bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pd Catalysts Supported on γ-Al2O3 Immobilized with Ionic Liquids: An Efficient and Recyclable System for C-C Bond Formation
Authors: Wang Ya-Jie , Li Guo-Hua , Wang Xu , Sun Hong-Xin 
Published: December 2024

Abstract

A novel γ-Al2O3/IL-Pd catalyst has been designed and synthesized, featuring the highly dispersed deposition of active palladium nanoparticles on the surface of porous γ-Al2O3, which enhances catalytic performance and recyclability. The incorporation of ionic liquids is critical to the catalyst's design, as it facilitates the complete dispersion of active metals through chelation, effectively preventing the formation of palladium black and addressing catalyst deactivation. The interaction between the ionic liquid and the support, likely through specific coordination chemistries, significantly enhances the recovery and reuse capabilities of the catalyst by preventing the leaching and sintering of the palladium nanoparticles. This approach not only resolves the agglomeration issue inherent to γ-Al2O3 but also optimizes the catalyst's performance further. In the Suzuki-Miyaura coupling reaction, the new catalyst demonstrates remarkable catalytic activity, achieving a yield of tetramethoxybiphenyl of up to 99% within a remarkably short reaction time of just 30 minutes, without the need for inert gas protection. Notably, after ten consecutive cycles, the catalyst's performance remained at 94%, outperforming many existing catalysts and confirming its exceptional stability and recyclability. Given its high efficiency, stability, and reusability, this catalyst is positioned to serve as a high-performance multifunctional catalytic platform for a range of significant organic synthesis reactions.
YOLOv8-Based Fracture Detection System
Authors: Shaban Waleed , El-Behaidy Wessam , badawy Wael 
Published: December 2024

Abstract

Emergency departments in hospitals regularly contend with a substantial influx of patients suffering from bone fractures, with pediatric wrist trauma fractures representing a notable portion of these cases. Preceding surgical interventions, pediatric surgeons must gather information about the fracture's cause and analyze the fracture's condition through the interpretation of X-ray images. This process typically requires a blend of skills from radiologists and surgeons, involving extensive training. The rise of deep learning in the field of computer vision has highlighted the development of network models for fracture detection as a crucial area of research. This paper is centered on the utilization of the YOLOv8 algorithm to train models using the GRAZPEDWRI-DX bone fracture dataset after pre-processing with the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, which contains 6,091 pediatric patients X-ray images suffering from wrist trauma. The principal objective is to enhance the detection of fractures in these cases. The results of rigorous experimentation illuminate the merits of various YOLOv8 algorithm models, notably emphasizing the superiority of the YOLOv8m model with a 1024-pixel image size, which achieves an impressive mean average precision (mAP 50) of 0.743.
Fabrication of Self-Assembled Cu Nanochains for Water-Based Lubrication Systems
Authors: Hua Keying , Guo Diange , Zhou Feng , Zhang Xia 
Published: December 2024

Abstract

The development of green and environmentally friendly lubricant additives is crucial in the field of friction. A green modifier, Cardanol polyoxyethylene ether xanthate (CPEOCS2K), was synthesized from natural cashew nut shells derived from Cardanol. Cu nanoparticles were prepared using a simple one-step reduction method, which involved modifying metallic Cu with CPEOCS2K. The long chains of the modifier on the surface of the nanocore interconnected to form a nano-network, and the resulting Cu nanochains exhibited excellent lubrication performance when applied to a water-ethylene glycol (EG) system as a lubricant additive. This encapsulated nanoparticle structure not only enhances the dispersion stability of the nanoparticles but also generates a chemically reactive film that adsorbs onto the wear surface during friction, thereby improving the lubrication effectiveness of the water-based lubricant. The use of natural organic compounds to synthesize modifiers enhances the environmental friendliness of metal nanoparticles as lubricant additives, and the outstanding lubricating performance demonstrates the potential of green lubrication technology.
Zerumbone And Doxorubicin Combination Induces MAPK3 Dependent Apoptosis in Triple Negative Breast Cancer Cells
Authors: Mehraj Umar , Sofi Shazia , Wani Nissar A , Mir Manzoor Ahmad 
Published: December 2024

Abstract

Aim
Doxorubicin (DOX) is one of the most commonly used chemotherapeutic medications for treating several malignancies, including TNBC. However, severe toxicity and the development of resistance to drugs, limits its therapeutic index. As a result, there is a crucial need for the advancement of novel medications that boost the efficacy of Doxorubicin while decreasing its toxicity. Studies have shown that the Z. zerumbet-isolated sesquiterpene zerumbone (ZER) is a potent anti-proliferative molecule against several cancers including BC. This study intended to assess the antitumor activity of ZER in TNBC cells in combination with Doxorubicin.
Methods
MTT and Chou-Talay techniques were used to evaluate cell viability and pharmacodynamic interactions. Western blotting. flow cytometry, and fluorescence microscopy were used to assess the molecular mechanism underlying the anti-tumor activity of DOX-ZER combination.
Results
DOX and ZER combination significantly reduced the cell viability and showed synergistic impact on migration, proliferation, and colony formation potential of TNBC cells. Moreover, DOX-ZER combination resulted in enhanced ROS accumulation, activating MAPK3 mediated apoptosis and cell cycle arrest.
Conclusions
Our findings show that ZER is a potent anticancer molecule that may be used in combination with DOX to enhance therapeutic efficacy and reduce the development of therapeutic resistance.
Aligning syntactic structure to the dynamics of verbal communication: a pipeline for annotating syntactic phrases onto speech acoustics
Authors: Iaia Cosimo , Tavano Alessandro 
Published: December 2024

Abstract

To investigate how the human brain encodes the complex dynamics of natural languages, any viable and reproducible analysis pipeline must rely on either manual annotations or Natural Language Processing (NLP) tools, which extract relevant physical (e.g., acoustic, gestural), and structure- building information from both speech and language signals. However, correctly annotating syntactic structure for a given human language is arguably a harder task than annotating the onset and offset of speech units such as phonemes and syllables, as the latter can be identified by relying at least in part on the physically overt and temporally measurable properties of the signal, while syntactic structure units are generally covert and their labelling is model-driven. We describe and validate a pipeline that takes into account both physical and theoretical aspects of speech and language signals, and operates a theory-driven and explicit alignment between overt speech units and covert syntactic units.
Arctic Ozone Amplifies Stratospheric Circulation Extremes
Authors: Hong Hao-Jhe , Reichler Thomas , Hsu Huang-Hsiung 
Published: December 2024

Abstract

Stratospheric ozone has long been suspected to drive interactions involving chemistry, radiation, and the circulation. However, the significance of these interactions, the underlying mechanisms, and the specific conditions that facilitate them remain poorly understood. In this study, we use a dry-dynamical core model with a simplified linear ozone scheme and a shortwave radiation parameterization to investigate these interactions. Our analysis, based on two long control simulations with either interactive or prescribed ozone, reveals that interactive ozone increases the persistence and interannual variability of the stratospheric circulation during northern spring, a period with sufficient solar radiation over the northern polar cap. This effect is closely linked to late-winter extreme stratospheric circulation events, such as stratospheric sudden warmings (SSWs) and vortex intensifications (VIs). While interactive ozone does not alter the frequency of these events, the ozone perturbations induced by the circulation amplify the associated temperature and wind anomalies. Specifically, late-winter VIs are followed by a colder and more persistent polar vortex in spring when interactive ozone is used, compared to fixed ozone. This results in a five-day delay in the breakdown date of the vortex and a more positive North Atlantic Oscillation at the surface. Although interactive ozone also amplifies perturbations following SSWs, these effects are less pronounced than those observed for VIs. Our findings contribute to a growing body of evidence highlighting the importance of ozone-dynamics interactions for simulating the stratospheric circulation, its variability, and its surface impacts.
AI and Kernel Influences: The Impact of Kernel Optimization on Energy Consumption Forecasting
Authors: Danish Muhammad Umair 
Published: December 2024

Abstract

This study addresses the complexities of short-term energy forecasting in the context of fluctuating consumer behaviors and seasonal variations. Our research primarily focused on the development and evaluation of advanced machine learning models and particularly understanding the effects of Kernels on the proposed HyperNetwork-based LSTM model to enhance the accuracy and reliability of energy predictions. We conducted a comprehensive analysis of various state-ofthe-art models, including MLPs, RNNs, and Transformers, rigorously evaluating them across standard metrics for fair comparisons. A significant contribution of our work is the proposed HyperNetwork-based LSTM model which provides consistently superior forecasting accuracy, particularly in certain seasonal contexts. However, upon seasonal evaluation, our proposed solution is not performing well in all seasons. The Python-based implementation of the entire project is available at GitHub Repository — Advanced AI.
Seasonal Dynamics in Energy Forecasting: A Deep Learning Approach for Student Residences
Authors: Danish Muhammad Umair 
Published: December 2024

Abstract

This study presents a comprehensive analysis of various machine-learning models for energy forecasting across different seasons in students' residential settings. Our research investigates the performance of baseline models, including LSTM, GRU, state-of-the-art forecasting models, including Autoregressive feedforward neural network, Transformer, and hybrid approaches, by evaluating their ability to predict energy usage with high accuracy. Seasonal patterns and the occurrence of anomalous events such as vacations, meteorological shifts, unpredictable nature of human activity leading to sudden spikes and drops in energy consumption are particular challenges. The results demonstrate that no single model uniformly outperforms others across all seasons, which shows the necessity for season-specific model selection or design. Our proposed HyperNetLSTM and MiniAutoEncXGBoost models are delivering robustness in learning to seasonal variations especially in capturing sudden changes in consumption during the summer. The research contributes to the field of energy forecasting by the significance of seasonal insight and model behavior.
Water quality polluted by total suspended solids classified within an artificial neural network approach
Authors: Luviano Soto Itzel , Sánchez Yajaira Concha , Raya Alfredo 
Published: December 2024

ABSTRACT

This study investigates the application of an artificial neural network (ANN) framework for analysing water pollution caused by solids. To address the challenge, we develop a convolutional neural network trained under a transfer learning strategy with AlexNet. We feed the network with pictures of samples of water with low, medium, and high concentrations of total suspended solids and achieve a high validation accuracy of 99.85% with a precision of 99.85%, which is highly competitive with other approaches. Our model demonstrates significant improvements in speed and reliability over conventional image processing methods, effectively predicting pollution levels. Our findings suggest that ANNs can serve as an effective tool for real-time monitoring and management of water pollution, facilitating proactive decision-making and policy formulation.
Probing the Mesenchymal Stem Cell Aging through In silico Assessment of Extracellular Vesicle-mediated miRNAs
Authors: Mi Ningning , Liu Xibin , Gao Yuhua , Bai Chunyu , Li Xiangchen 
Published: December 2024

Introduction:
During mesenchymal stem cell (MSCs) aging, a decrease in its proliferation
and regenerative capacity occurs, which is implicated in human aging. The MSCs aging process is
regulated by genetics, metabolism, the external environment, and various complex pathways

Method:
The aging of MSCs during in vitro culture poses a major challenge for developing cell therapy
aimed at combating human diseases and aging. To identify the contributing factors underlying
MSCs aging, we obtained datasets of mRNA expression changes before and after aging from the
Gene Expression Omnibus (GEO) database and datasets of extracellular vesicles (EVs) microRNAs
(miRNAs) expression changes (GSE153752, GSE195634, and GSE226464). We conducted an indepth
analysis to screen the correlation between EVs-miRNAs and MSCs aging.

Result:
Our analysis identified significant differences in the expression of hsa-miR-146a-5p, hsamiR-
432-5p, hsa-miR-7706, hsa-miR-409-3p, and hsa-miR-17-5p in EVs before and after MSCs
aging. These differences arise from the post-MSCs aging activation of signaling pathways, such as
FOXO and P53, which promote the expression of hsa-miR-146a-5p, hsa-miR-432-5p, hsa-miR-7706,
hsa-miR-409-3p, and hsa-miR-17-5p.

Conclusion:
Subsequently, these miRNAs are transported to EVs upon binding to the RNA-binding
proteins A2BP1, SFRS2, MBNL1, EIF4B, and ACO1. This study used the correlation between
MSCs aging and specific EVs-miRNAs to predict MSCs aging during the culture process.
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots
Authors: Tu Binbin , Fu Xibei , Han Xiaowei , Wang Fei , Wu Baoju 
Published: December 2024

Aims:
external interference and slow localization response. The primary objective is to
propose an algorithm that integrates admittance control and non-singular fast terminal sliding mode
control, verifying its effectiveness through simulation experiments while exploring its potential for
patent application.

Background:
Due to their versatility and efficiency, UAVs are increasingly utilized in various aerial
operations. However, they are susceptible to external disturbances, which may affect their stability
and accuracy during tasks such as contact operations. Additionally, inherent delays in localization
response speed may impact their performance in dynamic environments. Addressing these issues
is essential for improving the reliability and robustness of UAV-based systems.

Methods:
To achieve the objectives, the kinematics and dynamics of a hexacopter aerial carrier robotic
arm system were initially modeled. Subsequently, an external admittance controller was designed
to mitigate disturbances encountered during contact operations, achieving smooth control of
the robotic arm end-effector by adjusting the desired position to enhance system stability and disturbance
rejection. Additionally, to prevent performance degradation stemming from controller saturation,
an internal position control mechanism utilizing a non-singular fast terminal sliding mode
control algorithm was implemented. This approach enhances system robustness and convergence
speed, ensuring accurate positioning.

Results:
To validate the effectiveness and feasibility of the proposed control algorithm, numerical
simulations were conducted. The outer loop's admittance control exhibited a smoother control process,
particularly during sudden stiffness changes when the actuator contacts the environment. The
inner loop, employing Non-Singular Fast Terminal Sliding Mode Control (NFTSMC), improved
joint angle tracking speed by 41%-58% compared to PID control, and by 20%-50% compared to
traditional Sliding Mode Control (SMC). This algorithm demonstrated faster convergence rates and
smoother transitions, significantly reducing steady-state errors in contact force while exhibiting robustness
to environmental parameters. The findings indicate that the algorithm effectively addresses
the issues of external interference and sluggish localization response encountered by UAVs during
aerial operations.

Conclusion:
The algorithm based on admittance control and non-singular fast terminal sliding
mode control demonstrates superior performance compared to traditional sliding mode control and
PID control in mitigating external disturbances and enhancing the precision of UAV aerial operations.
This ensures the resilience to disturbances and the speed of localization response of the rotary-
wing flying robotic arm system during cleaning processes, thus enhancing its reliability and robustness
in dynamic environments.
Integrating Digital Twin Technology with AA7050 and AA5083 Alloys for Advanced Resistance Spot Welding Monitoring
Authors: R Girimurugan , Kumar Viranshu , D Magesh Babu , Shukla Anoop Kumar , L Girisha 
Published: December 2024

Introduction:
To improve product design and production iteratively, virtual-real interaction
was made possible by using a Digital Twin (DT) to build an information mapping bridge between
the virtual and physical worlds. Digital twins are an important tool in the execution of intelligent
manufacturing. Resistance spot welding techniques are used to construct thin sheet structures
in many aerospace and automotive industries. The production of high-quality joints during resistance
spot welding is highly dependent on the fusion nugget growth process. Due to its large, integrated,
complex process, it is difficult to exact quality monitoring using physical models of resistance
spot welding.

Method:
This study investigates the utilization of digital twin (DT) technology for the purpose of
monitoring and enhancing resistance spot welding (RSW) operations, with a specific emphasis on
the welding of AA7050/AA5083 materials. A full digital twin model was created by merging realtime
data collecting and processing using wavelet threshold noise removal analysis.

Result:
The model effectively achieved synchronization with the physical welding process, providing
a comprehensive examination of the temperature distribution and the kinetics of nugget production
during Resistance Spot Welding (RSW). The digital twin demonstrated remarkable accuracy,
achieving a 96% success rate in measuring the diameter of the molten core. This greatly improved
the precision of assessing the quality of welding.

Conclusion:
These findings validate the digital twin's capacity as a pragmatic instrument for realtime
process control and optimization in intricate industrial settings, thus promoting the fusion of
virtual models with physical production processes.
Effectiveness of an Internet-Based, Self-Guided, Short-Term Mindfulness Training (ISSMT) Program for Relieving Depressive Symptoms in the Adult Population in China: Single-Blind, Randomized Controlled Trial
Authors: Zhu Tingfei , Zhang Liuyi , Weng Wenqi , Gan Ruochen , Sun Limin , Wei Yanping , Zhu Yueping , Yu Hongyan , Xue Jiang , Chen Shulin 
Published: December 2024

Background
Depression is a significant global public health issue, and in China, access to mental health services remains limited despite high demand. Research has shown that mindfulness can effectively alleviate depressive symptoms and that telehealth solutions offer a promising avenue for addressing this service gap. Despite this potential, there are currently few studies in China focusing on short-term online mindfulness training. Most existing online mindfulness studies relied on traditional 8-week programs, which can be challenging for participant adherence due to limited accessibility and high dropout rates. Additionally, limited research exists on short-term online mindfulness interventions, and findings remain inconsistent.

Objective
This study aimed to develop and evaluate an internet-based, self-guided, short-term mindfulness training (ISSMT) program based on the Monitor and Acceptance Theory (MAT) to reduce depression symptoms.

Methods
The ISSMT program was delivered via an online platform, “Hi Emotion,” and was accessible to the general public. Interested individuals aged 18 years and older were randomized into either the ISSMT group or a wait-list control group. Participants in the ISSMT group received daily reminders to participate in a 15- to 20-minute session over a 14-day training period. Measurements, including mindfulness and depressive symptoms, were collected at baseline and weekly for the subsequent 3 weeks.

Results
A total of 205 adults participated in the 14-day online intervention. Linear mixed models were used to analyze both per-protocol (PP) and intention-to-treat (ITT) samples. Compared with the wait-list control group, participants in the ISSMT group showed significant improvements in mindfulness (Cohen d=0.44 for ITT; Cohen d=0.55 for PP) and reductions in depressive symptoms (Cohen d=0.50 for ITT; Cohen d=0.53 for PP). Furthermore, participants expressed high acceptance of this training format with a relatively low dropout rate (<40%).

Conclusions
The ISSMT program based on the MAT effectively enhanced mindfulness and alleviated depressive symptoms. This intervention could be considered for integration into psychosocial service systems to improve mental health outcomes and help bridge the gap between limited resources and the high demand for services in China. Future research should focus on personalizing these programs and incorporating advanced technologies to enhance their effectiveness and user engagement.

Trial Registration
Open Science Framework; https://doi.org/10.17605/OSF.IO/8P4V6