LINEARLY EXTENDED PYRYLIUM SALTS (LEPS) AND LINEARLY EXTENDED THIOPYRYLIUM SALTS (LETS) AS ORGANIC SEMICONDUCTORS

Research focused on the development of a new class of n-type organic semiconductors called linearly extended pyrylium salts (LEPS) and linearly extended thiopyrylium salts (LETS). While a lot of progress has been made on p-type organic semiconductors over the last decade, less attention has been paid to n-type organic semiconductors. Pyrylium and thiopyrylium cations are aromatic structures akin to benzene but with one methine carbon (CH) replaced by either a positively charged oxygen or sulfur atom, respectively. This makes these aromatic π-systems very electron-deficient. With pentacene-like backbones, LEPS and LETS compounds combine linearly conjugated π-systems of acenes with the highly electron-deficient nature of pyrylium and thiopyrylium salts. The LEPS and LETS compounds described here were synthesized efficiently using new reactions. Their properties were calculated through high-level computation and studied experimentally by UV-Vis-NIR spectroscopy, cyclic voltammetry, single crystal X-ray diffraction, ESR and variable temperature NMR (VT-NMR) experiments. The LEPS and LETS compounds described here show moderate to high solubilities in organic solvents like acetonitrile and dichloromethane. While they are resistant to oxidation due to their electron-deficient backbones, they are sensitive to nucleophiles like water. LEPS and LETS compounds with a mesityl or 2’,6’-dimethylphenyl substituent have much higher resistance to moisture compared to other LEPS and LETS compounds due to shielding of the reactive site via the o-methyl groups of the phenyl substituents. LEPS and LETS compounds have broad absorptions in the UV-Vis-NIR region and possess small HOMO-LUMO gaps close to that of pentacene. They also exhibited reversible electrochemical behavior including remarkably easy reductions. Single crystal X-ray diffraction studies show that LEPS and LETS compounds form intermolecular face-to-face π-π stacking thin films. Broad and indiscernible NMR signals in the aromatic region and strong ESR signals were detected for LEPS and LETS compounds bearing a mesityl substituent. Weak to moderate ESR signals were also observed for most of other LEPS and LETS compounds. The broad aromatic NMR signals for LETS 106 bearing a mesityl substituent in CD2Cl2 sharpened upon gradually decreasing temperature in VT-NMR experiments. This indicates a switch from a paramagnetic triplet state to a diamagnetic singlet state. We propose that the LEPS and LETS compounds showing strong ESR signals and broadened NMR spectra have paramagnetic, triplet excited states that lie close in energy to their corresponding diamagnetic ground states. In these cases, populating the paramagnetic excited states via the thermal excitation is possible at or near room temperature

Longitudinal trends in prostate cancer incidence, mortality, and survival of patients from two Shanghai city districts: a retrospective population-based cohort study, 2000-2009.

BackgroundProstate cancer is the fifth most common cancer affecting men of all ages in China, but robust surveillance data on its occurrence and outcome is lacking. The specific objective of this retrospective study was to analyze the longitudinal trends of prostate cancer incidence, mortality, and survival in Shanghai from 2000 to 2009.MethodsA retrospective population-based cohort study was performed using data from a central district (Putuo) and a suburban district (Jiading) of Shanghai. Records of all prostate cancer cases reported to the Shanghai Cancer Registry from 2000 to 2009 for the two districts were reviewed. Prostate cancer outcomes were ascertained by matching cases with individual mortality data (up to 2010) from the National Death Register. The Cox proportional hazards model was used to analyze factors associated with prostate cancer survival.ResultsA total of 1022 prostate cancer cases were diagnosed from 2000 to 2009. The average age of patients was 75 years. A rapid increase in incidence occurred during the study period. Compared with the year 2000, 2009 incidence was 3.28 times higher in Putuo and 5.33 times higher in Jiading. Prostate cancer mortality declined from 4.45 per 105 individuals per year in 2000 to 1.94 per 105 in 2009 in Putuo and from 5.45 per 105 to 3.5 per 105 in Jiading during the same period. One-year and 5-year prostate cancer survival rates were 95% and 56% in Putuo, and 88% and 51% in Jiading, respectively. Staging of disease, Karnofsky Performance Scale Index, and selection of chemotherapy were three independent factors influencing the survival of prostate cancer patients.ConclusionsThe prostate cancer incidence increased rapidly from 2000 to 2009, and prostate cancer survival rates decreased in urban and suburban Chinese populations. Early detection and prompt prostate cancer treatment is important for improving health and for increasing survival rates of the Shanghai male population

Production Control Using Real-Time Monitoring in Construction

Construction projects are known to be full of complexity because they interconnect high quantities of elements, including labor, tasks, and components. The complexity often results in risks such as poor work productivity and interruptions of production workflows, which further leads to unexpected and wasteful activities on-site. A well-functional production control system in construction is important in enabling smooth workflows with minimal waste and variability. Waste measurement is difficult and complex through conventional measurement techniques in construction. For instance, notable waste happens in labor movement and material flows, but the challenges of measuring the waste are still hard to address in construction. Therefore, it is of great benefit to develop a scalable and automated system that measures wasteful events and improves site operations in construction. If an automated real-time monitoring system in construction can be implemented with ease and satisfactory coverage and accuracy, it is then possible to assess the movement of labor and materials on-site. Next, the analyses of movement can be conducted to reflect upon the magnitude of variability at the project and task levels, which helps waste elimination and improvement of production control. The overall objective of the research is to improve production control in construction by estimating workers' presence on-site at the project and task levels to support task progress monitoring and to assess task workflow and material management practice. First, the thesis demonstrates how the proposed real-time monitoring system can be implemented in different types of indoor construction projects. The data accuracy and coverage of the system were evaluated, and heuristics were also proposed to improve the system's coverage. With this method, presence indices were calculated, matching previous studies in which value-added time was evaluated and the data were collected manually. Second, the thesis illustrates how the system can be installed to detect task start and finish times, measuring and validating task progress data automatically. Third, the thesis also shows how the proposed system could be applied for the automated detection and analyses of time-matching level of materials and workers based on their uninterrupted presence, which can be used to evaluate the kitting material solution practice. From a research perspective, this study makes it possible to measure the impact of construction management or digitalization interventions on the long-term presence of workers and materials in work locations. From a practical standpoint, managers can use the suggested presence information to compare efficiency in different projects. For project management, the daily measurement of presence in work locations could identify problems that are currently unknown to the management or highlight the impact of problems, e.g., the productivity impacts of delays

Accelerating Deep Reinforcement Learning With the Aid of Partial Model: Energy-Efficient Predictive Video Streaming

Predictive power allocation is conceived for energy-efficient video streaming over mobile networks using deep reinforcement learning. The goal is to minimize the accumulated energy consumption of each base station over a complete video streaming session under the constraint that avoids video playback interruptions. To handle the continuous state and action spaces, we resort to deep deterministic policy gradient (DDPG) algorithm for solving the formulated problem. In contrast to previous predictive power allocation policies that first predict future information with historical data and then optimize the power allocation based on the predicted information, the proposed policy operates in an on-line and end-to-end manner. By judiciously designing the action and state that only depend on slowly-varying average channel gains, we reduce the signaling overhead between the edge server and the base stations, and make it easier to learn a good policy. To further avoid playback interruption throughout the learning process and improve the convergence speed, we exploit the partially known model of the system dynamics by integrating the concepts of safety layer, post-decision state, and virtual experiences into the basic DDPG algorithm. Our simulation results show that the proposed policies converge to the optimal policy that is derived based on perfect large-scale channel prediction and outperform the first-predict-then-optimize policy in the presence of prediction errors. By harnessing the partially known model, the convergence speed can be dramatically improved

Impacts of FDI Renewable Energy Technology Spillover on China's Energy Industry Performance

Environmental friendly renewable energy plays an indispensable role in energy industry development. Foreign direct investment (FDI) in advanced renewable energy technology spillover is promising to improve technological capability and promote China’s energy industry performance growth. In this paper, the impacts of FDI renewable energy technology spillover on China’s energy industry performance are analyzed based on theoretical and empirical studies. Firstly, three hypotheses are proposed to illustrate the relationships between FDI renewable energy technology spillover and three energy industry performances including economic, environmental, and innovative performances. To verify the hypotheses, techniques including factor analysis and data envelopment analysis (DEA) are employed to quantify the FDI renewable energy technology spillover and the energy industry performance of China, respectively. Furthermore, a panel data regression model is proposed to measure the impacts of FDI renewable energy technology spillover on China’s energy industry performance. Finally, energy industries of 30 different provinces in China based on the yearbook data from 2005 to 2011 are comparatively analyzed for evaluating the impacts through the empirical research. The results demonstrate that FDI renewable energy technology spillover has positive impacts on China’s energy industry performance. It can also be found that the technology spillover effects are more obvious in economic and technological developed regions. Finally, four suggestions are provided to enhance energy industry performance and promote renewable energy technology spillover in China

Understanding Programs by Exploiting (Fuzzing) Test Cases

Semantic understanding of programs has attracted great attention in the community. Inspired by recent successes of large language models (LLMs) in natural language understanding, tremendous progress has been made by treating programming language as another sort of natural language and training LLMs on corpora of program code. However, programs are essentially different from texts after all, in a sense that they are normally heavily structured and syntax-strict. In particular, programs and their basic units (i.e., functions and subroutines) are designed to demonstrate a variety of behaviors and/or provide possible outputs, given different inputs. The relationship between inputs and possible outputs/behaviors represents the functions/subroutines and profiles the program as a whole. Therefore, we propose to incorporate such a relationship into learning, for achieving a deeper semantic understanding of programs. To obtain inputs that are representative enough to trigger the execution of most part of the code, we resort to fuzz testing and propose fuzz tuning to boost the performance of program understanding and code representation learning, given a pre-trained LLM. The effectiveness of the proposed method is verified on two program understanding tasks including code clone detection and code classification, and it outperforms current state-of-the-arts by large margins. Code is available at https://github.com/rabbitjy/FuzzTuning.Comment: Findings of the Association for Computational Linguistics: ACL 202