bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hydrogeological and speleological characterization of a karstic spring: Cokragan Cave System
Authors: BAYKARA Mehmet Oruç , Gemici Ünsal 
Published: February 2024

Abstract

Karstic landscapes, shaped by water dissolving rock, are unique ecosystems with complex water systems. Karst aquifers, vital for over 25% of the world's drinking water, offer a sustainable resource but are vulnerable to human activities. Their diverse plant, animal, and geological features contribute significantly to our planet's ecosystem. Notably, 49% of European geoparks contain karst, highlighting their importance in geoheritage and geotourism. Studying karst requires precise mapping of springs, ponors, channels, and caves to understand their unique hydrogeological processes. Here, we present a rare integrated study of Cokragan Cave (Spring) using both speleology and hydrogeology. This complex 2,050-meter cave system reveals multiple past groundwater levels through its geometry, with elevation differences suggesting tectonic influence. From 2003 to 2007, Cokragan Spring discharged and recharged 63.5 and 62.37 million cubic meters annually, respectively. Measured discharge ranged from a maximum of 1.488 cubic meters per second to a minimum of 0.108 cubic meters per second. In situ measurements and analysis of 13 samples revealed the groundwater's physicochemical characteristics, including major ions like calcium, magnesium, and bicarbonate, and trace elements like iron, manganese, and zinc.
The effect of Sambucus nigra fruit extract in three chemotropic cycles of cisplatin drug on male reproductive system of mice
Authors: Esmaeli Mojtaba , Ghanbari Ali , Roshankhah Shiva , Barazesh Mahdi 
Published: February 2024

Abstract

Background
Administration of chemotherapeutic agents, such as cisplatin, induces infertility as a side effect in men of childbearing age, while consumption of antioxidants can recover this process. Therefore, the present study was designed to evaluate the protective effect of Sambucus nigra (SNA) on cisplatin alone and in combination in reducing the toxicity of cisplatin on the reproductive system of mice.
Materials & methods
In this study, 30 adult balb/c mice were randomly divided into 5 groups (n = 6). Mice in the groups received cisplatin (0.4 mg/kg) and SNA (200 mg/kg) intraperitoneally for three periods of six days with an interval of 10 days for a total of 48 days. The positive and negative control groups received normal saline and no drug, respectively. At the end of the treatment period, various parameters such as semen analysis, changes in testicular histological structures, changes in hormone levels, expression of apoptotic genes and the amount of malondialdehyde (MDA), total superoxide dismutase (T-SOD), glutathione peroxidase (GSH-Px) and catalase (CAT) were evaluated.
Results
The results of the present study showed that SNA could significantly reduce abnormalities of sperm and testis due to cisplatin administration. In addition, SNA was able to reduce the expression of genes involved in the apoptotic pathway compared to the cisplatin group. Meanwhile, SNA reduced serum MDA rate and raised rate T-SOD, GSH-Px and CAT rate compared to the cisplatin group.
Conclusion
The findings of this study suggest that SNA may have a protective effect against cisplatin in testicular tissue via its antioxidant properties.
Functional investigation and two-sample Mendelian randomization study of Early gastric cancer (EGC) hub genes obtained by WGCNA analysis
Authors: Ren Xiao-Jun , Zhang Man-Ling , Zhao Hui-Hui , Shi Zhao-Hong 
Published: February 2024

Abstract
Objective: This study aimed to identify differentially expressed genes associated with early gastric cancer and analyze their potential functions through bioinformatics analysis. Additionally, we sought to validate the genetic causality of identified differential molecules related to early gastric cancer using Mendelian randomization.
Methods: We analyzed the expression profiles of 19 gastritis specimens and 20 early gastric cancer specimens (high-grade intraepithelial neoplasia) from the GEO database using combined differential gene expression analysis and weighted gene co-expression network analysis (WGCNA). Hub genes most relevant to early gastric cancer were selected and subjected to functional enrichment and signaling pathway analysis using GO and KEGG enrichment. The diagnostic efficacy of the predictive model based on the top five ranked hub genes was evaluated using ROC curves. Finally, Mendelian randomization analysis was employed to assess genetic causality between hub genes and early gastric cancer.
Results: A total of 755 hub genes were identified after intersecting the most relevant modular genes from WGCNA with genes exhibiting significant expression differences. GO and KEGG pathway enrichment analyses revealed significant differential expression of hub genes in responses to xenobiotic stimuli, hormone transport, apical cell parts, and oxidoreductase activity targeting the CH-OH group of donors, as well as involvement in pluripotency of stem cells, hepatocellular carcinoma, and axon guidance. The top five core genes—IL6, CLU, UGT2B15, NANOG, and NFE2L2—were analyzed using Cytoscape software. The column-linear graph prediction model demonstrated excellent performance in predicting the risk of early gastric cancer, as evidenced by ROC analysis. In the inverse variance weighting (IVW) method, we found that the core gene CLU was associated with an increased risk of early gastric cancer (OR = 1.157, 95% CI = 1.043-1.283, p = 0.0057).
Conclusion: Our bioinformatics analysis identified the CLU gene as genetically causally associated with early gastric cancer, suggesting its potential as a diagnostic or therapeutic target for this condition.
Response of Branched Broomrape (Phelipanche ramosa (L.) Pomel) to Amino Acid Treatments
Authors: Albayrak Hilal , Isik Doğan , Arslan Mehmet 
Published: February 2024

Abstract
Phelipanche ramose(branched broomrape), a root holoparasitic weed causes great trouble in agriculture along Mediterranean, Europe, Asia, Africa, North and South America, cannot be easily controlled by the known management practices applied for non-parasitic weeds. Determination of novel compounds with higher herbicidal activity against P. ramosa can provide effective control for this parasitic weed. Certain amino acids have potential to inhibit early development of broomrape without any injury to host plants. A series of pot and in vitro studies were conducted to identify amino acids with higher inhibition efficacy on broomrape germination and growth. Germination inhibition rates of amino acids were significantly different. The highest inhibitory rates were obtained from alanine, lysine and tryptophan, while the lowest was obtained from theanine, leucine and isoleucine under the in vitro conditions. The inhibitory effect of amino acids on germination enhanced with the increasing concentrations. In the polyethylene bag studies, the number of germinated seed per plant varied between 46.00 and 205.50. The lowest and the highest germinated seeds were obtained from proline and theanine, respectively. Tubercle number per plant varied between 0.00 and 3.50. Glutamic acid had the highest tubercle number, while leucine, glycine, tryptophan and isoleucine had the lowest tubercle number per plant. In general, lysine, proline, phenylalanine, asparagine are thought to the most effective inhibitors of brunched broomrape germination. Further studies are needed to evaluate the usefulness of amino acids in P. ramose control under field conditions.
RDMS: Reverse Distillation with Multiple Students of Different Scales for Anomaly Detection
Authors: Chen Ziheng , Lyu Chenzhi , Zhang Lei , Li ShaoKang , Xia Bin 
Published: February 2024

Abstract
Unsupervised anomaly detection is a critical task in computer vision, often approached as a one-class classification problem. Knowledge distillation has shown promising results in this area, particularly with the emergence of reverse distillation networks using encoder-decoder architectures, which further enhance anomaly detection accuracy. In this study, we propose a novel reverse knowledge distillation network with multiple scale student decoders, called RDMS. RDMS integrates a pretrained teacher encoding module, a trainable multi-level feature fusion connection module (MFFCM), and a student decoding module composed of three mutually independent decoders. Each student decoder is tasked with distilling a specific feature from the teacher encoder, effectively mitigating the overfitting issue that occurs when the student and teacher structures are similar or identical. Our model achieves an average of 99.3% image-level AUROC and 98.34% pixel-level AUROC on the publicly available dataset MVTec-AD, and also achieves state-of-the-art performance on the more challenging BTAD dataset. The proposed RDMS model demonstrates high accuracy for anomaly detection and local-ization, highlighting the potential of multi-student reverse distillation for improving unsupervised anomaly detection capabilities. Source code is available at https://github.com/zihengchen777/RDMS
Deep learning tool：Reconstruction of long missing climate data based on multilayer perceptron(MLP)
Authors: Zhang Yan , Xu Tianxin , Zhang Chenjia , Daokun Ma 
Published: February 2024

Abstract
Long-term monitoring of climate data is significant for grasping the law and development trend of climate change and guaranteeing food security. However, some weather stations lack monitoring data for even decades. In this study, 62 years of historical monitoring data from 105 weather stations in Xinjiang were used for missing sequence prediction, validating proposed data reconstruction tool. First of all, study area was divided into three parts according to the climatic characteristics and geographical locations. A deep learning tool based on multilayer perceptron (MLP) was established to reconstruct meteorological data with three time scales（Short term, cycle and long term）and one spatio dimension as inputing, filling in long sequence blank data. By designing an end-to-end model to autonomously detect the locations of missing data and make rolling predictions,we obtained complete meteorological monitoring data of Xinjiang from 1961 to 2022. Seven kinds of parameter reconstructed include maximum temperature (Max_T), minimum temperature (Min_T), mean temperature (Ave _ T), average water vapor pressure (Ave _ WVP), relative humidity (Ave _ RH), average wind speed (10 m Ave _ WS), and sunshine duration (Sun_H). The quality of reconstructed data was evaluated by calculating correlation coefficient with the monitored sequences of nearest station. Results show that,proposed model reached satisfied average correlation coefficient for Max_T, Min_T, Ave _ T and Ave _ WVP parameters are 0.969, 0.961, 0.971 and 0.942 respectively. The average correlation coefficient of Sun_H and Ave _ RH are 0.720 and 0.789. Although it is difficult to predict extreme values, it can still capture the period and trend; the reconstruction effect of 10 m Ave _ WS is poor, with the average similarity of 0.488. Finally, we published the trained parameter files and prediction codes as a micro service on the Agricultural Smart Brain platform, which provides firstly a deep learning tool for rapid and reliable reconstruction of meteorological monitoring data.
Structural characterization and bioactive screening of two homogeneity proteins from Tianshan red deer abomasum
Authors: Yang Zi , Wali Ahmidin , Wang Yahui , Arken Amina , Eli Yusupjan , Gao Yanhua , Yili Abulimiti , Aisa HajiAkber 
Published: February 2024

Abstract
In this study, response surface methodology (RSM) and Box-Behnken design (BBD) were used to optimize the process of extracting abomasum protein from Tianshan red deer. The final extraction conditions were as follows: ultrasonic power: 445 W, extraction temperature: 43 °C, and extraction solution salt concentration: 0.57 mol/L. Gel chromatography and anion exchange chromatography were applied to isolate two homogeneity proteins named A1 and A2 from the abomasum of Tianshan red deer. The protein contents of A1 and A2 were 71.57% ± 5.65 and 68.83%±4.35 respectively. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) determined the molecular weights of two homogeneity proteins to be 63 kDa and 55 kDa. The secondary structures of two homogeneity proteins are primarilyβ-folds and α-helixes. In addition, activity screening revealed the anti-inflammatory property of A1 (78.67% ± 2.23 inhibition of cyclooxygenase 2 (COX-2) at 0.1 mg/ml) and the anti-tumor activity of A2 (79.38% ± 3.52 and 85.89% ± 3.37 inhibition of human colon cancer cells (HCT-116) and human gastric cancer cells (HGC-27), respectively, at 1 mg/ml). These findings suggest that protein from the abomasum of Tianshan red deer may be useful as an efficacy factor in foods and nutraceuticals.
A cross-sectional analysis of severe SARS cases evolution in a brazilian municipality using data mining techniques
Authors: Júnior Silvano Herculano da Luz , Oliveira William Farias Carvalho , Neto Luis Cesar de Albuquerque , Souza Hugo Araujo , de Sant’Anna Yúri Faro Dantas , Adeodato Paulo Jorge Leitão 
Published: February 2024

Abstract
The first Severe Acute Respiratory Syndrome (SARS) outbreak occurred in China in November 2002. Since then, other coronavirus variants have emerged worldwide, such as Middle East Respiratory Syndrome (MERS) in 2012, 2019-nCOV in 2019, and Omicron in 2020. Several studies have been published, demonstrating the use of data mining (DM) to create relevant classification and decision systems for analyzing clinical data of patients with SARS. However, most of these studies lack the depth of consideration for the socioeconomic factors of the patients, such as income, education levels, race, among others, which could be relevant for classification algorithms. This study demonstrates the application of the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework and the use of DM techniques and tools, employing binary classification and decision algorithms to predict the progression of severe cases in SARS patients residing in the municipality of Recife, Pernambuco, Brazil. It is a cross-sectional study conducted using open datasets, considering various attributes related to symptoms, pre-existing conditions, and socioeconomic factors, including income, literacy rate, and patient domicile location. The analysis involved three healthcare experts (physicians). The results highlighted that the apriori algorithm performed better in rule induction, and the decision tree showed slightly better performance compared to logistic regression. Furthermore, the analysis brought to light interesting correlations between the progression of severe cases and the socioeconomic data of patients.
Identification of Hidrogeochemical Processes in the Montebello Lagunar System by Inverse Modeling in PHREEQC.
Authors: Villanueva Luis Antonio García , LozadaTrujillo José Abel , Rivas Dionisio Arturo Cabrera , Hernández Denise Michel Villarreal , Moctezuma Gustavo Alejandro Méndez , Moctezuma Alan Humberto Méndez 
Published: February 2024

Abstract
The aim of this article is to address a current problematic, which is concentrated in one of the most iconic locations in the state of Chiapas, Mexico. It is about a series of lakes found out at the Parque Nacional Lagunas de Montebello where has presented important alterations detected since the beginning of the 21st century. Making use of inverse modeling hydrogeochemical, it seeks to identify the predominant hydrogeochemical process in the place, since the chemical condition of the water will be known in greater detail in its constant interaction with geological conditions of the site to determine the degree of alteration owing anthropogenic activities developed around it, such as agriculture, industrial discharges and sewage that reach the lakes. Finding due to geological framework of the karst region, where there are rocks made of dolomites, limestone and plaster formations, one of the principal processes determinated by the modelation and comprobated by the comparation in site, is the precipitation of the plaster proper to the low solubility, reactions of oxidation and reduction of the organic matter, precipitation or dissolution of nitrogenous elements and salts that came from the unloading done to the bed of the great river that feed the study site.
Image-Driven Laser Ablation Optimization
Authors: Bukhtoyarov Vladimir V. , Ageev Daniel A. , Malashin Ivan P. , Tynchenko Vadim S. , Borodulin Aleksei S. , Neluyb Vladimir A. 
Published: February 2024

Abstract
This study presents the development of optimal nanostructures on surfaces post-laser ablation processinga software solution aimed at enhancing the efficiency of this process. Leveraging image processing techniques, particularly images from Scanning Electron Microscopy (SEM), the software optimizes laser system parameters to achieve desired nanostructure morphology. By integrating image analysis with laser system optimization, this approach offers a solution for improving the precision and efficacy of nanostructure fabrication. The proposed methodology holds significant promise for advancing laser ablation processes in material engineering and nanophotonics.
Thermomechanical Behaviour of Concrete Reinforced With Synthetic And Organic Fibers.
Authors: sahnoun Ilhem , Toumi Belgacem 
Published: February 2024

Abstract
The aim of this research work is to study the thermomechanical behavior of concrete with organic fibers such as camel fibers, chicken feather fibers and compare it with concrete without fibers and with synthetic fibers (polypropylene fibers). In order to study the influence of the introduction of fibers on the behavior of concretes subjected to high temperatures, tests are carried out to characterize the various physical and mechanical properties of concretes at high temperatures. Concrete formulated with different fiber content (0.5% chicken feather fiber (OCC2), 1.5% camel hair fiber (OCC3) and 0.9% polypropylene fiber (OCC4)) and ordinary concrete taken as control concrete (OCC1) are subjected to heating/cooling cycles at a rate of 1°C/min at 105°C,150°C, 250°C, 500°C and 750°C. The results showed that chicken feather fibers can be used to reinforce concrete by improving its behavior in the face of increased temperature and by having a better impact on the environment.
PSC Diffusion: Patch-based Simplified Conditional Diffusion Model for Low-light Image Enhancement
Authors: Wan Fei , Xu Bingxin , Pan Weiguo , Liu Hongzhe 
Published: February 2024

Abstract
Low-light image enhancement is pivotal for augmenting the utility and recognition of visuals captured under inadequate lighting conditions. Generative models are widely recognized as a mainstream approach by framing the challenge as an image-to-image translation task. This paper propose the Patch-based Simplified Conditional Diffusion Model (PSC Diffusion) for low-light image enhancement due to the outstanding performance of diffusion models in image generation. Specifically, recognizing the potential issue of gradient vanishing in extremely low-light images due to smaller pixel values, we design a simplified U-Net architecture with SimpleGate and Parameter-free attention (SimPF) block to predict noise. This architecture utilizes parameter-free attention mechanism and fewer convolutional layers to reduce multiplication operations across feature maps, resulting in a 12%-51% reduction in parameters compared to U-Nets used in several prominent diffusion models, which also accelerates the sampling speed. In addition, preserving intricate details in images during the diffusion process is achieved through employing a patch-based diffusion strategy, integrated with global structure-aware regularization, which effectively enhances the overall quality of the enhanced images. Experiments show that the method proposed in this paper achieves richer image details and better perceptual quality, while the sampling speed is over 35% faster than similar diffusion model-based methods.
In-Situ UNet-Based Heliostat Beam Characterization Method for Precise Flux Calculation Using the Camera-Target Method
Authors: Kuhl Mathias , Pargmann Max , Cherti Mehdi , Jitsev Jenia , Quinto Daniel Maldonado , Pitz-Paal Robert 
Published: February 2024

Abstract
Concentrated Solar Power (CSP) utilizing solar tower power plants exhibits significant potential for dispatchable sustainable power generation and fuel production, by concentrating up to hundred thousand focal spots of individual mirrors onto one surface. The irradiance on the receiver is a critical parameter for power plant operators, not only to prevent component-damaging temperature peaks and gradients but also to optimize the distribution of heliostat’s focal spots. Therefore, recent approaches aim to exploit images of isolated focal spots on a Lambertian target, taken on a day-to-day basis in most commercial power plants, to achieve more accurate flux predictions. State-of-the-art methods for flux measurements require additional equipment and are mostly time-intensive. This study introduces a novel methodology to derive single heliostat irradiance profiles using simple digital camera images, employing an image-to-image neural network. The model we propose is capable of generating accurate, high-resolution flux distributions from single images. It uniquely accounts for the target surface’s irregularities and reflectivity as well as background irradiation, factors critical for precise measurements of individual focal spots. By incorporating these factors, our methodology offers a new scaling approach for the measured flux density based on a calculated total flux, obviating the need for additional equipment such as flux gauges or moving bars. This enables to effortlessly be integrated into the daily calibration routine in-situ using the cameratarget method. This seamless integration not only supports existing flux prediction methodologies but also paves the way for future enhancements by potentially improving the accuracy of overall flux prediction.
Large-scale fermentation of Lactiplantibacillus pentosus 292 for the production of lactic acid and the storage strategy based on molasses as a preservative
Authors: chen xing , Wei Zhirong , Feng Ziqiao , Ren Wei , Che Yuhan , Wang Xinyi , Long Hao , Cai Xiaoni , Xie Zhenyu 
Published: February 2024

Abstract
A cheap medium containing 30 g/L glucose, 15 g/L yeast powder, 8 g/L K2HPO4, and 10 g/L tween-80 was developed for Lactiplantibacillus pentosus 292, and the lactic acid (LA) produced in this medium was up to 16.24 g/L increased by 83.48% compared with that in MRS medium after 24 h fermentation under optimal condition (temperature 35 ℃, pH 6, shaking speed 100 rpm, and inoculation amount 3%). Subsequently, the large-scale fermentation kinetics models of bacterial growth, substrate consumption, and product generation in a 200-L fermenter were built using better-fitting models of Logistic, Luedeking, and Luedeking-Piret, respectively. Then, the preservation strategy of fermentation broth with 1–3% molasses as preservative and temperature acceleration experiment were studied. This work besides constructing the large-scale fermentation kinetics models of L. pentosus 292, also developed a storage strategy based on the molasses as a preservative, thereby facilitating the application of 292-strain for large-scale production of lactic acid bacteria (LAB) preparation and its products.