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Vanadium (V) plays a crucial role in the formation of precipitates. In this study, different amounts of submicron-sized precipitates were introduced in high-carbon pearlitic CrMo wear-resistant cast steel by the addition of V. The result shows that the microstructure of high-carbon CrMo wear-resistant cast steel consists of lamellar pearlite and submicron-sized precipitates. With higher V content, the density of submicron-sized precipitates increases. The wear resistance of the steel also enhances with the addition of V, which is primarily attributed to the formation of submicron-sized precipitates resulting in the enhancement of initial mechanical properties and the creation of a harder work-hardening layer in worn surface. The wear mechanisms involve microploughing, microcutting, microfatigue, and microcracking in high-carbon pearlitic CrMo wear-resistant cast steel, but the introduction of submicron-sized precipitates leads to a substantial reduction in the depth of microploughing and microcutting.

Rare earth (RE) elements not only modify inclusions but also refine the steel's microstructure. This study investigates the evolution mechanism of cerium on inclusions and microstructure in low-alloy cast steel through experimental and thermodynamic analyses. After cerium is added to the steel, strip-like inclusions transform into spherical ones, accompanied by a reduction in size. The evolution path of inclusions follows MnS + MnS–Al2O3→Ce2O2S–Ce2S3–MnS + CeAlO3–Ce2S3–MnS with cerium addition. Notably, Ce2S3 and Ce2O2S exhibit an orientation relationship, with [1‾ 22]Ce2S3//[01 1‾ 1]Ce2O2S and (02 2‾)Ce2S3//(1‾ 011)Ce2O2S. Compared to Ce-free steel, the Ce-0.06 steel shows a reduced grain size and the absence of needle-shaped Widmanstätten structures, which may be mainly attributed to rare earth inclusions as heterogeneous nucleation of δ-Fe. This work can provide a theoretical basis for improving the overall mechanical properties of low-alloy cast steel by adding cerium.

Precipitation strengthening is a crucial method for enhancing the mechanical properties of steel. The bridgman directional solidification furnace, optical microscope (OM), scanning electron microscope (SEM), electron probe analyzer (EPMA) and transmission electron microscope (TEM) were used to investigate the effect of solidification cooling rate on the solidification microstructure, microsegregation, and nanoprecipitates in medium carbon CrMo cast steel. The results indicate that as the cooling rate increases, the solidification microstructure is significantly refined. The relationship between the cooling rate (CA) and primary dendrite arm spacing (PDAS, λ1) and secondary dendrite arm spacing (SDAS, λ2) are given by λ1 = 123.74 × C-0.4088A, λ2 = 66.63 × C-0.6532 A. Solute elements like Cr, Mo, and V exhibit noticeable positive segregation in the inter-dendritic. When the cooling rate does not exceed 0.47 °C/s, the solute concentrations in the inter-dendritic increase with higher cooling rate, leading to a decrease in the effective solute partition coefficient. This phenomenon is primarily attributed to the back diffusion of solute elements during solidification. Following heat treatment, precipitation of M6C, V(C,N), and M23C6 occurs within the inter-dendritic regions. If the solidification cooling rate does not exceed 0.47 °C/s, the density and volume fraction of nanoprecipitates increase with higher cooling rate. This trend primarily results from the reduction in critical nucleation radius and critical nucleation energy, facilitating easier nucleation of nanoprecipitates.

The information and communication technology (ICT) makes the smart city exchange information with the general public and deliver higher-quality services to citizens. The collection of waste is important in smart city service and smart technology has great potential for increasing garbage collection efficiency and quality all around the world. This is a waste of resources since garbage is emptied even half-full, city resources are misused, and vehicle gas is wasted. High costs and poor effectiveness are the two main problems with smart city garbage collection. This problem can be solved by recycling since it reduces the garbage that has to be disposed of and protects valuable storage space. Although the recycling rates are rising, estimates predict that people can generate more waste than ever before. A key focus of machine learning is developing algorithms that can acquire and utilize information in the learning process to make future predictions. Therefore, this paper’s automatic machine learning-based waste recycling framework (AMLWRF) has been proposed to classify and separate materials in a mixed recycling application for improved separation of complicated waste. The major goal of this research is to examine machine learning algorithms utilized in recycling systems. This paper suggests the ML and Internet of Things (IoT) for smart waste management to overcome this problem in the smart city. The IoT-powered devices may be installed in waste containers including recycling bins and it gives real-time data on garbage generating behavior. Image processing may be used to calculate a dump’s garbage index. They offer a clear picture of trash and recycling trends and suggestions on improving productivity. This research compiles the most recent advances in recycling-related machine learning.

Summary: Background: Mediastinal neoplasms are typical thoracic diseases with increasing incidence in the general global population and can lead to poor prognosis. In clinical practice, the mediastinum's complex anatomic structures and intertype confusion among different mediastinal neoplasm pathologies severely hinder accurate diagnosis. To solve these difficulties, we organised a multicentre national collaboration on the basis of privacy-secured federated learning and developed CAIMEN, an efficient chest CT-based artificial intelligence (AI) mediastinal neoplasm diagnosis system. Methods: In this multicentre cohort study, 7825 mediastinal neoplasm cases and 796 normal controls were collected from 24 centres in China to develop CAIMEN. We further enhanced CAIMEN with several novel algorithms in a multiview, knowledge-transferred, multilevel decision-making pattern. CAIMEN was tested by internal (929 cases at 15 centres), external (1216 cases at five centres and a real-world cohort of 11 162 cases), and human–AI (60 positive cases from four centres and radiologists from 15 institutions) test sets to evaluate its detection, segmentation, and classification performance. Findings: In the external test experiments, the area under the receiver operating characteristic curve for detecting mediastinal neoplasms of CAIMEN was 0·973 (95% CI 0·969–0·977). In the real-world cohort, CAIMEN detected 13 false-negative cases confirmed by radiologists. The dice score for segmenting mediastinal neoplasms of CAIMEN was 0·765 (0·738–0·792). The mediastinal neoplasm classification top-1 and top-3 accuracy of CAIMEN were 0·523 (0·497–0·554) and 0·799 (0·778–0·822), respectively. In the human–AI test experiments, CAIMEN outperformed clinicians with top-1 and top-3 accuracy of 0·500 (0·383–0·633) and 0·800 (0·700–0·900), respectively. Meanwhile, with assistance from the computer aided diagnosis software based on CAIMEN, the 46 clinicians improved their average top-1 accuracy by 19·1% (0·345–0·411) and top-3 accuracy by 13·0% (0·545–0·616). Interpretation: For mediastinal neoplasms, CAIMEN can produce high diagnostic accuracy and assist the diagnosis of human experts, showing its potential for clinical practice. Funding: National Key R&D Program of China, National Natural Science Foundation of China, and Beijing Natural Science Foundation.

Consensus is lacking regarding whether high-intensity interval training (HIIT) effectively improves VO2max (VO2peak) in elite athletes (Athlete must be involved in regular competition at the national level). This meta-analysis compared the effects of HIIT and conventional training methods (continuous training, repeated-sprint training, high volume low-intensity training, high-intensity continuous running, sprint-interval training, moderate-intensity continuous training)on VO2max in elite athletes. Nine studies were included, comprising 176 elite athletes (80 female). Compared to that with conventional training, VO2max was significantly increased after HIIT (overall: 0.58 [0.30, 0.87], I2 = 0.49, P = 0.03; males: 0.41 [0.06, 0.76], I2 = 0%, P = 0.89). VO2max had positive training effects when the HIIT recovery period had an interval time ≥2 min (0.44 [0.03, 0.84], I2 = 0%, P = 0.99) and recovery phase intensity ≤40% (0.38 [0.05, 0.71], I2 = 0%, P = 0.96). Thus, HIIT shows superiority over conventional training methods in improving VO2max, promoting aerobic capacity, in elite athletes.