This paper examines the interface development between a single crystalline Ag matrix and core-shell AgnCom nanoclusters that have been deposited with energies varying between 0.25 eV and 1.5 eV per atom using computer modeling techniques. Clusters undergo deformation as a result of the slowing down;they may also become epitaxial with the substrate and maintain their core-shell structure. A detailed analysis of the effects of the cluster-surface interaction is conducted over a realistic size and energy range, and a model is created to show how clusters accumulate. It is discovered that both the silver shells and the cobalt cluster cores exhibit limited epitaxy with the substrate, and that the contact produced is only a few atomic layers thick. The effect is higher for Ag shells than for Co cores, and it is not very energy dependent.
Odor pollution in landfill area has attracted more social attention in China. It is very important to control the generation of odor pollutants in situ. Analyzing odorous materials production form buried waste, simulated columns of different volatile solid (VS) content and different buried period waste were designed. Gas compounds produced from the columns were collected and analyzed by comprehensive two-dimensional gas chromatography (GC × GC) method. It has remarkable relationship between VS content and concentrations of odorous material. When VS content more than 40%, the total amount of odorous compounds increases remarkably. It can be inferred that reduced VS content of original waste may effective decreasing odorous materials production in landfill area. The old rubbish produced more odorous compounds than that of fresh one in simulated columns.
Peng LuYuanyuan ZhangLinan XingYing WangHong LuDongbei YueWei ChengJin Liu
The transformation of uterine fibroids is common in relation to their development. Giant forms of cystic degeneration are rare. They raise diagnostic difficulties with other pelvic tumors, such as ovarian tumors and leiomyosarcomas. Magnetic resonance imaging specifies the original organ, the volume and the main relationships of fibromyoma with adjacent structures. The diagnosis of certainty is based on laparotomy coupled with histology. The authors illustrate these difficulties by observing a giant cystic degenerative fibroma in a 26-year-old G1P1 woman in the postpartum period.
Roland AdjobyNdrin Denis EffohSoh Victor KoffiOkoin Paul José LobaNgolo Alassane SoroYapo Privat AkobéRamata Kouakou-KouraogoMichelle Gadji
Pancreatic acinar cell carcinoma(PACC)is a rare malignant tumor of pancreatic epithelial cells,which produces pancreatic exocrine enzymes.PACC originates from acinar cells and terminal branches of the pancreatic ducts in the exocrine tissue of the pancreas.PACC accounts for 1%−2%of pancreatic exocrine tumors[1].Herein,we present an elderly woman with PACC who recovered after effective laparoscopic surgery.The tumor was located on the left side of the abdomen;imaging suggested that it was a gastrointestinal stromal tumor of the gastric wall origin,infiltrating the tail of the pancreas and omentum,while postoperative pathology suggested PACC.
Ben-Jie LiuChen-Hui JinYan-Lun GuoZhi-Gang KeJun-Jun HuangLin-Ping Cao
A new simulation model for the development of gas condensate reservoirs is introduced based on the influence that phase change,non-Darcy flow,and capillary pressure have on the production of gas condensates.The model predicts well performance,including bottom-hole pressure,oil/gas production rate,oil/gas recovery,gaseoil ratio,and the change in produced fluid composition.It also calculates dynamic characters,such as the change of pressure field and oil/gas saturation field during the development of gas condensate reservoirs.The model is applicable to different boundary conditions(both constant-pressure and sealed boundary)and different production modes(both constant-pressure and constant-volume production modes).Model validation attempted using numerical simulation results for sealed boundary conditions with constant-pressure production mode has shown a relatively good match,proving its validity.For constant-pressure boundary conditions with constant-volume production mode,four stages are defined according to the dynamic behavior of production performance in the development of gas condensate reservoirs.
Continuous lifelong acquisition,updating,and finetuning of knowledge and skills is of crucial significance for the survival of humans.However,current neuromorphic devices exhibit obvious catastrophic forgetting when restimulated by new information.This remains a challenge for neuromorphic devices and artificial intelligence to achieve continuous learning.Herein,we propose an electric-induced cycloelimination strategy to realize an organic transistor nociceptor that can simulate synaptic and structural plasticity.The system benefits from the ring-opening characteristics of cross-linked poly(vinyl cinnamate)under a strong pulse voltage,during which new energy-level trap states are formed.The prepared organic transistor nociceptors exhibit both structural and synaptic plasticity.They simulate the characteristics of human nociceptors,including threshold,relaxation,sensitization,and maladaptation behavior.For the first time,we have simulated and explored the structural plasticity behavior in organisms based on electronic devices.More remarkably,the transistor nociceptors realize the reinput of information without forgetting the initial informa tion.The strategy developed for the preparation of organic transistor nociceptors provides insights for addressing the catastrophic forgetting in the lifelong learning of intelligent neuromorphic devices.
The breakage of brittle particulate materials into smaller particles under compressive or impact loads can be modelled as an instantiation of the population balance integro-differential equation.In this paper,the emerging computational science paradigm of physics-informed neural networks is studied for the first time for solving both linear and nonlinear variants of the governing dynamics.Unlike conventional methods,the proposed neural network provides rapid simulations of arbitrarily high resolution in particle size,predicting values on arbitrarily fine grids without the need for model retraining.The network is assigned a simple multi-head architecture tailored to uphold monotonicity of the modelled cumulative distribution function over particle sizes.The method is theoretically analyzed and validated against analytical results before being applied to real-world data of a batch grinding mill.The agreement between laboratory data and numerical simulation encourages the use of physics-informed neural nets for optimal planning and control of industrial comminution processes.
Complex disaster systems involve various components and mechanisms that could interact in complex ways and change over time,leading to significant deep uncertainty.Due to deep uncertainty,decision-makers have severe inadequacy of knowledge and often encounter unpredictable surprises that may emerge in the future,thus making it difficult to specify appropriate models and parameters to describe the system of interest.In this paper,we propose a dynamic exploratory hybrid modeling framework that fits data,models,and computational ex-periments together to simulate complex systems with deep uncertainty.In the framework,one needs to develop multiple plausible models from a hybrid modeling perspective and perform enormous computational experi-ments to explore the diversity of future scenarios.Real-time data is then incorporated into diverse forecasts to dynamically adjust the simulation system.This ultimately enables an ongoing modeling and analysis process in which deep uncertainty would be gradually mitigated.Our approach has been applied to a human-involved car-following system simulation under complex traffic conditions.The results show that the proposed approach can improve the prediction accuracy while enhancing the sensitivity of the simulation system to uncertain changes in the system of interest.
