Efficient C-C bond splitting on Pt monolayer and sub-monolayer catalysts during ethanol electro-oxidation: Pt layer strain and morphology effects

Efficient catalytic C–C bond splitting coupled with complete 12-electron oxidation of the ethanol molecule to CO2is reported on nanoscale electrocatalysts comprised of a Pt monolayer (ML) and sub-monolayer (sML) deposited on Au nanoparticles (Au@Pt ML/sML). The Au@Pt electrocatalysts were synthesized using surface limited redox replacement (SLRR) of an underpotentially deposited (UPD) Cu monolayer in an electrochemical cell reactor. Au@Pt ML showed improved catalytic activity for ethanol oxidation reaction (EOR) and, unlike their Pt bulk and Pt sML counterparts, was able to generate CO2at very low electrode potentials owing to efficient C–C bond splitting. To explain this, we explore the hypothesis that competing strain effects due to the Pt layer coverage/morphology (compressive) and the Pt–Au lattice mismatch (tensile) control surface chemisorption and overall activity. Control experiments on well-defined model Pt monolayer systems are carried out involving a wide array of methods such as high-energy X-ray diffraction, pair-distribution function (PDF) analysis,in situelectrochemical FTIR spectroscopy, andin situscanning tunneling microscopy. The vibrational fingerprints of adsorbed CO provide compelling evidence on the relation between surface bond strength, layer strain and morphology, and catalytic activity.BMBF, 16N11929, Netzwerk TU9/CN Elektromobilität - Teilvorhaben: Brennstoffzellen Range-Extende

Tomato Chlorosis Virus Infection Facilitates \u3cem\u3eBemisia tabaci\u3c/em\u3e MED Reproduction by Elevating \u3cem\u3eVitellogenin\u3c/em\u3e Expression

Transmission of plant pathogenic viruses mostly relies on insect vectors. Plant virus could enhance its transmission by modulating the vector. Previously, we showed that feeding on virus infected plants can promote the reproduction of the sweet potato whitefly, Bemisia tabaci MED (Q biotype). In this study, using a whitefly-Tomato chlorosis virus (ToCV)-tomato system, we investigated how ToCV modulates B. tabaci MED reproduction to facilitate its spread. Here, we hypothesized that ToCV-infected tomato plants would increase B. tabaci MED fecundity via elevated vitellogenin (Vg) gene expression. As a result, fecundity and the relative expression of B. tabaci MED Vg was measured on ToCV-infected and uninfected tomato plants on days 4, 8, 12, 16, 20 and 24. The role of Vg on B. tabaci MED reproduction was examined in the presence and absence of ToCV using dietary RNAi. ToCV infection significantly increased B. tabaci MED fecundity on days 12, 16 and 20, and elevated Vg expression on days 8, 12 and 16. Both ovarian development and fecundity of B. tabaci MED were suppressed when Vg was silenced with or without ToCV infection. These combined results suggest that ToCV infection increases B. tabaci MED fecundity via elevated Vg expression

Wind Turbine Performance Assessment Modeling Using Machine Learning Method for Condition Based Maintenance

This thesis work proposes a performance assessment framework to estimateoperation states of wind turbines for the sake of condition monitoring. Theframework uses the data in the supervisory control and data acquisition systemsas original input and some machine learning methods including K-NearestNeighbour, K-Means Clustering, Support Vector Machine and Artificial NeuralNetwork are implemented to analyze the data. The framework mainly consistsof three stages: power curve prediction, real time power tracking and turbineperformance assessment. At the first stage, two main methods including quartilemethod and k-means clustering and density-based clustering are implementedseparately for the elimination of bad measurements. Then di↵erent methods,including both parametric and non-parametric methods, are applied to estimatethe ideal wind turbine power curve, which is used as a reference value to assessthe real one. At the second stage, a sliding window method is introduced toanalyze the real time performance of wind turbines. The di↵erence between theexpected power output and real measurements are computed and used as theanomaly. At the third stage, performance zone is defined to evaluate the overallhealth condition of the turbines. The proposed approach has been applied withthe experience data of six onshore wind turbines in a single wind farm which islocated in southern Europe. The results show that the method in the frameworkcan monitor the wind turbine operation condition and evaluate the performancefor a wind turbine in this study case.I detta avhandlingsarbete presenteras en ram för prestationsbedömning föratt bedöma driftstillstånd för vindkraftverk för tillståndsövervakningens skull. Ramverket använder data i övervakningskontrolloch datainsamlingssystem som originalinmatning och vissa maskininlärningsmetoder inklusive K-närmaste granne, K-Means Clustering, Support Vector Machine och Artificial Neural Network implementeras för att analysera data. Ramverket består huvudsakligen av tre steg: prognos för kraftkurvor, realtidsspårning och turbins prestationsbedömning. I det första steget implementeras två huvudmetoder inklusive kvartilmetod och k-medel klusteroch täthetsbaserad klustring separat för eliminering av dåliga mätningar. Därefter appliceras olika metoder, inklusive både parametriska och icke parametriska metoder, för att uppskatta den ideala vindkraftverkskurvan, som används som referensvärde för att bedöma den verkliga. I det andra steget introduceras en glidfönstermetod för att analyseravindturbins realtidsprestanda. Skillnaden mellan den förväntade e↵ekten och reala mätningar beräknas och används som anomali. I tredje etappen definierasprestationszonen för att utvärdera turbins totala hälsotillstånd. Det föreslagna tillvägagångssättet har tillämpats med erfarenhetsdata från sex vindkraftverk på en enda vindkraftpark som ligger i södra Europa. Resultaten visar att metoden i ramen kan övervaka driften av vindkraftverk och utvärdera prestanda fören vindkraftverk i detta studiefall

Wind Turbine Performance Assessment Modeling Using Machine Learning Method for Condition Based Maintenance

This thesis work proposes a performance assessment framework to estimateoperation states of wind turbines for the sake of condition monitoring. Theframework uses the data in the supervisory control and data acquisition systemsas original input and some machine learning methods including K-NearestNeighbour, K-Means Clustering, Support Vector Machine and Artificial NeuralNetwork are implemented to analyze the data. The framework mainly consistsof three stages: power curve prediction, real time power tracking and turbineperformance assessment. At the first stage, two main methods including quartilemethod and k-means clustering and density-based clustering are implementedseparately for the elimination of bad measurements. Then di↵erent methods,including both parametric and non-parametric methods, are applied to estimatethe ideal wind turbine power curve, which is used as a reference value to assessthe real one. At the second stage, a sliding window method is introduced toanalyze the real time performance of wind turbines. The di↵erence between theexpected power output and real measurements are computed and used as theanomaly. At the third stage, performance zone is defined to evaluate the overallhealth condition of the turbines. The proposed approach has been applied withthe experience data of six onshore wind turbines in a single wind farm which islocated in southern Europe. The results show that the method in the frameworkcan monitor the wind turbine operation condition and evaluate the performancefor a wind turbine in this study case.I detta avhandlingsarbete presenteras en ram för prestationsbedömning föratt bedöma driftstillstånd för vindkraftverk för tillståndsövervakningens skull. Ramverket använder data i övervakningskontrolloch datainsamlingssystem som originalinmatning och vissa maskininlärningsmetoder inklusive K-närmaste granne, K-Means Clustering, Support Vector Machine och Artificial Neural Network implementeras för att analysera data. Ramverket består huvudsakligen av tre steg: prognos för kraftkurvor, realtidsspårning och turbins prestationsbedömning. I det första steget implementeras två huvudmetoder inklusive kvartilmetod och k-medel klusteroch täthetsbaserad klustring separat för eliminering av dåliga mätningar. Därefter appliceras olika metoder, inklusive både parametriska och icke parametriska metoder, för att uppskatta den ideala vindkraftverkskurvan, som används som referensvärde för att bedöma den verkliga. I det andra steget introduceras en glidfönstermetod för att analyseravindturbins realtidsprestanda. Skillnaden mellan den förväntade e↵ekten och reala mätningar beräknas och används som anomali. I tredje etappen definierasprestationszonen för att utvärdera turbins totala hälsotillstånd. Det föreslagna tillvägagångssättet har tillämpats med erfarenhetsdata från sex vindkraftverk på en enda vindkraftpark som ligger i södra Europa. Resultaten visar att metoden i ramen kan övervaka driften av vindkraftverk och utvärdera prestanda fören vindkraftverk i detta studiefall

The Impact of the Accessibility of Transportation Infrastructure on the Non-Farm Employment Choices of Rural Laborers: Empirical Analysis Based on China’s Micro Data

Non-agricultural employment plays a significant role in alleviating regional poverty. Using the micro data of the China Labor-Dynamics Survey (CLDS), this paper empirically analyzes the impact of the accessibility of rural transportation infrastructure on the non-agricultural employment choices of rural laborers by using the entropy method and the ordered Logit model. The results show that there is a significant positive correlation between the accessibility of rural transportation infrastructure and the non-agricultural employment of rural laborers. The study also finds that the laborers participating in non-agricultural employment in villages with good transportation infrastructure will prefer to be employed in nearby locations, and the development of the rural non-agricultural economy is an important reason. Further analysis clearly shows that gender, the family dependency ratio, and rural terrain characteristics affect the choices made by laborers with respect to non-agricultural employment. Based on the research results, focusing on a transportation and industry model and considering the construction of transportation infrastructure as a guide, especially in areas with poor terrain, promoting the development of rural non-agricultural industries can help solve the problem in rural areas and in women’s employment where family members or accompanying personnel are left behind, and can promote the orderly transfer of rural laborers

The Identification and Applicability of Regional Brand-Driving Modes for Agricultural Products

The regional brand-driven construction of agricultural products has taken shape in China. At present, the status quo entails the homogenization of the brand-driven mode of construction, making it a serious phenomenon in China. In addition, the misalignment between the brand-driven mode and resource conditions in some areas not only causes a waste of resources but also leads to a lack of competitiveness and premium capacity for agricultural products within the brand, which cannot increase farmers’ income. This article constructs a theoretical model of the brand-driven mode and uses the fuzzy set qualitative comparative analysis method to identify effective brand-driven modes and explore their applicable environmental conditions. This research can provide theoretical guidance for the local development of regional brands of characteristic agricultural products. The results of the driving mode validity analysis show that the four brand-driven modes, resource-dependent, technology-induced, culture-driven, and industry-based, are the main construction paths for regional brands of agricultural products in China. Among them, the effectiveness of the resource-dependent and technology-induced modes is the highest, reaching 0.90 or more. The results of the applicability analysis show that the resource-dependent mode is suitable for farming areas with well-developed supporting policies and infrastructure and good economic development. In addition, the use of the technology-induced mode requires local farmers to have a high level of education and a high-quality base

The Impact of the Accessibility of Transportation Infrastructure on the Non-Farm Employment Choices of Rural Laborers: Empirical Analysis Based on China’s Micro Data

Non-agricultural employment plays a significant role in alleviating regional poverty. Using the micro data of the China Labor-Dynamics Survey (CLDS), this paper empirically analyzes the impact of the accessibility of rural transportation infrastructure on the non-agricultural employment choices of rural laborers by using the entropy method and the ordered Logit model. The results show that there is a significant positive correlation between the accessibility of rural transportation infrastructure and the non-agricultural employment of rural laborers. The study also finds that the laborers participating in non-agricultural employment in villages with good transportation infrastructure will prefer to be employed in nearby locations, and the development of the rural non-agricultural economy is an important reason. Further analysis clearly shows that gender, the family dependency ratio, and rural terrain characteristics affect the choices made by laborers with respect to non-agricultural employment. Based on the research results, focusing on a transportation and industry model and considering the construction of transportation infrastructure as a guide, especially in areas with poor terrain, promoting the development of rural non-agricultural industries can help solve the problem in rural areas and in women’s employment where family members or accompanying personnel are left behind, and can promote the orderly transfer of rural laborers

Station-wise statistical joint assessment of wind speed and direction under future climates across the United States

This study develops a statistical conditional approach to evaluate climate model performance in wind speed and direction and to project their future changes under the representative concentration pathway 8.5 scenario over inland and offshore locations across the Continental United States. The proposed conditional approach extends the scope of existing studies by characterizing the changes of the full range of the joint wind speed and direction distribution. Directional wind speed distributions are estimated using two statistical methods: a Weibull distributional regression model and a quantile regression model, both of which enforce the circular constraint to their resulting estimates of directional distributions. Projected uncertainties associated with different climate models and model internal variability are investigated and compared with the climate change signal to quantify the statistical significance of the future projections. In particular this work extends the concept of internal variability to the standard deviation and high quantiles to assess the relative magnitudes to their projected changes. The evaluation results show that the studied climate model capture both historical wind speed, wind direction, and their dependencies reasonably well over both inland and offshore locations. In the future, most of the locations show no significant changes in mean wind speeds in both winter and summer, although the changes in standard deviation and 95th-quantile show some robust changes over certain locations in winter. The proposed conditional approach enables the characterization of the directional wind speed distributions, which offers additional insights for the joint assessment of speed and direction

Wind Data for Station-wise assessment of wind speed and direction under future climates across the United States

This study employs statistical techniques to evaluate climate model performance in wind speed and direction and their projected future changes under the representative concentration pathway (RCP) 8.5 scenario over inland and offshore across the Continental United States (CONUS). It extends the scope of existing studies by characterizing the changes of the full range of the joint wind speed and direction distribution via a conditional approach. Projected uncertainties associated with different climate models and model internal variability are investigated and compared with the climate change signal to quantify the statistical significance of the future projections. The proposed conditional approach provides a better way to characterize the directional wind speed distributions that offers additional insights for the joint assessment of speed and direction. WRF data: We focus on seasonal (December-January-February (winter hereafter) and June-July-August (summer hereafter) statistics computed from the 3-hourly RCM outputs on both wind speed and direction over ten locations with different local topological features. We use three WRF simulations driven by Community Climate System Model 4 (CCSM4), the Geophysical Fluid Dynamics Laboratory Earth System Model 2 (GFDL-ESM2G), and the Hadley Centre Global Environment Model version 2 (HadGEM2-ES). These three GCMs represent a range of climate sensitivities that encompasses most of the coupled model intercomparison project phase 5 (CMIP5) GCMs when projecting future temperature changes. In this work, we focus on RCP 8.5 scenario for future projections. A 16-member ensemble of one-year of RCM simulation using bias corrected CCSM-driven WRF is also generated for analyzing the uncertainty due to the RCM\u27s internal variability (IV). Benchmark data: Reanalysis data are used as a verification dataset in order to evaluate the RCMs\u27 wind conditions under study for the historical time period. For the seven inland locations, we use the second phase of the multi-institution North American Land Data Assimilation System project, phase 2, at a spatial resolution of 12 km and hourly resolution. NLDAS-2 is an offline data assimilation system featuring uncoupled land surface models driven by observation-based atmospheric forcing. The non-precipitation land surface forcing fields for NLDAS-2 are derived from the analysis fields of the NCEP North American Regional Reanalysis (NARR). NARR analysis fields are at a 32-km spatial resolution and 3-hourly temporal frequency. In-situ measurement: Since reanalysis data can present errors and uncertainties, ground measurements and offshore buoy measurements are used to consolidate the evaluation of RCMs\u27 wind conditions for inland and offshore locations in historical climates. Observational data are extracted from the Automated Surface Observing System (ASOS) network that consists stations covers the U.S. territory, available at ftp://ftp.ncdc.noaa.gov/pub/data/asos-onemin. The offshore downscaled wind speeds from the historical decade are compared with National Data Buoy Center (NDBC) buoy observations of near-surface wind velocities available at https://www.ndbc.noaa.gov. The observed winds at the NBDC anemometers are adjusted to 10-m above ground height and at 3-hourly rate

Fast Frame Synchronization Design and FPGA Implementation in SF-BOTDA

To address the issues of high time consumption of frame synchronization involved in a scanning-free Brillouin optical time-domain analysis (SF-BOTDA) system, a fast frame synchronization algorithm based on incremental updating was proposed. In comparison to the standard frame synchronization algorithm, the proposed one significantly reduced the processing time required for the BOTDA system frame synchronization by about 98%. In addition, to further accelerate the real-time performance of frame synchronization, a field programmable gate array (FPGA) hardware implementation architecture based on parallel processing and pipelining mechanisms was also proposed. Compared with the software implementation, it further raised the processing speed by 13.41 times. The proposed approach could lay a foundation for the BOTDA system in the field with the associated high real-time requirements