bluuebunny/crossref_metadata_2025 huggingface dataset:
Lone and Combine Effect of Accelerated Irradiation and Thermal Aging Stressors on Silica Reinforced Ethylenepropylenediene Monomer Rubber
Authors: Naz Sumaira , Waqas Hassan , Ahmed Muhammad Bin , Danish Muhammad
Published: June 2024

The aim of this study was to determine the affect of irradiation and thermal accelerated aging (i.e. equivalent to 8years) on Ethylenepropylenediene Monomer (EPDM) rubber contained silica as a filler and also grafted in polymeric matrix. For this purpose, accelerated thermal aging was performed at T = 123°C for 15 days. Whereas, accelerated i.e. short term irradiation aging was done separately under thermal neutron flux in research reactor for 3.6 hrs. The combine affect of thermal aging before and after irradiation was also studied. The results revealed that irradiation promoted crosslinking in EPDM rubber, while thermal aging preferably induced secissioning in silica grafted network chain structure. The further study revealed that irradiation after thermal aging have a propensity to cure the adverse effects of temperature by increase of crosslink density and elimination of pores which made the polymeric matrix more dense as observed by structural (XRD, pole density, FTIR) and microscopic (SEM) Analyses. However, in converse order i.e. thermal aging after irradiation aggravated detrimental effects on EPDM rubber due to prevailing scissioning over crosslinking in polymeric chain structure.
Molecular Identification of stink bug (Pentatomidae sp. BD39) and pod sucking bug (Mirperus sp.) associated with crops under rotation
Authors: Sanda Nafiu Bala , Hussaini Muhammad Auwal , Wudil Baba Sani , Lado Abdulrahman , Adnan Adnan Aminu , Abdulrahman Bassam Lawal , Garba Ismail Ibrahim , Kamara Alpha Yaya , Buhari Fatima Zahrah , Aliyu Ma’amun Da’u , Yakubu Bashir Hussaini , Abdullahi Abuhuraira
Published: June 2024

One of the problems facing agricultural production in Nigeria is the pests and diseases, which farmers depend heavily on the use of chemicals for their control. However, in time of outbreak of invasive species, difficulties amount on the proper identifications of these species for proper record and management strategies due to reliance on morphological methods of insect identification. The use of morphological methods of insect identification, not only slow but it sometimes leads to variations between experts. The use of Molecular techniques identifies insect species using molecular markers; which minimize false positive results by targeting the specific molecules of interests. This study was conducted to identify major insect pests of crops grown under rotation experiment. Cytochrome oxidase I (CO1) gene was used for the molecular identification of the insect pests of crops under rotation. The expected amplicons were sequenced and the results were analyzed using the nucleotide BLAST at the NCBI database for identification. The COI gene sequences were identified as Pentatomidae sp. BD39 and Mirperus sp. with 97.47% and 99.75% similarities, respectively. The former is a Stink bug and is a pest of cereal grains while the latter is a Pod sucker and is a major pest of cowpea and soybean in sub-Saharan Africa. Taken together, this knowledge is useful in developing different sustainable cropping options in order to monitor different types of insect pests and their population dynamics.
A Common Architecture-based Smart Home Hardware Forensics for Scalable Investigations
Authors: Kim Sungbum , Lee Gwangsik , Song Jian , Lee Insoo , Shon Taeshik
Published: June 2024

The smart home platform communicates with internet of things(IoT) devices, smartphones, and cloud servers to provide convenient services, storing user information and device operation and user behavior-related data. This data is crucial for criminal investigations, highlighting the importance of smart home forensics. Currently, advanced software and hardware technologies continue to be developed in the smart home market, and based on this, companies are releasing new services and devices. Therefore, scalable platform-oriented forensic research is needed for efficient digital investigation. This study identifies the components and structures of smart homes to derive a common architecture representing various environments. It proposes a three-stage smart home forensics framework: analyzing application functions to infer data, extracting and analyzing data from devices, and identifying data useful for criminal investigations. Its applicability is demonstrated with Samsung SmartThings and Xiaomi Mi Home platform testbeds.
The Role of TNF-R1-RIPK1/RIPK3 Signaling Pathway in Lead-Induced Necroptosis in the Hippocampus of Mice
Authors: Li Huishuai , Li Zhenning , Yang Chun , Wei Ruokun , Wei Peiqi , Yuan Haiyan , Aschner Michael , Ou Shiyan , Peng Dongjie , Li Shaojun
Published: June 2024

Lead (Pb), a dense, soft, blue-gray metal, is widely used in metallurgy, cables, storage batteries, pigments, and other industrial applications. Pb has been shown to cause hippocampal neurodegeneration. Necroptosis, a newly described cell death modality, is closely associated with neurodegenerative diseases. Whether Pb can cause programmed neuronal cell death and its mechanisms has yet to be characterized. Here, we explored the role of the TNF-R1-RIPK1/RIPK3 signaling pathway in necroptosis induced by Pb. Our results showed that Pb exposure elevated lead levels in murine whole blood and hippocampal tissue in a dose-response relationship. Protein expression levels of PARP, c-PARP, RIPK1, p-RIPK1, RIPK3, MLKL, and p-MLKL in the hippocampal tissues were elevated, while the protein expression of caspase-8 was decreased. Furthermore, Pb exposure reduced the survival rates in HT-22 cells and primary mouse hippocampal neurons, while increasing the protein expressions of RIPK1 and p-MLKL. Collectively, these novel findings suggest that the TNF-R1/RIPK1/RIPK3 signaling pathway mediates Pb-induced necroptosis in hippocampal neurons in mice.
Fake Review Detection and Text Analysis by Using Clustering Techniques on LSTM
Authors: Dai Tianle , Wang Xiaodong
Published: June 2024

With the growth of the internet, the issue of fake reviews has become increasingly prominent. Traditional models for fake review detection have underperformed on balanced datasets, leading to difficulties in differentiating between the genuine and the fake reviews. This paper introduces a model based on k-Means text clustering neural networks. Firstly, the genuine review texts are vectorized after stop-word removal using TF-IDF to determine the weights of each word and map them into a high-dimensional space. Then, the k-Means clustering algorithm is employed to cluster these reviews in the space. From each cluster center, the genuine reviews equal in number to the fake reviews are extracted. By analyzing keywords from different clusters, the model learns the linguistic behaviors distinguishing the genuine reviewers from the fake ones. These reviews are then processed by a Long Short-Term Memory (LSTM) network to extract and analyze their features. Finally, prediction outcomes are produced. Experimental results on the Yelp dataset demonstrate that the text clustering neural network model outperforms existing models, showcasing enhanced performance.
CGProNet: Continual Graph-based Prototypical Networks for Few-Shot Skin Disease Classification
Authors: Noman Abdulrahman , Beiji Zou , Zhu Chengzhang , Alhabib Mohammed , Al-Sabri Raeed
Published: June 2024

The prototypical network effectively classifies skin diseases in few-shot learning but faces challenges with prototype accuracy due to limited data and insufficient long-term knowledge retention, affecting generalization to new classes. However, the prototypical network faces challenges such as inaccurate prototype estimations due to limited data and insufficient long-term knowledge retention, affecting its generalization to new classes. This study introduces CGProNet (Continual Graph-based Prototypical Networks for Few-Shot Skin Disease Classification) to address these issues and improve the accuracy of uncommon skin disease identification. CGProNet employs prototype network-based support samples and considers their interdependencies while transferring knowledge across tasks. It uses Convolutional CNNs to generate initial features, graph-based methods to capture relational information, and gated recurrent units (GRUs) to preserve graph-aggregated features. CGProNet achieves 80.5\% accuracy on the ISIC-2018, 86.03\% on the Derm7pt, and 92.51\% on the SD-198 datasets in 5-shot learning scenarios, proving effective for skin disease classification with limited data.
Diffusion Model for Multi-scale Ship Object Detection and Recognition in Remote Sensing Images
Authors: Chen Lei , Wang Bin , Liu Ying , Zhao Shuang , Guan Qinghe , Li Guandian
Published: June 2024

Ship object detection and recognition in remote sensing images (RSIs) is a challenging task due to the multi-scale and complex background characteristics of ship objects. Currently, convolution-based methods cannot adequately solve these problems. Firstly, this article first applies the diffusion model to the task of ship object detection and recognition in RSIs, and proposes a new diffusion model for multi-scale ship object detection and recognition in remote sensing images (MSDiffDet). Secondly, in order to reduce the loss of multi-scale information in the feature extraction process, this article proposes the Channel Fusion FPN (CF-FPN) based on FPN and constructs the Large-Scale Feature Enhancement Module (LSFEM), which further enhances the algorithm's ability to extract large-scale ship object features and improves the detection accuracy of ship objects in RSIs. Finally, this article prunes and reconstructs MobileNetV2 to obtain the Sparse MobileNetV2, which is used as the backbone network of the image encoder, which enhances detection accuracy while reducing the overall parameter count of the algorithm. The experimental results demonstrate that the MSDiffDet algorithm is effective in detecting and recognizing four types of remote sensing ship objects: aircraft carriers, warships, commercial ships, and submarines. The mAP0.5 achieved a notable 89.8\%. A significant improvement of 5.8\% in mAP0.5 is observed compared to the DiffusionDet algorithm, indicating the potential of the MSDiffDet algorithm for applications in remote sensing ship object detection and recognition.
SWM: Enhancing Spatiotemporal Awareness in Remote Photoplethysmography for Non-Contact Heart Rate Estimation
Authors: Zhao Chaoyu , Sun Peng , Chen Zhencheng
Published: June 2024

Remote Photoplethysmography (rPPG) is a method for non-contact estimation of heart rate (HR) using facial video. In comparison to traditional electrocardiography (ECG), which requires physical contact and may cause discomfort to the subjects, rPPG technology offers a more comfortable and convenient solution. This is particularly applicable to patients such as newborns who find it challenging to wear monitoring devices, thereby reducing the risk of infections associated with contact devices.In recent years, Remote Photoplethysmography (rPPG) technology based on deep learning methods has advanced rapidly. Currently, most models use Convolutional Neural Networks (CNN) as feature extractors. However, the spatiotemporal perception capacity of CNN has certain limitations, potentially leading to the oversight of crucial information.Therefore, we propose a network with enhanced spatiotemporal awareness, named SWM. SWM employs Swin Transformer blocks as feature extractors, and after differencing and normalizing the video sequence, it enters Wrapping Temporal Shift Modules (WTSM).We proposed a subsampling method named Patch WTSM Mask, which facilitates the interaction of contextual information and weights facial features. SWM demonstrates excellent performance on the UBFC-rPPG dataset, with MAE=0.49, RMSE=1.21, MAPE=0.54, Pearson=0.99. The robustness of SWM is further validated across datasets, specifically on the UBFC-Phys dataset, where MAE=6.10, RMSE=10.82, MAPE=7.22, Pearson=0.70.In the end, we conducted ablation experiments and compared SWM with advanced methods, confirming its outstanding performance. SWM no longer relies on CNN, potentially heralding a new direction in the research field of rPPG.The data and code used in this study can be found at \url{https://github.com/tannixi/SWM}, and each record has a corresponding DOI: [10.5281/zenodo.11487732].
Distribution characteristics of pCO2 at the air-sea in the East China Sea and changes over the last 20 years
Authors: Ye Linan , Lin Chongyang , Ren Ming , Xu Guofeng , Zhang Haibo , Wei Yongjie , Jing Yudi
Published: June 2024

According to the carbon dioxide (pCO2), temperature (T), salinity (S) and pH of the surface seawater obtained from a survey of carbon dioxide (CO2) in the East China Sea(ECS) region from 2004 to 2023, the seasonal distribution characteristics of pCO2 in the ECS were analyzed and discussed. The results show that the overall trend of pCO2 in the ECS region indicated a slow increase followed by a decrease over the past 20 years, with the highest peak reached in 2016. The overall carbon sequestration capacity of the ECS shows a trend of first decreasing and then increasing. This is most likely related to China's adoption of more effective policies and measures in achieving carbon peak and carbon neutrality. In terms of seasonal changes, the distribution of pCO2 in the East Sea area roughly shows the differences among the offshore area. The annual seasonal changes are almost similar betweenthe continental shelf area and the outer sea area. In terms of the average distribution of pCO2 in surface seawater of the investigated area, pCO2 in autumn and summer is higher than that in winter and spring, while atmospheric pCO2 levelin spring and autumn is higher than that in summer and winter, and the seasons are all represented by the exchange of atmospheric CO2. The water system in the East Sea area is complicated, and the distribution of pCO2 in surface seawater is mainly affected by tfour major water systems: the Yangtze River, the continental shelf water, the Kuroshio water, and the coastal current of the Yellow Sea. It is generally affected by vertical mixing of water in winter and late autumn and biological activities in summer, late spring, and early autumn.
Machine Learning Regions of Reliability based on Sampling Distance Evaluation with Feature Decorrelation for Tabular Time Datasets
Authors: Askanazi Evan , Grinberg Ilya
Published: June 2024

Despite successful use in a wide variety of disciplines for data analysis and prediction, machine learning (ML) methods suffer from a lack of understanding of the reliability of predictions due to the lack of transparency and black-box nature of ML models. In materials science and other fields, typical ML model results include a significant number of low-quality predictions. This problem is known to be particularly acute for target systems which differ significantly from the data used for ML model training. However, to date, a general method for uncertainty quantification (UQ) of ML predictions has not been available. Focusing on the intuitive and computationally efficient similarity-based UQ, we show that a simple metric based on Euclidean feature space distance and sampling density together with the decorrelation of the features using Gram-Schmidt orthogonalization allows effective separation of the accurately predicted data points from data points with poor prediction accuracy. To demonstrate the generality of the method, we apply it to light GBM machine learning using a set of time series tabular data sets. We also show that this metric is a more effective UQ tool than the standard approach of using the average distance of k nearest neighbors (k = 1–10) in features space for similarity evaluation. The computational simplicity of this dataset combined with its applicability to time series datasets allows it to be readily used in numerous real world problems.
An evaluation of spirooxindoles as blocking agents of SARS-CoV-2 spike/ACE2 fusion and M pro inhibitory agents: Synthesis, biological evaluation and computational analysis
Authors: Ehinak Albert Enama , Lobe Maloba M. M. , Simoben Conrad V. , Tietjen Ian , Eni Donatus B. , Cassel Joel , Salvino Joseph M. , Montaner Luis J. , Sippl Wolfgang , Efange Simon M. N. , Ntie-Kang Fidele
Published: June 2024

Both tetrahydroisoquinolines (THIQs) and oxindoles (OXs) display a broad range of biological activities, including antiviral activity. They are, therefore, recognized as privileged scaffolds in drug discovery. Here, we describe the synthesis of spirofused tetrahydroisoquinoline–oxindole hybrids (spirooxindoles) and their evaluation as potential blocking agents of both SARS-CoV-2 spike/ACE fusion and inhibitors of the main protease (Mpro). The most active synthesized compound showed a 50% inhibitory concentration (IC50) of 3.6 µM against SARS-CoV-2 spike/ACE fusion. None of the tested compounds was shown to be active against Mpro. The most active compound possesses a bulky naphthyl group, which addresses voluminous hydrophobic regions of the ACE2 binding site and interacts with the hydrophobic residues of the target; this finding agrees with previous studies revealing that bulky compounds block spike/ACE2 fusion, e.g., the natural product hopeaphenol. Therefore, spirooxindoles may provide useful leads in the search for SARS-CoV-2 spike/ACE fusion blocking agents.
Listening to the women’s experience of perinatal domestic violence during the COVID-19 pandemic: a qualitative content analysis
Authors: Babazadeh Raheleh , Barez Malikeh Amel
Published: June 2024

Background
Pregnant women and their fetuses would be high risk population and more susceptible to COVID-19 infection. Since the outbreak of COVID-19, quarantine, social distancing and isolation, and the consequences were likely to dramatically increase the risk of domestic violence. The aim of this study was to explore women’s experience of perinatal domestic violence in COVID-19 pandemic.
Methods
This qualitative study was carried out from September 2020 to December 2022 in Mashhad, Iran. Data were collected through remote individual semi structured interviews with 9 women who experienced perinatal domestic violence, and 7 key informant with relevant specialist until the data saturation was achieved. The data was analyzed using conventional content analysis adopted by Graneheim & Lundman.
Findings:
The 4 main themes that emerged from the data analysis were "aggravators of perinatal violence", "enhanced vulnerability", "strategies for dealing with perinatal violence" and "neglected needs".
Conclusion
The high prevalence and the adverse consequences of perinatal domestic violence during COVID-19 crisis provide a golden opportunity for performing appropriate interventions. Perinatal period is a window of opportunity to identify and support abused pregnant who may have lost contact with other supportive services during COVID-19 pandemic. Psychological and physical support and social connectedness are important strategies during the crisis isolation for those who are at risk of domestic violence during COVID-19 pandemic.
Changing Precipitation Patterns and Extremes in Southern Thailand under Warmer Climate
Authors: Kuinkel Dipesh , Promchote Parichart , Upreti Khem R. , Wang S.-Y. Simon , Dahal Ngamindra , Pokharel Binod
Published: June 2024

Southern Thailand has experienced significant shifts in precipitation patterns in recent years, exerting substantial impacts on regional water resources and infrastructure systems. This study aims to elucidate these changes and underlying factors based on daily precipitation observations from Nakhon Si Thammarat Province spanning 1980 to 2022. Additionally, data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) is utilized to investigate projected changes in precipitation for 2015-2100 relative to the historical period (1980-2014), employing a comprehensive analysis considering two emissions scenarios (SSP245 and SSP585) across six models. Various precipitation indices are selected to assess trends and statistical significance using the Mann-Kendall test.
Both observed and climate model data indicate an increasing precipitation trend in Southern Thailand, with a reduced association with the El Niño-Southern Oscillation (ENSO) under warming conditions. Extreme precipitation indices also exhibit an increasing trend, with total precipitation and the 95th percentile of daily precipitation (R95p) revealing very wet conditions in recent years, projected to continue increasing. Contrastingly, the number of dry days is also mounting, suggesting that both dry and wet extremes will impact Southern Thailand under a warmer climate. The findings from this study provide an early indication of future precipitation and extreme event scenarios, which can inform the development of measures to mitigate climate change-related hazards in the region.
Study the Relationship between polymorphisms of Vitamin D Receptor (VDR FokI and VDR ApaI) Genes and colorectal cancer in some Iraqi population
Authors: Alwan Israa Ayoub , Moufak Shaimaa Khalid , Naji Tamara Sami , Abood Noor Adil , Ali Suad Turky
Published: June 2024

Background: Despite the unknown etiology of colorectal cancer (CRC), previous studies have suggested a potential contribution of genetics and environmental factors to CRC development. Therefore, investigating specific molecular pathways can provide valuable insights into CRC. This study aimed to explore the potential association between the SNP rs228570 (C/T) in the VDR FokI gene and rs7975232 (G/T) in the VDR ApaI gene with CRC. Additionally, the relationship between vitamin D levels, VDR FokI, and VDR ApaI polymorphisms was examined to evaluate the risk of colorectal cancer.
Methods: The study included a total of 140 participants, with 70 individuals diagnosed with CRC and 70 healthy controls. Vitamin D serum levels were measured using the ELFA technique in both CRC patients and the healthy control group. The prevalence of mutations in the VDR FokI rs228570 (C/T) and VDR ApaI rs7975232 (G/T) genes, as well as single nucleotide polymorphisms (SNPs), were evaluated in the CRC patient and control groups using the PCR-RFLP method.
Results: The results indicated a significant decrease (p < 0.05) in serum vitamin D levels in patients with colorectal cancer (CRC) compared to healthy controls. However, there was no significant difference in the genotype prevalence of VDR FokI rs228570 (C/T) and VDR ApaI rs7975232 (G/T), as well as allele frequencies between the CRC and control groups.
Conclusion: The findings suggest that serum vitamin D levels could serve as potential biomarkers for CRC. However, the polymorphisms in the VDR FokI and VDR ApaI genes are not considered risk factors for CRC in Iraq. This implies that further research focusing on additional loci or VDR is necessary.
A mixed-methods analysis of judges’ views and decision-making surrounding scientific evidence in criminal sentencing
Authors: Thomaidou Mia A. , Berryessa Colleen M. , Xie Sandy S.
Published: June 2024

Background: In contemporary criminal justice systems, the integration of bio-behavioral science evidence into legal proceedings poses complex challenges as well as opportunities. As psychiatric and mental health evidence may often not be accompanied by expert testimony, judges in criminal courts may be tasked with alone interpreting and incorporating this evidence into their decision-making processes.
Methods: This study investigates how judges–shaped by their views, beliefs, and personal characteristics–approach decision-making processes during sentencing in light of scientific explanations of behavior, as well as how their views on sentencing may be impacted by mental disorder diagnoses. We utilized a mixed-methods approach, including Natural Language Processing techniques (sentiment analysis and structural topic modeling) as well as qualitative analysis, to analyze data from semi-structured interviews with 34 judges from state criminal courts in the U.S.
Results: Results revealed varying degrees of belief in scientific determinism among judges, with corresponding sentiment analysis indicating differences in emotional tone across gender, age, geographical region, and professional background. Structural Topic Modeling identified key themes, including determinism, responsibility, treatment needs, and philosophical considerations surrounding punishment. Qualitative analysis enriched these results by unraveling the philosophical and legal considerations that judges grapple with when considering scientific explanations for defendants’ behavior.
Conclusions: Findings underscore the nuanced interplay between scientific understandings of behavior, personal beliefs, and judicial decision-making. This study offers valuable insights into the potential complexities of sentencing considerations involving scientific evidence and underscores the need for standardizing how scientific evidence is presented in courts and investing in science education for judges.
CSA-Lanenet: A Contiguous Spatial Attention Lane Detection Network with Vision Transformer Modules
Authors: Yang Wei-Jong , Ho Li-Yang
Published: June 2024

Lane detection, which is one of the important technologies for automatic driving to effectively avoid the accidents caused by vehicles deviating from their driving lanes. The lane detection task is challenging due to complex scenes and few features in distorted lane lines. Therefore, collecting useful spatial information of lane lines associated with feature maps becomes an important task for precision lane line detection. Considering the image captured from the front car camera, in this paper, we use deep learning segmentation technologies to precisely extract the lane lines by using contiguous spatial attentions. We first propose the shortened spatial attention module into the lane detection network through the correlated spatial local information and transfer to adjacent features to improve lane detection performance. In addition, due to the successful improvement of vision transformers, we also introduce several simplified poolformers into the proposed lane detection network for further improving lane detection exclusively. Simulation results show that the proposed shortened spatial attention module and the simplified poolformer can achieve effective and accurate lane detection. (The code is available at https://github.com/jong12/CSA-Lanet.)
Multiscale Modeling of Rough Contact Interfaces with Trapped Compressive Liquid Pockets
Authors: Alavi Parissa Sadat , Anciaux Guillaume , Rocchi Loris , Richard Julie , Leppin Christian , Molinari Jean-François
Published: June 2024

We introduce a novel numerical model that integrates a Boundary ElementMethod (BEM) code in Fourier space to solve the problem of lubricated fric-tional contact between rough surfaces. This model accounts for scenarios wherethe lubricant quantity is significant yet discontinuous, leading to the formationof trapped compressible lubricant pockets at the contact interface. Additionally,it incorporates the plastic behavior of solid surfaces through a simple plasticsaturation method, enabling comprehensive analysis of contact area, pressure dis-tribution, and variations in friction coefficient across a wide range of conditions,such as lubricant type, density, and surface roughness.
Comparison with experiments demonstrates that the developed model can esti-mate the influence of partial lubrication on the friction coefficients duringaluminum sheet deep-drawing with a good level of precision. Notably, the studyreveals the substantial impact of even minor adjustments in lubricant distributionand volume on the friction behavior of aluminum during forming processes.
Mechanism differences in the start time of sublingual immunotherapy in a mouse allergic airway inflammation model
Authors: Saito Akira , Koya Toshiyuki , Aoki Ami , Naramoto Shun , Ueno Hiroshi , Nishiyama Yuki , Shima Kenjiro , Kimura Yosuke , Hasegawa Takashi , Watanabe Satoshi , Ohshima Yasuyoshi , Suzuki Keisuke , Ohashi-Doi Katsuyo , Kikuchi Toshiaki
Published: June 2024

Sublingual immunotherapy (SLIT) has received considerable attention as a method for allergen immunotherapy (AIT). However, the mechanism of SLIT, especially its timing, has not been thoroughly investigated. We evaluated therapeutic and prophylactic SLIT in an allergic airway inflammation model and evaluated their efficacies. Mice were intranasally exposed to Dermatophagoides farinae (Der f) extract and received SLIT before (prophylactic model) and after (therapeutic model) intranasal exposure of Der f. We investigated airway responsiveness, airway inflammation, allergen-specific antibodies, lung histology and single-cell RNA sequencing (scRNA-seq) and T-cell receptor sequencing were also investigated. SLIT in the therapeutic model was effective; however, the effects of SLIT in the prophylactic model were stronger and immune tolerance was maintained for three months. ScRNA-seq of lung CD4+CD25+ T cells revealed that the expansion of induced T regulatory (iTreg) cells was greater in the prophylactic model than that in the therapeutic model. Additionally, the TCR repertoire of iTregs from the prophylactic model was abundant, sharing many clones with the TCR repertoire of effector T cells. These data suggest that the prophylactic model of AIT is extremely effective and persistent, and may respond to allergen diversity, and provide evidence for the clinical recommendation of preventive AIT.
Unveiling the crucial role of betaine: Modulation of GABA homeostasis via SLC6A1 transporter (GAT1)
Authors: Bhatt Manan , Lazzarin Erika , Alberto-Silva Ana Sofia , Domingo Guido , Zerlotti Rocco , Gradisch Ralph , Bazzone Andre , Sitte Harald H. , Stockner Thomas , Bossi Elena
Published: June 2024

Betaine is an endogenous osmolyte that exhibits therapeutic potential by mitigating various neurological disorders. However, the underlying cellular and molecular mechanisms responsible for its neuroprotective effects remain puzzling.
In this study, we describe a possible mechanism behind the positive impact of betaine in preserving neurons from excitotoxicity. Here we demonstrate that betaine at low concentration modulates the GABA uptake by GAT1 (slc6a1), the predominant GABA transporter in the central nervous system. This modulation occurs through the temporal inhibition of the transporter, wherein prolonged occupancy by betaine impedes the swift transition of the transporter to the inward conformation. Importantly, the modulatory effect of betaine on GAT1 is reversible, as the blocking of GAT1 disappears with increased extracellular GABA. Using electrophysiology, mass spectroscopy, radiolabelled cellular assay, and molecular dynamics simulation we demonstrate that betaine has a dual role in GAT1: at mM concentration acts as a slow substrate, and at µM as a temporal blocker of GABA, when it is below its K0.5. Given this unique modulatory characteristic and lack of any harmful side effects, betaine emerges as a promising neuromodulator of the inhibitory pathways improving GABA homeostasis via GAT1, thereby conferring neuroprotection against excitotoxicity.
The Dual Role of Environmental Regulations in Promoting and Hindering Innovation: A Comparative Analysis in the Korean Manufacturing Sector
Authors: Noh Daimin
Published: June 2024

This study compares innovation in manufacturing with and without environmental regulations. Many studies have either examined technological innovation in a general context without considering regulation or focused solely on innovation under environmental regulation. We examined technology sectors, firm sizes, and their interactions to understand innovation under both regulated and unregulated conditions. Our analysis of 4,000 Korean firms reveals a significant difference in innovation with and without regulation. Sectors with high pollution or green demand were more likely to innovate under environmental regulation, in contrast to the most innovative sectors. Large firms, regardless of sector, were more inclined to innovate under regulations. The results suggest that the relationship between environmental regulation and innovation varies according to a firm's capabilities and the technological attributes of the sector. This understanding informs the design of innovation strategies and appropriate levels of regulation for enhancing the cleanliness of manufacturing technology.
Improved peak detection at high particle image densities using single stage CNNs
Authors: Godbersen Philipp , Schanz Daniel , Schröder Andreas
Published: June 2024

An important step in the application of Lagrangian Particle Tracking (LPT) or in general for image based single particle identification techniques is the detection of particle image peaks on the measurement images and their sub-pixel accurate position estimation.In case of volumetric measurements, the peak detection constitutes the first step in the process of recovering 3D particle positions, which is usually performed by triangulation procedures. For two-component 2D measurements the peak detection results directly serve as input to the tracking algorithm.Depending on the quality of the image, the shape and size of the particle images, and the amount of particle image overlap, it can be difficult to find all, or even only the majority, of the projected particle peaks in a measurement image. Advanced strategies for 3D particle position reconstruction, such as Iterative Particle Reconstruction (IPR) are designed to work with incomplete 2D peak detection abilities but even they can greatly benefit from a more complete peak detection as ambiguities and position errors are reduced. We introduce a convolutional neural network (CNN) based peak detection scheme that significantly outperforms current conventional approaches, both on synthetic and experimental data, and enables peak detection with a vastly higher completeness even at high particle image densities.
Harnessing Satellite Imagery and Geospatial Analysis for Sustainable Nutrient Management in Agricultural Systems
Authors: Akanbi Olatunde D. , Bhuvanagiri} Deepa C. , Barcelos Erika I. , Nihar Arafath , Yarus Jeffrey M. , French Roger H.
Published: June 2024

Excess fertilizer use has increased crop yields at the cost of disrupting nutrient cycles and contaminating waterways. This research harnesses satellite remote sensing and geospatial modeling to enable sustainable nutrient management. MODIS-derived Normalized Difference Vegetation Index (NDVI) tracks crop growth to optimize fertilizer timing and application rates. Geospatiotemporal analysis of terrain, land use, soils, and hydrology data delineates nutrient distribution pathways to mitigate pollution. Kriging (Gaussian Process Regression) and conditional simulation were used to model soil nutrient distributions. The framework enhances productivity while minimizing agriculture’s environmental footprint through precision management. Geospatiotemporal techniques provide stakeholders with actionable insights, reducing costs and protecting water resources. This methodology offers a practical solution for balancing agricultural intensification and stewardship.
Identification of a colony- and dose-dependent worker aggregation pheromone in the subterranean termite Reticulitermes virginicus
Authors: Mitaka Yuki , Helms Anjel , Vargo Edward
Published: June 2024

Collective behaviors of social insects are often regulated by pheromones. In subterranean termites, some workers forage for and exploit decaying wood for new food resources while forming tunnels from their nest. Colonizing new food resources requires workers to build and disinfect tunnels and chambers inside the nest and ingest decaying wood; therefore subterranean termite colonies should have mechanisms to establish and maintain groups of workers to perform these functions. Recently, an aggregation pheromone was identified in workers of the termite Reticulitermes speratus, which induces quick attraction of nestmate workers and prolonged aggregation to the site of attraction. In this study, we extended this work to another species of Reticulitermes and identified a worker aggregation pheromone in the termite R. virginicus. GC-MS analysis and bioassays demonstrated that this pheromone consists of 3-octanone, 3-octanol, and palmitic acid and shows a colony-specific, dose-dependent attractant response but not an arrestant response. Furthermore, these pheromone components were most likely emitted from the surface of the body. This suggests that aggregation pheromone composition and function differ significantly among termite species, even within the same genus. This study advances our understanding of the regulatory mechanisms of termite aggregation behavior.
Enhancement in fire retardancy of thermoplastic polyurethane: Effect of pentaerythritol derived phosphorous based bicyclic polymer on the flammability
Authors: Shikha Shikha , Yadav Neelam Yadav
Published: June 2024

1-oxyphospha-4-hydroxymethyl-2,6,7-trioxabicyclo [2.2.2] octane (PEPA) was synthesized successfully in producing a PEPA-based monomer having an acrylate group and its polymers. Nuclear magnetic resonance (NMR), 1H, and 31P spectra were used to establish their structural integrity. Thermogravimetric analysis (TGA), the limited oxygen index (LOI), and the UL-94 test were used to examine the thermal characteristics and flame retardant behavior of the systems with 15 wt% of flame retardants (PhosPTPU15). Thermoplastic polyurethane (TPU), the results reveal that LOI increases from 19–27.8% with incorporation of 15 wt% PhosPTPU15. SEM-EDX was used to evaluate the morphology of the TPU and its blend, which exhibits that with the incorporation of PhosPTPU15 the blend showed compact morphology and supported the condensed phase mechanism of the blends.
Physical, Thermal, Linear, Non-linear Optical Properties of Bismuth Modified Borate Glasses Containing Zno, Cdo and Mgo
Authors: Rajiya Shaik , Ahmed Shaik Amer , Samee M. A. , Shareefuddin Md. , Sagar D. Karuna
Published: June 2024

The expression (70-x)B2O3-10ZnO-10CdO-10MgO-xBi2O3 Glasses containing B2O3, ZnO, CdO, MgO, and variable amounts of Bi2O3 were created using the melt quenching process. The observed rise in both density and molar volume of the glass samples as the Bi2O3 level increases suggests the formation of a more expanded structure with greater Bi2O3 content. There is a good degree of agreement between the observed values of band gap energy and Urbach energy estimated from the ASF model and Tauc model. The decrease in Eopt may be attributed to the increased concentration of non-bridging oxygen (NBO) inside the glass network, resulting in a disordered glass structure. The measured values of Λ and A indicate that the current glasses belong to group-II, namely typical ionic (basic) oxides. The non-linear optical properties of the current glasses, specifically n2, χ (1), and χ (3), showed an increase as the glass composition increased. The current glasses provide several benefits, such as a larger density. These glasses have a greater polarizability and an improved refractive index, making them highly sought-after materials for non-linear optical applications.
Centralized exhaust system simulation and parameter optimization
Authors: Lu Mingming , Miao Qian , Zhang Pengfei , Sun Guangwei , Yang Mingyue , Zhao Yao , Zhang Kai , Wang Chong , Wang An-ran , Zheng Tong-fu
Published: June 2024

With the enhancement of people's awareness of environmental protection and the improvement of environmental quality requirements, it is necessary to evaluate the effect of kitchen smoke exhaust on the environment. We introduced the structural definition of the parameters involved in the centralized exhaust flue and the range hood model in the centralized flue exhaust system. The iterative function calculation flow of the smoke output is clarified and the convergence of the iterative function was verified. To ensure the accuracy of parameters in the control equation which include the combined resistance coefficient of the pipeline's inner wall, we used CFD to solve resistance coefficients in the flue model of this smoke exhaust system. This solution was later verified experimentally. We deployed C# language for parametric programming, which allowed us to accommodate changes in different conditions. These include the number or height of layers, flue size, hood model, branch pipe length and type, valve angle, check valve type, and operating rate, among others. The results show that different pipe structures and smoke exhaust conditions are represented as a whole by parameters, which can be quickly calculated and displayed visually visualization program of flow changes in various layers inside the centralized exhaust flue. Lastly, we utilized the model to simulate a scenario, following which we evaluated the impact of the centralized exhaust flue in that scenario. The results of this study can guide the modeling of centralized flue systems and the evaluation of the effect of the exhaust pipe.
Preparation of graphene and its tribological properties based on deep eutectic solvent stripping method
Authors: Li Ting , Chen Yun , Wang Rui , Wang Junhai , Li Xinran , Zhang Lixiu
Published: June 2024

Graphene(GP), when used as a lubricant additive, not only reduces the friction coefficient but also enhances wear resistance by forming a protective lubrication film. However, there are still several challenges in practical applications related to graphene preparation. Therefore, this study employs a novel type of ionic liquid deep eutectic solvent as an interlayer agent for graphene preparation and investigates its tribological properties when used as an additive. We used choline chloride/ethylene glycol deep eutectic solvent as the intercalation agent and successfully prepared graphene samples using liquid phase exfoliation. The resulting graphene samples had a thickness of 4–5 layers. The peeling mechanism is analyzed through molecular dynamics simulations and characterization techniques such as Raman spectroscopy, XRD, SEM, and AFM. In friction experiments conducted with different mass fractions of 1.5 wt% DES and 0.05 wt% GP as lubricant additives, it is observed that the mixture exhibits optimal lubrication performance compared to base oil alone; specifically reducing average friction coefficient by 56.8% and depth of wear marks by 59.8%. This enhancement in friction performance can be attributed to both high wettability and synergistic effects between composite lubricants. Considering the wide range of available DESs and two-dimensional materials, these newly developed functional two-dimensional materials based on DES hold significant research potential.
A Digitization Model in Ugandan Technical, Vocational and Education Training Institutions: An Agency Theory Perspective
Authors: Muinda Patrick Emmanuel , Basaza-Ejiri Annabella Habinka , Maiga Gilbert , Mayoka Kituyi
Published: June 2024

This research examines global digital transformation project success, emphasizing the contrast between developed and developing economies. While developed nations experience declining failure rates, developing economies, notably Uganda, grapple with a 70% project failure rate. Focusing on Uganda's Technical, Vocational Education, and Training (TVET) institutions, this study investigates the integration of digital technologies for skill enhancement. By applying agency theory, a tailored model is introduced to mitigate project failures. Through a quantitative approach, variables (goal conflict, shirking, communication, task programability, contract type, process quality, and digital transformation) contributing to improved success are identified. The results show positive responses, ensuring high overall validity and reliability. Cronbach's alpha exceeded 0.7 for all variables and was lowest at 0.732. The convergent validity exceeds 0.5 for the independent variables. The validated model comprehensively addresses digital transformation success, highlighting the risks related to shirking and goal conflict. Future research should explore these risks, examine contributing factors, and refine the guiding model for digital transformation investments.
Radiologic Diagnosis of Rheumatoid Arthritis and Palindromic Arthritis
Authors: Khattab Khalid , Shehadeh Rawan , Alnahhas Fatema Alzahraa , Saleem Mjd , Farhan Nour
Published: June 2024

Background: Establishing an early diagnosis of rheumatoid disease is useful in preventing the progression of irreversible damage to the bones and joints. Many radiological investigations have been developed that have helped in diagnosis in the early stage and determining the severity and progression of the disease, in addition to determining the patients’ response to treatment. These methods include simple images, ultrasound, computed tomography, and magnetic resonance imaging.
Methods and Materials: A retrospective study, through which patient records at Al-Mowasat University Hospital were reviewed, and the number of patients confirmed to have a rheumatic or palindromic Arthritis from the year March 2021 until March 2024 was (572) male and female patients.
Results: The percentage of males was 25%, while the percentage of females was 75%. The percentage of those aged less than 50 years was 35%, while the percentage of those aged more than 50 years was 65%. The most common joints affected by rheumatoid arthritis are the joints of the feet and hands, with a percentage of 45%. The most common clinical symptoms are stiffness. Morning and muscle pain, 75% and 60%, respectively. The most common radiological method for diagnosis is the plain image, 100%, followed by magnetic resonance imaging, 74%. Most imaging positions were anteroposterior, 97%, and lateral, 75%. The most common radiological sign on the plain image is joint injury. Metacarpophalangeal, bone hypocalcification by 45%, joint pinches were seen by 45%, the most common sign in echography is thickening of the synovial membrane 77%.
Conclusion: It is necessary to adequately raise awareness and educate patients and medical personnel about the seriousness of this disease, Early detection of rheumatoid arthritis, and the negativity of laboratory tests and x-rays can be followed up by ultrasound, and magnetic resonance imaging is useful and is considered the gold standard in early detection of rheumatoid arthritis.
WITHDRAWN: Who Influences Whom about What?
Authors: Garg Prashant
Published: June 2024

This study examines the dynamics of opinion formation influenced by US senators on Twitter from 2018 to 2022, focusing on nine politically charged issues such as climate change, immigration, and race relations. Leveraging advanced topic and stance detection methods with Large Language Models (LLMs) and an event study design that exploits the intricacies of Twitter's recommendation algorithm to introduce exogenous variation, the research explores both agenda-setting and stance-setting effects. Key findings reveal that senators' tweets significantly shape public discourse, with variations in influence based on topic, partisan identity, and rhetorical style. While Democrats exhibit a progressive influence across issues, Republicans show a neutral/centrist stance on cultural topics, underscoring affective polarization on immigration and abortion rights. Cognitive language is most effective for economic topics, while emotional and egocentric language enhance engagement on climate action. Conversely, toxic language generally backfires, especially on climate topics. Additionally, political communication fosters inclusive over egocentric language and emotional over cognitive expression. These insights are critical for policymakers, social media platforms, and opinion leaders aiming to leverage digital political communication to foster informed and constructive public discourse.