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Chaotic Brillouin optical correlation domain analysis (BOCDA) has been proposed and experimentally demonstrated with the advantage of high spatial resolution. However, it faces the same issue of the temperature and strain cross-sensitivity. In this paper, the simultaneous measurement of temperature and strain can be preliminarily achieved by analyzing the two Brillouin frequencies of the chaotic laser in a large-effective-area fiber (LEAF). A temperature resolution of 1 °C and a strain resolution of 20 µε can be obtained with a spatial resolution of 3.9 cm. The actual temperature and strain measurement errors are 0.37 °C and 10 µε, respectively, which are within the maximum measurement errors.

Compared with conventional reservoir, the development efficiency of the carbonate reservoir is lower, because of the strong heterogeneity and complicated reservoir structure. How to accurately and quantitatively analyze development performance is critical to understand challenges faced, and to propose optimization plans to improve recovery. In the study, we develop a workflow to evaluate similarities and difference of well performance based on Machine Learning methods. A comprehensive Machine Learning evaluation approach for well performance is established by utilizing Principal Component Analysis (PCA) in combination with K-Means clustering. The multidimensional dataset used for analysis consists of over 15 years dynamic surveillance data of producers and static geology parameters of formation, such as oil/water/gas production, GOR, water cut (WC), porosity, permeability, thickness, and depth. This approach divides multidimensional data into several clusters by PCA and K-Means, and quantitatively evaluate the well performance based on clustering results. The approach is successfully developed to visualize (dis)similarities among dynamic and static data of heterogeneous carbonate reservoir, the optimal number of clusters of 27-dimension data is 4. This method provides a systematic framework for visually and quantitatively analyzing and evaluating the development performance of production wells. Reservoir engineers can efficiently propose targeted optimization measures based on the analysis results. This paper offers a reference case for well performance clustering and quantitative analysis and proposing optimization plans that will help engineers make better decision in similar situation.

C35H32N6O2, triclinic, P 1 ‾ $‾{1}$ (no. 2), a = 10.902(2) Å, b = 12.434(2) Å, c = 13.481(4) Å, α = 102.881(5)°, β = 104.935(5)°, γ = 114.542(3)°, V = 1488.6(6) Å3, Z = 2, R gt (F) = 0.0511, wR ref (F 2) = 0.1429, T = 296 K.

To improve the low thermal conductivities and poor wear resistances of TC4 (Ti-6Al-4V) alloy, the most widely used titanium alloy, the surface of TC4 alloys is modified by electroplating deposition of Ni and Cu layers, and then heat-treated to increase the diffusivity at the interface. In this paper, the corrosion behavior of Cu/Ni coatings on TC4 alloy at different heat treatment processes was investigated in 3.5 wt% NaCl by the electrochemical analysis, and the microstructure and composition of corrosion products was carried out to reveal the corrosion resistance mechanism of Cu/Ni coatings. It was found that the corrosion resistance was significantly influenced by heat treatment temperature. With the increasing diffusion treatment temperature from 500 to 700 °C, the corrosion potential positively shifted from −330.87 to −201.14 mV, and the corrosion current density decreased from 4.02×10−3 to 0.514×10−3 mA/cm2. However, when heat treatment temperature increased to 800 °C, the corrosion potential negatively shifted to -207.21 mV, and the current density increased to 1.62×10−3 mA/cm2.The diffusion behavior of Ti, Ni and Cu elements occurred and small amounts of Ni and Ti elements appeared on the specimen surface under different heat treatment temperature. Especially heattreated at 700 °C, the smaller pore size, dense Cu2O film, and highly stable TiO and NiO oxide layer were formed, which dramatically enhanced the corrosion resistance of Cu/Ni coatings. Finally, a novel model of corrosion resistance was proposed based on the analysis mentioned above.

Ponds are “islands” of independent habitats and research hotspots of regional biodiversity. This paper got some basic information about habitat conditions and aquatic macroinvertebrates in hilly ponds. Then we tried to provide a theoretical basis for the regional biodiversity conservation and sustainable development of the pond networks among basins. In this paper, the environmental and spatial variables concerned about aquatic macroinvertebrates in the ponds with different small basins of Liangping District, which is located in the east of Sichuan Basin, China, were investigated. The results showed that drainage basin effect did not occur among basins. The aquatic macroinvertebrates in the hilly ponds were mainly affected by altitude, nutrients, pH and other environmental factors. Within the basin scale, pond isolation was also one of the important factors affecting community structure. Considering the spatial pattern of the macroinvertebrate assemblages in the hilly ponds, the protection and management of those ponds need to focus on pond network other than a single pond, improve the habitat quality of the ponds, and ensure the cost-effectiveness and ecological benefits.

─Breast cancer subtype attains the great importance for disease diagnosis and individualized patient therapy. With the advance of the high-throughput sequencing techniques, huge and various types of genetic data have been produced. It has been shown that integration of multi-omics data contributes to cancer subtype identification. However, most of the existing methods used only gene expression to identify cancer subtypes; another major issue is that most of existing clustering methods completely ignore results from prior knowledge. In this paper, a new deep learning fusion clustering framework is proposed to integrate multi-omics data (mRNA expression, miRNA expression and DNA methylation) on the TCGA BRAC dataset for breast cancer subtype identification named as DLFC. Stacked autoencoder (SAE) and autoencoder (AE) are used to learn high-level data representations. Prior biological knowledge is used to guide the representation learning. The final learned high-level data representations is used as input to the clustering model for cancer subtype identification. The new deep learning fusion clustering framework is an effective method to integrate increasingly complex multi-omics data to identify breast cancer subtypes.

Diffusion process during heat treatment can be successfully employed to connect dissimilar metals. In order to get interconnected plating structure with a good biological performance on TC4 alloy, the most widely used titanium alloy, the surface of TC4 alloys were modified by deposition of Ni and Cu layers, and heat treatment was performed to increase the diffusivity at the interface. In this paper, the elements diffusion behaviors of Cu/Ni and Ni/Ti at the interfaces of Cu/Ni/Ti were investigated. A diffusion model was developed to reveal the diffusion coefficients and diffusion activation energies based on the Fick's law and Arrhenius-type equation. The adhesion strength and hardness of the coatings were also measured to evaluate the influence of diffusion behavior on mechanical properties. It was found that the diffusion behavior was significantly influenced by heat treatment temperature. The diffusion coefficient increased with the increase of heat treatment temperature, especially above 700 °C. The difference in the diffusion activation energies can be attributed to the difference in contact area and defect density caused by different coating processing technologies. In addition, the improvement of adhesion strength and hardness were mainly due to the solution strengthening and intermetallic compounds (IMCs) in the coating.

Hamatai lake represents important saline and alkaline resource in sandy land of Inner Mongolia plateau. In this study, the pollution degrees of heavy metals (Cu, Zn, Pb, Cd, Cr, Ni, Mn, As) in sediments of Hamatai Lake were analyzed, based on the national soil background value and the soil background value of Inner Mongolia, and single factor and potential ecological risk index. The results showed that the concentrations of heavy metals (Cu, Zn, Cu, Pb, Cd, Cr, Ni, Mn, As) were 15.22, 33.16, 5.32, 0.80, 61.60, 16.40, 286.50, 20.21 mg kg-1, respectively. Among them, the contents of Cd in eight samples went beyond the soil background value of Inner Mongolia, the national soil background value and the screening value. Meanwhile, Cd had the highest potential ecological risk through potential ecological risk assessment. Overall, the potential ecological risk of the central area of the lake was significantly greater than that of the surrounding area. The results of this study can provide a scientific basis for the prevention and control of heavy metal pollution in alkaline lake of Plateau in arid area.

The Middle East carbonate reservoirs are mostly reef flat sedimentary complexes, which are high heterogeneous and have extensive baffles. It brings great challenges to the efficient development of such reservoirs. Focus on the problem, a method of describing baffle distribution conjunctively using static and dynamic data is put forward in this paper. The distribution of baffles in the target formation is characterized based on core observation, well loggings, image logs, etc. Then, their impact towards reservoir performance is evaluated based on production data and dynamic surveillance data. Based on the study, it is acquired that cementation mainly controlled the upper baffles and compaction controlled the lower. Generally speaking, permeability of baffle is higher in the crest area and lower in the flank area. Baffles are stably distributed throughout the reservoir, although their ability to block fluid flow varies from region to region. The existence of baffles and their ability to block fluid flow had impact to the development effect, so development optimization and scheme comparison carried out. The corresponding development strategies are proposed for reservoirs with extensive baffles. Injectors and producers with highly-deviated well-type will help establish effective displacement system and achieve better reservoir production, it can improve the development effect and enhance oil recovery. This study offers a comprehensive case study for engineers and geologists to better understand this reservoir, it also provides a methodology that can be referred when developing similar fields.

Due to the variety of factors that may affect student achievement, individual students often come to schools with different levels of academic preparation. These students from diverse academic background come with different learning needs. So, to better serve them, schools have adopted a variety of strategies, including increasing instructional time, reducing class sizes, providing differentiated curriculum and improving teacher quality through professional development trainings. My dissertation consists of three papers that examine several education interventions targeting at students with different academic abilities. These studies examine the design and current uses of several popular education interventions, and provide actionable insights on improving these interventions to enhance learning experiences for students at different points of the achievement spectrum. Chapter one evaluates a multi-subject remedial program that provided additional instruction on math and reading to under-performing students in a large metropolitan school district. Using a fuzzy regression discontinuity design and school administrative records, I find that double-dose remedial math courses improve math test scores by 0.21 standard deviations at the end of the school year. Yet, the effect of double-dose reading courses on reading achievement is small and statistically insignificant. In addition, the required extra classes in math or reading do not have crowd-out impact on instruction time and student performances on non-targeted core subjects. Finally, the study shows that students who receive treatments in multiple subjects do not necessarily have larger gains. While double-dose math courses may improve students’ math skills, the effect disappears for students who were taking double-dose reading courses at the same time. This finding suggests that two separate double-dose courses in different subjects may be ineffective in improving student achievements. To support students who struggle with more than one subject, schools should consider redesigning the double-dose courses with alternative curriculum and instructional strategies to integrate content of different courses and to increase student engagement. Chapter two examines effects of taking accelerated math courses under a subject-based acceleration program for middle school students. Students assigned to accelerated courses were exposed to more advanced curriculum and higher-performing peers. Using school administrative records and fuzzy regression discontinuity approach, the study finds null effects of taking accelerated math courses on students’ end-of-grade math test scores. Specifically, the effects are insignificant for students who took accelerated courses in both math and English Language Arts, and for those who took accelerated courses only in math. Also, the effects are insignificant for low-income and minority students. These findings are unexpected given the treatments provided by accelerated courses. The study provides possible explanations to the findings, and suggests directions for future research. Chapter three examines the sustainability of teacher knowledge gains from teacher professional development (PD) programs. Teacher PD is seen as a promising intervention to improve teacher knowledge, instructional practice, and ultimately student learning. While research finds many instances of significant program effects on teacher knowledge, little is known about how long these effects last. If teachers forget what is learned from the professional development program, the contribution of the intervention will be diminished. Using a large-scale dataset with 3,340 in-service teachers from 161 programs, this study examines the sustainability of gains in teachers’ content knowledge for teaching mathematics (CKT-M). Results show that there is a negative rate of change in CKT after teachers complete the training and that this estimated rate is relatively stable over time, suggesting that the average gain in test scores before and after the program is lost in just 37 days. There is, however, variation in how quickly knowledge is lost, with teachers participating in summer programs losing more rapidly than those who attend programs that occur during school years. The implications of these findings for designing and evaluating professional development programs are discussed.

The subsurface identification of sequence stratigraphy and sedimentary environment commonly has a high uncertainty due to the ambiguity, low-resolution, or discontinuity of seismic data, wire log data, core description data or outcrop data. However, palynology can provide a relatively precise paleoecological and paleoenvironmental results and thus can be used as an explicit lateral correlation indicator for geologists. This paper shows a sequence stratigraphy and sedimentary environment determination case primarily based on palynology for the Upper Main Pay Member in Rumaila oilfield in Iraq. 640 samples in 8 wells were analyzed and grouped which occupied 93 palynological spices. They were clustered into 6 groups, including terrestrial algal group, arid terrestrial taxa group, wetland terrestrial taxa group, least marine dinocyst (dinoflagellate cyst) group, medium marine dinocyst group and most marine dinocyst group. The vertical and lateral changes and its rules of total counts and diversities for each group were studied after appropriate statistics based on their similarities and facies intervals. The distribution and variation of each groups from bottom to top and from proximal-source to distal-source showed some interesting and regular trends which could be associated to the results of sequence stratigraphy evolution and sedimentary environment switch. The arid terrestrial taxa group was a good indicator of the distance from the sea and of the transgressive process, which implied a continuous transgression process in the study area. The wane and wax among the least marine, medium marine and most marine dinocyst groups manifested a relatively variable salinity paleoenvironment. The simultaneous occurrence of peak total counts of the least marine dinocyst group and terrestrial algal group, combining with the relatively low total counts of the arid terrestrial taxa group, suggested a barrier island paleoenvironment. In total, the variety of the palynology in lateral and vertical dimensions recommended a transformation environment from delta to estuary, which is very difficult to be determined by other kinds of data. After the reconstruction of sedimentary environment, the influential factors, the sources (allogenic or authigenic) and the transportation processes about the terrestrialalgae, spores and pollen, and dinocysts were studied, integrated with energy distribution of estuary model. The grouping and interval samples summing statistics of palynological spices showed perfect assistances and verifications for the sequence stratigraphy determination and sedimentary environment identification. This could be helpful for lowering the uncertainty of geological studies, especially for the complex and ambiguous formations.

Considering the influence of the tower vibration such as fore-aft/ side-to-side in the offshore wind turbine, the Passivity-based Control (PBC) method is proposed to suppress the tower vibration based on the offshore Permanent Magnet Synchronous Generator (PMSG). The PCH model of the tower is established and the energy compensation of the system is adopted to increase the damping of the tower. In comparison with PID controller, the PBC method not only suppresses the vibration of the tower effectively while reduce the fatigue load of tower, but also reduces the influence of maximum capture of wind energy and the drive-train torsional vibration, stabilizing the output power of the generator. The effectiveness and superiority of the PBC method proposed is verified by the theoretical analysis and the simulations by comparing with PID.

In recent years, our understanding of complex networks has improved. Community structure as a common characteristic of complex networks has become an important direction in the study of complex networks. Meanwhile, people put forward many community detection algorithms. To original Largest Fitness Measure algorithm, the selection of seed node is random, community division needs to be improved, and it is difficult to achieve its end condition. Based on above problems, we propose a kind of Weight Largest Fitness Measure algorithm. According to the thought of potential energy, the new algorithm optimizes and handles initial node, simplify node fitness function and expand community according to potential queue. Finally, through two groups of experimental validate the performance of the algorithm. The experimental results show that, compared with Largest Fitness Measure algorithm, the new algorithm has higher accuracy and shorter run time.

This paper presents an uncertainty assessment project using Artificial Neural Network (ANN) for a giant multi-layered sandstone reservoir in Middle East, which contains several uncertainties and associated risks. Uncertainty quantification in history matching, production forecasting and optimization approaches often requires hundreds of thousands of forward flow simulations to explore the uncertain parameter space, causing forbidden computational time requirement, especially for large-scale reservoir models. In order to bypass this limitation, one can use a proxy to replace the time-consuming flow simulator. In this work, an optimized ANN is used as the proxy and an uncertainty assessment workflow is implemented for the giant Cretaceous multi-layered sandstone reservoir using a global optimizer. Using the ANN based uncertainty assessment framework, the impacts of the main uncertain parameters on production forecasting are assessed for this multi-layered sandstone reservoir. Then, field development optimization is also performed to optimize wells injection and production rates to maximize the economic measures considering uncertainties. Using a real field as a case study, the capability of our uncertainty assessment workflow is demonstrated. The optimized ANN successfully captures the complex nonlinear dynamics as a proxy and significantly reduced the computation time.