bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fatigue life study of milled surfaces based on stress concentration factor
Authors: Zhang Wei , Zhu Zhimin , Zheng Minli , Su Peibin 
Published: January 2025

Abstract
This paper adopts the method of combining theoretical modeling, experimental detection analysis and finite element simulation. The improved stress concentration factor solution equation is derived, and the fatigue life prediction model of plane milling surface is established accordingly. The fatigue life simulation analysis method based on surface stress concentration factor is used to obtain the influence law of surface morphology of typical parts on fatigue life, verify the accuracy of fatigue life prediction model, and provide an effective means for the analysis and prediction of fatigue performance of milling surface.
Production and testing of Al-12%Si composites strengthened with ZrO2 using an innovative disintegrated melt deposition manufacturing technique
Authors: Nataraja M , Balakumar G , Santhosh N , Rudra Naik M 
Published: January 2025

Abstract
This study aims to produce Al 12% Si-ZrO2 composites using the Disintegrated Melt Deposition (DMD) technique and assess their microstructure and mechanical properties. The Al-12%Si alloy is recognized for its strength and corrosion resistance, making it valuable in industries such as shipbuilding and aviation. ZrO2, a ceramic known for its corrosion resistance and hardness, enhances the strength and hardness of the composites. It also reduces elongation percentage and promotes lubrication between silicon and aluminum in the matrix alloy. Specimens with varying ZrO2 wt% (AS, AS0.5Z, AS1Z, AS1.5Z, AS2Z, AS2.5Z, AS3Z) were fabricated from DMD process. Results indicated that hardness increased from 59.7 HB (as cast) to 75.2 HB (2.5 wt% ZrO2), with a slight decrease at 3 wt% ZrO2. Elongation percentage decreased from 12.3% to 8.5%, ultimate tensile strength ranged from 239.2 MPa to 281.9 MPa, ultimate compression strength from 313.7 MPa to 375.6 MPa, and impact energy absorbed increased from 3.6 J to 5.3 J. These findings suggest potential applications of aluminum-12% Si-ZrO2 composites in various applications by improving their properties.
Dynamical structure factors of warm dense matter from time-dependent orbital-free and mixed-stochastic-deterministic density functional theory
Authors: White Alexander J 
Published: January 2025

Abstract
We present the first calculations of the inelastic part of the dynamical structure factor (DSF) for warm dense matter (WDM) using time-dependent orbital-free density functional theory (TD-OF-DFT) and mixed-stochastic-deterministic (mixed) Kohn Sham TD-DFT (KS TD-DFT). WDM is an intermediate phase of matter found in planetary cores and laser-driven experiments, where the accurate calculation of the DSF is critical for interpreting x-ray Thomson scattering measurements. Traditional TD-DFT methods, while highly accurate, are computationally expensive, motivating the exploration of TD-OF-DFT and mixed TD-KS-DFT as more efficient alternatives. We applied these methods to experimentally measured WDM systems, including solid-density aluminum and beryllium, compressed beryllium, and carbon–hydrogen mixtures. Our results show that TD-OF-DFT requires a dynamical kinetic energy potential in order to qualitatively capture the plasmon response. Additionally, it struggles with capturing bound electron contributions. In contrast, mixed TD-KS-DFT offers greater accuracy in distinguishing bound and free electron effects, aligning well with experimental data, though at a higher computational cost. This study highlights the trade-offs between computational efficiency and accuracy, demonstrating that TD-OF-DFT remains a valuable tool for rapid scans of parameter space, while mixed TD-KS-DFT should be preferred for high-fidelity simulations. Our findings provide insight into the future development of DFT methods for WDM and suggest potential improvements for TD-OF-DFT.
Outlier detection of monitored data and unsupervised recognition of construction activities during seismic performance enhancement of historic stone monuments
Authors: Xue Songtao , Shi Qinhao , Xie Liyu , Zhou Shuning , Lu Wensheng , Zhang Mansheng 
Published: January 2025

Abstract
To enhance the seismic resilience of historical and cultural heritage sites, protective measures were implemented through the installation of advanced heritage protection platform facilities. A structural health monitoring system was developed to safeguard historical relics during construction activities by continuously monitoring the overall condition of the relics and the integrity of critical components. Key parameters, such as settlement differences, tilt, crack width, and acceleration, were meticulously tracked, with predefined warning and alarm thresholds established. Alerts were triggered whenever these parameters exceeded their respective thresholds, ensuring timely interventions. To ensure the reliability and consistency of the collected data, this study proposes an evaluation method that integrates multi-source data fusion with statistical analysis techniques. Building on this foundation, an unsupervised algorithm was employed to identify construction activities impacting the structural health of the relics. The results demonstrate the effectiveness of combining multi-source data and intelligent algorithms for reliable monitoring and early detection of risks during conservation. The developed system offers automated, real-time assessments and can serve as a model for future heritage protection projects. Looking forward, integrating wireless sensors and diverse data sources could improve system accuracy, efficiency, and cost-effectiveness, further enhancing the protection of cultural heritage.
Application of improved multi-population genetic algorithm in structural optimization of automotive electrical equipment
Authors: Zhang Jia 
Published: January 2025

Abstract
The structure of automotive electrical equipment is becoming increasingly complex. How to optimize the structure without affecting the performance of the car itself to reduce the weight and manufacturing cost of electrical equipment has become particularly important. Based on this, this study introduces diversity preservation mechanism and adaptive mutation strategy to improve the multi-population genetic algorithm, and proposes the objective function and constraint conditions for the results of automotive electrical equipment, constructing an optimization model for the structure of automotive electrical equipment. The results showed that at 500 iterations, the improved multi-population genetic algorithm had optimal and average fitness values of 0.96 and 0.94, respectively, demonstrating higher accuracy and search capability compared to other algorithms. After using optimization models for different automotive electrical equipment structures, the average energy consumption decreased by 18%, the total cost decreased by 16.55%, and the average total weight of electrical equipment structures decreased by 16.475%. This model is effective in optimizing the structure of electrical equipment. This study contributes to improving the performance and design efficiency of automotive electrical equipment, and has significant implications for promoting the application of intelligent optimization technology in the automotive industry.
Effects of two wet exfoliation strategies on the yield and colloidal behavior of 2D hexagonal boron nitride nanosheets
Authors: Azme Anika , Escobar Isabel C , Tsyusko Olga , Aich Nirupam 
Published: January 2025

Abstract
We report the exfoliation process optimization, physicochemical characterizations, and comparative aggregation behavior of the inorganic 2D nanomaterial hexagonal Boron Nitride (h-BN) produced from two repetitive sonication-centrifugation processes with varying centrifugation speeds and recycle frequency: Continuous and Segmented protocols. Enhancing exfoliation efficiency and understanding aqueous stability are essential for sustainable design and environmental applications. Results showed that the Segmented protocol outperformed the Continuous protocol by having a six-fold increase in the exfoliated h-BN nanosheet yield by reusing the unexfoliated bulk h-BN and decreasing centrifugation speeds. Centrifugation speeds of 1880 and 950 rpm produced nanosheets of similar sizes due to the slight difference in the centrifugal force generated in both protocols. Moreover, nanosheets from both protocols had enhanced polarity due to the higher amounts of −OH bonds attached to the exposed edges of the nanosheets. However, the hydroxylation percentage of the nanosheets decreased with centrifugation speed. Both protocols produced h-BN nanosheets that were stable in DI water dispersion. The comparatively lower initial aggregation rate at all centrifugation speeds supported the fact that the Segmented protocol nanosheets were more stable than the Continuous ones. The Segmented protocol h-BN nanosheets showed better overall stability at lower speeds than the other centrifugation speeds. Segmented protocol nanosheets from 3750 rpm had the lowest aggregation rate than the other centrifugation speed. These findings assist in finding the balance between exfoliation protocol, environmental application, and implication of h-BN nanosheets.
Towards neuromorphic compression based neural sensing for next-generation wireless implantable brain machine interface
Authors: Mohan Vivek , Tay Wee Peng , Basu Arindam 
Published: January 2025

Abstract
This work introduces a neuromorphic compression based neural sensing architecture with address-event representation inspired readout protocol for massively parallel, next-gen wireless implantable brain machine interface (iBMI). The architectural trade-offs and implications of the proposed method are quantitatively analyzed in terms of compression ratio (CR) and spike information preservation. For the latter, we used metrics such as root-mean-square error and correlation coefficient (CC) between the original and recovered signals to assess the effect of neuromorphic compression on the spike shape. Furthermore, we use accuracy, sensitivity, and false detection rate to understand the effect of compression on downstream iBMI tasks, specifically, spike detection. We demonstrate that a data CR of 15–265 per channel can be achieved by transmitting address-event pulses for two different biological datasets. The CR further increases to 200–

50

K

per channel, 50 × more than in prior works, by the selective transmission of event pulses corresponding to neural spikes. A CC of ≈0.9 and spike detection accuracy of over 90% were obtained for the worst-case analysis involving

10

K

-channel simulated recording and typical analysis using 100 or 384-channel real neural recordings. We also analyzed the collision handling capability for up to 10K channels and observed no significant error, indicating the scalability of the proposed pipeline. We also present initial results to show the ability of intention decoders to work directly on the events generated by the neuromorphic front-end.
Ensemble modeling of the climate-energy nexus for renewable energy generation across multiple US states
Authors: Adul Joy , Kumar Rohini , Obringer Renee 
Published: January 2025

Abstract
The effects of climate change on renewable energy generation are of growing concern, as shifts in weather patterns and extreme events can significantly impact energy production. This study aims to leverage machine learning models to predict renewable energy generation based on the surrounding climate. We analyze data from four key states: California, New York, Florida, and Georgia, and focus on three critical renewable energy sources: hydroelectric, solar, and wind power. To determine the optimal model, we test six primary machine learning techniques, as well as an ensemble and a mean-only baseline. The results indicate that the ensemble approach improves the predictive accuracy of the model. Using this ensemble, we projected the changes to climate-sensitive portion of the renewable energy generation under climate change. Our results indicated that there was a wide variation of possible futures, depending on the state, source, and season. For example, the model projected a reduction in California’s monthly total renewable energy generation in the summer by 0.5%, or about 30 000 MWh, under SSP5-8.5, the worst-case scenario, but an increase of 0.5%, or about 25 000 MWh, in New York’s monthly total summer renewable energy generation under the same scenario. The modeling techniques detailed in this study can be applied across new regions, sources, or time periods. Ultimately, by understanding the influence of climate on renewable energy generation, we can improve the long-term planning process for the electricity grid, while building resilience and ensuring sustainable climate change mitigation and adaptation.
Combining experimental and observational data through a power likelihood
Authors: Lin Xi , Tarp Jens Magelund , Evans Robin J 
Published: January 2025

ABSTRACT
Randomized controlled trials are the gold standard for causal inference and play a pivotal role in modern evidence-based medicine. However, the sample sizes they use are often too limited to provide adequate power for drawing causal conclusions. In contrast, observational data are becoming increasingly accessible in large volumes but can be subject to bias as a result of hidden confounding. Given these complementary features, we propose a power likelihood approach to augmenting randomized controlled trials with observational data to improve the efficiency of treatment effect estimation. We provide a data-adaptive procedure for maximizing the expected log predictive density (ELPD) to select the learning rate that best regulates the information from the observational data. We validate our method through a simulation study that shows increased power while maintaining an approximate nominal coverage rate. Finally, we apply our method in a real-world data fusion study augmenting the PIONEER 6 clinical trial with a US health claims dataset, demonstrating the effectiveness of our method and providing detailed guidance on how to address practical considerations in its application.
Causal relationships between hippocampal volumetric traits and the risk of Alzheimer’s disease: a Mendelian randomization study
Authors: Guo Lining , Chen Yayuan , Sun Zuhao , Zhao Jiaxuan , Yao Jia , Zhang Zhihui , Lei Minghuan , Zhai Ying , Xu Jinglei , Jiang Yurong , Wang Ying , Xue Hui , Liu Mengge , Liu Feng 
Published: January 2025

Abstract
Alzheimer’s disease, a common and progressive neurodegenerative disorder, is associated with alterations in hippocampal volume, as revealed by neuroimaging research. However, the causal links between the volumes of the hippocampus and its subfield structures with Alzheimer’s disease remain unknown. A genetic correlation analysis using linkage disequilibrium score regression was conducted to identify hippocampal volumetric traits linked to Alzheimer’s disease. Following this, to examine the causal links between Alzheimer’s disease and hippocampal volumetric traits, we applied a two-sample Mendelian randomization approach, utilizing a bidirectional framework. Seven hippocampal volumetric traits were found as genetically correlated with Alzheimer’s disease in the genetic correlation analysis and were then included in the Mendelian randomization analyses. Inverse variance weighted Mendelian randomization analyses revealed that increased volumes in the left whole hippocampus, left hippocampal body, right presubiculum head and right cornu ammonis 1 head were causally related to higher risks of Alzheimer’s disease. Conversely, a higher risk of Alzheimer’s disease was causally associated with decreased volumes of the left hippocampal body and left whole hippocampus. These results were validated through other Mendelian randomization approaches and sensitivity analysis. Our findings uncover bidirectional causal relationships between Alzheimer’s disease and hippocampal volumetric traits, suggesting not only the potential significance of these traits in predicting Alzheimer’s disease but also the reciprocal influence of Alzheimer’s disease on hippocampal volumes.
Safety design of the piston rod seal structure for submerged high-pressure liquid hydrogen pump
Authors: Zhang Feng , Li Xuan , Zhang Xin , Xiong Xue , Cui Xin , Cai Xingye , He Guangli 
Published: January 2025

Abstract
For the submerged high-pressure liquid hydrogen pump, a core piece of equipment of liquid hydrogen transportation, extensive analysis, and research were conducted on its components and structure during the development process. By referencing typical failure probabilities in the energy industry and statistical data from prototype pump development and testing, a list of failure probabilities for each part of the liquid hydrogen pump was compiled, and the consequences of those failures were deduced. The nitrogen liquid test experiment verified the possibility of pump seal failure and identified the fault modes. It confirmed that the piston rod seal is one of the critical components prone to failure in the liquid hydrogen pump. Subsequently, six important technical optimization measures for the piston rod seal pair have been carried out, reducing the probability of failure. Moreover, based on the concept of inherent safety, the sealing structure of the piston rod was innovatively designed, transforming hidden failures into visible ones, facilitating timely detection and monitoring of failures, and greatly reducing the safety risks caused by piston rod seal failure. The improved liquid hydrogen pump successfully passed subsequent tests, ensuring its long-term safe operation.
MDD-SSTNet: detecting major depressive disorder by exploring spectral-spatial-temporal information on resting-state electroencephalography data based on deep neural network
Authors: Chen Qiurong , Xia Min , Li Jinfei , Luo Yiqian , Wang Xiuzhu , Li Fali , Liang Yi , Zhang Yangsong 
Published: January 2025

Abstract
Major depressive disorder (MDD) is a psychiatric disorder characterized by persistent lethargy that can lead to suicide in severe cases. Hence, timely and accurate diagnosis and treatment are crucial. Previous neuroscience studies have demonstrated that major depressive disorder subjects exhibit topological brain network changes and different temporal electroencephalography (EEG) characteristics compared to healthy controls. Based on these phenomena, we proposed a novel model, termed as MDD-SSTNet, for detecting major depressive disorder by exploring spectral-spatial-temporal information from resting-state EEG with deep convolutional neural network. Firstly, MDD-SSTNet used the Sinc filter to obtain specific frequency band features from pre-processed EEG data. Secondly, two parallel branches were used to extract temporal and spatial features through convolution and other operations. Finally, the model was trained with a combined loss function of center loss and Binary Cross-Entropy Loss. Using leave-one-subject-out cross-validation on the HUSM dataset and MODMA dataset, the MDD-SSTNet model outperformed six baseline models, achieving average classification accuracies of 93.85% and 65.08%, respectively. These results indicate that MDD-SSTNet could effectively mine spatial-temporal difference information between major depressive disorder subjects and healthy control subjects, and it holds promise to provide an efficient approach for MDD detection with EEG data.
Amygdala-centered fusional connections characterized nonmotor symptoms in Parkinson’s disease
Authors: Zhang Yi , Li Sixiu , Yu Jiali , Li Rong , Liao Wei , Chen Qin , Xing Haoyang , Lu Fengmei , Hu Xiaofei , Chen Huafu , Gao Qing 
Published: January 2025

Abstract
The importance of nonmotor symptoms in understanding the pathogenesis of the heterogeneity of Parkinson’s disease has been highlighted. However, the validation of specific brain network biomarkers in nonmotor symptom subtypes is currently lacking. By performing a new approach to compute functional connectivity with structural prior using magnetic resonance imaging, the present study computed both functional connectivity and fusional connectivity features in the nonmotor symptom subtypes of Parkinson’s disease, one characterized by cognitive impairment with late onset and the other depression with early onset. The functional connectivity and fusional connectivity features centered at the left amygdala were both detected. The fusional features significantly enhanced the classification performance. The amygdala-postcentral and amygdala-orbital frontal features were critical for cognitive impairment with late onset detection, while the amygdala-temporooccipital features were crucial for depression with early onset detection. Additionally, the fusional connectivity features between the amygdala and the junction sulcus of parietooccipital and temporooccipital regions contributed significantly to differentiating cognitive impairment with late onset and depression with early onset. The within-subtype correlation analysis revealed that age at onset and cognitive scores were associated with features of amygdala-somatosensory/visual-motor processing areas in cognitive impairment with late onset, while related to features of amygdala-emotional processing areas in depression with early onset. Our findings highlighted distinct amygdala-centered fusional connectivity features related to diverse nonmotor symptoms in Parkinson’s disease, offering new insights for pathogenesis-targeted treatments for specific Parkinson’s disease subtypes.
A New Headspace Gas Chromatography Method for Determination of Ethylene Oxide and Dioxane in Polyethylene Glycols
Authors: Premaratne Gayan , McAdoo Andrew , Tian Jingzhi , Rustum Abu 
Published: January 2025

Abstract
In this paper, a new headspace gas chromatography (HSGC) method has been developed for the determination of ethylene oxide (EO) and 1,4-Dioxane (Dioxane) in bulk lots of polyethylene glycols (PEG). PEG samples are dissolved in dimethyl sulfoxide, heated in a headspace oven maintained at 90°C for 10 min and then injected into the GC system for analysis. Analytes are separated through a thermal gradient elution from 36 to 240°C on an Agilent DB-624 column (60 m × 0.53 mm diameter, film thickness 3.00 μm). The carrier gas is helium and detector is flame ionization detector. Total run time of the new HSGC method is about 18 min. In the new HSGC method, other common residual solvents present in PEGs such as methanol (MeOH), methyl formate (MF) and acetyl aldehyde (Acetal) are also sufficiently separated from EO and Dioxane peaks. In the HSGC methods prescribed in USP monograph &lt;228&gt; and EP monograph &lt;2.4.25&gt;, EO co-elutes with MF. The new HSGC method was successfully validated according to current ICH/VICH guidelines and was found to be specific, linear, accurate, precise, robust and sensitive. This HSGC method is fast and quality control (QC) friendly and suitable for routine analysis for the determination of EO and Dioxane in bulk batches of PEGs in QC laboratories.