Integration Methodology of Spare Parts Supply Network Optimization and Decision-making

In order to optimize the spare parts supply network, a multi-objective optimization model is established with the objectives of the shortest supply time, the lowest risk, and the minimum supply cost. A decomposition-based multi-objective evolutionary algorithm with differential evolution strategy is introduced to solve the multi-objective model. A series of non-dominated solutions, that is, representing the optimal spare parts supply schemes are obtained. In order to comprehensively measure the performance of these solutions, suitable quantitative metrics are selected, and the secondary goal-based cross-efficiency Data Envelopment Analysis (DEA) model has been used to evaluate the efficiency of the obtained optimal schemes. The improved DEA model overcomes the problems that the efficient units cannot be sorted and the optimal weight is not unique in traditional DEA model. Finally, the self-evaluation efficiency and cross-evaluation efficiency of each scheme are obtained, and the optimal supply scheme is found based on their cross-evaluation efficiency.</p

Goal-Conditioned Reinforcement Learning with Disentanglement-based Reachability Planning

Goal-Conditioned Reinforcement Learning (GCRL) can enable agents to spontaneously set diverse goals to learn a set of skills. Despite the excellent works proposed in various fields, reaching distant goals in temporally extended tasks remains a challenge for GCRL. Current works tackled this problem by leveraging planning algorithms to plan intermediate subgoals to augment GCRL. Their methods need two crucial requirements: (i) a state representation space to search valid subgoals, and (ii) a distance function to measure the reachability of subgoals. However, they struggle to scale to high-dimensional state space due to their non-compact representations. Moreover, they cannot collect high-quality training data through standard GC policies, which results in an inaccurate distance function. Both affect the efficiency and performance of planning and policy learning. In the paper, we propose a goal-conditioned RL algorithm combined with Disentanglement-based Reachability Planning (REPlan) to solve temporally extended tasks. In REPlan, a Disentangled Representation Module (DRM) is proposed to learn compact representations which disentangle robot poses and object positions from high-dimensional observations in a self-supervised manner. A simple REachability discrimination Module (REM) is also designed to determine the temporal distance of subgoals. Moreover, REM computes intrinsic bonuses to encourage the collection of novel states for training. We evaluate our REPlan in three vision-based simulation tasks and one real-world task. The experiments demonstrate that our REPlan significantly outperforms the prior state-of-the-art methods in solving temporally extended tasks.Comment: Accepted by 2023 RAL with ICR

Prediction of Pressing Quality for Press-Fit Assembly Based on Press-Fit Curve and Maximum Press-Mounting Force

In order to predict pressing quality of precision press-fit assembly, press-fit curves and maximum press-mounting force of pressfit assemblies were investigated by finite element analysis (FEA). The analysis was based on a 3D Solidworks model using the real dimensions of the microparts and the subsequent FEA model that was built using ANSYS Workbench. The press-fit process could thus be simulated on the basis of static structure analysis. To verify the FEA results, experiments were carried out using a press-mounting apparatus. The results show that the press-fit curves obtained by FEA agree closely with the curves obtained using the experimental method. In addition, the maximum press-mounting force calculated by FEA agrees with that obtained by the experimental method, with the maximum deviation being 4.6%, a value that can be tolerated. The comparison shows that the press-fit curve and max press-mounting force calculated by FEA can be used for predicting the pressing quality during precision press-fit assembly

Nrf2 signaling pathway: current status and potential therapeutic targetable role in human cancers

Cancer is a borderless global health challenge that continues to threaten human health. Studies have found that oxidative stress (OS) is often associated with the etiology of many diseases, especially the aging process and cancer. Involved in the OS reaction as a key transcription factor, Nrf2 is a pivotal regulator of cellular redox state and detoxification. Nrf2 can prevent oxidative damage by regulating gene expression with antioxidant response elements (ARE) to promote the antioxidant response process. OS is generated with an imbalance in the redox state and promotes the accumulation of mutations and genome instability, thus associated with the establishment and development of different cancers. Nrf2 activation regulates a plethora of processes inducing cellular proliferation, differentiation and death, and is strongly associated with OS-mediated cancer. What’s more, Nrf2 activation is also involved in anti-inflammatory effects and metabolic disorders, neurodegenerative diseases, and multidrug resistance. Nrf2 is highly expressed in multiple human body parts of digestive system, respiratory system, reproductive system and nervous system. In oncology research, Nrf2 has emerged as a promising therapeutic target. Therefore, certain natural compounds and drugs can exert anti-cancer effects through the Nrf2 signaling pathway, and blocking the Nrf2 signaling pathway can reduce some types of tumor recurrence rates and increase sensitivity to chemotherapy. However, Nrf2’s dual role and controversial impact in cancer are inevitable consideration factors when treating Nrf2 as a therapeutic target. In this review, we summarized the current state of biological characteristics of Nrf2 and its dual role and development mechanism in different tumor cells, discussed Keap1/Nrf2/ARE signaling pathway and its downstream genes, elaborated the expression of related signaling pathways such as AMPK/mTOR and NF-κB. Besides, the main mechanism of Nrf2 as a cancer therapeutic target and the therapeutic strategies using Nrf2 inhibitors or activators, as well as the possible positive and negative effects of Nrf2 activation were also reviewed. It can be concluded that Nrf2 is related to OS and serves as an important factor in cancer formation and development, thus provides a basis for targeted therapy in human cancers

Filming a live cell by scanning electrochemical microscopy: label-free imaging of the dynamic morphology in real time

The morphology of a live cell reflects the organization of the cytoskeleton and the healthy status of the cell. We established a label-free platform for monitoring the changing morphology of live cells in real time based on scanning electrochemical microscopy (SECM). The dynamic morphology of a live human bladder cancer cell (T24) was revealed by time-lapse SECM with dissolved oxygen in the medium solution as the redox mediator. Detailed local movements of cell membrane were presented by time-lapse cross section lines extracted from time-lapse SECM. Vivid dynamic morphology is presented by a movie made of time-lapse SECM images. The morphological change of the T24 cell by non-physiological temperature is in consistence with the morphological feature of early apoptosis. To obtain dynamic cellular morphology with other methods is difficult. The non-invasive nature of SECM combined with high resolution realized filming the movements of live cells

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Dynamics and Distribution of Soil Salinity under Long-Term Mulched Drip Irrigation in an Arid Area of Northwestern China

Mulched drip irrigation has been widely used in agricultural planting in arid and semi-arid regions. The dynamics and distribution of soil salinity under mulched drip irrigation greatly affect crop growth and yield. However, there are still different views on the distribution and dynamics of soil salinity under long-term mulched drip irrigation due to complex factors (climate, groundwater, irrigation, and soil). Therefore, the soil salinity of newly reclaimed salt wasteland was monitored for 9 years (2008&ndash;2016), and the effects of soil water on soil salinity distribution under mulched drip irrigation have also been explored. The results indicated that the soil salinity decreased sharply in 3&ndash;4 years of implementation of mulched drip irrigation, and then began to fluctuate to different degrees and showed slight re-accumulation. During the growth period, soil salinity was relatively high at pre-sowing, and after a period of decline soil salinity tends to increase in the late harvest period. The vertical distribution of soil texture had a significant effect on the distribution of soil salinity. Salt accumulated near the soil layer transiting from coarse soil to fine soil. After a single irrigation, the soil water content in the 30&ndash;70 cm layer under the cotton plant undergoes a &lsquo;high&ndash;low&ndash;high&rsquo; change pattern, and the soil salt firstly moved to the deep layer (below 70 cm), and then showed upward migration tendency with the weakening of irrigation water infiltration. The results may contribute to the scientific extension of mulched drip irrigation and the farmland management under long-term mulched drip irrigation

Evaluation of Resilience of Battle Damage Equipment Based on BN-Cloud Model

In order to solve the problem of a lack of supportive means for evaluating the resilience of battle damage equipment, a Bayesian network cloud model is proposed to evaluate the resilience of battle damage equipment. The equipment functional features are analyzed to establish the equipment functional state evaluation model. Moreover, the samples of Bayesian network parameters training are obtained by inserting the results of battle damage simulation into the functional evaluation model. The simulation flow of parts state recovery probability is designed to determine the relationship between parts’ functional state and time. Based on the cloud model, the transformation model of functional state level probability to functional index is established. Hence, the equipment functional state level probability obtained by Bayesian network reasoning is transformed into a functional index and the transformation from uncertainty to certainty is realized. Considering self-propelled artillery as the object of resilience evaluation, the results of numerical examples show that by this method, the problem of equipment resilience evaluation can be effectively solved, and more information can be obtained by the accurate representation method compared to the traditional Bayesian network probabilistic evaluation results. This is greatly significant to the wartime maintenance support decision

On the Architecture and Key Technology for Digital Twin Oriented to Equipment Battle Damage Test Assessment

To overcome the technical bottleneck faced by the traditional equipment battle damage assessment method by analyzing the application status of digital twins in battle damage assessment, the application needs and the existing problems of current digital twin technology in damage assessment were summarized. Relying on battle damage tests, based on combing the current status of equipment battle damage test evaluation and digital twin technology research, the connotation and application features of digital twinning technology-oriented to equipment battle damage test assessment were explored. The architecture and implementation plan of the digital twin oriented to equipment battle damage test assessment were structured. The key technology and realization of digital twin oriented to battle damage test assessment were proposed. This study provided a theoretical reference and method guidance for the application of digital twins in battle damage assessment, which is of great reference significance for the development of digital twin battlefield construction and battle damage assessment

Prediction of Pressing Quality for Press-Fit Assembly Based on Press-Fit Curve and Maximum Press-Mounting Force

In order to predict pressing quality of precision press-fit assembly, press-fit curves and maximum press-mounting force of press-fit assemblies were investigated by finite element analysis (FEA). The analysis was based on a 3D Solidworks model using the real dimensions of the microparts and the subsequent FEA model that was built using ANSYS Workbench. The press-fit process could thus be simulated on the basis of static structure analysis. To verify the FEA results, experiments were carried out using a press-mounting apparatus. The results show that the press-fit curves obtained by FEA agree closely with the curves obtained using the experimental method. In addition, the maximum press-mounting force calculated by FEA agrees with that obtained by the experimental method, with the maximum deviation being 4.6%, a value that can be tolerated. The comparison shows that the press-fit curve and max press-mounting force calculated by FEA can be used for predicting the pressing quality during precision press-fit assembly