bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Impact of Artificial Intelligent Tools on Decision Making Behavioral and Neural Dynamics
Authors: Molina-Perez Edmundo , Cortes Pedro , Molina Isaac , Sobrino Fernanda , Tellez Mario , Orozco Yessica , Castellón Mitzi , Popper Steven , Serra Luis 
Published: July 2024

Abstract

Decision-making is a multifaceted cognitive process influenced by task complexity, information availability, individual cognitive strategies, and environmental settings. Yet, the neural mechanisms guiding everyday choices remain incompletely understood. This gap intensifies when integrating real-time aids, such as artificial intelligence tools (AIT), as cognitive decisionsupport especially for complex and ambiguous problems. This study explores the neural mechanisms of decision-making and examines how AIT influences these processes. Combining behavioral assessments and neurophysiological measurements, we investigate the dynamic interplay between human cognition and AIT through behavioral execution and electroencephalogram (EEG) activity. Experimental data from 54 participants suggest that in low-complexity decision-making, AIT is largely ignored in favor of heuristics. In high-complexity contexts, AIT positively influences decision-making outcomes while also increasing capacity for engagement with a challenging task as registered by EEG cortical activity. This suggests a non-linear effect of AIT in decision-making strategies highlighting its role as a complement to —rather than a replacement of—human cognitive processes.
A novel optimization method via Box-Behnken Design integrated with back propagation neural network - genetic algorithm on hydrogen purification
Authors: Zhang Nannan , Hu Sumeng 
Published: July 2024

Abstract

High purity hydrogen is a necessary need for fuel cell. Pressure swing adsorption (PSA) technology is one of the effective methods for hydrogen purification. The layered bed PSA model is built and validated. To simplify the calculation of PSA purification performance, the quadratic regression equations are obtained by Box-Behnken design (BBD) method.With adsorption time, pressure equalization time and feed flow rate from PSA process as independent optimization parameters of the BBD method, the hydrogen purity and productivity as two responses. The genetic algorithm (GA) is introduced to the back propagation neural network (BPNN) to solve the optimization problem of the PSA process. In order to explore the performance of optimization algorithms, a novel optimization method is proposed in this work. With the BBD method is integrated with BPNN-GA model to optimize the structure (BBD-BPNN-GA). The results showed that the BBD-BPNN-GA model have a better performance with the MSE of 0.0005, while the mean square error (MSE) of BPNN-GA model is 0.0035. And the correlation coefficient of R-values are much closer to 1of the BBD-BPNN-GA model, which is illustrated that the BBD-BPNN-GA model can be effectively applied to the prediction and optimization of PSA process.
The combined predictive value of renal function markers and blood lipid levels for the occurrence of preeclampsia
Authors: Zhang Jindi 
Published: July 2024

Abstract

Background and Objective: Preeclampsia(PE), a prevalent complication of pregnancy, could severely affect the health of gravidas and fetuses. Consequently, developing a convenient, cost-effective, and reliable method for predicting the occurrence of PE is crucial. This study aimed to assess the predictive value of combining renal function markers with blood lipid levels for PE.
Methods:This study conducted a retrospective analysis of 527 cases of pregnant women dilivering in Hangzhou Women’s Hospital during January 2021 to December 2023, which were divided into PE group(n=263) and control group(n=264).The levels of serum renal function markers and blood lipid were compared in both groups. Logistic regression analysis was used to explore the factors associated with PE. Then the receiver operating characteristic (ROC) curve was used to evaluate the predictive value of combining renal function markers with blood lipid levels for PE.
Results: Univariate analysis showed that the PE group had higher serum triglyceride(TG), creatinine(Cr), uric acid(UA)and urea than the control group. Logistic regression analysis showed that the high levels of serum TG,Cr and UA were independent risk factors for PE (P< 0.01). The areas under the curve (AUC) were 0.703(95%CI=0.658-0.747), 0.734(95%CI=0.691-0.777) and 0.822(95%CI=0.786-0.857) respectively. The AUC, sensitivity and specificity of the joint detection of TG,Cr and UA were 0.864(95%CI=0.833-0.896), 76.4%,and 84.8%,which showing a good predictive performance.
Conclusions: The increased levels of serum TG,Cr and UA suggest an increased risk of PE. And their joint detection has a certain predictive valuefor PE, which perhaps could offer a convenient and efficient method for the early diagnosis of PE.
False spring events in the eastern part of the Baltic Sea region
Authors: Klimavičius Laurynas , Rimkus Egidijus 
Published: July 2024

Abstract

Spring frosts can cause substantial economic and ecological damage, especially when occurring after the growing season has begun and leading to compound climate events, known as false spring (FS) events. This study examines the recurrence, spatial distribution and intensity of FS events in the eastern part of the Baltic Sea region from 1950 to 2022. These events were identified by determining the dates of the last spring frost (LSF) and the start of the growing season (SGS) for each grid cell annually. FS events were distinguished when the LSF date occurred later than the date of the SGS. The sum of growing degree days (tbase = 5°C), accumulated until the event, and the minimum air temperature (tmin) during the day of the FS event were used to assess the intensity of FS events. Throughout the study period, LSF and SGS dates shifted earlier by an average of 6.3 and 10.6 days, respectively. In 85.9% of the grid cells, the SGS date changed more rapidly than the LSF date. Although the number of FS events increased over the 73 years in most of the study area, the changes were statistically insignificant according to the Mann–Kendal test. However, comparing 1950–1969 and 2003–2022, 85.9% of grid cells saw more FS events, with the largest increase in the eastern part of the study area and southwestern Lithuania. Significant changes were determined in 30% of cells according to the t-test.
Bimetallic Chalcogenides Enable Highly Efficient Polysulfide Capture and Conversion in Lean-Electrolyte Li-S Batteries
Authors: Wang Junhua , Sun Hao , Zhao Weiran , Pan Chongxiang , Li Rongrong , Yao Yuan , Chang Caiyun 
Published: July 2024

Abstract

The commercialization of Li-S batteries is obstructed by the sluggish redox kinetics and serious shuttling behaviors of polysulfides. Herein, we report a rationally structured sulfur host material to tackle these issues, i.e. (Co, Ni)9S8 nanoparticles uniformly decorated on electrospun carbon nanofibers. The (Co, Ni)9S8 nanoparticles are demonstrated to effectively capture polysulfides and catalytically promote their redox conversions. Moreover, the interlinked porous architecture of (Co, Ni)9S8@CNFs also contributes to alleviate volume expansion of sulfur cathode and provide the rapid electron transfer paths and Li-ion diffusion channels. Benefiting from these attributes, the (Co, Ni)9S8@CNFs cathode delivers an excellent rate capability and long cycling stability (capacity decay of 0.142% per cycle over 300 cycles). Additionally, the (Co, Ni)9S8@CNFs with high sulfur content (83.3%) and lean electrolyte (5 μL mg-1) shows high capacity of 556 mAh g-1 at 0.5 C and 590 mAh g-1 after 180 cycles at 0.2 C, demonstrating highly efficient utilization of sulfur and extraordinary potential for practical application of Li-S batteries.
Al8P8 double nanoring as a high-performance sensor for SF6 decomposed gases: A DFT-D4 study
Authors: Ullah Faizan , Arfan Nur Hazimah Binti Zainal , Ayub Khurshid , Mahmood Tariq , Sheikh Nadeem S. 
Published: July 2024

Abstract

The efficacy of an Al8P8 double nanoring as a sensor for sulfur hexafluoride (SF6) decomposition gases (H2S, HF, SO2, SO2F2, and SOF2) is investigated using density functional theory with the PBE0-D4 functional and def2-TZVP basis set. Additionally, highly accurate DLPNO-CCSD(T)/cc-pVTZ single-point energy calculations are employed to refine the interaction energies. Interaction energies ranging from − 43.31 to − 63.92 kJ mol− 1 are reported, with H2S exhibiting the strongest adsorption. SO2 adsorption induces the most significant change in the HOMO-LUMO gap, narrowing it to 1.34 eV from 3.18 eV, which suggests a substantial enhancement in electrical conductivity upon interaction. NCI analysis reveals a diverse range of interaction types, including hydrogen bonding and van der Waals interactions, contributing to the adsorption behavior. Rapid recovery times are observed, indicating the reusability of the sensor. The findings demonstrate that the Al8P8 double nanoring shows promise as a sensitive, selective, and reusable sensor, particularly for SO2, with potential applications in industrial gas leak detection and environmental safety monitoring.
High-throughput separation of microalgae on a runway-shaped channel with ordered semicircular micro-obstacles
Authors: Hu Sheng , Jin Shuai , Chen Xiaoming , Tong Ruijie 
Published: July 2024

Abstract

Microalgae serve as a valuable biological resource in many industrial applications. Thus, it is essential to obtain a high-efficiency separation technique for microalgae precisely. In this study, a runway-shaped microchannel with ordered semicircular micro-obstacles was introduced to conduct the separation of microalgae with different sizes. The runway-shaped microchannel combined the spiral characteristics with a series of semicircular micro-obstacles to realize the advantage of a sheathless, high-throughput, and low aspect ratio configuration. These micro-obstacles improved the performance of particle focusing, which can promote the microalgae separation effectively. These simulated results demonstrated that the runway-shaped channel with ordered semicircular micro-obstacles could form the evident distribution of local Dean vortices to separate particles with different size or volume. When the flow rate is considered 4mL/min, the experiment indicated that the microchannel could separate the Chlorella vulgaris and Haematococcus pluvialis in 94.6% and 81.5%, respectively. The microchannel with the high throughput and separation efficiency is competent to carry out the task of microalgal screening and artificial cultivation.
Microfluidic Platform for Stroke Risk Prediction: Evaluation of Blood Viscosity by Shear Rate Variations
Authors: Chang Yii-Nuoh , Yao Da-Jeng 
Published: July 2024

Abstract

The incidence of stroke is on the rise globally, affecting one in every four individuals each year. An early warning and prevention systems are urgently required. Blood viscosity is a correlation factor that is worthy to study in the stroke risk evaluation model. For the first time, a microfluidic platform was used as the in-vitro blood property evaluation for stroke risk prediction. It can be also used to evaluate the variation of non-Newtonian fluid viscosity under different specific shear rate conditions. The rigorous microarray design is providing the meticulous shear rate which simulating the variable of blood viscosity during pulsation within blood vessels. Furthermore, the systolic blood viscosity (SBV) and diastolic blood viscosity (DBV) can be calculated by using the developed pulsatility flow concept. The results demonstrate an impressive accuracy of 95% and excellent reproducibility while compared to traditional viscometers and rheometer within the human blood viscosity range of 1-10cP. This monitoring system is capable of being an indispensable component in the stroke risk evaluation platform.
L-shaped association between leisure-time physical activity and depressive symptoms in individuals with chronic inflammatory airway disease: data from the NHANES (2007-2018)
Authors: Li Wenqiang , Bai Jingshan , Ge Yanlei , He Qian 
Published: July 2024

Abstract

Background
Chronic inflammatory airway diseases (CIAD) are well-known risk factors for depression symptoms. There exists a complex interplay between leisure-time physical activity (LTPA) and depressive symptoms. However, the relationship between LTPA duration and depressive symptoms in CIAD patients remains unclear.
Methods
We collected data from National Health and Nutrition Examination Survey (NHANES) 2007–2018. Weighted logistic regression was employed to analyze the correlation between CIAD and depressive symptoms. Next, CIAD patients with LTPA information, the similar method was also employed to examine the association between LTPA and depressive symptoms. Finally, the dose-response relationship between LTPA duration and depressive symptoms was explored using restricted cubic spline (RCS) plot.
Results
Consistent with previous research findings, patients with CIAD are more prone to experiencing symptoms of depression (P < 0.0001). Compared to CIAD patients without LTPA, patients who engage in LTPA have a lower probability of experiencing depressive symptoms (P < 0.0001). Interestingly, we had discovered that there is an L-shaped relationship between the duration of LTPA and the prevalence of depressive symptoms (non-linear P = 0.001). There is a threshold effect between the duration of LTPA and depressive symptoms. Within12 h/week, the risk of depressive symptoms in CIAD patients decreases with increased duration of LTPA. However, beyond 12 h/week, this association no longer exists.
Conclusion
Our research indicates that CIAD patients can increase the prevalence of depressive symptoms. And LTPA over a period of time is negatively correlated with depressive symptoms, but only up to a total duration of 12 h/week.
Multilevel Learning for Enhanced Traffic Congestion Prediction Using Anomaly Detection and Ensemble Learning.
Authors: Khasawneh Mohammed A. , Daraghmeh Mustafa , Awasthi Anjali , Agarwal Anjali 
Published: July 2024

Abstract

Traffic congestion is a major challenge in modern transportation systems, leading to increased travel time, greater fuel consumption, and higher levels of harmful emissions. Accurately forecasting traffic congestion at a high level of precision is crucial for implementing effective traffic control strategies, such as managing traffic lights. To generate accurate and reliable predictions, it is crucial to precisely detect abnormal traffic patterns in the data, particularly in densely populated urban areas. This study presents a novel approach for forecasting traffic congestion by employing a multilevel learning technique that integrates both anomaly detection and ensemble learning methods. As an initial step in the learning process, we evaluate different anomaly detection techniques to identify unusual traffic patterns in different locations over time. After predicting the anomaly, the data pattern is cleaned accordingly, and a set of baseline learner models is trained as a secondary learning process. The top-performing models are chosen and undergo an ensemble process to combine their results, evaluating both stacking and voting ensemble methods as a third learning process. We evaluate the efficacy of the proposed strategy by employing a real-world traffic dataset and diverse evaluation metrics. The dataset undergoes several preprocessing techniques, including the windowing process with various settings, to convert the time series data into frequency patterns and produce a more generalized model. The comparative results of this study demonstrate that the proposed multilevel learning approach achieves superior prediction accuracy compared to the baseline models, underscoring the influence of the multilevel learning strategy on the accuracy of predictions. This study highlights the utilization of anomaly detection and ensemble learning to enhance the precision of traffic congestion prediction, thereby promoting further exploration of this approach in intelligent transportation systems.
ParaMask, a new method to identify multicopy genomic regions, corrects major biases in whole-genome sequencing data
Authors: Tjeng Bastiaan , Arimond Male , Grindeland Helene Bråten , Libera Andrea Dalla , Fulgione Andrea 
Published: July 2024

Abstract

Multicopy genomic regions are repeated sequences that can bias genomics analyses. Here, we present a method to identify and filter multicopy regions in population-level genomic data of any species. The broad applicability of this method stems from a flexible Expectation-Maximization framework to detect excess heterozygosity while simultaneously fitting inbreeding levels. By combining this signature with read ratio deviations, excess sequencing coverage, and a clustering technique, our method attains high power. We show that multicopy regions create biases that confound evolutionary genomics analyses, and that by identifying these regions with our method and filtering them, we can correct these biases.
Association between pan-immune-inflammation value and hyperlipidemia in the United States population
Authors: Yan Yu , Jia Shanshan , Huo Xingwei , Liu Lu , Ma Shuangliang , Chen Xiaoping 
Published: July 2024

Abstract

Object: To investigate the possible association between pan-immune-inflammation value (PIV) and hyperlipidemia.
Methods: The authors selected the relevant data from National Health and Nutrition Examination Survey (NHANES) for a detailed cross-sectional study. The independent variable used the logarithmic form of PIV-log10 (PIV). The definition of dependent variable-hyperlipidemiawas based on the National Cholesterol Education Program standards. Both variables were calculated from measured laboratory data. Weighted multivariate logistic regression analyses and restricted cubic splines (RCS) were conducted to analyze the association between PIV and hyperlipidemia. Stratified analyses were used to identify potential associations between PIV and hyperlipidemia with other covariates. The study also constructed the receiver operating characteristic (ROC) curve to assess the predictive value for hyperlipidemia of PIV compared to systemic immune-inflammation index (SII).
Results: In the study, 7,715 participants from NHANES were included. After adjusting for all confounders, PIV and hyperglycemia had an significantly positive association (OR (95%CI): 1.55 (1.17-2.06); P = 0.002). Compared to participants with lowest quartile (Q1) of PIV, participants with the highest quartile (Q4) had a significantly higher risk of hyperlipidemia (OR (95%CI): 1.47 (1.21-1.79); P < 0.001). The RCS curve showed a linear relationship between PIV and hyperlipidemia (P-nonlinear = 0.0633, P-overall < 0.001). The ROC curve found that compared with SII, PIV had a slightly higher predictive value (0.547 vs 0.542, P = 0.267).
Conclusion: This national cross-sectional study discovered that PIV had a significantly positive relationship with hyperlipidemia, particularly in young overweight individuals. More prospective studies are needed to verify whether the PIV is a more reliable and effective index for assessing the risk of hyperlipidemia.
Adiabatic control of the phase memory relaxation in the anticrossing region
Authors: Kandrashkin Yu. E. 
Published: July 2024

Abstract

In the vicinity of the anticrossing region of the spin sublevels, the effective magnetic moment of the electron spin is reduced, resulting in an increase in the lifetime of the spin coherence. Based on this phenomenon, the modified electron spin echo protocol is proposed to extend the coherence relaxation time. It includes the Hahn echo sequence with the additional detuning of the magnetic field applied during the intervals between two pulses and between the refocusing pulse and the echo. The simplest system with anticrossing sublevels is an ion whose electron spin is strongly coupled to its own nucleus. To realize the proposed protocol, a narrow anticrossing region is required. The promising candidate to realize the proposed protocol is the crystal [Ho(W5O18)2]9−, whose spin transitions have been studied in the recent paper (K. Kundu, et al., Commun Phys. 6 (2023) 38). Near the clock transition, the spin evolution of the electron-nuclear spin system is described by a fictitious spin ½, but with the magnetic properties dictated by the properties of the ion. The numerical study shows that the spin coherence is not destroyed by the magnetic field detuning, but it reduces the phase relaxation. In addition, the detuning result in a phase shift of the coherence, which can be used differentiate between the target signal and the noise.
Avian Migration-Mediated Cross-Species Transmission and Recombination driving the Diversity of Gammacoronaviruses and Deltacoronaviruses
Authors: Wu Zhiqiang , Xu Yuting , Han Yelin , Xu Panpan , Zhou Siyu , Zhao Peng , Wang Yuyang , Hu Jie , Ma Min , Li Zirong , Bo Shunqi , Zhao Chenyao , Ji Lei , Yuan Yue , Zhao Wenliang , Wang Jianwei , Jin Qi , He Guimei 
Published: July 2024

Abstract

In the wake of pandemics like COVID-19, which have zoonotic origins, the role of wildlife as reservoirs for emerging infectious diseases has garnered heightened attention. Migratory birds, traversing continents, represent a potent but under-researched vector for the spread of infectious diseases, including novel coronaviruses. This study delves into the genetic diversity and cross-species transmission dynamics of coronaviruses in migratory birds, presenting pivotal findings. From April 2019 to April 2023, we screened 5,263 migratory bird samples collected from Shanghai, China, identifying 317 coronavirus-positive samples belonging to five avian-related coronavirus subgenera and subsequently obtaining 120 complete genome sequences. To facilitate further research with a global perspective, the study curated all currently available 19,000 avian-associated CoVs and expanded the original 12 species to the current 16 species, including three novel coronavirus species identified in our study and one re-classified species from the public domain. The study illuminates the intricate genetic evolution and cross-species transmission dynamics of birds-related coronaviruses on a global scale. A notable aspect of our research is the identification of complex recombination patterns within the spike protein across different virus species and subgenera, highlighting migratory birds as a reservoir of coronavirus. Notably, the coronaviruses found in migratory birds, predominantly from the orders Anseriformes, Charadriiformes, and Pelecaniformes, with domestic ducks from Anseriformes playing a key role in bridging the transmission of coronaviruses between migratory and non-migratory birds. These findings reveal the genetic and recombination characteristics of coronaviruses in migratory birds, emphasizing the critical role of ecologically pivotal bird species in coronavirus cross-species transmission and genetic diversity shaping.