Rainfall thresholds of landslides often are determined by empirical meteorological thresholds, but the reliability of this approach sometimes is limited by the lack of information about the ...hydrological processes that trigger landslides. Groundwater plays a critical role in triggering deep-seated landslides. In this study, we propose a methodology to estimate the rainfall threshold for a deep-seated landslide based on an integrated model that combines a model for predicting the level of the groundwater with a finite-element, strength-reduction model. First, in order to obtain more accurate results when predicting the level of the groundwater, a method is proposed to estimate the groundwater level fluctuation caused by rainfall (GLFR). Then, two different machine learning methods, i.e., the genetic algorithm back-propagation neural network (GA-BPNN) method and the genetic algorithm support vector machine (GA-SVM) method, are proposed for predicting the GLFR of the Duxiantou landslide located in Zhejiang Province, China. The results of the predictions showed that the performance of the GA-SVM model was better than that of the GA-BPNN model. Then, a series of numerical simulations was conducted to investigate the factor of safety (Fs) of the slope at different groundwater levels. At last, the probabilities of the occurrences of Duxiantou landslides for different return periods of rainfall intensity were evaluated to determine the rainfall threshold.
•A methodology to estimate the rainfall threshold based on integration of empirical and physically-based models is proposed•A empirically-based method to estimate the groundwater level fluctuation caused by rainfall (GLFR) is proposed•The occurrences of landslides for different rainfall intensity are evaluated to determine rainfall threshold
Sustainable poultry meat and egg production is important to provide safe and quality protein sources in human nutrition worldwide. The gastrointestinal (GI) tract of chickens harbor a diverse and ...complex microbiota that plays a vital role in digestion and absorption of nutrients, immune system development and pathogen exclusion. However, the integrity, functionality, and health of the chicken gut depends on many factors including the environment, feed, and the GI microbiota. The symbiotic interactions between host and microbe is fundamental to poultry health and production. The diversity of the chicken GI microbiota is largely influenced by the age of the birds, location in the digestive tract and diet. Until recently, research on the poultry GI microbiota relied on conventional microbiological techniques that can only culture a small proportion of the complex community comprising the GI microbiota. 16S rRNA based next generation sequencing is a powerful tool to investigate the biological and ecological roles of the GI microbiota in chicken. Although several challenges remain in understanding the chicken GI microbiome, optimizing the taxonomic composition and biochemical functions of the GI microbiome is an attainable goal in the post-genomic era. This article reviews the current knowledge on the chicken GI function and factors that influence the diversity of gut microbiota. Further, this review compares past and current approaches that are used in chicken GI microbiota research. A better understanding of the chicken gut function and microbiology will provide us new opportunities for the improvement of poultry health and production.
This paper aims to examine the impact of executive compensation incentive on corporate innovation capability by dividing executive compensation incentive into short-term monetary incentive and ...long-term equity incentive. We also investigate the interaction between the two types of executive compensation incentive. Data are collected from China’s agro-based companies during 2012–2019, and multiple regression analysis is utilized. The empirical results show that short-term monetary incentive has no impact on innovation capability, while long-term equity incentive stimulates innovation capability. Regarding company ownership, the impact of long-term equity incentive in state-owned enterprises is greater than that in private-owned enterprises. In addition, the complementary effect between short-term and long-term compensation incentive has a positive impact on innovation capability regardless of company ownership. The findings of this paper could help agribusiness managers to design the reasonable incentive system to incentivize corporate executives and enhance the capability of independent innovation.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•Pretreatment methods compared using same activated sludge for P release.•Release of P was not well correlated with those of COD or N.•Efficient P release by pH adjustment was due to orthophosphate ...dissolution.•Anaerobic digestion was ineffective for P release regardless of sludge pretreatment.
Phosphorus-rich waste activate sludge (WAS) has attracted considerable attention as an excellent material for P recovery from sewage. For a satisfactory recovery rate, an efficient release of P from WAS is critical. This paper investigated the effectiveness of four classes of sludge pretreatments, i.e., thermal hydrolysis, sonication, pH adjustment and ozonation, on P release from thickened WAS of an A2O wastewater treatment plant (WWTP). Standards, Measurements and Testing protocol (SMT, by European commission), 31P nuclear magnetic resonance (NMR), excitation-emission matrix (EEM) fluorescence spectroscopy and scanning electron microscopy (SEM) were used to elucidate the evolution of P fractions and their distribution during the pretreatments and the followed anaerobic sludge digestion (only sonicated and thermal hydrolyzed sludge). For the studied WAS (inorganic P content: 74%), the results show that pH adjustment, among the sludge pretreatment methods, was the most effective to release P with the resulting aqueous total P (TPaq) amounting to 30% and 34% of the sludge total P (TPmx) at pH2 and pH12, respectively. The releases of P were not noticeably correlated with those of COD or N, suggesting different sources and mechanisms. The pH adjustment-induced P releases were mostly due to the dissolution of orthophosphate. Anaerobic digestion (AD) was not an effective way to release P from WAS regardless of sludge pretreatments, while pretreatments resulting sludge disintegration significantly improved P mineralization during AD. Because of the difficulty in separating those fine phosphate particles (often observed in WWTP sludge) for recovery, we suggest induction of in situ crystallization of phosphate during AD to be more researched in addition to the conventional re-dissolution – crystallization route.
The firefly algorithm (FA) is proposed as a heuristic algorithm, inspired by natural phenomena. The FA has attracted a lot of attention due to its effectiveness in dealing with various global ...optimization problems. However, it could easily fall into a local optimal value or suffer from low accuracy when solving high-dimensional optimization problems. To improve the performance of the FA, this paper adds the self-adaptive logarithmic inertia weight to the updating formula of the FA, and proposes the introduction of a minimum attractiveness of a firefly, which greatly improves the convergence speed and balances the global exploration and local exploitation capabilities of FA. Additionally, a step-size decreasing factor is introduced to dynamically adjust the random step-size term. When the dimension of a search is high, the random step-size becomes very small. This strategy enables the FA to explore solution more accurately. This improved FA (LWFA) was evaluated with ten benchmark test functions under different dimensions (D = 10, 30, and 100) and with standard IEEE CEC 2010 benchmark functions. Simulation results show that the performance of improved FA is superior comparing to the standard FA and other algorithms, i.e., particle swarm optimization, the cuckoo search algorithm, the flower pollination algorithm, the sine cosine algorithm, and other modified FA. The LWFA also has high performance and optimal efficiency for a number of optimization problems.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In real industry, local damages often appear on multiple parts of bearings simultaneously and these compound faults coupling heavily troubles those regular diagnosis methods. In this article, an ...energy-constrained swarm decomposition and adaptive spectral amplitude modulation (ECSWD-ASAM) method is proposed to extract compound fault features from bearing vibration signals. First, a rectangular window is convolved with the energy spectrum to generate a smooth upper envelope curve to overcome the instant noisy peaks and highlight dominant frequencies in the vibration signals. Second, an energy filter is constructed to concentrate the energy of each oscillating component and suppress the spectrum overlap during the signal decomposition. Third, the envelope correlation coefficient (ECC) is used as the evaluation criterion to constrain the over-decomposition between adjacent components. Finally, a new index called kurtosis-enhanced entropy (KEE) is proposed to adaptively locate the optimal fault frequency band of the spectral amplitude modulation (SAM) results, which can enhance the weak fault features in the extracted components. ECSWD-ASAM effectively separates different fault components on simulated data, Paderborn University (PU) dataset, and vibration signals of bearing-gearbox testbed. The testing results indicate that ECSWD-ASAM has better denoising and compound fault extraction performance than these representative methods, i.e., successive variational mode decomposition (SVMD), improved complete ensemble empirical mode decomposition (EMD) adaptive noise (ICEEMDAN), and local mean decomposition (LMD).
Fault diagnosis is significant to guarantee the safety and reliability of the machinery. Local faults will make the collected vibration signals deviate from the normal signals. To extract ...fault-related features from vibration signals, many deep-learning-based methods have been proposed for machinery fault diagnosis recently. However, due to the extreme conditions (e.g., high background noise and limited labeled training samples), it is a challenging task to implement feature extraction from the collected signals with nonlinear and nonstationary characteristics. To implement feature extraction and noise reduction of vibration signals, this article proposes a novel network, that is, deep morphological shrinkage convolutional autoencoder (DMSCAE) for gearbox fault diagnosis considering the insufficient labeled training samples. First, a morphological convolutional autoencoder is proposed for noise filtering and feature extraction. Second, a multibranch structure with different structural elements (SEs) is used in the morphological layer to extract impulsive components. Finally, a soft thresholding-based shrinkage is employed to filter ineffective features, where an adaptative method is developed to adjust the threshold automatically in the backpropagation procedure. The experiments on two gearbox fault diagnosis tests are conducted to verify the performance of DMSCAE. The results indicate that DMSCAE obtains a better performance for fault diagnosis than other DNNs, for example, efficient channel attention network (ECANet) and self-calibrated convolutional network (SCNet).
A rising level of groundwater is a critical trigger for deep-seated landslides. Accordingly, an effective measure to improve the stability of a landslide is to reduce the groundwater level of a slope ...by using a drainage system. This study investigates the effectiveness of drainage tunnels in increasing the rainfall threshold of a deep-seated landslide. Monitoring results show that the movement of the landslide is highly sensitive to the prevailing groundwater level (GL), and the value of GL has a direct connection with the movement of a slope. Based on continuous monitoring of data of groundwater level (GL) and precipitation, the Particle Swarm Optimization Support Vector Machine (PSO-SVM) model was developed to predict GL based on antecedent rainfall. The calculated results show that the performance of the PSO-SVM model is acceptable. Using intensity-duration-frequency (IDF) analysis and the PSO-SVM model, the rainfall threshold of the landslide in this study was estimated to range from 63 to 78 mm before the drainage tunnel was completed. This contrasted with a rainfall threshold ranging from 144 to 162 mm after the drainage tunnel was completed. This shows that the construction of a drainage tunnel increased the rainfall threshold of the landslide significantly, nearly doubling it.
The study of two-dimensional (2D) superconductors is one of the most prominent areas in recent years and has remained a long-standing scientific challenge. Since the introduction of the new member of ...2D material family consisting of transition-metal elements with Borides (MBenes), the study of various properties of 2D metal borides has attracted widespread interest. In this work, we systematically investigate the phonon-mediated superconductivity in 2D MB4 (M = V, Nb, and Ta), by using the first-principles calculations. Starting from the dynamical stabilities of these structures, we perform a detailed analysis of the electron–phonon coupling and superconductivity of AB-stacked MBenes by solving the anisotropic Eliashberg equation. NbB4 has the largest electron–phonon coupling and the highest superconducting transition temperature Tc of 35.4 K. Our study broadens the 2D superconducting boride family, which is of great significance for the study of 2D superconductivity.
To understand the changes in chemical composition and sources of PM
under the extreme reduction background during the COVID-19 epidemic periods in Nanjing, hourly observation results of PM
components ...(water-soluble inorganic ions, carbonaceous components, and inorganic elements) of two epidemic events from January to March 2020 and June to August 2021 were analyzed. In comparison to that during pre-epidemic periods, the concentration of NO
during the two epidemic control periods decreased by 52.9% and 43.0%, respectively, which was larger than the decreases in NH
(46.4% and 31.6%) and SO
(33.8% and 16.5%). Since the observation site was located close to a main road, the decrease in elemental carbon (EC, 35.4% and 20.6%) was higher than that in organic carbon (OC, 11.1% and 16.2%). In reference to the variations in the characteristic ratios of the bulk components mentioned above, the epidemic control showed a more substantial influence on traffic emissions than industrial activities. The concentration time ser
PDF
