bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning
Authors: Abaker Mohammed , Dafaalla Hatim , Eisa Taiseer Abdalla Elfadil , Abdelgader Heba , Mohammed Ahmed , Burhanur Mohammed , Hasabelrsoul Aiman , Alfakey Mohammed Ibrahim , Morsi Mohammed Abdelghader
Published: September 2023

In recent years, several strategies have been introduced to enhance early warning systems and lower the risk of rock-falls. In this regard, this paper introduces a deep learning- and IoT-based framework for rock-fall early warning, devoted to reducing rock-fall risk with high accuracy. In this framework, the prediction accuracy was augmented by eliminating the uncertainties and confusion plaguing the prediction model. In order to achieve augmented prediction accuracy, this framework fused prediction model-based deep learning with a detection model-based Internet of Things. This study utilized parameters, namely, overall prediction performance measures based on a confusion matrix, to assess the performance of the framework in addition to its ability to reduce the risk. The result indicates an increase in prediction model accuracy from 86% to 98.8%. In addition, the framework reduced the risk probability from 1.51 × 10−3 to 8.57 × 10−9. Our findings demonstrate the high prediction accuracy of the framework, which also offers a reliable decision-making mechanism for providing early warning and reducing the potential hazards of rock falls.
Development of an Application That Implements a Brain–Computer Interface to an Upper-Limb Motor Assistance Robot to Facilitate Active Exercise in Patients: A Feasibility Study
Authors: Yamamoto Tadashi , Hamaguchi Toyohiro
Published: September 2023

In this study, we aimed to evaluate the effectiveness of a brain robot in rehabilitation that combines motor imagery (MI), robotic motor assistance, and electrical stimulation. Thirteen in-patients with severe post-stroke hemiplegia underwent electroencephalography (EEG), measured according to the international 10–20 method, during MI. The dicephalus robotic system (DiC) was activated by detecting event-related desynchronization (ERD) using the Markov switching model (MSM) and relative power (RP) from the EEG of the motor cortex (C3 and C4). The reaction times (the time between ERD detection and DiC activation) of the MSM and RP were compared using Wilcoxon’s signed rank sum test. ERD was detected in all 13 and 12 patients with the MSM and RP, respectively. The DiC reaction time for the ERD detection process was significantly shorter for the MSM (13.02 ± 0.16 s) than for the RP (19.95 ± 7.45 s) (W = 9, p = 0.0037). The results of this study suggest that ERD responses can be detected in the motor cortex during MI in patients with severe upper-extremity paralysis; the MSM is more effective than the RP in detecting ERD when the EEG signal is used as a switch to activate the robot, and the reaction time to detect the signal is shorter.
Geo-Environment Suitability Evaluation for Urban Construction in Rongcheng District of Xiong’an New Area, China
Authors: Liu Hongwei , Han Bo
Published: September 2023

Xiong’an New Area is a national event and a project planned for a millennium of China. Its high-quality construction is of great significance to easing the noncapital functions of Beijing and the coordinated development of the Beijing-Tianjin-Hebei region. As an emerging city, the development and construction of Xiong’an New Area is bound to be restricted by geological and resource conditions. Therefore, geo-environment suitability analysis is the necessary basis of urban development and construction. Geo-environment suitability analysis of urban construction is a complex process that requires various geological indicator information, and relevant expertise to analyze their relevance. This paper focuses on the analytic hierarchy process (AHP) for the assessment of geo-environment suitability for urban construction in Rongcheng district, which is a Start Construction Region in Xiong’an New Area. Multiple factors, including the characteristic value of bearing capacity of foundation soil, land subsidence rate, geological faults, ground fissures, potential liquefied sands, quality of groundwater chemistry, quality of soil chemistry, chemical corrosion of concrete by groundwater, chemical corrosion of steel by groundwater, and enrichment of deep groundwater and geothermal resource, were used for the suitability assessments. From the evaluation achievements, the high and very high suitable lands for urban construction, with an acreage percentage of 89.2%, were located in most parts of the study area. Meanwhile, for another 9.1% of the land, the impacts of geological faults, land subsidence, and potential liquefied sands needed to be noted preferentially for urban construction.
Characterization of Chemical Composition and Antioxidant Activity of Eucalyptus globulus Leaves under Different Extraction Conditions
Authors: Park Jae Yeon , Kim Ju Yeon , Son Yun Gon , Kang Seong Doo , Lee Sang Won , Kim Kwang Dong , Kim Jeong Yoon
Published: September 2023

Eucalyptus globulus leaves contain various types of phenolic metabolites related to their antioxidant effects such as acids, catechin, flavonoids, and others. To optimize its antioxidative phenolic contents, E. globulus was extracted under various solvent conditions using 0, 10, 30, 50, 70, 90, and 100% ethanol. The 50% ethanol extract possessed the highest content of total phenolics with 497.7 mg GAE (gallic acid equivalent)/g extract. In contrast, the highest content of total flavonoids was evaluated in the 100% ethanol extract, having 169.3 mg QE (quercetin equivalent)/g extract. The antioxidant activity of various extraction conditions was assessed against the radical scavenging effect of DPPH (SC50 = 188.2~5841.7 μg/mL) and ABTS (SC50 = 14.2~171.3 μg/mL). The major chemical composition of E. globulus leaves was identified as including salicylic acid β-D-glucuronide (1), chlorogenic acid (2), epicatechin (3), 2″-O-galloylhyperin (4), isoquercitrin (5), isorhapontin (6), quercitrin (7), and quercetin-3-O-glucuronide (8) using LC-Q-TOF/MS analysis. Among them, the identified metabolites were clarified and their contents in the extracts were calculated via quantitative analysis using HPLC at 254 nm. The flavonoids (4, 5, 7, and 8) were determined to have an influence on the TPC, TFC, and antioxidant activity of E. globulus leaves. The results suggested that optimizing the extraction conditions can result in appropriate chemical composition and antioxidant activity.
Scan to BIM Mapping Process Description for Building Representation in 3D GIS
Authors: Kang Taewook
Published: September 2023

This paper introduces a novel approach for mapping process description with Scan data to Building Information Modeling (BIM) in a 3D Geographic Information System (GIS). The methodology focuses on automatically generating building mass and facade information on the GIS platform using Point Cloud Data (PCD) of Airborne Laser Scanning (ALS). Advanced scanning techniques capture detailed geometry from the physical site and generate high-resolution point clouds, which are processed to create 3D models for GIS integration. The critical contribution of this research lies in a scalable Scan to BIM mapping process, which can be used for generating building footprints and masses, including attributes, on 3D GIS. The resulting integrated BIM–GIS dataset provides an accurate building mass, facade information, facility asset management, and architectural design and facilitates improved decision-making in urban planning based on enhanced visualization, analysis, and simulation. This study suggests a flexible Scan to BIM mapping process description based on use cases, including algorisms. Through prototype development, a case study demonstrates the effectiveness of the process approach, the automatic generation of BIM on a 3D GIS platform, and reducing the manual efforts. The proposed method automatically creates DEM, SHP, GeoJSON, IFC, and coordinate system information from scan data and can effectively map building objects in 3D GIS.
Detection of Wheat Yellow Rust Disease Severity Based on Improved GhostNetV2
Authors: Li Zhihui , Fang Xin , Zhen Tong , Zhu Yuhua
Published: September 2023

Wheat production safety is facing serious challenges because wheat yellow rust is a worldwide disease. Wheat yellow rust may have no obvious external manifestations in the early stage, and it is difficult to detect whether it is infected, but in the middle and late stages of onset, the symptoms of the disease are obvious, though the severity is difficult to distinguish. A traditional deep learning network model has a large number of parameters, a large amount of calculation, a long time for model training, and high resource consumption, making it difficult to transplant to mobile and edge terminals. To address the above issues, this study proposes an optimized GhostNetV2 approach. First, to increase communication between groups, a channel rearrangement operation is performed on the output of the Ghost module. Then, the first five G-bneck layers of the source model GhostNetV2 are replaced with Fused-MBConv to accelerate model training. Finally, to further improve the model’s identification of diseases, the source attention mechanism SE is replaced by ECA. After experimental comparison, the improved algorithm shortens the training time by 37.49%, and the accuracy rate reaches 95.44%, which is 2.24% higher than the GhostNetV2 algorithm. The detection accuracy and speed have major improvements compared with other lightweight model algorithms.
Performance Comparison of Traction Synchronous Motors with Ferrite Magnets for a Subway Train: Reluctance versus Homopolar Variants
Authors: Dmitrievskii Vladimir , Kazakbaev Vadim , Prakht Vladimir
Published: September 2023

Due to the high cost and the predicted shortage of rare earth elements in the near future, the task of developing energy-efficient electric machines without rare earth magnets is of great importance. This article presents a comparative analysis of optimized designs of a ferrite-assisted synchronous reluctance machine (FaSynRM) and a ferrite-assisted synchronous homopolar machine (FaSHM) in a 370-kW subway train drive. The objectives of optimizing these traction machines are to reduce their losses, maximum armature current, and torque ripple. The optimization considers the characteristics of the machines in the subway train moving cycle. The problem of the risk of irreversible demagnetization of ferrites in the FaSynRM and FaSHM is also considered. To reduce the computational burden, the Nelder-Mead method is used for the optimization. It is shown that the FaSHM demonstrates better field weakening capability, which can reduce the maximum current, power, and cost of the inverter power modules. At the same time, the FaSynRM requires less permanent magnet mass for the same torque density and is more resistant to irreversible demagnetization, which can reduce costs and improve the reliability of the electric machine.
X-ray Diffraction Data Analysis by Machine Learning Methods—A Review
Authors: Surdu Vasile-Adrian , Győrgy Romuald
Published: September 2023

X-ray diffraction (XRD) is a proven, powerful technique for determining the phase composition, structure, and microstructural features of crystalline materials. The use of machine learning (ML) techniques applied to crystalline materials research has increased significantly over the last decade. This review presents a survey of the scientific literature on applications of ML to XRD data analysis. Publications suitable for inclusion in this review were identified using the “machine learning X-ray diffraction” search term, keeping only English-language publications in which ML was employed to analyze XRD data specifically. The selected publications covered a wide range of applications, including XRD classification and phase identification, lattice and quantitative phase analyses, and detection of defects and substituents, as well as microstructural material characterization. Current trends in the field suggest that future efforts pertaining to the application of ML techniques to XRD data analysis will address shortcomings of ML approaches related to data quality and availability, interpretability of the results and model generalizability and robustness. Additionally, future research will likely incorporate more domain knowledge and physical constraints, integrate with quantum physical methods, and apply techniques like real-time data analysis and high-throughput screening to accelerate the discovery of tailored novel materials.
The Clinical Significance of Aspergillus Detected in Lower-Respiratory-Tract Samples of Critically Ill COVID-19-Positive Patients
Authors: Osman Heba , Shaik Asra N. , Nguyen Paul L. , Cantor Zachary , Kaafarani Mirna , Soubani Ayman O.
Published: September 2023

Objective: Critically ill patients with acute respiratory distress syndrome (ARDS) due to viral infection are at risk for secondary complications, including invasive aspergillosis. Our study aimed to characterize the clinical significance and outcome of Aspergillus species isolated from lower-respiratory-tract samples of critically ill OVID-19 patients at a single center. Design: We conducted a retrospective cohort study to evaluate the characteristics of patients with COVID-19 and aspergillus isolated from the lower respiratory tract and to identify predictors of outcomes in this population. Setting: The setting was a single-center hospital system within the metropolitan Detroit region. Results: The prevalence of Aspergillus isolated in hospitalized COVID-19 patients was 1.18% (30/2461 patients), and it was 4.6% in critically ill ICU patients with COVID-19. Probable COVID-19-associated invasive pulmonary aspergillosis (CAPA) was found in 21 critically ill patients, and 9 cases were classified as colonization. The in-hospital mortality of critically ill patients with CAPA and those with aspergillus colonization were high but not significantly different (76% vs. 67%, p = 1.00). Furthermore, the in-hospital mortality for ICU patients with or without Aspergillus isolated was not significantly different 73.3% vs. 64.5%, respectively (OR 1.53, CI 0.64–4.06, p = 0.43). In patients in whom Aspergillus was isolated, antifungal therapy (p = 0.035, OR 12.3, CI 1.74–252); vasopressors (0.016, OR 10.6, CI 1.75–81.8); and a higher mSOFA score (p = 0.043, OR 1.29 CI 1.03–1.72) were associated with a worse outcome. In a multivariable model adjusting for other significant variables, FiO2 was the only variable associated with in-hospital mortality in patients in whom Aspergillus was isolated (OR 1.07, 95% CI 1.01–1.27). Conclusions: The isolation of Aspergillus from lower-respiratory-tract samples of critically ill patients with COVID-19 is associated with high mortality. It is important to have a low threshold for superimposed infections such as CAPA in critically ill patients with COVID-19.
A Twisted Hand: Affective Iconography in Peter Paul Rubens’s Adoration of the Magi
Authors: Jonckheere Koenraad
Published: September 2023

After his return from Italy in 1608, Peter Paul Rubens received a commission to depict an Adoration of the Magi for the Statenkamer in Antwerp’s Town Hall. It was the first, grand display of his stylistic and iconographic innovations. By building on unexplored contemporary sources and close reading of the iconography, this article posits that Rubens’s canvas served as a questie on various matters under discussion at the time, and was designed to induce divergent affects in the beholders.
Integrating the Opposition Nelder–Mead Algorithm into the Selection Phase of the Genetic Algorithm for Enhanced Optimization
Authors: Zitouni Farouq , Harous Saad
Published: September 2023

In this paper, we propose a novel methodology that combines the opposition Nelder–Mead algorithm and the selection phase of the genetic algorithm. This integration aims to enhance the performance of the overall algorithm. To evaluate the effectiveness of our methodology, we conducted a comprehensive comparative study involving 11 state-of-the-art algorithms renowned for their exceptional performance in the 2022 IEEE Congress on Evolutionary Computation (CEC 2022). Following rigorous analysis, which included a Friedman test and subsequent Dunn’s post hoc test, our algorithm demonstrated outstanding performance. In fact, our methodology exhibited equal or superior performance compared to the other algorithms in the majority of cases examined. These results highlight the effectiveness and competitiveness of our proposed approach, showcasing its potential to achieve state-of-the-art performance in solving optimization problems.
Experimental and Numerical Investigation on the Damage Mechanism of a Loess–Mudstone Tunnel in Cold Regions
Authors: Wang Dongrui , Zhao Xueyi , Qiu Chenghu , Guo Xin , Du Yaohui , Li Xianhan , Gao Yue , Xuan Junjie
Published: September 2023

To address loess–mudstone tunnel damage resulting from mudstone swelling induced by water absorption in cold regions, model experiments and numerical simulations were employed to study the tunnel surrounding rock pressure distribution and the stress characteristics of support structures during mudstone swelling at the tunnel base. The findings reveal that the base uplift of the tunnel leads to a rapid stress increase on the arch, and the self-supporting of the interface is insufficient, causing uneven stress distribution on the tunnel. The stress peak value at the bottom of the outer arch is 30.8% of that at the inner side. The internal force of the tunnel lining at the arch crown is the largest. The compressive stress appears at the arch feet, while the tensile stress appears at the outer side of the lining. The bending moments of the inverted arch are larger than those of the arch shoulders and arch crown. The left arch shoulder and arch bottom are primarily subjected to negative bending moments, and the maximum values are about −500 kN·m and −400 kN·m, respectively. The left side of the inverted arch is first to crack, and two main cracks then appeared at the left and right arch shoulders, respectively. The formation and development of the longitudinal cracks in the arch induced by water absorption cause the inverted arch bulge failure. This study helps understand the damage mechanism of the loess–mudstone tunnel in cold regions.
Application of CNN-LSTM Algorithm for PM2.5 Concentration Forecasting in the Beijing-Tianjin-Hebei Metropolitan Area
Authors: Su Yuxuan , Li Junyu , Liu Lilong , Guo Xi , Huang Liangke , Hu Mingyun
Published: September 2023

Prolonged exposure to high concentrations of suspended particulate matter (SPM), especially aerodynamic fine particulate matter that is ≤2.5 μm in diameter (PM2.5), can cause serious harm to human health and life via the induction of respiratory diseases and lung cancer. Therefore, accurate prediction of PM2.5 concentrations is important for human health management and governmental environmental management decisions. However, the time-series processing of PM2.5 concentration based only on a single region and a special time period is less explanatory, and thus, the spatial-temporal applicability of the model is more restricted. To address this problem, this paper constructs a PM2.5 concentration prediction optimization model based on Convolutional Neural Networks-Long Short-Term Memory (CNN-LSTM). Hourly data of atmospheric pollutants, meteorological parameters, and Precipitable Water Vapor (PWV) of 10 cities in the Beijing-Tianjin-Hebei metropolitan area during the period of 1–30 September 2021/2022 were used as the training set, and the PM2.5 data of 1–7 October 2021/2022 were used for validation. The experimental results show that the CNN-LSTM model optimizes the average root mean square error (RMSE) by 25.52% and 14.30%, the average mean absolute error (MAE) by 26.23% and 15.01%, and the average mean absolute percentage error (MAPE) by 35.64% and 16.98%, as compared to the widely used Back Propagation Neural Network (BPNN) and Long Short-Term Memory (LSTM) models. In summary, the CNN-LSTM model is superior in terms of applicability and has the highest prediction accuracy in the Beijing-Tianjin-Hebei metropolitan area. The results of this study can provide a reference for the relevant departments in the Beijing-Tianjin-Hebei metropolitan area to predict PM2.5 concentration and its trend in specific time periods.
Spatial Inhomogeneity of New Particle Formation in the Urban and Mountainous Atmospheres of the North China Plain during the 2022 Winter Olympics
Authors: Shang Dongjie , Hu Min , Wang Xiaoyan , Tang Lizi , Clusius Petri S. , Qiu Yanting , Yu Xuena , Chen Zheng , Zhang Zirui , Sun Jiaqi , Dao Xu , Zeng Limin , Guo Song , Wu Zhijun , Boy Michael
Published: September 2023

The new particle formation (NPF) process is a significant source of atmospheric secondary particles, which has remarkable impacts on the regional air quality and global radiative forcing. Most NPF studies conduct their measurements at a single site, which can hardly provide information about the regionality of NPF events at large scales (>100 km). During the 2022 Winter Olympic and Paralympic Games, simultaneous measurements of particle number size distributions and NPF-associated precursors were conducted at a mountainous site close to the Winter Olympic Village in Chongli (CL), Zhangjiakou, and an urban site in Beijing (BJ) located 150 km southeast of the CL site. High NPF frequencies were observed at the CL (50%) and BJ (52%) sites; however, the fraction of concurrent NPF events was smaller than the results in other regions. In addition, the wind distributions exhibited distinct air mass origins at the two sites during the concurrent NPF events. Compared with the BJ site, the NPF growth rates were higher at the CL site due to higher levels of volatile organic compounds (VOCs) and radiation. Surprisingly, the formation rates at the CL site were lower than at the BJ site, even with a higher sulfuric acid concentration and lower CS, which may be attributed to lower dimethylamine concentrations in the mountainous area. This study reveals that, although NPF events are commonly thought to occur on regional scales, their intensity and mechanisms may have significant spatial inhomogeneity. Further studies are required to reduce the uncertainty when expanding the mechanisms based on the urban conditions to regional or global scales in the models.
Modeling of Precipitation Prediction Based on Causal Analysis and Machine Learning
Authors: Li Hongchen , Li Ming
Published: September 2023

The factors influencing precipitation in western China are quite complex, which increases the difficulty in determining accurate predictors. Hence, this paper models the monthly measured precipitation data from 240 meteorological stations in mainland China and the precipitation data from the European Centre for Medium-Range Weather Forecasts and the National Climate Centre and employs 88 atmospheric circulation indices to develop a precipitation prediction scheme. Specifically, a high-quality grid-point field is created by fusing and revising the precipitation data from multiple sources. This field is combined with the Empirical Orthogonal Function decomposition and the causal information flow. Next, the best predictors are screened through Empirical Orthogonal Function decomposition and causal information flow, and a data-driven precipitation prediction model is established using a Back Propagation Neural Network and a Random Forest algorithm to conduct the 1-month, 3-month, and 6-month precipitation predictions. The results show that: The machine learning-based precipitation prediction model has high accuracy and is generally able to predict the precipitation trend in the western region better. The Random Forest algorithm significantly outperforms the Back Propagation Neural Network algorithm in the prediction of the three starting times, and the prediction ability of both models gradually decreases as the starting time increases. Compared with the 2022 flood season prediction scores of the Institute of Atmospheric Sciences of the Chinese Academy of Sciences, the model improves the prediction of 1-month and 3-month precipitation in the western region and provides a new idea for the short-term climate prediction of precipitation in western China.
Variability of Air Pollutant Concentrations and Their Relationships with Meteorological Parameters during COVID-19 Lockdown in Western Macedonia
Authors: Begou Paraskevi , Evagelopoulos Vasilios , Charisiou Nikolaos D.
Published: September 2023

The lockdown implemented to tackle the spread of the COVID-19 pandemic had a positive impact on air quality. Globally, studies have shown that air pollutant levels decreased temporally during the restriction measures. In this study, we evaluated the impact of COVID-19 restrictions on the air quality of Western Macedonia, Greece, using the concentrations of PM2.5 and PM10 along with meteorological data from the Air Quality Monitoring Stations (AQMS) operated by the Lignite Center of Western Macedonia. In Western Macedonia, previous studies have identified a general reduction in air pollutant levels during the last decade due to the coal phase-out plan for power generation. During the lockdown, the levels of PM2.5 and PM10 decreased further. The reduced emissions from the local mining activities and lignite-fired power plant electricity generation, as well as the weather conditions, seem to contribute to improving air quality.
Barotropic-Baroclinic Coherent-Structure Rossby Waves in Two-Layer Cylindrical Fluids
Authors: Xu Jing , Fang Yong , Geng Jingxuan , Dong Huanhe
Published: September 2023

In this paper, the propagation of Rossby waves under barotropic-baroclinic interaction in polar co-ordinates is studied. By starting from the two-layer quasi-geotropic potential vorticity equation (of equal depth) with the β effect, the coupled KdV equations describing barotropic-baroclinic waves are derived using multi-scale analysis and the perturbation expansion method. Furthermore, in order to more accurately describe the propagation characteristics of barotropic-baroclinic waves, fifth-order coupled KdV-mKdV equations were obtained for the first time. On this basis, the Lie symmetry and conservation laws of the fifth-order coupled KdV-mKdV equations are analyzed in terms of their properties. Then, the elliptic function expansion method is applied to find the soliton solutions of the fifth-order coupled KdV-mKdV equations. Based on the solutions, we further simulate the evolution of Rossby wave amplitudes and investigate the influence of the high-order terms—time and wave number—on the propagation of barotropic waves and baroclinic waves. The results show that the appearance of the higher-order effect makes the amplitude of the wave lower, the width of the wave larger, and the whole wave flatter, which is obviously closer to actual Rossby wave propagation. The time and wave number will also influence wave amplitude and wave width.
Process Capability Control Charts for Monitoring Process Accuracy and Precision
Authors: Kuo Tsen-I , Chuang Tung-Lin
Published: September 2023

Process capability index (PCI) is a convenient and useful tool of process quality evaluation that allows a company to have a complete picture of its manufacturing process in order to prevent defective products while ensuring the product quality is at the required level. The aim of this study was to develop a control chart for process incapability index , which differentiates between information related to accuracy and precision. Index measures process inaccuracy as the degree to which the mean departs from the target value, while index measures imprecision in terms of process variation. The most important advantage of using these control charts of , , and is that practitioners can monitor and evaluate both the quality of the process and the differences in process capability. The and charts were instead of Shewhart’s and chart since the process target values and tolerances can be incorporated in the charts for evaluation as a whole, which makes the charts capable of monitoring process stability and quality simultaneously. The proposed , , and control charts enable practitioners to monitor and evaluate process quality as well as differences in process capability. The control charts are defined using probability limits, and operating characteristic (OC) curves used to detect shifts in process quality. The method proposed in this study can easily and accurately determine the process quality capability and a case is used to illustrate the application of control charts of , , and .
Investigating the Cell Result Multiplication Method for Emission Test of Battery Module
Authors: Ma Tianyi , Ma Xiaole , Wang Fang , Hao Weijian , Sun Zhipeng , Liu Lei , Xu Yue , Li Yupeng , Liu Shanming , Ma Haishuo , Dai Xiaoqian , Liu Yifan
Published: September 2023

The thermal safety of lithium-ion traction batteries is a highly concerning issue in the field of electric transportation. The large amount of gas emissions during the thermal runaway process of batteries has high safety hazards, such as fire and explosion. The quantitative analysis of emissions is one of the important challenges in testing and evaluating battery safety. Focusing on quantifying gas emissions using large-scale thermal propagation in battery modules and packs, based on the idea of cell result multiplication, this article conducts a thermal runaway emission analysis of a single cell and a module and compares the behavior of thermal runaway and gas emissions of the cell and module from the perspectives of temperature, pressure, gas composition, and battery morphology. The feasibility of the cell result multiplication method is verified from the perspective of experimental data.
Exploring the Discharge Performance of Li-ion Batteries Using Ohmic Drop Compensation
Authors: Magne-Tang Nicolas , Decaux Céline , Thivel Pierre-Xavier , Lefrou Christine
Published: September 2023

In this study, we investigate the use of the ohmic drop compensation method during battery discharges at different rates. Four different types of NMC Li-ion batteries are compared and three 18,650 cells of each type are tested to evaluate the performance dispersion. The cell type that shows significant performance improvement thanks to ohmic drop compensation in this first experimental part is then selected to complete the exploration. A drone-type usage profile is set up and demonstrates without any doubt the interest of using this type of protocol in such usage. Finally, a preliminary aging study is also performed on this type of cells: ohmic drop compensation use has no effect on low-power performance decrease during aging and has a moderate impact on high-power performances.
Sensory Analysis as a Simple and Low-Cost Tool to Evaluate and Valorize a New Product from Local Fruits in Rural Communities: The Case of Highly Aromatic Vinegar from Prickly Pear Fruits
Authors: Es-Sbata Ikram , Castro-Mejías Remedios , Rodríguez-Dodero Carmen , Zouhair Rachid , Durán-Guerrero Enrique
Published: September 2023

Prickly pear (Opuntia ficus-indica (L). Mill.) is a plant that belongs to the Cactaceae family and can grow in arid and semi-arid climates. This fruit is considered a good source of nutrients, antioxidant constituents and health-promoting substances, such as betalains and polyphenols. One of the ways in which the communities in the cultivation areas can revalorize this fruit is by diversifying the products that can be made from it. This research proposes the elaboration of prickly pear vinegars and their sensory characterization. Their aromatic profile has been determined using two different methodologies, the traditional Quantitative Descriptive Analysis (QDA) and the Free Choice Profiling (FCP). Similar aromatic profiles have been obtained by both techniques, QDA and FCP, although the last one has proven to be a less time- and effort-demanding method. The vinegars with the highest aromatic intensity and the highest fruity and floral notes were those that had been produced through submerged culture at 30 °C and using Acetobacter malorum strains. It was revealed that the Free Choice Profiling sensory technique is a useful and low-cost tool for innovating in products obtained from a local sugary raw material.
Alginate Conjugation Increases Toughness in Auricular Chondrocyte Seeded Collagen Hydrogels
Authors: Slyker Leigh , Bonassar Lawrence
Published: September 2023

Current auricular cartilage replacements for pediatric microtia fail to address the need for long-term integration and neocartilage formation. While collagen hydrogels have been successful in fostering neocartilage formation, the toughness and extensibility of these materials do not match that of native tissue. This study used the N-terminal functionalization of collagen with alginate oligomers to improve toughness and extensibility through metal–ion complexation. Alginate conjugation was confirmed via FTIR spectroscopy. The retention of native collagen fibrillar structure, thermal gelation, and helical conformation in functionalized gels was confirmed via scanning electron microscopy, oscillatory shear rheology, and circular dichroism spectroscopy, respectively. Alginate–calcium complexation enabled a more than two-fold increase in modulus and work density in functionalized collagen with the addition of 50 mM CaCl2, whereas unmodified collagen decreased in both modulus and work density with increasing calcium concentration. Additionally, the extensibility of alginate-functionalized collagen was increased at 25 and 50 mM CaCl2. Following 2-week culture with auricular chondrocytes, alginate-functionalization had no effect on the cytocompatibility of collagen gels, with no effects on cell density, and increased glycosaminoglycan deposition. Custom MATLAB video analysis was then used to quantify fracture toughness, which was more than 5-fold higher following culture in functionalized collagen and almost three-fold higher in unmodified collagen.
Emotion Recognition Using Hierarchical Spatiotemporal Electroencephalogram Information from Local to Global Brain Regions
Authors: Jeong Dong-Ki , Kim Hyoung-Gook , Kim Jin-Young
Published: September 2023

To understand human emotional states, local activities in various regions of the cerebral cortex and the interactions among different brain regions must be considered. This paper proposes a hierarchical emotional context feature learning model that improves multichannel electroencephalography (EEG)-based emotion recognition by learning spatiotemporal EEG features from a local brain region to a global brain region. The proposed method comprises a regional brain-level encoding module, a global brain-level encoding module, and a classifier. First, multichannel EEG signals grouped into nine regions based on the functional role of the brain are input into a regional brain-level encoding module to learn local spatiotemporal information. Subsequently, the global brain-level encoding module improved emotional classification performance by integrating local spatiotemporal information from various brain regions to learn the global context features of brain regions related to emotions. Next, we applied a two-layer bidirectional gated recurrent unit (BGRU) with self-attention to the regional brain-level module and a one-layer BGRU with self-attention to the global brain-level module. Experiments were conducted using three datasets to evaluate the EEG-based emotion recognition performance of the proposed method. The results proved that the proposed method achieves superior performance by reflecting the characteristics of multichannel EEG signals better than state-of-the-art methods.
Artificial Intelligence Advances in Transplant Pathology
Authors: Rahman Md Arafatur , Yilmaz Ibrahim , Albadri Sam T. , Salem Fadi E. , Dangott Bryan J. , Taner C. Burcin , Nassar Aziza , Akkus Zeynettin
Published: September 2023

Transplant pathology plays a critical role in ensuring that transplanted organs function properly and the immune systems of the recipients do not reject them. To improve outcomes for transplant recipients, accurate diagnosis and timely treatment are essential. Recent advances in artificial intelligence (AI)-empowered digital pathology could help monitor allograft rejection and weaning of immunosuppressive drugs. To explore the role of AI in transplant pathology, we conducted a systematic search of electronic databases from January 2010 to April 2023. The PRISMA checklist was used as a guide for screening article titles, abstracts, and full texts, and we selected articles that met our inclusion criteria. Through this search, we identified 68 articles from multiple databases. After careful screening, only 14 articles were included based on title and abstract. Our review focuses on the AI approaches applied to four transplant organs: heart, lungs, liver, and kidneys. Specifically, we found that several deep learning-based AI models have been developed to analyze digital pathology slides of biopsy specimens from transplant organs. The use of AI models could improve clinicians’ decision-making capabilities and reduce diagnostic variability. In conclusion, our review highlights the advancements and limitations of AI in transplant pathology. We believe that these AI technologies have the potential to significantly improve transplant outcomes and pave the way for future advancements in this field.
FOXO in Lymnaea: Its Probable Involvement in Memory Consolidation
Authors: Nakai Junko , Namiki Kengo , Fujimoto Kanta , Hatakeyama Dai , Ito Etsuro
Published: September 2023

Food deprivation activates forkhead box O (FOXO), a transcription factor downstream of insulin receptors. In the pond snail Lymnaea stagnalis, insulin signaling and food deprivation improve memory consolidation following conditioned taste aversion (CTA) learning. We investigated the subcellular localization of FOXO in Lymnaea and changes in its expression levels following food deprivation, CTA learning, and insulin administration. Immunohistochemistry revealed that Lymnaea FOXO (LymFOXO) was located in the central nervous system (CNS) neuronal cytoplasm in food-satiated snails but was mainly in neuronal nuclei in food-deprived snails. Following CTA acquisition, LymFOXO translocated to the nuclei in food-satiated snails and remained in the nuclei in food-deprived snails. Contrary to our expectations, insulin administered to the CNS did not induce LymFOXO translocation into the nuclei in food-satiated snails. Real-time PCR was used to quantify LymFOXO mRNA levels, its target genes, and insulin signaling pathway genes and revealed that LymFOXO mRNA was upregulated in food-deprived snails compared to food-satiated snails. Insulin applied to isolated CNSs from food-satiated snails increased LymFOXO compared to vehicle-treated samples. Food deprivation prepares FOXO to function in the nucleus and enhances CTA learning in snails. Insulin application did not directly affect LymFOXO protein localization. Thus, insulin administration may stimulate pathways other than the LymFOXO cascade.
The Long Scientific Journey of Sirolimus (Rapamycin): From the Soil of Easter Island (Rapa Nui) to Applied Research and Clinical Trials on β-Thalassemia and Other Hemoglobinopathies
Authors: Gambari Roberto , Zuccato Cristina , Cosenza Lucia Carmela , Zurlo Matteo , Gasparello Jessica , Finotti Alessia , Gamberini Maria Rita , Prosdocimi Marco
Published: September 2023

In this review article, we present the fascinating story of rapamycin (sirolimus), a drug able to induce γ-globin gene expression and increased production of fetal hemoglobin (HbF) in erythroid cells, including primary erythroid precursor cells (ErPCs) isolated from β-thalassemia patients. For this reason, rapamycin is considered of great interest for the treatment of β-thalassemia. In fact, high levels of HbF are known to be highly beneficial for β-thalassemia patients. The story of rapamycin discovery began in 1964, with METEI, the Medical Expedition to Easter Island (Rapa Nui). During this expedition, samples of the soil from different parts of the island were collected and, from this material, an antibiotic-producing microorganism (Streptomyces hygroscopicus) was identified. Rapamycin was extracted from the mycelium with organic solvents, isolated, and demonstrated to be very active as an anti-bacterial and anti-fungal agent. Later, rapamycin was demonstrated to inhibit the in vitro cell growth of tumor cell lines. More importantly, rapamycin was found to be an immunosuppressive agent applicable to prevent kidney rejection after transplantation. More recently, rapamycin was found to be a potent inducer of HbF both in vitro using ErPCs isolated from β-thalassemia patients, in vivo using experimental mice, and in patients treated with this compound. These studies were the basis for proposing clinical trials on β-thalassemia patients.
Mysterious Morphology: An Investigation of the Octopus Keel and Its Association with Burrowing
Authors: Springbett Cheyne , Cordero Katie , Ellis Garrett , Haeger Carly , Onthank Kirt L.
Published: September 2023

The octopus keel is a trait that has been hypothesized to be connected with burrowing in octopuses, but has never been explored in any detail. We investigated the association between these two traits using two approaches. First, we examined the phylogenetic correlation between the presence of a keel and known burrowing behavior in cirrate octopuses. Second, burrowing and non-burrowing captive Muusoctopus leioderma were evaluated for keel prominence to determine whether the keel is lost more rapidly in non-burrowing individuals. Pagel’s test for the coevolution of binary characteristics showed the model of best fit for the resulting phylogenetic tree to be one of evolutionary interdependence, and that non-burrowing Muusoctopus leioderma lost their keels over time, while burrowing individuals maintained their keels. Together, these results indicate the keel may be a trait associated with burrowing in octopuses.
Comparisons of the Oral Microbiota from Seven Species of Wild Venomous Snakes in Taiwan Using the High-Throughput Amplicon Sequencing of the Full-Length 16S rRNA Gene
Authors: Lin Wen-Hao , Tsai Tein-Shun
Published: September 2023

A venomous snake’s oral cavity may harbor pathogenic microorganisms that cause secondary infection at the wound site after being bitten. We collected oral samples from 37 individuals belonging to seven species of wild venomous snakes in Taiwan, including Naja atra (Na), Bungarus multicinctus (Bm), Protobothrops mucrosquamatus (Pm), Trimeresurus stejnegeri (Ts), Daboia siamensis (Ds), Deinagkistrodon acutus (Da), and alpine Trimeresurus gracilis (Tg). Bacterial species were identified using full-length 16S rRNA amplicon sequencing analysis, and this is the first study using this technique to investigate the oral microbiota of multiple Taiwanese snake species. Up to 1064 bacterial species were identified from the snake’s oral cavities, with 24 pathogenic and 24 non-pathogenic species among the most abundant ones. The most abundant oral bacterial species detected in our study were different from those found in previous studies, which varied by snake species, collection sites, sampling tissues, culture dependence, and analysis methods. Multivariate analysis revealed that the oral bacterial species compositions in Na, Bm, and Pm each were significantly different from the other species, whereas those among Ts, Ds, Da, and Tg showed fewer differences. Herein, we reveal the microbial diversity in multiple species of wild snakes and provide potential therapeutic implications regarding empiric antibiotic selection for wildlife medicine and snakebite management.
Deletion of the Neuronal Transcription Factor Satb1 Induced Disturbance of the Kinome and Mechanisms of Hypoxic Preconditioning
Authors: Turovsky Egor A. , Tarabykin Viktor S. , Varlamova Elena G.
Published: September 2023

Genetic disorders affecting the functioning of the brain lead not only to the development of numerous hereditary diseases but also to the development of neurodegenerative and cognitive disorders. The result of this may be the disability of part of the able-bodied population. Almost all pathological states of the brain are characterized by serious defects in the intracellular and intercellular signaling of neurons and glial cells. At the same time, the mechanisms of disruption of these signaling cascades are not well understood due to the large number of molecules, including transcription factors that, when mutated, cause brain malformations. The transcription factor Satb1 is one of the least studied factors in the cerebral cortex, and the effects of its deletion in the postnatal brain are practically not studied. Hyperexcitability of neurons is observed in many genetic diseases of the nervous system (Hirschsprung’s disease, Martin–Bell syndrome, Huntington’s disease, Alzheimer’s, etc.), as well as in ischemic brain phenomena and convulsive and epileptic conditions of the brain. In turn, all these disorders of brain physiology are associated with defects in intracellular and intercellular signaling and are often the result of genetic disorders. Using Satb1 mutant mice and calcium neuroimaging, we show that Satb1 deletion in projection neurons of the neocortex causes downregulation of protein kinases PKC, CaMKII, and AKT/PKB, while a partial deletion does not cause a dramatic disruption of kinome and Ca2+ signaling. As a result, Satb1-null neurons are characterized by increased spontaneous Ca2+ activity and hyperexcitability when modeling epileptiform activity. As a result of the deletion of Satb1, preconditioning mechanisms are disrupted in neurons during episodes of hypoxia. This occurs against the background of increased sensitivity of neurons to a decrease in the partial pressure of oxygen, which may indicate the vulnerability of neuronal networks and be accompanied by impaired expression of the Satb1 transcription factor. Here, we show that Satb1 deletion impaired the expression of a number of key kinases and neuronal hyperexcitation in models of epileptiform activity and hypoxia.
Inhibition of LNC EBLN3P Enhances Radiation-Induced Mitochondrial Damage in Lung Cancer Cells by Targeting the Keap1/Nrf2/HO-1 Axis
Authors: Tang Haoyi , Liu Shanghai , Yan Xiangyu , Jin Yusheng , He Xiangyang , Huang Hao , Liu Lu , Hu Wentao , Wu Anqing
Published: September 2023

Lung cancer remains the leading cause of cancer-related deaths in both women and men, claiming millions of lives worldwide. Radiotherapy is an effective modality for treating early-stage lung cancer; however, it cannot completely eradicate certain tumor cells due to their radioresistance. Radioresistance is commonly observed in conventionally fractionated radiotherapy, which can lead to treatment failure, metastasis, cancer recurrence, and poor prognosis for cancer patients. Identifying the underlying molecular mechanisms of radioresistance in lung cancer can promote the development of effective radiosensitizers, thereby improving patients’ life expectancy and curability. In this study, we identified LNC EBLN3P as a regulator of lung cancer cell proliferation and radiosensitivity. The repression of LNC EBLN3P could increase ROS production and mitochondrial injury in NSCLC cells. In addition, knocking down LNC EBLN3P increased the binding of Nrf2 to Keap1, resulting in enhanced Nrf2 degradation, decreased translocation of Nrf2 to the nucleus, reduced expression of antioxidant protein HO-1, weakened cellular antioxidant capacity, and increased radiosensitivity of NSCLC cells. These findings suggest that targeting LNC EBLN3P could be a promising strategy for developing novel radiosensitizers in the context of conventional radiotherapy for NSCLC.