bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lattice Boltzmann simulation of cross-linked polymer gel injection in porous media
Authors: Kamel Targhi Elahe , Emami Niri Mohammad , Rasaei Mohammad Reza , Zitha Pacelli L. J. 
Published: July 2024

AbstractThis study addresses the critical challenge of excessive water production in mature oil and gas reservoirs. It focuses on the effectiveness of polymer gel injection into porous media as a solution, with an emphasis on understanding its impact at the pore scale. A step-wise Lattice Boltzmann Method (LBM) is employed to simulate polymer gel injection into a 2D Berea sample, representing a realistic porous media. The non-Newtonian, time-dependent characteristics of polymer gel fluid necessitate this detailed pore-scale analysis. Validation of the simulation results is conducted at each procedural step. The study reveals that the methodology is successful in predicting the effect of polymer gel on reducing permeability as the gel was mainly formed in relatively larger pores, as it is desirable for controlling water cut. Mathematical model presented in this study accurately predicts permeability reductions up to 100% (complete blockage). In addition, simulations conducted over a wide range of gelation parameters, TD_factor from 1 to 1.14 and Threshold between 0.55 and 0.95, revealed a quadratic relationship between permeability reduction and these parameters. The result of this research indicates LBM can be considered as promising tool for investigating time-dependant fluids on porous media.
Cellulose, graphene and graphene-cellulose composite aerogels and their application in water treatment: a review
Authors: Sekwele Karabo G. , Tichapondwa Shepherd M. , Mhike Washington 
Published: July 2024

AbstractDue to their unique properties such as outstanding specific surface areas, aerogels can potentially offer a cost-effective and efficient water and wastewater treatment solution thereby solving the worldwide ever-growing water pollution conundrum. This review explores the developments and progress in the application of cellulose, graphene, and cellulose-graphene composite aerogels in addressing water treatment challenges. The objective of this study was to critically review the state-of-the-art with respect to synthesis methods and properties of cellulose, graphene, and cellulose-graphene composite aerogels and their application, effectiveness and performance in water and wastewater treatment. It is evident from the review that cost-effective aerogel production methods that can be scaled up easily still need to be developed to enable commercial viability of aerogels in water treatment. Furthermore, the mechanical properties of aerogels, particularly stiffness and strength, are a hinderance to expanding their utility. The potential for functionalization of the aerogels through fabrication of aerogel nanocomposites or incorporation of functional groups was recognized as a viable method to enhance their properties and effectiveness in water treatment and other advanced industrial-scale applications. Graphene-cellulose composite aerogels have enhanced properties, removal capacities and efficiencies for pollutants from water compared to aerogels prepared from the individual materials. The regeneration ability and reusability of the composite aerogels make them an attractive option for practical applications. Although the potential of cellulose, graphene, and cellulose-graphene composite aerogels in water treatment applications is apparent, the impact of their production, utilization and subsequent disposal on sustainability and the environmental is yet to be fully studied.
The continuing trend of air warming and humidifying in Kowloon, Hong Kong—is there an effective intervention?
Authors: Li Yuguo , Fan Yifan , Yin Shi , Little John C. 
Published: July 2024

AbstractThe Kowloon Peninsula, an urban area of Hong Kong, might be one of the most studied cities in terms of urban climate in the world. Both annual mean urban air temperature and humidity have been on the rise following the continual increase in the building volume and continual reduction in the average wind speed in the urban canopy layer. The observed urban warming and humidifying phenomena are a direct result of the city form (i.e., the city shape and size), including the building height and density. We call for a system-of-systems approach to be adopted in urban climate studies.
First report of Mugilogobius rambaiae (Smith, 1945) (Family: Gobiidae) from India along with geographical expansion of two goby fishes from Kerala
Authors: Sreeraj C. R. , Hussna N. , Sen Arya , Limna Mol V. P. 
Published: July 2024

AbstractThe discovery of three previously undocumented gobioid fish species in Kerala's mangrove ecosystems, namely Eugnathogobius mindora, Mugilogobius mertoni, and Mugilogobius rambaiae, marks a significant advancement in our understanding of the region's aquatic biodiversity. These findings highlight the importance of targeted sampling initiatives and underscore the need for continued exploration and conservation efforts to protect Kerala's fragile mangrove habitats. The adaptability of gobioid fish species from the genera Eugnathogobius and Mugilogobius to fluctuating salinity levels makes them particularly well-suited to thrive in the dynamic environments of shallow intertidal and subtidal estuaries, as well as mangrove ecosystems. Their presence is crucial in maintaining the ecological balance of coastal habitats in the Indo-Pacific region, including Kerala. The discovery of these gobioid fish species not only enhances the ecological richness of Kerala but also offers a significant understanding of the intricate relationships between these fish and their habitats. By enhancing the understanding of the range of gobioid fish species found in the mangroves of Kerala, more effective conservation and management approaches to safeguard these vital coastal ecosystems can be developed. Continuing study and conservation efforts are crucial for the preservation of Kerala’s mangrove ecosystems and the species diversity. By comprehending the enigmas of gobioid fish diversity and other fundamental facets of mangrove ecosystems, substantial advancements can be made in guaranteeing enduring sustainability for future generations.
The Role of Climate, Mineralogy and Stable Aggregates for Soil Organic Carbon Dynamics Along a Geoclimatic Gradient
Authors: Wasner Daniel , Abramoff Rose , Griepentrog Marco , Venegas Erick Zagal , Boeckx Pascal , Doetterl Sebastian 
Published: July 2024

AbstractOrganic matter accumulation in soil is understood as the result of the dynamics between mineral‐associated (more decomposed, microbial derived) organic matter and free particulate (less decomposed, plant derived) organic matter. However, from regional to global scales, patterns and drivers behind main soil organic carbon (SOC) fractions are not well understood and remain poorly linked to the pedogenetic variation across soil types. Here, we separated SOC associated with silt‐ and clay‐sized particles (S + C), stable aggregates (>63 μm, SA) and particulate organic matter (POM) from a diverse range of grassland topsoils sampled along a geoclimatic gradient. The relative contribution of the two mineral‐associated fractions (S + C & SA) to SOC differed significantly across the gradient, while POM was never the dominant SOC fraction. Stable aggregates (>63 μm) emerged as the major SOC fraction in carbon‐rich soils. The degree of decomposition of carbon in stable aggregates (>63 μm) was consistently between that of the S + C and POM fractions and did not change along the investigated gradient. In contrast, carbon associated with the S + C fraction was less microbially decomposed in carbon‐rich soils than in carbon‐poor soils. The amount of SOC in the S + C fraction was positively correlated to pedogenic oxide contents and texture, whereas the amount of SOC associated with stable aggregates (>63 μm) was positively correlated to pedogenic oxide contents and negatively to temperature. We present a conceptual summary of our findings, which integrates the role of stable aggregates (>63 μm) with other major SOC fractions and illustrates their changing importance across (soil‐)environmental gradients.
Stabilization of EREG via STT3B-mediated N-glycosylation is critical for PDL1 upregulation and immune evasion in head and neck squamous cell carcinoma
Authors: Xu Shengming , Wang Haifeng , Zhu Yu , Han Yong , Liu Liu , Zhang Xiangkai , Hu Jingzhou , Zhang Wuchang , Duan Shengzhong , Deng Jiong , Zhang Zhiyuan , Liu Shuli 
Published: July 2024

AbstractDysregulated Epiregulin (EREG) can activate epidermal growth factor receptor (EGFR) and promote tumor progression in head and neck squamous cell carcinoma (HNSCC). However, the mechanisms underlying EREG dysregulation remain largely unknown. Here, we showed that dysregulated EREG was highly associated with enhanced PDL1 in HNSCC tissues. Treatment of HNSCC cells with EREG resulted in upregulated PDL1 via the c-myc pathway. Of note, we found that N-glycosylation of EREG was essential for its stability, membrane location, biological function, and upregulation of its downstream target PDL1 in HNSCC. EREG was glycosylated at N47 via STT3B glycosyltransferases, whereas mutations at N47 site abrogated N-glycosylation and destabilized EREG. Consistently, knockdown of STT3B suppressed glycosylated EREG and inhibited PDL1 in HNSCC cells. Moreover, treatment of HNSCC cells with NGI-1, an inhibitor of STT3B, blocked STT3B-mediated glycosylation of EREG, leading to its degradation and suppression of PDL1. Finally, combination of NGI-1 treatment with anti-PDLl therapy synergistically enhanced the efficacy of immunotherapy of HNSCC in vivo. Taken together, STT3B-mediated N-glycosylation is essential for stabilization of EREG, which mediates PDL1 upregulation and immune evasion in HNSCC.
VAMP2 regulates phase separation of α-synuclein
Authors: Agarwal Aishwarya , Chandran Aswathy , Raza Farheen , Ungureanu Irina-Maria , Hilcenko Christine , Stott Katherine , Bright Nicholas A. , Morone Nobuhiro , Warren Alan J. , Lautenschläger Janin 
Published: July 2024

Abstractα-Synuclein (αSYN), a pivotal synaptic protein implicated in synucleinopathies such as Parkinson’s disease and Lewy body dementia, undergoes protein phase separation. We reveal that vesicle-associated membrane protein 2 (VAMP2) orchestrates αSYN phase separation both in vitro and in cells. Electrostatic interactions, specifically mediated by VAMP2 via its juxtamembrane domain and the αSYN C-terminal region, drive phase separation. Condensate formation is specific for R-SNARE VAMP2 and dependent on αSYN lipid membrane binding. Our results delineate a regulatory mechanism for αSYN phase separation in cells. Furthermore, we show that αSYN condensates sequester vesicles and attract complexin-1 and -2, thus supporting a role in synaptic physiology and pathophysiology.
Estimation of methane greenhouse gas emissions from livestock in Egypt during 1989 to 2021
Authors: Maze Mona , Taqi Mohamed Omar , Tolba Rania , Abdel-Wareth Ahmed A. A. , Lohakare Jayant 
Published: July 2024

AbstractThis study investigates methane emissions from the livestock sector, representing by enteric fermentation and manure management, in Egypt from 1989 to 2021, focusing on spatial and temporal variations at the governorate level. Utilizing IPCC guidelines and emission factors, methane emissions were estimated for dairy and non-dairy cattle, buffalo, sheep and goat, poultry, and other livestock categories. Results reveal fluctuating emission patterns over the study period, with notable declines in certain governorates such as Kafr El-Sheikh and Red Sea, attributed to reductions in livestock populations. However, increasing trends were observed overall, driven by population growth in other regions. Hotspots of methane emissions were identified in delta governorates like Behera and Sharkia, as well as agriculturally rich regions including Menia and Suhag. While livestock populations varied between regions, factors such as water availability, climatic conditions, and farming practices influenced distribution. Notably, cluster analysis did not reveal regional clustering among governorates, suggesting emissions changes were not dependent on specific geographic or climatic boundaries. Manure management accounted for only 5–6% of total emissions, with emissions at their lowest in the last three years due to population declines. Despite the highest livestock populations being sheep and goats, emissions from enteric fermentation and manure management were highest from buffalo and cattle. This study underscores the importance of accurate data collection and adherence to IPCC recommendations for estimating GHG emissions, enabling the development of targeted mitigation strategies to address climate change challenges in the livestock sector.
Analysis of banana plant health using machine learning techniques
Authors: Thiagarajan Joshva Devadas , Kulkarni Siddharaj Vitthal , Jadhav Shreyas Anil , Waghe Ayush Ashish , Raja S. P. , Rajagopal Sivakumar , Poddar Harshit , Subramaniam Shamala 
Published: July 2024

AbstractThe Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves. Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. This manuscript examines the prediction and detection of diseases in banana leaves, exploring various diseases, machine learning algorithms, and methodologies. The study makes a contribution by proposing two approaches for improved performance and suggesting future research directions. In summary, the objective is to advance understanding and stimulate progress in the prediction and detection of diseases in banana leaves. The need for enhanced disease identification processes is highlighted by the results of the survey. Existing models face a challenge due to their lack of rotation and scale invariance. While algorithms such as random forest and decision trees are less affected, initially convolutional neural networks (CNNs) is considered for disease prediction. Though the Convolutional Neural Network models demonstrated impressive accuracy in many research but it lacks in invariance to scale and rotation. Moreover, it is observed that due its inherent design it cannot be combined with feature extraction methods to identify the banana leaf diseases. Due to this reason two alternative models that combine ANN with scale-invariant Feature transform (SIFT) model or histogram of oriented gradients (HOG) combined with local binary patterns (LBP) model are suggested. The first model ANN with SIFT identify the disease by using the activation functions to process the features extracted by the SIFT by distinguishing the complex patterns. The second integrate the combined features of HOG and LBP to identify the disease thus by representing the local pattern and gradients in an image. This paves a way for the ANN to learn and identify the banana leaf disease. Moving forward, exploring datasets in video formats for disease detection in banana leaves through tailored machine learning algorithms presents a promising avenue for research.
Automated detection, labelling and radiological grading of clinical spinal MRIs
Authors: Windsor Rhydian , Jamaludin Amir , Kadir Timor , Zisserman Andrew 
Published: July 2024

AbstractSpinal magnetic resonance (MR) scans are a vital tool for diagnosing the cause of back pain for many diseases and conditions. However, interpreting clinically useful information from these scans can be challenging, time-consuming and hard to reproduce across different radiologists. In this paper, we alleviate these problems by introducing a multi-stage automated pipeline for analysing spinal MR scans. This pipeline first detects and labels vertebral bodies across several commonly used sequences (e.g. T1w, T2w and STIR) and fields of view (e.g. lumbar, cervical, whole spine). Using these detections it then performs automated diagnosis for several spinal disorders, including intervertebral disc degenerative changes in T1w and T2w lumbar scans, and spinal metastases, cord compression and vertebral fractures. To achieve this, we propose a new method of vertebrae detection and labelling, using vector fields to group together detected vertebral landmarks and a language-modelling inspired beam search to determine the corresponding levels of the detections. We also employ a new transformer-based architecture to perform radiological grading which incorporates context from multiple vertebrae and sequences, as a real radiologist would. The performance of each stage of the pipeline is tested in isolation on several clinical datasets, each consisting of 66 to 421 scans. The outputs are compared to manual annotations of expert radiologists, demonstrating accurate vertebrae detection across a range of scan parameters. Similarly, the model’s grading predictions for various types of disc degeneration and detection of spinal metastases closely match those of an expert radiologist. To aid future research, our code and trained models are made publicly available.
Scattering integral equation formulation for intravascular inclusion biosensing
Authors: Valagiannopoulos Constantinos , Tosi Daniele 
Published: July 2024

AbstractA dielectric waveguide, inserted into blood vessels, supports its basic mode that is being scattered by a near-field intravascular inclusion. A rigorous integral equation formulation is performed and the electromagnetic response from that inhomogeneity is semi-analytically evaluated. The detectability of the formation, based on spatial distribution of the recorded signal, is estimated by considering various inclusion sizes, locations and textural contrasts. The proposed technique, with its variants and generalizations, provides a generic versatile toolbox to efficiently model biosensor layouts involved in healthcare monitoring and disease screening.
Comparison of digital and analog [68Ga]Ga-PSMA-11 PET/CT for detecting post-prostatectomy biochemical recurrence in prostate cancer patients: a prospective study
Authors: Kim Yong-il , Lee Dong Yun , Sung Changhwan , Lee Sang Ju , Oh Seung Jun , Oh Jungsu S. , Yoon Shinkyo , Lee Jae Lyun , Lim Bumjin , Suh Jungyo , Park Juhyun , You Dalsan , Jeong In Gab , Hong Jun Hyuk , Ahn Hanjong , Kim Choung-Soo , Ryu Jin-Sook 
Published: July 2024

AbstractDigital positron emission tomography/computed tomography (PET/CT) has shown enhanced sensitivity and spatial resolution compared with analog PET/CT. The present study compared the diagnostic performance of digital and analog PET/CT with [68Ga]Ga-PSMA-11 in prostate cancer patients who experienced biochemical recurrence (BCR) after prostatectomy. Forty prostate cancer patients who experienced BCR, defined as serum prostate-specific antigen (PSA) concentrations exceeding 0.2 ng/mL after prostatectomy, were prospectively recruited. These patients were stratified into three groups based on their serum PSA levels. [68Ga]Ga-PSMA-11 was injected into each patient, and images were acquired using both analog and digital PET/CT scanners. Analog and digital PET/CT showed comparable lesion detection rate (71.8% vs. 74.4%), sensitivity (85.0% vs. 90.0%), and positive predictive value (PPV, 100.0% vs. 100.0%). However, digital PET/CT detected more lesions (139 vs. 111) and had higher maximum standardized uptake values (SUVmax, 14.3 vs. 10.3) and higher kappa index (0.657 vs. 0.502) than analog PET/CT, regardless of serum PSA levels. On both analog and digital PET/CT, lesion detection rates and interrater agreement increased with increasing serum PSA levels. Compared with analog PET/CT, digital PET/CT detected more lesions with a higher SUVmax and better interrater agreement in prostate cancer patients who experienced BCR after prostatectomy.
AgriGAN: unpaired image dehazing via a cycle-consistent generative adversarial network for the agricultural plant phenotype
Authors: Ding Jin-Ting , Peng Yong-Yu , Huang Min , Zhou Sheng-Jun 
Published: July 2024

AbstractArtificially extracted agricultural phenotype information exhibits high subjectivity and low accuracy, while the utilization of image extraction information is susceptible to interference from haze. Furthermore, the effectiveness of the agricultural image dehazing method used for extracting such information is limited due to unclear texture details and color representation in the images. To address these limitations, we propose AgriGAN (unpaired image dehazing via a cycle-consistent generative adversarial network) for enhancing the dehazing performance in agricultural plant phenotyping. The algorithm incorporates an atmospheric scattering model to improve the discriminator model and employs a whole-detail consistent discrimination approach to enhance discriminator efficiency, thereby accelerating convergence towards Nash equilibrium state within the adversarial network. Finally, by training with network adversarial loss + cycle consistent loss, clear images are obtained after dehazing process. Experimental evaluations and comparative analysis were conducted to assess this algorithm's performance, demonstrating improved accuracy in dehazing agricultural images while preserving detailed texture information and mitigating color deviation issues.
Complex artificial intelligence models for energy sustainability in educational buildings
Authors: Tariq Rasikh , Mohammed Awsan , Alshibani Adel , Ramírez-Montoya Maria Soledad 
Published: July 2024

AbstractEnergy consumption of constructed educational facilities significantly impacts economic, social and environment sustainable development. It contributes to approximately 37% of the carbon dioxide emissions associated with energy use and procedures. This paper aims to introduce a study that investigates several artificial intelligence-based models to predict the energy consumption of the most important educational buildings; schools. These models include decision trees, K-nearest neighbors, gradient boosting, and long-term memory networks. The research also investigates the relationship between the input parameters and the yearly energy usage of educational buildings. It has been discovered that the school sizes and AC capacities are the most impact variable associated with higher energy consumption. While 'Type of School' is less direct or weaker correlation with 'Annual Consumption'. The four developed models were evaluated and compared in training and testing stages. The Decision Tree model demonstrates strong performance on the training data with an average prediction error of about 3.58%. The K-Nearest Neighbors model has significantly higher errors, with RMSE on training data as high as 38,429.4, which may be indicative of overfitting. In contrast, Gradient Boosting can almost perfectly predict the variations within the training dataset. The performance metrics suggest that some models manage this variability better than others, with Gradient Boosting and LSTM standing out in terms of their ability to handle diverse data ranges, from the minimum consumption of approximately 99,274.95 to the maximum of 683,191.8. This research underscores the importance of sustainable educational buildings not only as physical learning spaces but also as dynamic environments that contribute to informal educational processes. Sustainable buildings serve as real-world examples of environmental stewardship, teaching students about energy efficiency and sustainability through their design and operation. By incorporating advanced AI-driven tools to optimize energy consumption, educational facilities can become interactive learning hubs that encourage students to engage with concepts of sustainability in their everyday surroundings.