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The development of automated grading equipment requires achieving high throughput and precise detection of disease spots on jujubes. However, the current algorithms are inadequate in accomplishing these objectives due to their high density, varying sizes and shapes, and limited location information regarding disease spots on jujubes. This paper proposes a method called JujubeSSD, to boost the precision of identifying disease spots in jujubes based on a single shot multi-box detector (SSD) network. In this study, a diverse dataset comprising disease spots of varied sizes and shapes, varying densities, and multiple location details on jujubes was created through artificial collection and data augmentation. The parameter information obtained from transfer learning into the backbone feature extraction network of the SSD model, which reduced the time of spot detection to 0.14 s. To enhance the learning of target detail features and improve the recognition of weak information, the traditional convolution layer was replaced with deformable convolutional networks (DCNs). Furthermore, to address the challenge of varying sizes and shapes of disease spot regions on jujubes, the path aggregation feature pyramid network (PAFPN) and balanced feature pyramid (BFP) were integrated into the SSD network. Experimental results demonstrate that the mean average precision at the IoU (intersection over union) threshold of 0.5 ( mAP@0.5 ) of JujubeSSD reached 97.1%, representing an improvement of approximately 6.35% compared to the original algorithm. When compared to existing algorithms, such as YOLOv5 and Faster R-CNN, the improvements in mAP@0.5 were 16.84% and 8.61%, respectively. Therefore, the proposed method for detecting jujube disease spot achieves superior performance in jujube surface disease detection and meets the requirements for practical application in agricultural production.

Cotton phenomics plays a crucial role in understanding and managing the growth and development of cotton plants. The segmentation of point clouds, a process that underpins the measurement of plant organ structures through 3D point clouds, is necessary for obtaining precise phenotypic parameters. This study proposes a cotton point cloud organ semantic segmentation method named TPointNetPlus, which combines PointNet++ and Transformer algorithms. Firstly, a dedicated point cloud dataset for cotton plants is constructed using multi-view images. Secondly, the attention module Transformer is introduced into the PointNet++ model to increase the accuracy of feature extraction. Finally, organ-level cotton plant point cloud segmentation is performed using the HDBSCAN algorithm, successfully segmenting cotton leaves, bolls, and branches from the entire plant, and obtaining their phenotypic feature parameters. The research results indicate that the TPointNetPlus model achieved a high accuracy of 98.39% in leaf semantic segmentation. The correlation coefficients between the measured values of four phenotypic parameters (plant height, leaf area, and boll volume) ranged from 0.95 to 0.97, demonstrating the accurate predictive capability of the model for these key traits. The proposed method, which enables automated data analysis from a plant's 3D point cloud to phenotypic parameters, provides a reliable reference for in-depth studies of plant phenotypes.

The amount of concrete needed in the construction of projects is enormous. The concrete dispatching in construction is, however, chaotic and causes grievous waste due to the tight construction schedule, heavy tasks, and improper transport vehicle dispatching by the constructor. This paper proposes a more realistic objective function in the vehicle dispatching algorithm and offers a solution to the rational dispatching of concrete transport vehicles. The analysis of the calculation example validates the feasibility of the built model, which provides more appropriate dispatching and more balanced distribution, meets the needs of the worksite and mixing station to the greatest extent and improves the level of project refinement and process management. Therefore, the model is worth promoting in future practice.

Point cloud completion is a challenging task that involves predicting missing parts in incomplete 3D shapes. While existing strategies have shown effectiveness on point cloud datasets with regular shapes and continuous surfaces, they struggled to manage the morphologically diverse structures commonly encountered in real-world scenarios. This research proposed a new point cloud completion method, called SegCompletion, to derive complete 3D geometries from a partial shape with different structures and discontinuous surfaces. To achieve this, morphological segmentation was introduced before point cloud completion by deep hierarchical feature learning on point sets, and thus, the complex morphological structure was segmented into regular shapes and continuous surfaces. Additionally, each instance of a point cloud that belonged to the same type of feature could also be effectively identified using HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise). Furthermore, the multiscale generative network achieved sophisticated patching of missing point clouds under the same geometric feature based on feature points. To compensate for the variance in the mean distances between the centers of the patches and their closest neighbors, a simple yet effective uniform loss was utilized. A number of experiments on ShapeNet and Pheno4D datasets have shown the performance of SegCompletion on public datasets. Moreover, the contribution of SegCompletion to our dataset (Cotton3D) was discussed. The experimental results demonstrated that SegCompletion performed better than existing methods reported in the literature.

This paper extends a one-dimensional model of adsorbed contaminants in saturated porous media to establish a three-dimensional case suitable for unsaturated porous media. The effects of hydrothermal influences on the distributions of temperature and moisture, evolution of the bulk strain and migration of nuclides are simulated and analysed, and the coupled thermo-hydro-mechanical governing equations determined by the author are presented. The results show that the saturation in the buffer layer gradually rises to 0.96 from the initial value of 0.5 after approximately 450 years, and increases slowly in the initial stage of the simulation. The nuclide concentration in the solidified body first increases and then decreases, reaching a maximum of 173.05 mol/m 3 in approximately 110 years. Moreover, most of the radionuclides are released by buffer layer adsorption and the adsorbed concentration is almost 100 times the solute concentration. In addition, the calculation also shows that the nuclides penetrate the buffer and reach the surrounding rock approximately 400 years later.

A novel type of NH2-functionalized carbon-coated Fe3O4 core-shell nanoparticles (Fe3O4@C-NH2) soil remediation agent was prepared. The effects of the Fe3O4@C-NH2 application rate, pH, water content, soil conductivity, soil organic matter (SOM) and solidification time on the solidification efficiency of cadmium (Cd) in Cd-contaminated soils were investigated. The results showed that the structure of Fe3O4@C-NH2 had a hollow core-shell, the carbon shell was well dispersed, the -NH2 bonding was obvious and the adsorption of Fe3O4@C-NH2 to Cd2+ in water quickly reached equilibrium within 20 min, with a saturated adsorption capacity of 78.83 rng.g-1. The Fe3O4@C-NH2 application rate and soil organic matter (SOM) had a great influence on solidification efficiency. SOM mainly affected the transition from weak acidity to reducible and oxidizable state, while solidification time affected the transition from oxidizable state to residual state. The optimal conditions for Fe3O4@C-NH2 solidification of Cd are: application rate of 3%-4% (3.3%), pH of 6-10 (7.33), moisture content of 40%-60% (50%), conductivity of 90-400 pS.cmr1 (142.6 pS.cmr1), SOM of 45.00-65.00 g.kg-1 (63.04 g.kg'1) and solidification time of no less than 10 days. Under the optimal conditions, the solidification efficiency of Fe3O4@C-NH2 for Cd was 42.95±1.89%, which significantly reduced the leaching and migration of Cd and achieved the goal of remediation.

The antioxidant N -isopropyl- N ′-phenyl- p -phenylenediamine (4010NA) was dissolved in ethanol and impregnated into silica aerogel (SAG) via vacuum-pressure cycles, yielding composite particles (A-N) with enhanced sustained-release and reinforcing capabilities. The effect of A-N on the mechanical properties and thermal-oxidative aging resistance of styrene-butadiene rubber (SBR) vulcanizates was investigated. TGA and BET assessments indicated that the loading efficiency of 4010NA in SAG reached 14.26% within ethanol’s solubility limit. Incorporating A-N into SBR vulcanizates significantly elevated tensile strength by 17.5% and elongation at break by 41.9% over those with fumed silica and free 4010NA. Furthermore, A-N notably enhanced the thermal-oxidative aging resistance of SBR. After aging for 96 h at 100 °C, the tensile strength and elongation at break of SBR with A-N sustained 70.09% and 58.61% of their initial values, respectively, with the retention rate of elongation at break being 62.8% higher than that of SBR with fumed silica and free antioxidant. The study revealed that A-N composite particles significantly inhibited the crosslinking in SBR’s molecular chains, reducing hardening and embrittlement during later thermal-oxidative aging stages.

Sewage sludge composting is a process entailing a continuous succession of microorganisms. To understand the microbial mechanisms involved in sewage sludge composting, we performed an aerobic static composting of sewage sludge and sawdust (ratio = 3:1 m/m) in medium-scale bioreactor systems. The associated changes in physico-chemical parameters (i.e., temperature, organic matter, pH, ammonium nitrogen) were studied parallelly to those in the microbial (i.e., bacteria, fungi, archaea) succession. Additionally, we discussed correlations between these physico-chemical parameters and the microbial communities. The results showed that the pile temperature went through mesophilic phase, thermophilic phase, and cooling phase. The pile temperature reached a maximum of 78.68°C by day 3 and remained above 55°C for more than 6 days, complying with the harmless composting requirements. The organic matter content decreased gradually, the pH increased after a first decrease, and the NH4+-N content showed a consistent trend. The dominant bacteria during composting were Ureibacillus, Bacillus, Sphaerobacter and Thermobifida, while the dominant fungi were unclassified_f_ Trichocomaceae, unclassified_d_Eukaryota, Hypocrea and Thysanophora; finally, the dominant archaea were Methanobrevibacter, Methanosaeta, Methanobacterium and unclassified_k_norank. The composting stages were characterized by different microbial compositions. The mesophilic phase presented a relatively uniform proportion of bacterial genera, while the thermophilic and cooling phases were dominated by Ureibacillus and Bacillus, respectively. The fungus unclassified_f_Trichocomaceae played a major role during the mesophilic, thermophilic, and cooling phases, while unclassified_d_Eukaryota played a major role during the mesophilic and thermophilic phases. For what concerns the archaea, Methanobrevibacter played a major role in the mesophilic, thermophilic, and cooling phases, Methanosaeta during the mesophilic and thermophilic phases, and Methanobacterium during the thermophilic and cooling phases. Additionally, the bacterium Ureibacillus and the archaea Methanospirillum were positively correlated with temperature, while the bacteria norank_Pem15, norank_JG30-KF-CM45, and the archaea Methanosphaera were negatively correlated with temperature. The fungi Thysanophora, unclassified_d_ Eukaryota, and unclassified_p_Ascomycota were negatively correlated with pH. Moreover, the bacterium norank_c_1-20, the fungi Trichosporon, norank_o_Saccharomycetales, unclassified_o_Pleosporales, and the archaea Methanosaeta, Methanomethylovorans were positively correlated with organic matter. On the other hand, the bacteria Bacillus, Thermobifida, the fungus unclassified_f_Trichocomaceae, and the archaea Methanobrevibacter were negatively correlated with organic matter. Finally, the bacteria Bacillus, Thermobifida, the fungus unclassified_f_Trichocomaceae, and the archaea Methanobrevibacter were positively correlated with ammonium nitrogen, while the bacterium norank_c_1-20, the fungi Trichosporon, norank_o_Saccharomycetales, unclassified_o_Pleosporales, and the archaea Methanosaeta, Methanomethylovorans were negatively correlated with ammonium nitrogen. This paper provides new solid bases to understand changes in microbial composition and their correlation with physico-chemical parameters during sewage sludge composting.

The influence of different initial moisture content on the degradation of phenanthrene was studied during sewage sludge composting under the same aerobic fermentation conditions. Wood chips were used as amendment and the moisture content of mixture was adjusted to 56.21% (treatment 1) or 60.89% (treatment 2). The results showed that initial moisture content has a significant effect on sewage sludge composting, the composting of two treatments all lasted 12 days. In addition, variations in moisture content led to different temperature tendencies, with the temperature of treatment 1 increasing faster than that of treatment 2, while the thermophilic stage of treatment 2 lasted for longer than that of treatment 1. The overall oxygen content, moisture content, volatile solids, pH, and total nitrogen content of the two treatments were basically the same. Microbial degradation of phenanthrene during sewage sludge composting primarily occurred in the rapid heating stage and thermophilic temperature stage, and a portion of the 4-5 ring polycyclic aromatic hydrocarbons would concentrate relatively. The phenanthrene degradation rate of treatment 1 and treatment 2 were 52.37% and 45.64%, respectively. Based on the phenanthrene degradation efficiency, an initial moisture water content of 56.21% should be used during the sewage sludge composting.

The steady-state temperature field of horizontal core-filling continuous casting （HCFC） for producing copper cladding aluminum rods was simulated by finite element method to investigate the effects of key processing parameters on the positions of solid-liquid interfaces （SLIs） of copper and aluminum. It is found that mandrel tube length and mean withdrawing speed have significant effects on the SLI positions of both copper and aluminum. Aluminum casting temperature （TAI） （1003-1123 K） and secondary cooling water flux （600-900 L.h-1） have little effect on the SLI of copper but cause the SLI of aluminum to move 2-4 mm. When TA1 is in a range of 1043-1123 K, the liquid aluminum can fill continuously into the pre-solidified copper tube. Based on the numerical simulation, reasonable processing parameters were determined.