bluuebunny/crossref_metadata_2025_split huggingface dataset:
A robust audio steganography technique based on image encryption using different chaotic maps
Authors: Nasr Marwa A. , El-Shafai Walid , El-Rabaie El-Sayed M. , El-Fishawy Adel S. , El-Hoseny Heba M. , Abd El-Samie Fathi E. , Abdel-Salam Nariman 
Published: September 2024

AbstractThe development of innovative methods for concealing critical data in multimedia files has exploded in information security in recent years. Cryptography and steganography cannot be used alone to protect data; rather, they can be combined and used in a single system. Audio steganography is among the most important information security techniques. It involves the concealment of information within audio signals to achieve covert communication. This paper introduces a comprehensive technique that integrates chaos Henon, Baker, and Arnold maps for image encryption with audio steganography to create a robust and secure audio steganography technique. First, the target image is encrypted using chaotic maps. Then, it is embeded within the high frequencies of the cover audio signal based on the Inverse Short Time Fourier Transform (ISTFT) to be transmitted to the destination through the channel. By integrating both encryption and concealment techniques, the cover audio signal quality can be preserved. Moreover, the hidden image security and robustness are improved, making the technique resistant to many types of attacks. The simulation results confirm that the suggested technique is robust in the presence of attacks. It achieves a distinct perceptual quality with an appreciated peak signal-to-noise ratio (PSNR) of 91.2 dB and a Mean Square Error (MSE) of 7.5 × 10–10. The randomness of the resulting encrypted image has successfully passed the National Institute of Standards and Technology (NIST) statistical test suite.
Improvement of Vicia faba plant tolerance under salinity stress by the application of thiamine and pyridoxine vitamins
Authors: Ahmed Eman Zakaria , Sattar Amira Mohamed Abd El 
Published: September 2024

AbstractEnhancement of plant growth at early growth stages is usually associated with the stimulation of various metabolic activities, which is reflected on morphological features and yield quantity and quality. Vitamins is considered as anatural plant metabolites which makes it a safe and ecofriendly treatment when used in appropriate doses, for that this research aimed to study the effect of two different vitamin B forms (thiamine and pyridoxine) on Vicia faba plants as agrowth stimutator in addition to study it’s effect on plant as astrong antioxidant under salinity stress.Our findings demonstrated that both vitamin forms significantly increased seedling growth at germination and early growth stages, especially at 50 ppm for pyridoxine and 100 ppm for thiamine. Pyridoxine at 50 ppm increased seedling length by approximately 35% compared to control, while thiamine at 100 ppm significantly promoted seedling fresh and dry wt by 4.36 and 1.36 g, respectively, compared to control seedling fresh wt 2.17 g and dry weight 1.07 g. Irrigation with 100 mM NaCl had a negative impact on plant growth and processes as well as the uptake of several critical ions, such as K+ and Mg+2, increasing Na uptake in comparison to that in control plants. Compared to control plants irrigated with NaCl solution, the photosynthetic pigments, soluble sugars, soluble proteins, and total antioxidant capacity increased in the presence of pyridoxine and thiamine, both at 50 and 100 ppm salinity. The proline content increased in both treated and untreated plants subjected to salt stress compared to that in control plants. Thiamine, especially at 50 ppm, was more effective than pyridoxine at improving plant health under saline conditions. An increase in Vicia faba plant tolerance to salinity was established by enhancing antioxidant capacity via foliar application of vitamin B through direct and indirect scavenging methods, which protect cell macromolecules from damage by oxidative stress, the highest antioxidant capacity value 28.14% was recorded at 50 ppm thiamine under salinity stress.The provided results is aguide for more researches in plant physiology and molecular biology to explain plant response to vitamins application and the suggest the sequence by which vitamins work inside plant cell.
Elevated α-1,2-mannosidase MAN1C1 in glioma stem cells and its implications for immunological changes and prognosis in glioma patients
Authors: Batara Don Carlo , Kim Hyun-Jin , Phan Le Thi , Kim Minseo , Son Young-Ok , Lee Seongsoo , Park Sang-Ik , Choi Young Sun , Beck Samuel , Kim Sung-Hak 
Published: September 2024

AbstractGlioblastoma multiforme (GBM) is the most aggressive type of primary brain tumor, and the presence of glioma stem cells (GSCs) has been linked to its resistance to treatments and recurrence. Additionally, aberrant glycosylation has been implicated in the aggressiveness of cancers. However, the influence and underlying mechanism of N-glycosylation on the GSC phenotype and GBM malignancy remain elusive. Here, we performed an in-silico analysis approach on publicly available datasets to examine the function of N-glycosylation-related genes in GSCs and gliomas, accompanied by a qRT-PCR validation experiment. We found that high α-1,2-mannosidase MAN1C1 is associated with immunological functions and worse survival of glioma patients. Differential gene expression analysis and qRT-PCR validation revealed that MAN1C1 is highly expressed in GSCs. Furthermore, higher MAN1C1 expression predicts worse outcomes in glioma patients. Also, MAN1C1 expression is increased in the perinecrotic region of GBM and is associated with immunological and inflammatory functions, a hallmark of the GBM mesenchymal subtype. Further analysis confirmed that MAN1C1 expression is closely associated with infiltrating immune cells and disrupted immune response in the GBM microenvironment. These suggest that MAN1C1 is a potential biomarker for gliomas and may be important as an immunotherapeutic target for GBM.
Infrared neuroglial modulation of spinal locomotor networks
Authors: Dumas Nathan , Pecchi Emilie , O’Connor Rodney , Bos Rémi , Moreau David 
Published: September 2024

AbstractInfrared neural stimulation (INS) emerges as a promising tool for stimulating the nervous system by its high spatial precision and absence of the use of exogenous agents into the tissue, which led to the first successful proof of concept in human brain. While neural networks have been the focal point of INS research, this technique is also non cell type specific as it triggers activity in non electrically excitable cells. Despite increasing interest, there remains to demonstrate well defined simultaneous astrocytic and neuronal signals in response to INS. Using calcium imaging, we show that INS has the capacity to initiate calcium signaling in both astrocytes and neurons simultaneously from the rostral lumbar spinal cord, each exhibiting distinct temporal and amplitude characteristics. Importantly, the mechanism underlying infrared-induced neuronal and astrocytic calcium signaling differ, with neuronal activity relying on sodium channels, whereas induced astrocytic signaling is predominantly influenced by extracellular calcium and TRPV4 channels. Furthermore, our findings demonstrate the frequency shift of neuronal calcium oscillations through infrared stimulation. By deepening our understanding in INS fundamentals, this technique holds great promise for advancing neuroscience, deepening our understanding of pathologies, and potentially paving the way for future clinical applications.
Smartphone addiction in Iranian schoolchildren: a population-based study
Authors: Azizi Alireza , Emamian Mohammad Hassan , Hashemi Hassan , Fotouhi Akbar 
Published: September 2024

AbstractSmartphone addiction is a public health problem and is increasing globally. It is important to know the latest status of this phenomenon in different region of the world. This study aimed to investigate smartphone addiction in an Iranian student population. The participants were a random sample of Shahroud Schoolchildren Eye Cohort study in 2018. Smartphone addiction was measured by the Smartphone Addiction Scale Short Version (SAS-sv) questionnaire. The prevalence and mean estimates were reported with 95% confidence intervals. The analysis of variance, t-test and multiple logistic regression were used for analysis of data. The mean age of 2682 participating students was 13.5 year with an age range of 10 to 15 year, and 1197 (44.6%) of them were girls. The mean SAS-sv score was 27.96 (95% CI: 27.36–28.58) for boys and 26.10 (95% CI: 25.34–26.80) for girls. This score follows a U-shaped pattern with a lower mean score observed at age 8. The Prevalence of smartphone addiction was 29.8% (95% CI: 28.1–31.5) in studied population. Compared to 10 year old students, smartphone addiction was higher in 14 (Odds Ratio (OR) = 1.7, 95% CI: 1.1–2.7) and 15 (OR = 2.4, 95% CI: 1.5–3.8) years old students. Smartphone addiction was higher in boys (OR = 1.9; 95% CI, 1.5–3.0; P value < 0.001). Smartphone addiction was not different in urban and rural students after adjusting for age and gender (P = 0.089). Compared to European countries the prevalence of smartphone addiction in Iranian students is relatively high and need prompt intervention especially in boys and older adolescents.
DINO-Mix enhancing visual place recognition with foundational vision model and feature mixing
Authors: Huang Gaoshuang , Zhou Yang , Hu Xiaofei , Zhang Chenglong , Zhao Luying , Gan Wenjian 
Published: September 2024

AbstractUsing visual place recognition (VPR) technology to ascertain the geographical location of publicly available images is a pressing issue. Although most current VPR methods achieve favorable results under ideal conditions, their performance in complex environments, characterized by lighting variations, seasonal changes, and occlusions, is generally unsatisfactory. Therefore, obtaining efficient and robust image feature descriptors in complex environments is a pressing issue. In this study, we utilized the DINOv2 model as the backbone for trimming and fine-tuning to extract robust image features and employed a feature mix module to aggregate image features, resulting in globally robust and generalizable descriptors that enable high-precision VPR. We experimentally demonstrated that the proposed DINO-Mix outperforms the current state-of-the-art (SOTA) methods. Using test sets having lighting variations, seasonal changes, and occlusions such as Tokyo24/7, Nordland, and SF-XL-Testv1, our proposed architecture achieved Top-1 accuracy rates of 91.75%, 80.18%, and 82%, respectively, and exhibited an average accuracy improvement of 5.14%. In addition, we compared it with other SOTA methods using representative image retrieval case studies, and our architecture outperformed its competitors in terms of VPR performance. Furthermore, we visualized the attention maps of DINO-Mix and other methods to provide a more intuitive understanding of their respective strengths. These visualizations serve as compelling evidence of the superiority of the DINO-Mix framework in this domain.
Robust tissue pattern formation by coupling morphogen signal and cell adhesion
Authors: Mizuno Kosuke , Hirashima Tsuyoshi , Toda Satoshi 
Published: September 2024

AbstractMorphogens, locally produced signaling molecules, form a concentration gradient to guide tissue patterning. Tissue patterns emerge as a collaboration between morphogen diffusion and responsive cell behaviors, but the mechanisms through which diffusing morphogens define precise spatial patterns amidst biological fluctuations remain unclear. To investigate how cells respond to diffusing proteins to generate tissue patterns, we develop SYMPLE3D, a 3D culture platform. By engineering gene expression responsive to artificial morphogens, we observe that coupling morphogen signals with cadherin-based adhesion is sufficient to convert a morphogen gradient into distinct tissue domains. Morphogen-induced cadherins gather activated cells into a single domain, removing ectopically activated cells. In addition, we reveal a switch-like induction of cadherin-mediated compaction and cell mixing, homogenizing activated cells within the morphogen gradient to form a uniformly activated domain with a sharp boundary. These findings highlight the cooperation between morphogen gradients and cell adhesion in robust tissue patterning and introduce a novel method for tissue engineering to develop new tissue domains in organoids.
Electric load forecasting under false data injection attacks via denoising deep learning and generative adversarial networks
Authors: Mahmoudnezhad Fayezeh , Moradzadeh Arash , Mohammadi‐Ivatloo Behnam , Zare Kazem , Ghorbani Reza 
Published: September 2024

AbstractAccurate electric load forecasting at various time periods is considered a necessary challenge for electricity consumers and generators to maximize their economic efficiency in energy markets. Hence, the accuracy and effectiveness of existing electric load forecasting approaches depends on the data quality. Nowadays, with the implementation of modern power systems and Internet of Things technology, forecasting models are faced with a large volume of data, which puts the security and health of data at risk due to the use of numerous measuring devices and the threat of cyber‐attackers. In this study, a cyber‐resilient hybrid deep learning‐based model is developed that accurately forecasts electric load in short‐term and long‐term time horizons. The architecture of the proposed model systematically integrates stacked multilayer denoising autoencoder (SMDAE) and generative adversarial network (GAN) and is called SMDAE‐GAN. In the proposed framework, SMDAE layer is used to pre‐process and remove real fs and intentional anomalies in data, and GAN layer is also utilized to forecast electric load values. The effectiveness of the SMDAE‐GAN structure is studied using realistic electrical load data monitored in the distribution network of Tabriz, Iran, and meteorological data measured in weather station there. Compared with other conventional load forecasting approaches, the developed framework has the highest accuracy in both cases of using normal data with real‐world noise and damaged data under false data injection attacks.
Wind turbine generator failure analysis and fault diagnosis: A review
Authors: Liu Huan , Wang YuZe , Zeng Tao , Wang HaiFeng , Chan Shing‐Chow , Ran Li 
Published: September 2024

AbstractThe large scale deployment of modern wind turbines and the yearly increase of installed capacity have drawn attention to their operation and maintenance issues. The development of highly reliable and low‐maintenance wind turbines is an urgent demand in order to achieve the low‐carbon goals, and the arrival of fault diagnosis provides assurance for its satisfactory operation and maintenance. Numerous statistical studies have pointed out that generator failures are a main cause of wind turbine system downtime. The generator, as one of the core components, converts rotating mechanical energy into electrical energy. However, the generators can hardly operate reliably towards the end of the turbine life owing to the variable‐speed conditions and harsh electromagnetic environments. This article first provides a comprehensive and up‐to‐date review of the electrical and mechanical failures of various parts (stator, rotor, air gap and bearings) of the generator. Then the fault characteristics and diagnostic processes of generators are investigated, and the principles and processes of fault diagnosis are discussed. Finally, the application of four categories of model‐based, signal‐based, knowledge‐based and hybrid approaches to wind turbine generator fault diagnosis is summarized. The comprehensive review shows that the hybrid approach is now the leading and most accurate tool for real‐time fault diagnosis for wind turbine generators. A qualitative and quantitative assessment of algorithm performance using false alarm rates is proposed. The methodology can subsequently be applied to the wind industry.
An adaptive method for real‐time photovoltaic power forecasting utilizing mathematics and statistics: Case studies in Australia and Vietnam
Authors: Nguyen‐Duc Tuyen , Vu‐Xuan‐Son Huu , Do‐Dinh Hieu , Nguyen‐Vu‐Nhat Nam , Fujita Goro , Tran‐Thanh Son 
Published: September 2024

AbstractThe advancement of Photovoltaic technology has undergone rapid acceleration in recent years. Nonetheless, the most significant drawback of Photovoltaic is its intermittence, making it an obvious source of power fluctuation. This study proposes a novel scheme for real‐time or intraday PV power forecasting by adopting two predictive models, namely, White‐box and Combination. The White‐box model is implemented employing mathematical calculations and statistics called Exceedance Probability. Meanwhile, the Combination model is an aggregation of several predictive models' outputs including the White‐box model and benchmark ones by dynamically adjusting the weight coefficient of each model based on their forecasting accuracy. The experimental results, which are verified on two PV systems corresponding to two case studies located at Vietnam and Australia, indicate that the two proposed models outperform other referenced models as improves approximately 40% and 38% in terms of the first and second case study, respectively. In particular, the White‐box model shows superiority by updating the forecast every 10 min, which can adapt to the fluctuation of weather conditions whereas the Combination one yields acceptable precision, indicating its flexible application.
First map of D2H+ emission revealing the true centre of a prestellar core: Further insights into deuterium chemistry
Authors: Pagani L. , Belloche A. , Parise B. 
Published: September 2024

Context. IRAS 16293E is a rare case of a prestellar core being subjected to the effects of at least one outflow.
Aims. We want to disentangle the actual structure of the core from the outflow impact and evaluate the evolutionary stage of the core.
Methods. Prestellar cores being cold and depleted, the best tracers of their central regions are the two isotopologues of the trihydrogen cation that are observable from the ground: ortho-H2D+ and para-D2H+. We used the Atacama Pathfinder EXperiment (APEX) telescope to map the para-D2H+ emission in IRAS 16293E and collected James Clerk Maxwell Telescope (JCMT) archival data of ortho-H2D+. We compared their emission to that of other tracers, including dust emission, and analysed their abundance with the help of a 1D radiative transfer tool. The ratio of the abundances of ortho-H2D+ to para-D2H+ can be used to estimate the stage of the chemical evolution of the core.
Results. We have obtained the first complete map of para-D2H+ emission in a prestellar core. We compare it to a map of ortho-H2D+ and show their partial anti-correlation. This reveals a strongly evolved core with a para-D2H+/ortho-H2D+ abundance ratio towards the centre for which we obtain a conservative lower limit from 3.9 (at 12 K) to 8.3 (at 8 K), while the high extinction of the core is indicative of a central temperature below 10 K. This ratio is higher than predicted by the known chemical models found in the literature. Para-D2H+ (and indirectly ortho-H2D+) is the only species that reveals the true centre of this core, while the emission of other molecular tracers and dust are biased by the temperature structure that results from the impact of the outflow.
Conclusions. This study is an invitation to reconsider the analysis of previous observations of this source and possibly questions the validity of the deuteration chemical models or of the reaction and inelastic collisional rate coefficients of the
H+3
isotopologue family. This could impact the deuteration clock predictions for all sources.
A novel optimal transport-based approach for interpolating spectral time series
Authors: Ramirez Mauricio , Pignata Giuliano , Förster Francisco , González-Gaitán Santiago , Gutiérrez Claudia P. , Ayala Bastian , Cabrera-Vives Guillermo , Catelan Márcio , Arancibia Alejandra M. Muñoz , Pineda-García Jonathan 
Published: September 2024

Context. The Vera C. Rubin Observatory is set to discover 1 million supernovae (SNe) within its first operational year. Given the impracticality of spectroscopic classification at such scales, it is mandatory to develop a reliable photometric classification framework.
Aims. This paper introduces a novel method for creating spectral time series that can be used not only to generate synthetic light curves for photometric classification, but also in applications such as K-corrections and bolometric corrections. This approach is particularly valuable in the era of large astronomical surveys, where it can significantly enhance the analysis and understanding of an increasing number of SNe, even in the absence of extensive spectroscopic data.
Methods. By employing interpolations based on optimal transport theory, starting from a spectroscopic sequence, we derive weighted average spectra with high cadence. The weights incorporate an uncertainty factor for penalizing interpolations between spectra that show significant epoch differences and lead to a poor match between the synthetic and observed photometry.
Results. Our analysis reveals that even with a phase difference of up to 40 days between pairs of spectra, optical transport can generate interpolated spectral time series that closely resemble the original ones. Synthetic photometry extracted from these spectral time series aligns well with observed photometry. The best results are achieved in the V band, with relative residuals of less than 10% for 87% and 84% of the data for type Ia and II, respectively. For the B, g, R, and r bands, the relative residuals are between 65% and 87% within the previously mentioned 10% threshold for both classes. The worse results correspond to the i and I bands, where, in the case of SN Ia, the values drop to 53% and 42%, respectively.
Conclusions. We introduce a new method for constructing spectral time series for individual SNe starting from a sparse spectroscopic sequence, and demonstrate its capability to produce reliable light curves that can be used for photometric classification.