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[Display omitted] The leachate collection system (LCS) is an important component of barrier systems in municipal solid waste (MSW) landfills. LCS Failure due to clogging could lead to leachate accumulation, potential soil and groundwater pollution and slope failure. LCS clogging is generally ascribed to particle deposition, biofilm growth, and calcium carbonate precipitation, while temperature strongly influences microbial activity and thus inevitably affects the clogging process of LCSs. However, the role of temperature in the process of clogging and leachate accumulation in LCSs remains unclear. The main objective of this study was to bridge this research gap by developing reasonable mathematical models and comprehensively analyzing this issue. The mathematical model involved leachate flow, solute migration, physical–chemical-biological clogging, and heat transfer processes. By conducting a series of numerical simulations, the temperature variation, clogging development, and corresponding leachate accumulation inside the system during long-term operation in both the geotextile and gravel layers were numerically analyzed and predicted. The results demonstrated that, in the gravel layer, due to the higher temperature, more thorough microbial substrate consumption led to higher production of carbonate ions and hence faster and more notable calcium carbonate precipitation. Overall, the bioactivity changes due to high temperatures could considerably accelerate the failure of LCS. [ABSTRACT FROM AUTHOR]

• An intelligent analysis framework of the super-large diameter shield machine performance is established. • The key parameters which affect the TAS (tunnelling advance speed) of shield machine are obtained. • Compared with traditional GRU and LSTM deep learning prediction models. The tunnelling advance speed (TAS) of a super-large-diameter tunnel boring machine (TBM) significantly affects project progress and safety. Effective prediction of the TAS of a TBM is crucial for early warning and evaluation of its excavation performance. This paper proposes a model based on convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) to predict TAS. This model can more effectively extract and retain many features; additionally, it considers the temporal relationships among the operating parameters of the TBM. Additionally, a Kalman filter (KF) is incorporated into the model to effectively remove input parameter noise, leading to a more accurate prediction of TBM excavation speed. A case study indicated that the pressure of the excavation chamber and the pitch angle of the shield tunnelling machine were correlated with the advance speed. The research results show that the proposed model can accurately and effectively predict TAS. [ABSTRACT FROM AUTHOR]

Soil water-dispersible colloidal phosphorus (WCP) presents high mobility, however, the regulatory effect of biochar-coupled organic fertilizer is rarely known, especially under different cropping patterns. This study investigated the P adsorption, soil aggregate stability, and WCP in three paddy and three vegetable fields. These soils were amended with different fertilizers (chemical fertilizer, CF; substitution of solid-sheep manure or liquid-biogas slurry organic fertilizer, SOF/LOF; substitution of biochar-coupled organic fertilizers, BSOF/BLOF). Results presented that the LOF averagely increased the WCP contents by 50.2% across the sites, but the SOF and BSOF/BLOF averagely decreased their contents by 38.5% and 50.7% in comparison with the CF. The WCP decline in the BSOF/BLOF-amended soils was mainly attributed to the intensive P adsorption capacity and soil aggregate stability. The BSOF/BLOF increased the amorphous Fe and Al contents in the fields in comparison with the CF, which improved the adsorption capacity of soil particles, further improving the maximum absorbed P (Q max) and reducing the dissolved organic matter (DOC), leading to the improvement of > 2 mm water-stable aggregate (WSA >2mm) and subsequent WCP decrease. This was proved by the remarkable negative associations between the WCP and Q max (R2 = 0.78, p < 0.01) and WSA >2mm (R2 = 0.74, p < 0.01). This study manifests that biochar-coupled organic fertilizer could effectively reduce soil WCP content via the improvement of P adsorption and aggregate stability. [Display omitted] • Biochar addition decreased soil water–dispersible colloidal P content. • Biochar addition increased soil P adsorption capacity. • Biochar addition improved soil aggregate stability. [ABSTRACT FROM AUTHOR]

A novel type of biochar was produced by mixing bamboo sawdust with sewage sludge (1:1, w/w) via a co-pyrolysis process at 400–600 °C. Changes in physico-chemical properties and the intrinsic speciation of metals were investigated before and after pyrolysis. Co-pyrolysis resulted in a lower biochar yield but a higher C content in the end product compared with use of sludge alone as the raw material. FT-IR analysis indicates that phosphine derivatives containing P H bonds were formed in the co-pyrolyzed biochars. In addition, co-pyrolysis of sludge with bamboo sawdust transformed the potentially toxic metals in the sludge into more stable fractions, leading to a considerable decrease in their direct toxicity and bioavailability in the co-pyrolyzed biochar. In conclusion, the co-pyrolysis technology provides a feasible method for the safe disposal of metal-contaminated sewage sludge in an attempt to minimize the environmental risk from potentially toxic metals after land application. [ABSTRACT FROM AUTHOR]

Phosphorus (P) availability and loss risk are linked to P species; however, their alternations in the soil amended with biochar-blended organic fertilizer is not well known, particularly under contrasting soil properties and land management. In this study, the variance of soil P species extracted by sequential chemical extraction (SCE) and 31P NMR techniques, as well as the degree of P saturation (DPS), were investigated throughout three paddy and three vegetable fields. These fields were amended with three different fertilizers at the same P application rate: chemical fertilizer (CF), organic fertilizer substitution (sheep manure/biogas slurry, SM/BS), and biochar-blended organic fertilizer substitution (BSM/BBS). Results showed that the BSM/BBS and SM increased the total P contents by 7.5% and 5.9% (TP) and available P contents by 30.1% and 19.2% (AP), but decreased the DPS values by 19.4% and 11.7%, compared to the CF treatment. Yet, the BS decreased the TP and AP contents but increased the DPS values across the experimental sites. In the BSM/BBS amended soils, high AP contents were due to the increased inorganic P (NaHCO 3 -P i), while the increased organic P (monoester and DNA) induced low DPS values and reduced soil P loss risk. Our study highlights that biochar-blended organic fertilizer is an effective agronomic way for improving P availability and decreasing P loss risk via the alteration of soil P species. [Display omitted] • High proportion of organic P and low P loss risk in the paddy fields. • Biochar addition increased stable P dissolution and organic P retention. • Biochar addition improved soil P availability and retention. [ABSTRACT FROM AUTHOR]

Class imbalance problems pose significant challenges in the field of data mining. The skewed distribution of classes in imbalanced datasets often leads conventional classification methods to neglect the importance of minority classes, favoring the majority ones. In this paper, we propose an imbalanced complemented subspace representation model with adaptive weight learning to address imbalanced classification challenges. This approach incorporates a novel regularization technique and an adaptive weighting mechanism. The regularization technique integrates a complemented subspace term based on space theory into the efficient collaborative representation-based classification (CRC) framework, enhancing the overall accuracy of existing CRC-based methods. Furthermore, it takes into account both intra-class and inter-class density information to assign greater weights to minority classes. This highlights the contribution of minority classes and mitigates bias towards majority classes. Additionally, it is demonstrated that the proposed model can be efficiently solved with a closed-form solution. Extensive experiments conducted on diverse imbalanced datasets showcase the superiority of our method over most state-of-the-art imbalanced classification algorithms. • A complemented subspace regularization term is introduced into CRC. • An adaptive weight learning strategy is present. • The closed-form solution is provided to ensure the efficiency of our model. • Experimental results show the effectiveness of our method. [ABSTRACT FROM AUTHOR]

This study investigates the effect of a stress state change at an intermediate stage during cyclic loading on the drained cyclic deformation of soil using Discrete Element Method (DEM) simulations. Drained cyclic triaxial simulations were conducted on an assembly of irregularly shaped particles, with the stress state shifting along different stress paths at an intermediate stage of cyclic loading. The analysis of the macro- and microscopic responses reveals a link between the cyclic stress–dilatancy behaviour and the anisotropy of the granular assembly. In the simulations without the stress state change or with a stress ratio increase, the strain accumulation direction aligns with the prediction of the Modified Cam Clay (MCC) flow rule; and the strain accumulation is faster at higher stress ratios when the contact distribution is more anisotropic. However, when the particle assembly experiences a stress ratio decrease during cyclic loading, the subsequent strain accumulation direction deviates from the MCC flow rule, and strains accumulate more rapidly at lower stress ratios when the contact distribution is nearly isotropic. A tentative explanation is proposed to explain the altered strain accumulation direction after the stress ratio decrease. [ABSTRACT FROM AUTHOR]

• ε -dragging technique was introduced into BLS to improve the discriminative ability. • Regularization is adopted to optimize network structure. • Theoretical analysis verifies the convergence of the algorithm. • Numerical simulations are conducted to verify the performance of the algorithm. This paper presents the framework of the smoothing group L 1 / 2 regularized discriminative broad learning system for pattern classification and regression. The core idea is to improve the sparseness of the standard broad learning system and improve performance on recognition and generalization. First, the ε -dragging technique is introduced into the standard broad learning system to relax regression targets and enlarge distances between categories. Then, we integrate the group L 1 / 2 regularization to optimize the network architecture to achieve sparsity. For the original group L 1 / 2 regularization, the objective function is non-convex and non-smooth, which is hard for theoretical analysis. Therefore, we propose a simple and effective smoothing technique, i.e.,smoothing group L 1 / 2 regularization, which can effectively eliminate the deficiency of the original group L 1 / 2 regularization. As a result, the final weights projection matrix has a compact form and shows discriminative power capability. In addition, the alternating direction method of multipliers was adopted to optimize the algorithm. The simulation results show that the proposed algorithm has redundancy control capability and improved performance on recognition and generalization. The simulation results proves the efficiency of the theoretical analysis. [ABSTRACT FROM AUTHOR]

Inorganic contaminants accumulation in drinking water distribution systems (DWDS) is a great threat to water quality and safety. This work assessed the main risk factors for different water pipes and discovered the release profile of accumulated materials in a full scale distribution system frequently suffered from water discoloration problem. Physicochemical characterization of pipe deposits were performed using X-ray fluorescence, scanning electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy. The metal release profile was obtained through continuous monitoring of a full-scale DWDS area. The results showed that aluminum and manganese were the main metals of deposits in nonmetallic pipes, while iron was dominant in iron-based pipe corrosion scales. Manganese primarily existed as MnO 2 without well crystalline form. The relative abundance of Mn and Fe in deposits changed with their distance from the water treatment plant. Compared with iron in corrosion scales, Mn and Al were more labile to be released back into bulk water during unidirectional flushing process. A main finding of this work is the co-release behavior of Mn and Al in particulate form and significant correlation exists between these two metals. Dual control of manganese and aluminum in treated water is proposed to be essential to cope with discoloration and trace metal contamination in DWDS. [ABSTRACT FROM AUTHOR]

• Phosphorus (P) input and crop type cause differences in P loss via runoff. • Modified biochar reduced the runoff P concentration and loss load in all cropping systems. • Solid organic fertilizer decreased particulate P and colloidal P losses. • Biogas slurry increased the P loss in the runoff of the rice-wheat rotation system. Agricultural cropping systems involve the application of high fertilizer rates, which lead to phosphorus (P) losses via surface runoff to the aquatic environment, thereby resulting in severe eutrophication. Here, to evaluate the nutrient loss, we established three monitoring stations in Zhejiang Province, China, in each of which the crop types were double cropping rice, rice–wheat rotation, and vegetable cultivation. Field experiments were conducted with four treatments and three replications over a whole planting year. The four treatments at the same P application rate were: 1) no fertilization (control; CK), 2) chemical fertilization (CF), 3) substitution of 30 % chemical P in CF by organic P in solid sheep manure (OF) or in liquid biogas slurry (BS), and 4) substitution of 30 % chemical P in CF by organic P fertilizers (solid sheep manure or liquid biogas slurry) +1.5 t ha−1 polyacrylamide modified BC (PSB). The runoff losses of various P forms, that is, the total P, total dissolved P, particulate P, and colloidal P losses were analyzed. The total P concentration varied greatly with the crop growth stages. Compared with CF treatment, PSB treatment significantly reduced the concentration of P fractions in the runoff in all three cropping systems, while the OF treatment significantly reduced the particulate P and colloidal P concentrations by coating colloidal particles with organic carbon. However, BS treatment increased particulate P and colloidal P concentrations. Compared with CF treatment, PSB treatment reduced total P loss by 41.1 %, 29.7 %, and 37.8 % in the double cropping rice, rice–wheat, and vegetable systems, respectively. The PSB and OF treatments significantly reduced the particulate P and colloidal P losses, while BS treatment increased the loss of various P forms. Compared with the CF treatment, the PSB treatment significantly increased the available P contents by 12.7–85.4 mg kg-1, and decreased the colloidal P contents by 26.7 %–51.4 % in the soils examined. The results of the redundancy analysis confirmed that P loss via runoff was mainly caused by differences in the P inputs and crop types. The results of this study highlight the important role of PSB in reducing P loss via runoff from agricultural cropping systems. [ABSTRACT FROM AUTHOR]