bluuebunny/crossref_metadata_2025_split huggingface dataset:
AerialIRGAN: Unpaired Aerial Visible-to-Infrared Image Translation with Dual-Encoder Structure
Authors: Ma Decao , Su Juan , Li Shaopeng , Xian Yong 
Published: July 2024

Abstract

Due to the high cost of equipment and the constraints of shooting conditions, obtaining aerial infrared images of specific targets is very challenging. Most methods using Generative Adversarial Networks for translating visible images to infrared greatly depend on registered data and struggle to handle the diversity and complexity of scenes in aerial infrared targets. This paper proposes a one side end-to-end unpaired aerial visible-to-infrared image translation algorithm, termed AerialIRGAN. AerialIRGAN introduces a dual-encoder structure, where one encoder is designed based on the Segment Anything Model to extract deep semantic features from visible images, and the other encoder is designed based on UniRepLKNet to capture small-scale patterns and sparse patterns from visible images. Subsequently, AerialIRGAN constructs a bridging module to deeply integrate the features of both encoders and their corresponding decoders. Finally, AerialIRGAN proposes a structural appearance consistency loss to guide the synthetic infrared images to maintain the structure of the source image while possessing distinct infrared characteristics. The experimental results show that compared to the existing typical infrared image generation algorithms, the proposed method can generate higher-quality infrared images and achieve better performance in both subjective visual description and objective metric evaluation.
The Role of Exogenous Mitochondria in Enhancing the Survival of Transplanted Fat Tissue
Authors: Li Haoran , Li Zhengyao , Zhang Xiaoyu , Lin Yan , Zhang Tongtong , Gan Leijuan , Mu Dali 
Published: July 2024

Abstract

Background
Despite the pivotal role of fat grafting in the realms of plastic, reconstructive, and aesthetic surgery, the erratic survival rates of transplanted adipose tissue, primarily due to early ischemic and hypoxic insults, pose a substantial challenge. The strategic infusion of healthy mitochondria has emerged as a critical intervention for the recuperation of tissues from ischemic, hypoxic, and a variety of damages across numerous organ systems.
Objectives
This investigation seeks to assess the impact of supplementing human adipose tissue grafts with healthy exogenous mitochondria on their volume and mass retention rates when transplanted into the subcutaneous layers of nude mice. This novel approach aims to refine and enhance the efficacy of fat grafting techniques.
Methods
Human adipose tissues were preconditioned with exogenous mitochondria (10µg/mL), a combination of exogenous mitochondria and the inhibitor Dyngo-4a, Dyngo-4a alone, and PBS, and subsequently transplanted into the subcutaneous tissue of 24 nude mice. Samples were harvested at 1 and 3 months post-transplantation for analysis of mass and volume retention. The structural morphology and integrity of the adipose tissues were evaluated using Hematoxylin and Eosin (H&E) staining.
Results
The incorporation of mitochondrial preconditioning significantly enhanced the retention of mass and volume in fat grafts, demonstrating superior structural morphology and integrity in comparison to those observed in the control group.
Conclusions
The outcomes of this study underscore the significant potential of exogenous mitochondrial augmentation in fat transplantation to substantially increase fat graft survival and, consequently, optimize the success rates of fat grafting interventions.
stDyer enables spatial domain clustering with dynamic graph embedding
Authors: Xu Ke , Xu Yu , Wang Zirui , Zhou Xin , Zhang Lu 
Published: July 2024

Abstract

Spatially resolved transcriptomics (SRT) data provide critical insights into gene expression patterns within tissue contexts, necessitating effective methods for identifying spatial domains. Traditional clustering techniques often overlook spatial information, leading to disjointed domains. Current computational approaches integrate spatial information but still face challenges in recognizing domain boundaries, scalability, and the need of independent clustering steps. We introduce stDyer, an end-to-end deep learning framework designed for spatial domain clustering in SRT data. stDyer combines a Gaussian Mixture Variational AutoEncoder (GMVAE) with graph attention networks (GATs) to simultaneously learn deep representations and perform clustering for units. A unique feature of stDyer is the dynamic graphs it adopts, which adaptively links units based on Gaussian Mixture assignments in the latent space, thereby improving spatial domain clustering and producing smoother domain boundaries. Additionally, stDyer's mini-batch neighbor sampling strategy facilitates scalability to large datasets and enables multi-GPU training. Benchmarking against state-of-the-art tools across various SRT technologies, stDyer demonstrates superior performance in spatial domain clustering, multi-slice analysis, and large-scale dataset handling.
Detection and Classification of Concrete Pavement Cracks Based on Residual Neural Networks and the Bisection Method
Authors: yu zhi , Wu Qiong , Tang Changhua , Shi Qingtao , Song Wei , Si Junrui 
Published: July 2024

Abstract

Concrete pavement cracks can reduce road safety and lead to traffic accidents. Detecting road cracks and implementing appropriate maintenance measures holds significant practical importance. Addressing the low detection accuracy of existing research methods for road crack detection and the limited studies on classifying and rating the severity of road cracks, this study first proposes a COTECANet model based on the ResNet50 architecture. This model effectively detects pavement cracks with a recognition accuracy of 99.8%, surpassing other compared deep learning models. Subsequently, for roads with detected cracks, the maximum inscribed circle radius of the crack contours in the images is computed using the bisection method, thereby obtaining the maximum pixel width of the road cracks. Finally, by proportional conversion, the actual width of the measured pavement cracks is obtained, and the damage severity of the road cracks is classified and rated according to relevant standards. This research can help highway management departments implement corresponding maintenance measures based on the actual conditions of road damage, thereby extending the lifespan of roads and possessing practical application significance.
Chemically induced deceleration of nuclear spin relaxation (CIDER) preserves hyperpolarization
Authors: Peters Josh , Assaf Charbel , Brahms Arne , Them Kolja , Gerdsen Mirco , Herges Rainer , Hövener Jan-Bernd , Pravdivtsev Andrey 
Published: July 2024

Abstract

The hyperpolarization of nuclear spins has enabled real-time molecular magnetic resonance imaging. Hyperpolarized tracers like pyruvate have enabled visualization of real-time metabolism in vitro and in vivo. However, the scope of hyperpolarizable molecules remains limited. We found that specific molecules such as 15N-pyridine, 1-15N-nicotinamide, and 15N-nicotinic acid can be hyperpolarized well using dynamic nuclear polarization; however, the polarization does not persist after dissolution. The primary contribution to this swift relaxation is a rapid chemical exchange of protons and possibly other interactions, which accelerate relaxation at low magnetic fields. We discovered that adding molecules, nicotinamide, urea, nicotinic acid, and glycerol to the dissolution medium, protected the tracers from relaxation and dramatically improved 15N polarization. This chemically induced deceleration of nuclear spin relaxation (CIDER) effect allowed us to observe the highest 15N hyperpolarization reported in literature so far, almost 30% for the biomolecule 1-15N nicotinamide. We anticipate that CIDER will be very useful to prolong the T1 of various molecules, thereby extending the scope and boundaries of hyperpolarization molecular imaging and beyond.
What is Beautiful is Still Good: The Attractiveness Halo Effect in the era of Beauty Filters
Authors: Gulati Aditya , Martinez-Garcia Marina , Fernandez Daniel , Lozano Miguel Angel , Lepri Bruno , Oliver Nuria 
Published: July 2024

Abstract

The impact of cognitive biases on decision-making in the digital world remains under-explored despite its well-documented effects in physical contexts. This study addresses this gap by investigating the attractiveness halo effect using AI-based beauty filters. We conduct a large-scale online user study involving 2,748 participants who rated facial images from a diverse set of 462 distinct individuals in two conditions: original and attractive after applying a beauty filter.
Our study reveals that the same individuals receive statistically significantly higher ratings of attractiveness and other traits, such as intelligence and trustworthiness, in the attractive condition. We also study the impact of age, gender, and ethnicity and identify a weakening of the halo effect in the beautified condition, resolving conflicting findings from the literature and suggesting that filters could mitigate this cognitive bias. Finally, our findings raise ethical concerns regarding the use of beauty filters.
Research Trends Of Clinical Learning Environment In Nursing And Midwifery Education: A Bibliometric Analysis
Authors: Hidigow Abdishakur Mohamud Hassan , Dirie Najib isse , Warsame Zakarie Abdi 
Published: July 2024

Abstract

The clinical learning environment (CLE) is at the heart of nursing and midwifery education. This research aims to contribute to the discourse on the CLE by analyzing trends and patterns and uncover collaboration networks among researchers and key terms in the research literature through bibliometric analysis and visualization. A descriptive bibliometric study was carried out on June 7, 2024. The Scopus database was utilized in this study to gather all the CLE research performance from inception until June 2024. VOS viewer was used to analyze scientific collaborations. The search in the Scopus database found 3179 articles; finally, after the inclusion and exclusion criteria, the number of articles was lowered to 3077. Regarding publications and citations, the United States and Australia have been the top contributors from an international perspective. Key journals such as "Nurse Education Today," "Nurse Education in Practice," "Journal of Advanced Nursing," and "Journal of Clinical Nursing" were critical in spreading research, with large numbers of publications and citations. Prominent author contributions included Saarikoski M., Levett-Jones T., and Henderson A. The co-occurrence network of terms highlights how student experiences, clinical competence, psychological factors, and teaching methods are all connected issues. Regarding limitations, the databases used to analyze the bibliometric data may not include all relevant articles, especially those published in fewer prominent journals or languages other than English. Furthermore, citation metrics can be impacted by external variables like self-citations or citation practices within specific fields or areas. Studying the implications of virtual and digital learning environments, the significance of cultural competency in varied clinical settings, and the incorporation of modern technology like simulation and virtual reality in clinical education are crucial.
A cross-sectional study of the prevalence of ketoacidosis and its causes in patients with type 1 diabetes compared to patients with type 2 diabetes.
Authors: Suleman Yazan Jabr AL , Alhassani Mahmoud Yahia , Hammoud Taghrid 
Published: July 2024

Abstract

Research objective: Studying the local characteristics of ketoacidosis, comparing it with medical literature in other societies, and develop an educational and treatment plan for doctors dealing with diabetics, patients, and their families, which will improve the results of managing this acute complication and preventing its occurrence.
Methods and materials: The study sample included 201 patients with ketoacidosis admitted to the endocrinology department of Damascus Hospital (Al-Mujtahid) between 2019 and 2024. We conducted a descriptive, retrospective cross-sectional observational study of the files of patients who met the exclusion and inclusion criteria.
Results: Ketoacidosis was the diagnosing manifestation of diabetes in 19 patients (9.45%), while the distribution of the sample according to gender, females: males, was 1.2: 1, and the average age of the patients was 42.6 ± 14.3 years, with the vast majority of patients between 20-39 years, and the first type was Diabetes was the most common in our study at a rate of 66.2%, and insulin was the most common treatment used to lower blood sugar in the study sample at a rate of 69.7% of patients previously diagnosed with diabetes. As for the prevalence of symptoms, they were: abdominal pain 84.1%, followed by diarrhea 82.6%, then fatigue and general weakness 72.1 %, fever, diarrhea, dyspnea, urinary heartburn, and vomiting, and when studying the factors inciting ketoacidosis, infection in general was the most common, followed by non-adherence to treatment, then systemic distress, then diabetes diagnosed for the first time, and 6% of patients had an unknown cause, noting that the urinary tract The most common site of infection was 66.2%, followed by respiratory infection 29.9%, gastrointestinal infection 23.9%, and dental abscess 19.4%.
Conclusion: Our study showed a slight tendency for acidosis to occur in females, and more likely in patients with type 1 diabetes. It presents with symptoms, the most common of which are abdominal pain and vomiting, and that one of its most important causes in our region is sepsis, especially urinary tract infection.
Determination of the effects of irrigation with recycled wastewater and biochar treatments on crop and soil properties in maize cultivation
Authors: Yerli Caner 
Published: July 2024

ABSTRACT
The study was conducted under two water qualities (fresh water (FW), recycled wastewater (RWW)) and two biochar treatments (no biochar (No-B) and biochar (B)). It was determined that B reduced the actual evapotranspiration by saving irrigation water and that biomass yield increased in RWW and B; thus, RWW and B provided higher WPirrigation and WP. RWW and B increased OM, TN, P2O5, K2O, CEC, porosity, and aggregate stability, thus encouraging the development of the physical–physiological properties, ADF-NDF content, and biomass yields of the crop, but causing EC to increase. RWW and B resulted in higher macro–microelement contents and heavy metal (HM) contamination in the soil; thus, increases were observed in the macro–microelements and the HM content of the crop grown in RWW and B, but the absorption and buffering capacity of B limited the Na–Cd–Cr–Ni uptake of maize. However, the HM contents of the soil–crop did not exceed international standards in all treatments except the Cd content of maize. It was found that the use of B in irrigation with RWW can be recommended, considering the productivity-increasing contribution and the effectiveness of B in reducing the possible HM risks of RWW in maize cultivation, but monitoring the Cd content of maize and the EC of the soil.
Developing and evaluating decision support indicators (DSIs) of climate change impacts on flood and drought: a case study in Western Norway
Authors: Huang Shaochun , Eisner Stephanie , Beldring Stein 
Published: July 2024

ABSTRACT

The decision support indicators (DSIs) are specifically designed to inform local and regional stakeholders on the characteristics of a predicted event to facilitate decision-making. They can be classified as conventional, impact-based and event-based DSIs. This study aims to develop methodologies for calculating event-based DSIs and to evaluate the usefulness of different classes of DSIs for climate impact assessment and climate actions by learning about users' perceptions. The DSIs are calculated based on an ensemble of hydrological projections in western Norway under two representative concentration pathway (RCP) scenarios. The definitions, methodologies and results of the indicators are summarized in questionnaires and evaluated by key stakeholders in terms of understandability, importance, plausibility and applicability. Based on the feedback, we conclude that the conventional DSIs are still preferred by stakeholders and an appropriate selection of conventional DSIs may overcome the understanding problems between the scientists and stakeholders. The DSIs based on well-known historical events are easy to understand and can be a useful tool to convey climate information to the public. However, they are not readily implemented by stakeholders in the decision-making process. The impact-based DSI is generally easy to understand and important but it can be restricted to specific impact sectors.
Comparing the hybrid-lumped-LSTM model with a semi-distributed model for improved hydrological modeling
Authors: Zarei Erfan , Saleh Farzin Nasiri , Dalir Afsaneh Nobakht 
Published: July 2024

ABSTRACT
Accurate hydrological modeling is essential for understanding and managing water resources. This study conducts a comparative analysis of hydrological modeling strategies in a date-scarce region. This study examines lumped (IHACRES), semi-distributed (HEC-HMS), and hybrid-lumped/long short-term memory (LSTM) models, aiming to assess their performance and accuracy in a data-scarce region. It investigates whether lump models can accurately simulate flow and evaluates the impact of combining lump models with machine learning to enhance accuracy, compared to semi-distributed models. The IHACRES model underestimates discharge, but its commendable NSE during calibration (0.628) and validation (0.681) signifies reliable simulation. The HEC-HMS model accurately depicts daily streamflow but struggles with extreme events, showcasing limitations in predicting maximum flows. The hybrid-lumped/LSTM model exhibits improved accuracy over IHACRES. Despite some underestimation, it mitigates IHACRES limitations during extreme events. However, challenges persist in simulating high flows, emphasizing the necessity for further refinement. The findings contribute to the discourse on merging machine learning with traditional hydrological models in data-scarce regions. The hybrid model offers promise but underscores the need for ongoing research to optimize performance, especially during extreme events. This study provides valuable insights for advancing hydrological modeling capabilities in complex watersheds.
Determination of climate change impacts on Mediterranean streamflows: a case study of Edremit Eybek Creek, Türkiye
Authors: Iltas Ayşenur , Demircan Mesut , Gokdag Erkan , Aksu Hakan 
Published: July 2024

ABSTRACT
The growing population contributes to an increase in greenhouse gases and affects the environment in a negative manner. To determine and predict the impact of climate change on hydrological processes, and to examine the status of our current/future water resources, hydrological modeling is of great importance and various models are utilized in this regard. In this study, the hydrological impact of climate change on a river in Türkiye, located in the Mediterranean Basin, has been revealed using a hydrological model, Hydrologiska Byråns Vattenbalansavdelning (HBV). Precipitation, temperature, and streamflow data from 1979–2021 were used for model calibration, validation, and warming processes, and model performance was assessed using the Nash–Sutcliffe efficiency (NSE) coefficient criterion. The established model's NSE performance has been determined as 0.66 for calibration and 0.69 for validation. The hydrological model was run with climate projection data (representative concentration pathways (RCP4.5 and RCP8.5)) to predict streamflows for the projection period (2023–2092). The evaluations conducted using the hydrological model for the period between 2023 and 2092 vunder the RCP8.5 scenario indicate a statistically significant decreasing trend of streamflows due to climate change. However, for RCP4.5, no trend was detected in streamflows for the projection period. From a seasonal perspective, while the greatest decrease in trends is expected to occur in autumn according to the RCP 8.5 scenario, all seasons are anticipated to exhibit significant decreasing.
Virtual sensors for the Hias process
Authors: Komulainen Tiina M. , Baqeri A. Malik , Jansen Katrine Marsteng , Saltnes Torgeir , Bech Axel Tveiten , Korostynska Olga 
Published: July 2024

ABSTRACT

This article presents the development of virtual sensors for estimation of phosphates (PO4) and soluble COD profiles in a novel, continuous flow, moving bed bioreactor with enhanced biological phosphorus removal and simultaneous nitrification and denitrification, the Hias process. The virtual sensors combine online measurements with additional electrical conductivity (EC), oxidation–reduction potential (ORP) measurements, and state-space models at inlet, zone 3 and zone 7. The data were collected from Hias municipal WRRF, Norway from March to July 2023, and include both online data and laboratory data. Input variables were selected using correlation plots. Linear measurement equations were fitted to relate PO4 and COD concentrations in the laboratory data set with the online measurements including EC/ORP measurements. The state-space models were identified from the online data with model accuracy from moderate to strong. The estimated PO4 and COD concentrations correspond to most of the scarce laboratory data points at inlet and zone 3, whereas the model in zone 7 requires more work. A Kalman filter was developed for zone 3 and implemented in KYB IIoT platform. Future work is suggested on improvement of the model accuracy in zone 7, and development of energy-efficient control strategies using the virtual sensors.
Chemoprophylaxis Effect of EGCG on the Recurrence of Colorectal Cancer: A Systematic Review and Meta-Analysis
Authors: He Benyu , Kang Shuhui , Su Runze , Wu Sha , Liu Xue , Liu Maosheng , Chen Si 
Published: July 2024

Background and Aims:
The recurrence rate of Colorectal Cancer (CRC) after cure is always high.
The purpose of this study was to investigate whether green tea extract (-)-Epigallocatechin gallate (EGCG) has
an effective preventive effect on the recurrence of CRC.

Methods:
We conducted a systematic literature review and meta-analysis of the effects of taking EGCG or
placebo on disease recurrence in patients after colon polyp removal.

Results:
Five Randomized Controlled Trials (RCTs) were included in this review. A double-blind drug trial involving
1389 participants involved EGCG and placebo. The results showed no significant publication bias or
heterogeneity in the five studies (I2 = 38%; p = 0.17). Patients taking EGCG had a lower recurrence rate of
CRC than those in the placebo group. The results were statistically significant (Z=2.83, p < 0.05).

Conclusion:
This study demonstrated that long-term EGCG can prevent CRC recurrence to a certain extent.