bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lithium-ion battery components are at the nexus of sustainable energy and environmental release of per- and polyfluoroalkyl substances
Authors: Guelfo Jennifer L. , Ferguson P. Lee , Beck Jonathan , Chernick Melissa , Doria-Manzur Alonso , Faught Patrick W. , Flug Thomas , Gray Evan P. , Jayasundara Nishad , Knappe Detlef R. U. , Joyce Abigail S. , Meng Pingping , Shojaei Marzieh 
Published: July 2024

AbstractLithium-ion batteries (LiBs) are used globally as a key component of clean and sustainable energy infrastructure, and emerging LiB technologies have incorporated a class of per- and polyfluoroalkyl substances (PFAS) known as bis-perfluoroalkyl sulfonimides (bis-FASIs). PFAS are recognized internationally as recalcitrant contaminants, a subset of which are known to be mobile and toxic, but little is known about environmental impacts of bis-FASIs released during LiB manufacture, use, and disposal. Here we demonstrate that environmental concentrations proximal to manufacturers, ecotoxicity, and treatability of bis-FASIs are comparable to PFAS such as perfluorooctanoic acid that are now prohibited and highly regulated worldwide, and we confirm the clean energy sector as an unrecognized and potentially growing source of international PFAS release. Results underscore that environmental impacts of clean energy infrastructure merit scrutiny to ensure that reduced CO2 emissions are not achieved at the expense of increasing global releases of persistent organic pollutants.
Temporal stratification of amyotrophic lateral sclerosis patients using disease progression patterns
Authors: M. Amaral Daniela , Soares Diogo F. , Gromicho Marta , de Carvalho Mamede , Madeira Sara C. , Tomás Pedro , Aidos Helena 
Published: July 2024

AbstractIdentifying groups of patients with similar disease progression patterns is key to understand disease heterogeneity, guide clinical decisions and improve patient care. In this paper, we propose a data-driven temporal stratification approach, ClusTric, combining triclustering and hierarchical clustering. The proposed approach enables the discovery of complex disease progression patterns not found by univariate temporal analyses. As a case study, we use Amyotrophic Lateral Sclerosis (ALS), a neurodegenerative disease with a non-linear and heterogeneous disease progression. In this context, we applied ClusTric to stratify a hospital-based population (Lisbon ALS Clinic dataset) and validate it in a clinical trial population. The results unravelled four clinically relevant disease progression groups: slow progressors, moderate bulbar and spinal progressors, and fast progressors. We compared ClusTric with a state-of-the-art method, showing its effectiveness in capturing the heterogeneity of ALS disease progression in a lower number of clinically relevant progression groups.
Fluorinated chlorin chromophores for red-light-driven CO2 reduction
Authors: Yang Shuang , Yuan Huiqing , Guo Kai , Wei Zuting , Ming Mei , Yi Jinzhi , Jiang Long , Han Zhiji 
Published: July 2024

AbstractThe utilization of low-energy photons in light-driven reactions is an effective strategy for improving the efficiency of solar energy conversion. In nature, photosynthetic organisms use chlorophylls to harvest the red portion of sunlight, which ultimately drives the reduction of CO2. However, a molecular system that mimics such function is extremely rare in non-noble-metal catalysis. Here we report a series of synthetic fluorinated chlorins as biomimetic chromophores for CO2 reduction, which catalytically produces CO under both 630 nm and 730 nm light irradiation, with turnover numbers of 1790 and 510, respectively. Under appropriate conditions, the system lasts over 240 h and stays active under 1% concentration of CO2. Mechanistic studies reveal that chlorin and chlorinphlorin are two key intermediates in red-light-driven CO2 reduction, while corresponding porphyrin and bacteriochlorin are much less active forms of chromophores.
Plasma metabolomics reveals the shared and distinct metabolic disturbances associated with cardiovascular events in coronary artery disease
Authors: Lv Jiali , Pan Chang , Cai Yuping , Han Xinyue , Wang Cheng , Ma Jingjing , Pang Jiaojiao , Xu Feng , Wu Shuo , Kou Tianzhang , Ren Fandong , Zhu Zheng-Jiang , Zhang Tao , Wang Jiali , Chen Yuguo 
Published: July 2024

AbstractRisk prediction for subsequent cardiovascular events remains an unmet clinical issue in patients with coronary artery disease. We aimed to investigate prognostic metabolic biomarkers by considering both shared and distinct metabolic disturbance associated with the composite and individual cardiovascular events. Here, we conducted an untargeted metabolomics analysis for 333 incident cardiovascular events and 333 matched controls. The cardiovascular events were designated as cardiovascular death, myocardial infarction/stroke and heart failure. A total of 23 shared differential metabolites were associated with the composite of cardiovascular events. The majority were middle and long chain acylcarnitines. Distinct metabolic patterns for individual events were revealed, and glycerophospholipids alteration was specific to heart failure. Notably, the addition of metabolites to clinical markers significantly improved heart failure risk prediction. This study highlights the potential significance of plasma metabolites on tailed risk assessment of cardiovascular events, and strengthens the understanding of the heterogenic mechanisms across different events.
Environment random interaction of rime optimization with Nelder-Mead simplex for parameter estimation of photovoltaic models
Authors: Shi Jinge , Chen Yi , Heidari Ali Asghar , Cai Zhennao , Chen Huiling , Chen Yipeng , Liang Guoxi 
Published: July 2024

AbstractAs countries attach importance to environmental protection, clean energy has become a hot topic. Among them, solar energy, as one of the efficient and easily accessible clean energy sources, has received widespread attention. An essential component in converting solar energy into electricity are solar cells. However, a major optimization difficulty remains in precisely and effectively calculating the parameters of photovoltaic (PV) models. In this regard, this study introduces an improved rime optimization algorithm (RIME), namely ERINMRIME, which integrates the Nelder-Mead simplex (NMs) with the environment random interaction (ERI) strategy. In the later phases of ERINMRIME, the ERI strategy serves as a complementary mechanism for augmenting the solution space exploration ability of the agent. By facilitating external interactions, this method improves the algorithm’s efficacy in conducting a global search by keeping it from becoming stuck in local optima. Moreover, by incorporating NMs, ERINMRIME enhances its ability to do local searches, leading to improved space exploration. To evaluate ERINMRIME's optimization performance on PV models, this study conducted experiments on four different models: the single diode model (SDM), the double diode model (DDM), the three-diode model (TDM), and the photovoltaic (PV) module model. The experimental results show that ERINMRIME reduces root mean square error for SDM, DDM, TDM, and PV module models by 46.23%, 59.32%, 61.49%, and 23.95%, respectively, compared with the original RIME. Furthermore, this study compared ERINMRIME with nine improved classical algorithms. The results show that ERINMRIME is a remarkable competitor. Ultimately, this study evaluated the performance of ERINMRIME across three distinct commercial PV models, while considering varying irradiation and temperature conditions. The performance of ERINMRIME is superior to existing similar algorithms in different irradiation and temperature conditions. Therefore, ERINMRIME is an algorithm with great potential in identifying and recognizing unknown parameters of PV models.
Complete realization of energy landscapes and non-equilibrium trapping dynamics in small spin glass and optimization problems
Authors: Po Ho Fai , Yeung Chi Ho 
Published: July 2024

AbstractEnergy landscapes are high-dimensional surfaces underlie all physical systems, which determine crucially the energetic and behavioral dependence of the systems on variable configurations, but are difficult to be analyzed due to their high-dimensional nature. Here we introduce an approach to reveal for the complete energy landscapes of spin glasses and Boolean satisfiability problems with a small system size, and unravels their non-equilibrium dynamics at an arbitrary temperature for an arbitrarily long time. Remarkably, our results show that it can be less likely for the system to attain ground states when temperature decreases, due to trapping in individual local minima, which ceases at a different time, leading to multiple abrupt jumps in the ground-state probability. For large systems, we introduce a variant approach to extract partially the energy landscapes and observe both semi-analytically and in simulations similar phenomena. This work introduces new methodology to unravel the energy landscapes and non-equilibrium dynamics of glassy systems, and provides us with a clear, complete and new physical picture on their long-time behaviors inaccessible by existing approaches.
Assessing the impacts of vaccination and viral evolution in contact networks
Authors: Blanco-Rodríguez Rodolfo , Tetteh Josephine N. A. , Hernández-Vargas Esteban 
Published: July 2024

AbstractA key lesson learned with COVID-19 is that public health measures were very different from country to country. In this study, we provide an analysis of epidemic dynamics using three well-known stochastic network models—small-world networks (Watts–Strogatz), random networks (Erdös–Rényi), and scale-free networks (Barabási–Albert)—to assess the impact of different viral strains, lockdown strategies, and vaccination campaigns. We highlight the significant role of highly connected nodes in the spread of infections, particularly within Barabási–Albert networks. These networks experienced earlier and higher peaks in infection rates, but ultimately had the lowest total number of infections, indicating their rapid transmission dynamics. We also found that intermittent lockdown strategies, particularly those with 7-day intervals, effectively reduce the total number of infections, serving as viable alternatives to prolonged continuous lockdowns. When simulating vaccination campaigns, we observed a bimodal distribution leading to two distinct outcomes: pandemic contraction and pandemic expansion. For WS and ER networks, rapid mass vaccination campaigns significantly reduced infection rates compared to slower campaigns; however, for BA networks, differences between vaccination strategies were minimal. To account for the evolution of a virus into a more transmissible strain, we modeled vaccination scenarios that varied vaccine efficacy against the wild-type virus and noted a decline in this efficacy over time against a second variant. Our results showed that vaccination coverage above 40% significantly flattened infection peaks for the wild-type virus, while at least 80% coverage was required to similarly reduce peaks for variant 2. Furthermore, the effect of vaccine efficacy on reducing the peak of variant 2 infection was minimal. Although vaccination strategies targeting hub nodes in scale-free networks did not substantially reduce the total number of infections, they were effective in increasing the probability of preventing pandemic outbreaks. These findings underscore the need to consider the network structure for effective pandemic control.
Effect of electromigration on microstructure and properties of CeO2 nanopartical-reinforced Sn58Bi/Cu solder joints
Authors: Chen Weiming , Zhang Keke , Fan Yuchun , Zhang Chao , Wang Nannan 
Published: July 2024

AbstractTo mitigate the decrease in mechanical performance of Sn58Bi/Cu solder joints resulting from electromigration-induced damage. The CeO2 nanoparticles were incorporated into Sn58Bi solder by a melt-casting method, and their effects on the microstructure and properties of Sn58Bi/Cu solder joints under electromigration were investigated. The study results demonstrate that the addition of 0.125 ~ 0.5 wt% CeO2 nanoparticles refines the eutectic microstructure of Sn58Bi solder alloy. At an addition amount of 0.5 wt%, the composite solder alloy exhibits the maximum tensile strength of 68.9 MPa, which is 37% higher than that of the base solder. CeO2 nanoparticle-reinforced Sn58Bi solder can achieve excellent solderbility with Cu substrates and the joints can significantly inhibit the growth of the anodic Bi-rich layer, which is responsible for electromigration. With the extension of current stressing time, Bi-rich and Sn-rich layer are respectively formed on the anode and cathode in the joints. The intermetallic compound (IMC) layer grows asymmetrically, transitioning from a fan-shaped morphology to a flattened structure at the anode and to a thickened mountain-like morphology at the cathode. Adding the CeO2 nanoparticles helps to mitigate the decrease in mechanical performance caused by electromigration damage during current application to some extent. Over the current stressing period of 288 ~ 480 h, the fracture position shifts from the anodic IMC/Bi-rich interface to the cathodic Sn-rich/IMC interface. The fracture mechanism transitions from a brittle fracture characterized by plate-like cleavage to a ductile–brittle mixed fracture with fine dimples and cleavage.
Refining silicon nitride waveguide quality through femtosecond laser annealing
Authors: Wang Pei-Hsun , Chen Chien-Hung , Hou Nien-Lin , Cao Jia-Hao , Zheng He-Yuan , Chen Hung-Wen 
Published: July 2024

AbstractWe present a method for modification of silicon nitride (Si3N4) waveguide resonators using femtosecond laser annealing. The quality (Q) factor of the waveguide resonators can be improved by approximately 1.3 times after annealing. Notably, waveguides that originally had a high Q value maintained their quality after the annealing process. However, those with a lower initial Q value experienced a noticeable improvement post-annealing. To characterize the annealing effect, the surface morphologies of Si3N4 films, both pre- and post-annealing, were analyzed using atomic force microscopy. The findings suggest a potential enhancement in surface refinement. Furthermore, Raman spectroscopy confirmed that the Si3N4 film's composition remains largely consistent with its original state within the annealing power range of 0.6–1.6 W. This research underscores the potential of femtosecond laser annealing as an efficient, cost-effective, and localized technique for fabricating low-loss integrated photonics.
Predictive modeling of lower extreme deep vein thrombosis following radical gastrectomy for gastric cancer: based on multiple machine learning methods
Authors: Zhou Haiyan , Jin Yongyan , Chen Guofeng , Jin Xiaoli , Chen Jian , Wang Jun 
Published: July 2024

AbstractPostoperative venous thromboembolic events (VTEs), such as lower extremity deep vein thrombosis (DVT), are major risk factors for gastric cancer (GC) patients following radical gastrectomy. Accurately predicting and managing these risks is crucial for optimal patient care. This retrospective case‒control study involved 693 GC patients from our hospital who underwent radical gastrectomy. We collected plentiful and comprehensive clinical indicators including a total of 49 baseline, preoperative, surgical and pathological clinical data. Using univariate logistic regression, we identified potential risk factors, followed by feature selection through the Boruta algorithm. We then constructed the final predictive model using multivariate logistic regression and evaluated it using receiver operating characteristic (ROC) curve analysis, calibration plots, decision curve analysis, and other methods. Additionally, we applied various machine learning techniques, including decision trees and random forests, to assess our model’s predictive strength. This retrospective case‒control study involved 693 GC patients from our hospital who underwent radical gastrectomy. We collected plentiful and comprehensive clinical indicators including a total of 49 baseline, preoperative, surgical and pathological clinical data. Using univariate logistic regression, we identified potential risk factors, followed by feature selection through the Boruta algorithm. We then constructed the final predictive model using multivariate logistic regression and evaluated it using receiver operating characteristic (ROC) curve analysis, calibration plots, decision curve analysis, and other methods. Additionally, we applied various machine learning techniques, including decision trees and random forests, to assess our model’s predictive strength. Univariate logistic analysis revealed 14 risk factors associated with postoperative lower limb DVT. Based on the Boruta algorithm, six significant clinical factors were selected, namely, age, D-dimer (D-D) level, low-density lipoprotein, CA125, and calcium and chloride ion levels. A nomogram was developed using the outcomes from the multivariate logistic regression analysis. The predictive model showed high accuracy, with an area under the curve of 0.936 in the training set and 0.875 in the validation set. Various machine learning algorithms confirmed its strong predictive capacity. MR analysis revealed meaningful causal relationships between key clinical factors and DVT risk. Based on various machine learning methods, we developed an effective predictive diagnostic model for postoperative lower extremity DVT in GC patients. This model demonstrated excellent predictive value in both the training and validation sets. This novel model is a valuable tool for clinicians to use in identifying and managing thrombotic risks in this patient population.
Ionic liquid based surfactant-free microemulsion as a new protocol for preparation of visible light active poly(methyl methacrylate)/TiO2 nanocomposite
Authors: Salabat Alireza , Mirhoseini Behnia Sadat , Mirhoseini Farid 
Published: July 2024

AbstractThe practical application of sensitized TiO2 nanocomposites is very satisfying due to their high photon utilization in visible light, simple recovery without affecting the photocatalytic performance, high energy efficiency, low potential environmental risk, and low operational costs. The objective of this study is developing the ionic liquid (IL)-based surfactant-free microemulsion, as a soft template, for preparation of a novel type of sensitized poly(methyl methacrylate)/TiO2 nanocomposite (PMMA/TiO2/IL). For this purpose, a series of visible light-responsive PMMA/TiO2/IL transparent nanocomposites were prepared in microemulsion composed of methyl methacrylate monomer, 1-buthyl-3-methylimidazolium tetrafluoroborate ([bmim][BF4]), and 1-buthanol as amphi-solvent. Techniques such as diffuse reflectance spectroscopy (DRS)), attenuated total reflectance-fourier transform infrared (ATR-FTIR) spectroscopy, atomic force microscopy (AFM), field emission scanning electron microscopy (FE-SEM), and energy dispersive X-ray analysis (EDX) were used to characterize prepared nanocomposites. Photocatalytic degradation of methyl orange dye under visible light illumination, as an application in wastewater treatment, with the investigation of the influence of TiO2 content in the nanocomposite, pH, and nanocomposite reusability on photodegradation efficiency was studied and maximum value of 93.9% obtained at optimum conditions. The FESEM analysis indicated that the utilization of a relatively low amount of ionic liquid and also in absence of the surfactant ensures the monodispersity of the visible light sensitized TiO2 nanoparticles in the polymer matrix.
Optimal scheduling of the stand‐alone micro grids considering the reliability cost
Authors: Nargeszar Ayoub , Ghaedi Amir , Nafar Mehdi , Simab Mohsen 
Published: July 2024

AbstractIn order to prevent wastage of generated power of renewable resources, the energy storage systems can be utilized in the stand‐alone micro grids to store the excess produced power of the renewable generation units. When the generated power of the renewable resources is less than the required load, the energy storage systems can help to compensate all or part of the power shortage. In the current study, a stand‐alone micro grid including wind and tidal turbines, PV systems, batteries and fuel‐based generation units is considered to supply the required load of the micro grid. The generated power of each dispatch‐able generation units is determined in such a way as to minimize the operating cost. In the operating cost of the micro grid, the operating cost of the fuel‐based generation units and the reliability cost associated to the penalty of the curtailed loads are considered. To calculate the reliability cost of the micro grid, a comprehensive reliability evaluation of the micro grid considering the resource‐dependent failure rates for all composed components is performed. To study the effectiveness of the proposed reliability‐based scheduling approach, the numerical results associated to a stand‐alone micro grid containing wind, tidal, PV and fuel‐based generation units connected to the batteries are given.
Power cable monitoring method based on UHF‐RFID and deep learning in edge computing environment
Authors: Gu Xiongfei , Shang Jian , Shen Changlu 
Published: July 2024

AbstractThis research addresses the challenge faced by most existing prediction methods in handling nonlinear data of cables. Furthermore, it proposes a novel power cable monitoring method utilizing UHF‐RFID and deep learning within an edge computing environment, specifically targeting the currently suboptimal wireless monitoring of cables. First, based on edge computing, a power cable monitoring system is designed to migrate the analysis of massive data to the edge of the network to improve the monitoring efficiency. Then, the temperature sensing chip and RFID chip were integrated to design a UHF‐RFID temperature tag, which was fixed at the cable temperature measurement point to achieve passive wireless monitoring of the cable. Finally, the parameters of the GRNN model are optimized using the beetle antennae search algorithm, and the EEMD decomposed data is input into the BAS‐GRNN model for learning to output temperature prediction results. Based on the establishment of an experimental platform, the method was demonstrated, and results showed that the maximum error between the UHF‐RFID temperature tag temperature measurement results and the thermocouple was within 0.3°C, and the average relative error of the proposed method was only 0.01, which can meet the accuracy requirements of actual monitoring of power cables.
L’intestin, lanceur d’alerte, dans les prémices de la maladie de Parkinson
Authors: Hazart Doriane , Rolli-Derkinderen Malvyne , Delhomme Brigitte , Derkinderen Pascal , Oheim Martin , Ricard Clément 
Published: July 2024

Le système nerveux entérique (SNE), souvent qualifié de « deuxième cerveau », joue un rôle crucial dans la régulation des fonctions digestives. Des dysfonctionnements du SNE sont associés à diverses maladies telles que la maladie de Parkinson. Des études récentes suggèrent que les troubles digestifs précoces, notamment la constipation chronique, pourraient être des signes avant-coureurs de cette maladie neurodégénérative. L’imagerie tridimensionnelle du SNE offre de nouvelles perspectives pour un diagnostic précoce via notamment l’analyse de biopsies intestinales. Ce nouvel axe de recherche soulève des questions sur l’origine intestinale de la maladie de Parkinson et ouvre la porte à une meilleure compréhension et une prise en charge anticipée de cette maladie.