Validation of qPCR method for enterococci quantification at Toronto beaches: Application for rapid recreational water monitoring

Rapid quantitative PCR-based methods for enterococci monitoring can allow public health authorities to make more timely beach posting decisions. However, qPCR methods must be assessed for proposed sites as locale-specific factors may affect DNA recovery or qPCR inhibition. We assessed the feasibility of the USEPA 1609.1 qPCR-based (Enterococcus) method at two urban Toronto beaches and three recreational areas at nearby river mouths in parallel with culture-based methods on the same water samples. A strong positive correlation was observed between the Enterococcus qPCR method and culturing-based quantification methods for E. coli and enterococci at both beaches and two river mouth areas. One river, known to be highly sewage-impacted, did not yield DNA suitable for qPCR analyses. qPCR results from biological replicates were strongly correlated and showed coefficients of variation as low as or lower than culture-based methods. With respect to Beach Action Value exceedances, the USEPA 1609.1 qPCR method provided an 80–90% level of agreement with E. coli enumeration results and >90% with enterococci enumeration. Results indicated that if recreational water locations and sampling conditions met the requirements of the USEPA 1609.1 qPCR method, the method can meet or exceed all quality control requirements and provide water quality results within 3.5 h for diverse recreational water settings around the City of Toronto.     

优化GD-模糊规则的滑坡预报模型研究

为了提高滑坡灾害预报准确率，改善传统的滑坡监测和预报中存在的参数选取困难及模糊控制系统作为预报模型精确度不高的问题，首先采用山体结构稳定性分析法进行滑坡灾害参数的选取，得出降雨量、含水率、土压力及岩土表面位移增量作为预报参数的结论；其次将选取的参数作为模糊系统的输入，建立滑坡灾害发生概率模型，并引入优化的GD算法修正预报模型中的动态参数，使模糊控制模型具有自适应性；同时与未优化的模糊控制模型以及单独模糊控制模型进行仿真对比，仿真结果表明，该控制算法收敛速度快，具有很好的收敛性；最后将该模型在某滑坡重点灾区实验区进行实验测试，实验结果显示该模型具有较好的收敛性，且预报精度达到90%。     

Introduction to crystallographic refinement of macromolecular atomic models

Refinement of atomic models is a necessary step in solving the macromolecular structure by X-ray diffraction methods. Nowadays, high automation and well-developed interfaces give a possibility to use the most popular refinement programs as black boxes. Nevertheless, working with complex objects requires an understanding of the internal structure and principles of operation of these programs and critical assessment of the results of refinement. In this review, we discuss the basic principles of the organization of refinement programs and the history of their improvement and development, as the studied objects became more and more complicated. The discussions are kept at the level of basic mathematic knowledge avoiding unnecessary formalism and too detailed expressions.     

Two-dimensional temperature distribution estimation for a cross-flow planar solid oxide fuel cell stack

The uniform temperature distribution of a cross-flow planar solid oxide fuel cell (SOFC) stack plays an essential role in stack thermal safety and electrical property. However, because of the strict requirements in stack sealing struture, it is hard to acquire the temperature inside the stack using thermal detection devices within an acceptable cost. Therefore, accurately estimating the two-dimensional (2-D) temperature distribution of the cross-flow stack is crucial for its thermal management. In this paper, Firstly, a 2-D mechanism model of a cross-flow planar SOFC stack is established. The stack is divided into 5*5 nodes along the gas flow directions, which can reflect the stack states with moderate computational burden. Then, experimental test data is utilized to modify and validate the stack model, guaranteeing the model accuracy as well as the reliability of model-based state estimator design. Finally, easily-measured stack inputs and outputs are selected, and a temperature distribution estimator combined with unscented kalman filter (UFK) approach is developed to achieve accurate and fast temperature distribution estimation of a cross-flow SOFC stack. Simulation results demonstrate that the UKF-based temperature distribution estimator can precisely and quickly achieve the temperature distribution estimation of the cross-flow stack under both static state and dynamic state changes and is applicable to cross-flow stacks with different size or cell number as well, the maximum estimated absolute error is less than 0.15 K with an absolute error rate of 0.015%, which indicates the developed estimator has good estimation performances.     

带有蓄热型直接冷凝式加热板的空气源热泵系统性能研究

针对现有的空气源热泵冬季供热系统，提出了一种使用新型蓄热型直接冷凝式加热板（RHP）的空气源热泵供热系统，并测试了RHP的热性能和系统的运行特性，同时分析了系统的效率和经济性。实验结果表明，在39℃的冷凝温度下，RHP的热容量高达1044 W，RHP的蓄热量大于1000 kJ。在室外空气温度为8℃时，系统COP高达3.7。此外，对于20 m²的居住房间而言，该系统的供暖初始投资成本和总运行成本分别为3174.7 CNY和510.7 CNY，在居住建筑冬季供暖领域具有较大的竞争力。     

Critical state model for structured soil

Structure is an evident determinant for macroscopic behaviors of soils. However, this is not taken into account in most constitutive models, as structure is a rather complex issue in models. For this, it is important to develop and implement simple models that can reflect this important aspect of soil behavior. This paper tried to model structured soils based on well-established concepts, such as critical state and sub-loading. Critical state is the core of the classic Cam Clay model. The sub-loading concept implies adoption of an inner (sub-loading) yield surface, according to specific hardening rules for some internal strain-like state variables. Nakai and co-workers proposed such internal variables for controlling density (ρ) and structure (ω), using a modified stress space, called t_ij. Herein, similar variables are used in the context of the better-known invariants (p and q) of the Cam Clay model. This change requires explicit adoption of a non-associated flow rule for the sub-loading surface. This is accomplished by modifying the dilatancy ratio of the Cam Clay model, as a function of the new internal variables. These modifications are described and implemented under three-dimensional (3D) conditions. The model is then applied to simulating laboratory tests under different stress paths and the results are compared to experiments reported for different types of structured soils. The good agreements show the capacity and potential of the proposed model.     

Signature of Meyer-Neldel compensation rule in iso-conversional crystallization under the influence of γ-ray irradiation

In the current article, the influence of three different doses of gamma-rays on the thermally assisted crystal growth in a novel ternary chalcogenide glassy Se₇₈Te₂₀Sn₂ semiconductor has been reported in terms of the re-crystallization. The iso-conversional kinetic approach is used in the present study. The variation of crystal growth rate with temperature obeys the Arrhenius relation for all the doses of gamma-rays irradiation. Further analysis confirms that pre-factor K₀ of crystal growth rate and the corresponding energy ΔE involved in thermally governed crystallization follows Meyer-Neldel compensation rule (MNCR). Further, we have observed a linear correlation i.e., Further Meyer-Neldel compensation rule (FMNCR) between Meyer-Neldel pre-factor K₀₀ and Meyer-Neldel energy kT₀. The observed correlations between these significant parameters (MNCR between K₀ & ΔE and FMNCR between K₀₀ & kT₀) may open a new gateway for revealing the external effects on crystal growth rate during the iso-conversional analysis of crystallization kinetics.     

Temperature of post‐flashover compartment fires—Calculations and validation

This paper describes and validates by comparisons with tests a one‐zone model for computing temperature of fully developed compartment fires. Like other similar models, the model is based on an analysis of the energy and mass balance assuming combustion being limited by the availability of oxygen, ie, a ventilation‐controlled compartment fire. However, the mathematical solution techniques in this model have been altered. To this end, a maximum fire temperature has been defined depending on combustion efficiency and opening heights only. This temperature together with well‐defined fire compartment parameters was then used as a fictitious thermal boundary condition of the surrounding structure. The temperature of that structure could then be calculated with various numerical and analytical methods as a matter of choice, and the fire temperature could be identified as a weighted average between the maximum fire temperature and the calculated surface temperature of the surrounding structure as a function of time. It is demonstrated that the model can be used to predict fire temperatures in compartments with boundaries of semi‐infinitely thick structures as well as with boundaries of insulated and noninsulated steel sheets where the entire heat capacity of the surrounding structure is assumed to be concentrated to the steel core. With these assumptions, fire temperatures could be calculated with spreadsheet calculation methods. For more advanced problems, a general finite element solid temperature calculation code was used to calculate the temperature in the boundary structure. With this code, it is possible to analyze surrounding structures of various kinds, for example, structures comprising several materials with properties varying with temperature as well as voids. The validation experiments were accurately defined and surveyed. In all the tests, a propane diffusion burner was used as the only fire source. Temperatures were measured with thermocouples and plate thermometers at several positions.     

GA-based learning for rule identification in fuzzy neural networks

Employing an effective learning process is a critical topic in designing a fuzzy neural network, especially when expert knowledge is not available. This paper presents a genetic algorithm (GA) based learning approach for a specific type of fuzzy neural network. The proposed learning approach consists of three stages. In the first stage the membership functions of both input and output variables are initialized by determining their centers and widths using a self-organizing algorithm. The second stage employs the proposed GA based learning algorithm to identify the fuzzy rules while the final stage tunes the derived structure and parameters using a back-propagation learning algorithm. The capabilities of the proposed GA-based learning approach are evaluated using a well-examined benchmark example and its effectiveness is analyzed by means of a comparative study with other approaches. The usefulness of the proposed GA-based learning approach is also illustrated in a practical case study where it is used to predict the performance of road traffic control actions. Results from the benchmarking exercise and case study effectively demonstrate the ability of the proposed three stages learning approach to identify relevant fuzzy rules from a training data set with a higher prediction accuracy than alternative approaches.