bluuebunny/crossref_metadata_2025 huggingface dataset:
Diet Impacts on Gene Expression in Healthy Colon Tissue: Insights from the BarcUVa-Seq Study
Authors: Obón-Santacana Mireia , Moratalla-Navarro Ferran , Guinó Elisabet , Carreras-Torres Robert , Díez-Obrero Virginia , Bars-Cortina David , Ibáñez-Sanz Gemma , Rodríguez-Alonso Lorena , Mata Alfredo , García-Rodríguez Ana , Devall Matthew , Casey Graham , Li Li , Moreno Victor
Published: September 2024

(1) Introduction: The global rise of gastrointestinal diseases, including colorectal cancer and inflammatory bowel diseases, highlights the need to understand their causes. Diet is a common risk factor and a crucial regulator of gene expression, with alterations observed in both conditions. This study aims to elucidate the specific biological mechanisms through which diet influences the risk of bowel diseases. (2) Methods: We analyzed data from 436 participants from the BarcUVa-Seq population-based cross-sectional study utilizing gene expression profiles (RNA-Seq) from frozen colonic mucosal biopsies and dietary information from a semi-quantitative food frequency questionnaire. Dietary variables were evaluated based on two dietary patterns and as individual variables. Differential expression gene (DEG) analysis was performed for each dietary factor using edgeR. Protein–protein interaction (PPI) analysis was conducted with STRINGdb v11 for food groups with more than 10 statistically significant DEGs, followed by Reactome-based enrichment analysis for the resulting networks. (3) Results: Our findings reveal that food intake, specifically the consumption of blue fish, alcohol, and potatoes, significantly influences gene expression in the colon of individuals without tumor pathology, particularly in pathways related to DNA repair, immune system function, and protein glycosylation. (4) Discussion: These results demonstrate how these dietary components may influence human metabolic processes and affect the risk of bowel diseases.
Metabolic Footprint of Treponema phagedenis and Treponema pedis Reveals Potential Interaction Towards Community Succession and Pathogenesis in Bovine Digital Dermatitis
Authors: Espiritu Hector M. , Valete Edeneil Jerome P. , Mamuad Lovelia L. , Jung Myunghwan , Paik Man-Jeong , Lee Sang-Suk , Cho Yong-Il
Published: September 2024

Bovine digital dermatitis (BDD) is a cattle infection causing hoof lesions and lameness, with treponemes as key pathogens. We analyzed the metabolic activity of Treponema phagedenis and Treponema pedis using gas chromatography–mass spectrometry for organic acids (OAs), amino acids (AAs), and fatty acids (FAs), and high-performance liquid chromatography for short-chain fatty acids (SCFAs). Key findings include a 61.5% reduction in pyruvic acid in T. pedis and 81.0% in T. phagedenis. 2-hydroxybutyric acid increased by 493.8% in T. pedis, while succinic acid increased by 31.3%, potentially supporting T. phagedenis. Among AAs, glycine was reduced by 97.4% in T. pedis but increased by 64.1% in T. phagedenis. Proline increased by 76.6% in T. pedis but decreased by 13.6% in T. phagedenis. Methionine and glutamic acid were competitively utilized, with methionine reduced by 41.8% in T. pedis and 11.9% in T. phagedenis. Both species showed significant utilization of palmitic acid (reduced by 82.8% in T. pedis and 87.2% in T. phagedenis). Butyric acid production increased by 620.2% in T. phagedenis, and propionic acid increased by 932.8% in T. pedis and 395.6% in T. phagedenis. These reveal metabolic interactions between the pathogens, contributing to disease progression and offering insights to BDD pathogenesis.
Molecular Confirmation, Epidemiology, and Pathophysiology of Ehrlichia canis Prevalence in Eastern India
Authors: Chakraborty Ankita , Rath Prasana Kumar , Panda Susen Kumar , Mishra Bidyut Prava , Dehuri Manaswini , Biswal Sangram , Jena Manoj Kumar , Sahu Basanta Pravas , Paital Biswaranjan , Sahoo Dipak Kumar
Published: September 2024

The present study aimed to investigate pathological epidemiology and molecular confirmation of Ehrlichia canis among pet dogs in Bhubaneswar, Odisha, a state in eastern India. A total of 178 dogs were screened for Ehrlichiosis based on history, clinical signs, blood, and buffy coat smear examination, resulting in only 56 dogs (31.46%) screening positive. The epidemiological study recorded a non-significant (p ≥ 0.05) increase in incidences among male dogs (68%), German Shepherds (25%), dogs more than 20 kg body weight (75%), in the summer months (55%), and dogs housed in pukka houses with exposure to the outside (59%). The majority of the infected dogs had a history of tick infestation (79%) at some point in their lives. Clinical signs showed non-typical manifestations like fever, lethargy, diarrhoea, epistaxis, hind limb edema, and corneal opacity. Haematological studies revealed anaemia and thrombocytopenia along with neutrophilia with relative lymphopenia and monocytosis. A decreasing trend was observed in the levels of total protein and albumin, with an increase in the levels of globulin, alanine aminotransferase, alanine aminotransferase, aspartate aminotransferase, blood urea nitrogen, and creatinine. The ultrasonography studies revealed hepatosplenomegaly along with hyper-echogenicity in various organs. Proteinuria and haematuria were consistent, along with the presence of bile salts in the urine of affected dogs. Molecular confirmation from n-type PCR data using Ehrlichia-specific primers targeting the p28 gene (843 bp) was done, and the identified gene sequences submitted to NCBI databases have accession numbers OQ383671-OQ383674 and OP886674-OP886677. Ticks collected from dogs were identified morphologically through microscopy and scanning electron microscopy as Rhipicephalus sanguineus.
Synthesis and Biological Evaluation of Novel Cationic Rhenium and Technetium-99m Complexes Bearing Quinazoline Derivative for Epidermal Growth Factor Receptor Targeting
Authors: Triantopoulou Sotiria , Roupa Ioanna , Shegani Antonio , Pirmettis Nektarios N. , Terzoudi Georgia I. , Chiotellis Aristeidis , Tolia Maria , Damilakis John , Pirmettis Ioannis , Paravatou-Petsota Maria
Published: September 2024

Background/Objectives: Epidermal growth factor receptor (EGFR) plays a vital role in cell proliferation and survival, with its overexpression linked to various malignancies, including non-small cell lung cancer (NSCLC). Although EGFR tyrosine kinase inhibitors (TKIs) are a key therapeutic strategy, acquired resistance and relapse remain challenges. This study aimed to synthesize and evaluate novel rhenium-based complexes incorporating EGFR TKIs to enhance anticancer efficacy, particularly in radiosensitization. Methods: We synthesized a rhenium tricarbonyl complex (Complex 2) and its 99mTc analog (Complex 2’) by incorporating triphenylphosphine instead of bromine as the monodentate ligand and PF6− as the counter-ion, resulting in a positively charged compound that forms cationic structures. Cytotoxicity and EGFR inhibition were evaluated in A431 cells overexpressing EGFR using MTT assays, Western blotting, and flow cytometry. Radiosensitization was tested through MTT and clonogenic assays. The 99mTc complex’s radiochemical yield, stability, and lipophilicity were also assessed. Results: Complex 2 exhibited significant cytotoxicity with an IC50 of 2.6 μM and EGFR phosphorylation inhibition with an IC50 of 130.6 nM. Both complex 1 and 2 induced G0/G1 cell cycle arrest, with Complex 2 causing apoptosis. Radiosensitization was observed at doses above 2 Gy. Complex 2’ demonstrated high stability and favorable lipophilicity (LogD7.4 3.2), showing 12% cellular uptake after 30 min. Conclusions: Complexes 2 and 2’ show promise as dual-function anticancer agents, offering EGFR inhibition, apoptosis induction, and radiosensitization. Their potential as radiopharmaceuticals warrants further in-depth investigation in preclinical models.
Titanium Dioxide Nanomaterials: Progress in Synthesis and Application in Drug Delivery
Authors: Zuo Fanjiao , Zhu Yameng , Wu Tiantian , Li Caixia , Liu Yang , Wu Xiwei , Ma Jinyue , Zhang Kaili , Ouyang Huizi , Qiu Xilong , He Jun
Published: September 2024

Background: Recent developments in nanotechnology have provided efficient and promising methods for the treatment of diseases to achieve better therapeutic results and lower side effects. Titanium dioxide (TiO2) nanomaterials are emerging inorganic nanomaterials with excellent properties such as low toxicity and easy functionalization. TiO2 with special nanostructures can be used as delivery vehicles for drugs, genes and antigens for various therapeutic options. The exploration of TiO2-based drug delivery systems shows great promise for translating nanotechnology into clinical applications; Methods: Comprehensive data on titanium dioxide were collected from reputable online databases including PubMed, GreenMedical, Web of Science, Google Scholar, China National Knowledge Infrastructure Database, and National Intellectual Property Administration; Results: In this review, we discuss the synthesis pathways and functionalization strategies of TiO2. Recent advances of TiO2 as a drug delivery system, including sustained and controlled drug release delivery systems were introduced. Rigorous long-term systematic toxicity assessment is an extremely critical step in application to the clinic, and toxicity is still a problem that needs to be closely monitored; Conclusions: Despite the great progress made in TiO2-based smart systems, there is still a great potential for development. Future research may focus on developing dual-reaction delivery systems and single-reaction delivery systems like redox and enzyme reactions. Undertaking thorough in vivo investigations is necessary prior to initiating human clinical trials. The high versatility of these smart drug delivery systems will drive the development of novel nanomedicines for personalized treatment and diagnosis of many diseases with poor prognosis.
Economic Evaluation of Conservation through Use of an Araucaria angustifolia Provenance and Progeny Test
Authors: Machado José Arimatéia Rabelo , Freitas Miguel Luiz Menezes , Paiva Daniela Ivana , Souza Bruno Marchetti de , Sousa Valderês Aparecida De , Martins Karina , Oliveira Edilson Batista , Aguiar Ananda Virginia De
Published: September 2024

Araucaria angustifolia is a species known for its valuable wood and nuts, but it is threatened with extinction. The plantation of forests for genetic resource conservation is a complementary strategy designed to reduce the species’ genetic variability loss. This study aimed to evaluate the technical and economic viability of A. angustifolia for genetic conservation through use. The analyzed provenance and progeny trial was established in 1982 in Itapeva, Brazil. It was structured using a compact family blocks design with 110 open-pollinated progenies from five natural populations, three replicates, ten plants per subplot, and 3.0 m × 2.0 m spacing. After 33 years, the trial was evaluated for total height, diameter at breast height, wood volume, and survival. The variance components and genetic parameter estimates were performed using Restricted Maximum Likelihood/Best Linear Unbiased Prediction methods (REML/BLUP) methods with the Selegen software (version 2014). The production and management scenarios were obtained using the SisAraucaria software (version 2003). Sensitivity analysis and economic parameter estimates were obtained through various economic evaluation methods using the Planin software (version 1995). In general, the genetic parameters indicated that the population has enough variability for both conservation and breeding purposes, suggesting technical viability for the establishment of a seed orchard. The economic parameters indicated that the commercialization of wood and araucaria nuts proved to be more profitable than wood production by itself. In conclusion, araucaria genetic conservation through use is a technically and economically viable ex situ conservation strategy.
Impacts of Distribution Data on Accurate Species Modeling: A Case Study of Litsea auriculata (Lauraceae)
Authors: Tan Chao , Ferguson David Kay , Yang Yong
Published: September 2024

Global warming has caused many species to become endangered or even extinct. Describing and predicting how species will respond to global warming is one of the hotspots of biodiversity research. Species distribution models predict the potential distribution of species based on species occurrence data. However, the impact of the accuracy of the distribution data on the prediction results is poorly studied. In this study, we used the endemic plant Litsea auriculata (Lauraceae) as a case study. By collecting and assembling six different datasets of this species, we used MaxEnt to perform species distribution modeling and then conducted comparative analyses. The results show that, based on our updated complete correct dataset (dataset 1), the suitable distribution of this species is mainly located in the Ta-pieh Mountain, southwestern Hubei and northern Zhejiang, and that mean diurnal temperature range (MDTR) and temperature annual range (TAR) play important roles in shaping the distribution of Litsea auriculata. Compared with the correct data, the wrong data leads to a larger and expanded range in the predicted distribution area, whereas the species modeling based on the correct but incomplete data predicts a small and contracted range. We found that only about 23.38% of Litsea auriculata is located within nature reserves, so there is a huge conservation gap. Our study emphasized the importance of correct and complete distribution data for accurate prediction of species distribution regions; both incomplete and incorrect data can give misleading prediction results. In addition, our study also revealed the distribution characteristics and conservation gap of Litsea auriculata, laying the foundation for the development of reasonable conservation strategies for this species.
Biological Decline of Alfalfa Is Accompanied by Negative Succession of Rhizosphere Soil Microbial Communities
Authors: Ma Yuanyuan , Shen Yan , Zhou Xiaoping , Ma Hongbin , Lan Jian , Fu Bingzhe , Xue Quanhong
Published: September 2024

The growth and biological decline of alfalfa may be linked to the rhizosphere microbiome. However, plant–microbe interactions in the rhizosphere of alfalfa and associated microbial community variations with stand age remain elusive. This study explored the successional pattern of rhizosphere microbial communities across different aged alfalfa stands and its relationship with alfalfa decline. Rhizosphere soils were collected from 2- and 6-year-old alfalfa stands. Control soils were collected from interspaces between alfalfa plants in the same stands. Soil bacterial and fungal communities were characterized by 16S and ITS rRNA gene sequencing, respectively. Specific microbial taxa colonized the rhizosphere soils, but not the control soils. The rhizosphere-specific taxa mainly included potentially beneficial genera (e.g., Dechloromonas, Verrucomicrobium) in the young stand and harmful genera (e.g., Peziza, Campylocarpon) in the old stand. Alfalfa roots regulated soil microbial communities by selective promotion or inhibition of distinct taxa. The majority of time-enriched taxa were reported as harmful fungi, whose relative abundances were negatively correlated with plant traits. Time-depleted taxa were mostly known as beneficial bacteria, which had relative abundances positively correlated with plant traits. The relative abundances of functional bacterial genes associated with vancomycin biosynthesis, zeatin biosynthesis, and amino acid metabolism trended lower in rhizosphere soils from the old stand. An upward trend was observed for fungal pathogens and wood saprotrophs with increasing stand age. The results suggest that root activity drives the negative succession of rhizosphere microbial communities during alfalfa decline in old stands.
Biodegradable Natural Hydrogels for Tissue Engineering, Controlled Release, and Soil Remediation
Authors: Garcia-Garcia Ane , Muñana-González Sara , Lanceros-Mendez Senentxu , Ruiz-Rubio Leire , Alvarez Leyre Perez , Vilas-Vilela José Luis
Published: September 2024

This article provides insights into hydrogels of the most promising biodegradable natural polymers and their mechanisms of degradation, highlighting the different possibilities of controlling hydrogel degradation rates. Since biodegradable hydrogels can be designed as scaffolding materials to mimic the physical and biochemical properties of natural tissues, these hydrogels have found widespread application in the field of tissue engineering and controlled release. In the same manner, their potential as water reservoirs, macro- and microelement carriers, or matrixes for the selective adsorption of pollutants make them excellent candidates for sustainable soil amendment solutions. Accordingly, this article summarizes the recent advances in natural biodegradable hydrogels in the fields of tissue engineering, controlled release, and soil remediation, emphasizing the new opportunities that degradability and its tunability offer for the design and applicability of hydrogels.
Optimization of 3D Printing Parameters of High Viscosity PEEK/30GF Composites
Authors: Stepanov Dmitry Yu. , Dontsov Yuri V. , Panin Sergey V. , Buslovich Dmitry G. , Alexenko Vladislav O. , Bochkareva Svetlana A. , Batranin Andrey V. , Kosmachev Pavel V.
Published: September 2024

The aim of this study was to optimize a set of technological parameters (travel speed, extruder temperature, and extrusion rate) for 3D printing with a PEEK-based composite reinforced with 30 wt.% glass fibers (GFs). For this purpose, both Taguchi and finite element methods (FEM) were utilized. The artificial neural networks (ANNs) were implemented for computer simulation of full-scale experiments. Computed tomography of the additively manufactured (AM) samples showed that the optimal 3D printing parameters were the extruder temperature of 460 °C, the travel speed of 20 mm/min, and the extrusion rate of 4 rpm (the microextruder screw rotation speed). These values correlated well with those obtained by computer simulation using the ANNs. In such cases, the homogeneous micro- and macro-structures were formed with minimal sample distortions and porosity levels within 10 vol.% of both structures. The most likely reason for porosity was the expansion of the molten polymer when it had been squeezed out from the microextruder nozzle. It was concluded that the mechanical properties of such samples can be improved both by changing the 3D printing strategy to ensure the preferential orientation of GFs along the building direction and by reducing porosity via post-printing treatment or ultrasonic compaction.
Dual-Method Characterization and Optimization of Drilling Parameters for Picosecond Laser Drilling Quality in CFRP
Authors: Zheng Zhao , Ma Yao , Wang Zhonghe , Liu Siqi , Wu Chunting
Published: September 2024

Carbon fiber-reinforced polymer (CFRP), known for its light weight, high strength, and corrosion-resistant properties, is extensively used in the lightweight design of satellite components, the optimization of electronic device casings, and the processing of high-performance composite materials in the defense sector. This study employs picosecond laser drilling technology for the precision machining of CFRP, demonstrating its advantages over traditional mechanical drilling and other unconventional methods in significantly reducing the heat-affected zone (HAZ) and enhancing hole wall quality. The optimization of laser power, scanning speed, and fill times via response surface methodology (RSM) significantly reduced the hole wall taper to 4.160° and confined the HAZ to within 18.577 μm, thereby enhancing machining precision. The actual test results show that the deviations in the hole taper and HAZ width were 5.0% and 2.2%, respectively, further verifying the effectiveness of the optimization method. This technique not only improves processing quality but also offers significant industrial application value in the machining of materials for related high-tech fields.
Patterning of Organic Semiconductors Leads to Functional Integration: From Unit Device to Integrated Electronics
Authors: Choi Wangmyung , Kim Yeo Eun , Yoo Hocheon
Published: September 2024

The use of organic semiconductors in electronic devices, including transistors, sensors, and memories, unlocks innovative possibilities such as streamlined fabrication processes, enhanced mechanical flexibility, and potential new applications. Nevertheless, the increasing technical demand for patterning organic semiconductors requires greater integration and functional implementation. This paper overviews recent efforts to pattern organic semiconductors compatible with electronic devices. The review categorizes the contributions of organic semiconductor patterning approaches, such as surface-grafting polymers, capillary force lithography, wettability, evaporation, and diffusion in organic semiconductor-based transistors and sensors, offering a timely perspective on unconventional approaches to enable the patterning of organic semiconductors with a strong focus on the advantages of organic semiconductor utilization. In addition, this review explores the opportunities and challenges of organic semiconductor-based integration, emphasizing the issues related to patterning and interconnection.
Synergistic Effects of Radical Distributions of Soluble and Insoluble Polymers within Electrospun Nanofibers for an Extending Release of Ferulic Acid
Authors: Dong Ran , Gong Wenjian , Guo Qiuyun , Liu Hui , Yu Deng-Guang
Published: September 2024

Polymeric composites for manipulating the sustained release of an encapsulated active ingredient are highly sought after for many practical applications; particularly, water-insoluble polymers and core–shell structures are frequently explored to manipulate the release behaviors of drug molecules over an extended time period. In this study, electrospun core–shell nanostructures were utilized to develop a brand-new strategy to tailor the spatial distributions of both an insoluble polymer (ethylcellulose, EC) and soluble polymer (polyvinylpyrrolidone, PVP) within the nanofibers, thereby manipulating the extended-release behaviors of the loaded active ingredient, ferulic acid (FA). Scanning electron microscopy and transmission electron microscopy assessments revealed that all the prepared nanofibers had a linear morphology without beads or spindles, and those from the coaxial processes had an obvious core–shell structure. X-ray diffraction and attenuated total reflectance Fourier transform infrared spectroscopic tests confirmed that FA had fine compatibility with EC and PVP, and presented in all the nanofibers in an amorphous state. In vitro dissolution tests indicated that the radical distributions of EC (decreasing from shell to core) and PVP (increasing from shell to core) were able to play their important role in manipulating the release behaviors of FA elaborately. On one hand, the core–shell nanofibers F3 had the advantages of homogeneous composite nanofibers F1 with a higher content of EC prepared from the shell solutions to inhibit the initial burst release and provide a longer time period of sustained release. On the other hand, F3 had the advantages of nanofibers F2 with a higher content of PVP prepared from the core solutions to inhibit the negative tailing-off release. The key element was the water permeation rates, controlled by the ratios of soluble and insoluble polymers. The new strategy based on core–shell structure paves a way for developing a wide variety of polymeric composites with heterogeneous distributions for realizing the desired functional performances.
Robots for the Energy Transition: A Review
Authors: Taraglio Sergio , Chiesa Stefano , De Vito Saverio , Paoloni Marco , Piantadosi Gabriele , Zanela Andrea , Di Francia Girolamo
Published: September 2024

The energy transition relies on an increasingly massive and pervasive use of renewable energy sources, mainly photovoltaic and wind, characterized by an intrinsic degree of production uncertainty, mostly due to meteorological conditions variability that, even if accurately estimated, can hardly be kept under control. Because of this limit, continuously monitoring the operative status of each renewable energy-based power plant becomes relevant in order to timely face any other uncertainty source such as those related to the plant operation and maintenance (O&M), whose effect may become relevant in terms of the levelized cost of energy. In this frame, the use of robots, which incorporate fully automatic platforms capable of monitoring each plant and also allow effective and efficient process operation, can be considered a feasible solution. This paper carries out a review on the use of robots for the O&M of photovoltaic, wind, hydroelectric, and concentrated solar power, including robot applications for controlling power lines, whose role can in fact be considered a key complementary issue within the energy transition. It is shown that various robotic solutions have so far been proposed both by the academy and by industries and that implementing their use should be considered mandatory for the energy transition scenario.
Identification of Flow Pressure-Driven Leakage Zones Using Improved EDNN-PP-LCNetV2 with Deep Learning Framework in Water Distribution System
Authors: Dong Bo , Shu Shihu , Li Dengxin
Published: September 2024

This study introduces a novel deep learning framework for detecting leakage in water distribution systems (WDSs). The key innovation lies in a two-step process: First, the WDS is partitioned using a K-means clustering algorithm based on pressure sensitivity analysis. Then, an encoder–decoder neural network (EDNN) model is employed to extract and process the pressure and flow sensitivities. The core of the framework is the PP-LCNetV2 architecture that ensures the model’s lightweight, which is optimized for CPU devices. This combination ensures rapid, accurate leakage detection. Three cases are employed to evaluate the method. By applying data augmentation techniques, including the demand and measurement noises, the framework demonstrates robustness across different noise levels. Compared with other methods, the results show this method can efficiently detect over 90% of leakage across different operating conditions while maintaining a higher recognition of the magnitude of leakages. This research offers a significant improvement in computational efficiency and detection accuracy over existing approaches.
Computational Fluid Dynamics (CFD) Investigation of NREL Phase VI Wind Turbine Performance Using Various Turbulence Models
Authors: Al-Ttowi Abobakr , Mohammed Akmal Nizam , Al-Alimi Sami , Zhou Wenbin , Saif Yazid , Ismail Iman Fitri
Published: September 2024

This study presents a detailed computational fluid dynamics (CFD) investigation into the aerodynamic performance of the NREL Phase VI wind turbine, focusing on torque and power generation under different turbulence models. The primary objective was to analyse the effect of various turbulence models and their responses in wind turbine torque generation. Furthermore, it also investigates the distance effect on wind velocity deficit. The research utilizes 2D and 3D simulations of the S809 airfoil and the full rotor, examining the predictive capabilities of the k-epsilon, k-omega, and k-omega SST turbulence models. The study incorporates both experimental validation and wake analysis using the Gaussian wake model to assess wind velocity deficits. Simulations were conducted for a wind speed range of (6–10 m/s), with results indicating that the k-epsilon model provided the closest match to experimental data, particularly at higher wind speeds within the targeted range. Even though k-epsilon results had better agreement when validated with experimental data, theoretically k-omega (SST) should perform better as it combines k-epsilon and k-omega advantages in predicting the flow regardless of its farness from the wall. However, in simulations using the k-omega (SST), the separation of flow and the shear stress transients were only visible at wind speeds of 10 m/s or higher. Wake effects, on the other hand, were found to cause significant velocity deficits behind the turbine, following an exponential decay pattern. The findings offer valuable insights into improving wind turbine performance through turbulence model selection and wake impact analysis, providing practical guidelines for future wind energy optimizations.
Coordination of Renewable Energy Integration and Peak Shaving through Evolutionary Game Theory
Authors: Sun Jian , Wu Fan , Shi Mingming , Yuan Xiaodong
Published: September 2024

This paper presents a novel approach to optimizing the coordination between renewable energy generation enterprises and power grid companies using evolutionary game theory. The research focuses on resolving conflicts and distributing benefits between these key stakeholders in the context of large-scale renewable energy integration. A theoretical model based on replicator dynamics is developed to simulate and analyze the evolutionary stable strategies of power generation enterprises and grid companies with particular emphasis on peak shaving services and electricity bidding. These simulations are based on theoretical models and do not incorporate real-world data directly, but they aim to replicate scenarios that reflect realistic behaviors within the electricity market. The model is validated through dynamic simulation under various scenarios, demonstrating that the final strategic choices of both thermal power and renewable energy enterprises tend to evolve towards either high-price or low-price bidding strategies, significantly influenced by initial system parameters. Additionally, this study explores how the introduction of peak shaving compensation affects the coordination process and stability of renewable energy integration, providing insights into improving grid efficiency and enhancing renewable energy adoption. Although the results are simulation-based, they are designed to offer practical recommendations for grid management and policy development, particularly for the integration of renewable energies such as wind power in competitive electricity markets. The findings suggest that effective government regulation, alongside well-designed compensation mechanisms, can help establish a balanced interest distribution between stakeholders. By offering a clear framework for analyzing the dynamics of renewable energy integration, this work provides valuable policy recommendations to promote cooperation and stability in electricity markets. This study contributes to the understanding of the complex interactions in the electricity market and offers practical solutions for enhancing the integration of renewable energy into the grid.
Promoting Gas Extraction Technology with Screen Pipe for Long Borehole Protection in Soft Seam
Authors: Wang Lin , Wang Dezhang , Chen Xiangjun , Min Rui
Published: September 2024

The inherent properties of soft coal seams and their mechanical environment make long boreholes susceptible to issues like collapse, deformation, and blockages. These problems shorten the service life of the boreholes and hinder extraction efficiency. This paper tackles these challenges by analyzing the deformation and damage patterns of long boreholes in soft coal seams. It examines the stress distribution and deformation characteristics around both protected and unprotected boreholes at different burial depths. Additionally, it recommends using screen pipe protection technology to improve gas extraction and mining operations, as demonstrated in Changping Coal Mine. The results show that screen pipe protection substantially improves the stress distribution and deformation stability of coal seam boreholes. The flow attenuation coefficients of two boreholes equipped with protection technology decreased by 48% and 61%. After 50 days of extraction from boreholes with a protection rate exceeding 90%, gas concentration remained above 50%, which is 2.59 times higher than that of unprotected boreholes. This technology effectively addresses the frequent accidents, poor extraction performance, and inefficiency of long boreholes in soft coal seams, ensuring the mine’s safe and efficient production.
Christian and Indigenous: Multiple “Religions” in Contemporary Toraja Funerals
Authors: Maćkowiak Anna M.
Published: September 2024

The theoretical framework of “religion” is problematic, especially in studying non-Western realities. In the field, I often encountered its Indonesian and Toraja most common equivalents—agama and aluk. There were also categories assigned to the realm of “culture” rather than “religion”. Toraja funeral ceremonies, which originated from the indigenous religion and became predominantly Christianized, are defined in religious and/or cultural categories. How do these related categories manifest in the utterances of the ritual actors of Toraja funerals? This article is based primarily on interviews; it refers to statements from 34 purposively chosen research participants. The attitudes towards the Toraja funeral tradition vary based on religious affiliation. The lines between different perspectives and categorization characteristics of Christianities and the minority indigenous religion are blurred but distinguishable.
Evaluating Satellite-Based Water Quality Sensing of Inland Waters on Basis of 100+ German Water Bodies Using 2 Different Processing Chains
Authors: Schmidt Susanne I. , Schröder Tanja , Kutzner Rebecca D. , Laue Pia , Bernert Hendrik , Stelzer Kerstin , Friese Kurt , Rinke Karsten
Published: September 2024

Remote sensing for water quality evaluation has advanced, with more satellites providing longer data series. Validations of remote sensing-derived data for water quality characteristics, such as chlorophyll-a, Secchi depth, and turbidity, have often remained restricted to small numbers of water bodies and have included local calibration. Here, we present an evaluation of > 100 water bodies in Germany covering different sizes, maximum depths, and trophic states. Data from Sentinel-2 MSI and Sentinel-3 OLCI were analyzed by two processing chains. Our work focuses on analysis of the accuracy of remote sensing products by comparing them to a large in situ data set from governmental monitoring from 13 federal states in Germany and, hence, achieves a national scale assessment. We quantified the fit between the remote sensing data and in situ data among processing chains, satellite instruments, and our three target water quality variables. In general, overall regressions between in situ data and remote sensing data followed the 1:1 regression. Remote sensing may, thus, be regarded as a valuable tool for complementing in situ monitoring by useful information on higher spatial and temporal scales in order to support water management, e.g., for the European Water Framework Directive (WFD) and the Bathing Water Directive (BWD).
The Link between Surface Visible Light Spectral Features and Water–Salt Transfer in Saline Soils—Investigation Based on Soil Column Laboratory Experiments
Authors: Qin Shaofeng , Zhang Yong , Ding Jianli , Wang Jinjie , Han Lijing , Zhao Shuang , Zhu Chuanmei
Published: September 2024

Monitoring soil salinity with remote sensing is difficult, but knowing the link between saline soil surface spectra, soil water, and salt transport processes might help in modeling for soil salinity monitoring. In this study, we used an indoor soil column experiment, an unmanned aerial vehicle multispectral sensor camera, and a soil moisture sensor to study the water and salt transport process in the soil column under different water addition conditions and investigate the relationship between the soil water and salt transport process and the spectral reflectance of the image on the soil surface. The observation results of the soil column show that the soil water and salt transportation process conforms to the basic transportation law of “salt moves together with water, and when water evaporates, salt is retained in the soil weight”. The salt accumulation phenomenon increases the image spectral reflectance of the surface layer of the soil column, while soil temperature has no effect on the reflectance. As the water percolates down, water and salt accumulate at the bottom of the soil column. The salinity index decreases instantly after the addition of brine and then tends to increase slowly. The experimental results indicate that this work can capture the relationship between the water and salt transport process and remote sensing spectra, which can provide theoretical basis and reference for soil water salinity monitoring.
Context-Aware DGCN-Based Ship Formation Recognition in Remote Sensing Images
Authors: Zhang Tao , Yang Xiaogang , Lu Ruitao , Xie Xueli , Wang Siyu , Su Shuang
Published: September 2024

Ship detection and formation recognition in remote sensing have increasingly garnered attention. However, research remains challenging due to arbitrary orientation, dense arrangement, and the complex background of ships. To enhance the analysis of ship situations in channels, we model the ships as the key points and propose a context-aware DGCN-based ship formation recognition method. First, we develop a center point-based ship detection subnetwork, which employs depth-separable convolution to reduce parameter redundancy and combines coordinate attention with an oriented response network to generate direction-invariant feature maps. The center point of each ship is predicted by regression of the offset, target scale, and angle to realize the ship detection. Then, we adopt the spatial similarity of the ship center points to cluster the ship group, utilizing the Delaunay triangulation method to establish the topological graph structure of the ship group. Finally, we design a context-aware Dense Graph Convolutional Network (DGCN) with graph structure to achieve formation recognition. Experimental results on HRSD2016 and SGF datasets demonstrate that the proposed method can detect arbitrarily oriented ships and identify formations, attaining state-of-the-art performance.
Assessing Air Quality Dynamics during Short-Period Social Upheaval Events in Quito, Ecuador, Using a Remote Sensing Framework
Authors: Alvarez Cesar Ivan , López Santiago , Vásquez David , Gualotuña Dayana
Published: September 2024

This study uses a remote sensing approach to investigate air quality fluctuations during two short-period social upheaval events caused by civil protests in 2019 and the COVID-19 pandemic in 2020 in Quito, Ecuador. We used data from the TROPOMI Sentinel-P5 satellite to evaluate the concentrations of two greenhouse gases, namely O3 and NO2. TROPOMI Sentinel-P5 satellite data are becoming essential in air quality monitoring, particularly for countries that lack ground-based monitoring systems. For a better approximation of satellite data with ground data, we related the remotely sensed data using ground station data and Pearson correlation analysis, which revealed a significant association between the two sources (0.43 ≤ r ≤ 0.78). Using paired t-test comparisons, we evaluated the differences in mean gas concentrations at 30 randomly selected intervals to identify significant changes before and after the events. The results indicate noticeable changes in the two gases over the three analysis periods. O3 significantly decreased between September and November 2019 and between March and May 2020, while NO2 significantly increased. NO2 levels decreased by 18% between February and March 2020 across the study area, as indicated by remote sensing data. The geovisualization of remotely sensed data over these periods supports these patterns, suggesting a potential connection with population density. The results show the complexity of drawing global conclusions about the impact of social disruptions on the atmosphere and emphasize the advantages of using remote sensing as an effective framework to address air quality changes over short periods of time. This study also highlights the advantages of a remote sensing approach to monitor atmospheric conditions in countries with limited air quality monitoring infrastructure and provides a valuable approach for the evaluation of short-term alterations in atmospheric conditions due to social disturbance events.
CFD Analyses of Density Gradients under Conditions of Supersonic Flow at Low Pressures
Authors: Bayer Robert , Bača Petr , Maxa Jiří , Šabacká Pavla , Binar Tomáš , Vyroubal Petr
Published: September 2024

This paper deals with CFD analyses of the difference in the nature of the shock waves in supersonic flow under atmospheric pressure and pressure conditions at the boundary of continuum mechanics for electron microscopy. The first part describes the verification of the CFD analyses in combination with the experimental chamber results and the initial analyses using optical methods at low pressures on the boundary of continuum mechanics that were performed. The second part describes the analyses on an underexpanded nozzle performed to analyze the characteristics of normal shock waves in a pressure range from atmospheric pressure to pressures at the boundary of continuum mechanics. The results obtained by CFD modeling are prepared as a basis for the design of the planned experimental sensing of density gradients using optical methods, and for validation, the expected pressure and temperature courses from selected locations suitable for the placement of temperature and pressure sensors are prepared from the CFD analyses.
Near-Surface Air Temperature Estimation Based on an Improved Conditional Generative Adversarial Network
Authors: Zheng Jiaqi , Wu Xi , Li Xiaojie , Peng Jing
Published: September 2024

To address the issue of missing near-surface air temperature data caused by the uneven distribution of ground meteorological observation stations, we propose a method for near-surface air temperature estimation based on an improved conditional generative adversarial network (CGAN) framework. Leveraging the all-weather coverage advantage of Fengyun meteorological satellites, Fengyun-4A (FY-4A) satellite remote sensing data are utilized as conditional guiding information for the CGAN, helping to direct and constrain the near-surface air temperature estimation process. In the proposed network model of the method based on the conditional generative adversarial network structure, the generator combining a self-attention mechanism and cascaded residual blocks is designed with U-Net as the backbone, which extracts implicit feature information and suppresses the irrelevant information in the Fengyun satellite data. Furthermore, a discriminator with multi-level and multi-scale spatial feature fusion is constructed to enhance the network’s perception of details and the global structure, enabling accurate air temperature estimation. The experimental results demonstrate that, compared with Attention U-Net, Pix2pix, and other deep learning models, the method presents significant improvements of 68.75% and 10.53%, respectively in the root mean square error (RMSE) and Pearson’s correlation coefficient (CC). These results indicate the superior performance of the proposed model for near-surface air temperature estimation.
Aerogel-Lined Capillaries as Liquid-Core Waveguides for Raman Signal Gain of Aqueous Samples: Advanced Manufacturing and Performance Characterization
Authors: Spiske Felix , Jakob Lara Sophie , Lippold Maximilian , Rahimi Parvaneh , Joseph Yvonne , Braeuer Andreas Siegfried
Published: September 2024

An advanced process for the manufacturing of aerogel-lined capillaries is presented; these are applicable as liquid-core waveguides for gaining the Raman signal of aqueous samples. With respect to the spin-coating process we have used so far for the manufacturing of aerogel-lined capillaries, the here-presented manufacturing process is advanced as it enables (i) the lining of longer capillaries, (ii) the adjustment of the lining-thickness via the lining velocity, and (iii) the reproducible generation of crack-free linings. The key parameters of the advanced process and their effect on the fabrication of aerogel-lined capillaries with optimal Raman signal gain are reported and related to the thickness and topography of the aerogel linings by the support of scanning electron microscopy.
An Underwater Crack Detection System Combining New Underwater Image-Processing Technology and an Improved YOLOv9 Network
Authors: Huang Xinbo , Liang Chenxi , Li Xinyu , Kang Fei
Published: September 2024

Underwater cracks are difficult to detect and observe, posing a major challenge to crack detection. Currently, deep learning-based underwater crack detection methods rely heavily on a large number of crack images that are difficult to collect due to their complex and hazardous underwater environments. This study proposes a new underwater image-processing method that combines a novel white balance method and bilateral filtering denoising method to transform underwater crack images into high-quality above-water images with original crack features. Crack detection is then performed based on an improved YOLOv9-OREPA model. Through experiments, it is found that the new image-processing method proposed in this study significantly improves the evaluation indicators of new images, compared with other methods. The improved YOLOv9-OREPA also exhibits a significantly improved performance. The experimental results demonstrate that the method proposed in this study is a new approach suitable for detecting underwater cracks in dams and achieves the goal of transforming underwater images into above-water images.
Research on the Performance of Thermoelectric Self−Powered Systems for Wireless Sensor Based on Industrial Waste Heat
Authors: Jiang Yong , Wang Yupeng , Yan Junhao , Shen Limei , Qin Jiang
Published: September 2024

The issue of energy supply for wireless sensors is becoming increasingly severe with the advancement of the Fourth Industrial Revolution. Thus, this paper proposed a thermoelectric self−powered wireless sensor that can harvest industrial waste heat for self−powered operations. The results show that this self−powered wireless sensor can operate stably under the data transmission cycle of 39.38 s when the heat source temperature is 70 °C. Only 19.57% of electricity generated by a thermoelectric power generation system (TPGS) is available for use. Before this, the power consumption of this wireless sensor had been accurately measured, which is 326 mW in 0.08 s active mode and 5.45 μW in dormant mode. Then, the verified simulation model was established and used to investigate the generation performance of the TPGS under the Dirichlet, Neumann, and Robin boundary conditions. The minimum demand for a heat source is cleared for various data transmission cycles of wireless sensors. Low−temperature industrial waste heat is enough to drive the wireless sensor with a data transmission cycle of 30 s. Subsequently, the economic benefit of the thermoelectric self−powered system was also analyzed. The cost of one thermoelectric self−powered system is EUR 9.1, only 42% of the high−performance battery cost. Finally, the SEPIC converter model was established to conduct MPPT optimization for the TEG module and the output power can increase by up to approximately 47%. This thermoelectric self−powered wireless sensor can accelerate the process of achieving energy independence for wireless sensors and promote the Fourth Industrial Revolution.