Search Results (31)

Extracting causal relations from natural language texts is crucial for uncovering causality, and most existing causal relation extraction models are single-task learning-based models, which can not comprehensively address attributes such as part-of-speech tagging and chunk analysis. However, the characteristics of words with multi-domains are more relevant for causal relation extraction, due to words such as adjectives, linking verbs, etc., bringing more noise data limiting the effectiveness of the single-task-based learning methods. Furthermore, causalities from diverse domains also raise a challenge, as existing models tend to falter in multiple domains compared to a single one. In light of this, we propose a multi-task sequence tagging model, MPC−CE, which utilizes more information about causality and relevant tasks to improve causal relation extraction in noised data. By modeling auxiliary tasks, MPC−CE promotes a hierarchical understanding of linguistic structure and semantic roles, filtering noise and isolating salient entities. Furthermore, the sparse sharing paradigm extracts only the most broadly beneficial parameters by pruning redundant ones during training, enhancing model generalization. The empirical results on two datasets show 2.19% and 3.12% F1 improvement, respectively, compared to baselines, demonstrating that our proposed model can effectively enhance causal relation extraction with semantic features across multiple syntactic tasks, offering the representational power to overcome pervasive noise and cross-domain issues. Full article

Soil porosity, as an essential indicator for assessing soil quality, plays a key role in guiding agricultural production, so it is beneficial to detect soil porosity. However, the currently available methods do not apply to high-precision and rapid detection of soil with a black-box nature in the field, so this paper proposes a soil porosity detection method based on ultrasound and multi-scale CNN-LSTM. Firstly, a series of ring cutter soil samples with different porosities were prepared manually to simulate soil collected in the field using a ring cutter, followed by ultrasonic signal acquisition of the soil samples. The acquired signals were subjected to three kinds of data augmentation processes to enrich the dataset: adding Gaussian white noise, time shift transformation, and random perturbation. Since the collected ultrasonic signals belong to long-time series data and there are different frequency and sequence features, this study constructs a multi-scale CNN-LSTM deep neural network model using large convolution kernels based on the idea of multi-scale feature extraction, which uses multiple large convolution kernels of different sizes to downsize the collected ultra-long time series data and extract local features in the sequences, and combining the ability of LSTM to capture global and long-term dependent features enhances the feature expression ability of the model. The multi-head self-attention mechanism is added at the end of the model to infer the before-and-after relationship of the sequence data to improve the degradation of the model performance caused by waveform distortion. Finally, the model was trained, validated, and tested using ultrasonic signal data collected from soil samples to demonstrate the accuracy of the detection method. The model has a coefficient of determination of 0.9990 for detecting soil porosity, with a percentage root mean square error of only 0.66%. It outperforms other advanced comparative models, making it very promising for application. Full article

Symbolic regression plays a crucial role in machine learning and data science by allowing the extraction of meaningful mathematical models directly from data without imposing a specific structure. This level of adaptability is especially beneficial in scientific and engineering fields, where comprehending and articulating the underlying data relationships is just as important as making accurate predictions. Genetic Programming (GP) has been extensively utilized for symbolic regression and has demonstrated remarkable success in diverse domains. However, GP’s heavy reliance on evolutionary mechanisms makes it computationally intensive and challenging to handle. On the other hand, Particle Swarm Optimization (PSO) has demonstrated remarkable performance in numerical optimization with parallelism, simplicity, and rapid convergence. These attributes position PSO as a compelling option for Automatic Programming (AP), which focuses on the automatic generation of programs or mathematical models. Particle Swarm Programming (PSP) has emerged as an alternative to Genetic Programming (GP), with a specific emphasis on harnessing the efficiency of PSO for symbolic regression. However, PSP remains unsolved due to the high-dimensional search spaces and local optimal regions in AP, where traditional PSO can encounter issues such as premature convergence and stagnation. To tackle these challenges, we introduce Dynamical Sphere Regrouping PSO Programming (DSRegPSOP), an innovative PSP implementation that integrates DSRegPSO’s dynamical sphere regrouping and momentum conservation mechanisms. DSRegPSOP is specifically developed to deal with large-scale, high-dimensional search spaces featuring numerous local optima, thus proving effective behavior for symbolic regression tasks. We assess DSRegPSOP by generating 10 mathematical expressions for mapping points from functions with varying complexity, including noise in position and cost evaluation. Moreover, we also evaluate its performance using real-world datasets. Our results show that DSRegPSOP effectively addresses the shortcomings of PSO in PSP by producing mathematical models entirely generated by AP that achieve accuracy similar to other machine learning algorithms optimized for regression tasks involving numerical structures. Additionally, DSRegPSOP combines the benefits of symbolic regression with the efficiency of PSO. Full article

The role of forest community configurations in multiple ecosystem functions remains poorly understood due to the absence of quantifiable metrics for evaluating these configurations. This limitation hinders our ability to use forests to enhance urban well-being effectively. This study integrates both observation and experimentation to elucidate the effects of community configurations on the multifunctionality of forests. We examine seven ecosystem functions in Changchun’s urban forests: carbon sequestration, rainwater interception, temperature reduction, humidity increase, particulate matter reduction, noise reduction, and water conservation. Assortment indices, derived from traditional diversity metrics and relative importance values, reveal a negative correlation with multifunctionality. This suggests that improving forest multifunctionality requires a strategically planned species composition rather than simply increasing diversity. Furthermore, the creation of comprehensive configuration indices for evaluating intraspecific configurations has confirmed their beneficial impact on multifunctionality. Our results highlight the significance of intraspecific structural configurations and advocate for using mixed-species plantings in urban forestry practices. We propose practical management strategies to enhance urban forest multifunctionality, including selecting tree species for their functional benefits, implementing uneven-aged plantings, and integrating both shade-tolerant and sun-loving species. Together, our findings underscore the essential role of community configuration in sustaining multifunctionality and strongly support the management of urban forests. Full article

The timely and accurate mapping of sugarcane cultivation is significant to ensure the sustainability of the sugarcane industry, including sugarcane production, rural society, sugar futures, and crop insurance. Synthetic aperture radar (SAR), due to its all-weather and all-time imaging capability, plays an important role in mapping sugarcane cultivation in cloudy areas. However, the inherent speckle noise of SAR data worsens the “salt and pepper” effect in the sugarcane map. Therefore, in previous studies, an additional land cover map or optical image was still required. This study proposes a new application paradigm of time series SAR data for sugarcane mapping to tackle this limitation. First, the locally estimated scatterplot smoothing (LOESS) smoothing technique was exploited to reconstruct time series SAR data and reduce SAR noise in the time domain. Second, temporal importance was evaluated using RF MDA ranking, and basic parcel units were obtained only based on multi-temporal SAR images with high importance values. Lastly, the parcel-based classification method, combining time series smoothing SAR data, RF classifier, and basic parcel units, was used to generate a sugarcane extent map without unreasonable sugarcane spots. The proposed paradigm was applied to map sugarcane cultivation in Suixi County, China. Results showed that the proposed paradigm was able to produce an accurate sugarcane cultivation map with an overall accuracy of 96.09% and a Kappa coefficient of 0.91. Compared with the pixel-based classification result with original time series SAR data, the new paradigm performed much better in reducing the “salt and pepper” spots and improving the completeness of the sugarcane plots. In particular, the unreasonable non-vegetation spots in the sugarcane map were eliminated. The results demonstrated the efficacy of the new paradigm for mapping sugarcane cultivation. Unlike traditional methods that rely on optical remote sensing data, the new paradigm offers a high level of practicality for mapping sugarcane in large regions. This is particularly beneficial in cloudy areas where optical remote sensing data is frequently unavailable. Full article

The changes in the properties of three biological events that occur with cerebral aging are discussed. These adverse changes already begin to develop early in mid-life and gradually become more pronounced with senescence. Essentially, they are reflections of the progressive decline in effectiveness of key processes, resulting in the deviation of essential biochemical trajectories to ineffective and ultimately harmful variants of these programs. The emphasis of this review is the major role played by the mitochondria in the transition of these three important processes toward more deleterious variants as brain aging proceeds. The immune system: the shift away from an efficient immune response to a more unfocused, continuing inflammatory condition. Such a state is both ineffective and harmful. Reactive oxygen species are important intracellular signaling systems. Additionally, microglial phagocytic activity utilizing short lived reactive oxygen species contribute to the removal of aberrant or dead cells and bacteria. These processes are transformed into an excessive, untargeted, and persistent generation of pro-oxidant free radicals (oxidative stress). The normal efficient neural transmission is modified to a state of undirected, chronic low-level excitatory activity. Each of these changes is characterized by the occurrence of continuous activity that is inefficient and diffused. The signal/noise ratio of several critical biological events is thus reduced as beneficial responses are gradually replaced by their impaired and deleterious variants. Full article

The microbiota in gastrointestinal tracts is recognized to play a pivotal role in the health of their hosts. Music and noise are prevalent environmental factors in human society and animal production and are reported to impact their welfare and physiological conditions; however, the information on the relationship between the microbiota, physiological status, and sound is limited. This study investigated the impact of music and white noise exposure in mice through 16s rRNA gene sequencing, enzyme assay, and qPCR. The results demonstrate that white noise induced oxidative stress in animals by decreasing serum SOD and GSH-PX activity while increasing LDH activity and MDA levels (p < 0.05). Conversely, no oxidative stress was observed in the music treatment group. The relative gene expression of IFN-γ and IL-1β decreased in the white noise group compared to the music and control groups. The 16s rRNA gene amplicon sequencing revealed that Bacteroidetes, Firmicutes, Verrucomicrobia, and Proteobacteria were dominant among all the groups. Furthermore, the proportion of Firmicutes increased in the music treatment group but decreased in the white noise treatment group compared to the control group. In conclusion, white noise has detrimental impacts on the gut microbiota, antioxidant activity, and immunity of mice, while music is potentially beneficial. Full article

Current research shows that the wavy shape can play an important role in drag reduction. Meanwhile, it also has the potential of noise reduction. In the present study, a kind of wavy shape of periodic cosine profile with a large spanwise wavelength and large aspect ratio was applied to the circular cylinder model. The experiments on the influence of various aspect ratios (ratio of wave wavelength to amplitude) on the far-field noise of the wavy cylinder were carried out in a 0.55 m × 0.4 m aeroacoustic wind tunnel. It is shown that the maximum decrease of the far-field SPL (Sound Pressure Level) between the wavy cylinder and baseline cylinder exceeded 37 dB within the frequency between 200 Hz and 1000 Hz. The noise reduction effect of the wavy cylinder will become better along with the increasing aspect ratio. However, there exists a critical aspect ratio near λ/a = 30. If the aspect ratio continues increasing, the noise reduction effect of the wavy cylinder will decrease instead of increasing. Finally, the computational fluid dynamics method is applied to reveal the noise reduction mechanism of this kind of wavy cylinder with a large spanwise wavelength and large aspect ratio. It is found that the periodic shedding vortex is disturbed and tends to be banded instead of showing alternate oscillation. The turbulence intensity and velocity fluctuation around the wavy cylinder will be also reduced. According to the vortex and sound theory, these changes are beneficial to the noise reduction. The large spanwise wavelength and large aspect ratio play a significant role in controlling the shedding vortex variation and adjusting the local flow field around the crest and trough of the wavy cylinder, which is the key factor to change the flow field and reduce the flow-noise of the wavy cylinder. Full article

Providing restorative green areas is important, especially in the city, where the level of stress and noise is relatively high. Therefore, green areas, such as urban parks, should provide coherent audio–visual stimuli to achieve positive perception by the residents. Therefore, this study aims to investigate the potential for psychological regeneration in urban parks in terms of visual and soundscape assessment as well as to assess the role of the intensity of different types of sound contributing to the positive perception of the soundscape. In order to achieve this aim, we chose eight urban parks in the city of Wrocław to provide audio and visual stimuli and used a group of young adults as survey respondents. The results show that visual stimuli are perceived as undoubtedly more important than the soundscape, and that talking, footsteps, music, children (playing), birds, and vehicles are the most significant types of sound that contribute to the perception of soundscape depending on the level of intensity of the sound (with children and vehicles being beneficial if they are completely inaudible). We conclude that the quality of the soundscape is essential to improve the restorative potential of urban parks and, in consequence, to improve the well-being and health of the city dwellers, and there is a necessity for strategies and development plans including sensually coherent and inclusive public parks in the city of Wrocław. Full article

With increasing urbanization in China, the traffic-induced urban environmental noise pollution problem is becoming more and more serious, and it has become a common urban malady that cannot be ignored. Traffic green belts are an important part of the urban ecosystem and play a role in traffic noise reduction, and simultaneously provide ecosystem services, such as creating a natural landscape and retaining dust. Therefore, they are a category of Nature-based Solutions (NbSs) that have multiple ecosystem service provisions. The relationship between NbSs and urban ecosystem services is one of the current research hot spots. However, regarding the assessment of ecosystem services on the urban scale, the role of vegetation in reducing noise pollution as a service has rarely been studied. Taking Shenzhen City as an example, through monitoring 217 sample plots in the city, this paper analyzes the relationship between vegetation coverage and the ability of green belts to reduce noise by using the IUEMS platform combined with the high-resolution spatial distribution data of green spaces. Then, we evaluated the product amount and the value of the roadside green belts in Shenzhen when acting as a noise reduction service. The work of this study, to a certain extent, improves the problems related to the inadequate consideration of vegetation characteristics in current urban-scale noise assessment models. The results show the following: (i) In the respective analysis buffer zones of the Grade I to Grade IV roads in Shenzhen, on average, for every 1% increase in the vegetation coverage of green belts, noise can be reduced by 0.4 dB, 1.0 dB, 0.2 dB, and 0.6 dB, respectively. (ii) The product value of the noise reduction service provided by roadside green belts is CNY 1.16 billion in Shenzhen. (iii) The road traffic noise greatly exceeds the standard in Shenzhen, but traffic noise can be decreased by increasing the vegetation coverage of green belts. This is not only beneficial to the scientific understanding of the ecological service value of green spaces by evaluating the noise reduction service of traffic green belts, as well as its influencing factors, but is also beneficial to making improvements in construction and management ideas for urban green spaces. Full article

In this work, we address the beneficial role of noise in two different contexts, the human brain and financial markets. In particular, the similitude between the ability of financial markets to maintain in equilibrium asset prices is compared with the ability of the human nervous system to balance a stick on a fingertip. Numerical simulations of the human stick balancing phenomenon show that after the introduction of a small quantity of noise and a proper calibration of the main control parameters, intermittent changes in the angular velocity of the stick are able to reproduce the most basilar stylized facts involving price returns in financial markets. These results could also shed light on the relevance of the idea of the “planetary nervous system”, already introduced elsewhere, in the financial context. Full article

Aiming to address the problems of the high bit error rate (BER) of demodulation or low classification accuracy of modulation signals with a low signal-to-noise ratio (SNR), we propose a double-residual denoising autoencoder method with a channel attention mechanism, referred to as DRdA-CA, to improve the SNR of modulation signals. The proposed DRdA-CA consists of an encoding module and a decoding module. A squeeze-and-excitation (SE) ResNet module containing one residual connection is modified and then introduced into the autoencoder as the channel attention mechanism, to better extract the characteristics of the modulation signals and reduce the computational complexity of the model. Moreover, the other residual connection is further added inside the encoding and decoding modules to optimize the network degradation problem, which is beneficial for fully exploiting the multi-level features of modulation signals and improving the reconstruction quality of the signal. The ablation experiments prove that both the improved SE module and dual residual connections in the proposed method play an important role in improving the denoising performance. The subsequent experimental results show that the proposed DRdA-CA significantly improves the SNR values of eight modulation types in the range of −12 dB to 8 dB. Especially for 16QAM and 64QAM, the SNR is improved by 8.38 dB and 8.27 dB on average, respectively. Compared to the DnCNN denoising method, the proposed DRdA-CA makes the average classification accuracy increase by 67.59∼74.94% over the entire SNR range. When it comes to the demodulation, compared with the RLS and the DnCNN denoising algorithms, the proposed denoising method reduces the BER of 16QAM by an average of 63.5% and 40.5%, and reduces the BER of 64QAM by an average of 46.7% and 18.6%. The above results show that the proposed DRdA-CA achieves the optimal noise reduction effect. Full article

FAPI PET/CT is a novel imaging tool targeting fibroblast activation protein (FAP), with high tumor uptake rate and low background noise. Therefore, the appearance of FAPI PET/CT provides a good tumor-to-background ratio between tumor and non-tumor tissues, which is beneficial to staging, tumor description and detection. Colorectal cancer has the biological characteristics of high expression of FAP, which provides the foundation for targeted FAP imaging. FAPI PET/CT may have a potential role in changing the staging and re-staging of colorectal cancer, monitoring recurrence and treatment management, and improving the prognosis of patients. This review will summarize the application status of FAPI PET/CT in colorectal cancer and provide directions for further application research. Full article

Volatile organic compounds (VOCs) comprises a broad class of small molecules (up to ~300 g/mol) produced by biological and non-biological sources. VOCs play a vital role in an organism’s metabolism during its growth, defense, and reproduction. The well-known 6-pentyl-$\alpha$-pyrone (6-PP) molecule is an example of a major volatile biosynthesized by Trichoderma atroviride that modulates the expression of PIN auxin-transport proteins in primary roots of Arabidopsis thaliana during their relationship. Their beneficial relation includes lateral root formation, defense induction, and increased plant biomass production. The role of 6-PP has been widely studied due to its relevance in this cross-kingdom relationship. Conventional VOCs measurements are often destructive; samples require further preparation, and the time resolution is low (around hours). Some techniques enable at-line or real-time analyses but are highly selective to defined compounds. Due to these technical constraints, it is difficult to acquire relevant information about the dynamics of VOCs in biological systems. Low-temperature plasma (LTP) ionization allows the analysis of a wide range of VOCs by mass spectrometry (MS). In addition, LTP-MS requires no sample preparation, is solvent-free, and enables the detection of 6-PP faster than conventional analytical methods. Applying static statistical methods such as Principal Component Analysis (PCA) and Discriminant Factorial Analysis (DFA) leads to a loss of information since the biological systems are dynamic. Thus, we applied a time series analysis to find patterns in the signal changes. Our results indicate that the 6-PP signal is constitutively emitted by T. atroviride only; the signal shows high skewness and kurtosis. In A. thaliana grown alone, no signal corresponding to 6-PP is detected above the white noise level. However, during T. atroviride-A. thaliana interaction, the signal performance showed reduced skewness and kurtosis with high autocorrelation. These results suggest that 6-PP is a physiological variable that promotes homeostasis during the plant-fungal relationship. Although the molecular mechanism of this cross-kingdom control is still unknown, our study indicates that 6-PP has to be regulated by A. thaliana during their interaction. Full article

The coil structure of the inductive wear debris sensor plays a significant role in the effect of wear debris detection. According to the characteristics of LC parallel resonance, the capacitor and coil are connected in parallel to make sensor coils in the LC parallel resonance state, which is beneficial to improve the ability to detect wear particles. In this paper, the mathematical model of output-induced electromotance of the detection coil is established to analyze the influence of the structure on the detection sensitivity and enhance the sensor’s current rate of change to the disturbance magnetic field, which is essential to resist noise interference. Based on the coherent demodulation principle, the AD630 lock-in amplifier is applied to the test platform to amplify and identify weak signals. In addition, experiments are designed to test the output signals of debris under the condition of different original output voltages of the sensor with a parallel structure. Meanwhile, the near-resonance state of the detection coil with LC parallel circuit is tested by output signal information. Results show that the sensor detection sensitivity will be effectively improved with the LC parallel coil structure. For the sensor structure parameters designed in this paper, the optimal raw output amplification voltage for abrasive particle detection is 4.49 V. The detection performance of ferromagnetic particles and non-ferromagnetic particles is tested under this condition, realizing the detection ability of 103.33 μm ferromagnetic abrasive particles and 320.74 μm non-ferromagnetic abrasive particles. Full article