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The stability of coal pillar is extremely important to the control of rock strata movement and surface subsidence. It is of great significance for mining design to analyze the stability and failure characteristics of coal and rock pillars left after mining and to study the failure characteristics and rheological properties of coal and rock with different shapes. In this paper, based on uniaxial compression and rheological tests on rock samples, the rheological properties of rock samples with different shapes were discussed by using the nonlinear theoretical mechanics and damage theory, and the rheological mechanical characteristics of coarse yellow sandstone samples under the action of different free surface areas and the same loading contact area were investigated by means of experimental research, theoretical analysis and numerical simulation. The following conclusions were drawn: the failure characteristics and dynamic change process of rock samples with different shapes under the same loading contact area are obtained by uniaxial compression test and multi-stage rheological loading. The uniaxial compressive strengths of rock samples with the same loading contact surface area and different free surface areas are inversely proportional to their free surface areas. For the round sample, the stress level in the rheological test is obviously lower than the instantaneous peak uniaxial compression strength, while for the other samples, the stress level in the rheological test is close to the instantaneous peak uniaxial compression strength. For rock all these samples, both the ratio of steady-state rheological time to final failure time and the deformation degree decrease with the increase of free surface area. [ABSTRACT FROM AUTHOR]

Due to the harsh operating environment and ultralong operating hours of wireless sensor networks (WSNs), node failures are inevitable. Ensuring the reliability of the data collected by the WSN necessitates the utmost importance of diagnosing faults in nodes within the WSN. Typically, the initial step in the fault diagnosis of WSN nodes involves extracting numerical features from neighboring nodes. A solitary data feature is often assigned a high weight, resulting in the failure to effectively distinguish between all types of faults. Therefore, this study introduces an enhanced variant of the traditional belief rule base (BRB), called the belief rule base with adaptive attribute weights (BRB-AAW). First, the data features are extracted as input attributes for the model. Second, a fault diagnosis model for WSN nodes, incorporating BRB-AAW, is established by integrating parameters initialized by expert knowledge with the extracted data features. Third, to optimize the model's initial parameters, the projection covariance matrix adaptive evolution strategy (P-CMA-ES) algorithm is employed. Finally, a comprehensive case study is designed to verify the accuracy and effectiveness of the proposed method. The results of the case study indicate that compared with the traditional BRB method, the accuracy of the proposed model in WSN node fault diagnosis is significantly improved. [ABSTRACT FROM AUTHOR]

Due to the harsh operating environment and ultralong operating hours of wireless sensor networks (WSNs), node failures are inevitable. Ensuring the reliability of the data collected by the WSN necessitates the utmost importance of diagnosing faults in nodes within the WSN. Typically, the initial step in the fault diagnosis of WSN nodes involves extracting numerical features from neighboring nodes. A solitary data feature is often assigned a high weight, resulting in the failure to effectively distinguish between all types of faults. Therefore, this study introduces an enhanced variant of the traditional belief rule base (BRB), called the belief rule base with adaptive attribute weights (BRB-AAW). First, the data features are extracted as input attributes for the model. Second, a fault diagnosis model for WSN nodes, incorporating BRB-AAW, is established by integrating parameters initialized by expert knowledge with the extracted data features. Third, to optimize the model's initial parameters, the projection covariance matrix adaptive evolution strategy (P-CMA-ES) algorithm is employed. Finally, a comprehensive case study is designed to verify the accuracy and effectiveness of the proposed method. The results of the case study indicate that compared with the traditional BRB method, the accuracy of the proposed model in WSN node fault diagnosis is significantly improved. [ABSTRACT FROM AUTHOR]

Reservoir flood control scheduling is a challenging optimization task, particularly due to the complexity of various constraints. This paper proposes an innovative algorithm design approach to address this challenge. Combining the basic walrus optimization algorithm with the adaptive ε-constraint method and introducing the SPM chaotic mapping for population initialization, spiral search strategy, and local enhancement search strategy based on Cauchy mutation and reverse learning significantly enhances the algorithm's optimization performance. On this basis, innovate an adaptive approach ε A New Algorithm for Constraints and Multi Strategy Optimization Improvement (ε-IWOA). To validate the performance of the ε-IWOA algorithm, 24 constrained optimization test functions are used to test its optimization capabilities and effectiveness in solving constrained optimization problems. Experimental results demonstrate that the ε-IWOA algorithm exhibits excellent optimization ability and stable performance. Taking the Taolinkou Reservoir, Daheiting Reservoir, and Panjiakou Reservoir in the middle and lower reaches of the Luanhe River Basin as a case study, this paper applies the ε-IWOA algorithm to practical reservoir scheduling problems by constructing a three-reservoir flood control scheduling system with Luanxian as the control point. A comparative analysis is conducted with the ε-WOA, ε-DE and ε-PSO (particle swarm optimization) algorithms.The experimental results indicate that ε-IWOA algorithm performs the best in optimization, with the occupied flood control capacity of the three reservoirs reaching 89.32%, 90.02%, and 80.95%, respectively. The control points in Luan County can reduce the peak by 49%.This provides a practical and effective solution method for reservoir optimization scheduling models. This study offers new ideas and solutions for flood control optimization scheduling of reservoir groups, contributing to the optimization and development of reservoir scheduling work. [ABSTRACT FROM AUTHOR]

Pancreatic adenocarcinoma (PAAD) is a frequent type of cancer in adults worldwide, and the search for better biomarkers is one of the current challenges. Although RAB7A is associated with tumour progression in multiple tumour types, there are only a few reports in PAAD. Therefore, in this paper, RNA sequencing data were obtained from TCGA(The Cancer Genome Atlas) and GTEx to analyse RAB7A expression and differentially expressed genes (DEGs) in PAAD. The functional enrichment of RAB7A-associated DEGs was analysed by protein‒protein interaction (PPI) networks, immune cell infiltration analysis and GO/KEGG analyses. Additionally, Kaplan‒Meier and Cox regression analyses were used to determine the clinical significance of RAB7A in PAAD. High RAB7A expression was associated with poor prognosis in 182 PAAD specimens, including subgroups of patients aged ≤ 65 years, with male sex, not receiving radiotherapy, and with a history of previous alcohol consumption (P < 0.05). Cox regression analysis showed that elevated RAB7A was an independent prognostic factor, and the prognostic nomogram model included radiotherapy status, presence of postoperative tumour residual and histologic grade. Overall, RAB7A overexpression may serve as a biomarker for poor outcome in pancreatic cancer. The DEGs and pathways revealed in this work provide a tentative molecular mechanism for the pathogenesis and progression of PAAD. [ABSTRACT FROM AUTHOR]

Pancreatic adenocarcinoma (PAAD) is a frequent type of cancer in adults worldwide, and the search for better biomarkers is one of the current challenges. Although RAB7A is associated with tumour progression in multiple tumour types, there are only a few reports in PAAD. Therefore, in this paper, RNA sequencing data were obtained from TCGA(The Cancer Genome Atlas) and GTEx to analyse RAB7A expression and differentially expressed genes (DEGs) in PAAD. The functional enrichment of RAB7A-associated DEGs was analysed by protein‒protein interaction (PPI) networks, immune cell infiltration analysis and GO/KEGG analyses. Additionally, Kaplan‒Meier and Cox regression analyses were used to determine the clinical significance of RAB7A in PAAD. High RAB7A expression was associated with poor prognosis in 182 PAAD specimens, including subgroups of patients aged ≤ 65 years, with male sex, not receiving radiotherapy, and with a history of previous alcohol consumption (P < 0.05). Cox regression analysis showed that elevated RAB7A was an independent prognostic factor, and the prognostic nomogram model included radiotherapy status, presence of postoperative tumour residual and histologic grade. Overall, RAB7A overexpression may serve as a biomarker for poor outcome in pancreatic cancer. The DEGs and pathways revealed in this work provide a tentative molecular mechanism for the pathogenesis and progression of PAAD. [ABSTRACT FROM AUTHOR]

The optimal oxygen concentration is unclear for normothermic machine perfusion (NMP) of livers from donation after circulatory death (DCD). Our purposes were to investigate the effect of air-ventilated NMP on the DCD liver, analyze the underlying mechanism and select the targets to predict liver functional recovery with NMP. NMP was performed using the NMP system with either air ventilation or oxygen ventilation for 2 h in the rat liver following warm ischemia and cold-storage preservation. Proteomics and metabolomics were used to reveal the significant molecular networks. The bioinformation analysis was validated by administering peroxisome proliferator activator receptor-γ (PPARγ) antagonist and agonist via perfusion circuit in the air-ventilated NMP. Results showed that air-ventilated NMP conferred a better functional recovery and a less inflammatory response in the rat DCD liver; integrated proteomics and metabolomics analysis indicated that intrahepatic docosapentaenoic acid downregulation and upregulation of cytochrome P450 2E1 (CYP2E1) expression and activity were associated with DCD liver functional recovery with air-ventilated NMP; PPARγ antagonist worsened liver function under air-oxygenated NMP whereas PPARγ agonist played the opposite role. In conclusion, air-ventilated NMP confers a better liver function from DCD rats through the DAP-PPARγ-CYP2E1 axis; CYP2E1 activity provides a biomarker of liver functional recovery from DCD. [ABSTRACT FROM AUTHOR]

To investigate the effects of extended light/dark (L/D) cycle period (relative to the diurnal L/D cycle) on lettuce and explore potential advantages of abnormal L/D cycles, butter leaf lettuce were grown in a plant factory with artificial light (PFAL) and exposed to mixed red (R) and blue (B) LED light with different L/D cycles that were respectively 16 h light/8 h dark (L16/D8, as control), L24/D12, L48/D24, L96/D48 and L120/D60. The results showed that, all the abnormal L/D cycles increased shoot dry weight (DW) of lettuce (by 34–83%) compared with the control, and lettuce DW increased with the L/D cycle period prolonged. The contents of soluble sugar and crude fiber in lettuce showed an overall upward trend with the length of L/D cycle extended, and the highest vitamin C content as well as low nitrate content were both detected in lettuce treated with L120/D60. The light use efficiency (LUE) and electric use efficiency (EUE) of lettuce reached the maximum (respectively 5.37% and 1.76%) under L120/D60 treatment and so were DW, Assimilation rate (A), RC/CS, ABS/CS, TRo/CS and DIo/CS, indicating that longer L/D cycle period was beneficial for the assimilation efficiency and dry matter accumulation in lettuce leaves. The highest shoot fresh weight (FW) and nitrate content detected in lettuce subjected to L24/D12 may be related to the vigorous growth of root, specific L/D cycle seemed to strengthen root growth and water absorption of lettuce. The openness level of RC in PSII (Ψo), ETo/CS, and PIabs were all the highest in lettuce treated with L24/D12, implying that slightly extending the L/D cycle period might promote the energy flowing to the final electron transfer chain. In general, irradiation modes with extended L/D cycle period had the potential to improve energy use efficiency and biomass of lettuce in PFAL. No obvious stress or injury was detected in lettuce subjected to prolonged L/D cycles in terms of plant growth and production. From the perspective of shoot FW, the optimal treatment in this study was L24/D12, while L120/D60 was the recommended treatment as regards of the energy use efficiency and nutritional quality. [ABSTRACT FROM AUTHOR]