Rank-Based Learning and Local Model Based Evolutionary Algorithm for High-Dimensional Expensive Multi-Objective Problems

Surrogate-assisted evolutionary algorithms have been widely developed to solve complex and computationally expensive multi-objective optimization problems in recent years. However, when dealing with high-dimensional optimization problems, the performance of these surrogate-assisted multi-objective evolutionary algorithms deteriorate drastically. In this work, a novel Classifier-assisted rank-based learning and Local Model based multi-objective Evolutionary Algorithm (CLMEA) is proposed for high-dimensional expensive multi-objective optimization problems. The proposed algorithm consists of three parts: classifier-assisted rank-based learning, hypervolume-based non-dominated search, and local search in the relatively sparse objective space. Specifically, a probabilistic neural network is built as classifier to divide the offspring into a number of ranks. The offspring in different ranks uses rank-based learning strategy to generate more promising and informative candidates for real function evaluations. Then, radial basis function networks are built as surrogates to approximate the objective functions. After searching non-dominated solutions assisted by the surrogate model, the candidates with higher hypervolume improvement are selected for real evaluations. Subsequently, in order to maintain the diversity of solutions, the most uncertain sample point from the non-dominated solutions measured by the crowding distance is selected as the guided parent to further infill in the uncertain region of the front. The experimental results of benchmark problems and a real-world application on geothermal reservoir heat extraction optimization demonstrate that the proposed algorithm shows superior performance compared with the state-of-the-art surrogate-assisted multi-objective evolutionary algorithms. The source code for this work is available at https://github.com/JellyChen7/CLMEA

Development and validation of a deep learning-based model to distinguish acetabular fractures on pelvic anteroposterior radiographs

Objective: To develop and test a deep learning (DL) model to distinguish acetabular fractures (AFs) on pelvic anteroposterior radiographs (PARs) and compare its performance to that of clinicians.Materials and methods: A total of 1,120 patients from a big level-I trauma center were enrolled and allocated at a 3:1 ratio for the DL model’s development and internal test. Another 86 patients from two independent hospitals were collected for external validation. A DL model for identifying AFs was constructed based on DenseNet. AFs were classified into types A, B, and C according to the three-column classification theory. Ten clinicians were recruited for AF detection. A potential misdiagnosed case (PMC) was defined based on clinicians’ detection results. The detection performance of the clinicians and DL model were evaluated and compared. The detection performance of different subtypes using DL was assessed using the area under the receiver operating characteristic curve (AUC).Results: The means of 10 clinicians’ sensitivity, specificity, and accuracy to identify AFs were 0.750/0.735, 0.909/0.909, and 0.829/0.822, in the internal test/external validation set, respectively. The sensitivity, specificity, and accuracy of the DL detection model were 0.926/0.872, 0.978/0.988, and 0.952/0.930, respectively. The DL model identified type A fractures with an AUC of 0.963 [95% confidence interval (CI): 0.927–0.985]/0.950 (95% CI: 0.867–0.989); type B fractures with an AUC of 0.991 (95% CI: 0.967–0.999)/0.989 (95% CI: 0.930–1.000); and type C fractures with an AUC of 1.000 (95% CI: 0.975–1.000)/1.000 (95% CI: 0.897–1.000) in the test/validation set. The DL model correctly recognized 56.5% (26/46) of PMCs.Conclusion: A DL model for distinguishing AFs on PARs is feasible. In this study, the DL model achieved a diagnostic performance comparable to or even superior to that of clinicians

The efficacy and neural mechanism of acupuncture therapy in the treatment of visceral hypersensitivity in irritable bowel syndrome

Irritable Bowel Syndrome (IBS) is a complex functional gastrointestinal disorder primarily characterized by chronic abdominal pain, bloating, and altered bowel habits. Chronic abdominal pain caused by visceral Hypersensitivity (VH) is the main reason why patients with IBS seek medication. Significant research effort has been devoted to the efficacy of acupuncture as a non-drug alternative therapy for visceral-hyperalgesia-induced IBS. Herein, we examined the central and peripheral analgesic mechanisms of acupuncture in IBS treatment. Acupuncture can improve inflammation and relieve pain by reducing 5-hydroxytryptamine and 5-HT3A receptor expression and increasing 5-HT4 receptor expression in peripheral intestinal sensory endings. Moreover, acupuncture can also activate the transient receptor potential vanillin 1 channel, block the activity of intestinal glial cells, and reduce the secretion of local pain-related neurotransmitters, thereby weakening peripheral sensitization. Moreover, by inhibiting the activation of N-methyl-D-aspartate receptor ion channels in the dorsal horn of the spinal cord and anterior cingulate cortex or releasing opioids, acupuncture can block excessive stimulation of abnormal pain signals in the brain and spinal cord. It can also stimulate glial cells (through the P2X7 and prokinetic protein pathways) to block VH pain perception and cognition. Furthermore, acupuncture can regulate the emotional components of IBS by targeting hypothalamic-pituitary-adrenal axis-related hormones and neurotransmitters via relevant brain nuclei, hence improving the IBS-induced VH response. These findings provide a scientific basis for acupuncture as an effective clinical adjuvant therapy for IBS pain

Structural characteristics and tectonic evolution of Mesozoic-Cenozoic faults in the Shunbei area, Tarim Basin

Oil and gas resources in the Shunbei area of the Tarim Basin are rich in Paleozoic strata.Recent petroleum exploration indicates that Mesozoic-Cenozoic sedimentary rocks have a good potential in hydrocarbon extraction. In this study, recently acquired 3D seismic surveys were used to systematically analyze fault types, geometrical characteristics, and evolution of Mesozoic-Cenozoic faults in the Shunbei area. The objective is to understand the characteristics and development mechanisms of faults that have the key controlling factors of hydrocarbon accumulation.In the Shunbei area, the dominant faults are tensional normal faults with few thrust faults and strike-slip faults. The district has many small scale, disorderly distributed faults. Combined with the regional setting and basin evolution stage, we suggest that the development of Mesozoic-Cenozoic faults in the Shunbei area experienced three tectonic cycles during the Indosinian, Yanshanian, and Himalayan periods. The structural position, middle Permian igneous rocks, and the sandy mudstone of the Lower Triassic Tongketuer Formation have significant control on fault evolution.Detailed interpretation of faults in the T90-T50 stratigraphic interval in the Shunbei area shows that the central part of the No.5 fault and south central part of the No.7 fault are two large faults that connect downward to the source rock of the basin. In these two areas, some faults cut upward through T90 to T50, which is good forvertical migration of hydrocarbon from Paleozoic to Mesozoic-Cenozoic strata and therefore are ideal locations for petroleum exploration

Animal-Protein-Based and Synthetic-Based Foamed Mixture Lightweight Soil Doped with Bauxite Tailings: Macro and Microscopic Properties

In order to explore the effect of the foaming agent type on the properties of foamed mixture lightweight soil mixed with bauxite tailings (FMLSB), low-density (437.5 kg/m3 and 670 kg/m3) and high-density (902.5 kg/m3 and 1170 kg/m3) FMLSB were prepared using protein-based and synthetic-based foaming agents (AF and SF, respectively). The foam stability, micro characteristics, compressive strength, fluidity, and volume of water absorption of the FMLSB were investigated. The results showed that the foam made from AF had better strength and stability compared to SF. The internal pore sizes of both AF- and SF-FMLSB at low density were large, but at high density the internal pore sizes and area porosity of AF-FMLSB were smaller than those of SF-FMLSB. In terms of compressive strength, the compressive strength of AF-FMLSB was improved by 17.5% to 43.2% compared to SF-FMLSB. At low density, the fluidity of AF- and SF-FMLSB is similar, while at high density the fluidity of AF-FMLSB is much higher than that of SF-FMLSB. In addition, the stable volume of water absorption of SF-FMLSB is smaller than that of AF-FMLSB at low density, and the corresponding water resistance is better, but the situation is reversed at high density

Potassium Tethered Carbons with Unparalleled Adsorption Capacity and Selectivity for Low-Cost Carbon Dioxide Capture from Flue Gas

Carbons are considered less favorable for postcombustion CO₂ capture because of their low affinity toward CO₂, and nitrogen doping was widely studied to enhance CO₂ adsorption, but the results are still unsatisfactory. Herein, we report a simple, scalable, and controllable strategy of tethering potassium to a carbon matrix, which can enhance carbon–CO₂ interaction effectively, and a remarkable working capacity of ca. 4.5 wt % under flue gas conditions was achieved, which is among the highest for carbon-based materials. More interestingly, a high CO₂/N₂ selectivity of 404 was obtained. Density functional theory calculations evidenced that the introduced potassium carboxylate moieties are responsible for such excellent performances. We also show the effectiveness of this strategy to be universal, and thus, cheaper precursors can be used, holding great promise for low-cost carbon capture from flue gas

DataSheet1_A novel LUAD prognosis prediction model based on immune checkpoint-related lncRNAs.xlsx

Lung adenocarcinoma (LUAD) is a malignant disease with an extremely poor prognosis, and there is currently a lack of clinical methods for early diagnosis and precise treatment and management. With the deepening of tumor research, more and more attention has been paid to the role of immune checkpoints (ICP) and long non-coding RNAs (lncRNAs) regulation in tumor development. Therefore, this study downloaded LUAD patient data from the TCGA database, and finally screened 14 key ICP-related lncRNAs based on ICP-related genes using univariate/multivariate COX regression analysis and LASSO regression analysis to construct a risk prediction model and corresponding nomogram. After multi-dimensional testing of the model, the model showed good prognostic prediction ability. In addition, to further elucidate how ICP plays a role in LUAD, we jointly analyzed the immune microenvironmental changes in LAUD patients and performed a functional enrichment analysis. Furthermore, to enhance the clinical significance of this study, we performed a sensitivity analysis of common antitumor drugs. All the above works aim to point to new directions for the treatment of LUAD.</p