bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Dietary Energy Level during Late Gestation on Mineral Contents in Colostrum, Milk, and Plasma of Lactating Jennies
Authors: Hui Fang , Tong Manman , Li Shuyi , Zhao Yanli , Guo Xiaoyu , Guo Yongmei , Shi Binlin , Yan Sumei
Published: August 2024

This study investigated the effects of dietary energy levels during late gestation on mineral content in the plasma, colostrum, and milk of jennies postpartum. Twenty-four pregnant multiparous DeZhou jennies, aged 6.0  ±  0.1 years, with a body weight of 292  ±  33 kg, an average parity number of 2.7  ±  0.1, and similar expected dates of confinement (74  ±  4 days), were randomly allocated to three groups and fed three diets: high energy (12.54 MJ/kg, HE), medium energy (12.03 MJ/kg, ME), and low energy (11.39 MJ/kg, LE). Blood samples were collected from the jugular vein of each jenny at time points of 0 h, 24 h, 48 h, 5 d, 7 d, and 14 d after parturition. Additionally, milk samples were collected through manual milking, and an analysis of the mineral content was conducted. The results showed that compared with HE, both ME and LE significantly increased the levels of calcium (Ca), phosphorus (P), zinc (Zn), selenium (Se), molybdenum (Mo), and cobalt (Co) in the plasma and Ca, P, magnesium (Mg), copper (Cu), manganese (Mn), Zn, selenium (Se), molybdenum (Mo), and Co in the milk of jennies postpartum (p < 0.05); ME also increased the levels of potassium (K), iron (Fe), and Mn in plasma and K and Fe in milk (p < 0.05). The levels of Ca, K, Mg, P, Fe, Cu, Mn, Co, Se, Zn, and Mo in plasma and milk gradually decreased with increasing postpartum time. Their contents were the highest at 0 h postpartum, rapidly decreased after 24 h postpartum, and declined to the lowest on day 14 postpartum. The interaction between dietary energy level and postpartum time showed that although the concentrations of the minerals Ca, P, K, Mg, Fe, Cu, Mn, Zn, Co, Se, and Mo decreased in jennies’ plasma and milk in the treatment groups with different energy levels as postpartum time increased, the pattern of change was also influenced by dietary energy level. The influence of dietary energy level in late gestation on the mineral content of milk and plasma during the postpartum colostrum phase was higher than that during the milk phase. In conclusion, this study demonstrated that, under the current experimental conditions, the mineral content of the colostrum, milk, and plasma of jennies after parturition was dependent on the dietary energy level during late gestation.
Secure Beamforming Design for UAV-Empowered Integrated Sensing and Communication
Authors: Chen Chuang , Zhu Qi
Published: August 2024

Integrated sensing and communication (ISAC) systems assisted by unmanned aerial vehicles (UAVs) are susceptible to eavesdropping, making secure information transmission a critical area of research. We propose a joint optimization algorithm to improve the security of UAV-assisted ISAC systems through coordinated adjustments of transmit beamforming and UAV position. The optimization problem aims to minimize the maximum eavesdropping signal-to-interference-plus-noise ratio (SINR) across multiple legitimate users while adhering to three distinct constraints: service quality for legitimate users, sensing requirements for detecting eavesdroppers, and limitations on system transmit power. To address the non-convex optimization challenge effectively, the problem is decomposed into sub-problems focusing on the optimization of transmit beamforming and UAV position. These sub-problems are solved using semidefinite relaxation (SDR), the Dinkelbach method, and successive convex approximation (SCA), with iterative alternation achieving optimal values for the transmit beamforming and UAV position while also proving the convergence of the algorithm. Lastly, simulation results illustrate that the proposed algorithm markedly improves the system’s security performance while preserving its communication and sensing capabilities.
Spatial Dynamic Interaction Effects and Formation Mechanisms of Air Pollution in the Central Plains Urban Agglomeration in China
Authors: Huang Jie , Lu Hongyang , Huang Yajun
Published: August 2024

Accurately identifying the dynamic interaction effects and network structure characteristics of air pollution is essential for effective collaborative governance. This study investigates the spatial dynamic interactions of air pollution among 30 cities in the Central Plains Urban Agglomeration using convergent cross mapping. Social network analysis is applied to assess the overall and node characteristics of the spatial interaction network, while key driving factors are analyzed using an exponential random graph model. The findings reveal that air pollution levels in the Central Plains Urban Agglomeration initially increase before they decrease, with heavily polluted cities transitioning from centralized to sporadic distribution. Among the interactions, Heze’s air pollution impact on Kaifeng was the strongest, while Xinxiang’s impact on Changzhi was the weakest. The emission and receiving effects peaked during 2010–2012. The air pollution interactions among cities exhibit significant network characteristics, with block model results indicating that emitting and receiving relationships are primarily concentrated in the bidirectional spillover plate. Natural factors such as temperature and precipitation significantly influence the spatial interaction network. Economic and social factors like economic level and industrial sector proportion also have a significant impact. However, population density does not influence the spatial interaction network. This study contributes to understanding the spatial network of air pollution, thereby enhancing strategies for optimizing regional collaborative governance efforts to address air pollution.
Generating Daily High-Resolution Regional XCO2 by Deep Neural Network and Multi-Source Data
Authors: Tian Wenjie , Zhang Lili , Yu Tao , Yao Dong , Zhang Wenhao , Wang Chunmei
Published: August 2024

CO2 is one of the primary greenhouse gases impacting global climate change, making it crucial to understand the spatiotemporal variations of CO2. Currently, commonly used satellites serve as the primary means of CO2 observation, but they often suffer from striping issues and fail to achieve complete coverage. This paper proposes a method for constructing a comprehensive high-spatiotemporal-resolution XCO2 dataset based on multiple auxiliary data sources and satellite observations, utilizing multiple simple deep neural network (DNN) models. Global validation results against ground-based TCCON data demonstrate the excellent accuracy of the constructed XCO2 dataset (R is 0.94, RMSE is 0.98 ppm). Using this method, we analyze the spatiotemporal variations of CO2 in China and its surroundings (region: 0°–60° N, 70°–140° E) from 2019 to 2020. The gapless and fine-scale CO2 generation method enhances people’s understanding of CO2 spatiotemporal variations, supporting carbon-related research.
BERTGuard: Two-Tiered Multi-Domain Fake News Detection with Class Imbalance Mitigation
Authors: Alnabhan Mohammad Q. , Branco Paula
Published: August 2024

In an era where misinformation and fake news undermine social well-being, this work provides a complete approach to multi-domain fake news detection. Multi-domain news refers to handling diverse content across various subject areas such as politics, health, research, crime, and social concerns. Recognizing the lack of systematic research in multi-domain fake news detection, we present a fundamental structure by combining datasets from several news domains. Our two-tiered detection approach, BERTGuard, starts with domain classification, which uses a BERT-based model trained on a combined multi-domain dataset to determine the domain of a given news piece. Following that, domain-specific BERT models evaluate the correctness of news inside each designated domain, assuring precision and reliability tailored to each domain’s unique characteristics. Rigorous testing on previously encountered datasets from critical life areas such as politics, health, research, crime, and society proves the system’s performance and generalizability. For addressing the class imbalance challenges inherent when combining datasets, our study rigorously evaluates the impact on detection accuracy and explores handling alternatives—random oversampling, random upsampling, and class weight adjustment. These criteria provide baselines for comparison, fortifying the detection system against the complexities of imbalanced datasets.
The Impact of Green Physical Crosslinking Methods on the Development of Sericin-Based Biohydrogels for Wound Healing
Authors: Arango Maria C. , Jaramillo-Quiceno Natalia , Badia José David , Cháfer Amparo , Cerisuelo Josep Pasqual , Álvarez-López Catalina
Published: August 2024

Silk sericin (SS)–based hydrogels show promise for wound healing due to their biocompatibility, moisture regulation, and cell proliferation properties. However, there is still a need to develop green crosslinking methods to obtain non-toxic, absorbent, and mechanically strong SS hydrogels. This study investigated the effects of three green crosslinking methods, annealing treatment (T), exposure to an absolute ethanol vapor atmosphere (V.E), and water vapor (V.A), on the physicochemical and mechanical properties of SS and poly (vinyl alcohol) (PVA) biohydrogels. X-ray diffraction and Fourier-transform infrared spectroscopy were used to determine chemical structures. Thermal properties and morphological changes were studied through thermogravimetric analysis and scanning electron microscopy, respectively. The water absorption capacity, mass loss, sericin release in phosphate-buffered saline (PBS), and compressive strength were also evaluated. The results showed that physical crosslinking methods induced different structural transitions in the biohydrogels, impacting their mechanical properties. In particular, V.A hydrogen presented the highest compressive strength at 80% deformation owing to its compact and porous structure with crystallization and bonding sites. Moreover, both the V.A and T hydrogels exhibited improved absorption capacity, stability, and slow SS release in PBS. These results demonstrate the potential of green physical crosslinking techniques for producing SS/PVA biomaterials for wound healing applications.
Surface Electromyography-Based Recognition of Electronic Taste Sensations
Authors: Ullah Asif , Zhang Fengqi , Song Zhendong , Wang You , Zhao Shuo , Riaz Waqar , Li Guang
Published: August 2024

Taste sensation recognition is a core for taste-related queries. Most prior research has been devoted to recognizing the basic taste sensations using the Brain–Computer Interface (BCI), which includes EEG, MEG, EMG, and fMRI. This research aims to recognize electronic taste (E-Taste) sensations based on surface electromyography (sEMG). Silver electrodes with platinum plating of the E-Taste device were placed on the tongue’s tip to stimulate various tastes and flavors. In contrast, the electrodes of the sEMG were placed on facial muscles to collect the data. The dataset was organized and preprocessed, and a random forest classifier was applied, giving a five-fold accuracy of 70.43%. The random forest classifier was used on each participant dataset individually and in groups, providing the highest accuracy of 84.79% for a single participant. Moreover, various feature combinations were extracted and acquired 72.56% accuracy after extracting eight features. For a future perspective, this research offers guidance for electronic taste recognition based on sEMG.
EEG–fNIRS-Based Emotion Recognition Using Graph Convolution and Capsule Attention Network
Authors: Chen Guijun , Liu Yue , Zhang Xueying
Published: August 2024

Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) can objectively reflect a person’s emotional state and have been widely studied in emotion recognition. However, the effective feature fusion and discriminative feature learning from EEG–fNIRS data is challenging. In order to improve the accuracy of emotion recognition, a graph convolution and capsule attention network model (GCN-CA-CapsNet) is proposed. Firstly, EEG–fNIRS signals are collected from 50 subjects induced by emotional video clips. And then, the features of the EEG and fNIRS are extracted; the EEG–fNIRS features are fused to generate higher-quality primary capsules by graph convolution with the Pearson correlation adjacency matrix. Finally, the capsule attention module is introduced to assign different weights to the primary capsules, and higher-quality primary capsules are selected to generate better classification capsules in the dynamic routing mechanism. We validate the efficacy of the proposed method on our emotional EEG–fNIRS dataset with an ablation study. Extensive experiments demonstrate that the proposed GCN-CA-CapsNet method achieves a more satisfactory performance against the state-of-the-art methods, and the average accuracy can increase by 3–11%.
From Editorial Board of Special Issue Entitled “Post-Stroke Rehabilitation”
Authors: Nakhostin Ansari Noureddin , Hassanzadeh Gholamreza , Shariat Ardalan
Published: August 2024

Diseases affecting the nervous system are diverse [...]
An Energy Portrait-Based Identification Method of Building Users for Demand Response
Authors: Zhang Ying , Ling Zaixun , Liu Manjia , Gang Wenjie , Su Lihong
Published: August 2024

Demand response is an effective solution for balancing supply and demand in modern energy supply systems. For utility or load aggregators, it is important to accurately target potential consumers to participate in demand response programs to recruit a massive number of users. This is especially important for the invitation-based demand response mode, which is currently often used in China. In this paper, a portrait-based method is proposed to effectively identify potential consumers for different demand response tasks based on historical loads. Eight indicators are proposed to quantify the energy consumption characteristics from different aspects, and an evaluation method is introduced. Then, a selection method based on the K-means clustering algorithm and support vector machine classifiers is proposed. The method is tested under two scenarios, including load shifting and monthly peak shaving. The results show that the proposed method can identify potential users effectively, and the accuracy of the trained classification model exceeds 99.9%. The proposed portrait-based identification method provides an effective way to describe users’ energy consumption characteristics and select potential users effectively, which is very useful for helping the utility or virtual plant with load management.
Unveiling the Dynamic Interplay between Cancer Stem Cells and the Tumor Microenvironment in Melanoma: Implications for Novel Therapeutic Strategies
Authors: Limonta Patrizia , Chiaramonte Raffaella , Casati Lavinia
Published: August 2024

Cutaneous melanoma still represents a significant health burden worldwide, being responsible for the majority of skin cancer deaths. Key advances in therapeutic strategies have significantly improved patient outcomes; however, most patients experience drug resistance and tumor relapse. Cancer stem cells (CSCs) are a small subpopulation of cells in different tumors, including melanoma, endowed with distinctive capacities of self-renewal and differentiation into bulk tumor cells. Melanoma CSCs are characterized by the expression of specific biomarkers and intracellular pathways; moreover, they play a pivotal role in tumor onset, progression and drug resistance. In recent years, great efforts have been made to dissect the molecular mechanisms underlying the protumor activities of melanoma CSCs to provide the basis for novel CSC-targeted therapies. Herein, we highlight the intricate crosstalk between melanoma CSCs and bystander cells in the tumor microenvironment (TME), including immune cells, endothelial cells and cancer-associated fibroblasts (CAFs), and its role in melanoma progression. Specifically, we discuss the peculiar capacities of melanoma CSCs to escape the host immune surveillance, to recruit immunosuppressive cells and to educate immune cells toward an immunosuppressive and protumor phenotype. We also address currently investigated CSC-targeted strategies that could pave the way for new promising therapeutic approaches for melanoma care.
Back-Health Knowledge and Misconceptions Related to the Daily Life Activities of Secondary School Students
Authors: Monfort-Pañego Manuel , Bosch-Biviá Antonio Hans , Miñana-Signes Vicente , Noll Matias
Published: August 2024

High school students with better knowledge about back care have fewer problems, but conceptual errors can hinder the acquisition of essential knowledge necessary for developing healthy habits. This study analyzed secondary school students’ declarative knowledge and misconceptions related to back care in daily activities. An exploratory cross-sectional study was conducted with 80 girls and 89 boys aged 14–18 years (M = 15.68, SD = 2.12). The Health Questionnaire on Back Care Knowledge in Activities of Daily Living was used to evaluate knowledge using the true answer model (TAM) and the misconception model (MM). Using the test–retest method, both models’ reliability was confirmed (TAM = 0.75; MM = 0.77), while only a minimal measurement error was identified (TAM = −0.01; MM = −0.07). The average scores were 6.23 for the TAM and 2.29 for the MM. The results showed no significant differences in both models. The analysis indicated that students had the most accurate knowledge of the location and function of the spine, whereas misconceptions regarding anatomical understanding and body posture usage were common. An analysis of the results under Reassumption Theory emphasizes the significance of comprehending concepts such as load transmission and spinal stability to maintain back health, thus highlighting the need for improved education in these areas to address misconceptions and enhance overall back-care knowledge.
Green Tea Polyphenol (-)-Epicatechin Pretreatment Mitigates Hepatic Steatosis in an In Vitro MASLD Model
Authors: Hefer Marija , Petrovic Ana , Roguljic Lucija Kuna , Kolaric Tea Omanovic , Kizivat Tomislav , Wu Catherine H. , Tabll Ashraf A. , Smolic Robert , Vcev Aleksandar , Smolic Martina
Published: August 2024

Abstract: Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as non-alcoholic fatty liver disease (NAFLD), is becoming more prominent globally due to an increase in the prevalence of obesity, dyslipidemia, and type 2 diabetes. A great deal of studies have proposed potential treatments for MASLD, with few of them demonstrating promising results. The aim of this study was to investigate the potential effects of (-)-epicatechin (EPI) on the development of MASLD in an in vitro model using the HepG2 cell line by determining the metabolic viability of the cells and the levels of PPARα, PPARγ, and GSH. HepG2 cells were pretreated with 10, 30, 50, and 100 μM EPI for 4 h to assess the potential effects of EPI on lipid metabolism. A MASLD cell culture model was established using HepG2 hepatocytes which were exposed to 1.5 mM oleic acid (OA) for 24 h. Moreover, colorimetric MTS assay was used in order to determine the metabolic viability of the cells, PPARα and PPARγ protein levels were determined using enzyme-linked immunosorbent assay (ELISA), and lipid accumulation was visualized using the Oil Red O Staining method. Also, the levels of intracellular glutathione (GSH) were measured to determine the level of oxidative stress. EPI was shown to increase the metabolic viability of the cells treated with OA. The metabolic viability of HepG2 cells, after 24 h incubation with OA, was significantly decreased, with a metabolic viability of 71%, compared to the cells pretreated with EPI, where the metabolic viability was 74–86% with respect to the concentration of EPI used in the experiment. Furthermore, the levels of PPARα, PPARγ, and GSH exhibited a decrease in response to increasing EPI concentrations. Pretreatment with EPI has demonstrated a great effect on the levels of PPARα, PPARγ, and GSH in vitro. Therefore, considering that EPI mediates lipid metabolism in MASLD, it should be considered a promising hepatoprotective agent in future research.
Study of PVD-Coated Inserts’ Lifetime in High-Pressure Die Casting Regarding the Requirements for Surface Quality of Castings
Authors: Sütőová Andrea , Kočiško Róbert , Petroušek Patrik , Kotus Martin , Petryshynets Ivan , Pylypenko Andrii
Published: August 2024

The wear and degradation of tools applied in the high-pressure die casting of Al alloys induce significant financial losses. The formation of failures on the surface of mold parts caused by erosion, thermal fatigue, corrosion, and soldering negatively affects the surface quality of castings. In this study, the lifetime of inserts protected by physical-vapor-deposited coatings (TiN, TiAlN, and CrAlSiN) is examined under real manufacturing conditions while considering requirements for the castings’ surface quality (maximum average roughness Ra) defined by the customer. The goal was to identify the most suitable solution for HPDC in the foundry organization. After the deposition of PVD coatings on the inserts, the hardness (HRC) values increased from two to five depending on the coating used, and also the surface roughness was higher in the case of all inserts (Ra values increased from 0.24 to 0.36 µm). The lifetime of all PVD-coated inserts was higher compared to the uncoated insert. The highest lifetime was achieved by the application of a TiN coating, when 15,000 shots were achieved until the inserts’ wear negatively affected (increased) the surface roughness of castings, considering the customer requirements for the maximum Ra value. SEM analysis was used to identify examples of wear and degradation on the surface of the TiN coated insert.
A3B Zn(II)-Porphyrin-Coated Carbon Electrodes Obtained Using Different Procedures and Tested for Water Electrolysis
Authors: Taranu Bogdan-Ovidiu , Rus Florina Stefania , Fagadar-Cosma Eugenia
Published: August 2024

In the context of water electrolysis being highlighted as a promising technology for the large-scale sustainable production of hydrogen, the water-splitting electrocatalytic properties of an asymmetrically functionalized A3B zinc metalated porphyrin, namely, Zn(II) 5-(4-pyridyl)-10,15,20-tris(4-phenoxyphenyl)-porphyrin, were evaluated in a wide pH range. Two different electrode manufacturing procedures were employed to outline the porphyrin’s applicative potential for the O2 and H2 evolution reactions (OER and HER). The electrode, manufactured by coating the catalyst on a graphite support from a dimethylsulfoxide solution, displayed electrocatalytic activity for the OER in an acidic electrolyte. An overpotential value of 0.44 V (at i = 10 mA/cm2) and a Tafel slope of 0.135 V/dec were obtained. The modified electrode that resulted from applying a Zn(II)-porphyrin-containing catalyst ink onto the same substrate type was identified as a bifunctional water-splitting catalyst in a neutral medium. OER and HER overpotentials of 0.78 and 1.02 V and Tafel slopes of 0.39 and 0.249 V/dec were determined. This is the first Zn(II)-porphyrin to be reported as a heterogenous bifunctional water-splitting electrocatalyst in neutral aqueous electrolyte solution and is one of very few porphyrins behaving as such. The TEM analysis of the porphyrin’s self-assembly behavior revealed a wide variety of architectures.
The Influence of Distance Pulsed Plasma Treatment on the Structure and Properties of Detonation Coatings from Eutectic Alloy Fe-TiB2-CrB2
Authors: Rakhadilov Bauyrzhan , Kakimzhanov Dauir , Seitkhanova Ainur , Kengesbekov Aidar , Muktanova Nazerke
Published: August 2024

This work presents the results of research on the effect of a pulsed plasma treatment on the structure, phase composition, hardness, roughness, and elemental composition of Fe-TiB2-CrB2-based coatings. The Fe-TiB2-CrB2 coating was applied via the detonation method. Fe-TiB2-CrB2 powder mixtures were used for coating on AISI 1017 steel substrate with the coating surface being modified using a pulsed plasma treatment. The effects of the pulsed plasma treatment on the microstructure, phase composition, and mechanical properties of Fe-TiB2-CrB2 detonation coatings were investigated using an optical microscope, X-ray diffraction (XRD), scanning electron microscopy (SEM), a nanohardness tester, and a Leica 3D profilometer. The mechanical test results showed that the hardness of the Fe-TiB2-CrB2 coating increased from 8.22 Gpa to 15.6 GPa after the pulsed plasma treatment. The results of the tribological tests show that after the pulsed plasma treatment of Fe-TiB2-CrB2 coatings, a wear-resistant modified layer consisting of (Ti,Cr)B2 and alpha-Fe formed on its surface. It is determined that the surface modified coating layer has a low porosity compared to the coating base. In addition, it is determined that after the pulsed plasma treatment, a decrease in the average pore size is observed in the subsurface layer of the coating. The pulsed plasma treatment resulted in a decrease in the roughness parameter (Ra) from 12.2 μm to 6.6 μm, which is due to the melting of protruding particles.
Current Status of Image Recognition Technology in the Field of Corrosion Protection Applications
Authors: Wang Xinran , Zhang Wei , Lin Zhifeng , Li Haojie , Zhang Yuanqing , Quan Weiyin , Chen Zhiwei , You Xueqiang , Zeng Yang , Wang Gang , Luo Bolin , Yu Zhenghua
Published: August 2024

Corrosion brings serious losses to the economy annually. Therefore, various corrosion protection and detection techniques are widely used in the daily maintenance of large metal engineering structures. The emergence of image recognition technology has brought a more convenient and faster way for nondestructive testing. Existing image recognition technology can be divided into two categories according to the algorithm: traditional image recognition technology and image recognition technology based on deep learning. These two types of technologies have been widely used in the three fields of metal, coating, and electrochemical data images. A large amount of work has been carried out to identify defects in metals and coatings, and deep learning-based methods also show potential for identifying electrochemical data images. Matching electrochemical images with the detection of defect morphology will bring a deeper understanding of image recognition techniques for metals and coatings. A database of accumulated morphology and electrochemical parameters will make it possible to predict the life of steel and coatings using image recognition techniques.
Infrared and Harsh Light Visible Image Fusion Using an Environmental Light Perception Network
Authors: Yan Aiyun , Gao Shang , Lu Zhenlin , Jin Shuowei , Chen Jingrong
Published: August 2024

The complementary combination of emphasizing target objects in infrared images and rich texture details in visible images can effectively enhance the information entropy of fused images, thereby providing substantial assistance for downstream composite high-level vision tasks, such as nighttime vehicle intelligent driving. However, mainstream fusion algorithms lack specific research on the contradiction between the low information entropy and high pixel intensity of visible images under harsh light nighttime road environments. As a result, fusion algorithms that perform well in normal conditions can only produce low information entropy fusion images similar to the information distribution of visible images under harsh light interference. In response to these problems, we designed an image fusion network resilient to harsh light environment interference, incorporating entropy and information theory principles to enhance robustness and information retention. Specifically, an edge feature extraction module was designed to extract key edge features of salient targets to optimize fusion information entropy. Additionally, a harsh light environment aware (HLEA) module was proposed to avoid the decrease in fusion image quality caused by the contradiction between low information entropy and high pixel intensity based on the information distribution characteristics of harsh light visible images. Finally, an edge-guided hierarchical fusion (EGHF) module was designed to achieve robust feature fusion, minimizing irrelevant noise entropy and maximizing useful information entropy. Extensive experiments demonstrate that, compared to other advanced algorithms, the method proposed fusion results contain more useful information and have significant advantages in high-level vision tasks under harsh nighttime lighting conditions.
The Monetary Model of Exchange Rate Determination for South Africa
Authors: Msomi Simiso , Ngalawa Harold
Published: August 2024

The disconnect between the exchange rate and its macroeconomic fundamentals has been extensively discussed in the literature. It nonetheless continues to pose theoretical and empirical challenges in the literature. This study examines the relationship between the exchange rate and its fundamentals. This study used the monetary model of exchange rate determination developed in the 1970s. The study used the TAR to estimate the exchange change rate behaviour in response to variations in monetary variables. We found that the exchange rates respond to the interest rate differential, consistent with the predictions of the monetary model of exchange rate determination. Furthermore, in all the regimes, the sizes of coefficients are different, which shows that the exchange rate behaviour is non-linear (asymmetric). While the impact of the interest rate differential in regime 1 and 2 leads to exchange rates appreciating although in regime 2 the results are insignificant, this occurs when the exchange rates fluctuate below 0.87 percentage points. In regime 3, on average, a marginal increase in interest rate deferential leads to an exchange rate depreciation. In some instances, the exchange rates respond to the monetary variables’ changes in line with the predictions of the monetary theory of exchange rate determination. An increase in interest rates in some instances leads to an improvement in the value of the exchange rate. However, the conditions are not constant—they vary depending on the state of exchange rate fluctuation.
Improving Accuracy and Efficiency of Monocular Depth Estimation in Power Grid Environments Using Point Cloud Optimization and Knowledge Distillation
Authors: Xiao Jian , Zhang Keren , Xu Xianyong , Liu Shuai , Wu Sheng , Huang Zhihong , Li Linfeng
Published: August 2024

In the context of distribution networks, constructing 3D point cloud maps is crucial, particularly for UAV navigation and path planning tasks. Methods that utilize reflections from surfaces, such as laser and structured light, to obtain depth point clouds for surface modeling and environmental depth estimation are already quite mature in some professional scenarios. However, acquiring dense and accurate depth information typically requires very high costs. In contrast, monocular image-based depth estimation methods do not require relatively expensive equipment and specialized personnel, making them available for a wider range of applications. To achieve high precision and efficiency in UAV distribution networks, inspired by knowledge distillation, we employ a teacher–student architecture to enable efficient inference. This approach maintains high-quality depth estimation while optimizing the point cloud to obtain more precise results. In this paper, we propose KD-MonoRec, which integrates knowledge distillation into the semi-supervised MonoRec framework for UAV distribution networks. Our method demonstrates excellent performance on the KITTI dataset and performs well in collected distribution network environments.
A Review of Hydrogen Storage and Transportation: Progresses and Challenges
Authors: Xie Zongao , Jin Qihang , Su Guanli , Lu Wei
Published: August 2024

This review aims to summarize the recent advancements and prevailing challenges within the realm of hydrogen storage and transportation, thereby providing guidance and impetus for future research and practical applications in this domain. Through a systematic selection and analysis of the latest literature, this study highlights the strengths, limitations, and technological progress of various hydrogen storage methods, including compressed gaseous hydrogen, cryogenic liquid hydrogen, organic liquid hydrogen, and solid material hydrogen storage, as well as the feasibility, efficiency, and infrastructure requirements of different transportation modes such as pipeline, road, and seaborne transportation. The findings reveal that challenges such as low storage density, high costs, and inadequate infrastructure persist despite progress in high-pressure storage and cryogenic liquefaction. This review also underscores the potential of emerging technologies and innovative concepts, including metal–organic frameworks, nanomaterials, and underground storage, along with the potential synergies with renewable energy integration and hydrogen production facilities. In conclusion, interdisciplinary collaboration, policy support, and ongoing research are essential in harnessing hydrogen’s full potential as a clean energy carrier. This review concludes that research in hydrogen storage and transportation is vital to global energy transformation and climate change mitigation.
Managing Energy Consumption of Linear Delta Robots Using Neural Network Models
Authors: Vodovozov Valery , Lehtla Madis , Raud Zoja , Semjonova Natalia , Petlenkov Eduard
Published: August 2024

A new approach to managing linear Delta robots is developed and two problems of their energy-efficient operation are solved in this work based on neural network models. The first solution concentrates on the minimization of the power consumed by the robot at various tool positions as a function of joint configurations. This problem is actually faced in industrial processes, in which the steady-state placing and holding phases of the pick-and-place cycle continue for much more time than picking, such as quality control, welding, packaging, and wrapping. The second solution relates to searching for the shortest path through all targets, considering all possible robot joint configurations, so that total energy consumption is minimal. This problem is essential to processes that require the fastest picking and placing cycles, such as assembling, loading, or painting. The outlined power monitoring procedure aligns with detailed power estimation at different joint configurations, with joint route optimization used to reduce energy demand. The feasibility and applicability of the proposed neural network-based methodology are confirmed via experimental testing on the Festo EXPT-45-E1 robot.
A Systematic Review of Microplastic Contamination in Commercially Important Bony Fish and Its Implications for Health
Authors: de Souza Júlia Scarpa , de Pinho Júlia Vianna , de Almeida Rodrigues Paloma , de Melo Anita Corrêa , Bergsten-Torralba Ludmila Rosa , Conte-Junior Carlos Adam
Published: August 2024

The increasing production of plastic products has raised concerns about environmental impacts related to microplastic formation, which harms ecosystems and human health. This systematic review aims to present the concentration of microplastics in commercially important bony fish and discuss the impacts on animal health and the possibility of these contaminants reaching the end consumer. The PICO methodology was used, and 517 articles were retrieved from four databases (PubMed, Embase, Web of Science, and Scopus); after selecting articles that complement the research objective, 70 articles were used to compose this review. According to the results, line-shaped microplastics, polypropylene, and polystyrene polymers were the most frequently identified in the articles. Additionally, the effects of microplastics on animal health, including false satiety and physical injuries, as well as risks to human health, such as epithelial inflammation, oxidative stress, and cell contamination, were discussed. Understanding the concentration of microplastics in commercially important bony fish is necessary for protecting human health and maintaining the health of marine ecosystems. It is necessary to adopt legislative measures for proper plastic disposal.
Reconstructing the Temperature and Precipitation Changes in the Northern Part of the Greater Khingan Mountains over the Past 210 Years Using Tree Ring Width Data
Authors: Wang Zhaopeng , Zhang Dongyou , Zhang Tongwen , Li Xiangyou , Wang Xinrui , Luo Taoran , Zhong Shubing , Song Kexin
Published: August 2024

In northeastern China, simultaneous reconstruction of temperature and precipitation changes in the same region using tree ring data has not yet been reported, limiting our understanding of the historical climate. Using tree ring samples from the Greater Khingan Mountains, it was established that there are five standardized tree ring width chronologies of Pinus sylvestris var. mongolica at five elevations. Correlation analyses revealed significant relationships between the tree ring chronologies and climate data for multiple months. Specifically, the correlation coefficient between the average minimum temperature from May to July and the composite chronologies of mid–high and mid-elevations was 0.726, whereas that between the total precipitation from August to July and the low-elevation chronology was 0.648 (p < 0.01). Based on these findings, we reconstructed two series: the average minimum temperature from May to July over the past 211 years and the total precipitation from August to July over the past 214 years. The reconstructed sequences revealed changes in the average minimum temperature from 1812 to 2022 and precipitation from 1809 to 2022 in the northern part of the Greater Khingan Mountains. The variances explained by the reconstruction equations were 0.528 and 0.421 (adjusted R-squared: 0.520 and 0.411), with F-test values of 65.896 and 42.850, respectively, exceeding the significance level of 0.01. The reliability of the reconstructed sequences was validated by historical records of meteorological disasters and the reconstruction results in the surrounding area. The reconstructed temperature and precipitation sequences exhibited distinct patterns of temperature fluctuations, dry–wet changes, and periodic oscillations. The region experienced two warm periods (1896–1909 and 2006–2020), two cold periods (1882–1888 and 1961–1987), a wet period (1928–1938), a drought period (1912–1914), and a period prone to severe drought events (1893–1919) during the past 210 years. The temperature series showed periodicities of 2–2.5 years, 3.9 years, 5.2 years, and 68 years, while the precipitation series exhibited periodicities of 2.1 years, 2.5 years, and 2.8 years, possibly related to El Niño–Southern Oscillation (ENSO) events, quasi-biennial oscillation, and Pacific Decadal Oscillation (PDO). Spatial correlation analysis indicated that the reconstructed temperature and precipitation sequences accurately represented the hydrothermal changes in the study area.
Insights into Agitated Bacterial Cellulose Production with Microbial Consortia and Agro-Industrial Wastes
Authors: Páez María Augusta , Casa-Villegas Mary , Aldas Miguel , Luna Maribel , Cabrera-Valle Daniel , López Orestes , Fernández Danae , Cruz María Alejandra , Flor-Unda Omar , García Mario D. , Cerda-Mejía Liliana
Published: August 2024

Bacterial cellulose (BC) is emerging as an attractive large-scale polymer due to its superior properties. The dominant static culture for BC fermentation by bacteria or microbial consortium results in low productivity. Agitated culture, as an industrially projected technique, has been widely investigated but exclusively for cellulose-producing bacterial strains. Addressing this concern and evaluating the potential of residues as feedstock, this study highlights the utilization of microbial consortium BA2 and seven agro-industrial wastes including cocoa husks, sugarcane bagasse and others. Remarkably, rice bran (RB) appears as a promising substrate, achieving 2.14 g/L (dry basis) and outperforming the traditional HS medium, evident from a 15-day fermentation. A complex interplay between oxygen availability, glucose consumption and BC yield was revealed; while orbital and magnetic stirring with forced air ventilation (AFV) showed low BC yields and early biomass saturation, 4.07 g/L (dry basis) was targeted by magnetic stirring at 100 rpm from the start using only headspace air. However, beyond controlled operating conditions, mechanical agitation and favorable cellulose adhesion to metal in the stirred tank bioreactor negatively affect BC yield. This pattern uncovers the need for a further approach to the design of bioreactors when the microbial consortium is considered.
Ensiling of High-Moisture Plant By-Products: Fermentation Quality, Nutritional Values, and Animal Performance
Authors: Nkosi Bhutikini D. , Malebana Ingrid M. M. , Rios Sergio Á. , Nkukwana Thobela T. , Meeske Robin
Published: August 2024

Animal feeds under ruminant production are a challenge, and ruminants are mostly fed on fibrous plants including high-moisture plant by-products (HMPBs). These HMPBs are available during the food processing periods and cannot be fed entirely in their fresh form. These resources are conserved in the form of silage for future feeding. Silage-making entails the anaerobic preservation of forages with the aid of additives that reduce the pH of the ensiled materials and preserve the forage. Most silage research work focuses mainly on the preservation of forages/plants, with less attention on HMPBs. This review focuses on the silage production from HMPBs (e.g., pulps/pomaces), challenges involved in the ensiling of these resources, use of additives (e.g., chemical additives), and growth performance of ruminants fed silage from these resources. This review will assist farmers from developing countries who rely on HMPBs as sources of animal feed.
Advances and Future Directions in the Use of Lactobacillus in Forage Storage and Processing
Authors: Zhao Jie , Mu Lin , Guan Hao , Cheng Qiming , Shao Tao
Published: August 2024

The “The Use of Lactobacillus in Forage Storage and Processing” Special Issue (SI) in Fermentation has brought together groundbreaking research and innovative methodologies in the realm of forage preservation [...]
The Application of Lophatherum Gracile Brongn Flavonoids in Wheat Flour Products: Effects on the Structural and Functional Characteristics of Wheat Dough
Authors: Li Qin , Liu Yi , Bao Huimei , Zhang Haihua
Published: August 2024

The effects of lophatherum gracile brongn flavonoids on the multiscale structure and functional properties of wheat dough were investigated. Wheat dough samples with varying contents of lophatherum gracile brongn flavonoids were analyzed to assess changes in thermal-mechanical rheological properties, microstructure, chemical interactions, water distribution, and macropolymer formation by Mixolab mixer, fluorescence microscopy, and low-field nuclear magnetic resonance (LF-NMR). The findings revealed that lophatherum gracile brongn flavonoids disrupted the three-dimensional network of gluten proteins in the wheat dough, leading to decreased water-binding capacity and reduced gluten protein crosslinking while enhancing thermal stability and inhibiting the starch retrogradation of the dough. This study provided important insights into the interaction mechanisms between lophatherum gracile brongn flavonoids and the proteins/starch in wheat dough, offering theoretical guidance for the development of novel wheat-based products for industrialization and practical production.
The Influence of Food Names with Different Levels of Concreteness on Evaluations of Food Deliciousness and Healthiness
Authors: Yu Zhao , Kang Yixin , Liu Peipei , Ou Haokai , Zhang Wei , He Xianyou
Published: August 2024

Recently, many restaurateurs in the food and beverage industry started using vague and abstract names to label their dishes. However, the influence of the concreteness of food names on consumers’ evaluations of food remains unclear. Therefore, the present study investigated people’s perceptions of food names with different levels of concreteness and their evaluations of food deliciousness and healthiness through two experiments. Experiment 1 investigated the likelihood of names with different levels of concreteness being perceived as foods or dishes through subjective guessing tasks. In line with the hypothesis of mental imagery consistency, the results revealed that individuals were more inclined to perceive high-concreteness names as actual food or dishes than low-concrete names. Experiment 2 further explored the impact of food names with different levels of concreteness on consumers’ perceptions and evaluations of food in terms of the direct sensory (deliciousness) and indirect inference (healthiness) dimension. The results showed that in terms of deliciousness, consistent with the feelings-as-information theory, high-concreteness food names were rated significantly higher than low-concreteness ones. In terms of healthiness, consistent with the incongruence theory, low-concreteness food names were rated significantly higher than high-concreteness ones. These results indicated that high-concreteness names were more likely to be perceived as foods or dishes. Moreover, they also had advantages in the direct sensory dimension (deliciousness) but were perceived as less healthy in the indirect inference dimension (healthiness). The present findings provide new evidence for studies related to food naming and the evaluation of deliciousness and healthiness and offer suggestions and strategies for the food and beverage industry in naming foods and dishes.
Consumer Values, Attitudes, and Behavior towards Plant-Based Alternatives
Authors: Park Cho-I , Namkung Young
Published: August 2024

This study investigated the impact of consumer values and attitudes to consuming plant-based alternatives (PBAs), using the value–attitude–behavior framework. The research model and hypotheses were tested using a two-step approach to structural equation modeling on 392 responses collected from PBA consumers through a research company in Korea in November 2023. The results indicated that environmental consciousness and health consciousness significantly affected attitudes toward PBAs. Also, positive attitudes toward PBAs were critical for the formation of repurchase intentions for PBAs and PBA restaurant visit behavior. Our study contributes to both academics and PBA practitioners by showing how consumer values are associated with attitudes, PBAs repurchase intentions, and PBAs restaurant visits.