bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization of Photoautotrophic Growth Regimens of Scenedesmaceae alga: The Influence of Light Conditions and Carbon Dioxide Concentrations
Authors: Ziganshina Elvira E. , Bulynina Svetlana S. , Yureva Ksenia A. , Ziganshin Ayrat M.
Published: November 2023

Improving methods for landless production of bioproducts is considered an important stage in the development of the modern bioeconomy. In this context, microalgal biomass is one of the most promising sources of valuable substances due to its rich biochemical composition. Despite the high adaptability of microalgae to various environmental factors, the effectiveness of cultivation systems depends on precisely selected parameters. Both the light conditions and the supply of inorganic carbon sources are key in determining the efficiency of photoautotrophic cultivation. In this work, the effect of a high daily photosynthetic photon flux density (PPFD) ranging from 37.44 to 112.32 mol m−2 day−1 on the growth and productivity of a novel Scenedesmaceae alga, strain EZ-B1, was assessed. The next stage of cultivation consisted of selecting the optimal CO2 concentration. Improved performance of microalga during cultivation in a photobioreactor was achieved at 112.32 mol m−2 day−1 (24 h photoperiod) and by supplying 2% CO2, as evidenced by the high biomass productivity (0.69 g L−1 day−1), total biomass yield (5.23 g L−1), and ammonium nitrogen consumption rate. The data obtained suggest that a higher level of PPFD led to the highest growth rate of the novel strain and the highest biomass productivity, which, in practice, will increase production capacity.
Novel Network Intrusion Detection Based on Feature Filtering Using FLAME and New Cuckoo Selection in a Genetic Algorithm
Authors: Alzboon Kawthar , Al-Nihoud Jehad , Alsharafat Wafa
Published: November 2023

Recently, networks have faced a significant challenge in terms of security due to constant unauthorized access by hackers, resulting in the compromise of network user data. To enhance network security, there are various approaches that can be employed, including the utilization of firewalls, encryption, and antivirus software. Among these methods, one type of system that can be implemented is an intrusion detection system (IDS), which actively monitors the network to identify any intrusions. In order to effectively detect any unauthorized or malicious activities, sophisticated techniques such as genetic algorithms, cuckoo searches, and FLAME are employed. This research proposes a novel IDS that aims to improve the detection of intrusions. The proposed IDS initially conducts feature filtration using fuzzy clustering through the local approximation of the membership algorithm (FLAME), which effectively reduces the number of features that need to be analyzed and processed. Subsequently, the system detects attacks by implementing an extended classifier system (XCS) that incorporates a genetic algorithm (GA) to enhance the accuracy of intrusion detection. By incorporating the cuckoo search and selection within GA, while considering different crossover and mutation probabilities, instead of solely relying on traditional GA, it is anticipated that intrusion detection accuracy will be improved. To evaluate the performance of the proposed IDS, it was tested on the KDD99 dataset and compared with several other IDSs that were also tested on the same dataset. The experimental results clearly indicate that the proposed IDS significantly enhances the detection rate (DR) and accuracy while also significantly reducing the false alarm rate (FAR) and these impressive results were 100%, 99.99% and 0.05% respectively. The performance comparisons reveal that, overall, the proposed IDS outperforms several existing IDSs in terms of intrusion detection performance.
Flywheel Vibration Isolation of Satellite Structure by Applying Structural Plates with Elastic Boundary Instead of Restrained Boundary
Authors: Kong Xiangsen , Li Hao , Zhou Xubin , Xiang Xiujuan , Shen Xing
Published: November 2023

Flywheels play a critical role as core components in satellite attitude control systems. However, their high-speed rotation inevitably generates vibrations that have a detrimental impact on the in-orbit imaging capabilities of high-precision remote sensing payloads. This study focuses on the passive vibration isolation design of satellite flywheels. The flywheel-mounted structural plate and flywheel vibration isolation platform are considered as a whole system (termed a plate-isolator system). In this system, the structural plate is treated as an elastomer. By simplifying the plate-isolator system as a 2-degree-of-freedom vibration system, it becomes evident that obtaining an ideal vibration isolation effect through the optimization of the flywheel vibration isolation platform (FVIP) alone is difficult. In order to enhance the passive vibration isolation effect for satellite flywheels, this study introduces the concept of an elastic boundary applied to the flywheel-mounted structural plate, thus treating the elastic boundary as a design factor. Consequently, the plate-isolator system can be simplified as a 3-stage vibration isolation system. The optimization of the elastic boundary condition of the structural plate is performed using the kinetic model of the simplified 3-stage system. The vibration isolation effect of the plate-isolator system with an elastic boundary is further confirmed through finite element simulation. The calculation results demonstrate that, after establishing a reasonable elastic boundary for the satellite structural plate, the overall vibration/force transmission rate of the plate-isolator system becomes similar to that of a single-degree-of-freedom dynamic system. Finally, the proposed concept is validated through kinetic response analysis of a cube satellite. The results reveal that the vibration amplitude of the satellite’s top and side structural plates can be effectively lowered if the elastic boundary condition is set for the flywheel-mounted bottom structural plate.
Research on Quantization Parameter Decision Scheme for High Efficiency Video Coding
Authors: Jin Xuesong , Chai Yansong
Published: November 2023

High-Efficiency Video Coding (HEVC) is one of the most widely studied coding standards. It still uses the block-based hybrid coding framework of Advanced Video Coding (AVC), and compared to AVC, it can double the compression ratio while maintaining the same quality of reconstructed video. The quantization module is an important module in video coding. In the process of quantization, quantization parameter is an important factor in determining the bitrate in video coding, especially in the case of limited channel bandwidth. It is particularly important to select a reasonable quantization parameter to make the bitrate as close as possible to the target bitrate. Aiming at the problem of unreasonable selection of quantization parameters in codecs, this paper proposes using a differential evolution algorithm to assign quantization parameter values to the coding tree unit (CTU) in each frame of 360-degree panoramic video based on HEVC so as to strike a balance between bitrate and distortion. Firstly, the number of CTU rows in a 360-degree panoramic video frame is considered as the dimension of the optimization problem. Then, a trial vector is obtained by randomly selecting the vectors in the population for mutation and crossover. In the mutation step, the algorithm generates a new parameter vector by adding the weighted difference between two population vectors to a third vector. And the elements in the new parameter vector are selected according to the crossover rate. Finally, the trial vector is regarded as the quantization parameter of each CTU in the CTU row to encode, and the vector with the least rate distortion is selected. The algorithm will produce the optimal quantization parameter combination for the current video. The experimental results show that compared to the benchmark algorithm of HEVC reference software HM-16.20, the proposed algorithm can provide a bit saving of 1.86%, while the peak signal-to-noise ratio (PSNR) can be improved by 0.07 dB.
Analysis and Reduction of Nonlinear Effects in Optical Fiber Frequency Transfer
Authors: Gao Jing , Zhang Linbo , Jiao Dongdong , Xu Guanjun , Bu Jiayu , Wu Mengfan , Zang Qi , Zhang Xiang , Dong Ruifang , Liu Tao , Zhang Shougang
Published: November 2023

Nonlinear effects in optical fiber frequency transfer have a significant impact on the precision of frequency transfer. We investigate the main nonlinear effects, including the Brillouin scattering and the Raman scattering, in optical fiber frequency transfer through theoretical and simulation calculations in detail. The calculation results show that the threshold powers of the Brillouin scattering and the Raman scattering decrease with the increase in the fiber length; however, the fiber length has little to no impact on the threshold powers when the fiber length is greater than 10 km. The threshold powers, including the Brillouin scattering and the Raman scattering, increase as the attenuation coefficient increases. Conversely, when it comes to the gain coefficients, the outcomes exhibit a reverse trend. When the linewidth Δvlaser of the laser source is from 1 Hz to 1 MHz, the linewidth Δvlaser does not affect the threshold powers of the Brillouin scattering. This study seeks to offer design guidance aimed at mitigating nonlinear effects in optical fiber frequency transfer. The calculated results hold considerable potential in guiding various applications reliant on Brillouin and Raman scattering properties, such as laser technology and optical fiber sensing.
Enhancing Traceability of Italian Almonds through IR Spectroscopy and Chemometric Classifiers
Authors: Scappaticci Claudia , Foschi Martina , Plaku Alessio , Biancolillo Alessandra , D’Archivio Angelo Antonio
Published: November 2023

Almonds are the seeds of the almond (Prunus Amygdalus) tree and are a nut consumed worldwide. The present study utilized the ATR FT-IR technique followed by a chemometric analysis to develop predictive models for determining the geographical origin of almonds from three regions in Southern Italy (Apulia, Calabria, and Sicily). IR spectra were collected on both the almond shell and the edible kernel to accurately characterize the three different geographical origins. The spectroscopic data obtained were processed using Soft Independent Modeling of Class Analogies (SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA). Both SIMCA and PLS-DA revealed that the shell spectra are more useful for assessing the geographical origin of samples. In particular, the PLS-DA model applied to these data achieved a 100% correct classification rate (on the external test set of individuals) for all the investigated classes.
Thermal-Mechanical Behaviour of Road-Embedded Wireless Charging Pads for EVs
Authors: Li Kai-Yeung , Allen Tom , Bickerton Simon , Kelly Piaras
Published: November 2023

Road-embedded inductive power transfer (IPT) systems have the potential to accelerate the electrification of the transportation sector. For these systems to be economically viable, however, they need to have a similar durability and lifespan to those of asphalt roads. One area that has lacked investigation is thermally induced stresses in a primary IPT pad, which are caused by the increase in the temperature of the pad when it is energized and the differing thermal expansion of the materials within. This paper presents an experimental and a finite element-based methodology for investigating the thermal–mechanical behaviour of a ¼-scale double-D pad, which was energized while suspended in air, as well as energized when embedded in pavement. A focus was placed on the measurement and prediction of strains in the magnetic ferrite cores because of their brittleness. Ferrite strains were measured using a combination of resistive strain gauges and non-metallic fibre Bragg grating (FBG) sensors. Coupled electromagnetic–thermal–structural simulations were conducted to predict temperature and strains in the system, with temperature-dependent properties obtained through physical testing. At an ambient temperature of 50 °C, the temperature in the middle of the copper litz wire coil was predicted to be 100 °C in both the suspended and embedded case. There was an excellent correlation with the experimental results, with a difference of less than 10% for most temperature measurements. When energized, the pad was predicted to experience an upward bow due to its temperature rise, resulting in bending strains in the ferrite cores. At an ambient temperature of 50 °C, the maximum tensile strain in the ferrites of the embedded pad was measured to be 62 microstrains (με), with a root-mean square error that was 18 με across three sensors. The experimental and validated numerical methodology can be applied to full-scale operational IPT pads to analyse and improve their thermal–mechanical performance.
Steelmaking Predictive Analytics Based on Random Forest and Semantic Reasoning
Authors: Beden Sadeer , Lakshmanan Kayal , Giannetti Cinzia , Beckmann Arnold
Published: November 2023

This paper proposes a human-in-the-loop framework that integrates machine learning models with semantic technologies to aid decision making in the domain of steelmaking. To achieve this, we convert a random forest (RF) into rules in a Semantic Web Rule Language (SWRL) format and represent real-world data as a knowledge graph in a Resource Description Framework (RDF) format, capturing the meta-data as part of the model. A rule engine is deployed that applies logical inference on the knowledge graph, resulting in a semantically enriched classification. This new classification is combined with external domain-expert knowledge to provide improved, knowledge-guided assistance for the human-in-the-loop system. A case study in the steel manufacturing domain is introduced, where this application is used for real-world predictive analytic purposes.
Implementation of the SoftMax Activation for Reconfigurable Neural Network Hardware Accelerators
Authors: Shatravin Vladislav , Shashev Dmitriy , Shidlovskiy Stanislav
Published: November 2023

In recent decades, machine-learning algorithms have been extensively utilized to tackle various complex tasks. To achieve the high performance and efficiency of these algorithms, various hardware accelerators are used. Typically, these devices are specialized for specific neural network architectures and activation functions. However, state-of-the-art complex autonomous and mobile systems may require different algorithms for different tasks. Reconfigurable accelerators can be used to resolve this problem. They possess the capability to support diverse neural network architectures and allow for significant alterations to the implemented model at runtime. Thus, a single device can be used to address entirely different tasks. Our research focuses on dynamically reconfigurable accelerators based on reconfigurable computing environments (RCE). To implement the required neural networks on such devices, their algorithms need to be adapted to the homogeneous structure of RCE. This article proposes the first implementation of the widely used SoftMax activation for hardware accelerators based on RCE. The implementation leverages spatial distribution and incorporates several optimizations to enhance its performance. The timing simulation of the proposed implementation on FPGA shows a high throughput of 1.12 Gbps at 23 MHz. The result is comparable to counterparts lacking reconfiguration capability. However, this flexibility comes at the expense of the increased consumption of logic elements.
Study on the Degradation of SOFC Anodes Induced by Chemical and Electrochemical Sintering Using EIS and µ-CT
Authors: Sourkouni Georgia , Argirusis Christos
Published: November 2023

The goal of the present study was to quantify degradation phenomena on anodes that can be attributed to chemical (thermal) and/or electrochemical sintering, to find out the underlying mechanisms, and to propose countermeasures. The samples were thermally aged for times from 0 to 1000 h, and additional samples of the same type were subjected to electrochemical loading over the same period. The cells were then examined for microstructural changes using FE-SEM/EDS and micro-computed tomography (µ-CT), and the results are correlated with electrochemical impedance spectroscopy (EIS) parameters of long-term electrochemical experiments under dry and humid conditions. It has been shown that it is possible to distinguish between the thermal (chemical) and the electrochemical part of the microstructure degradation. Humidity is an important factor that affects the microstructure in the long term. Tortuosity, porosity, and specific resistance change with time, depending on the humidity of the fuel. Tortuosity changes by one order of magnitude in the direction perpendicular to the electrode surface, while in the other two directions in the plane, the changes are only moderate. Porosity increases in all electrochemically treated samples by 1–5% depending on dry ore humidity conditions and time. As all other experimental parameters are the same in all experiments, the EIS results confirm through the increasing specific resistance, mainly the influence of the changes on the microstructure on the electrochemical properties of the cells.
Improving the Air Quality Management: The Air Pollutant and Carbon Emission and Air Quality Model for Air Pollutant and Carbon Emission Reduction in the Iron and Steel Industries of Tangshan, Hebei Province, China
Authors: Chen Shaobo , Li Jianhui , You Qian , Wang Zhaotong , Shan Wanyue , Bo Xin , Zhu Rongjie
Published: November 2023

Currently, Tangshan confronts the dual challenge of elevated carbon emissions and substantial pollution discharge from the iron and steel industries (ISIs). While significant efforts have been made to mitigate air pollutants and carbon emissions within the ISIs, there remains a gap in comprehending the control of carbon emissions, air pollutant emissions, and their contributions to air pollutant concentrations at the enterprise level. In this study, we devised the Air Pollutant and Carbon Emission and Air Quality (ACEA) model to identify enterprises with noteworthy air pollution and carbon emissions, as well as substantial contributions to air pollutant concentrations. We constructed a detailed inventory of air pollutants and CO2 emissions from the iron and steel industry in Tangshan for the year 2019. The findings reveal that in 2019, Tangshan emitted 5.75 × 104 t of SO2, 13.47 × 104 t of NOx, 3.55 × 104 t of PM10, 1.80 × 104 t of PM2.5, 5.79 × 106 t of CO and 219.62 Mt of CO2. The ACEA model effectively pinpointed key links between ISI enterprises emitting air pollutants and carbon dioxide, notably in pre-iron-making processes (coking, sintering, pelletizing) and the Blast furnace. By utilizing the developed air pollutant emission inventory, the CALPUFF model assessed the impact of ISI enterprises on air quality in the Tangshan region. Subsequently, we graded the performance of air pollutant and CO2 emissions following established criteria. The ACEA model successfully identified eight enterprises with significant air pollution and carbon emissions, exerting notable influence on air pollutant concentrations. Furthermore, the ACEA outcomes offer the potential for enhancing regional air quality in Tangshan and provide a scientific instrument for mitigating air pollutants and carbon emissions. The effective application of the ACEA model in Tangshan’s steel industry holds promise for supporting carbon reduction initiatives and elevating environmental standards in other industrial cities across China.
Assessing the Risk of Extreme Storm Surges from Tropical Cyclones under Climate Change Using Bidirectional Attention-Based LSTM for Improved Prediction
Authors: Ian Vai-Kei , Tang Su-Kit , Pau Giovanni
Published: November 2023

Accurate prediction of storm surges is crucial for mitigating the impact of extreme weather events. This paper introduces the Bidirectional Attention-based Long Short-Term Memory (LSTM) Storm Surge Architecture, BALSSA, addressing limitations in traditional physical models. By leveraging machine learning techniques and extensive historical and real-time data, BALSSA significantly enhances prediction accuracy. Utilizing a bidirectional attention-based LSTM framework, it captures complex, non-linear relationships and long-term dependencies, improving the accuracy of storm surge predictions. The enhanced model, D-BALSSA, further amplifies predictive capability through a doubled bidirectional attention-based structure. Training and evaluation involve a comprehensive dataset from over 70 typhoon incidents in Macao between 2017 and 2022. The results showcase the outstanding performance of BALSSA, delivering highly accurate storm surge forecasts with a lead time of up to 72 h. Notably, the model exhibits a low Mean Absolute Error (MAE) of 0.0287 m and Root Mean Squared Error (RMSE) of 0.0357 m, crucial indicators measuring the accuracy of storm surge predictions in water level anomalies. These metrics comprehensively evaluate the model’s accuracy within the specified timeframe, enabling timely evacuation and early warnings for effective disaster mitigation. An adaptive system, integrating real-time alerts, tropical cyclone (TC) chaser, and prospective visualizations of meteorological and tidal measurements, enhances BALSSA’s capabilities for improved storm surge prediction. Positioned as a comprehensive tool for risk management, BALSSA supports decision makers, civil protection agencies, and governments involved in disaster preparedness and response. By leveraging advanced machine learning techniques and extensive data, BALSSA enables precise and timely predictions, empowering coastal communities to proactively prepare and respond to extreme weather events. This enhanced accuracy strengthens the resilience of coastal communities and protects lives and infrastructure from the escalating threats of climate change.
Discrete Parameter-Free Zone Distribution and Its Application in Normality Testing
Authors: Avdović Atif , Jevremović Vesna
Published: November 2023

In recent research endeavors, discrete models have gained considerable attention, even in cases where the observed variables are continuous. These variables can often be effectively approximated by a normal distribution. Given the prevalence of processes requiring robust quality control, models associated with the normal distribution have found widespread applicability; nevertheless, there remains a persistent need for enhanced accuracy in normality analysis, prompting the exploration of novel and improved solutions. This paper introduces a discrete parameter-free distribution linked to the normal distribution, derived from a quality control methodology rooted in the renowned ‘3-sigma’ rule. The development of a novel normality test, based on this distribution, is presented. A comprehensive examination encompasses mathematical derivation, distribution tables generated through Monte Carlo simulation studies, properties, power analysis, and comparative analysis, all with key features illustrated graphically. Notably, the proposed normality test surpasses conventional methods in performance. Termed the ‘Zone distribution’, this newly introduced distribution, along with its accompanying ‘Zone test’, demonstrates superior efficacy through illustrative examples. This research contributes a valuable tool to the field of normality analysis, offering a robust alternative for applications requiring precise and reliable assessments.
Artificial Intelligence in the Interpretation of Videofluoroscopic Swallow Studies: Implications and Advances for Speech–Language Pathologists
Authors: Girardi Anna M. , Cardell Elizabeth A. , Bird Stephen P.
Published: November 2023

Radiological imaging is an essential component of a swallowing assessment. Artificial intelligence (AI), especially deep learning (DL) models, has enhanced the efficiency and efficacy through which imaging is interpreted, and subsequently, it has important implications for swallow diagnostics and intervention planning. However, the application of AI for the interpretation of videofluoroscopic swallow studies (VFSS) is still emerging. This review showcases the recent literature on the use of AI to interpret VFSS and highlights clinical implications for speech–language pathologists (SLPs). With a surge in AI research, there have been advances in dysphagia assessments. Several studies have demonstrated the successful implementation of DL algorithms to analyze VFSS. Notably, convolutional neural networks (CNNs), which involve training a multi-layered model to recognize specific image or video components, have been used to detect pertinent aspects of the swallowing process with high levels of precision. DL algorithms have the potential to streamline VFSS interpretation, improve efficiency and accuracy, and enable the precise interpretation of an instrumental dysphagia evaluation, which is especially advantageous when access to skilled clinicians is not ubiquitous. By enhancing the precision, speed, and depth of VFSS interpretation, SLPs can obtain a more comprehensive understanding of swallow physiology and deliver a targeted and timely intervention that is tailored towards the individual. This has practical applications for both clinical practice and dysphagia research. As this research area grows and AI technologies progress, the application of DL in the field of VFSS interpretation is clinically beneficial and has the potential to transform dysphagia assessment and management. With broader validation and inter-disciplinary collaborations, AI-augmented VFSS interpretation will likely transform swallow evaluations and ultimately improve outcomes for individuals with dysphagia. However, despite AI’s potential to streamline imaging interpretation, practitioners still need to consider the challenges and limitations of AI implementation, including the need for large training datasets, interpretability and adaptability issues, and the potential for bias.
Quantitative Proteomics Analysis Reveals the Effect of a MarR Family Transcriptional Regulator AHA_2124 on Aeromonas hydrophila
Authors: Li Zhen , Li Wanxin , Lu Jinlian , Liu Ziqiu , Lin Xiangmin , Liu Yanling
Published: November 2023

The transcriptional regulators of the MarR family play an important role in diverse bacterial physiologic functions, whereas their effect and intrinsic regulatory mechanism on the aquatic pathogenic bacterium Aeromonas hydrophila are, clearly, still unknown. In this study, we firstly constructed a deletion strain of AHA_2124 (ΔAHA_2124) of a MarR family transcriptional regulator in Aeromonas hydrophila ATCC 7966 (wild type), and found that the deletion of AHA_2124 caused significantly enhanced hemolytic activity, extracellular protease activity, and motility when compared with the wild type. The differentially abundant proteins (DAPs) were compared by using data-independent acquisition (DIA), based on a quantitative proteomics technology, between the ΔAHA_2124 strain and wild type, and there were 178 DAPs including 80 proteins up-regulated and 98 proteins down-regulated. The bioinformatics analysis showed that the deletion of gene AHA_2124 led to some changes in the abundance of proteins related to multiple biological processes, such as translation, peptide transport, and oxidation and reduction. These results provided a theoretical basis for better exploring the regulatory mechanism of the MarR family transcriptional regulators of Aeromonas hydrophila on bacterial physiological functions.
Fungal Laccases: Fundamentals, Engineering and Classification Update
Authors: Aza Pablo , Camarero Susana
Published: November 2023

Multicopper oxidases (MCOs) share a common catalytic mechanism of activation by oxygen and cupredoxin-like folding, along with some common structural determinants. Laccases constitute the largest group of MCOs, with fungal laccases having the greatest biotechnological applicability due to their superior ability to oxidize a wide range of aromatic compounds and lignin, which is enhanced in the presence of redox mediators. The adaptation of these versatile enzymes to specific application processes can be achieved through the directed evolution of the recombinant enzymes. On the other hand, their substrate versatility and the low sequence homology among laccases make their exact classification difficult. Many of the ever-increasing amounts of MCO entries from fungal genomes are automatically (and often wrongly) annotated as laccases. In a recent comparative genomic study of 52 basidiomycete fungi, MCO classification was revised based on their phylogeny. The enzymes clustered according to common structural motifs and theoretical activities, revealing three novel groups of laccase-like enzymes. This review provides an overview of the structure, catalytic activity, and oxidative mechanism of fungal laccases and how their biotechnological potential as biocatalysts in industry can be greatly enhanced by protein engineering. Finally, recent information on newly identified MCOs with laccase-like activity is included.
ICF1-Syndrome-Associated DNMT3B Mutations Prevent De Novo Methylation at a Subset of Imprinted Loci during iPSC Reprogramming
Authors: Verma Ankit , Poondi Krishnan Varsha , Cecere Francesco , D’Angelo Emilia , Lullo Vincenzo , Strazzullo Maria , Selig Sara , Angelini Claudia , Matarazzo Maria R. , Riccio Andrea
Published: November 2023

Parent-of-origin-dependent gene expression of a few hundred human genes is achieved by differential DNA methylation of both parental alleles. This imprinting is required for normal development, and defects in this process lead to human disease. Induced pluripotent stem cells (iPSCs) serve as a valuable tool for in vitro disease modeling. However, a wave of de novo DNA methylation during reprogramming of iPSCs affects DNA methylation, thus limiting their use. The DNA methyltransferase 3B (DNMT3B) gene is highly expressed in human iPSCs; however, whether the hypermethylation of imprinted loci depends on DNMT3B activity has been poorly investigated. To explore the role of DNMT3B in mediating de novo DNA methylation at imprinted DMRs, we utilized iPSCs generated from patients with immunodeficiency, centromeric instability, facial anomalies type I (ICF1) syndrome that harbor biallelic hypomorphic DNMT3B mutations. Using a whole-genome array-based approach, we observed a gain of methylation at several imprinted loci in control iPSCs but not in ICF1 iPSCs compared to their parental fibroblasts. Moreover, in corrected ICF1 iPSCs, which restore DNMT3B enzymatic activity, imprinted DMRs did not acquire control DNA methylation levels, in contrast to the majority of the hypomethylated CpGs in the genome that were rescued in the corrected iPSC clones. Overall, our study indicates that DNMT3B is responsible for de novo methylation of a subset of imprinted DMRs during iPSC reprogramming and suggests that imprinting is unstable during a specific time window of this process, after which the epigenetic state at these regions becomes resistant to perturbation.
The Moment Criterion of Anthropomorphicity of Prosthetic Feet as a Potential Predictor of Their Functionality for Transtibial Amputees
Authors: Pitkin Mark
Published: November 2023

The purpose of this paper is to discuss a new quantitative mechanical parameter of prosthetic feet called the Index of Anthropomorphicity (IA), which has the potential to be adopted as an objective predictor of their functionality. The objectives are to present the research findings supporting the introduction of IA and unify previous results into a coherent theory. The IA is founded on the moment criterion of the anthropomorphicity of prosthetic feet. The term “anthropomorphicity” is defined for this application. Studies with a small number of human subjects and prostheses have shown that the value of the parameter is positively correlated with patient comfort and with the restoration of certain normal gait characteristics. Confirmatory studies with controlled human trials and mechanical tests with a wider selection of prosthesis types can give prosthesis manufacturers a new criterion to follow in the design process, and prosthetists may use the IA for selecting more suitable prostheses for a patient’s comfort and health.
Parallel Monitoring of Glucose, Free Amino Acids, and Vitamin C in Fruits Using a High-Throughput Paper-Based Sensor Modified with Poly(carboxybetaine acrylamide)
Authors: Yin Xinru , Zhao Cheng , Zhao Yong , Zhu Yongheng
Published: November 2023

Herein, a cost-effective and portable microfluidic paper-based sensor is proposed for the simultaneous and rapid detection of glucose, free amino acids, and vitamin C in fruit. The device was constructed by embedding a poly(carboxybetaine acrylamide) (pCBAA)-modified cellulose paper chip within a hydrophobic acrylic plate. We successfully showcased the capabilities of a filter paper-based microfluidic sensor for the detection of fruit nutrients using three distinct colorimetric analyses. Within a single paper chip, we simultaneously detected glucose, free amino acids, and vitamin C in the vivid hues of cyan blue, purple, and Turnbull’s blue, respectively, in three distinctive detection zones. Notably, we employed more stable silver nanoparticles for glucose detection, replacing the traditional peroxidase approach. The detection limits for glucose reached a low level of 0.049 mmol/L. Meanwhile, the detection limits for free amino acids and vitamin C were found to be 0.236 mmol/L and 0.125 mmol/L, respectively. The feasibility of the proposed sensor was validated in 13 different practical fruit samples using spectrophotometry. Cellulose paper utilizes capillary action to process trace fluids in tiny channels, and combined with pCBAA, which has superior hydrophilicity and anti-pollution properties, it greatly improves the sensitivity and practicality of paper-based sensors. Therefore, the paper-based colorimetric device is expected to provide technical support for the nutritional value assessment of fruits in the field of rapid detection.
Continuous In Vivo Monitoring of Indole-3-Acetic Acid and Salicylic Acid in Tomato Leaf Veins Based on an Electrochemical Microsensor
Authors: Tang Lingjuan , Li Daodong , Liu Wei , Sun Yafang , Dai Ying , Cui Wenjing , Geng Xinliu , Li Dayong , Song Fengming , Sun Lijun
Published: November 2023

Indole-3-acetic acid (IAA) and salicylic acid (SA), as critical plant hormones, are involved in multiple physiological regulatory processes of plants. Simultaneous and continuous in vivo detection of IAA and SA will help clarify the mechanisms of their regulation and crosstalk. First, this study reports the development and application of an electrochemical microsensor for simultaneous and continuous in vivo detection of IAA and SA. This electrochemical microsensor system consisted of a tip (length, 2 mm) of platinum wire (diameter, 0.1 mm) modified with carbon cement and multi-walled carbon nanotubes, an untreated tip (length, 2 mm) of platinum wire (diameter, 0.1 mm), as well as a tip (length, 2 mm) of Ag/AgCl wire (diameter, 0.1 mm). It was capable of detecting IAA in the level ranging from 0.1 to 30 µM and SA ranging from 0.1 to 50 µM based on the differential pulse voltammetry or amperometric i-t., respectively. The dynamics of IAA and SA levels in tomato leaf veins under high salinity stress were continuously detected in vivo, and very little damage occurred. Compared to conventional detection methods, the constructed microsensor is not only suitable for continuously detecting IAA and SA in microscopic plant tissue in vivo, it also reduces the damage done to plants during the detection. More importantly, the continuous and dynamic changes in IAA and SA data obtained in stiu through this system not only can help clarify the interaction mechanisms of IAA and SA in plants, it also helps to evaluate the health status of plants, which will promote the development of basic research in botany and precision agriculture.
Spanish Version of a Scale to Evaluate the Quality of Work Life in Teachers: An Adaptation of Walton’s QWL Model in the Peruvian Context
Authors: Muguerza-Florián Edgardo , García-Salirrosas Elizabeth Emperatriz , Villar-Guevara Miluska , Fernández-Mallma Israel
Published: November 2023

Workers’ job satisfaction benefits the organization, which constitutes a competitive advantage. This is why the Quality of Work Life (QoWL) study has gained relevance in recent years. For this reason, various scales have been developed to evaluate this organizational variable constantly. However, to date, there is no evidence in the scientific literature of a Spanish version that evaluates the validity and reliability of these scales in a Peruvian context. Thus, this study aimed to translate into Spanish and adapt and analyze the validity and reliability of a scale to assess the quality of work life in Peruvian teachers based on Walton’s model. For this purpose, 457 regular basic education teachers from a private educational network located in the three regions of Peru were surveyed. The analyses used the Structural Equation Model (SEM) with the AMOS 24 statistical software. Confirmatory Factor Analysis provided an excellent fit model of eight factors and 31 elements (CMIN/DF = 2.351; CFI = 0.955; SRMR = 0.062; RMSEA = 0.054; Pclose = 0.052). It also demonstrated good internal consistency (α = between 0.806 and 0.938; CR = between 0.824 and 0.939; AVE = between 0.547 and 0.794). These results contribute to the study of QoWL in Peru.
On the Similarity Relationship between the Structural-Steel Prototype and the 304-Stainless-Steel Dynamic Scale Model
Authors: Xu Jie , Wang Guangyong , Liu Renjie
Published: November 2023

Due to the size limitations of shaking tables, dynamic scale models of large-span space structures for engineering have small cross-sections and thin wall thicknesses. It is difficult to use the structural steels commonly used in prototypes to make dynamic scale models. In this paper, 304 stainless steel is proposed for making the scale model, and the similarity relationship between the structural-steel prototype and the 304-stainless-steel dynamic scale model was studied. Firstly, a uniaxial test was conducted to study the elastic modulus similarity and the yielding stress similarity. The test results demonstrated that the elastic modulus similarity ratio was 1:1, and the stress similarity ratios of the 304 stainless steel and the three typical structural steels were 1:1 (Q235 steel), 1:1.5 (Q355 steel) and 1:1.8 (Q420 steel). Then, the similarities of other variables were derived using the dimensional analysis method. In the end, a numerical analysis was conducted to verify the similarity relationship between the structural-steel prototype and the 304-stainless-steel dynamic scale model. In the numerical analysis, a single-layer spherical reticulated shell structure and a dynamic scale model with a length similarity ratio of 1:20 were established by using the ABAQUS 2021 software, and the node displacement, the element internal force and natural vibration characteristics were analyzed. The results show that standard deviations of the displacements, the internal forces and the natural vibration frequencies between the prototype and the scale model were within 5%. It turns out that the proposed similarity between the structural-steel prototype and the 304-stainless-steel dynamic scale model was applicable in the elastic stage. The findings provide a reference for designing a dynamic scale model of large-span space structures for engineering by using 304 stainless steel.
Properties of Fine Graded Perlite-Based Lightweight Cement Mortars Subjected to Elevated Temperatures
Authors: Pizoń Jan , Konečný Petr , Mynarz Miroslav , Bílek Vlastimil
Published: November 2023

The present research compared the behaviours of lightweight mortars based on ordinary Portland cement (OPC), calcium sulphoaluminate cement (CSAC), and calcium aluminate cement (CAC) containing expanded perlite and subjected to elevated temperatures. The perlite substituted natural sand in amounts of 25, 50, 75, and 100% by volume. The mortars were subjected to heating at up to 300 °C, 650 °C, and 1000 °C at a rate of 20 °C/min. The consistency and density of fresh mortars, compressive strength and density of hardened mortars after heating and cooling, and absorbability were assessed. Such a holistic testing approach is the main novelty of this research, which is related to the aforementioned mixtures and elevated temperatures. The main contribution of this article is a comparison of various cement types coupled with variations in the level of sand replacement with expanded perlite. In previous studies, comparisons were made in pairs of OPC-CSAC and OPC-CAC for normal-weight concrete. There is a gap in our knowledge of triple comparisons and lightweight cement composites which is filled by the current study. The use of OPC at up to 650 °C is recommended because it is the most common solution, its performance is similar to that of CAC, and it is cheaper than other solutions. Above 650 °C and up to 1000 °C, CAC is the only solution because it performs better than other cements. CSAC is not suitable for use at elevated temperatures because of its poor strength performance, even if it is the best solution from an environmental point of view. Sand replacement with perlite does not increase the strength performance under elevated temperatures, but its efficiency is different for various types of cement.
CO2 Hydrogenation over Fe-Co Bimetallic Catalyst Derived from the Thermolysis of [Co(NH3)6][Fe(CN)6]
Authors: Gosteva Alevtina N. , Kulikova Mayya V. , Ivantsov Mikhail I. , Grabchak Alena A. , Semushina Yulya P. , Lapuk Semen E. , Gerasimov Alexander V. , Tsvetov Nikita S.
Published: November 2023

Reducing the amount of CO2 in the atmosphere is a very important task. Therefore, the development and search for new approaches to the synthesis of catalytic systems, allowing for the catalytic conversion of CO2 into valuable products, is an urgent task. In this work, the catalyst was obtained by the thermolysis of a double complex compound. In this regard, kinetic studies of the parameters of the thermolysis process of double complex salts-[Co(NH)3]6][Fe(CN)6] were additionally determined using isoconversion and model approaches of non-isothermal kinetics. The catalyst was studied using various physicochemical methods—X-ray diffraction (XRD), infrared (IR)-spectroscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS). It was shown that, at the stage of catalyst preparation, the formation of a CoFe alloy occurred, while the surface mainly consisted of carbon in sp2-hybridization, and the metals existed in the form of spinel CoFe2O4. It was shown that catalysts based on bimetallic salts were active in the process of hydrogenation of carbon dioxide without a pre-activation stage (CO2 conversion reached 28%, with a specific activity of 4.0 µmolCO2/gMe·s). It was established that it was possible to change the selectivity of the carbon dioxide hydrogenation process by pre-treating the catalyst with hydrogen (selectivity for methane formation in the presence of an unreduced catalyst is 46.4–68.0%, whereas in the presence of a reduced catalyst it is 5.1–16.5%).
Photocatalytic Applications of SnO2 and Ag2O-Decorated SnO2 Coatings on Cement Paste
Authors: Vendramini Danilo da Silva , Benatto Victoria Gabriela , Ashtiani Alireza Mohebi , La Porta Felipe de Almeida
Published: November 2023

Recently, the production of new photocatalytic materials has attracted considerable attention as a promising strategy to mitigate anthropogenic environmental degradation. In this study, cement paste composites (water/cement ratio = 0.5) were prepared using a coating based on nanoparticles of SnO2 (SnO2/cement paste) and SnO2 decorated with Ag2O (Ag2O-decorated SnO2/cement paste) for photocatalytic applications. These coatings were prepared in this study by using the hydrothermal method as the strategy. Thus, photocatalyst efficiency was evaluated through the degradation of methylene blue (MB) and methyl red (MR) as cationic and anionic dyes, respectively, and the simultaneous degradation of MB/MR (1:1 v/v) dyes. Moreover, the photocatalytic mechanism was investigated in the presence of scavengers. Notably, an increase in pH in the range of 2–6 resulted in selective degradation of the MB/MR dye mixtures. Overall, the photocatalytic performance of these materials provides a novel platform technology focused on advanced civil engineering applications, which consequently facilitates the mitigation of various environmental problems.
Antifibrotic Effect of Selenium-Containing Nanoparticles on a Model of TAA-Induced Liver Fibrosis
Authors: Varlamova Elena G. , Goltyaev Michail Victorovich , Rogachev Vladimir Vladimirovich , Gudkov Sergey V. , Karaduleva Elena V. , Turovsky Egor A.
Published: November 2023

For the first time, based on the expression analysis of a wide range of pro- and anti-fibrotic, pro- and anti-inflammatory, and pro- and anti-apoptotic genes, key markers of endoplasmic reticulum stress (ER-stress), molecular mechanisms for the regulation of fibrosis, and accompanying negative processes caused by thioacetamide (TAA) injections and subsequent injections of selenium-containing nanoparticles and sorafenib have been proposed. We found that selenium nanoparticles of two types (doped with and without sorafenib) led to a significant decrease in almost all pro-fibrotic and pro-inflammatory genes. Sorafenib injections also reduced mRNA expression of pro-fibrotic and pro-inflammatory genes but less effectively than both types of nanoparticles. In addition, it was shown for the first time that TAA can be an inducer of ER-stress, most likely activating the IRE1α and PERK signaling pathways of the UPR, an inducer of apoptosis and pyroptosis. Sorafenib, despite a pronounced anti-apoptotic effect, still did not reduce the expression of caspase-3 and 12 or mitogen-activated kinase JNK1 to control values, which increases the risk of persistent apoptosis in liver cells. After injections of selenium-containing nanoparticles, the negative effects caused by TAA were leveled, causing an adaptive UPR signaling response through activation of the PERK signaling pathway. The advantages of selenium-containing nanoparticles over sorafenib, established in this work, once again emphasize the unique properties of this microelement and serve as an important factor for the further introduction of drugs based on it into clinical practice.
The Shift to Synergies in China’s Climate Planning: Aligning Goals with Policies and Institutions
Authors: Cai Qianyi , Zusman Eric , Meng Guobi
Published: November 2023

China has long sought to address climate change in line with other development goals. However, research supporting this alignment often employs data-driven models that downplay the policies and institutions needed to achieve the multiple benefits that studies feature in their analyses. This oversight is troubling because it neglects gaps between goals and the actual integration of climate and development or co-control of air pollution and greenhouse gases (GHGs). Additionally, this oversight may overlook growing implementation challenges as China pursues synergies between net-zero emissions, biodiversity, and circularity. This article illustrates these challenges by tracing the goals and policies/institutions in China over three phases: (1) integration (1979–2010), (2) co-control (2011–2019), and (3) synergies (2020–present). This article argues that China needs to strengthen the science–policy interface and ensure that new market-based policy instruments (such as emissions trading programs) as well as the leadership responsibility system incentivize reductions in overall GHG emissions while shrinking ecological footprints in the shifts to synergies.