bluuebunny/crossref_metadata_2025 huggingface dataset:
Acute Responses of Low-Load Resistance Exercise with Blood Flow Restriction
Authors: Jagim Andrew R. , Schuler Jordan , Szymanski Elijah , Khurelbaatar Chinguun , Carpenter Makenna , Fields Jennifer B. , Jones Margaret T.
Published: December 2024

Blood flow restriction (BFR) is a popular resistance exercise technique purported to increase metabolic stress and augment training adaptations over time. However, short-term use may lead to acute neuromuscular fatigue and higher exertion ratings. Objective: The purpose of the current study was to examine acute physiological responses to low-load resistance exercise utilizing BFR compared to higher-load, non-BFR resistance exercise. Methods: Recreationally trained males (n = 6) and females (n = 7) (mean ± standard deviation, age: 20 ± 1 yrs.; height: 172 ± 8 cm; weight: 73 ± 11 kg; BMI: 24.4 ± 2.2 kg·m−2; training experience: 4 ± 2 yrs.) had limb occlusion pressure determined (50%; right leg: 118 ± 11 mmHg; left leg: 121 ± 13 mmHg) using an automated, self-inflating cuff system during baseline testing. In subsequent sessions, using a randomized, cross-over design, participants completed one of two experimental conditions: (1) Low-load + BFR and (2) High load + non-BFR. In both conditions, participants completed one set of back squats at either 30% (BFR) or 60% (non-BFR) of an estimated 1RM for a max of 30 repetitions, followed by three additional sets with the same loads and a target of 15 repetitions per set. Blood lactate and countermovement jump (CMJ) height were measured pre- and post-back squat. Ratings of perceived exertion (RPE) were assessed following each set. Results: When collapsed across all sets, participants completed significantly more total repetitions in the BFR condition compared to non-BFR (75.0 ± 0.0 vs. 68.23 ± 9.27 reps; p = 0.015; ES: 1.03), but a lower training load volume (2380 ± 728 vs. 4756 ± 1538 kg; p < 0.001; ES: 1.97). There was a significant time-by-condition interaction (p < 0.001), with a greater increase in blood lactate occurring from baseline to post-back squat in the non-BFR condition (11.61 mmol/L, 95%CI: 9.93, 13.28 mmol/L) compared to BFR (5.98 mmol/L, 95%CI: 4.30, 7.65 mmol/L). There was another significant time-by-condition interaction (p = 0.043), with a greater reduction in CMJ occurring in the non-BFR condition (−6.01, 95%CI: −9.14, −2.88 cm; p < 0.001) compared to BFR (−1.50, 95%CI: −1.50, 4.51 cm; p = 0.312). Conclusions: Utilizing a low-load BFR protocol may allow for a higher training volume, yet lower metabolic stress and reduce neuromuscular fatigue compared to lifting at a higher load without the use of BFR.
Local Remelting in Laser Powder Bed Fusion
Authors: Lehmann Janno , Weise Martin , Köhler Markus , von Lacroix Frank , Ploshikhin Vasily , Dilger Klaus
Published: December 2024

In Laser Powder Bed Fusion, process material defects such as a lack of fusion, powder inclusions and cavities occur repeatedly by chance. These stochastically distributed defects can significantly reduce the mechanical performance of the components during operation. Possible in situ repair solutions such as multiple remelting of specific layer areas are promising approaches to avoid these defects in the finished component, thus improving the overall properties. In this context, the present study investigates the remelting of artificially introduced defects using the example of M789 tool steel. In the first step, the process parameter settings and mechanical properties were evaluated using a tensile test, and the density of the local repair was examined using X-ray computer tomography and a metallographic analysis. The results demonstrate that the mechanical properties of the tensile test are comparable with those of the reference samples while successfully increasing the component quality. This indicates that defects that arise during the process can be remelted without the loss of mechanical characteristics.
A De-Nesting Hybrid Reliability Analysis Method and Its Application in Marine Structure
Authors: Li Chenfeng , Jin Tenglong , Chen Zequan , Wei Guanchen
Published: December 2024

In recent years, marine structures have been widely used in the world, making significant contributions to the utilization of marine resources. In the design of marine structures, there is a hybrid reliability problem arising from aleatory uncertainty and epistemic uncertainty. In many cases, epistemic uncertainty is estimated by interval parameters. Traditional methods for hybrid reliability analysis usually require a nested optimization framework, which will lead to too many calls to the limit state function (LSF) and result in poor computational efficiency. In response to this problem, this paper proposes a de-nesting hybrid reliability analysis method creatively. Firstly, it uses the p-box model to describe the epistemic uncertainty variables, and then the linear approximation (LA) model and the two-point adaptive nonlinear approximation (TANA) model are combined to approximate the upper and lower bounds of LSF with epistemic uncertainty. Based on the first-order reliability method (FORM), an iterative operation is used to obtain the interval of the non-probability hybrid reliability index. The traditional nested optimization structure is effectively eliminated by the above approximation method, which efficiently reduces the times of LSF calls and increases the calculation speed while preserving sufficient accuracy. Finally, one numerical example and two engineering examples are provided to show the greater effectiveness of this method than the traditional nested optimization method.
Spatial Sequential Matching Enhanced Underwater Single-Photon Lidar Imaging Algorithm
Authors: Zhu Qiguang , Wang Yuhang , Wang Chenxu , Rong Tian , Li Buxiao , Ying Xiaotian
Published: December 2024

Traditional LiDAR and air-medium-based single-photon LiDAR struggle to perform effectively in high-scattering environments. The laser beams are subject to severe medium absorption and multiple scattering phenomena in such conditions, greatly limiting the maximum operational range and imaging quality of the system. The high sensitivity and high temporal resolution of single-photon LiDAR enable high-resolution depth information acquisition under limited illumination power, making it highly suitable for operation in environments with extremely poor visibility. In this study, we focus on the data distribution characteristics of active single-photon LiDAR operating underwater, without relying on time-consuming deep learning frameworks. By leveraging the differences in time-domain distribution between noise and echo signals, as well as the hidden spatial information among echo signals from different pixels, we rapidly obtain imaging results across various distances and attenuation coefficients. We have experimentally verified that the proposed spatial sequential matching enhanced (SSME) algorithm can effectively enhance the reconstruction quality of reflection intensity maps and depth maps in strong scattering underwater environments. Through additional experiments, we demonstrated the algorithm’s reconstruction effect on different geometric shapes and the system’s resolution at different distances. This rapidly implementable reconstruction algorithm provides a convenient way for researchers to preview data during underwater single-photon LiDAR studies.
Effect of Propeller Face Camber Ratio on the Reduction of Fuel Consumption
Authors: Tadros Mina , Sun Zehao , Shi Weichao
Published: December 2024

This paper presents the effect of the face camber ratio (FCR) on propeller performance, cavitation, and fuel consumption of a bulk carrier in calm water. First, using a developed propeller optimization model coupling a ship performance prediction tool (NavCad) and a nonlinear optimizer in MATLAB, an optimized propeller design at the optimal engine operating point with minimum fuel consumption is selected. This optimized propeller demonstrates superior fuel efficiency compared to the one selected by using the traditional selection methods that prioritize only higher propeller efficiency. Afterward, the FCR is applied to the propeller geometry to evaluate the effect on propeller performance. The open water curves of propellers with different FCRs ranging from 0% to 1.5% are computed based on empirical formulas and computational fluid dynamics (CFD) simulations. Between the two techniques, a good agreement is noted in verifying the predictions. Then, the open water curves from CFD models are implemented into NavCad to evaluate the overall hydrodynamic performance of the propeller at the design point in terms of efficiency, quantify reductions in fuel consumption, and analyze changes in cavitation and noise criteria. The computed results show a reduction in fuel consumption by 3% with a higher FCR. This work offers a preliminary evaluation of propeller performance-based FCR and shows its benefits. This technique offers a promising solution for improving the energy efficiency of the ship and lowering the level of fuel consumption and exhaust emissions.
Parameter Identification of an Unmanned Sailboat Combining Experiments and Numerical Analysis
Authors: Chen Yifan , Liu Shuo , Xie Tian , Zhang Zhaozhao , Zhang Yu , Lin Wanglin , Jiang Kaiyou , Wang Tao
Published: December 2024

It is meaningful to develop an accurate model to predict the dynamical motion of an unmanned sailboat. Considering cost and convenience, this work proposes a parameter identification method based on the combination of experiments and numerical analysis. Firstly, a free-running trial is carried out by utilizing the propellers on the studied sailboat to acquire real navigation information. Secondly, particle swarm optimization (PSO), which is highly efficient and easily implemented, is designed to acquire the hydrodynamic parameters of the sailboat’s hull. At the same time, the aerodynamic parameters of the sail are acquired by computational fluid dynamics (CFD) simulation. Finally, a three degree-of-freedom (DOF) model is established, the effectiveness of which is verified through comparisons between sea trials and simulation. The results prove that this parameter identification method has the desired accuracy and reliability.
Boldenone and Testosterone Production from Phytosterol via One-Pot Cascade Biotransformations
Authors: Kollerov Vyacheslav V. , Timakova Tatiana A. , Shutov Andrei A. , Donova Marina V.
Published: December 2024

Testosterone (TS) and its 1(2)-dehydrogenated derivative boldenone (BD) are widely used in medicine, veterinary science and as precursors in organic synthesis of many therapeutic steroids. Green production of these compounds is possible from androstenedione (AD) enzymatically, or from phytosterol (PS) using fermentation stages. In this study, the ascomycete Curvularia sp. VKM F-3040 was shown to convert androstadienedione (ADD, 4 and 10 g/L) to yield 97% and 78% (mol/mol) of BD, respectively. Based on its high 17β-hydroxysteroid dehydrogenase (17β-HSD) activity, a novel cascade biotransformation of PS was developed for production of TS and BD. At the first stage, the strains of Mycolicibacterium neoaurum VKM Ac-1815D or M. neoaurum VKM Ac-1816D converted PS (5 or 10 g/L) into AD or ADD (each in a concentration of 2.5 or 5 g/L), respectively. At the second stage, mycelium of the fungus under the revealed optimal conditions reduced AD or ADD with more than 90% efficiency to form TS or BD, respectively. Based on transcriptome analysis, six candidate genes that might encode 17β-HSDs in the Curvularia sp. genome were revealed. Along with 17β-HSDs, the fungus possessed inducible P450cur 7-monooxygenase, which led to the accumulation of 7α-hydroxytestosterone (7α-OH-TS) as a major product from AD (up to 83% within 24 h after mycelium addition at the second stage of cascade biotransformation). The presence of protein synthesis inhibitor cycloheximide (CHX) prevented 7α/β-hydroxylation due to inhibition of de novo synthesis of the enzyme in the fungal cells. The results demonstrate the high biotechnological potential of the Curvularia sp. strain and open up prospects for the synthesis of valuable 17β-reduced and 7-hydroxylated steroids by cascade biotransformations.
The Hemibiotrophic Apple Scab Fungus Venturia inaequalis Induces a Biotrophic Interface but Lacks a Necrotrophic Stage
Authors: Steiner Ulrike , Oerke Erich-Christian
Published: December 2024

Microscopic evidence demonstrated a strictly biotrophic lifestyle of the scab fungus Venturia inaequalis on growing apple leaves and characterised its hemibiotrophy as the combination of biotrophy and saprotrophy not described before. The pathogen–host interface was characterised by the formation of knob-like structures of the fungal stroma appressed to epidermal cells as early as 1 day after host penetration, very thin fan-shaped cells covering large parts of the host cell lumen, and enzymatic cuticle penetration from the subcuticular space limited to the protruding conidiophores. The V. inaequalis cell wall had numerous orifices, facilitating intimate contact with the host tissue. Pathogen-induced modifications of host cells included partial degradation of the cell wall, transition of epidermal cells into transfer cells, modification of epidermal pit fields to manipulate the flow of nutrients and other compounds, and formation of globular protuberances of mesophyll cells without contact with the pathogen. The non-haustorial biotrophy was characterised by enlarged areas of intimate contact with host cells, often mediated by a matrix between the pathogen and plant structures. The new microscopic evidence and information on the pathogens’ biochemistry and secretome from the literature gave rise to a model of the lifestyle of V. inaequalis, lacking a necrotrophic stage that covers and explains its holomorphic development.
Novel Monoclonal Antibodies 1D2 and 4E4 Against Aspergillus Glycoprotein Antigens Detect Early Invasive Aspergillosis in Mice
Authors: Lian Xihua , Scott-Thomas Amy , Lewis John G. , Bhatia Madhav , Chambers Stephen T.
Published: December 2024

Due to the high morbidity and mortality rates of invasive aspergillosis (IA) and the importance of early IA detection for successful treatment and subsequent outcome, this study aimed to determine a time course of detectable antigen in a mouse model of IA and correlate it with tissue invasion by using two novel monoclonal antibodies, 1D2 and 4E4, that can be used to detect the Aspergillus-derived glycoproteins. Immunocompromised mice were randomly divided into five groups: uninfected control, and inoculation with conidia from Aspergillus fumigatus, Aspergillus flavus, Aspergillus niger, and Aspergillus terreus. Conidia (2 × 106 cells/mL) were administered intravenously via tail vein injection. Three mice from each group were euthanised at each time point (6 h, 12 h, 18 h, 24 h, and 48 h) after inoculation. Urine and blood were collected for analysis using a double-sandwich ELISA using 1D2 and 4E4. Liver, spleen, and kidney tissues were harvested for tissue staining. The levels of liver injury in the IA mice progressively increased with time after inoculation with Aspergillus conidia. Following inoculation with A. fumigatus, swollen conidia were identified in the spleen, as well as antigens in blood after 18 h. Hyphae were detected in the spleen, liver, and kidney after 48 h. For A. flavus, the antibodies detected hyphae in the liver and spleen as well as circulating antigens in blood samples 48 h after inoculation. Tissue injury was observed in the mice inoculated with A. terreus and A. niger, but there was no evidence of fungal invasion or antigens in the blood. Antigens were not detectable in mouse urine but could be detected in glomeruli of the kidney by immunofluorescence. In conclusion, the mAb-based antigen detection double-sandwich ELISA results were consistent with the IHC results in this study. Novel monoclonal antibodies 1D2 and 4E4 can serve as tools for the early identification of IA in mice infected by A. fumigatus and A. flavus. This study also suggests the potential usefulness of this approach in human disease.
Structures and Biological Activities of Secondary Metabolites from Daldinia spp.
Authors: Yu Miao , Chen Shiji , Shi Jueying , Chen Weikang , Qiu Yikang , Lan Jing , Qu Shiyan , Feng Jiayi , Wang Ru , Lin Fangru , Huang Guolei , Zheng Caijuan
Published: December 2024

The genus Daldinia have long been recognized as a source of structural novel, pharmaceutically relevant natural products. We reviewed the structures and activities of secondary metabolites isolated from the genus of Daldinia from January 1995 to June 2024, and 280 compounds, including six major categories—terpenoids, alkaloids, polyketides, polyphenols, steroids, and other classes—are presented in this review. Among these metabolites, 196 were identified as new structures. Remarkably, 112 compounds exhibited a range of biological activities, including cytotoxic, antimicrobial, anti-inflammatory, antifungal, anti-virus, and enzyme-inhibitory activities. This review highlights the bioactive metabolites discovered in the past three decades from the genus of Daldinia while also exploring the potential of these symbiotic fungi as rich sources of novel and diverse natural products. The varying bioactivities of these metabolites offer a vast array of promising lead compounds and also could significantly contribute to the development of new medicines.
New Species and Records Expand the Checklist of Cellular Slime Molds (Dictyostelids) in Jilin Province, China
Authors: Zhang Zhaojuan , He Liang , Sun Yuqing , Li Zhuang , Yang Yingkun , Zhai Chao , Stephenson Steven L. , Xie Xiangrui , Li Yu , Liu Pu
Published: December 2024

Dictyostelids represent a crucial element in the protist community, and their abundant presence in Jilin Province underscores their indispensable role in biodiversity conservation. In the present study, a resource survey of dictyostelids used random sampling to collect 28 soil samples from five localities in Changbai Korean Autonomous County, Jilin Province. In addition, a compilation of dictyostelid species reported from Jilin Province was developed, based on a thorough review of the literature. The survey yielded fifteen isolates of dictyostelids, comprising six species from four genera. Notably, two new species (Dictyostelium longigracilis sp. nov. and Dictyostelium macrosoriobrevipes sp. nov.) were described using morphological characteristics and SSU gene-based phylogenetic analyses. One other species (Polysphondylium patagonicum) was recorded as new for China, while another (Cavenderia aureostipes) was recorded as a new record for Jilin Province. The dictyostelid assemblage in Jilin Province is dominated by the genus Dictyostelium (51.4%), with a total of 35 species, which represent 59.3% of the current total known for all of China. These findings provide a scientific basis for the protection of species diversity and resource utilization of dictyostelids in Jilin Province.
Bedrock-Dependent Effects of Climate Change on Terricolous Lichens Along Elevational Gradients in the Alps
Authors: Vallese Chiara , Di Nuzzo Luca , Francesconi Luana , Giordani Paolo , Spitale Daniel , Benesperi Renato , Gheza Gabriele , Mair Petra , Nascimbene Juri
Published: December 2024

In this study, we focused on the bedrock-dependent effects of climate change on terricolous lichen communities along elevational gradients in the Alps. In particular, we contrasted between carbonatic and siliceous bedrock, hypothesizing more favourable conditions on siliceous than on carbonatic bedrock, where dryer conditions may exacerbate the effects of climate change. To test this hypothesis, we compared terricolous lichen diversity patterns between the two bedrock types in terms of (1) species richness, (2) beta-diversity, (3) proportion of cryophilous species, and (4) functional diversity, also testing the effect of the elevational gradient as a proxy for expected climate warming. Our results indicate that the most cold-adapted part of the terricolus lichen biota of the Alps could be especially threatened in the near future, mainly on carbonatic bedrock. Actually, contrasting diversity patterns were found between carbonatic and siliceous bedrock, clearly revealing a bedrock-dependent effect of climate change on terricolous lichens of the Alps. As hypothesized, siliceous bedrock hosts a richer lichen biota than carbonatic bedrock, reflecting a general richness pattern at the national level. In general, siliceous bedrock seems to be less prone to rapid pauperization of its lichen biota, providing more suitable climatic refugia that can mitigate the effects of climate warming on terricolous lichens.
Control of Postharvest Green Mold in Citrus by the Antimicrobial Peptide BP15 and Its Lipopeptides
Authors: Lei Yu , Lyu Aiyuan , Pan Mengjuan , Shi Qingxia , Xu Haowan , Li Dong , Deng Mengsheng
Published: December 2024

This study examined the efficacy and mechanisms of action of the antimicrobial peptide BP15 and its lipopeptides, HBP15 and LBP15, against Penicillium digitatum, the primary causative agent of green mold in citrus fruits. The findings revealed that all three antimicrobial peptides markedly inhibited the spore germination and mycelial growth of P. digitatum, with minimum inhibitory concentrations (MICs) of 3.12 μM for BP15, HBP15, and LBP15. The peptides induced morphological alterations in hyphae and elevated intracellular Sytox Green (SG) fluorescence signals, which is indicative of increased cell membrane permeability and disruption. This membrane damage was further supported by the heightened extracellular conductivity and the release of intracellular nucleic acid and protein. A gel retardation assay demonstrated that the peptides showed significant DNA binding and retardation effects. Furthermore, the peptides exhibited significantly lower hemolytic activity (p < 0.05) compared to commercial prochloraz in normal mammalian erythrocytes (sheep erythrocytes) at the tested concentrations. Therefore, BP15 and its lipopeptides, HBP15 and LBP15, show potential as effective agents for preventing green mold in citrus fruits.
Genome Sequencing and Comparative Genomic Analysis of Attenuated Strain Gibellulopsis nigrescens GnVn.1 Causing Mild Wilt in Sunflower
Authors: Dong Baozhu , Liu Wanyou , Zhao Yingjie , Quan Wei , Hao Lijun , Wang Dong , Zhou Hongyou , Zhao Mingmin , Hao Jianxiu
Published: December 2024

Gibellulopsis nigrescens, previously classified in the Verticillium genus until 2007, is an attenuated pathogen known to provide cross-protection against Verticillium wilt in various crops. To investigate the potential mechanisms underlying its reduced virulence, we conducted genome sequencing, annotation, and a comparative genome analysis of G. nigrescens GnVn.1 (GnVn.1), an attenuated strain isolated from sunflower. The genome sequencing and annotation results revealed that the GnVn.1 genome consists of 22 contigs, with a total size of 31.79 Mb. We predicted 10,876 genes, resulting in a gene density of 342 genes per Mb. The pathogenicity gene prediction results indicated 1733 high-confidence pathogenicity factors (HCPFs), 895 carbohydrate-active enzymes (CAZys), and 359 effectors. Moreover, we predicted 40 secondary metabolite clusters (SMCs). The comparative genome analysis indicated that GnVn.1 contains more CAZys, SMCs, predicted effectors, and HCPF genes than Verticillium dahliae (VdLs.17) and Verticillium alfalfae (VaMas.102). The core–pan analysis results showed that GnVn.1 had more specific HCPFs, effectors, CAZys, and secreted protein (SP) genes, and lost many critical pathogenic genes compared to VdLs.17 and VaMs.102. Our results indicate that the GnVn.1 genome harbors more pathogenicity-related genes than the VdLs.17 and VaMs.102 genomes. These abundant genes may play critical roles in regulating virulence. The loss of critical pathogenic genes causes weak virulence and confers biocontrol strategies to GnVn.1.
Will the Real Immunogens Please Stand Up: Exploiting the Immunogenic Potential of Cryptococcal Cell Antigens in Fungal Vaccine Development
Authors: Avina Samantha L. , Pawar Siddhi , Rivera Amariliz , Xue Chaoyang
Published: December 2024

Cryptococcus neoformans is an opportunistic fungal pathogen that is a continuous global health concern, especially for immunocompromised populations. The World Health Organization recognized C. neoformans as one of four critical fungal pathogens, thus emphasizing the need for increased research efforts and clinical resource expansion. Currently, there are no fungal vaccines available for clinical use. Exciting new findings in cryptococcal vaccine development have identified whole cell-based and subunit-based vaccinations to help mitigate health risks and make commercialization attainable. Importantly, recent work has focused on how different cryptococcal cell-wall antigens modified in these vaccine candidates allow us to manipulate their immunogenicity to produce a desired long-term protective anti-fungal immune response. In this review, we discuss the different cryptococcal cell immunogens, namely the polysaccharide capsule, glucans, chitin/chitosan, mannoproteins, and extracellular vesicles, and their role in novel cryptococcal vaccination approaches. Additionally, we examine the immunological mechanisms responsible for protection in these vaccine candidates and the similar host response-stimulation pathways induced through different immunogen exposure.
Evaluating Financial Inclusion in Peru: A Cluster Analysis Using Self-Organizing Maps
Authors: Talavera Alvaro , Maehara Rocío , Benites Luis , Arriaga Benjamin , Aybar-Flores Alejandro
Published: December 2024

This study evaluates financial inclusion in Peru through self-organizing maps. Financial inclusion is a multidimensional issue of great importance on the global agenda and continues to concern various actors internationally. In this context, the objective is to assess the financial inclusion situation in the country and determine how self-organizing maps can complement standard models for this purpose. The empirical aim is to demonstrate how this technique can help identify priority areas and vulnerable groups, thus facilitating decision-making and policy design to improve the access to and use of financial services among Peruvian consumers by finding clearly defined profiles that allow the identification of potential problems within each category. This makes it possible to create customized strategies for each group, such as addressing the financial inclusion barriers faced by rural residents, compounded by low income and educational levels.
Spatio-Temporal Evolution, Internal Diversity, and Driving Factors of Economy of Guanzhong Plain Urban Agglomeration in Northwestern China Based on Nighttime Light Data
Authors: Liu Limeng , Wu Wenheng , Bai Xiaoying , Shang Wanying
Published: December 2024

Urban agglomeration (UA) is a highly developed spatial form of urban complex, which is one of the important carriers of regional economic cooperation, international industrial division of labor, and flow of capital and information elements. In China, urban agglomerations (UAs) have become the spatial subject of the national new-type urbanization strategy since the early 21st century and have made irreplaceable contributions to China’s urbanization and economic development. The Guanzhong Plain urban agglomeration (GPUA) is an important economic growth pole in northwest China and a key node in China’s open-door pattern. Exploring the spatial and temporal characteristics and driving factors of its economic development will be an important revelation for the promotion of high-quality economic development of the GPUA. This paper characterizes the level of economic development of GPUA with a long series of nighttime light data between 2002 and 2022. The standard deviation ellipse, spatial autocorrelation analysis, the economic difference index, and grey correlation analysis are used to analyze the characteristics of spatio-temporal evolution, internal diversity, and driving factors of economic development of the GPUA. The results show that the economic development level of the GPUA continued to increase from 2002 to 2022. The spatial distribution of the GPUA economy is “northeast-southwest” axial distribution, and the center of gravity of economic development gradually moves westward. The differences in the level of economic development within the GPUA show a typical core–periphery structure, but the degree of difference tends to weaken over time. The internal expansion force and economic promotion force were the dominant factors for the economic development of the GPUA in the early years. However, with the passage of time, scientific and technological support and government support have gradually become the main influencing factors for the economic development of the GPUA nowadays.
Lipid-Based Niclosamide Delivery: Comparative Efficacy, Bioavailability, and Potential as a Cancer Drug
Authors: Woo Jihoo , Wiggins Russell W. , Mito Shizue
Published: December 2024

Niclosamide, an FDA-approved anti-parasitic drug, has demonstrated significant potential as a repurposed anti-cancer agent due to its ability to interfere with multiple oncogenic pathways. However, its clinical application has been hindered by poor solubility and bioavailability. Lipid-based nanocarrier systems such as liposomes, solid lipid nanoparticles (SLNs), nanostructured lipid carriers (NLCs), and lipid nanoemulsions (LNE), along with lipid prodrugs, have successfully been employed by researchers to overcome these limitations and improve niclosamide’s pharmacokinetic profile. Lipids are the core organic compounds which serve as the foundation of these advanced drug delivery methods and in turn play a critical role in enhancing niclosamide’s therapeutic efficacy through improving drug solubility and bioavailability. Lipid-based nanoparticles encapsulate niclosamide, protect it from degradation, facilitate drug delivery and release, and may facilitate targeted delivery in the future. While niclosamide holds significant potential as an anticancer agent due to its multi-pathway inhibitory effects, the challenges associated with its poor bioavailability and rapid clearance underscore the need for innovative delivery methods and chemical modifications to unlock its full therapeutic potential. This review aims to present the latest instances of lipid-based delivery of niclosamide and to compile successful strategies which may be employed when aiming to develop effective anticancer therapies.
Optimizing Project Time and Cost Prediction Using a Hybrid XGBoost and Simulated Annealing Algorithm
Authors: ForouzeshNejad Ali Akbar , Arabikhan Farzad , Aheleroff Shohin
Published: December 2024

Machine learning technologies have recently emerged as transformative tools for enhancing project management accuracy and efficiency. This study introduces a data-driven model that leverages the hybrid eXtreme Gradient Boosting-Simulated Annealing (XGBoost-SA) algorithm to predict the time and cost of construction projects. By accounting for the complexity of activity networks and uncertainties within project environments, the model aims to address key challenges in project forecasting. Unlike traditional methods such as Earned Value Management (EVM) and Earned Schedule Method (ESM), which rely on static metrics, the XGBoost-SA model adapts dynamically to project data, achieving 92% prediction accuracy. This advanced model offers a more precise forecasting approach by incorporating and optimizing features from historical data. Results reveal that XGBoost-SA reduces cost prediction error by nearly 50% and time prediction error by approximately 80% compared to EVM and ESM, underscoring its effectiveness in complex scenarios. Furthermore, the model’s ability to manage limited and evolving data offers a practical solution for real-time adjustments in project planning. With these capabilities, XGBoost-SA provides project managers with a powerful tool for informed decision-making, efficient resource allocation, and proactive risk management, making it highly applicable to complex construction projects where precision and adaptability are essential. The main limitation of the developed model in this study is the reliance on data from similar projects, which necessitates additional data for application to other industries.
An Efficient Lane Detection Network with Channel-Enhanced Coordinate Attention
Authors: Xu Ke , Hao Zhicheng , Zhu Ming , Wang Jiarong
Published: December 2024

Lane detection based on semantic segmentation can achieve high accuracy, but, in recent years, it does not have a mobile-friendly cost, which is caused by the complex iteration and costly convolutions in convolutional neural networks (CNNs) and state-of-the-art (SOTA) models based on CNNs, such as spatial CNNs (SCNNs). Although the SCNN has shown its capacity to capture the spatial relationships of pixels across rows and columns of an image, the computational cost and memory requirement needed cannot be afforded with mobile lane detection. Inspired by the channel attention and self-attention machine, we propose an integrated coordinate attention (ICA) module to capture the spatial-wise relationships of pixels. Furthermore, due to the lack of enhancement in the channel dimension, we created an efficient network with a channel-enhanced coordinate attention block named CCA, composed of ICA and other channel attention modules, for all-dimension feature enhancement. As a result, by replacing many repeated or iterative convolutions with the attention mechanism, CCA reduces the computational complexity. Thus, our method achieves a balance of accuracy and speed and has better performance on two lane datasets—TuSimple and ILane. At less than a few tenths of the computational cost, our CCA achieves superior accuracy compared to the SCNN. These results show that the low cost and great performance of our design enable the use of the lane detection task in autopilot scenarios.
Fault Feature Extraction Based on Variational Modal Decomposition and Lifting Wavelet Transform: Application in Gear of Mine Scraper Conveyor Gearbox
Authors: Lu Zhengxiong , Li Linyue , Zhang Chuanwei , Zhao Shuanfeng , Gong Lingxiao
Published: December 2024

Vibration-based fault diagnosis of chain conveyor gearboxes is challenging under high load and strong shock conditions. This paper applies motor current characteristic analysis technology to scraper conveyor gearbox fault diagnosis and proposes a fault feature extraction method. Firstly, a variational mode decomposition algorithm combined with a genetic algorithm is used to divide the original current signal into several sub-bands with different frequency modulation information, and irrelevant information is preliminarily eliminated. Secondly, an intrinsic mode function (IMF) sub-band fault information extraction method based on lifting wavelet transform is proposed. The minimum entropy value is used to set the sensitive parameters involved in lifting wavelet transform, and the power supply current frequency and noise interference information of a scraper conveyor are removed from the current signal. Finally, it is proved that variational mode decomposition combined with lifting wavelet transform can effectively diagnose the fault of a scraper conveyor by comparative experiments.
Comparing Skill Transfer Between Full Demonstrations and Segmented Sub-Tasks for Neural Dynamic Motion Primitives
Authors: Hanks Geoffrey , Venture Gentiane , Hu Yue
Published: December 2024

Programming by demonstration has shown potential in reducing the technical barriers to teaching complex skills to robots. Dynamic motion primitives (DMPs) are an efficient method of learning trajectories from individual demonstrations using second-order dynamic equations. They can be expanded using neural networks to learn longer and more complex skills. However, the length and complexity of a skill may come with trade-offs in terms of accuracy, the time required by experts, and task flexibility. This paper compares neural DMPs that learn from a full demonstration to those that learn from simpler sub-tasks for a pouring scenario in a framework that requires few demonstrations. While both methods were successful in completing the task, we find that the models trained using sub-tasks are more accurate and have more task flexibility but can require a larger investment from the human expert.
Control of Parallel Quadruped Robots Based on Adaptive Dynamic Programming Control
Authors: Liang Junwei , Tang Shenyu , Jia Bingyi
Published: December 2024

With the rapid development of robotics technology, quadruped robots have shown significant potential in navigating complex terrains due to their excellent stability and adaptability. This paper proposes an adaptive dynamic programming control method based on policy iteration, aimed at improving the motion performance and autonomous adaptability of parallel quadruped robots in unknown environments. First, the study establishes a kinematic model of the robot and performs inverse kinematics calculations to determine the angular functions for each joint of the robot’s legs. To improve the robot’s mobility on challenging terrains, we design an optimal tracking controller based on Generalized Policy Iteration (GPI). This approach reduces the model’s dependency on strict requirements and is applied to the control of quadruped robots. Finally, kinematic simulations are conducted based on pre-planned robot gaits. In addition, experiments are then conducted based on the simulation results. The results of simulation experiments indicate that the quadruped robot, under the adaptive optimal control algorithm, can achieve smooth walking on complex terrains, verifying the rationality and effectiveness of the parallel quadruped robot in handling such conditions. The experimental results further demonstrate that this strategy significantly improves the stability and robustness of the robot across various terrains.
Study of a Companion Trajectory Kinematics Analysis Method for the Five-Blade Rotor Swing Scraper Pump
Authors: Wang Chong , Zhang Shigong , Zhang Tiezhu , Zhang Hongxin , Li Minghao
Published: December 2024

This paper proposes a five-blade rotor swing scraper pump (FRSSP) to overcome traditional volumetric pumps’ drawbacks, such as poor sealing performance, low volumetric efficiency, and complex structure. This pump employs a rotating cam-swing scraper mechanism to achieve fluid intake and discharge. The FRSSP is compact in structure, self-sealing, and highly efficient in volumetric utilization, offering promising applications. A companion trajectory kinematic analysis method of the FRSSP is proposed. The polar coordinate equation of the companion trajectory is derived from the profile equation of the five-blade rotor cam. Based on this trajectory, a kinematic model of the scraper pump is established, resulting in the kinematic equations for the swing angle of the scraper, the pressure angle of the scraper, the rotation angle of the rotor, the angular velocity of the scraper, and the angular acceleration of the scraper. The kinematics of the FRSSP were simulated and validated using ADAMS. Comparing the results of theoretical calculations and simulation reveals that the error in the scraper swing angle is 1.85%, the maximum error in the scraper angular velocity is 4.93%, and the maximum error in the scraper angular acceleration is 2.47%, confirming the accuracy of the kinematic analysis method. A sensitivity analysis was performed on the kinematic research method for companion trajectories. After modifying the dimensions of key components in the scraper pump, the discrepancies between theoretical calculations and simulation results were within 5%, confirming the accuracy and robustness of the method. Flow field simulation analysis and experimental tests on the scraper pump revealed that the deviation between the simulated and experimental outlet flow rates was less than 5%, validating the feasibility of the pump’s structural principles and the reliability of the simulations. Furthermore, these findings indirectly affirmed the correctness of the companion trajectory kinematic analysis method.
Aero-Engine Fault Detection with an LSTM Auto-Encoder Combined with a Self-Attention Mechanism
Authors: Du Wenyou , Zhang Jingyi , Meng Guanglei , Zhang Haoran
Published: December 2024

The safe operation of aero-engines is crucial for ensuring flight safety, and effective fault detection methods are fundamental to achieving this objective. In this paper, we propose a novel approach that integrates an auto-encoder with long short-term memory (LSTM) networks and a self-attention mechanism for the anomaly detection of aero-engine time-series data. The dataset utilized in this study was simulated from real data and injected with fault information. A fault detection model is developed utilizing normal data samples for training and faulty data samples for testing. The LSTM auto-encoder processes the time-series data through an encoder–decoder architecture, extracting latent representations and reconstructing the original inputs. Furthermore, the self-attention mechanism captures long-range dependencies and significant features within the sequences, thereby enhancing the detection accuracy of the model. Comparative analyses with the traditional LSTM auto-encoder, as well as one-class support vector machines (OC-SVM) and isolation forests (IF), reveal that the experimental results substantiate the feasibility and effectiveness of the proposed method, highlighting its potential value in engineering applications.
Research on the Entrance Damage of Carbon Fiber-Reinforced Polymer/Ti6Al4V Stacks in Six-Degrees-of-Freedom Robot Drilling
Authors: Zhong Hao , Zhang Ziqiang , Wang Xue , Jiao Feng , Li Yuanxiao
Published: December 2024

Carbon fiber-reinforced polymer (CFRP)/titanium alloy (Ti6Al4V) stacks are widely used in the aerospace industry due to their excellent physical properties. The substantial demand for drilling components in the aerospace industry necessitates the implementation of enhanced processing efficiency and drilling quality standards. Six-degrees-of-freedom robots are commonly used in the aerospace industry due to their high production efficiency, high flexibility, and low labor costs. However, due to the weak stiffness, chatter is prone to occur during processing, which has a detrimental impact on the quality of the finished product. As an advanced processing technology, ultrasonic-assisted machining technology can effectively reduce the cutting force and suppress the chatter in the drilling process, so it is widely used in production. In this paper, first, the robot kinematic (dexterity) and stiffness performance is analyzed. Then, the appropriate range of the machining plane and the posture of the robot in the workspace are selected. Finally, the vibration and CFRP entrance damage during the machining process are compared and studied in conventional robotic drilling (CRD) and ultrasonic-assisted robotic drilling (UARD). The experimental results demonstrate that the UARD is an effective method for reducing vibration during the machining process. Compared with the CRD, the CFRP entrance delamination damage in UARD is reduced. Under the appropriate processing parameters, the entrance delamination factor could be reduced by 15%, and the burr height could be reduced by 45%. Obviously, the UARD is a promising process to improve the CFRP entrance delamination damage.
Synthesis of a Novel Magnetic Biochar from Lemon Peels via Impregnation-Pyrolysis for the Removal of Methyl Orange from Wastewater
Authors: Daffalla Samah , Da’na Enshirah , Taha Amel , El-Aassar Mohamed R.
Published: December 2024

This research examined the elimination of methyl orange (MO) utilizing a novel magnetic biochar adsorbent (MLPB) derived from lemon peels via an impregnation-pyrolysis method. Material characterization was conducted using SEM, XRD, TGA, FTIR, and nitrogen adsorption isotherms. SEM-EDX analysis indicates that MLPB is a homogeneous and porous composite comprising Fe, O, and C, with iron oxide uniformly dispersed throughout the material. Also, MLPB is porous with an average pore diameter of 4.65 nm and surface area value (111.45 m2/g). This study evaluated pH, MO concentration, and contact time to analyze the adsorption process, kinetics, and isothermal behavior. Under optimal conditions, MLPB was able to remove MO dye from aqueous solutions with an efficiency of 90.87%. Results showed optimal MO removal at pH 4, suggesting a favorable electrostatic interaction between the adsorbent and dye. To ascertain the adsorption kinetics, the experimental findings were compared using several adsorption models, first- and second-orders, and intra-particle diffusion. According to the findings, the pseudo-second-order model described the adsorption kinetic promoting the formation of the chemisorption phase well. Modeling of intra-particle diffusion revealed that intra-particle diffusion is not the only rate-limiting step. A study involving isothermal systems showed that Langmuir is a good representation of experimental results; the maximum adsorption capacity of MLPB was 17.21 mg/g. According to the results, after four cycles of regeneration, the produced magnetic material regained more than 88% of its adsorption ability.
Phase Formation, Microstructure, and Permeability of Fe-Deficient Ni-Cu-Zn Ferrites (II): Effect of Oxygen Partial Pressure
Authors: Priese Christoph , Töpfer Jörg
Published: December 2024

We have investigated the phase formation, microstructure, and permeability of stoichiometric and Fe-deficient Ni-Cu-Zn ferrites of composition Ni0.30Cu0.20Zn0.50+zFe2−zO4−(z/2) with 0 ≤ z ≤ 0.06 sintered at 1000 °C in various oxygen partial pressures pO2, which range from 0.21 atm down to 10−5 atm. The density of the sintered samples is almost independent of the pO2, whereas the grain size of the Fe-deficient ferrites decreases in more reducing atmospheres. Stoichiometric ferrites show a regular growth of single-phase ferrite grains if sintered in air. Sintering at pO2 ≤ 10−2 atm leads to the formation of a small amount of Cu2O at grain boundaries and triple points. Fe-deficient compositions (z > 0) form Cu-poor stoichiometric ferrites, which coexist with a minority CuO phase homogeneously distributed between the grains after sintering in air. At pO2 ≤ 10−2 atm, the CuO grain boundary phase starts to transform into Cu2O, which concentrates at some triple points at pO2 = 10−2 atm, and it is more homogeneously distributed between the ferrite grains at the lower pO2. Formation of the Cu oxide second phases is investigated using XRD, SEM, and EDX. The permeability at 1 MHz of the stoichiometric ferrites (z = 0) is between µ′ = 200 and µ′ = 300 within the studied range of the pO2. The permeability at 1 MHz of the Fe-deficient samples decreases with the pO2, e.g., from µ′ = 750 at pO2 = 0.21 atm to µ′ = 320 at pO2 = 10−5 atm for z = 0.02, respectively.
EGNAS: Efficient Graph Neural Architecture Search Through Evolutionary Algorithm
Authors: Jwa Younkyung , Ahn Chang Wook , Kim Man-Je
Published: December 2024

The primary objective of our research is to enhance the efficiency and effectiveness of Neural Architecture Search (NAS) with regard to Graph Neural Networks (GNNs). GNNs have emerged as powerful tools for learning from unstructured network data, compensating for several known limitations of Convolutional Neural Networks (CNNs). However, the automatic search for optimal GNN architectures has seen little progressive advancement so far. To address this gap, we introduce the Efficient Graph Neural Architecture Search (EGNAS), a method that leverages the advantages of evolutionary search strategies. EGNAS incorporates inherited parameter sharing, allowing offspring to inherit parameters from their parents, and utilizes half epochs to improve optimization stability. In addition, EGNAS employs a combined evolutionary search, which explores both the model structure and the hyperparameters within a large search space, resulting in improved performance. Our experimental results demonstrate that EGNAS outperforms state-of-the-art methods in node classification tasks on the Cora, Citeseer, and PubMed datasets while maintaining a high degree of computational efficiency. In particular, EGNAS is the fastest GNN architecture search method in terms of search time, particularly when compared to precedently suggested evolutionary search strategies, delivering performance up to 40 times faster.