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Using vacuum evaporation and magnetron sputtering technology, with copper phthalocyanine (CuPc) as the active layer, a vertical organic thin-film transistor (VOTFT) with a structure of Cu/CuPc/Al/CuPc/Cu was fabricated. The static photoelectric and dynamic photoelectric characteristics of the VOTFT were experimented, and its photocurrent multiplication mechanism was analyzed. The results of the static electrical characteristics experiment show that the base Al and the adjacent two layers of CuPc form good Schottky contact and the output characteristics show unsaturated characteristics. Illumination lowers the Schottky barrier between the active layer and the base, which is conducive to carrier transport. A dynamic light signal with a frequency of 200 Hz is applied to the VOTFT; according to the experiment, its turn-on time ton = 1.5 ms, and its turn-off time toff = 1.8 ms, due to its shorter conductive channel and smaller junction capacitance.

In this work, we fabricated vertical organic thin film transistors (VOTFTs) with lead phthalocyanine (PbPc) as the active layer and analyzed the operating characteristics and current transfer mechanism of VOTFTs. The results show that the quiescent output current of VOTFTs is not saturated, and the output current can be modulated by requiring different gate voltages. The switching parameters of the VOTFTs are ton = 2.5 μs, toff = 3.5μs, and the switching characteristic time reaches the microsecond level. When a sine wave dynamic signal is applied to the gate electrode, the cut-off frequency fc = 77 kHz. The experimental results demonstrate that short channel and vertical structure of VOTFTs make up for organic semiconductor material defects of low carrier mobility and realize high speed and low driving voltage of organic devices. Electric field intensity of depletion layer in the gate region is 7×107V/m, and high electric field intensity makes carriers tunneling become the major transport mode. The tunnel injection model is used to analyze the carrier transport inside the transistor. The results show that the operating current of the transistor conforms to the tunneling effect theory.

The organic film co-deposited by Zinc Phthalocyanine (ZnPc) and Lead Phthalocyanine (PbPc) was used as the active layer of the vertical organic film diode, and we obtained the co-deposition ratio of the devices with the better performance of 1:1. We made the vertical organic thin film transistor (VOTFT) with the same process again, compared the co-deposited VOTFT with the VOTFT with a single organic material (ZnPc, PbPc) as the active layer and tested the static operating characteristics of the device. The results show that the carrier mobility of ZnPc-PbPc-VOTFT is more than three times higher than that of ZnPc-VOTF and PbPc-VOTFT. When [Formula: see text][Formula: see text]V and [Formula: see text][Formula: see text]V, the output current [Formula: see text] of ZnPc-PbPc-VOTFT is up to 0.2[Formula: see text]mA, which is 12 times that of ZnPc-VOTFT and 27 times that of PbPc-VOTFT. Overall, the electrical performance of ZnPc-PbPc-VOTFT is better than ZnPc-VOTFT and PbPc-VOTFT.

Research objectives Clostriodiodes difficile infection (CDI) is a major cause of healthcare-associated diarrhoea with high mortality. There is a lack of validated predictors for severe outcomes in CDI. The aim of this study is to derive and validate a clinical prediction tool for CDI in-hospital mortality using a large critical care database. Methodology The demographics, clinical parameters, laboratory results and mortality of CDI were extracted from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. We subsequently trained three machine learning models: logistic regression (LR), random forest (RF) and gradient boosting machine (GBM) to predict in-hospital mortality. The individual performances of the models were compared against current severity scores (Clostridiodes difficile Associated Risk of Death Score (CARDS) and ATLAS (Age, Treatment with systemic antibiotics, leukocyte count, Albumin and Serum creatinine as a measure of renal function) by calculating area under receiver operating curve (AUROC). We identified factors associated with higher mortality risk in each model. Summary of results From 61 532 intensive care unit stays in the MIMIC-III database, there were 1315 CDI cases. The mortality rate for CDI in the study cohort was 18.33%. AUROC was 0.69 (95% CI, 0.60 to 0.76) for LR, 0.71 (95% CI, 0.62 to 0.77) for RF and 0.72 (95% CI, 0.64 to 0.78) for GBM, while previously AUROC was 0.57 (95% CI, 0.51 to 0.65) for CARDS and 0.63 (95% CI, 0.54 to 0.70) for ATLAS. Albumin, lactate and bicarbonate were significant mortality factors for all the models. Free calcium, potassium, white blood cell, urea, platelet and mean blood pressure were present in at least two of the three models. Conclusion Our machine learning derived CDI in-hospital mortality prediction model identified pertinent factors that can assist critical care clinicians in identifying patients at high risk of dying from CDI.

Research objectivesClostriodiodes difficile infection (CDI) is a major cause of healthcare-associated diarrhoea with high mortality. There is a lack of validated predictors for severe outcomes in CDI. The aim of this study is to derive and validate a clinical prediction tool for CDI in-hospital mortality using a large critical care database.MethodologyThe demographics, clinical parameters, laboratory results and mortality of CDI were extracted from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. We subsequently trained three machine learning models: logistic regression (LR), random forest (RF) and gradient boosting machine (GBM) to predict in-hospital mortality. The individual performances of the models were compared against current severity scores (Clostridiodes difficile Associated Risk of Death Score (CARDS) and ATLAS (Age, Treatment with systemic antibiotics, leukocyte count, Albumin and Serum creatinine as a measure of renal function) by calculating area under receiver operating curve (AUROC). We identified factors associated with higher mortality risk in each model.Summary of resultsFrom 61 532 intensive care unit stays in the MIMIC-III database, there were 1315 CDI cases. The mortality rate for CDI in the study cohort was 18.33%. AUROC was 0.69 (95% CI, 0.60 to 0.76) for LR, 0.71 (95% CI, 0.62 to 0.77) for RF and 0.72 (95% CI, 0.64 to 0.78) for GBM, while previously AUROC was 0.57 (95% CI, 0.51 to 0.65) for CARDS and 0.63 (95% CI, 0.54 to 0.70) for ATLAS. Albumin, lactate and bicarbonate were significant mortality factors for all the models. Free calcium, potassium, white blood cell, urea, platelet and mean blood pressure were present in at least two of the three models.ConclusionOur machine learning derived CDI in-hospital mortality prediction model identified pertinent factors that can assist critical care clinicians in identifying patients at high risk of dying from CDI.

Mobile devices such as smartphones, tablets and various tiny smart equipment are widely subjected to the limit of energy consumption. Moreover, user experience has become an up-to-the-moment research topic in mobile devices. In most mobile devices, the processor is usually set in a higher frequency level to provide better performance, which would cause the increment of energy consumption. In order to address this issue, in this paper, we propose a novel task scheduling model to harvest the energy saving and user experience improvement in energy-sensitive mobile devices. In our task scheduling model, we introduce the concept of user-centric task, and present tow judgmental principles to determine user-centric tasks (i.e., foreground/interactive tasks). For enhancing user experience, we reduce slack time of the user-centric tasks properly to achieve a shorter response time. We then present two algorithms to calculate the optimal frequencies and allocate the CPU resources. The experimental evaluation results show that our task scheduling model can achieve a tradeoff between energy consumption and performance, and enhance user experience.

In order to ease the contradiction between the development of industrial structure and the supply of land resources, people must explore the relationship between the adjustment of industrial structure and the intensive utilization of land resources, realize the organic combination of two sides, and realize the intensive utilization of land resources within all industries. This paper analyzed the interactive relationship between the industrial structure adjustment and regional land resources utilization, introduced the present situation of the land use and development of industrial structure in Hebei province, and put forward concrete Suggestions to realize intensive utilization of land and adjustment of industrial structure.