Predicting Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae) populations and associated grain damage in smallholder farmers’ maize stores: A machine learning approach

Prostephanus truncatus is a notorious pest of stored-maize grain and its spread throughout sub-Saharan Africa has led to increased levels of grain storage losses. The current study developed models to predict the level of P. truncatus infestation and associated damage of maize grain in smallholder farmer stores. Data were gathered from grain storage trials conducted in Hwedza and Mbire districts of Zimbabwe and correlated with weather data for each site. Insect counts of P. truncatus and other common stored grain insect pests had a strong correlation with time of year with highest recorded numbers from January to May. Correlation analysis showed insect-generated grain dust from boring and feeding activity to be the best indicator of P. truncatus presence in stores (r = 0.70), while a moderate correlation (r = 0.48) was found between P. truncatus numbers and storage insect parasitic wasps, and grain damage levels significantly correlated with the presence of Tribolium castaneum (r = 0.60). Models were developed for predicting P. truncatus infestation and grain damage using parameter selection algorithms and decision-tree machine learning algorithms with 10-fold cross-validation. The P. truncatus population size prediction model performance was weak (r = 0.43) due to the complicated sampling and detection of the pest and eight-week long period between sampling events. The grain damage prediction model had a stronger correlation coefficient (r = 0.93) and is a good estimator for in situ stored grain insect damage. The models were developed for use under southern African climatic conditions and can be improved with more input data to create more precise models for building decision-support tools for smallholder maize-based production systems.     

Structural modification of aminoclay for catalytic applications

Abstract

An eco-friendly catalyst system was synthesized using three step reaction. The Cu nanoparticles bridged aminoclay catalyst was analyzed using various analytical techniques. The structure of the catalyst was confirmed by FTIR and XRD. The synthesized catalyst system was used for the reduction of p-nitrophenol (NP), Cr(VI), and fluorescein (Flur) dye individually, as well as in their mixture forms. The k_app value was computed to access the efficiency of the catalyst. The results indicated that the k_app value of the individual system is higher than that of the mixture systems due to the absence of the complex formation reaction. The catalytic performance of the catalyst was also tested for the Schiff base (SB) formation between poly(ethyleneglycol) (PEG) and aniline (ANI) in an oxygen atmosphere. The ¹H-NMR spectroscopy result indicates that the present catalyst system produced 86% yield.     

转载溜槽衬板磨损预测的DEM仿真方法

针对EDEM自动划分网格较稀疏问题,采用Hypermesh划分仿真模型网格,利用Herz-Mindlin接触理论的Archard磨损模型对转载溜槽磨损问题进行仿真,在网格单元上提取出漏斗和溜管衬板的接触能量和磨损量,分析了磨损量与接触能量的关系。假设转载溜槽衬板磨损深度为物料与溜槽衬板的使用时间为线性函数,根据现场衬板磨损量的测量值,将衬板的磨损系数关系的看作隐式的单变量方程,应用EDEM软件的仿真结果求解出磨损系数的标定值。分析了漏斗和溜管的磨损机理。该方法可用于转载溜槽的磨损预测。     

面向医院建筑项目的IPD模式应用研究

医院建筑项目是非常复杂且具有挑战性的建设项目。传统的交付模式无法满足人们对医院建筑建设、运营方面的高要求,为此开展面向医院建筑项目的IPD模式应用研究。在对IPD模式发展现状整理的基础上,阐述医院建筑项目IPD模式的适用性。分析采用IPD模式的医院建筑项目特点,并以上海市某公立三甲专科医院项目为例,对IPD模式在国内的应用进行探索性研究。     

Research on optimization design of PWR flow distribution device based on numerical simulation

ABSTRACT

In the lower chamber of pressurized water reactor (PWR), the flow distribution device is the core module to distribute coolant into the core. It has complex structure and numerous design parameters. Therefore, it has important theoretical and practical significance to optimize the device. The mesh independence verification, turbulence model selection, and data processing all can influence the numerical simulation results of the lower chamber, in order to research the influence, a numerical simulation method based on the original model of CNP1000 reactor lower chamber is proposed in this paper. In the method, an optimization design method of flow distribution device is established based on surrogate model. The main design variables and optimization objectives are determined based on the device’s structure and function characteristics. And then it respectively adopts Kriging algorithm and multi-objective genetic algorithm to establish a surrogate model of flow distribution device and optimize it globally. Finally, the optimal design variables are obtained. Compared with the device’s performance before optimization, the after optimization has smaller total pressure loss and more uniform flow. The effectiveness and practicability of proposed optimization design method can be verified.     

Optimal design of nonlinear model predictive controller based on new modified multitracker optimization algorithm

The controller design for the robotic manipulator faces different challenges such as the system's nonlinearities and the uncertainties of the parameters. Furthermore, the tracking of different linear and nonlinear trajectories represents a vital role by the manipulator. This paper suggests an optimal design for the nonlinear model predictive control (NLMPC) based on a new improved intelligent technique and it is named modified multitracker optimization algorithm (MMTOA). The proposed modification of the MTOA is carried out based on opposition-based learning (OBL) and quasi OBL approaches. This modification improves the exploration behavior of the MTOA to prevent it from becoming trapped in a local optimum. The proposed method is applied on the robotic manipulator to track different linear and nonlinear trajectories. The NLMPC parameters are tuned by the MMTOA rather than the trial and error method of the designer. The proposed NLMPC based on MMTOA is compared with the original MTOA, genetic algorithm, and cuckoo search algorithm in literature. The superiority and effectiveness of the proposed controller are confirmed to track different linear and nonlinear trajectories. Furthermore, the robustness of the proposed method is emphasized against the uncertainties of the parameters.     

芽孢杆菌Bacillus sp.dwc-2对模拟地下水中U(Ⅵ)的还原行为研究

在厌氧条件下研究了西南地区一种典型土壤微生物芽孢杆菌Bacillus sp.dwc-2对模拟地下水中U(Ⅵ)的还原行为，重点考察了时间、无机阴离子、腐殖酸（HA）及富里酸（FA）对还原的影响，并利用TEM、EDS、SAED和XPS对还原后的样品进行了表征。结果表明：在pH=7.0、c_NaHCO3=5 mmol/L和T=303 K条件下，Bacillus sp.dwc-2对U(Ⅵ)的还原率随时间的增加而增加，24 h内最大还原率为12.2%，此后则随时间的增加逐渐降低；HA和FA对U(Ⅵ)的微生物还原行为有一定影响，其中HA和FA浓度为25 mg/L时，U(Ⅵ)的还原在24 h最明显，其还原率分别为14.2%和16.2%，但随着HA和FA浓度的继续增加，因在U(Ⅵ)离子与HA、FA形成的配合物表面形成致密的腐殖层，抑制了电子的转移，阻止了U(Ⅵ)的还原。此外，研究表明HCO₃^-也会抑制U(Ⅵ)的还原。TEM-SAED和XPS分析证实了还原过程中U(Ⅳ)的存在。上述结果可为真实环境中微生物还原U(Ⅵ)提供基础数据和参考。     

用于厚屏蔽小探测器的蒙特卡罗模拟减方差方法研究

反应堆屏蔽计算中经常出现厚屏蔽、小探测器问题，常规蒙特卡罗方法难以有效解决。基于自动重要抽样（AIS）方法，本文提出了小探测器自动重要抽样(SDAIS)方法，并针对小探测器问题，优化了AIS方法的虚粒子赌分裂算法。该方法在自主开发的蒙特卡罗屏蔽程序MCShield上进行了实现。使用NUREG/CR-6115 PWR基准题验证该方法的正确性和计算效率。结果表明，SDAIS方法可有效地解决厚屏蔽小探测器问题，相比AIS方法及传统的重要性方法，计算效率提升1~2个量级。     

Mathematical modeling of growth of Salmonella spp. and spoilage microorganisms in raw oysters

The main objective of this study was to develop the primary and secondary models to describe the growth kinetics of Salmonella as well as background microorganisms in raw, shucked oysters. Samples, inoculated with a cocktail of two Salmonella serotypes, S. Typhimurium (CICC22956) and S. Enteritidis (CICC21482), were incubated at 4, 8, 12, 16, 20, 25, 30, 33, 37, 40, and 43 °C. Growth of Salmonella was observed at all temperatures, except at 4 °C. The background microorganisms grew at all temperatures. All growth curves clearly exhibited lag, exponential and stationary phases, and were analyzed using the Huang growth model. Three secondary models (Ratkowsky square-root, Huang square-root, and Cardinal parameter models) were compared for evaluating the effect of temperature on bacterial growth rates. Data analysis was performed using IPMP 2013, a free predictive microbiology software tool developed by the USDA ARS.The Cardinal parameters model underestimated the specific rates of the microorganisms at low temperatures. The Huang square-root model was more suitable than the Ratkowsky square-root model for describing the effect of temperature on growth of Salmonella, while the Ratkowsky square-root model, on the other hand, was more suitable for background microorganisms. For both Salmonella and background microorganisms, the logarithms of the lag phase were expressed as linear functions of the logarithms of specific growth rates. The results of this study can be used by the food retailers and regulatory agencies to estimate the microbial shelf-life of raw, shucked oysters.     

Evaluation of hydrodynamic behavior of the perforated gas distributor of industrial gas phase polymerization reactor using CFD-PBM coupled model

A 2D computational fluid dynamics (Eulerian–Eulerian) multiphase flow model coupled with a population balance model (CFD-PBM) was implemented to investigate the fluidization structure in terms of entrance region in an industrial-scale gas phase fluidized bed reactor. The simulation results were compared with the industrial data, and good agreement was observed. Two cases including perforated distributor and complete sparger were applied to examine the flow structure through the bed. The parametric sensitivity analysis of time step, number of node, drag coefficient, and specularity coefficient was carried out. It was found that the results were more sensitive to the drag model. The results showed that the entrance configuration has significant effect on the flow structure. While the dead zones are created in both corners of the distributors, the perforated distributor generates more startup bubbles, heterogeneous flow field, and better gas–solid interaction above the entrance region due to jet formation.