bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigating PEM Fuel Cells as an Alternative Power Source for Electric UAVs: Modeling, Optimization, and Performance Analysis
Authors: Shuhayeu Pavel , Martsinchyk Aliaksandr , Martsinchyk Katsiaryna , Milewski Jaroslaw
Published: September 2024

Unmanned aerial vehicles (UAVs) have become an integral part of modern life, serving both civilian and military applications across various sectors. However, existing power supply systems, such as batteries, often fail to provide stable, long-duration flights, limiting their applications. Previous studies have primarily focused on battery-based power, which offers limited flight endurance due to lower energy densities and higher system mass. Proton exchange membrane (PEM) fuel cells present a promising alternative, providing high power and efficiency without noise, vibrations, or greenhouse gas emissions. Due to hydrogen’s high specific energy, which is substantially higher than that of combustion engines and battery-based alternatives, UAV operational time can be significantly extended. This paper investigates the potential of PEM fuel cells as an alternative power source for electric propulsion in UAVs. This study introduces an adaptive, fully functioning PEM fuel cell model, developed using a reduced-order modeling approach and optimized for UAV applications. This research demonstrates that PEM fuel cells can effectively double the flight endurance of UAVs compared to traditional battery systems, achieving energy densities of around 1700 Wh/kg versus 150–250 Wh/kg for batteries. Despite a slight increase in system mass, fuel cells enable significantly longer UAV operations. The scope of this study encompasses the comparison of battery-based and fuel cell-based propulsion systems in terms of power, mass, and flight endurance. This paper identifies the limitations and optimal applications for fuel cells, providing strong evidence for their use in UAVs where extended flight time and efficiency are critical.
Assessing the Static Security of the Italian Grid by Means of the N-1 Three-Phase Contingency Analysis
Authors: Gardan Giovanni , Rusalen Luca , Benato Roberto
Published: September 2024

The ongoing replacement of synchronous machine generators (SMs) with converter-interface generators (CIGs) is raising the voltage unbalance of power systems, affecting power quality and grid stability. This paper focuses on a key power quality index for power systems, i.e., the voltage unbalance factor. The purpose of this work is twofold. First, it presents the generalization of a three-phase power flow algorithm developed by University of Padova, named PFPD_3P, to assess the voltage unbalance factors of power systems supplied by CIGs. In particular, it is demonstrated that CIGs can be modelled as three-phase PV/PQ constraints embedding their positive-, negative- and zero-sequence admittances. Then, the concept of three-phase contingency analysis is introduced. Indeed, for static security evaluation, the classical single-phase contingency analysis may no longer be sufficient, as it lacks power quality computations, e.g., voltage/current unbalance factors. Numerical simulations evaluating the unbalance factors due to different generation mix scenarios and contingencies are tested on the Italian extra-high-voltage/high-voltage (EHV/HV) grid. The choice of this network relies on its representativeness, as CIGs are the majority of new installations in the Italian generation mix.
A Snake Optimization Algorithm-Based Power System Inertia Estimation Method Considering the Effects of Transient Frequency and Voltage Changes
Authors: Pang Yanzhen , Li Feng , Qian Haiya , Liu Xiaofeng , Yao Yunting
Published: September 2024

Inertia is the measure of a power system’s ability to resist power interference. The accurate estimation and prediction of inertia are crucial for the safe operation of the power system. To obtain the accurate power system inertia provided by generators, this paper proposes an estimation method considering the influence of frequency and voltage characteristics on the power deficit during transients. Specifically, the traditional swing equations-based inertia estimation model is improved by embedding linearized frequency and voltage factors. On this basis, the snake optimization algorithm is utilized to identify the power system inertia constant due to its strong global search ability and fast convergence speed. Finally, the proposed inertia estimation method is validated in four test systems, and the results show the effectiveness of the proposed method.
Gasification of the Char Residues with High Ash Content by Carbon Dioxide
Authors: Xue Junjie , Dong Zhen , Chen Hao , Zhang Mengyuan , Zhao Yufeng , Chen Yanpeng , Chen Shanshan
Published: September 2024

To increase the carbon conversion of char in gasification, this paper aimed to reveal the gasification behaviours of char residues. Char residues with different ash contents in this work were prepared from Shenmu char and Tejing char. Those char residues were gasified by different CO2 gas mixtures at different temperatures. The gasification process of char residue was different from the end stage of the gasification process of the corresponding raw char: the gasification rate of the char residue increased at first and then decreased, whereas the gasification rate of the corresponding raw char kept decreasing during the end stage of gasification. The highest gasification rate was achieved at a lower conversion in the gasification of char residue than in the gasification of the corresponding raw char. Catalytic minerals, high temperature, and high CO2 partial pressure benefited the gasification of gasified char residues. The char residues that contained more catalytic minerals were more reactive in gasification and were less sensitive to changes in temperature and CO2 partial pressure. The Modified Random Pore Model (MRPM) and Random Pore Model (RPM) were used to predict the gasification kinetics of the chars, and the MRPM describes the gasification processes of gasified char residues well.
Thermal History and Hydrocarbon Accumulation Stages in Majiagou Formation of Ordovician in the East-Central Ordos Basin
Authors: Tao Hua , Cui Junping , Zhao Fanfan , Ren Zhanli , Qi Kai , Liu Hao , Su Shihao
Published: September 2024

The marine carbonates in the Ordovician Majiagou Formation in the Ordos Basin have significant exploration potential. Research has focused on their thermal history and hydrocarbon accumulation stages, as these are essential for guiding the exploration and development of hydrocarbons. In this paper, we study the thermal evolution history of the carbonate reservoirs of the Ordovician Majiagou Formation in the east-central Ordos Basin. Furthermore, petrographic and homogenization temperature studies of fluid inclusions were carried out to further reveal the hydrocarbon accumulation stages. The results demonstrate that the degree of thermal evolution of the Ordovician carbonate reservoirs is predominantly influenced by the deep thermal structure, exhibiting a trend of high to low values from south to north in the central region of the basin. The Fuxian area is located in the center of the Early Cretaceous thermal anomalies, with the maturity degree of the organic matter ranging from 1 to 3.2%, with a maximum value of 3.2%. The present geothermal gradient of the Ordovician Formation exhibits the characteristics of east–high and west–low, with an average of 28.6 °C/km. The average paleo-geotemperature gradient is 54.2 °C/km, the paleoheat flux is 55 mW/m2, and the maximum paleo-geotemperature reaches up to 270 °C. The thermal history recovery indicates that the Ordovician in the central part of the basin underwent three thermal evolution stages: (i) a slow warming stage before the Late Permian; (ii) a rapid warming stage from the end of the Late Permian to the end of the Early Cretaceous; (iii) a cooling stage after the Early Cretaceous, with the hydrocarbon production of hydrocarbon source rocks weakening. In the central part of the basin, the carbonate rock strata of the Majiagou Formation mainly developed asphalt inclusions, natural gas inclusions, and aqueous inclusions. The fluid inclusions can be classified into two stages. The early-stage fluid inclusions are mainly present in dissolution holes. The homogenization temperature is 110–130 °C; this coincides with the hydrocarbon charging period of 210–165 Ma, which corresponds to the end of the Triassic to the end of the Middle Jurassic. The late-stage fluid inclusions are in the dolomite vein or late calcite that filled the gypsum-model pores. The homogenization temperature is 160–170 °C; this coincides with the hydrocarbon charging period of 123–97 Ma, which corresponds to the late Early Cretaceous. Both hydrocarbon charging periods are in the rapid stratigraphic warming stage.
Agri-PV (Agrivoltaics) in Developing Countries: Advancing Sustainable Farming to Address the Water–Energy–Food Nexus
Authors: Mehta Kedar , Shah Meeth Jeetendra , Zörner Wilfried
Published: September 2024

The escalating demand for water, energy, and food, coupled with the imperative for sustainable development, necessitates innovative solutions to address the complex interdependencies within the water–energy–food nexus. In this context, agriculture and photovoltaics (Agri-PV or Agri–voltaics) systems have emerged as a promising approach to promoting sustainable agricultural practices while enhancing energy efficiency and food production. However, limited research, especially on the technical aspects of Agri-PV, has resulted in a knowledge gap regarding how to model and determine the suitability of Agri-PV for different crops based on local conditions. This study presents a novel approach to modeling and simulating Agri-PV systems for various major crops in developing countries, using Uzbekistan as a case study. It provides a blueprint for selecting suitable Agri-PV systems. The research investigates the technical feasibility of Agri-PV technology tailored to Uzbekistan’s agricultural landscape, with broader implications for Central Asia. Employing a systematic methodology, the study begins by selecting appropriate sites and crops for Agri-PV system testing, ensuring the relevance and applicability of the research findings to the local context. Using advanced software tools such as PVSyst, the study accurately calculates photosynthetically active radiation (PAR) values specific to selected crops, bridging a significant knowledge gap and providing empirical data essential for informed decision making. The methodology further incorporates an in-depth analysis of economic and technical considerations in selecting PV modules and inverters, enhancing the scientific accuracy of the study. By strategically modeling Agri-PV systems based on parameters like row density, module distance, and tilt angle, this research aims to optimize the integration of photovoltaic technology with agricultural practices in Uzbekistan. Moreover, this study helps to understand the impact of Agri-PV systems on the water–energy–food nexus, providing valuable insights into the potential benefits and challenges specific to the region. The study identifies the positive impact of Agri-PV on major crops and provides a suitable design and modeling approach for sustainable farming practices.
Evaluation of Distributed Photovoltaic Economic Access Capacity in Distribution Networks Considering Proper Photovoltaic Power Curtailment
Authors: Hao Wenbo , Xiao Weisong , Yan Qingyu , Jia Qingquan , Hu Benran , Li Pan
Published: September 2024

The high proportion of distributed photovoltaic (DPV) access has changed the traditional distribution network structure and operation mode, posing a huge threat to the stable operation and economy of the distribution network. Aiming at a reasonable access capacity of DPV in the distribution network, this paper proposes an economic access capacity evaluation method for DPV in the distribution network considering proper PV power curtailment. Firstly, a method for generating typical joint light intensity and load power operation scenarios based on an improved K-means clustering algorithm is proposed, which provides comprehensive scenario support for the evaluation. Secondly, based on active and reactive power regulation, this paper proposes a DPV access capacity enhancement method to improve the DPV access capacity. Thirdly, considering proper PV power curtailment, an evaluation model of DPV economic access capacity in the distribution network is established to solve the maximum DPV economic access capacity in the distribution network. And aiming at the nonlinear problem in the model, the second-order cone relaxation method is employed to transform the model into the second-order cone programming model, so as to solve the objective function conveniently and efficiently. Finally, based on the improved IEEE 33-node distribution network analysis, the results show that the proposed method can be more comprehensive and effective in evaluating the DPV economic access capacity in the distribution network, and proper PV power curtailment can significantly increase the DPV economic access capacity in the distribution network.
Lightweight Model Development for Forest Region Unstructured Road Recognition Based on Tightly Coupled Multisource Information
Authors: Lei Guannan , Guan Peng , Zheng Yili , Zhou Jinjie , Shen Xingquan
Published: September 2024

Promoting the deployment and application of embedded systems in complex forest scenarios is an inevitable developmental trend in advanced intelligent forestry equipment. Unstructured roads, which lack effective artificial traffic signs and reference objects, pose significant challenges for driverless technology in forest scenarios, owing to their high nonlinearity and uncertainty. In this research, an unstructured road parameterization construction method, “DeepLab-Road”, based on tight coupling of multisource information is proposed, which aims to provide a new segmented architecture scheme for the embedded deployment of a forestry engineering vehicle driving assistance system. DeepLab-Road utilizes MobileNetV2 as the backbone network that improves the completeness of feature extraction through the inverse residual strategy. Then, it integrates pluggable modules including DenseASPP and strip-pooling mechanisms. They can connect the dilated convolutions in a denser manner to improve feature resolution without significantly increasing the model size. The boundary pixel tensor expansion is then completed through a cascade of two-dimensional Lidar point cloud information. Combined with the coordinate transformation, a quasi-structured road parameterization model in the vehicle coordinate system is established. The strategy is trained on a self-built Unstructured Road Scene Dataset and transplanted into our intelligent experimental platform to verify its effectiveness. Experimental results show that the system can meet real-time data processing requirements (≥12 frames/s) under low-speed conditions (≤1.5 m/s). For the trackable road centerline, the average matching error between the image and the Lidar was 0.11 m. This study offers valuable technical support for the rejection of satellite signals and autonomous navigation in unstructured environments devoid of high-precision maps, such as forest product transportation, agricultural and forestry management, autonomous inspection and spraying, nursery stock harvesting, skidding, and transportation.
Vulnerability Detection and Classification of Ethereum Smart Contracts Using Deep Learning
Authors: Bani-Hani Raed M. , Shatnawi Ahmed S. , Al-Yahya Lana
Published: September 2024

Smart contracts are programs that reside and execute on a blockchain, like any transaction. They are automatically executed when preprogrammed terms and conditions are met. Although the smart contract (SC) must be presented in the blockchain for the integrity of data and transactions stored within it, it is highly exposed to several vulnerabilities attackers exploit to access the data. In this paper, classification and detection of vulnerabilities targeting smart contracts are performed using deep learning algorithms over two datasets containing 12,253 smart contracts. These contracts are converted into RGB and Grayscale images and then inserted into Residual Network (ResNet50), Visual Geometry Group-19 (VGG19), Dense Convolutional Network (DenseNet201), k-nearest Neighbors (KNN), and Random Forest (RF) algorithms for binary and multi-label classification. A comprehensive analysis is conducted to detect and classify vulnerabilities using different performance metrics. The performance of these algorithms was outstanding, accurately classifying vulnerabilities with high F1 scores and accuracy rates. For binary classification, RF emerged in RGB images as the best algorithm based on the highest F1 score of 86.66% and accuracy of 86.66%. Moving on to multi-label classification, VGG19 stood out in RGB images as the standout algorithm, achieving an impressive accuracy of 89.14% and an F1 score of 85.87%. To the best of our knowledge, and according to the available literature, this study is the first to investigate binary classification of vulnerabilities targeting Ethereum smart contracts, and the experimental results of the proposed methodology for multi-label vulnerability classification outperform existing literature.
Schistosoma japonicum sja-let-7 Inhibits the Growth of Hepatocellular Carcinoma Cells via Cross-Species Regulation of Col1α2
Authors: Zhong Haoran , Dong Bowen , Zhu Danlin , Fu Zhiqiang , Liu Jinming , Guan Guiquan , Jin Yamei
Published: September 2024

Liver fibrosis, a critical precursor to hepatocellular carcinoma (HCC), results from chronic liver injury and significantly contributes to HCC progression. Schistosomiasis, a neglected tropical disease, is known to cause liver fibrosis; however, this process can be modulated by schistosome-derived miRNAs. Previous studies from our laboratory have demonstrated that Schistosoma japonicum extracellular vesicles (EVs) deliver sja-let-7 to hepatic stellate cells, leading to the inhibition of Col1α2 expression and alleviation of liver fibrosis. Given the well-documented antifibrotic and antiproliferative properties of the let-7 miRNA family, this study aims to preliminarily investigate the effects of the sja-let-7/Col1α2 axis on BALB/c mice and HCC cell line SNU387, providing a basis for the potential application of parasite-derived molecules in HCC therapy. In the present study, schistosome-induced fibrosis datasets were analyzed to identify the role of Col1α2 in extracellular matrix organization. Pan-cancer analysis revealed that Col1α2 is upregulated in various cancers, including HCC, with significant associations with immune cell infiltration and clinical parameters, highlighting its diagnostic importance. Functional assays demonstrated that transfection with sja-let-7 mimics significantly reduced Col1α2 expression, inhibited HCC cell proliferation, migration, and colony formation. These findings suggest that sja-let-7, by targeting Col1α2, has the potential to serve as a therapeutic agent in HCC treatment. This study indicates the pivotal role of Col1α2 in liver fibrosis and HCC, and the promising therapeutic application of helminth-derived miRNAs.
Beyond the Screen: A Comprehensive Analysis of Emotional Skills and Social Networking in French Young Adults
Authors: Guarnaccia Cinzia , Rasho Abdul Rahman , Testé Benoit , Delouvée Sylvain
Published: September 2024

(1) Background: This study investigates the influence of social networks on young adults, focusing on both positive and negative impacts. It considers problematic social networking site use (PSNSU), emotional intelligence, empathy, and the phenomenon of Fear of Missing Out (FoMO). (2) Methods: A comprehensive online survey was conducted with 442 participants aged 18 to 30 years that used various psychometric tools to assess emotional intelligence, empathy, internet and social media use, and FoMO. (3) Results: The study revealed that young adults display low competencies in emotional intelligence and empathy, with a tendency towards problematic internet and social media use. High FoMO scores were observed, correlating with negative internet use outcomes. Gender differences in these aspects were also explored. (4) Conclusions: The findings suggest a complex interaction between social network use, emotional skills, and FoMO that impacts young adults’ mental health and social behaviors. This study highlights the need for a nuanced understanding of these relationships and their implications for well-being and social interaction in the digital age.
Deep Learning-Based Obesity Identification System for Young Adults Using Smartphone Inertial Measurements
Authors: Degbey Gou-Sung , Hwang Eunmin , Park Jinyoung , Lee Sungchul
Published: September 2024

Obesity recognition in adolescents is a growing concern. This study presents a deep learning-based obesity identification framework that integrates smartphone inertial measurements with deep learning models to address this issue. Utilizing data from accelerometers, gyroscopes, and rotation vectors collected via a mobile health application, we analyzed gait patterns for obesity indicators. Our framework employs three deep learning models: convolutional neural networks (CNNs), long-short-term memory network (LSTM), and a hybrid CNN–LSTM model. Trained on data from 138 subjects, including both normal and obese individuals, and tested on an additional 35 subjects, the hybrid model achieved the highest accuracy of 97%, followed by the LSTM model at 96.31% and the CNN model at 95.81%. Despite the promising outcomes, the study has limitations, such as a small sample and the exclusion of individuals with distorted gait. In future work, we aim to develop more generalized models that accommodate a broader range of gait patterns, including those with medical conditions.
Fetal Growth Following Electronic Cigarette Use in Pregnancy
Authors: Bailey Beth A. , Azar Michelle , Nadolski Katherine , Dodge Phoebe
Published: September 2024

Electronic cigarette (e-cig) use in pregnancy is common, but potential effects on fetal development are largely unknown. This study’s goal was to examine the association between e-cig exposure and fetal growth. Data were extracted from medical charts in this single-site retrospective study. The sample, excluding those with known tobacco, alcohol, illicit drug, opioid, and benzodiazepine use, contained women who used e-cigs throughout pregnancy and non-e-cig user controls. Fetal size measurements from second- and third-trimester ultrasounds and at birth were expressed as percentiles for gestational age. Following adjustment for confounding factors, in the second trimester, only femur length was significant, with an adjusted deficit of 11.5 percentile points for e-cig exposure compared to controls. By the third trimester, the femur length difference was 28.5 points, with the fetal weight difference also significant (17.2 points). At birth, all three size parameter differences between groups were significant. Significant size deficits were predicted by prenatal e-cig exposure, becoming larger and impacting more parameters with increasing gestation. While additional studies are warranted to confirm and expand upon these findings, this study adds to emerging data pointing to specific harms following e-cig exposure in pregnancy and suggests that e-cigs may not be a “safer” alternative to combustible cigarette smoking in pregnancy.
Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection
Authors: Messa Letizia , Testa Carolina , Carelli Stephana , Rey Federica , Jacchetti Emanuela , Cereda Cristina , Raimondi Manuela Teresa , Ceri Stefano , Pinoli Pietro
Published: September 2024

The vast corpus of heterogeneous biomedical data stored in databases, ontologies, and terminologies presents a unique opportunity for drug design. Integrating and fusing these sources is essential to develop data representations that can be analyzed using artificial intelligence methods to generate novel drug candidates or hypotheses. Here, we propose Non-Negative Matrix Tri-Factorization as an invaluable tool for integrating and fusing data, as well as for representation learning. Additionally, we demonstrate how representations learned by Non-Negative Matrix Tri-Factorization can effectively be utilized by traditional artificial intelligence methods. While this approach is domain-agnostic and applicable to any field with vast amounts of structured and semi-structured data, we apply it specifically to computational pharmacology and drug repurposing. This field is poised to benefit significantly from artificial intelligence, particularly in personalized medicine. We conducted extensive experiments to evaluate the performance of the proposed method, yielding exciting results, particularly compared to traditional methods. Novel drug–target predictions have also been validated in the literature, further confirming their validity. Additionally, we tested our method to predict drug synergism, where constructing a classical matrix dataset is challenging. The method demonstrated great flexibility, suggesting its applicability to a wide range of tasks in drug design and discovery.
Mitochondrial NME6 Influences Basic Cellular Processes in Tumor Cells In Vitro
Authors: Proust Bastien , Horvat Anđela , Tadijan Ana , Vlašić Ignacija , Herak Bosnar Maja
Published: September 2024

NME6 belongs to the family of nucleoside diphosphate kinase enzymes, whose major role is to transfer the terminal phosphate from NTPs, mostly ATP, to other (d)NDPs via a high-energy intermediate. Beside this basic enzymatic activity, the family, comprising 10 genes/proteins in humans, executes a number of diverse biochemical/biological functions in the cell. A few previous studies have reported that NME6 resides in the mitochondria and influences oxidative phosphorylation while interacting with RCC1L, a GTPase involved in mitochondrial ribosome assembly and translation. Considering the multifunctional role of NME family members, the goal of the present study was to assess the influence of the overexpression or silencing of NME6 on fundamental cellular events of MDA-MB-231T metastatic breast cancer cells. Using flow cytometry, Western blotting, and a wound-healing assay, we demonstrated that the overexpression of NME6 reduces cell migration and alters the expression of EMT (epithelial–mesenchymal transition) markers. In addition, NME6 overexpression influences cell cycle distribution exclusively upon DNA damage and impacts the MAPK/ERK signaling pathway, while it has no effect on apoptosis. To conclude, our results demonstrate that NME6 is involved in different cellular processes, providing a solid basis for future, more precise investigations of its role.
Revolutionizing Bone Regeneration with Grinder-Based Dentin Biomaterial: A Systematic Review
Authors: Olchowy Anna , Olchowy Cyprian , Zawiślak Ireneusz , Matys Jacek , Dobrzyński Maciej
Published: September 2024

Bone tissue regeneration is a critical aspect of dental surgery, given the common occurrence of bone resorption leading to alveolar bone defects. The aim of this paper was to conduct a systematic review to provide a comprehensive summary of the evidence regarding the regenerative properties of dentin biomaterial. This systematic review was conducted through comprehensive searches in the PubMed, Scopus, and Web of Science databases, as well as an extensive exploration of the gray literature sources, including WorldCat, The New York Academy of Medicine Library, and Trip Database, following the established PRISMA protocol. Keywords such as tooth, dentin, grinder, and autograft guided the search, with a focus on a standardized procedure involving dentin grinders within laboratory, experimental, and clinical settings. Initially, a pool of 1942 articles was identified with 452 duplicates removed. An additional 1474 articles were excluded for not aligning with the predefined topics, and three more were excluded due to the unavailability of the full text. Ultimately, 13 articles met the strict inclusion criteria and were included in the review. The chemical composition of the dentin particles was similar to natural bone in terms of oxygen, carbon, calcium, phosphorus, sodium, and magnesium content, as well as in terms of the Ca/P ratio. In addition, the dentin also contained amide I and amide II structures, as well as aliphatic and hydroxyl functional groups. The chemically treated dentin was free of microorganisms. The dentin had characteristic tubules that opened after chemical treatment. At the cellular level, dentin released bone morphogenetic protein 2, induced significant cell growth, and stimulated the reorganization of the fibroblast cytoskeleton. Most clinical studies have focused on alveolar bone regeneration. After the transplantation of demineralized dentin particles, studies have observed new bone formation, a reduction in residual bone, and an increase in connective tissue. Clinical reports consistently indicate uncomplicated healing and recovery post-transplantation. However, there is a notable gap in the evidence concerning complication rates, patient-reported outcomes, and the presence of pro-inflammatory factors. In conclusion, dentin biomaterial emerges as a versatile bone substitute, demonstrating high biocompatibility and ease of acquisition. The preservation of its internal structure containing organic matter and growth factors enhances its potential for effective bone regeneration. Particularly, in dental surgery, dentin-derived materials present a promising alternative to traditional autologous bone autografts, offering the potential to reduce patient morbidity and treatment costs.
Hyperspectral Imaging Combined with Deep Transfer Learning to Evaluate Flavonoids Content in Ginkgo biloba Leaves
Authors: Lu Jinkai , Jiang Yanbing , Jin Biao , Sun Chengming , Wang Li
Published: September 2024

Ginkgo biloba is a famous economic tree. Ginkgo leaves have been utilized as raw materials for medicines and health products due to their rich active ingredient composition, especially flavonoids. Since the routine measurement of total flavones is time-consuming and destructive, rapid, non-destructive detection of total flavones in ginkgo leaves is of significant importance to producers and consumers. Hyperspectral imaging technology is a rapid and non-destructive technique for determining the total flavonoid content. In this study, we discuss five modeling methods, and three spectral preprocessing methods are discussed. Bayesian Ridge (BR) and multiplicative scatter correction (MCS) were selected as the best model and the best pretreatment method, respectively. The spectral prediction results based on the BR + MCS treatment were very accurate (RTest2 = 0.87; RMSETest = 1.03 mg/g), showing a high correlation with the analytical measurements. In addition, we also found that the more and deeper the leaf cracks, the higher the flavonoid content, which helps to evaluate leaf quality more quickly and easily. In short, hyperspectral imaging is an effective technique for rapid and accurate determination of total flavonoids in ginkgo leaves and has great potential for developing an online quality detection system for ginkgo leaves.
Natural Products and Altered Metabolism in Cancer: Therapeutic Targets and Mechanisms of Action
Authors: Talib Wamidh H. , Baban Media Mohammad , Bulbul Mais Fuad , Al-Zaidaneen Esraa , Allan Aya , Al-Rousan Eiman Wasef , Ahmad Rahaf Hamed Yousef , Alshaeri Heba K. , Alasmari Moudi M. , Law Douglas
Published: September 2024

Cancer is characterized by uncontrolled cell proliferation and the dysregulation of numerous biological functions, including metabolism. Because of the potential implications of targeted therapies, the metabolic alterations seen in cancer cells, such as the Warburg effect and disruptions in lipid and amino acid metabolism, have gained attention in cancer research. In this review, we delve into recent research examining the influence of natural products on altered cancer metabolism. Natural products were selected based on their ability to target cancer’s altered metabolism. We identified the targets and explored the mechanisms of action of these natural products in influencing cellular energetics. Studies discussed in this review provide a solid ground for researchers to consider natural products in cancer treatment alone and in combination with conventional anticancer therapies.
Deciphering the Potential Role of Specialized Pro-Resolving Mediators in Obesity-Associated Metabolic Disorders
Authors: Kim Nahyun , Shin Ha Youn
Published: September 2024

Obesity-related metabolic disorders, including diabetes, non-alcoholic fatty liver disease (NAFLD), and cardiovascular disease, increasingly threaten global health. Uncontrolled inflammation is a key pathophysiological factor in many of these conditions. In the human body, inflammatory responses generate specialized pro-resolving mediators (SPMs), which are crucial for resolving inflammation and restoring tissue balance. SPMs derived from omega-3 polyunsaturated fatty acids (n-3 PUFAs) such as resolvins, protectins, and maresins hold promise in attenuating the chronic inflammatory diseases associated with lipid metabolism disorders. Recent research has highlighted the therapeutic potential of n-3 PUFA-derived metabolites in addressing these metabolic disorders. However, the understanding of the pharmacological aspects of SPMs, particularly in obesity-related metabolic disorders, remains limited. This review comprehensively summarizes recent advances in understanding the role of SPMs in resolving metabolic disorders, based on studies in animal models and humans. These studies indicate that SPMs have potential as therapeutic targets for combating obesity, as well as offering insights into their mechanisms of action.
Elevation and Human Disturbance Interactively Influence the Patterns of Insect Diversity on the Southeastern Periphery of the Tibetan Plateau
Authors: Liao Zhouyang , Zhang Jinlu , Shen Xuemei , Zhu Mi , Lan Xinlin , Cui Junming , Guan Yunfang , Zhang Ying , Deng Zhongjian , Tang Tiantian , Liu Fang , Yang Darong , Zhang Yuan
Published: September 2024

The maintenance of biodiversity and ecological balance heavily relies on the diversity of insects. In order to investigate the impacts of elevation and human disturbance, as well as their interactions on insect diversity, we conducted an intensive survey of insects in the Hengduan Mountain Range, which is situated on the southeastern periphery of the Tibetan Plateau in China. A total of 50 line transects were established in this study to investigate the impact of elevation and human disturbance on insect diversity and distribution patterns. Designed insect surveys were conducted at various elevations and levels of human disturbance, and statistical methods such as generalized linear modeling and redundancy analysis were employed for data analysis. The results of this study indicated a negative correlation between insect diversity and elevation. Additionally, moderate disturbance was found to have a positive impact on insect diversity to some extent. The explanatory power of the model for the distribution of insect diversity could be improved if elevation and human disturbance were included as an interaction effect into the model, and there were differences in the effects of human disturbances on insect diversity at different elevation levels. The highest insect diversity was observed under low disturbance conditions below elevation of ~2200 m, whereas above this threshold, insect diversity was the highest under moderate disturbance compared to low disturbance. The response of different insect taxa to the interactions of elevation and human disturbance varied. The findings imply that when formulating strategies for managing insect diversity, it is crucial to thoroughly consider the interaction of environmental factors and disturbance response of individual insect taxa.
Susceptibility of Bemisia tabaci Gennadius (Hemiptera: Aleyrodidae) Mediterranean Populations Found in São Paulo, Brazil to 11 Insecticides and Characterization of Their Endosymbionts
Authors: Alvarez Daniel de Lima , Hayashida Rafael , Cavallaro Michael C. , Santos Daniel Mariano , Santos Lucas Moraes , Müller Cristiane , Watanabe Luís Fernando Maranho , Bello Vinicius Henrique , Krause-Sakate Renate , Hoback William Wyatt , Oliveira Regiane Cristina de
Published: September 2024

The silverleaf whitefly, Bemisia tabaci Gennadius (Hemiptera: Aleyrodidae), is a significant agricultural pest worldwide, impacting a variety of crop yields. Since the introduction of B. tabaci Mediterranean (MED) species in Brazil, limited research has measured the relative efficacy of the primary insecticides used in whitefly management. This study evaluated the susceptibility of three distinct B. tabaci MED populations to 11 insecticide active ingredients and characterized the bacterial endosymbionts within each population. The insecticides tested were acetamiprid, bifenthrin, cyantraniliprole, diafenthiuron, spiromesifen, imidacloprid, pymetrozine, pyriproxyfen, sulfoxaflor, and thiamethoxam. Results showed varying LC50 and LC90 values among tested insecticides and populations. Notably, populations varied in response to imidacloprid and thiamethoxam with some populations having a 6× higher tolerance. Sequencing data of endosymbionts revealed that individuals from the most susceptible B. tabaci population harbored Rickettsia and Arsenophonus, whereas these bacteria were not detected in the resistant populations. These findings highlight the need for frequent insecticide toxicity bioassays of distinct B. tabaci populations and the adoption of integrated pest management strategies to preserve the efficacy of insecticides for B. tabaci control. Additionally, the role of infection by endosymbionts to alter susceptibility should be further explored.
Randomised Placebo-Controlled Pilot Trial Evaluating the Anti-Anginal Efficacy of Ticagrelor in Patients with Angina with Nonobstructive Coronary Arteries and Coronary Slow Flow Phenomenon
Authors: Pasupathy Sivabaskari , Tavella Rosanna , Zeitz Christopher , Edwards Suzanne , Worthley Matthew , Arstall Margaret , Beltrame John F.
Published: September 2024

Background: The coronary slow flow phenomenon (CSFP) is an angiographic finding characterised by the delayed passage of contrast through the coronary arteries, despite the absence of obstructive coronary artery disease (defined as less than 50% narrowing of the vessel lumen). Patients with the CSFP experience recurrent angina, for which there are limited evidence-based therapies. Ticagrelor may serve as an effective anti-anginal therapy for these patients by increasing adenosine levels, which could alleviate coronary microvascular dysfunction and its associated angina due to its vasodilatory properties. This study aimed to determine the anti-anginal efficacy of ticagrelor 90 mg taken twice daily on spontaneous angina episodes in patients with refractory angina (i.e., episodes ≥3/week despite two anti-anginals) and documented CSFP. Methods: In a randomised, double-blind, placebo-controlled, cross-over trial, the anti-anginal efficacy of a 4-week ticagrelor therapy regimen was evaluated in 20 patients with refractory angina (mean age 61.5 ± 10.5 years; 40% women) who had documented slow coronary flow. The primary endpoint was the frequency of angina episodes, recorded using an angina diary. Secondary endpoints included the duration and severity of angina episodes, consumption of short-acting nitrates, and health status evaluations using the Seattle Angina Questionnaire (SAQ) and the Short Form-36 (SF-36) indices. Results: During the four weeks of therapy, ticagrelor did not significantly improve angina symptoms compared to the placebo (placebo 25.7 (16.7)) vs. ticagrelor 19.8 (18.1), p > 0.05). Furthermore, it did not impact other patient-related outcome measures, including angina severity, duration, frequency of prolonged angina episodes, nitrate consumption, or the SAQ/SF-36 health outcome indices. No serious adverse events related to the study drug were observed. Conclusions: In patients with documented CSFP who were unresponsive to standard anti-anginal therapy, ticagrelor did not reduce the frequency of spontaneous angina episodes or the consumption of nitrates. Further confirmation of the potential benefits of this therapy may be obtained through a larger clinical trial.
Optimal Signal Wavelengths for Underwater Optical Wireless Communication under Sunlight in Stratified Waters
Authors: Waduge Tharuka Govinda , Seet Boon-Chong , Vopel Kay
Published: September 2024

Underwater optical wireless communication (UOWC) is a field of research that has gained popularity with the development of unmanned underwater vehicle (UUV) technologies. Its utilization is crucial in offshore industries engaging in sustainable alternatives for food production and energy security. Although UOWC can meet the high data rate and low latency requirements of underwater video transmission for UUV operations, the links that enable such communication are affected by the inhomogeneous light attenuation and the presence of sunlight. Here, we present how the underwater spectral distribution of the light field can be modeled along the depths of eight stratified oceanic water types. We considered other established models, such as SPCTRL2, Haltrin’s single parameter model for inherent optical properties, and a model for the estimation of the depth distribution of chlorophyll-a, and present insights based on transmission wavelength for the maximum signal-to-noise ratio (SNR) under different optical link parameter combinations such as beam divergence and transmit power under “daytime” and “nighttime” conditions. The results seem to challenge the common notion that the blue-green spectrum is the most suitable for underwater optical communication. We highlight a unique relationship between the transmission wavelength for the optimal SNR and the link parameters and distance, which varies with depth depending on the type of oceanic water stratification. Our analyses further highlighted potential implications for solar discriminatory approaches and strategies for routing in cooperative optical wireless networks in the photic region.
Exploring Consumer Acceptance of AI-Generated Advertisements: From the Perspectives of Perceived Eeriness and Perceived Intelligence
Authors: Gu Chenyan , Jia Shuyue , Lai Jiaying , Chen Ruli , Chang Xinsiyu
Published: September 2024

The rapid popularity of ChatGPT has brought generative AI into broad focus. The content generation model represented by AI-generated content (AIGC) has reshaped the advertising industry. This study explores the mechanisms by which the characteristics of AI-generated advertisements affect consumers’ willingness to accept these advertisements from the perspectives of perceived eeriness and perceived intelligence. It found that the verisimilitude and imagination of AI-generated advertisements negatively affect the degree of perceived eeriness by consumers, while synthesis positively affects it. Conversely, verisimilitude, vitality, and imagination positively affect the perceived intelligence, while synthesis negatively affects it. Meanwhile, consumers’ perceived eeriness negatively affects their acceptance of AI-generated advertisements, while perceived intelligence positively affects their willingness to accept AI-generated advertisements. This study helps explain consumers’ attitudes toward AI-generated advertisements and offers strategies for brands and advertisers for how to use AI technology more scientifically to optimize advertisements. Advertisers should cautiously assess the possible impact of AI-generated advertisements according to their characteristics, allowing generative AI to play a more valuable role in advertising.
Beyond Barriers: Constructing the Cloud Migration Complexity Index for China’s Digital Transformation
Authors: Wen Weiwei , Zhang Chenglei , Ye Qin
Published: September 2024

In the digital era, cloud computing stands as a pivotal tool in the transformative journey of enterprises, with China’s tech landscape serving as a prime exemplar. However, when enterprises embrace cloud computing, they face complex hurdles, mainly technical ones. To understand how the complexity of cloud migration affects their digital transformation, our research meticulously constructed business process models for business deployment in both non-cloud and cloud contexts, spanning the IaaS, PaaS, and SaaS levels. By harnessing China’s public cloud market data, we constructed the Cloud Migration Complexity Index, providing a tangible metric to gauge the intricacies of cloud migration and their implications on digital transformation. The findings illustrate that a decrease in cloud migration complexity significantly accelerates digital transformation, with the reduction in SaaS complexity having the most profound impact. Analyzing businesses of varying scales, the diminishing complexity of SaaS predominantly boosts digital transformation for non-SME enterprises, while reduced PaaS complexity is most beneficial for SMEs. This study advocates for the government to expand the PaaS market and suggest that cloud providers develop more PaaS-based products to optimize cloud migration both technically and economically.
Factors Influencing Spatiotemporal Changes in the Urban Blue-Green Space Cooling Effect in Beijing–Tianjin–Hebei Based on Multi-Source Remote Sensing Data
Authors: Gong Haiying , Cao Yongqiang , Yao Jiaqi , Xu Nan , Chang Huanyu , Wu Shuqi , Hu Liuru , Liu Zihua , Liu Tong , Zhang Zihao
Published: September 2024

Owing to rapid urbanization, the Beijing–Tianjin–Hebei region in China faces considerable urban heat island (UHI) effects, which can be mitigated by blue-green space construction. In this study, we used multi-source remote sensing products and the InVEST model’s urban cooling module to analyze the spatiotemporal changes in blue-green space cooling effects from 1990 to 2020. The wavelet coherence theory was used to explore these changes, as well as the environmental factors that affect cooling. The key findings indicate that the cooling effect is closely related to urbanization, as similar trends and significant temporal differences in cooling indices were observed in central urban areas, the urban fringe, and the city center. In addition, climatic factors such as temperature and precipitation substantially influenced cooling, with an average wavelet coherence of 0.88. Seasonal variations in cooling were notable, with temperature exhibiting the best coherence across all time–frequency scales (averaging 0.55). The findings highlight the critical role of blue-green spaces for mitigating UHI effects, which provides scientific insights for urban planning and environmental management.