bluuebunny/crossref_metadata_2025 huggingface dataset:
Promising Approaches for Heat Utilization in Agricultural Machinery Engines
Authors: Kalinichenko Antonina , Hruban Vasyl , Marchenko Dmytro
Published: September 2024

The methods of increasing the efficiency of internal combustion engines through heat utilization are examined. A proposed classification of heat utilization systems for mobile energy vehicles is presented. External utilization harnesses the heat generated by a diesel engine to fulfill the needs of consumers not directly related to the engine, such as interior heating and air conditioning systems. Internal recycling focuses on enhancing the power, environmental performance, and economic performance of an engine and its related systems. Various heat utilization schemes are compared. For the economic conditions of the European Union (EU), a turbocompounding diesel engine is acceptable if its agricultural tractor rated power is more than 275 kW and its combine harvester rated power is more than 310 kW. Steam injection into the combustion chamber is utilized to improve the technical and economic performance of gas turbine engines. This technology is also produced in Ukraine and is proposed for use in internal combustion engines. It is suggested to inject water vapor into a turbocharger turbine. This approach reduces the number of components in the heat recovery system, thereby lowering its cost. A recycling chiller can be employed to cool the air after it passes through the air cleaner, enhance the efficiency of the air cooler, and improve the performance of the thermoelectric generators. This device is particularly effective at relatively high air temperatures and can be recommended for agricultural machinery that operates in such conditions, such as combines. With the application of this new technology, it is possible to increase the power of diesel engines by 15…20% and reduce fuel consumption by up to 14%. Further research will focus on substantiating the parameters of recycling systems for different classes of vehicles. Developing a methodology to justify the effective application of heat utilization systems in agricultural mobile energy vehicles is advisable.
Improved Performance of Bifacial Photovoltaic Modules with Low-Temperature Processed Textured Rear Reflector
Authors: Song Hyung-Jun , Lee Deukgwang , Kim Chungil , Na Jun-Hee
Published: September 2024

Bifacial photovoltaic (PV) modules can capture both front and rear incident light simultaneously, thereby enhancing their power output. Achieving uniformity in rear incident light is crucial for an efficient and a stable operation. In this study, we present a simple, yet effective textured rear reflector, designed to optimize the performance and stability of bifacial PV modules. The three-dimensional textured surface was created using an ethylene vinyl acetate sheet (EVA) through a hot-press method at 150 °C. Subsequently, the textured EVA surface was coated with solution-processed silver ink, increasing the reflectance of the textured reflector through a low-temperature process. The integration of the developed textured rear reflector into bifacial crystalline silicon (c-Si) PV modules resulted in an additional 6.9% improvement in power conversion efficiency compared to bifacial PV modules without a rear reflector, particularly when the rear reflector is close to the PV module. Furthermore, the textured rear reflector may mitigate current mismatch among cells by randomizing incident light and uniformly redistributing the reflected light to the PV cells. Consequently, the proposed textured reflector contributes to the enhanced performance and stability of bifacial PV modules.
Improvement of Optical-Induced Thermography Defect Detectability by Equivalent Heating and Non-Uniformity Compensation in Polyetheretherketone
Authors: Chung Yoonjae , Kim Chunyoung , Lee Seungju , Suh Hyunkyu , Kim Wontae
Published: September 2024

This paper deals with the experimental procedures of lock-in thermography (LIT) for polyetheretherketone (PEEK), which is used as a lightweight material in various industrial fields. The LIT has limitations due to non-uniform heating by external optic sources and the non-uniformity correction (NUC) of the infrared (IR) camera. It is generating unintended contrast in the IR image in thermal imaging inspection, reducing detection performance. In this study, the non-uniformity effect was primarily improved by producing an equivalent array halogen lamp. Then, we presented absolute temperature compensation (ATC) and temperature ratio compensation (TRC) techniques, which can equalize the thermal contrast of the test samples by compensating for them using reference samples. By applying compensation techniques to data acquired from the test samples, defect detectability improvement was quantitatively presented. In addition, binarization was performed and detection performance was verified by evaluating the roundness of the detected defects. As a result, the contrast of the IR image was greatly improved by applying the compensation technique. In particular, raw data were enhanced by up to 54% using the ATC compensation technique. Additionally, due to improved contrast, the signal-to-noise ratio (SNR) was improved by 7.93%, and the R2 value of the linear trend equation exceeded 0.99, demonstrating improved proportionality between the defect condition and SNR.
Research on the Fatigue Strength of Brazed Joints in Gas-Wave Refrigerator Oscillating Tubes Based on Cohesive Zone Model
Authors: Zhang Yongle , Xing Aidong , Sang Cheng , Li Qingsheng
Published: September 2024

Gas-wave refrigerators are widely used devices that utilize gas pressure to produce wave motion inside an oscillating tube for high-power refrigeration. This paper manufactures the core component of gas-wave refrigerators—the oscillating tubes—using hub and blade brazing. The tensile strength and fatigue life of the brazed joints were obtained through experiments. A finite element simulation based on the cohesive zone model determined that the critical damage stress of the brazed-joint brazing seam was 525 MPa. Under cyclic displacements of U = [0.4 mm, 0.5 mm, 0.6 mm], the errors between the simulated and experimental values of fatigue life are 9.09%, 4.38%, and 4.44%, respectively, which verifies the accuracy of the VUMAT subroutine of the cyclic CZM. The fatigue life relationship formula for the butt-brazed joints was fitted as y=5458.97x−3.545. Through the simulation analysis of the impact of the butt-joint angles on the fatigue life of brazed joints, it was found that as the butt-joint angle increased, the fatigue damage decreased, and the fatigue life increased. A finite element model of an oscillating tube with a DN (Nominal Diameter) of 285 × 450 mm was established to analyze the fatigue life of the butt-brazed structure of the rotor blade. The fatigue life of the brazed structure with a butt-joint angle of α = 45° increased by over 102.92% than that of α = 0°.
Design of a Steady-State Adjustment Method and Sensitivity Analysis for an ORC System with Plate Heat Exchangers
Authors: Ji Lantian , Wang Xiao , He Zhilong , Xing Ziwen
Published: September 2024

Because of its low concentration and low energy in an organic Rankine cycle (ORC) system, more than 50% of low-grade thermal energy with a temperature below 300 °C is not taken seriously. The relationship between changeable working condition parameters and operating parameters is typically not taken into account in a model while studying ORC systems. It is insufficient to analyze the system performance changes solely on changes in the performance parameters of the heat exchangers. Furthermore, a model perspective of the system control solution is absent from the steady-state control of the system when the heat source varies. In this paper, we created a system model for the application scenario of a 100–200 kW ORC system with brazed plate heat exchangers in order to address the aforementioned issues using MATLAB R2016b. Additionally, a sensitivity analysis of the system was conducted based on heat exchangers of performance variations. In addition, a modeling computation and adjustment scheme were put forth to guarantee that the system could continue to produce steady power production even when the temperature of the heat source fluctuated. Preliminary results showed that the system’s internal parameters included its evaporation pressure and condensation temperature. An increase in evaporation pressure will reduce the mass flow rate and heat flux of an evaporator, thus reducing the heat transfer coefficient. The two-phase region of the evaporator accounted for the highest proportion, of 70.22%, and continued to increase to 75.83%, followed by the supercooling region, and gradually decreasing. The utilization rate of the evaporator decreased from 74.85% to 38.32%. The system output power increased first and then decreased, with a maximum value of 153.11 kW. The system efficiency increased from 5.74% to 11.04%. The increase in condensation temperature increased the proportion of the two-phase region and the superheating region, and the mass flow rate did not change much. The increase in heat flux increased the heat transfer coefficient from 1721.31 W/(m2·K) to 2374.77 W/(m2·K), and the utilization rate of the evaporator decreased from 70.56% to 51.91%. The aforementioned change rules were used in the building of the steady-state regulation model to balance out the changes in the generating power as the heat source temperature increased. The model output was cross-checked and validated against pertinent experimental test literature data. The results of this research can serve as a valid and useful guide for ORC system design and practical applications.
End-to-End Electrocardiogram Signal Transformation from Continuous-Wave Radar Signal Using Deep Learning Model with Maximum-Overlap Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Network Layers
Authors: Kim Tae-Wan , Kwak Keun-Chang
Published: September 2024

This paper is concerned with an end-to-end electrocardiogram (ECG) signal transformation from a continuous-wave (CW) radar signal using a specialized deep learning model. For this purpose, the presented deep learning model is designed using convolutional neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) with a maximum-overlap discrete wavelet transform (MODWT) layer and an adaptive neuro-fuzzy network (ANFN) layer. The proposed method has the advantage of developing existing deep networks and machine learning to reconstruct signals through CW radars to acquire ECG biological information in a non-contact manner. The fully connected (FC) layer of the CNN is replaced by an ANFN layer suitable for resolving black boxes and handling complex nonlinear data. The MODWT layer is activated via discrete wavelet transform frequency decomposition with maximum-overlap to extract ECG-related frequency components from radar signals to generate essential information. In order to evaluate the performance of the proposed model, we use a dataset of clinically recorded vital signs with a synchronized reference sensor signal measured simultaneously. As a result of the experiment, the performance is evaluated by the mean squared error (MSE) between the measured and reconstructed ECG signals. The experimental results reveal that the proposed model shows good performance in comparison to the existing deep learning model. From the performance comparison, we confirm that the ANFN layer preserves the nonlinearity of information received from the model by replacing the fully connected layer used in the conventional deep learning model.
Study on the Optimization of Site Selection for Emergency Supply Reserve Warehouses Based on Multi-Attribute Weighted Intelligent Gray Target Decision-Making Evaluation Model
Authors: Li Linyan , Bai Xiao , Xia Hongshan
Published: September 2024

To reasonably allocate emergency supply reserve warehouses and enhance the utilization efficiency of emergency supply reserves, this paper proposes a new optimization method for the site selection of emergency supply reserve warehouses based on gray system theory—the multi-attribute weighted intelligent gray target decision-making evaluation model. This method innovatively incorporates the density of road networks, population density, economic density, and post-disaster response capability as key indicators. It then utilizes the multi-attribute weighted intelligent gray target decision-making evaluation model to evaluate the synthetic effect. Based on the evaluation results, optimization recommendations for the site selection of emergency supply reserve warehouses are provided. To validate the credibility of the proposed method, a comparative analysis is then conducted using the EEM-TOPSIS and TOPSIS–Gray Correlation Degree methods, resulting in largely consistent evaluation results. The study demonstrates that the multi-attribute weighted intelligent gray target decision-making evaluation model accounts for both hitting-the-target and missing-the-target scenarios for effect values and vectors of objectives. This approach effectively addresses the limitation of traditional multi-attribute evaluation methods, which can only rank evaluation schemes without effectively distinguishing between superior and inferior ones. This method also proves to be more user-friendly compared to others.
Multi-Scale Price Forecasting Based on Data Augmentation
Authors: Yue Ting , Liu Yahui
Published: September 2024

When considering agricultural commodity transaction data, long sampling intervals or data sparsity may lead to small samples. Furthermore, training on small samples can lead to overfitting and makes it hard to capture the fine-grained fluctuations in the data. In this study, a multi-scale forecasting approach combined with a Generative Adversarial Network (GAN) and Temporal Convolutional Network (TCN) is proposed to address the problems related to small sample prediction. First, a Time-series Generative Adversarial Network (TimeGAN) is used to expand the multi-dimensional data and t-SNE is utilized to evaluate the similarity between the original and synthetic data. Second, a greedy algorithm is exploited to calculate the information gain, in order to obtain important features, based on XGBoost. Meanwhile, TCN residual blocks and dilated convolutions are used to tackle the issue of gradient disappearance. Finally, an attention mechanism is added to the TCN, which is beneficial in terms of improving the forecasting accuracy. Experiments are conducted on three products, garlic, ginger and chili. Taking garlic as an example, the RMSE of the proposed method was reduced by 1.7% and 1% when compared to the SVR and RF models, respectively. Its R2 accuracy was also improved (by 4.3% and 3.4%, respectively). Furthermore, TCN-attention and TCN were found to require less time compared to GRU and LSTM. The accuracy of the proposed method increased by about 5% when compared to that without TimeGAN in the ablation study. Moreover, compared with TCN, the Gated Recurrent Unit (GRU), and the Long Short-term Memory (LSTM) model in the multi-scale price forecasting task, the proposed method can better utilize small samples and high-dimensional data, leading to improved performance. Additionally, the proposed model is compared to the Transformer and TimesNet models in terms of its accuracy, deployment cost, and other metrics.
Hierarchical QAM and Inter-Layer FEC for Multi-View Video Plus Depth Format in Two-Way Relay Channels
Authors: You Dongho , Kim Sung-Hoon , Kim Dong Ho
Published: September 2024

This paper presents an enhanced method for the transmission of 3D video in the Multi-view Video plus Depth (MVD) format over Two-Way Relay Channels (TWRC). Our approach addresses the unique challenges of MVD-based 3D video by combining Hierarchical Quadrature Amplitude Modulation (HQAM), a method that prioritizes data layers based on importance, and Inter-Layer Forward Error Correction (IL-FEC), which protects critical data from errors. These are specifically designed to handle the dual-layer data structure where color data and depth information require different levels of error protection, and it reduces transmission errors and enhances the quality of MVD-based 3D video over TWRC. In the TWRC scenario, the proposed scheme optimizes transmission by reducing the number of relayed bitstreams by half while maintaining high-quality requirements, as demonstrated by significant improvements in the Structural Similarity Index (SSIM) for virtually synthesized views. Furthermore, we identify and optimize the hierarchical modulation parameter (α), which controls the priority and protection levels of different data streams. Systematically varying α reveals its substantial impact on the quality of the reconstructed 3D video, as measured by SSIM. Our results demonstrate that the proposed combination of HQAM and IL-FEC not only maintains the target SSIM of 0.9 for the virtually synthesized view under various relay conditions but also reveals the optimal α value for balancing the error protection between the color and depth map data streams. Notably, while increasing α enhances the protection of critical data (such as color video streams), it may concurrently degrade the quality of less important streams (like depth maps), highlighting the importance of fine-tuning α to achieve the best overall video quality. These findings suggest that our method provides a flexible and effective solution for high-quality 3D video transmission in challenging communication environments, potentially advancing the development of future 3D video delivery systems.
Prediction of PM2.5 Concentration Based on Deep Learning for High-Dimensional Time Series
Authors: Hu Jie , Jia Yuan , Jia Zhen-Hong , He Cong-Bing , Shi Fei , Huang Xiao-Hui
Published: September 2024

PM2.5 poses a serious threat to human life and health, so the accurate prediction of PM2.5 concentration is essential for controlling air pollution. However, previous studies lacked the generalization ability to predict high-dimensional PM2.5 concentration time series. Therefore, a new model for predicting PM2.5 concentration was proposed to address this in this paper. Firstly, the linear rectification function with leakage (LeakyRelu) was used to replace the activation function in the Temporal Convolutional Network (TCN) to better capture the dependence of feature data over long distances. Next, the residual structure, dilated rate, and feature-matching convolution position of the TCN were adjusted to improve the performance of the improved TCN (LR-TCN) and reduce the amount of computation. Finally, a new prediction model (GRU-LR-TCN) was established, which adaptively integrated the prediction of the fused Gated Recurrent Unit (GRU) and LR-TCN based on the inverse ratio of root mean square error (RMSE) weighting. The experimental results show that, for monitoring station #1001, LR-TCN increased the RMSE, mean absolute error (MAE), and determination coefficient (R2) by 12.9%, 11.3%, and 3.8%, respectively, compared with baselines. Compared with LR-TCN, GRU-LR-TCN improved the index symmetric mean absolute percentage error (SMAPE) by 7.1%. In addition, by comparing the estimation results with other models on other air quality datasets, all the indicators have advantages, and it is further demonstrated that the GRU-LR-TCN model exhibits superior generalization across various datasets, proving to be more efficient and applicable in predicting urban PM2.5 concentration. This can contribute to enhancing air quality and safeguarding public health.
A Stock Prediction Method Based on Deep Reinforcement Learning and Sentiment Analysis
Authors: Du Sha , Shen Hailong
Published: September 2024

Most previous stock investing methods were unable to predict newly listed stocks because they did not have historical data on newly listed stocks. In this paper, we use the Q-learning algorithm based on a convolutional neural network and add sentiment analysis to establish a prediction method for Chinese stock investment tasks. There are 118 companies that are ranked in the Chinese top 150 list for two consecutive years in both 2022 and 2023. We collected all comments under the stock bar of these 118 stocks for each day from 1 January 2022 to 1 July 2024, totaling nearly 10 million comments. There are 90 stocks left after the preprocessing of 118 stocks. We use these 90 stocks as the dataset. The stock’s closing price, volume, and comment text data are fed together to the agent, and the trained agent outputs investment behaviors that maximize future returns. We apply the trained model to two test sets that are completely different from the training set and compare it to several other methods. Our proposed method called SADQN-S obtains results of 1.1229 and 1.1054 on the two test sets. SADQN-S obtained higher final total assets than the other methods on both test sets. This shows that our model can help stock investors earn high returns on newly listed stocks.
How Automated Machine Learning Can Improve Business
Authors: Rosário Albérico Travassos , Boechat Anna Carolina
Published: September 2024

Automated Machine Learning (AutoML) is revolutionizing how businesses utilize data, but there seems to be a lack of clarity and a holistic view regarding all its advantages, especially concerning the benefits of AutoML for companies. In order to deeply understand how AutoML can improve businesses, a systematic review examined the bibliometric literature of this field, analyzing 74 academic and scientific documents from the Scopus database. Results showed that AutoML (i) reduces the time and resources needed to develop and deploy machine learning models, (ii) accelerates decision-making and enables quicker responses to market changes, (iii) empowers businesses to build accurate predictive models using sophisticated algorithms, (iv) optimizing model performance for reliable insights and better outcomes, and (v) enhances accessibility by reducing technical barriers and democratizing innovation. As businesses grow, AutoML scales to handle larger datasets and more complex problems without extensive manual intervention. In sum, AutoML enhances efficiency, accuracy, and scalability, becoming a crucial driver of business innovation and success.
Forecasting the Total Output Value of Agriculture, Forestry, Animal Husbandry, and Fishery in Various Provinces of China via NPP-VIIRS Nighttime Light Data
Authors: Yang Rongchao , Zhou Qingbo , Xu Lei , Zhang Yi , Wei Tongyang
Published: September 2024

This paper attempts to establish the accurate and timely forecasting model for the total output value of agriculture, forestry, animal husbandry, and fishery (TOVAFAF) in various provinces of China using NPP-VIIRS nighttime light (NTL) remote sensing data and machine learning algorithms. It can provide important data references for timely assessment of agricultural economic development level and policy adjustment. Firstly, multiple NTL indices for provincial-level administrative regions of China were constructed based on NTL images from 2013 to 2023 and various statistics. The results of correlation analysis and significance test show that the constructed total nighttime light index (TNLI), luminous pixel quantity index (LPQI), luminous pixel ratio index (LPRI), and nighttime light squared deviation sum index (NLSDSI) are highly correlated with the TOVAFAF. Subsequently, using the relevant data from 2013 to 2020 as the training set, the four NTL indices were separately taken as single independent variable to establish the linear model, exponential model, logarithmic model, power exponential model, and polynomial model. And all the four NTL indices were taken as the input features together to establish the multiple linear regression (MLR), extreme learning machine (ELM), and particle swarm optimization-ELM (PSO-ELM) models. The relevant data from 2021 to 2022 were taken as the validation set for the adjustment and optimization of the model weight parameters and the preliminary evaluation of the modeling effect. Finally, the established models were employed to forecast the TOVAFAF in 2023. The experimental results show that the ELM and PSO-ELM models can better explore and characterize the potential nonlinear relationship between NTL data and the TOVAFAF than all the models established based on single NTL index and the MLR model, and the PSO-ELM model achieves the best forecasting effect in 2023 with the MRE value for 32.20% and the R2 values of the linear relationship between the actual values and the forecasting values for 0.6460.
A Novel FECAM-iTransformer Algorithm for Assisting INS/GNSS Navigation System during GNSS Outages
Authors: Kuang Xinghong , Yan Biyun
Published: September 2024

In the field of navigation and positioning, the inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation system is known for providing stable and high-precision navigation services for vehicles. However, in extreme scenarios where GNSS navigation data are completely interrupted, the positioning accuracy of these integrated systems declines sharply. While there has been considerable research into using neural networks to replace the GNSS signal output during such interruptions, these approaches often lack targeted modeling of sensor information, resulting in poor navigation stability. In this study, we propose an integrated navigation system assisted by a novel neural network: an inverted-Transformer (iTransformer) and the application of a frequency-enhanced channel attention mechanism (FECAM) to enhance its performance, called an INS/FECAM-iTransformer integrated navigation system. The key advantage of this system lies in its ability to simultaneously extract features from both the time and frequency domains and capture the variable correlations among multi-channel measurements, thereby enhancing the modeling capabilities for sensor data. In the experimental part, a public dataset and a private dataset are used for testing. The best experimental results show that compared to a pure INS inertial navigation system, the position error of the INS/FECAM-iTransformer integrated navigation system reduces by up to 99.9%. Compared to the INS/LSTM (long short-term memory) and INS/GRU (gated recurrent unit) integrated navigation systems, the position error of the proposed method decreases by up to 82.4% and 78.2%, respectively. The proposed approach offers significantly higher navigation accuracy and stability.
Exploring the Intriguing World of Fungal Diversity in the Oral Cavities of a Native Community in Siltepec, Chiapas, Mexico
Authors: Pérez-González Orquídea , Valencia-Ampudia Franco , de la Garza-Ramos Myriam A , Aguirre-Arzola Victor E.
Published: September 2024

This study explores the relationship between filamentous fungi and dental caries in isolated indigenous communities in Siltepec, Chiapas, Mexico. A total of 37 oral swabs were collected, with 22 participants harboring filamentous fungi, primarily from the genus Cladosporium. Statistical analysis using Student’s t-test and the Mann–Whitney U test revealed a significant reduction in extensive and fully cavitated caries (p < 0.0001) in individuals with fungi, while those without fungi exhibited higher rates of dental decay. Participants with fungi had a higher prevalence of healthy teeth and incipient caries. The findings suggest that traditional maize-based diets, particularly fermented beverages like pozol, may promote the growth of beneficial fungi in the oral microbiome, offering a protective effect against dental caries through microbial competition and the alteration of the oral environment. These results underline the need for further research into the long-term impact of traditional diets on oral health and the potential use of natural substances, such as probiotics and plant-based antimicrobials, to maintain oral homeostasis and prevent caries.
Comprehensive Air Quality Model with Extensions: Formulation and Evaluation for Ozone and Particulate Matter over the US
Authors: Emery Christopher , Baker Kirk , Wilson Gary , Yarwood Greg
Published: September 2024

The Comprehensive Air Quality Model with extensions (CAMx) is an open-source, state-of-the-science photochemical grid model that addresses tropospheric air pollution (ozone, particulates, air toxics) over spatial scales ranging from neighborhoods to continents. CAMx has been in continuous development for over 25 years and has been used by numerous entities ranging from government to industry to academia to support regulatory actions and scientific research addressing a variety of air quality issues. Here, we describe the technical formulation of CAMx v7.20, publicly released in May 2022. To illustrate an example of regional and seasonal model performance for predicted ozone and fine particulate matter (PM2.5), we summarize a model evaluation from a recent 2016 national-scale CAMx application over nine climate zones contained within the conterminous US. We show that statistical performance for warm season maximum 8 h ozone is consistently within benchmark statistical criteria for bias, gross error, and correlation over all climate zones, and often near statistical goals. Statistical performance for 24 h PM2.5 and constituents fluctuate around statistical criteria with more seasonal and regional variability that can be attributed to different sources of uncertainty among PM2.5 species (e.g., weather influences, chemical treatments and interactions, emissions uncertainty, and ammonia treatments). We close with a mention of new features and capabilities that are planned for the next public releases of the model in 2024 and beyond.
A General Fixed Point Theorem for a Sequence of Multivalued Mappings in S-Metric Spaces
Authors: Popa Valeriu , Patriciu Alina-Mihaela
Published: September 2024

Over the years, the concept of metric space has been extended in several directions, and numerous common fixed point theorems for multivalued mappings in complete metric space have been demonstrated. In this paper, we prove a general fixed point theorem for a pair of multivalued mappings satisfying implicit relations, extending some results from the literature to S-metric spaces. As an application, we obtain new results for a sequence of multivalued mappings in S-metric spaces, generalizing some known results.
Enhanced Electrochemical Performance of Lithium Iron Phosphate Cathodes Using Plasma-Assisted Reduced Graphene Oxide Additives for Lithium-Ion Batteries
Authors: Jekal Suk , Kim Chan-Gyo , Kim Jiwon , Kim Ha-Yeong , Chu Yeon-Ryong , Ra Yoon-Ho , Otgonbayar Zambaga , Yoon Chang-Min
Published: September 2024

One-dimensional lithium-ion transport channels in lithium iron phosphate (LFP) used as a cathode in lithium-ion batteries (LIBs) result in low electrical conductivity and reduced electrochemical performance. To overcome this limitation, three-dimensional plasma-treated reduced graphene oxide (rGO) was synthesized in this study and used as an additive for LFP in LIB cathodes. Graphene oxide was synthesized using Hummers’ method, followed by mixing with LFP, lyophilization, and plasma treatment to obtain LFP@rGO. The plasma treatment achieved the highest degree of reduction and porosity in rGO, creating ion transfer channels. The structure of LFP@rGO was verified through scanning electron microscopy (SEM) analysis, which demonstrated that incorporating 10.0 wt% of rGO into LFP resulted in successful coverage by the rGO layer, forming LFP@rGO-10. In half-cell tests, LFP@rGO-10 exhibited a specific capacity of 142.7 mAh g−1 at the 1.0 C-rate, which is higher than that of LFP. The full-cell exhibited 86.8% capacity retention after 200 cycles, demonstrating the effectiveness of rGO in enhancing the performance of LFP as an LIB cathode material. The outstanding efficiency and performance of the LFP@rGO-10//graphite cell highlight the promising potential of rGO-modified LFP as a cathode material for high-performance LIBs, providing both increased capacity and stability.
Distribution and Ecological Risk of Ludwigia peploides in South Korea
Authors: Jo Aram , Lee Soo In , Choi Donghui , Kim Youngha , Lee Yong Ho , Hong Sun Hee
Published: September 2024

The number of alien species introduced into South Korea continues to increase over the years. In particular, several plants have been introduced as ornamentals. Ludwigia peploides, which is native to the Americas and Australia, is believed to have been planted as an ornamental aquatic plant called “water primrose” and “primrose”. It spread to natural ecosystems through rivers, and its distribution is gradually expanding in Suwon, Hwaseong, Busan, and Jeju. However, there has been no specific study on the ecological risk of L. peploides introduced into South Korea. This study, therefore, investigates the distribution status and ecological risks of L. peploides in South Korea through field surveys and allelopathic material analysis, as well as assessing abiotic risk factors. The distribution was confirmed at a total of 19 sites, with high-density mats of a single species forming along the water’s edge and on the water surface. The maximum distribution area was 13,922 m2 in Chilgok Reservoir in Anseong. Stems and plant fragments transported along waterways were continuously forming colonies through vegetative propagation. When evaluating the overall risk, it is determined that L. peploides has a high potential to cause significant damage to the ecosystem if not managed promptly. Therefore, continuous monitoring is necessary to effectively manage and prevent the habitat expansion of L. peploides. The results of this study are expected to aid in the identification of the current distribution and potential ecological risks of L. peploides in South Korea, providing essential data for ecological risk assessment and the development of effective management strategies.
Characterization of New Tropicoporus Species (Basidiomycota, Hymenochaetales, Hymenochaetaceae) Discovered in Tamil Nadu, India
Authors: Arumugam Elangovan , Murugadoss Ramesh , Gunaseelan Sugantha , Karunarathna Samantha C. , Elgorban Abdallah M. , Rampelotto Pabulo Henrique , Kaliyaperumal Malarvizhi
Published: September 2024

This study aimed to investigate the morphological characteristics and phylogenetic relationships of three new species of Tropicoporus from the southern parts of India. The analyses of the ITS and nLSU regions revealed the novelty of these species, which have been named T. pannaensis, T. subindicus, and T. xerophyticus. All three species possess pileate basidiomes, a monomitic hyphal system in the context, and the presence of cystidioles and setae. However, they differ significantly in their phylogenetic placements and other morpho-taxonomic features. Tropicoporus pannaensis is characterized by a meagrely ungulate basidiome, indistinct zones, and an obtuse margin. Tropicoporus subindicus has a triquetrous basidiome and a radially cracked, crusted pileal surface with an acute margin, while T. xerophyticus is distinguished by an imbricate, perennial basidiome with an abundantly warted pileal surface. A phylogenetic tree is provided to show the placement of the three new species, along with detailed descriptions and illustrations. Additionally, a key for the identification of the Asian species of Tropicoporus is presented.
Effects of Extended Treatment with Protium heptaphyllum Liposomes on Metabolic Parameters of Obese Rats
Authors: Patias Naiéle Sartori , Maia Sara Vieira , Ferreira Yasmin Gabriele , de Oliveira Natalhya Letícia Ferreira , Ferrarini Stela Regina , Bomfim Gisele Facholi , Sinhorin Adilson Paulo , Aguiar Danilo Henrique , de Queiroz Eveline Aparecida Isquierdo Fonseca , Sinhorin Valéria Dornelles Gindri
Published: September 2024

Protium heptaphyllum (P. heptaphyllum), popularly known as “almacega” or “white pitch”, is widely used in folk medicine due to its antioxidant, anti-inflammatory and healing properties, attributed to its richness in flavonoids and terpenes. Therefore, this study aimed to evaluate the effects of treatment for 28 days with liposomes containing P. heptaphyllum leaf extract in obese animals. Male Wistar rats, subjected to a hypercaloric diet for 8 weeks to induce obesity (hypercaloric chow and water enriched with 30% sucrose, ad libitum), were treated with the plant formulation (1 mg kg−1day−1, via gavage) for 28 days. The study investigated morphological, metabolic, redox state, immunological and histological parameters in adipose and liver tissue. Rats were divided into four groups: control (C), liposomes with extract (H), obese (O) and obese treated with liposomes containing extract (OH). The results indicated that the obese group (O) presented weight gain, hepatic steatosis and alterations in metabolic and inflammatory parameters. However, treatment with liposomes (OH) reduced glucose, alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), creatinine and the lipid profile. In adipose tissue, the OH group showed decreased superoxide dismutase (SOD) activity and increased glutathione S-transferase (GST) activity, in contrast to the effects observed in liver GST. In the analysis of thiobarbituric-acid-reactive substances (TBARS), it was possible to observe an increase in all groups in adipose tissue and in group O in liver tissue, in addition to a reduction in TBARS in group OH in the liver, indicating modulation of oxidative stress. The treatment also increased the concentration of IL-10 and IL-17 in the liver and decreased that of IL-6 in adipose tissue. After 28 days of treatment, these results point to the therapeutic potential of treatment with P. heptaphyllum, not necessarily only against obesity, but also an effect per se of the liposomes, possibly due to the high concentration of flavonoids present in the plant extract.
Catching the Big Fish in Big Data: A Meta-Analysis of Zebrafish Kidney scRNA-Seq Datasets Highlights Conserved Molecular Profiles of Macrophages and Neutrophils in Vertebrates
Authors: Bobrovskikh Aleksandr V. , Zubairova Ulyana S. , Naumenko Ludmila G. , Doroshkov Alexey V.
Published: September 2024

The innate immune system (IIS) is an ancient and essential defense mechanism that protects animals against a wide range of pathogens and diseases. Although extensively studied in mammals, our understanding of the IIS in other taxa remains limited. The zebrafish (Danio rerio) serves as a promising model organism for investigating IIS-related processes, yet the immunogenetics of fish are not fully elucidated. To address this gap, we conducted a meta-analysis of single-cell RNA sequencing (scRNA-seq) datasets from zebrafish kidney marrow, encompassing approximately 250,000 immune cells. Our analysis confirms the presence of key genetic pathways in zebrafish innate immune cells that are similar to those identified in mammals. Zebrafish macrophages specifically express genes encoding cathepsins, major histocompatibility complex class II proteins, integral membrane proteins, and the V-ATPase complex and demonstrate the enrichment of oxidative phosphorylation ferroptosis processes. Neutrophils are characterized by the significant expression of genes encoding actins, cytoskeleton organizing proteins, the Arp2/3 complex, and glycolysis enzymes and have demonstrated their involvement in GnRH and CLR signaling pathways, adherents, and tight junctions. Both macrophages and neutrophils highly express genes of NOD-like receptors, phagosomes, and lysosome pathways and genes involved in apoptosis. Our findings reinforce the idea about the existence of a wide spectrum of immune cell phenotypes in fish since we found only a small number of cells with clear pro- or anti-inflammatory signatures.
Molecular Targets for Breast Cancer Therapy
Authors: Montazeri Aliabadi Hamidreza
Published: September 2024

Breast cancer is by far the most common cancer in women, and for a while, it surpassed lung cancer as the most diagnosed cancer, regardless of gender, in 2020 [...]
Chloramphenicol Interferes with 50S Ribosomal Subunit Maturation via Direct and Indirect Mechanisms
Authors: Yu Ting , Zeng Fuxing
Published: September 2024

Chloramphenicol (CAM), a well-known broad-spectrum antibiotic, inhibits peptide bond formation in bacterial ribosomes. It has been reported to affect ribosome assembly mainly through disrupting the balance of ribosomal proteins. The present study investigates the multifaceted effects of CAM on the maturation of the 50S ribosomal subunit in Escherichia coli (E. coli). Using label-free quantitative mass spectrometry (LFQ-MS), we observed that CAM treatment also leads to the upregulation of assembly factors. Further cryo-electron microscopy (cryo-EM) analysis of the ribosomal precursors characterized the CAM-treatment-accumulated pre-50S intermediates. Heterogeneous reconstruction identified 26 distinct pre-50S intermediates, which were categorized into nine main states based on their structural features. Our structural analysis highlighted that CAM severely impedes the formation of the central protuberance (CP), H89, and H58 during 50S ribosomal subunit maturation. The ELISA assay further demonstrated the direct binding of CAM to the ribosomal precursors, suggesting that the interference with 50S maturation occurs through a combination of direct and indirect mechanisms. These findings provide new insights into the mechanism of the action of CAM and provide a foundation for a better understanding of the assembly landscapes of the ribosome.
Portable Electrochemical System and Platform with Point-of-Care Determination of Urine Albumin-to-Creatinine Ratio to Evaluate Chronic Kidney Disease and Cardiorenal Syndrome
Authors: Lee Shuenn-Yuh , Ciou Ding-Siang , Lee Hao-Yun , Chen Ju-Yi , Wei Yi-Chieh , Shieh Meng-Dar
Published: September 2024

Abstract: The urine albumin (Alb)-to-creatinine (Crn) ratio (UACR) is a sensitive and early indicator of chronic kidney disease (CKD) and cardiorenal syndrome. This study developed a portable and wireless electrochemical-sensing platform for the sensitive and accurate determination of UACR. The developed platform consists of a carbon nanotube (CNT)-2,2′-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid)(ABTS)-based modified UACR sensor, a miniaturised potentiostat, a cup holder embedded with a magnetic stirrer and a smartphone app. The UACR sensing electrode is composed of two screen-printed carbon working electrodes, one screen-printed carbon counter electrode and a screen-printed AgCl reference electrode. The miniaturised potentiostat, which is controlled by the developed app, performs cyclic voltammetry and amperometry to detect Alb and Crn, respectively. Clinical trials of the proposed system by using spot urine samples from 30 diabetic patients indicate that it can accurately classify all three CKD risk statuses within 30 min. The high accuracy of our proposed sensing system exhibits satisfactory agreement with the commercial biochemical analyser TBA-25FR (Y = 0.999X, R2 = 0.995). The proposed UACR sensing system offers a convenient, reliable and affordable solution for personal mobile health monitoring and point-of-care urinalysis.
Video Game Skills across Diverse Genres and Cognitive Functioning in Early Adulthood: Verbal and Visuospatial Short-Term and Working Memory, Hand–Eye Coordination, and Empathy
Authors: Zioga Triantafyllia , Ferentinos Aristotelis , Konsolaki Eleni , Nega Chrysanthi , Kourtesis Panagiotis
Published: September 2024

The cognitive and affective impacts of video games are subjects of ongoing debate, with recent research recognizing their potential benefits. This study employs the Gaming Skill Questionnaire (GSQ) to evaluate participants’ gaming skills across six genres and overall proficiency. A total of 88 individuals aged 20–40 participated, completing assessments of empathy and six cognitive abilities: verbal short-term memory, verbal working memory, visuospatial short-term memory, visuospatial working memory, psychomotor speed (hand–eye coordination), and attention. Participants’ cognitive abilities were examined using the Digit Span Test, Corsi Block Test, and Deary–Liewald Reaction Time Task, while empathy was assessed using the Empathy Quotient Questionnaire. Findings indicate that higher levels of videogaming proficiency are linked to improvements in visuospatial short-term and working memory, psychomotor speed, and attention. Specific genres enhanced particular skills: RPGs were positively associated with both verbal working memory and visuospatial short-term memory, but were negatively associated with empathy; action games improved psychomotor speed and attention; and puzzle games showed a positive relationship with visuospatial working memory. These results add to ongoing research on the cognitive and affective effects of video games, suggesting their potential to enhance specific cognitive functions. They also highlight the complex relationship between video games and empathy. Future research should explore the long-term impacts and genre-specific effects.
Artificial Neural Network-Based Automated Finite Element Model Updating with an Integrated Graphical User Interface for Operational Modal Analysis of Structures
Authors: Hasani Hamed , Freddi Francesco
Published: September 2024

This paper presents an artificial neural network-based graphical user interface, designed to automate finite element model updating using data from operational modal analysis. The approach aims to reduce the uncertainties inherent in both the experimental data and the computational model. A key feature of this method is the application of a discrete wavelet transform-based approach for denoising OMA data. The graphical interface streamlines the FEMU process by employing neural networks to automatically optimize FEM inputs, allowing for real-time adjustments and continuous structural health monitoring under varying environmental and operational conditions. This approach was validated with OMA results, demonstrating its effectiveness in enhancing model accuracy and reliability. Additionally, the adaptability of this method makes it suitable for a wide range of structural types, and its potential integration with emerging technologies such as the Internet of Things further amplifies its relevance.
Effect of the Addition of Manganese Dioxide Nanoparticles on the Mechanical Properties of Concrete against Carbonation and Sulfate Attack
Authors: Torre Ana , Shuan Luisa , Quintana Noemi , Moromi Isabel , Basurto Jesus , Mosquera Luis , Cortez Noe
Published: September 2024

This study evaluates the impact of the addition of nanoparticles of anodic manganese dioxide (NAMD) on the mechanical properties and resistance to chemical attack of concrete. The research focused on nine concrete mixtures with water/cement ratios of 0.40, 0.45, and 0.50 and NAMD contents of 0, 5, and 10%. The properties of NAMD were analyzed, and fresh concrete properties such as temperature, unit weight, and consistency were measured. The compressive strength was determined at different ages (7, 14, 28, 56, and 90 days). The tensile and flexural strength were evaluated at 28 days, and the longitudinal change generated by the SO4Mg attack was monitored until 90 days. In addition, an accelerated carbonation test was performed on concrete samples with 28 days of curing exposed to an atmosphere of 6% CO2 for one week. The addition of NAMD did not significantly affect the temperature or unit weight of the fresh concrete, but it did influence the consistency. An increase in compressive, tensile, and flexural strength was observed, especially at early ages and for low w/c ratios. The addition of NAMD reduced the expansion of concrete exposed to magnesium sulfate, with 5% being the most effective dose, and reduced the carbonation rate of concrete by up to 40% in mixes with w/c ratios of 0.40 and 0.50. It was shown that the addition of 5% as an effective dose of NAMD improves the mechanical and durability properties of concrete, especially in mixtures with a low water/cement ratio, contributing to the improvement of the quality and strength of concrete.