsprung masses. For this model, the equations of motion were derived using Lagrange’s equation. To verify the model, the suspension deformations computed using the model were compared with those measured through three actual vehicle driving tests: the slalom, double lane change, and step steer tests. Furthermore, we investigated the effects of suspension stiffness, suspension damping, and anti-roll bar torsional stiffness on the dynamic characteristics and responses of the vehicle model. This study presented a new full-car model that can analyze a turning vehicle’s behavior in response to changes in the steering angle input. The developed dynamic vehicle model may help vehicle designers predict the dynamic responses of a vehicle through simulation without performing a driving test.
Facade Style Mixing Using Artificial Intelligence for Urban Infill
Authors: Ali Ahmed Khairadeen , Lee One Jae
Published: May 2023

Artificial intelligence and machine learning, in particular, have made rapid advances in image processing. However, their incorporation into architectural design is still in its early stages compared to other disciplines. Therefore, this paper addresses the development of an integrated bottom–up digital design approach and describes a research framework for incorporating the deep convolutional generative adversarial network (GAN) for early stage design exploration and the generation of intricate and complex alternative facade designs for urban interiors. In this paper, a novel facade design is proposed using the architectural style, size, scale, and openings of two adjacent buildings as references to create a new building design in the same neighborhood for urban infill. This newly created building contains the outline, style and shape of the two main buildings. A 2D building design is generated as an image, where (1) neighboring buildings are imported as a reference using the cell phone and (2) iFACADE decodes their spatial neighborhood. It is illustrated that iFACADE will be useful for designers in the early design phase to create new facades in relation to existing buildings in a short time, saving time and energy. Moreover, building owners can use iFACADE to show their preferred architectural facade to their architects by mixing two building styles and creating a new building. Therefore, it is presented that iFACADE can become a communication platform in the early design phases between architects and builders. The initial results define a heuristic function for generating abstract facade elements and sufficiently illustrate the desired functionality of the prototype we developed.
Molecular and Cellular Mechanisms of Inflammation and Tissue Regeneration
Authors: Favier Anne-Laure , Nikovics Krisztina
Published: May 2023

Over the past 70 years, significant progress has been made in understanding the molecular and cellular mechanisms of inflammation and tissue regeneration [...]
Adipokines in Pregnancy: A Systematic Review of Clinical Data
Authors: Kabbani Noura , Blüher Matthias , Stepan Holger , Stumvoll Michael , Ebert Thomas , Tönjes Anke , Schrey-Petersen Susanne
Published: May 2023

Adipokines are signaling proteins involved in metabolic, endocrinological, vascular and immunogenic processes. Associations of various adipokines with not only insulin resistance but also with increased insulin sensitivity, increased systolic blood pressure, and atherosclerosis highlight the significance of adipokines in several components of metabolic syndrome and metabolic diseases in general. As pregnancy presents a unique metabolic state, the role of adipokines in pregnancy, and even in various pregnancy complications, appears to be key to elucidating these metabolic processes. Many studies in recent years have attempted to clarify the role of adipokines in pregnancy and gestational pathologies. In this review, we aim to investigate the changes in maternal adipokine levels in physiological gestation, as well as the association of adipokines with pregnancy pathologies, such as gestational diabetes mellitus (GDM) and preeclampsia (PE). Furthermore, we will analyze the association of adipokines in both maternal serum and cord blood with parameters of intrauterine growth and various pregnancy outcomes.
Enhanced Competitive Immunomagnetic Beads Assay Assisted with PAMAM-Gold Nanoparticles Multi-Enzyme Probes for Detection of Deoxynivalenol
Authors: Zeng Kun , Yang Jian , Su Hao , Yang Sheng , Gu Xinkai , Zhang Zhen , Zhao Hongjun
Published: May 2023

Contamination of deoxynivalenol (DON) in grains has attracted widespread concern. It is urgently needed to develop a highly sensitive and robust assay for DON high-throughput screening. Antibody against DON was assembled on the surface of immunomagnetic beads orientationally by the aid of Protein G. AuNPs were obtained under the scaffolding of poly(amidoamine) dendrimer (PAMAM). DON-horseradish peroxidase (HRP) was combined on the periphery of AuNPs/PAMAM by a covalent link to develop DON-HRP/AuNPs/PAMAM. Magnetic immunoassay based on DON-HRP/AuNPs/PAMAM was optimized and that based on DON-HRP/AuNPs and DON-HRP was adopted as comparison. The limits of detection (LODs) were 0.447 ng/mL, 0.127 ng/mL and 0.035 ng/mL for magnetic immunoassays based on DON-HRP, DON-HRP/Au and DON-HRP/Au/PAMAM, respectively. Magnetic immunoassay based on DON-HRP/AuNPs/PAMAM displayed higher specificity towards DON and was utilized to analyze grain samples. The recovery for the spiked DON in grain samples was 90.8–116.2% and the method presented a good correlation with UPLC/MS. It was found that the concentration of DON was in the range of ND-3.76 ng/mL. This method allows the integration of dendrimer–inorganic NPs with signal amplification properties for applications in food safety analysis.
Abnormal Brain Structure Is Associated with Social and Communication Deficits in Children with Autism Spectrum Disorder: A Voxel-Based Morphometry Analysis
Authors: Xu Ming-Xiang , Ju Xing-Da
Published: May 2023

Structural magnetic resonance imaging (sMRI) studies have shown abnormalities in the brain structure of ASD patients, but the relationship between structural changes and social communication problems is still unclear. This study aims to explore the structural mechanisms of clinical dysfunction in the brain of ASD children through voxel-based morphometry (VBM). After screening T1 structural images from the Autism Brain Imaging Data Exchange (ABIDE) database, 98 children aged 8–12 years old with ASD were matched with 105 children aged 8–12 years old with typical development (TD). Firstly, this study compared the differences in gray matter volume (GMV) between the two groups. Then, this study evaluated the relationship between GMV and the subtotal score of communications and social interaction on the Autism Diagnostic Observation Schedule (ADOS) in ASD children. Research has found that abnormal brain structures in ASD include the midbrain, pontine, bilateral hippocampus, left parahippocampal gyrus, left superior temporal gyrus, left temporal pole, left middle temporal gyrus and left superior occipital gyrus. In addition, in ASD children, the subtotal score of communications and social interaction on the ADOS were only significantly positively correlated with GMV in the left hippocampus, left superior temporal gyrus and left middle temporal gyrus. In summary, the gray matter structure of ASD children is abnormal, and different clinical dysfunction in ASD children is related to structural abnormalities in specific regions.
Influence of Fatigue Damage on Criticality Cell Ultimate Load Capacity of Steel–Concrete Composite Section
Authors: Wang Haibo , Wu Shasha
Published: May 2023

In order to investigate the impact of fatigue damage on the ultimate load capacity of criticality cells in steel–concrete composite segments and to address the complex design challenges associated with bridge steel–concrete composite segments in practical engineering, this study designs two scaled criticality cell specimens with a scale ratio of 1:2 and performs ultimate load capacity tests after fatigue cyclic loading. By analyzing the stress distribution of each component and the force transmission ratio and combining the results from finite element model calculations, this study introduces the degree of structural fatigue damage and proposes a predictive model for the ultimate load capacity of steel–concrete composite segment criticality cells that is easy to apply. This model is compared with the finite element calculation results and experimental values, and the results are found to be in good agreement. Additionally, the number of shear connection members in the model is optimized based on the calculation results. The research findings indicate that the main failure mode of criticality cells is inclined compression failure. The strength of each part decreases in the following order: steel–concrete composite segment, steel structure segment, and concrete segment. Furthermore, fatigue damage has a significant impact on criticality cells. The optimized model exhibits similar stress performance and force transmission ratio to the original model and provides a reference for the design of practical engineering.
Liquid Biopsy in Early-Stage Lung Cancer: Current and Future Clinical Applications
Authors: Vandekerckhove Olivia , Cuppens Kristof , Pat Karin , Du Pont Bert , Froyen Guy , Maes Brigitte
Published: May 2023

Lung cancer remains the leading cause of cancer death worldwide, with the majority of cases diagnosed in an advanced stage. Early-stage disease non-small cell lung cancer (NSCLC) has a better outcome, nevertheless the 5-year survival rates drop from 60% for stage IIA to 36% for stage IIIA disease. Early detection and optimized perioperative systemic treatment are frontrunner strategies to reduce this burden. The rapid advancements in molecular diagnostics as well as the growing availability of targeted therapies call for the most efficient detection of actionable biomarkers. Liquid biopsies have already proven their added value in the management of advanced NSCLC but can also optimize patient care in early-stage NSCLC. In addition to having known diagnostic benefits of speed, accessibility, and enhanced biomarker detection compared to tissue biopsy, liquid biopsy could be implemented for screening, diagnostic, and prognostic purposes. Furthermore, liquid biopsy can optimize therapeutic management by overcoming the issue of tumor heterogeneity, monitoring tumor burden, and detecting minimal residual disease (MRD), i.e., the presence of tumor-specific ctDNA, post-operatively. The latter is strongly prognostic and is likely to become a guidance in the postsurgical management. In this review, we present the current evidence on the clinical utility of liquid biopsy in early-stage lung cancer, discuss a selection of key trials, and suggest future applications.
The French Cohort of DNA Repair-Deficient Xeroderma Pigmentosum Patients: Risk of Hematological Malignancies
Authors: Sarasin Alain
Published: May 2023

Background: Xeroderma pigmentosum (XP) is a rare genetic disorder characterized by a high incidence of skin cancers. These patients are deficient in nucleotide excision repair caused by mutations in one of the 7 XP genes. Methods: We diagnosed 181 XP patients using UV-induced DNA repair measurements and/or DNA sequencing from 1982 to 2022 in France. Results: As all XP patients, the French ones are very sensitive to UV exposure but since they are usually very well protected, they develop relatively few skin cancers. A majority of French XP patients originate from North Africa and bear a founder mutation on the XPC gene. The striking discovery is that these patients are at a very high risk to develop aggressive and lethal internal tumors such as hematological malignancies (more than a 100-fold risk compared to the general population for myelodysplasia/leukemia) with a median age of death of 25 years, and brain, gynecological, and thyroid tumors with even lower median ages of death. The high mutation rates found in XP-C internal tumors allow us to think that these XP patients could be successfully treated by immunotherapies. A full analysis of the molecular origins of these DNA repair-deficient tumors is discussed. Several explanations for this high predisposition risk are proposed. Conclusions: As the age of the XP population is increasing due to better photo-protection, the risk of lethal internal tumors is a new Damocles sword that hangs over XP-C patients. This review of the French cohort is of particular importance for alerting physicians and families to the prevention and early detection of aggressive internal tumors in XP patients.
Sex-Specific Associations of MDM2 and MDM4 Variants with Risk of Multiple Primary Melanomas and Melanoma Survival in Non-Hispanic Whites
Authors: Ward Sarah V. , Autuori Isidora , Luo Li , LaPilla Emily , Yoo Sarah , Sharma Ajay , Busam Klaus J. , Olilla David W. , Dwyer Terence , Anton-Culver Hoda , Zanetti Roberto , Sacchetto Lidia , Cust Anne E. , Gallagher Richard P. , Kanetsky Peter A. , Rosso Stefano , Begg Colin B. , Berwick Marianne , Thomas Nancy E. , Orlow Irene
Published: May 2023

MDM2-SNP309 (rs2279744), a common genetic modifier of cancer incidence in Li-Fraumeni syndrome, modifies risk, age of onset, or prognosis in a variety of cancers. Melanoma incidence and outcomes vary by sex, and although SNP309 exerts an effect on the estrogen receptor, no consensus exists on its effect on melanoma. MDM2 and MDM4 restrain p53-mediated tumor suppression, independently or together. We investigated SNP309, an a priori MDM4-rs4245739, and two coinherited variants, in a population-based cohort of 3663 primary incident melanomas. Per-allele and per-haplotype (MDM2_SNP309-SNP285; MDM4_rs4245739-rs1563828) odds ratios (OR) for multiple-melanoma were estimated with logistic regression models. Hazard ratios (HR) for melanoma death were estimated with Cox proportional hazards models. In analyses adjusted for covariates, females carrying MDM4-rs4245739*C were more likely to develop multiple melanomas (ORper-allele = 1.25, 95% CI 1.03–1.51, and Ptrend = 0.03), while MDM2-rs2279744*G was inversely associated with melanoma-death (HRper-allele = 0.63, 95% CI 0.42–0.95, and Ptrend = 0.03). We identified 16 coinherited expression quantitative loci that control the expression of MDM2, MDM4, and other genes in the skin, brain, and lungs. Our results suggest that MDM4/MDM2 variants are associated with the development of subsequent primaries and with the death of melanoma in a sex-dependent manner. Further investigations of the complex MDM2/MDM4 motif, and its contribution to the tumor microenvironment and observed associations, are warranted.
Silver-Doped Zeolitic Imidazolate Framework (Ag@ZIF-8): An Efficient Electrocatalyst for CO2 Conversion to Syngas
Authors: Usman Muhammad , Suliman Munzir H.
Published: May 2023

To enable the reuse of carbon dioxide (CO2), electrocatalytic reduction of CO2 (CO2RR) into syngas with a controllable H2/CO ratio is considered a cost-effective and intriguing approach. Here, a number of silver (Ag)-doped, zeolitic imidazole framework composites were prepared by a facile method. The outcomes demonstrate that CO2 electroreduction on Ag-doped ZIF-8 catalysts produces just CO and H2, without having any liquid fuel, resulting in a total faradaic efficiency approaching 100%. The most optimal Ag-Zn-ZIF-8 (10% Ag, 90% Zn) demonstrates good selectivity for syngas (CO and H2) that can be easily adjusted from 3:1 to 1:3 (H2/CO) by changing the applied voltage during the CO2 conversion process.
Tweaking of Peripheral Moieties in Catalytic Amyloid for Modulating Hydrogel Strength and Hydrolase Activity
Authors: Patra Soumya , Mavlankar Nimisha A. , Ramesan Lakshminarayan , Singh Ashmeet , Pal Asish
Published: May 2023

The de novo design and synthesis of peptide-based biocatalysts that can mimic the activity of natural enzymes is an exciting field with unique opportunities and challenges. In a natural enzyme, the active site is composed of an assembly of different amino acid residues, often coordinated with a metal ion. A metalloenzyme’s catalytic activity results from the dynamic and concerted interplay of various interactions among the residues and metal ions. Aiming to mimic such enzymes, simple peptide fragments, drawing structural inspiration from natural enzymes, can be utilized as a model. In our effort to mimic a metal-containing hydrolase, we designed peptide amphiphiles (PA) 1 and 2 with a terminal histidine having amide and acid functionalities, respectively, at its C-terminal, imparting differential ability to coordinate with Zn and Cu ions. The PAs demonstrate remarkable self-assembly behavior forming excellent nanofibers. Upon coordination with metal ions, depending on the coordination site the nanofibers become rigidified or weakened. Rheological studies revealed excellent mechanical properties of the hydrogels formed by the PAs and the PA–metal co-assemblies. Using such co-assemblies, we mimic hydrolase activity against a p-nitrophenyl acetate (p-NPA) substrate. Michaelis–Menten’s enzyme kinetic parameters indicated superior catalytic activity of 2 with Zn amongst all the assemblies.
Experimental Seismic Evaluation of Novel Buckling-Restrained Braced Frames Containing Tire-Derived Aggregate Concrete
Authors: Pathan Nasreen B. , Couch Logan , Tehrani Fariborz M. , Naghshineh Ali , Fischer Oliver
Published: May 2023

Existing literature suggests that substituting mineral aggregates with tire-derived aggregate (TDA) in concrete increases the ductility and toughness of the concrete at the cost of lowering its strength and stiffness. Hence, TDA concrete has applications in dynamic systems, such as lateral resisting frames sustaining seismic loads. This study investigated the application of TDA concrete in buckling-restrained braced frames (BRBFs). Buckling-restrained brace (BRB) specimens included steel plates encased with concrete mixtures containing TDA coarse aggregates compared to conventional concrete. Testing involved shake-table testing of a single-span, one-story, steel-braced frame with single-leg conventional or TDA concrete BRBs under harmonic, periodic, impulse, and ground motion loadings. Results included time-history responses and backbone curves of the BRBF specimens. Analytical interpretation of results included determining effective mass, stiffness, damping ratio, toughness, and ductility of BRBFs for TDA versus conventional concrete.
Simulation of Turbulent Flow Structure and Particle Deposition in a Three-Dimensional Heat Transfer Duct with Convex Dimples
Authors: Lu Hao , Han Zunshi , Li Hongchang , Chang Xiqiang , Dong Lijiang , Fan Mao , Kong Dean , Jing Xuehui
Published: May 2023

In engineering applications, dust deposition on the heat transfer channel greatly reduces the efficiency of heat transfer. Therefore, it is very significant to study the characteristics of particle deposition for thermal energy engineering applications. In this study, the Reynolds stress model (RSM) and the discrete phrase model (DPM) were used to simulate particle deposition in a 3D convex-dimpled rough channel. A discrete random walk model (DRW) was used for the turbulent diffusion of particles, and user-defined functions were developed for collisions between particles and walls. An improved deposition model of rebound between particles was developed. The flow structure, secondary flow, temperature distribution, Q criterion, and particle deposition distribution in the convex-dimpled rough channel were analyzed after a study of the grid independence and a numerical validation. The results showed that these mechanisms affected the flow structure in the flow field. For tiny particles (dp ≤ 10 μm), the presence of convex dimples promoted their deposition. The rates of particle deposition in the presence of convex dimples were 535, 768, 269, and 2 times higher than in smooth channels (particle sizes of 1, 3, 5, and 10 μm, respectively). However, for large particles (dp > 10 μm), although the presence of convex dimples had a certain effect on the location distribution of particle deposition, it had little effect on the deposition rates of large particles, which were 0.99, 0.98, 0.97 and 0.96 times those in the smooth channel, respectively.
Preparation and Investigation of the SPF and Antioxidant Properties of O/W and W/O Emulsions Containing Vitamins A, C and E for Cosmetic Applications
Authors: Bikiaris Nikolaos D. , Koumentakou Ioanna , Hatzistamatiou Katerina , Lykidou Smaro , Barmpalexis Panagiotis , Nikolaidis Nikolaos
Published: May 2023

In the current work, Oil in Water (O/W) and Water in Oil (W/O) emulsions containing Vitamins A, C and E in 0.5, 1 and 2% wt concentrations were prepared. The pH and viscosity stability over storage, as well as the sunscreen and antioxidant properties of the obtained emulsions, were investigated. The results obtained showed that vitamins slightly increased the pH of the blank emulsions; however, their pH values were within the acceptable values (pH = 4–6). Nevertheless, all emulsions presented excellent pH stability during storage for up to 90 days. Similar results were observed by rheological measurements as the prepared emulsions did not exhibit viscosity instabilities deriving during storage. Moreover, emulsions containing Vitamin A exhibited higher UV protection than the other emulsions, as the W/O emulsion containing 2% wt Vitamin A presented the highest SPF value at 22.6.
Growth of β-Ga2O3 Single-Crystal Microbelts by the Optical Vapor Supersaturated Precipitation Method
Authors: Pan Yongman , Wang Qiang , Yan Yinzhou , Yang Lixue , Wan Lingyu , Yao Rongcheng , Jiang Yijian
Published: May 2023

Monoclinic β-Ga2O3 microbelts were successfully fabricated using a one-step optical vapor supersaturated precipitation method, which exhibited advantages including a free-standing substrate, prefect surface, and low cost. The as-grown microbelts possessed a well-defined geometry and perfect crystallinity. The dimensions of individual β-Ga2O3 microbelts were a width of ~50 μm, length of ~5 mm, and thickness of ~3 μm. The SEM, XRD, HRTEM, XPS, and Raman spectra demonstrated the high single-crystalline structure of β-Ga2O3 microbelts. Twelve frequency modes were activated in Raman spectra. The optical band gap of the β-Ga2O3 microbelt was calculated to be ~4.45 eV. Upon 266 nm excitation, 2 strong UV emissions occurred in photoluminescence spectra through the radiative recombination of self-trapped excitons, and the blue emission band was attributed to the presence of donor-acceptor-pair transition. The individual β-Ga2O3 microbelt was employed as metal-semiconductor-metal deep-ultraviolet photodetector, which exhibits the photoresponse under 254 nm. This work provides a simple and economical route to fabricate high-quality β-Ga2O3 single-crystal microbelts, which should be a potential synthetic strategy for ultra-wide bandgap semiconductor materials.
Structure, Growth and Histology of Gnathal Elements in Dunkleosteus (Arthrodira, Placodermi), with a Description of a New Species from the Famennian (Upper Devonian) of the Tver Region (North-Western Russia)
Authors: Lebedev Oleg A. , Engelman Russell K. , Skutschas Pavel P. , Johanson Zerina , Smith Moya M. , Kolchanov Veniamin V. , Trinajstic Kate , Linkevich Valeriy V.
Published: May 2023

A new species of Dunkleosteus, D. tuderensis sp. nov., is named based on an infragnathal from the Famennian of the Tver Region, Russia. CT scanning of the holotype revealed two high-density bony constituents comparable in position and interrelations to components described for coccosteomorph arthrodires, supported by the presence of at least two clusters of large vascular canals marking separate arterial supplies. Coccosteomorph and dunkleosteid pachyosteomorphs exhibit similar growth patterns including labio-basal depositions of vascularized bone in the infragnathals and basally in the supragnathals. In contrast to coccosteomorphs, dunkleosteid reinforcement of the occlusal margins occurred via the formation of dense osteonal bone, in parallel with resorption forming extensive lingual fossae. Active bone remodeling proceeded without a complete reworking of the primary osteonal bone structure and the original arrangement of vascular canals. Due to inconsistent anatomical terminology in gnathal elements of dunkleosteid arthrodires, a revised terminology is suggested and new terms are introduced.
Preimplantation Testing for Polygenic Disease (PGT-P): Brave New World or Mad Pursuit?
Authors: Griffin Darren K. , Gordon Anthony T.
Published: May 2023

In preimplantation testing for monogenic disease (PGT-M), we are used to specific and directed diagnoses. Preimplantation testing for polygenic disease (PGT-P), however, represents a further level of complexity in that multiple genes are tested for with an associated polygenic risk score (PRS), usually established by a genome-wide association study (GWAS). PGT-P has a series of pros and cons and, like many areas of genetics in reproductive medicine, there are vocal proponents and opponents on both sides. As with all things, the question needs to be asked, how much benefit does PGT-P provide in comparison to the risks involved? For each disease, a case will need to be made for PGT-P, as will a justification that the family involved will actually benefit; the worry is that this might be more work than the cost justifies.
Open Circuit Fault Detection of T-Type Grid Connected Inverters Using Fast S Transform and Random Forest
Authors: You Li , Ling Zaixun , Cui Yibo , Cai Wanli , He Shunfan
Published: May 2023

To detect open circuit faults of grid-connected T-type inverters, this paper proposed a real-time method based on fast S transform and random forest. The three-phase fault currents of the inverter were used as the inputs of the new method and no additional sensors were needed. Some fault current harmonics and direct current components were selected as the fault features. Then, fast S transform was used to extract the features of fault currents, and random forest was used to recognize the features and the fault type, as well as locate the faulted switches. The simulation and experiments showed that the new method could detect open-circuit faults with low computation complexity and the detection accuracy was 100%. The real-time and accurate open circuit fault detection method was proven effective for grid-connected T-type inverter monitoring.
Novel Schemes of No-Slip Boundary Conditions for the Discrete Unified Gas Kinetic Scheme Based on the Moment Constraints
Authors: Guo Wenqiang , Hou Guoxiang
Published: May 2023

The boundary conditions are crucial for numerical methods. This study aims to contribute to this growing area of research by exploring boundary conditions for the discrete unified gas kinetic scheme (DUGKS). The importance and originality of this study are that it assesses and validates the novel schemes of the bounce back (BB), non-equilibrium bounce back (NEBB), and Moment-based boundary conditions for the DUGKS, which translate boundary conditions into constraints on the transformed distribution functions at a half time step based on the moment constraints. A theoretical assessment shows that both present NEBB and Moment-based schemes for the DUGKS can implement a no-slip condition at the wall boundary without slip error. The present schemes are validated by numerical simulations of Couette flow, Poiseuille flow, Lid-driven cavity flow, dipole–wall collision, and Rayleigh–Taylor instability. The present schemes of second-order accuracy are more accurate than the original schemes. Both present NEBB and Moment-based schemes are more accurate than the present BB scheme in most cases and have higher computational efficiency than the present BB scheme in the simulation of Couette flow at high Re. The present Moment-based scheme is more accurate than the present BB, NEBB schemes, and reference schemes in the simulation of Poiseuille flow and dipole–wall collision, compared to the analytical solution and reference data. Good agreement with reference data in the numerical simulation of Rayleigh–Taylor instability shows that they are also of use to the multiphase flow. The present Moment-based scheme is more competitive in boundary conditions for the DUGKS.
Exploring Gamification Approaches for Enhancing Computational Thinking in Young Learners
Authors: del Olmo-Muñoz Javier , Bueno-Baquero Andrea , Cózar-Gutiérrez Ramón , González-Calero José Antonio
Published: May 2023

The integration of computational thinking (CT) in mathematics education is increasingly important due to its intersections with mathematical thinking and its role in the 21st century. Recent years have seen a growing interest in the use of innovative tools to enhance mathematics education, including the exploration of gamification techniques to improve student engagement. While previous research suggests that the inclusion of CT activities at an early age can positively impact students’ motivation and CT skills, the potential benefits of incorporating gamification techniques into CT instruction remain unexplored. In this study, we investigated the impact of shallow and deep gamification techniques on the CT skills and the intrinsic and extrinsic motivation of second-grade students in a blended teaching sequence that combined both unplugged and plugged CT activities. A quasi-experimental design was used with a control group and an experimental group. The findings suggest that both shallow and deep gamification techniques can be effective in improving CT skills in young students, but deep gamification may have a stronger impact on motivation. These results contribute to the growing body of literature on the integration of gamification techniques in CT instruction.
Impact of Air Gap Defects on the Electrical and Mechanical Properties of a 320 kV Direct Current Gas Insulated Transmission Line Spacer
Authors: Deng Yuan , Fan Xianhao , Luo Hanhua , Wang Yao , Wu Keyan , Liang Fangwei , Li Chuanyang
Published: May 2023

Air gap defects inside a spacer reduce its insulation performance, resulting in stress concentration, partial discharge, and even flashover. If such gap defects are located at the interface between the insulation and conductor, a decrease in mechanical stress may occur. In this work, a finite element method-based simulation model is developed to analyze the influence of gap defects on the electrical and mechanical properties of a ±320 kV direct current gas insulated line (DC GIL) spacer. Present findings reveal that a radially distributed air gap produces a more significant effect on the electric field distribution, and an electric field strength 1.7 times greater than that of the maximum surface value is observed at the air gap. The axial distribution dominates the distortion of the surface stress by generating a stress concentration region in which the maximum stress of the air gap is twice the pressure in the surrounding area.
A Review on Defect Detection of Electroluminescence-Based Photovoltaic Cell Surface Images Using Computer Vision
Authors: Hussain Tahir , Hussain Muhammad , Al-Aqrabi Hussain , Alsboui Tariq , Hill Richard
Published: May 2023

The past two decades have seen an increase in the deployment of photovoltaic installations as nations around the world try to play their part in dampening the impacts of global warming. The manufacturing of solar cells can be defined as a rigorous process starting with silicon extraction. The increase in demand has multiple implications for manual quality inspection. With automated inspection as the ultimate goal, researchers are actively experimenting with convolutional neural network architectures. This review presents an overview of the electroluminescence image-extraction process, conventional image-processing techniques deployed for solar cell defect detection, arising challenges, the present landscape shifting towards computer vision architectures, and emerging trends.
Description of Pressure-Multiplying Efficiency Model Creation Used for Pressure Intensifiers Based on Rotary Flow Dividers
Authors: Hradecký Jakub Milan
Published: May 2023

Pressure intensifiers have their utilization in hydraulic circuits, when there is a need for pressure multiplication for some period of time. Using intensifiers provides energy savings, cost reductions or smaller dimensioning. This article is about a pressure intensifier based on rotary flow dividers. Its functioning is evaluated by its pressure-multiplying efficiency. This efficiency is always calculated from values gained from measurements. However, it would be very convenient to be able to evaluate these types of intensifiers without a need to provide specific measurements for every type of intensifier and rather calculate their efficiency using the general parameters of a hydraulic circuit. Therefore, this article is about the description of a model which calculates the pressure-multiplying efficiency only with two input values: the pressure and the speed of an intensifier. Final derived formulas are also used for the efficiency prediction at pressure levels never measured before and then verified by measurements.