bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi Microgrid Framework for Resilience Enhancement Considering Mobile Energy Storage Systems and Parking Lots
Authors: Abdulrazzaq Oraibi Waleed , Mohammadi-Ivatloo Behnam , Hosseini Seyed Hossein , Abapour Mehdi
Published: January 2023

This paper proposes a practical and effective planning approach that takes advantage of the mobility and flexibility of mobile energy storage systems (MESSs) to increase distribution system resilience against complete area blackouts. MESSs will be very useful for boosting the system’s resilience in places affected by disasters when the transmission lines are damaged. A joint post-disaster restoration strategy for MESSs and PEV-PLs is proposed, along with distributed generation and network reconfigurations, to reduce total system costs, which include customer interruption costs, generation costs, and MESS operation and transportation costs. The integrated strategy accounts for the uncertainty of the production of wind- and solar-powered microgrids (MGs) and different forms of load demand. Therefore, this paper assesses the effect of MESSs on distribution system (DS) resilience in respect of MG cost reduction and flexibility. Due to the multiple networks, PEV-PLs, DGs, and MESS limitations, the suggested restoration problem is stated as a mixed-integer linear programming problem. The suggested framework is complemented using a benchmark testing system (i.e., 33-bus DS). To assess the effectiveness of the proposed model, the model’s output is contrasted with results from typical planning and a traditional model. The comparison of the data shows that the suggested model, in addition to MESSs, effectively achieves a large decrease in cost and enhances the DS resilience level.
Water Quality Evaluation and Prediction Based on a Combined Model
Authors: Jiao Guimei , Chen Shaokang , Wang Fei , Wang Zhaoyang , Wang Fanjuan , Li Hao , Zhang Fangjie , Cai Jiali , Jin Jing
Published: January 2023

Along with increasingly serious water pollution, water environmental problems have become major factors that hinder the sustainable development of our economy and society. Reliable evaluation of water quality and accurate prediction of water pollution indicators are the key links in water resource management and water pollution control. In this paper, the water quality data of Lanzhou Xincheng Bridge section in the Yellow River Basin and Sichuan Panzhihua Longdong section in the Yangtze River Basin were used to establish a water quality evaluation model and a prediction model. For the water quality evaluation model, we constructed the research samples by means of equal intervals and uniform distribution of interpolated water quality index data according to Environmental Quality Standards for Surface Water. The training samples were determined by a stratified sampling method, and the water quality evaluation model was established using a T-S fuzzy neural network. The experimental results show that the highest accuracy achieved by the evaluation model in water quality classification was 94.12%. With respect to the water quality prediction model, we propose ARIMA-WNN, which combines the autoregressive integrated moving average model (ARIMA) and a wavelet neural network (WNN) with the bat algorithm (BA) to determine the optimal weight of each individual model. The experimental results show that the highest prediction accuracy of ARIMA-WNN is 68.06% higher than that of the original model.
Evaluating the Efficacy of Common Treatments Used for Vairimorpha (Nosema) spp. Control
Authors: Prouty Cody , Jack Cameron , Sagili Ramesh , Ellis James D.
Published: January 2023

Vairimorpha (formerly Nosema) apis and V. ceranae are microsporidian pathogens that are of concern for managed honey bee colonies. Multiple treatments have been proposed to be effective in reducing the prevalence and intensity of Vairimorpha spp. infections. Here, we test the efficacy of these products in one lab-based experiment and three field experiments. In the lab experiment, we found no reductions in Vairimorpha spp. prevalence (proportion of individuals infected with Vairimorpha spp.) or intensity (number of Vairimorpha spp. spores per individual), but we did find a decrease in honey bee survival after treatment with Fumagilin-B, Honey-B-Healthy®, and Nozevit Plus. The first field experiment showed increased Vairimorpha spp. intensity in colonies treated with Fumagilin-B and HiveAlive® compared to a negative control (sucrose syrup alone). The second field experiment showed a weak reduction in Vairimorpha spp. intensity after 3 weeks post treatment with Fumagilin-B compared to Nozevit. However, Vairimorpha spp. intensity returned to levels comparable to those of other treatment groups after 5 weeks post treatment and remained similar to those of other groups for the duration of the experiment. The final field trial showed no positive or negative effects of treatment with Fumagilin-B or Nosevit on Vairimorpha spp. prevalence or intensity. These findings raise questions regarding the efficacy of the products currently being used by beekeepers to control Vairimorpha spp. We argue that the observed reduction of Vairimorpha spp. is more likely relevant to the phenology of spore prevalence and intensity in honey bee colonies than to chemical treatment.
An Automatic ECG Signal Quality Assessment Method Based on Resnet and Self-Attention
Authors: Liu Yuying , Zhang Hao , Zhao Kun , Liu Haiyang , Long Fei , Chen Liping , Yang Yaguang
Published: January 2023

Electrocardiogram (ECG) signals are among the significant physiological signals that indicate the essential properties of the human body. In recent years, the measurement of ECG signals has become more portable thanks to the increasing usage of wearable health testing technology. However, the enormous amount of signal data gathered over a long period of time does impose a heavy load on medical professionals. In addition, false alarms might occur due to the potential for the detected signal to become jumbled with noise and motion perturbations. Therefore, analyzing the quality of the measured raw ECG signal automatically is a valuable task. In this paper, we propose a new single-channel ECG signal quality assessment method that combines the Resnet network structure and the principle of self-attention to extract ECG signal features using the principle of similarity between individual QRS heartbeats within a time slice of ten seconds. In addition, an improved self-attention module is introduced into the deep neural network to learn the similarity between features. Finally, the network distinguishes between acceptable and unacceptable ECG segments. The model test results indicate that the F1-score can approach 0.954, which leads to a more accurate assessment of the ECG signal quality.
Structural Evaluation of Cable Bolts under Static Loading
Authors: Tahmasebinia Faham , Yang Adam , Feghali Patrick , Skrzypkowski Krzysztof
Published: January 2023

Rock bursts are a natural phenomenon that are caused by high stresses and faults within the deep geological profile. The framework within deep mining excavations, comprising various rock and face supports such as cable bolts, is required to withstand rock bursts. These mechanisms are subject to static and dynamic loading conditions which possess unique challenges. This study focused on the shearing impact of static loads on cable bolts, a key structural support mechanism designed to absorb energy and investigate the impacts of bolt diameter and strength. A double shear test was modelled using the Finite Element Analysis (FEA) software ABAQUS/Explicit. A double shear test was modelled using Finite Element Modelling (FEM) by creating individual parts, assigning material and contact properties and applying a load directly on the central block. Because ABAQUS/Explicit was used, a primarily dynamic analysis tool, quasi-static loading, was applied to negate the natural time scale. A total of six bolt diameters and six bolt strengths were tested. A positive correlation was exhibited between the bolt diameter, yield strength and the maximum force and displacement.
Cosmeceutical Potentials of Equisetum debile Roxb. ex Vaucher Extracts
Authors: Thammarat Phanit , Jiaranaikulwanitch Jutamas , Phongpradist Rungsinee , Raiwa Araya , Pandith Hataichanok , Sawangrat Kasirawat , Sirilun Sasithorn
Published: January 2023

Trends in skin and hair treatments focus on natural products due to undesired effects of chemically synthetic ingredients. This study aims to investigate the cosmeceutical effects of Equisetum debile (horsetail) extracts relating to anti-hyperpigmentation via tyrosinase, anti-wrinkle formation via matrix metalloproteinases (MMPs), and anti-androgenic alopecia via 5α-reductase. Ethanolic extracts were sequentially partitioned into semi-purified fractions hexane, dichloromethane, ethyl acetate, methanol, and methanol insoluble residue. The ethyl acetate fraction possessed the highest total phenolic content (39.24 ± 0.72 mg gallic acid/g), the strongest anti-tyrosinase activities (583.33 ± 23.59 mg kojic acid/g), and potent collagenase inhibitions (IC50 MMP-1 and MMP-2 of 0.82 ± 0.09 and 0.94 ± 0.11 mg/mL, respectively). All extracts showed considerable inhibitions of 5α-reductase ranging from 44.59 ± 0.40 to 83.07 ± 3.46% with the strongest activity from the dichloromethane fraction (1.48 ± 0.06 mg finasteride/g). In conclusion, E. debile extracts exhibit cosmeceutical potentials. This study suggests that the E. debile ethyl acetate fraction could be used as a promising ingredient to organically treat hyperpigmentation and delay the skin aging process. In addition, compared to the current recommended intake of finasteride (1 mg/day) for androgenic alopecia, the dichloromethane fraction is proposed as an alternative source to naturally remediate hair loss.
Cascade Analysis Method of Multilayer Optical Films Structure Based on Two Port Transmission Line Theory
Authors: Wen Kui , Huang Xianjun , Liang Yuanlong , Liu Wencong , Xu Yanlin , Liu Peiguo
Published: January 2023

The infrared band is one of the important communication windows. Most of the detectors and sensors working in this band are designed and manufactured based on micro- and nano-lithography technology. In this article, we cut the giant-sized thickness of the transparent substrate and the metal film was uniformly sliced. Then, we used the CST software to simulate the sliced substrate and the metal film to obtain the optical response parameters for each slice. Finally, the combination of metal film and substrate was realized by cascading calculation of the two port transmission line theory, which solves problems such as overlong simulation time and cumbersome running load caused by huge grid divisions due to the difference between the substrate thickness and the response wavelength in the process of simulating light propagation. On the other hand, the cascade analysis method was experimentally verified by constructing a surface plasmon filter in the medium infrared band, which provides an effective idea and solution for bridging the gap between simulation and engineering application.
Explore Long-Range Context Features for Speaker Verification
Authors: Li Zhuo , Zhao Zhenduo , Wang Wenchao , Zhang Pengyuan , Zhao Qingwei
Published: January 2023

Multi-scale context information, especially long-range dependency, has shown to be beneficial for speaker verification (SV) tasks. In this paper, we propose three methods to systematically explore long-range context SV feature extraction based on ResNet and analyze their complementarity. Firstly, the Hierarchical-split block (HS-block) is introduced to enlarge the receptive fields (RFs) and extract long-range context information over the feature maps of a single layer, where the multi-channel feature maps are split into multiple groups and then stacked together. Then, by analyzing the contribution of each location of the convolution kernel to SV, we find the traditional convolution with a square kernel is not effective for long-range feature extraction. Therefore, we propose cross convolution kernel (cross-conv), which replaces the original 3 × 3 convolution kernel with a 1 × 5 and 5 × 1 convolution kernel. Cross-conv further enlarges the RFs with the same FLOPs and parameters. Finally, the Depthwise Separable Self-Attention (DSSA) module uses an explicit sparse attention strategy to capture effective long-range dependencies globally in each channel. Experiments are conducted on the VoxCeleb and CnCeleb to verify the effectiveness and robustness of the proposed system. Experimental results show that the combination of HS-block, cross-conv, and DSSA module achieves better performance than any single method, which demonstrates the complementarity of these three methods.
Virtualization Airborne Trusted General Computing Technology
Authors: Zhang Shuang , Wang Yuanxun , Wan Xinyu , Li Zhihui , Guo Yangming
Published: January 2023

Aircraft information service systems, such as airborne information systems, airborne integrated maintenance management systems, and cabin management systems, have greatly improved the ease of use and maintenance of civil aircraft. The current computing platforms used for accommodating these systems are unable to satisfy the multifaceted requirements of future information-based aircraft, such as energy conservation, emission reduction, high-performance computing, and information security protection, due to their high computing capacity, weight, and power consumption. Based on multi-core multi-threaded processors, a security hardware unit with microkernel virtualization technology and a virtualization airborne trusted general computing service architecture is proposed, and key technologies, including a high-performance processing and high-security hardware unit, virtualization management software unit, and virtualization security protection architecture were designed. By building a verification environment, the proposed platform was verified in terms of its application accommodation function, platform performance, and network security protection, for comparison with the existing platforms. The results showed that our method can fulfill the requirements of these technical indicators and is applicable, not only to new-generation civil aircraft, but also to unmanned aerial vehicles (UAVs) and emergency rescue aircraft with high-performance safety-critical computing needs.
Several Tree-Based Solutions for Predicting Flyrock Distance Due to Mine Blasting
Authors: Yari Mojtaba , Armaghani Danial Jahed , Maraveas Chrysanthos , Ejlali Alireza Nouri , Mohamad Edy Tonnizam , Asteris Panagiotis G.
Published: January 2023

Blasting operations involve some undesirable environmental issues that may cause damage to equipment and surrounding areas. One of them, and probably the most important one, is flyrock induced by blasting, where its accurate estimation before the operation is essential to identify the blasting zone’s safety zone. This study introduces several tree-based solutions for an accurate prediction of flyrock. This has been done using four techniques, i.e., decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost). The modelling of tree-based techniques was conducted with in-depth knowledge and understanding of their most influential factors. The mentioned factors were designed through the use of several parametric investigations, which can also be utilized in other engineering fields. As a result, all four tree-based models are capable enough for blasting-induced flyrock prediction. However, the most accurate predicted flyrock values were obtained using the AdaBoost technique. Observed and forecasted flyrock by AdaBoost for the training and testing phases received coefficients of determination (R2) of 0.99 and 0.99, respectively, which confirm the power of this technique in estimating flyrock. Additionally, according to the results of the input parameters, the powder factor had the highest influence on flyrock, whereas burden and spacing had the lowest impact on flyrock.
Research on the Spatial Effects of Green Process Innovation, Environmental Regulation, and Precipitation on Environmental Air Pollution
Authors: Zhou Jingkun , Li Yating , Tian Juan , Ma Zhifei
Published: January 2023

In recent years, air pollution events have occurred frequently in China and have had serious impacts on people’s work and life. The research objective of the article is to find out the spatial effects of green process innovation, environmental regulation, rainfall, and pollution from industrial development on air pollution by compiling a development index of the air pollution industry and constructing spatial autoregression and spatial Durbin models. The study found that green technology innovation significantly reduced sulfur dioxide emissions and fog haze pollution. The development of industrial air pollution is an important source of sulfur dioxide and pollutant haze and has significant spatial spillover effects. The strengthening of environmental regulations leads to a decrease in life pollution but causes a transfer of industrial pollution. Rainfall is conducive to reducing the concentrations of pollutants in the air, such as those from chimneys and smog. Policy recommendations are proposed, such as establishing different environmental policies for different green technological innovations to promote green technology innovation; constructing a joint defense control system for regional air pollution; increasing the construction of artificial wetlands in areas with serious air pollution; and creating scientific plans to coordinate the development of industry, technology, and the environment, to provide help for China to reduce the emission of atmospheric environmental pollutants.
Magnetic Assessment of Transplanted Tillandsia spp.: Biomonitors of Air Particulate Matter for High Rainfall Environments
Authors: Buitrago Posada Daniela , Chaparro Marcos A. E. , Duque-Trujillo José F.
Published: January 2023

Complementary methodologies in air quality monitoring, such as magnetic biomonitoring, are currently implemented since atmospheric particle pollution is a relevant problem for human health and ecosystems. We carried out magnetic biomonitoring using transplanted species of Tillandsia recurvata and T. usneoides to study their retention capacity of airborne magnetic particles AMP, the influence of precipitation, and magnetic properties. Plants of both epiphytic species were exposed for two, three and twelve months under uncovered and covered rain exposure conditions. The mass-specific magnetic susceptibility χ increases for both species over time, mean (s.d.) values of χ = 6.1 (6.4)–47.9 (37.6) × 10−8 m3 kg−1. The comparison of χ between exposure conditions suggests an insignificant rainfall influence on the accumulation/loss of magnetic particles in the studied plants. Magnetic parameters, scanning electron microscopy, and X-ray energy dispersive spectroscopy indicate the presence of magnetite and Fe-rich particles with sizes between <0.1 and 5 µm, a harmful particle category to human health. It is concluded that both species of the genus Tillandsia are efficient biological indicators of AMP and convenient for air particle pollution assessment in high-precipitation environments.
Pole Allocation for Rational Gauss Quadrature Rules for Matrix Functionals Defined by a Stieltjes Function
Authors: Alahmadi Jihan , Pranić Miroslav , Reichel Lothar
Published: January 2023

This paper considers the computation of approximations of matrix functionals of form F(A):=vTf(A)v, where A is a large symmetric positive definite matrix, v is a vector, and f is a Stieltjes function. The functional F(A) is approximated by a rational Gauss quadrature rule with poles on the negative real axis (or part thereof) in the complex plane, and we focus on the allocation of the poles. Specifically, we propose that the poles, when considered positive point charges, be allocated to make the negative real axis (or part thereof) approximate an equipotential curve. This is easily achieved with the aid of conformal mapping.
A Method for Detecting Outliers from the Gamma Distribution
Authors: Liao Xiou , Wang Tongtong , Zou Guohua
Published: January 2023

Outliers often occur during data collection, which could impact the result seriously and lead to a large inference error; therefore, it is important to detect outliers before data analysis. Gamma distribution is a popular distribution in statistics; this paper proposes a method for detecting multiple upper outliers from gamma (m,θ). For computing the critical value of the test statistic in our method, we derive the density function for the case of a single outlier and design two algorithms based on the Monte Carlo and the kernel density estimation for the case of multiple upper outliers. A simulation study shows that the test statistic proposed in this paper outperforms some common test statistics. Finally, we propose an improved testing method to reduce the impact of the swamping effect, which is demonstrated by real data analyses.
Molecular Actors of Inflammation and Their Signaling Pathways: Mechanistic Insights from Zebrafish
Authors: Leiba Jade , Özbilgiç Resul , Hernández Liz , Demou Maria , Lutfalla Georges , Yatime Laure , Nguyen-Chi Mai
Published: January 2023

Inflammation is a hallmark of the physiological response to aggressions. It is orchestrated by a plethora of molecules that detect the danger, signal intracellularly, and activate immune mechanisms to fight the threat. Understanding these processes at a level that allows to modulate their fate in a pathological context strongly relies on in vivo studies, as these can capture the complexity of the whole process and integrate the intricate interplay between the cellular and molecular actors of inflammation. Over the years, zebrafish has proven to be a well-recognized model to study immune responses linked to human physiopathology. We here provide a systematic review of the molecular effectors of inflammation known in this vertebrate and recapitulate their modes of action, as inferred from sterile or infection-based inflammatory models. We present a comprehensive analysis of their sequence, expression, and tissue distribution and summarize the tools that have been developed to study their function. We further highlight how these tools helped gain insights into the mechanisms of immune cell activation, induction, or resolution of inflammation, by uncovering downstream receptors and signaling pathways. These progresses pave the way for more refined models of inflammation, mimicking human diseases and enabling drug development using zebrafish models.
Evidence Based on an Integrative Analysis of Multi-Omics Data on METTL7A as a Molecular Marker in Pan-Cancer
Authors: Liu Zikai , Chen Yiqun , Shen Tong
Published: January 2023

Methyltransferase-like protein 7A (METTL7A), an RNA N6-methyladenosine (m6A) methyltransferase, has attracted much attention as it has been found to be closely associated with various types of tumorigenesis and progression. This study provides a comprehensive assessment of METTL7A from a pan-cancer perspective using multi-omics data. The gene ontology enrichment analysis of METTL7A-binding proteins revealed a close association with methylation and lipid metabolism. We then explored the expression of METTL7A in normal tissues, cell lines, different subtypes and cancers, and found that METTL7A was differentially expressed in various cancer species, tumor molecular subtypes and immune subtypes. Evaluation of the diagnostic and prognostic value of METTL7A in pan-cancer revealed that METTL7A had high accuracy in tumor prediction. Moreover, the low expression of METTL7A significantly correlated with the poor prognosis, including kidney renal clear cell carcinoma (KIRC), mesothelioma and sarcoma, indicating that METTL7A could be a potential biomarker for tumor diagnosis and prognosis. We focused on KIRC after pre-screening and analyzed its expression and prognostic value in various clinical subgroups. We found that METTL7A was significantly related to tumor stage, metastasis stage, pathologic stage, primary therapy outcome, histologic grade and gender, and that low METTL7A expression was associated with poorer outcomes. Finally, we analyzed the immune infiltration and co-expressed genes of METTL7A as well as the differentially expressed genes in the high and low expression groups. In conclusion, METTL7A is a better molecular marker for pan-cancer diagnosis and prognosis and has high potential as a diagnostic and prognostic biomarker for KIRC.
Oligodendrocyte Progenitors Display Enhanced Proliferation and Autophagy after Space Flight
Authors: Tran Victoria , Carpo Nicholas , Cepeda Carlos , Espinosa-Jeffrey Araceli
Published: January 2023

Intracranial hypertension (ICP) and visual impairment intracranial pressure (VIIP) are some of the consequences of long-term space missions. Here we examined the behavior of oligodendrocyte progenitors (OLPs) after space flight using time-lapse microscopy. We show that most OLPs divided more than ground control (GC) counterparts did. Nonetheless, a subpopulation of OLPs flown to space presented a significant increase in autophagic cell death. Examination of the proteomic profile of the secretome of space flown OLPs (SPC-OLPs) revealed that the stress protein heat shock protein-90 beta “HSP-90β” was the 5th most enriched (6.8 times) and the secreted protein acidic and rich in cysteine “SPARC” was the 7th most enriched (5.2 times), with respect to ground control cells. SPARC induces endoplasmic reticulum stress, which leads to autophagy. Given the roles and importance of these two proteins in mammalian cells’ metabolism, their upregulation may hold the key to modulating cell proliferation and autophagy, in order to mitigate ICP and VIIP during and after space missions.
Electrospinning of Cyclodextrin–Oligolactide Derivatives
Authors: Opalkova Siskova Alena , Sacarescu Liviu , Opalek Andrej , Mosnacek Jaroslav , Peptu Cristian
Published: January 2023

The materials used for the preparation of electrospun mats exhibit a large variety. Among them, cyclodextrins (CDs) and their derivatives have received thorough attention. Herein, we focus on the preparation of electrospun fibers based on biodegradable cyclodextrin–oligolactide (CDLA) derivatives, which may be qualified as polymer-free cyclodextrin. CDLA was prepared by ring opening of L-lactide initiated by the β-cyclodextrin. A clear structural image of the high-purity CDLA product was proved by MALDI MS. Preparation of the electrospun mats was optimized by taking into consideration the electrospinning parameters such as applied voltage, needle-to-collector distance, flow rate, the concentration of cyclodextrin solutions, and solvent type. The obtained electrospun fibers were morphologically characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and small-angle X-ray scattering (SAXS). SEM allowed the optimization of the electrospinning process to obtain beadless fibers with submicronic diameters. Further analysis by TEM and SAXS revealed the inner structural features of the CDLA-based filaments. Our results showed that the high purity CDLA materials, structurally well-defined at the molecular level, are suitable for the preparation of electrospun mats by using dimethylformamide or a water/acetonitrile mixture as electrospinning solvents, similar to lower molecular weight commercial cyclodextrin derivatives.
The Sound of Emotion: Pinpointing Emotional Voice Processing Via Frequency Tagging EEG
Authors: Vos Silke , Collignon Olivier , Boets Bart
Published: January 2023

Successfully engaging in social communication requires efficient processing of subtle socio-communicative cues. Voices convey a wealth of social information, such as gender, identity, and the emotional state of the speaker. We tested whether our brain can systematically and automatically differentiate and track a periodic stream of emotional utterances among a series of neutral vocal utterances. We recorded frequency-tagged EEG responses of 20 neurotypical male adults while presenting streams of neutral utterances at a 4 Hz base rate, interleaved with emotional utterances every third stimulus, hence at a 1.333 Hz oddball frequency. Four emotions (happy, sad, angry, and fear) were presented as different conditions in different streams. To control the impact of low-level acoustic cues, we maximized variability among the stimuli and included a control condition with scrambled utterances. This scrambling preserves low-level acoustic characteristics but ensures that the emotional character is no longer recognizable. Results revealed significant oddball EEG responses for all conditions, indicating that every emotion category can be discriminated from the neutral stimuli, and every emotional oddball response was significantly higher than the response for the scrambled utterances. These findings demonstrate that emotion discrimination is fast, automatic, and is not merely driven by low-level perceptual features. Eventually, here, we present a new database for vocal emotion research with short emotional utterances (EVID) together with an innovative frequency-tagging EEG paradigm for implicit vocal emotion discrimination.
Supervised Learning Algorithm Based on Spike Train Inner Product for Deep Spiking Neural Networks
Authors: Lin Xianghong , Zhang Zhen , Zheng Donghao
Published: January 2023

By mimicking the hierarchical structure of human brain, deep spiking neural networks (DSNNs) can extract features from a lower level to a higher level gradually, and improve the performance for the processing of spatio-temporal information. Due to the complex hierarchical structure and implicit nonlinear mechanism, the formulation of spike train level supervised learning methods for DSNNs remains an important problem in this research area. Based on the definition of kernel function and spike trains inner product (STIP) as well as the idea of error backpropagation (BP), this paper firstly proposes a deep supervised learning algorithm for DSNNs named BP-STIP. Furthermore, in order to alleviate the intrinsic weight transport problem of the BP mechanism, feedback alignment (FA) and broadcast alignment (BA) mechanisms are utilized to optimize the error feedback mode of BP-STIP, and two deep supervised learning algorithms named FA-STIP and BA-STIP are also proposed. In the experiments, the effectiveness of the proposed three DSNN algorithms is verified on the MNIST digital image benchmark dataset, and the influence of different kernel functions on the learning performance of DSNNs with different network scales is analyzed. Experimental results show that the FA-STIP and BP-STIP algorithms can achieve 94.73% and 95.65% classification accuracy, which apparently possess better learning performance and stability compared with the benchmark algorithm BP-STIP.
Cognitive Performance in Short Sleep Young Adults with Different Physical Activity Levels: A Cross-Sectional fNIRS Study
Authors: You Yanwei , Liu Jianxiu , Wang Dizhi , Fu Yingyao , Liu Ruidong , Ma Xindong
Published: January 2023

Short sleep is a common issue nowadays. The purpose of this study was to investigate prefrontal cortical hemodynamics by evaluating changes in concentrations of oxygenated hemoglobin (HbO) in cognitive tests among short-sleep young adults and to explore the relationship between sleep duration, physical activity level, and cognitive function in this specific population. A total of 46 participants (25 males and 21 females) were included in our study, and among them, the average sleep duration was 358 min/day. Stroop performance in the short sleep population was linked to higher levels cortical activation in distinct parts of the left middle frontal gyrus. This study found that moderate-to-vigorous physical activity (MVPA) was significantly associated with lower accuracy of incongruent Stroop test. The dose-response relationship between sleep duration and Stroop performance under different levels of light-intensity physical activity (LPA) and MVPA was further explored, and increasing sleep time for different PA level was associated with better Stroop performance. In summary, this present study provided neurobehavioral evidence between cortical hemodynamics and cognitive function in the short sleep population. Furthermore, our findings indicated that, in younger adults with short sleep, more MVPA was associated with worse cognitive performance. Short sleep young adults should increase sleep time, rather than more MVPA, to achieve better cognitive function.
Performance of Rubberized Concrete and the Effect of Temperature and Stainless Steel Fibers
Authors: El-Zohairy Ayman , Sanchez Matthew , Abediniangerabi Bahram , Moler Perry
Published: January 2023

Rubberized concrete is widely used in construction by utilizing the advantages of partially replacing fine or coarse aggregate with rubber to enhance several properties of concrete and provide an environmentally friendly solution. This paper experimentally explores the influence of utilizing crumb rubber (CR) as an alternate coarse aggregate in concrete. Concrete specimens were prepared with different percentages of rubber (0%, 5%, 10%, 15%, and 20%). Additionally, other parameters, such as freezing–thawing cycles, temperature, and stainless steel fibers (SSFs), were investigated. The workability of fresh concrete and the compression properties of hardened concrete were examined. Reductions in the mechanical properties of rubberized concrete were obtained. The compressive strength reductions ranged between 13% and 50%, based on the percentage of CR in the concrete mix. However, a lesser unit weight and higher toughness were obtained relative to conventional concrete. The average unit weight decreased by 1.3%, 2.5%, 3.4%, and 5.7% of the control mixture when 5%, 10%, 15%, and 20% of the CR were incorporated into the concrete mixtures, respectively. Regression models to predict the compressive strength and unit weight of concrete with CR were developed. In addition, a life cycle cost analysis (LCCA) to identify and quantify the possible benefits of using CR in concrete mixes was carried out. Using rubberized concrete mixtures for thin whitetopping offered a slightly lower net present value compared to the ordinary concrete mix.
Experiments and Numerical Simulations on the Seismic Performance of Steel-Frame Composite Wallboard Shear Walls
Authors: Wang Zhen , Liu Zhe , Yan Jia-Bao , Ju Xiao , Han Lei
Published: January 2023

In order to expand the applications of cold-formed thin-walled steel structures, this study proposes a new type of composite wallboard composed of cold-formed thin-walled C-shaped steel and multi-layer concrete, in which C-shaped steel serves as the skeleton, foam concrete acts as the thermal insulation material, and fine aggregate concrete and cement mortar play the part of envelopes. The composite wallboard can be made in a factory assembly line, meeting the requirements of the building (civil and structural) industry. Two steel-frame composite wallboard shear walls were subjected to reciprocating loading, with the connection mode as the design parameter, to investigate the seismic performance of the structure. The failure mode, hysteresis curve, skeleton curve, strength degradation, stiffness degradation, ductility, and energy dissipation capacity of the specimens were analyzed. On this basis, the finite element (FE) model of the steel-frame composite wallboard was established, and the model’s accuracy was verified by comparing the bearing capacity and the skeleton curve. Results show that the structure shows shear failure characteristics, and the cement mortar layer and the fine aggregate concrete layer are separated from the C-shaped steel after being crushed. The infilled foam concrete is also crushed, and the welding seams between the extended C-shaped steel and steel frame of the WP-1 specimen are damaged. The hysteresis curves of the two specimens have a clear pinch, but the area enclosed by the hysteresis loop is large, and the energy dissipation capacity is also present. The yield load and ultimate load of the WP-2 specimen are higher than those of the WP-1 specimen, indicating that the higher the connection strength between the composite wallboard and the steel frame, the greater the ultimate carrying capacity of the specimen. The established FE model can accurately estimate the seismic performance of steel-frame composite wallboard shear walls.
Damage States Investigation of Infilled Frame Structure Based on Meso Modeling Approach
Authors: Hapsari Isyana Ratna , Kristiawan Stefanus Adi , Sangadji Senot , Gan Buntara Sthenly
Published: January 2023

The non-linear behavior of infilled frames is very complex. The behavior of this structure may be studied by experimental and numerical approaches. An experimental test can provide a more realistic output but has the disadvantages of high costs, relatively long time and specific room usage. A numerical analysis can be an alternative to analyze the behavior of infilled frames. One of the most powerful numerical approaches is meso-modeling. This approach has the advantage of being able to capture local damage to the panel. For this reason, the progressive damage identified in the meso-model can be used as a basis for determining damage state criteria. The grouping of damage states is proposed based on the initial identification in the form of local damage linked to global damage, i.e., IDR. This study’s proposed level of infilled frame damage is DS1 = 0.17%, DS2 = 0.52%, DS3 = 0.79% and DS4 = 1.99%. However, the quantification results of the structural damage level cannot be generalized because many complex factors influence the behavior of infilled frames. Subsequently, a parametric study was carried out to determine the contribution of the mechanical properties of the infilled frame material to the degree of structural damage.
Experimental Study on Impact Behavior of Concrete Panel with and without Polypropylene Macrofibers
Authors: Youm Kwangsoo , Moon Jiho
Published: January 2023

Macrofibers have often been used to increase the tensile strength, durability, crack resistance, spalling, impact resistance, and toughness of concrete. However, the impact behavior of fiber-reinforced concrete (FRC) structures is quite different from their static behavior, and the effectiveness of macrofibers in improving impact resistance should be carefully evaluated. In this study, the impact behavior of FRC with polypropylene (PP) macrofibers was studied through a series of drop-weight impact tests. First, the material characteristics of the FRC with PP fibers under static conditions were evaluated. Test specimens were constructed and drop-weight impact tests were performed. The main parameters were the presence or absence of PP fibers and the drop height, which is related to the magnitude of the impact energy. From the results, it can be found that the crack width of the FRC specimen was smaller than the normal concrete specimen for a similar residual deflection after the impact test due to the bridging effect of the macrofibers. However, the effect of PP fibers on the impact resistance was not significant, even though there was a considerable increase in tensile and flexural performance under static conditions, since the hardening effect after the sharp reduction in strength shown in the static test of FRC is not effective in the impact test.
The β-Blocker Carvedilol Prevents Benzo(a)pyrene-Induced Lung Toxicity, Inflammation and Carcinogenesis
Authors: Shahid Ayaz , Chen Mengbing , Lin Carol , Andresen Bradley T. , Parsa Cyrus , Orlando Robert , Huang Ying
Published: January 2023

The current study evaluated the effects of the β-blocker carvedilol on benzo(a)pyrene (B(a)P) and its active metabolite benzo(a)pyrene diol epoxide (BPDE)-induced lung toxicity, inflammation and carcinogenesis and explored the potential mechanisms. Carvedilol blocked the BPDE-induced malignant transformation of human bronchial epithelial cells BEAS-2B. In BEAS-2B cells, B(a)P strongly activated ELK-1, a transcription factor regulating serum response element (SRE) signaling, which was attenuated by carvedilol. Carvedilol also inhibited the B(a)P-induced AhR/xenobiotic responsive element (XRE) and mRNA expression of CYP1A1 and attenuated B(a)P-induced NF-κB activation. In a B(a)P-induced acute lung toxicity model in CD-1/IGS mice, pretreatment with carvedilol for 7 days before B(a)P exposure effectively inhibited the B(a)P-induced plasma levels of lactate dehydrogenase and malondialdehyde, inflammatory cell infiltration and histopathologic abnormalities in the lung, and upregulated the expression of GADD45α, caspase-3 and COX-2 in the lung. In a B(a)P-induced lung carcinogenesis model in A/J mice, carvedilol treatment for 20 weeks did not affect body weight but significantly attenuated tumor multiplicity and volume. These data reveal a previously unexplored role of carvedilol in preventing B(a)P-induced lung inflammation and carcinogenesis by inhibiting the cross-talk of the oncogenic transcription factors ELK-1, AhR and NF-κB.
Synovial Sarcoma Preclinical Modeling: Integrating Transgenic Mouse Models and Patient-Derived Models for Translational Research
Authors: Landuzzi Lorena , Ruzzi Francesca , Lollini Pier-Luigi , Scotlandi Katia
Published: January 2023

Synovial sarcomas (SyS) are rare malignant tumors predominantly affecting children, adolescents, and young adults. The genetic hallmark of SyS is the t(X;18) translocation encoding the SS18-SSX fusion gene. The fusion protein interacts with both the BAF enhancer and polycomb repressor complexes, and either activates or represses target gene transcription, resulting in genome-wide epigenetic perturbations and altered gene expression. Several experimental in in vivo models, including conditional transgenic mouse models expressing the SS18-SSX fusion protein and spontaneously developing SyS, are available. In addition, patient-derived xenografts have been estab-lished in immunodeficient mice, faithfully reproducing the complex clinical heterogeneity. This review focuses on the main molecular features of SyS and the related preclinical in vivo and in vitro models. We will analyze the different conditional SyS mouse models that, after combination with some of the few other recurrent alterations, such as gains in BCL2, Wnt-β-catenin signaling, FGFR family, or loss of PTEN and SMARCB1, have provided additional insight into the mechanisms of synovial sarcomagenesis. The recent advancements in the understanding of SyS biology and improvements in preclinical modeling pave the way to the development of new epigenetic drugs and immunotherapeutic approaches conducive to new treatment options.
Technical–Economic Assessment—The Missing Piece for Increasing the Attractiveness of Applied Biocatalysis in Ester Syntheses?
Authors: de Sousa Ronaldo Rodrigues , de Castro Rui de Paula Vieira , Assis Nadinne Medeiros , da Silva Ayla Sant’Ana , Freire Denise Maria Guimarães , Fernandez-Lafuente Roberto , Ferreira-Leitão Viridiana Santana
Published: January 2023

Although the current literature describes significant advances in biocatalytic ester syntheses, few industrial plants worldwide are currently producing esters using biocatalysts. Green and sustainable esters can be obtained via a biocatalytic route, including some operational advantages over conventional syntheses. An analysis of the literature revealed that most articles neglect or describe the economic issues generically, without quantitative information. Scaling-up studies are also scarce in this field. The main disadvantage of biocatalysis using immobilized lipases—their cost—has not been studied at the same level of depth as other technical aspects. This gap in the literature is less intense in enzymatic biodiesel production studies and, despite the lack of a strict correlation, enzymatic biodiesel commercial plants are relatively more common. Preliminary techno-economic assessments are crucial to identify and circumvent the economic drawbacks of biocatalytic ester syntheses, opening the way to broader application of this technology in a large-scale context.
Experimental Study on Kinetics and Mechanism of Ciprofloxacin Degradation in Aqueous Phase Using Ag-TiO2/rGO/Halloysite Photocatalyst
Authors: Ngo Ha-Son , Nguyen Thi-Linh , Tran Ngoc-Tuan , Le Hanh-Chi
Published: January 2023

In this study, Ag-TiO2/rGO/halloysite nanotubes were synthesised from natural sources using a simple method. The material was characterised by X-ray diffraction (XRD), Fourier-transform infrared (FTIR), Raman spectroscopy, BET, scanning electron microscopy (SEM) and UV-vis DRS techniques. The as-synthesised material has a sandwich-like shape, with the active phase distributed evenly over the rGO/HNT support. Compared to pure TiO2, the material has a lower band gap energy (~2.7 eV) and a suitable specific surface area (~80 m2/g), making it able to participate effectively in the photochemical degradation of pollutants. The catalyst showed exceptional activity in the degradation of CIP antibiotics in water, achieving a conversion of about 90% after 5 h of irradiation at an initial CIP concentration of 20 ppm. This efficiency was significantly higher than that of pure TiO2 and Ag-TiO2, which could prove the important effect of the support and silver doping. The results of the experiments show that the process follows a pseudo-first-order kinetic model in the case of (1%)Ag wt. and pseudo-second-order in the case of (3%)Ag wt., which could be explained by the aggregation of silver and the increasing role of chemisorption. Tests with radical scavengers showed that the •OH radical had the greatest effect on CIP decomposition, while •O2− had the least. The neutral pH value and the high degree of mineralisation (approx. 80%) confirm the potential of the material for use in wastewater treatment.
Immobilization of the Amidohydrolase MxcM and Its Application for Biocatalytic Flow Synthesis of Pseudochelin A
Authors: Winand Lea , Theisen Stefanie , Lütz Stephan , Rosenthal Katrin , Nett Markus
Published: January 2023

The chemical synthesis of heterocycles typically requires elevated temperature and acid or base addition to form the desired product. Moreover, these reactions often involve hazardous reagents, which is why biocatalytic routes for heterocycle formation have gained increasing attention. In recent years, several enzymes belonging to the amidohydrolase superfamily have been identified to generate heterocycles via cyclocondensation reactions. Of particular interest is the amidohydrolase MxcM, which catalyzes the formation of an imidazoline moiety in the biosynthesis of the anti-inflammatory natural product pseudochelin A. In this study, we present a concept for the immobilization of this enzyme using a fused hexahistidine tag for fixation onto a solid, porous carrier. Notably, the immobilization improves the enzyme’s tolerance to organic solvents. The immobilized MxcM exhibits a residual activity of 169% in the polar solvent acetonitrile compared to the free enzyme, and the storage stability in the presence of 20 vol% acetonitrile was ameliorated. In addition, an immobilized enzyme reactor (IMER) was designed that can be operated under flow conditions. The MxcM-IMER retains its biocatalytic activity and mechanic stability over the tested operation time. These results provide important insights for the integration of heterocycle-forming amidohydrolases in chemical processes.