Miracle-dz/UniFAR-FTUs huggingface dataset:
Strengthened Relationship between Eastern ENSO and Summer Precipitation over Northeastern China

AbstractThis paper reveals a strengthened relationship between the spring eastern ENSO and summer precipitation over northeastern China (NEC) after the late 1990s. The relationship of the spring eastern ENSO with summer NEC precipitation is insignificant during 1983–97, whereas it becomes significantly positive during 1998–2012. In the earlier period, a zonal tripolar pattern in the middle to high latitudes of the North Asia–Pacific region and an anomalous anticyclone over Japan are the main factors responsible for NEC summer precipitation, although these atmospheric patterns have a weak correlation with the spring eastern ENSO. However, the atmospheric circulation that influences precipitation in NEC develops into the Siberian high and an anomalous cyclone over NEC after the late 1990s, which are strongly correlated with the spring eastern ENSO. The changes in associated atmospheric circulation partially contribute to the strengthening of the eastern ENSO–precipitation relationship. Additionally, the eas.
Analysis of the Thickness of Multilayered Porous Silicon in the Cold Emission Property

This work aims to investigate the influence of thickness of multilayered porous silicon (MPS) on its cold emission properties. The MPS layer with different thicknesses was prepared by adjusting the charge quantity <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$\text {mA}\cdot \text {min}$ </tex-math></inline-formula>) in the pulsed electrochemical etching process and oxidized through rapid thermal oxidation (RTO). The microstructure of the oxidized MPS was characterized by the scanning electron microscope (SEM) and the cold emission performance of the MPS-based emitters were also measured in vacuum. The studies show that, to fully utilize the advantages of a multilayered structure, it is essential to use the appropriate thickness and achieve sufficient oxidation for the MPS in this electron emitter fabrication. The optimal electron emission performance, with a stable efficiency of nearly 1%, a high current density of about <inline-formula> <tex-math notation="LaTeX">$300~\mu \text{A}$ </tex-math></inline-formula>/cm2, and an emission current fluctuation of only 9.8% can be achieved with a 5.5-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> thickness in the present MPS configuration and RTO condition at the operating voltage of 20 V. This work helps explore the intrinsic relationship between multilayered structures and the cold emission property of PS-based cathodes, facilitating their application in vacuum microelectronic devices.
Effectiveness of a multicomponent exercise intervention in community-dwelling older Chinese people with cognitive frailty: protocol for a mixed-methods research

Aims To evaluate the effectiveness of a multicomponent exercise intervention and to clarify the underlying mechanisms of the program in community-dwelling older adults with cognitive frailty. Additionally, the perception of participants in the program will be explored. Design A mixed-methods design, including a randomized controlled trial and an exploratory qualitative study, was used. Methods Each group consists of 41 participants. The experimental group will undergo a 12-week multicomponent exercise intervention, including warm-up, exergaming aerobic exercise, elastic-band resistance exercise, and cool-down. This intervention was developed based on the Health Belief Model (HBM) and Self-Efficacy Model (SEM). The control group will not receive any intervention. Physical frailty and cognitive function will be considered as primary outcomes. Data will be collected both at baseline and at the end of the intervention period. Fisher’s exact test, analysis of covariance, and generalized linear models will be conducted to compare mean changes between the two groups. Additionally, the mediation models will be used to examine whether any intervention effects are mediated through exercise self-efficacy. Discussion The findings of this study are anticipated to provide valuable insights for healthcare providers, enabling them to learn about effective strategies to enhance exercise adherence and promote improved functionality, independence, and quality of life for older adults with cognitive frailty. Clinical trial registration: [https://clinicaltrials.gov/], identifier [ChiCTR2200058850].
A fault feature extraction method for single-channel signal of rotary machinery based on VMD and KICA

A feature extraction method combined with variational mode decomposition (VMD) and kernel independent component analysis (KICA) is proposed to improve the fault feature extraction of vibration signal of rotary machinery. Firstly, VMD is used to decompose the single-channel vibration signal. Secondly, calculate the correlation coefficient between each component and the original signal. Finally, a new multidimensional observation signal is formed with high correlation components, and the fault signals will be extracted from the new observation signal by KICA. Compared with some typical fault feature extraction methods, the better performance of the proposed method is demonstrated by two experiments which are faulty rolling bearing experiment and a comprehensive experiment with faulty rolling bearing and faulty gear. Furthermore, an experiment of faulty rotary shaft verifies the effectiveness of this method. The results demonstrate that the proposed method is efficient for fault feature extraction of single-channel vibration signal of rotary machinery.
Method for retrieval of vertical profiles of wind from Stream Line lidar data with allowance that the noise component of recorded signal differs from white noise

A new method is proposed for retrieval of vertical profiles of the wind speed and direction angle from data measured by the Stream Line pulsed coherent Doppler lidar with conical scanning by the probing beam. Components of the wind velocity vector are estimated by the method of filtered sine-wave fitting with allowance that the noise component of the signal measured by the lidar differs from the white noise. The analysis of results of numerical simulation has shown that this approach allows the maximal height of retrieval of wind profiles to be increased one and half times.
Seismic Interferometry at the TIMO2-network, Germany

Using seismic interferometry, we analyze the seismic noise wavefield around the city of Landau, Germany. The crosscorrelation functions (CCFs) are investigated with respect to signals which might contain information on the underground and its temporal variations. The used data set comes from the TIMO2-project (TIMO: Deep Structure of the Central Upper Rhine Graben). Since the summer of 2009, seismic stations have been installed especially to detect induced seismicity in the region of Landau. The obtained CCFs are dominated by signals with frequencies between 0.4 and 0.8 Hz which are strongly asymmetric. We will present the results of the ongoing work to characterize and identify the source(s) of these signals.
Heat Transfer Characteristics of Endothermic Hydrocarbon Fuel in C/SiC Composites Cooling Channels

Regenerative cooling is one of the most effective thermal protection techniques for hypersonic vehicles. Because of the low density and high-temperature resistance of C/SiC composites, they have great potential to improve the cooling capacity of thermal protection systems. In this study, three regenerative cooling configurations applying C/SiC composites channels for a cylindrical ramjet engine combustion chamber are designed. The heat transfer characteristics and the mass flow distribution of endothermic hydrocarbon fuel in the composites channels under the influence of the cooling equivalence ratio (0.4$\sim$1.0) and the anisotropic thermal conductivity are investigated. The results show that C/SiC composites can significantly improve the heat resistance of the whole regenerative cooling structure, which prevents the inner metallic cooling structure from exceeding the material maximum service temperature. However, flow distribution deterioration phenomenon can be observed in the cooling channels due to the decrease in coolant density and the increase of channel flow resistance under certain conditions. C/SiC composites with higher thermal conductivity in the circumferential direction are found to be beneficial for the uniformity of temperature distribution.
Construction and Application of Knowledge Graph for Risk Identification Methods of Counterfeit Cigarettes and Wine

In response to the sharp increase in cases of counterfeit cigarettes and counterfeit alcohol in recent years, based on the application of knowledge graphs in social entities and social relationships, a knowledge graph for risk identification of counterfeit cigarettes and counterfeit alcohol was constructed and applied. Based on the historical cases of counterfeit cigarettes and counterfeit wines and the relevant groups, we process the data through ontology construction, knowledge extraction, and knowledge fusion, and build a knowledge map of relevant personnel for testing counterfeit wines. We use the TransH model to complete the constructed knowledge graph to obtain a more complete knowledge graph for risk identification of counterfeit cigarettes and counterfeit alcohol, and then use PageRank and Louvain community discovery algorithms to screen out communities where people with major suspicions gather, and control the system. Gangs selling counterfeit cigarettes and alcohol provide risk warning.
Exploring the causal relationship between gut microbiota and multiple myeloma risk based on Mendelian randomization and biological annotation

Introduction The microbial genome-wide association studies (mbGWAS) have highlighted significant host-microbiome interactions based on microbiome heritability. However, establishing causal relationships between particular microbiota and multiple myeloma (MM) remains challenging due to limited sample sizes. Methods Gut microbiota data from a GWAS with 18,340 participants and MM summary statistics from 456,348 individuals. The inverse variance-weighted (IVW) method was used as the main bidirectional Mendelian randomization (MR) analysis. To assess the robustness of our results, we further performed supplementary analyses, including MR pleiotropy residual sum and outlier (MR-PRESSO) test, MR-Egger, Weighted median, Simple mode, and Weighted mode. Moreover, a backward MR analysis was conducted to investigate the potential for reverse causation. Finally, gene and gene-set-based analyses were then conducted to explore the common biological factors connecting gut microbiota and MM. Results We discovered that 10 gut microbial taxa were causally related to MM risk. Among them, family Acidaminococcaceae, Bacteroidales family S24-7, family Porphyromonadaceae, genus Eubacterium ruminantium group, genus Parabacteroides, and genus Turicibacter were positively correlated with MM. Conversely, class Verrucomicrobia, family Verrucomicrobiaceae, genus Akkermansia, and order Verrucomicrobiales were negatively correlated with MM. The heterogeneity test revealed no Heterogeneity. MR-Egger and MR-PRESSO tests showed no significant horizontal pleiotropy. Importantly, leave-one-out analysis confirmed the robustness of MR results. In the backward MR analysis, no statistically significant associations were discovered between MM and 10 gut microbiota taxa. Lastly, we identified novel host-microbiome shared genes (AUTS2, CDK2, ERBB3, IKZF4, PMEL, SUOX, and RAB5B) that are associated with immunoregulation and prognosis in MM through biological annotation. Discussion Overall, this study provides evidence supporting a potential causal relationship between gut microbiota and MM risk, while also revealing novel host-microbiome shared genes relevant to MM immunoregulation and clinical prognosis.
Modulating Driver Alertness via Ambient Olfactory Stimulation: A Wearable Electroencephalography Study

Poor alertness levels and related changes in cognitive efficiency are common when performing monotonous tasks such as extended driving. Recent studies have investigated driver alertness decrement and possible strategies for modulating alertness with the goal of improving reaction times to safety critical events. However, most studies rely on subjective measures in assessing alertness changes, while the use of olfactory stimuli, which are known to be strong modulators of cognitive states, has not been commensurately explored in driving alertness settings. To address this gap, in the present study we investigated the effectiveness of olfactory stimuli in modulating the alertness state of drivers and explored the utility of electroencephalography (EEG) in developing objective brain-based tools for assessing the resulting changes in cortical activity. Olfactory stimulation induced a significant differential effect on braking reaction time. The corresponding effect to the cortical activity was characterized using EEG-derived metrics and the devised machine learning framework yielded a high discriminating accuracy (92.1%). Furthermore, neural activity in the alpha frequency band was found to be significantly associated with the observed drivers’ behavioral changes. Overall, our results demonstrate the potential of olfactory stimuli to modulate the alertness state and the efficiency of EEG in objectively assessing the resulting cognitive changes.
A comparison of balance velocities with remotely sensed surface velocities on a Svalbard ice cap

Over the past few decades Arctic average temperatures have risen at almost twice the rate of the rest of the world and this trend is projected to accelerate over this century (ACIA, 2004). The water contained in ice caps and glaciers outside of Antarctica and Greenland could contribute an estimated 0.5 m to global sea level rise (Church et al., 2001). There are several large ice caps on Svalbard which have been the focus of recent research. Vestfonna is the third largest and covers an area of 2,455 km (Hagen et al., 1993). It is drained to the south by four well defined outlet glaciers, two of which were shown to have negative net mass balances in the early 1970s (Dowdeswell and Collin, 1990).
Dietary Acrylamide: A Detailed Review on Formation, Detection, Mitigation, and Its Health Impacts

In today’s fast-paced world, people increasingly rely on a variety of processed foods due to their busy lifestyles. The enhanced flavors, vibrant colors, and ease of accessibility at reasonable prices have made ready-to-eat foods the easiest and simplest choice to satiate hunger, especially those that undergo thermal processing. However, these foods often contain an unsaturated amide called ‘Acrylamide’, known by its chemical name 2-propenamide, which is a contaminant formed when a carbohydrate- or protein-rich food product is thermally processed at more than 120 °C through methods like frying, baking, or roasting. Consuming foods with elevated levels of acrylamide can induce harmful toxicity such as neurotoxicity, hepatoxicity, cardiovascular toxicity, reproductive toxicity, and prenatal and postnatal toxicity. This review delves into the major pathways and factors influencing acrylamide formation in food, discusses its adverse effects on human health, and explores recent techniques for the detection and mitigation of acrylamide in food. This review could be of interest to a wide audience in the food industry that manufactures processed foods. A multi-faceted strategy is necessary to identify and resolve the factors responsible for the browning of food, ensure safety standards, and preserve essential food quality traits.
Adaptive Sliding Mode Control with Artificial Potential Field for Ground Robots in Precision Agriculture

This work considers autonomous Guidance, Navigation and Control (GNC) of Unmanned Ground Vehicles (UGVs) for Precision Agriculture (PA) applications. In the agricultural environment, the GPS signal can be weak for various reasons, which makes GPS-based navigation unreliable. Therefore, we consider a GPS-denied situation and localise the robot with several sensors and an Extended Kalman Filter (EKF). The path planner exploits a suitably designed Artificial Potential Field (APF), which ensures attraction to the target while avoiding obstacles. The motion controller, on the other hand, is based on Sliding Mode Control (SMC) and proposes a new direct adaptive law to adjust a control parameter in real time and achieve better tracking of the reference path.The navigation strategy was validated in Matlab/Simulink through numerical simulations, employing simulation scenarios which reflect typical applications in precision agriculture.
Can soil organic carbon in long–term experiments be detected using Vis-NIR spectroscopy?

The determination of soil organic carbon (SOC) stock and its evolution over time is important for understanding the cycling of carbon in soils, and its relationship with agricultural practices and agro-environmental conditions to develop more sustainable management practices. Moreover, this issue is of great importance in the development of monitoring, reporting, and verification (MRV) systems as part of climate change mitigation measures affecting the agricultural sector. In this context, the present study aims to investigate the reliability of visible and near-infrared (Vis-NIR) spectroscopy as a fast method to determine and monitor the SOC content in an MRV system framework. To that aim, soils from a Long-term field experiment (LTE), located in northern Italy and comparing various maize-based forage systems with contrasting residues and fertilization management was used as a case study. Three sampling campaigns (i.e. 2003, 2012, and 2018) were utilized for a total of 162 soil samples collected in the LTE (54 each). Soil samples archived after each campaign were retrieved and scanned using a Vis-NIR spectrometer to create a site-specific soil spectral library (Site-SSL). Aiming to implement a local prediction model, the 54 soil samples and spectra collected in 2003 were used as a training data set to estimate the SOC of the soil samples collected in 2012 and 2018 (108 in total). In practice, 2003 soil survey represented the time zero reference system. Concurrently, a second prediction model was performed from a regional soil sample archive (Reg-SSL). This is a dataset containing 122 soil samples and spectra collected in alluvial soils from the Po Plain, in the same pedo-landscape of the LTE. The Reg-SSL was merged with the 2003 Site-SSL to implement a regional prediction model. The objective of this activity was to compare the SOC estimations for 2012 and 2018 obtained with both Site-SSL and Reg-SSL models. In addition, three strategies were compared, including Random Forest (RF), Cubist (CU), and Memory based learning (MBL) on Site-SSL and Reg-SSL models (totally, six models were created). We also carried out a sensitivity analysis about the influence of training sample size on the accuracy of different models. Results revealed high correlations between measured and predicted values and for this kind of application, the best results were identified for the MBL model and the Site-SSL dataset. This exercise pointed out that Vis-NIR spectral libraries could be used for detecting changes in SOC. In addition, comparing the use of regional and site-specific libraries for SOC estimation, we confirmed that the accuracy of the Reg-SSL model, although more stable, was lower than that of Site-SSL. A sample size of N=40 samples resulted large enough for building a site-specific spectral library.
EVOLUTION TRENDENCY IN PLUVIOMETRIC INDEXES USED TO MONITOR GLOBAL CLIMATE CHANGES

Most popular pluviometric indexes used to monitor climate hazards are: Hellman, Criterion, Topor pluviometric index, Rainfall deciles, Angot pluviometric index, Anomaly of standardized precipitation, Bhalme-Mooley drought index, Effective drought index and the Standardized Precipitation Index (SPI). Analysis of rainfall recorded in Oradea, in the period of agricultural years 1971-2011, with SPI indicates that a series of 4 consecutive years (1995-1999) are rainy and 5 consecutive years (1988-1993) are drought and a trend toward rainy years. Annual SPI values determined in Oradea during 1971-2011 are correlated linearly with rainfall deciles, the correlation was very significant (R2 = 0.9638) because both pluviometric indexes are based for determining on the cumulative probability of precipitation.
A Review on the Use of Deep Eutectic Solvents in Protection Reactions

Given the recent research on the application of eco-sustainable methods in organic chemistry, we have focused our attention on the derivatization processes for fundamental functional groups in organic chemistry, such as amino, hydroxyl and carbonyl groups. Protection reactions are needed to temporarily block a certain reactive site on a molecule. The use of green solvents in this context has made an excellent contribution to the development of eco-sustainable methods. In recent years, deep eutectic solvents (DESs) have had great success as a new class of green solvents used in various chemical applications, such as extraction or synthetic processes. These solvents are biodegradable and nontoxic. In this framework, a list of relevant works found in the literature is described, considering DESs to be a good alternative to classic toxic solvents in the protection reactions of important functional groups.
Merge-and-Split Graph Convolutional Network for Skeleton-Based Interaction Recognition

We introduce an innovative approach to address a significant challenge in interaction recognition, specifically the capture of correlation features between different interaction body parts. These features are often overlooked by traditional graph convolution networks commonly used in interaction recognition tasks. Our solution, the Merge-and-Split Graph Convolutional Network, takes a unique perspective, treating interaction recognition as a global problem. It leverages a Merge-and-Split Graph structure to effectively capture dependencies between interaction body parts. To extract the essential interaction features, we introduce the Merge-and-Split Graph Convolution module, which seamlessly combines the Merge-and-Split Graph with Graph Convolutional Networks. This fusion enables the extraction of rich semantic information between adjacent joint points. In addition, we introduce a Short-term Dependence module designed to extract joint and motion characteristics specific to each type of interaction. Furthermore, to extract correlation features between different hierarchical sets, we present the Hierarchical Guided Attention Module. This module plays a crucial role in highlighting the relevant hierarchical sets that contain essential interaction information. The effectiveness of our proposed model is demonstrated by achieving state-of-the-art performance on 2 widely recognized datasets, namely, the NTU60 and NTU120 interaction datasets. Our model’s efficacy is rigorously validated through extensive experiments, and we have made the code available for the research community at https://github.com/wanghq05/MS-GCN/.