Analysis of characteristic and postexposure practices of occupational blood and body fluid exposures among health care workers in Chinese tertiary hospitals: a retrospective ten-year study.

BACKGROUND: Occupational blood and body fluid exposure (OBEs) is a highly concerning global health problem in health facilities. Improper or inadequate post-exposure practices increase the risk of infection with bloodborne pathogens. Understanding risk factors for OBEs and evaluating the post-exposure practices will contribute to healthcare workers' (HCWs) well-being. METHODS: This study retrospectively synthesized and reviewed the 10-year data (from 2010 to 2020) on OBEs in a tertiary teaching hospital. RESULTS: A total of 519 HCWs have reported OBEs, increasing yearly from 2010 to 2020. Of these, most were nurses (247 [47.2%]), female (390 [75.1%]), at 23-27 years old (207 [39.9%]). The hepatitis B was the primary bloodborne pathogen exposed to HCWs, with 285 (54.9%) cases, internal medicine was the main exposure site (161 [31.0%]), and sharp injury was the main exposure route (439 [84.6%]). Data analysis shows that there are significant differences between exposure route, exposed pathogens, and exposure site among the different occupational categories (X2 = 14.5, 43.7, 94.3, all P < 0.001). 3.3% of HCWs did not take any post-exposure practices. For percutaneous exposure, 4.7% did not rinse the wound, 3.3% did not squeeze out the wound, and 2.3% did not disinfect the wound. In the case of mucosal exposure, 90.4% clean the exposure area immediately. CONCLUSIONS: The data from the past decade underscores the seriousness of current situation of OBEs in Chinese tertiary hospital, particularly among young HCWs, and with hepatitis B as the predominant blood-borne pathogen. This study also identifies HCWs may take incorrect post-exposure practices. It's crucial in the future to discuss the effectiveness of main groups targeted for focused specialty-specific guidance for the prevention of such accidents, meanwhile, to include blood-borne disease immunity testing in mandatory health check-ups. Additionally, focus on optimizing post-exposure practices, offering significant steps toward prevention of such incidents and reducing infection risks should also be considered in future studies.

Higher level of neutrophil-to-lymphocyte is associated with severe COVID-19.

In December 2019, cases of severe coronavirus 2019 (COVID-19) infection rapidly progressed to acute respiratory failure. This study aims to assess the association between the neutrophil-to-lymphocyte ratio (NLR) and the incidence of severe COVID-19 infection. A retrospective cohort study was conducted on 210 patients with COVID-19 infection who were admitted to the Central Hospital of Wuhan from 27 January 2020 to 9 March 2020. Peripheral blood samples were collected and examined for lymphocyte subsets by flow cytometry. Associations between tertiles of NLR and the incidence of severe illness were analysed by logistic regression.Of the 210 patients with COVID-19, 87 were diagnosed as severe cases. The mean NLR of the severe group was higher than that of the mild group (6.6 vs. 3.3, P < 0.001). The highest tertile of NLR (5.1-19.7) exhibited a 5.9-fold (95% CI 1.3-28.5) increased incidence of severity relative to that of the lowest tertile (0.6-2.5) after adjustments for age, diabetes, hypertension and other confounders. The number of T cells significantly decreased in the severe group (0.5 vs. 0.9, P < 0.001). COVID-19 might mainly act on lymphocytes, particularly T lymphocytes. NLR was identified as an early risk factor for severe COVID-19 illness. Patients with increased NLR should be admitted to an isolation ward with respiratory monitoring and supportive care.

[Apoptosis of hepatocellular induced by active ingredients of Zanthoxyli Radix by Raman spectroscopy].

The apoptosis of mono-hepatocellular induced by the active ingredients of the Zanthoxyli Radix was investigated using laser Raman spectroscopy. Hepatoma cells (BEL-7404) were treated with 10 mg•L⁻¹ nitidine chloride and 3 g•L⁻¹ the extracts of Zanthoxyli Radix, respectively, then were divided into two parts, one for fluorescence staining, the other for determination of Raman spectroscopy. The acquired spectra were then processed by background elimination, smoothing, and normalization. Fluorescence staining results showed that the nucleuses from untreated group were uniformly stained, while those from the group treated for 48 hours were densely stained and broken. The spectra results revealed that the intensity of peaks associated with nucleic acid and protein decreased after the cells were incubated with the extracts of Zanthoxyli Radix for 12, 24, 36 and 48 hours. The intensity of peaks at 785,1 002,1 175,1 660 cm⁻¹ was decreased with the time of the cells were incubated by the extracts of Zanthoxyli Radix. The results indicated that the extracts of Zanthoxyli Radix could induce the apoptosis of hepatoma cells and reduce the amount of nucleic acid and protein in the cells. There is a certain relevance between the drug treatment time and the efficacy. The above results suggest that Raman spectra can provide abundant information about the changes in biological macromolecules within the cells after incubated by the extracts of Zanthoxyli Radix and serve as an effective method for the real time measurement of apoptosis.

Investigation of contemporary college students' mental health status and construction of a risk prediction model.

BACKGROUND: Due to academic pressure, social relations, and the change of adapting to independent life, college students are under high levels of pressure. Therefore, it is very important to study the mental health problems of college students. Developing a predictive model that can detect early warning signals of college students' mental health risks can help support early intervention and improve overall well-being. AIM: To investigate college students' present psychological well-being, identify the contributing factors to its decline, and construct a predictive nomogram model. METHODS: We analyzed the psychological health status of 40874 university students in selected universities in Hubei Province, China from March 1 to 15, 2022, using online questionnaires and random sampling. Factors influencing their mental health were also analyzed using the logistic regression approach, and R4.2.3 software was employed to develop a nomogram model for risk prediction. RESULTS: We randomly selected 918 valid data and found that 11.3% of college students had psychological problems. The results of the general data survey showed that the mental health problems of doctoral students were more prominent than those of junior college students, and the mental health of students from rural areas was more likely to be abnormal than that of urban students. In addition, students who had experienced significant life events and divorced parents were more likely to have an abnormal status. The abnormal group exhibited significantly higher Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 scores than the healthy group, with these differences being statistically significant (P < 0.05). The nomogram prediction model drawn by multivariate analysis included six predictors: The place of origin, whether they were single children, whether there were significant life events, parents' marital status, regular exercise, intimate friends, and the PHQ-9 score. The training set demonstrated an area under the receiver operating characteristic (ROC) curve (AUC) of 0.972 [95% confidence interval (CI): 0.947-0.997], a specificity of 0.888 and a sensitivity of 0.972. Similarly, the validation set had a ROC AUC of 0.979 (95%CI: 0.955-1.000), with a specificity of 0.942 and a sensitivity of 0.939. The H-L deviation test result was χ2 = 32.476, P = 0.000007, suggesting that the model calibration was good. CONCLUSION: In this study, nearly 11.3% of contemporary college students had psychological problems, the risk factors include students from rural areas, divorced parents, non-single children, infrequent exercise, and significant life events.

The effects of nurse-led family pulmonary rehabilitation intervention on quality of life and exercise capacity in rural patients with COPD.

AIM: To assess the effects of nurse-led family pulmonary rehabilitation intervention on quality of life and exercise capacity in rural patients with chronic obstructive pulmonary disease (COPD). DESIGN: A non-randomized clinical trial. METHODS: This study enrolled COPD patients at two hospitals from January 2019 to January 2020. The primary outcome was the quality of life. The secondary outcome was exercise capacity. RESULTS: Seventy-four rural COPD patients were enrolled, 38 in the comprehensive nursing group and 36 in the routine nursing group. After the intervention, the St. George's Respiratory Questionnaire (SGRQ) scores in the comprehensive nursing group were significantly lower than in the routine nursing group, especially in symptoms, activities and total scores. The SGRQ scores of the comprehensive nursing group were significantly lower than baseline, while the SGRQ scores of the routine nursing group were significantly higher than baseline. After the intervention, there was a significant difference in six-minute walking distance(6MWD) between the two groups.

Inhibition of acid sensing ion channels by eugenol in rat trigeminal ganglion neurons.

Eugenol is widely used as an analgesic in the dental treatment. The underlying mechanisms may involve its modulation of various ion channels. Acid-sensing ion channels (ASICs) are pH sensors and expressed in trigeminal ganglion (TG) neurons. In the present study, we found that eugenol concentration-dependently inhibited ASIC currents in TG neurons with an IC50 of 98.8 ± 7.4 µM. Eugenol decreased the maximum response to acidic pH and did not alter pH0.5 in the concentration-response curve of acidic pH, suggesting a noncompetitive inhibition of ASICs by eugenol. G-proteins were not involved in eugenol-induced inhibition, since pre-application of eugenol also decreased ASIC currents in the presence of the G-protein blocker GDP-ß-S. In addition, eugenol also partly inhibited ASIC3 currents in Chinese hamster ovary cells transfected with ASIC3. In conclusion, eugenol partly inhibited ASIC currents in TG neurons in a concentration-dependent, non-competitive and G-protein independent manner. These results suggested that the ASICs could be a molecular target for eugenol in TG neurons, which contributed to its analgesic effect.

[Calibration methods of infrared spectra for geographical origins determination of Radix Zanthoxyli].

The instrument and the experimental environment influence the infrared spectra, which may limited the identification of the samples by a prediction model. Based on the Fourier transform infrared spectroscopy (FTIR) technology, the authors performed different infrared spectral calibration methods for Radix Zanthoxyli geographical origins determination, the SIMCA was used to establish an identification models, and the model was used to distinguish samples from four different regions of Guangxi. According to the result of prediction, the authors could obtain the most suitable calibration method for the identification model. The results showed that, respectively, by the multiple scattering correction and standard normal variation, their PCA data distribution and the distance between models is ideal, suggesting that we can eliminate the interference from the environmental and human factors by these two correction methods, and also separate each samples of different habitats. The test using the method to measure the geographical origins of Radix Zanthoxyli proved that the recognition rate and rejection rate are both at or near 100%. Visible, and both the multiplicative scatter correction and the standard normal variation are all the ideal calibration methods for Radix Zanthoxyli infrared spectral geographical origins determination.

Effect of Zhujingqiaoyun Receptivity of Infertility with Kidney Deficiency Based on Ultrasonic Evaluation.

Objective: This study aims to explore the effect of the prescription for Zhujingqiaoyun receptivity in patients with infertility. Methods: This project is a prospective randomized controlled clinical study, including infertility diagnostic criteria and dialectical kidney deficiency patients. 60 cases were randomly divided into 2 groups: the control group, where medication complex packing estradiol tablets were given, and the treatment group, on the basis of the control group, which was given Zhujingqiaoyun receptivity plus or minus. Transvaginal ultrasound was used to observe the endometrial thickness, endometrial volume, endometrial blood supply, and other aspects of patients in the two groups to evaluate the endometrial receptivity before and after treatment, and to record the pregnancy rate and safety of patients in the two groups after three menstrual cycles. Results: There was no significant difference in age, course of disease, and endometrial thickness between the two groups (P > 0.05). Before and after treatment, the endometrial thickness of the two groups increased significantly, and the uterine artery blood flow pulsatility index (PI) and resistance index (RI) decreased significantly (P < 0.05). The endometrial volume in the control group was significantly lower than that in the treatment group, and the difference was statistically significant (P < 0.05). The endometrial FI and VFI in the control group were significantly lower than those in the treatment group, and the difference was statistically significant (P < 0.05). In the treatment group, 30 cases were treated for 3 months, and 11 of those were pregnant (36.7%). There were 30 cases in the control group, and 5 cases were pregnant (16.67%). Both groups had good safety. SPSS 22.0 statistical software was used for the chi-square test. Conclusion: Zhujingqiaoyun receptivity on endometrial receptivity can treat infertility patients with good efficacy, increasing endometrial thickness and reducing uterine artery blood flow index. It is worthy of clinical promotion to improve pregnancy rates.

An automatic method to generate voxel-based absorbed doses from radioactivity distributions for nuclear medicine using generative adversarial networks: a feasibility study.

An approach to autogenerate voxel-based absorbed dose for nuclear medicine is proposed using generative adversarial networks. The method is based on image-to-image transformation and promises to achieve real-time visualization of the absorbed dose and optimization of therapeutic strategies. The activity-density superimposed image is input to generator (G) as a reference image to generate a pseudoabsorbed dose image (DI), which is then mixed with ground truth (GT) DI and recognized by discriminator (D). If the pseudoimage is recognized, the information is fed back, and G regenerates a pseudodose image until D drops to obtain a lifelike DI. As a feasibility study, we used the dose distribution of segmented human anatomy from different sources and activities as training and test datasets. The activity source was assumed to be 1, 2, 3, 4, or 7 subsource blocks. The 3-subsource model was used as the test dataset, and the others were used as the training dataset. The activity distribution in the subsource was assumed to be uniform or heterogeneous (i.e., Gaussian diffusion with sigma 0.0, 0.3, or 0.6). Differences were assessed by Gamma analysis. Results showed that the same or quasi-inhomogeneity model can well predict the dose distribution of different activity-inhomogeneity. Although the 1-source model was trained with very few datasets, it showed an optimal balance between accuracy and training efficiency. There were offsets in the mean absorbed dose between the predicted and GT, but they all showed a higher Gamma-pass-rate (> 93%) and ~ 10% std.

Relationship between Occupational Metal Exposure and Hypertension Risk Based on Conditional Logistic Regression Analysis.

Occupational exposure is a significant source of metal contact; previous studies have been limited regarding the effect of occupational metal exposure on the development of hypertension. This study was conducted to assess the levels of exposure of certain metals (chromium (Cr), iron (Fe), manganese (Mn), and nickel (Ni)) in hypertensive and non-hypertensive workers and to assess the relationship between the risk of hypertension and metal exposure level. Our study included 138 hypertensive patients as case groups and 138 non-hypertensive participants as controls. The exposure risk level was divided according to the limit value after collecting and testing the metal dust in the workshop. Considering the influence of single- and poly-metal, single factor analysis and conditional logistic regression analysis of poly-metal were carried out. The results of the model indicated that the incidence of hypertension increased with an increase in Cr exposure level, and the risk of hypertension was 1.85 times higher in the highest exposure than in the lowest exposure (95% CI: 1.20−2.86, p < 0.05). Mn has the same effect as Cr. There was no significant correlation between Fe or Ni and hypertension. Our findings suggested that Cr and Mn exposure in the work environment might increase the risk of hypertension, while no effect of Fe and Ni on blood pressure was found. Prospective study designs in larger populations are needed to confirm our findings.

[Application of two FTIR pattern recognition methods to the Zanthoxylum nitidum geographical origins determination].

In the present work, the authors explored a rapid method of the Zanthoxylum nitidum geographical origins determination. Based on Fourier transform infrared spectroscopy (FTIR) technology, the band of 1 800-400 cm(-1) which is the IR fingerprint of Zanthoxylum nitidum, the Fisher ratio and the soft independent modeling of class analogies (SIMCA) were used to build a classification model. Respectively, four kinds of Zanthoxylum nitidum in the Guangxi region were detected by the model, and the model was verified by calculating their recognition rate and rejection rate. The results show that the authors can accurately extract the overall information of Chinese herbal medicines by using the FTIR, also established a pattern recognition model to predict unknown samples, and obtained satisfactory recognition rate and rejection rate, indicating that the model has stronger ability of identification. The detection on real time was carried out rapidly with the Fisher model, suggesting that the model has more practical value.

Fluid dynamics analysis and experimental study for solar radiation error correction of sounding humidity sensor.

In order to correct the solar radiation error of relative humidity, the mainstream capacitive sounding humidity sensor HC103M2 is selected and investigated by simulation analysis and experimental verification. First, the basic theories for solar radiation error and sensor error itself are elaborated, and simulational and experimental platforms are introduced. The computational fluid dynamics (CFD) method is utilized to theoretically investigate the dry error of the humidity sensor caused by solar radiation heating, which is related to radiation intensity, altitude, and solar elevation angle as well as reflectivity, thickness, and shape of the shield. Then, in order to verify the accuracy of the simulation, an experimental platform including a humidity sensor and two temperature sensors to measure the solar radiation heating is built to analyze the relative error of humidity obtained by the CFD simulation and experiment. It is found that their maximum deviation is 3.30% and the average error is 1.94%, which indicates that the calculation using the CFD method is accurate and feasible. In order to easily and operationally predict the solar radiation heating of the humidity sensor, a back propagation (BP) neural network fusion algorithm based on three inputs of radiation intensity, air pressure, and solar elevation angle is proposed. Compared with the solar radiation heating obtained by CFD simulation, the maximum absolute error is about 0.2 K, and the relative error of humidity is about ±1.30%. Finally, a case of vertical humidity profile correction considering the temperature-sensitive error of HC103M2 is analyzed. The response time of sensor measurement and the airflow into the shield are discussed as well. The corrected results after taking solar radiation heating into account are more similar to those measured by RS92 and cryogenic frost point hygrometer (CFH). This result shows that the prediction model is accurate, which may be applied to correct the dry error and further improve the accuracy of humidity measurement.

Magnetron sputtered ZnO electron transporting layers for high performance perovskite solar cells.

An ideal electron transporting layer (ETL) of perovskite solar cells (PSCs) requires reasonable energy levels, high electrical conductivity and excellent charge extraction. The low processing temperature makes ZnO a promising ETL for PSCs; however the widely used solution-processed ZnO films often suffer from high-density surface defect states, which might cause severe charge recombinations at the ETL/perovskite interface and accelerate the chemical decomposition of perovskite materials. In this work, we employed the vacuum-based magnetron sputtering method to deposit ZnO ETLs, which significantly reduces the number of oxygen vacancies and hydroxyl groups on the ZnO surface. The magnetron sputtered ZnO based CH3NH3PbI3 PSCs yield a considerable power conversion efficiency (PCE) of 13.04% with excellent long-term device stability. Furthermore, aiming to improve the ETL/perovskite interface for more efficient electron extraction, a bilayer ZnO/SnO2 ETL was designed for constructing high-efficiency PSCs. The detailed morphology characterization confirms that the bilayer ZnO/SnO2 provides a low-roughness film surface for the deposition of high-quality perovskite films with full coverage and long-range continuity. The carrier dynamic study reveals that the presence of the SnO2 layer results in the formation of favorable cascade energy alignments and facilitates the electron extraction at the ETL/perovskite interface. As a result, compared with the ZnO-based PSCs, the device constructed with the bilayer ZnO/SnO2 ETL delivers an improved PCE of 15.82%, coupled with a reduced hysteresis.

Clinical and hematological characteristics of 88 patients with COVID-19.

INTRODUCTION: To retrospectively analyze epidemiological, clinical and hematological characteristics of COVID-19 patients. METHODS: The demographic, symptoms, and physiological parameters of 88 patients were collected and analyzed. The performance of complete blood count (CBC) indexes for monitoring and predicting the severity of COVID-19 in patients was evaluated by analyzing and comparing CBC results among different COVID-19 patient groups. RESULTS: White blood cells (WBCs), the neutrophil percentage (Neu%), absolute neutrophil count (Neu#), and neutrophil-to-lymphocyte ratio (NLR) were significantly higher in the critical group than in the other three groups (P < .05), while the lymphocyte percentage (Lym%), monocyte percentage (Mon%), lymphocyte count (Lym#), and lymphocyte-to-monocyte ratio (LMR) were significantly lower in the critical group than in the other three groups (P < .05). WBCs, the Neu%, Neu#, NLR, and neutrophil-to-monocyte ratio (NMR) were significantly higher in the severe group than in the mild and moderate groups (P < .05), while the Lym% was significantly lower in the severe group than in the mild and moderate groups (P < .05). The Mon%, Lym#, and LMR were significantly lower in the severe group than in the moderate group (P < .05). Using receiver operating characteristic (ROC) curve analysis to differentiate severe and nonsevere patients, the areas under the curve (AUCs) for the NLR, Neu%, and Lym% were 0.733, 0.732, and 0.730, respectively. When differentiating critical patients from noncritical patients, the AUCs for the NLR, Neu%, and Lym% were 0.832, 0.831, and 0.831. CONCLUSIONS: The NLR is valuable for differentiating and predicting patients who will become critical within 4 weeks after the onset of COVID-19.

Ship Emission Impacts on Air Quality and Human Health in the Pearl River Delta (PRD) Region, China, in 2015, With Projections to 2030.

Ship emissions contribute to air pollution, increasing the adverse health impacts on people living in coastal cities. We estimated the impacts caused by ship emissions, both on air quality and human health, in 2015 and future (2030) within the Pearl River Delta (PRD) region of China. In addition, we assessed the potential health benefits of implementing an Emission Control Area (ECA) in the region by predicting avoided premature mortality with and without an ECA. In 2015, ship emissions increased PM2.5 concentrations and O3 mixing ratios by 1.4 µg/m3 and 1.9 ppb, respectively, within the PRD region. This resulted in 466 and 346 excess premature acute deaths from PM2.5 and O3, respectively. Premature mortality from chronic exposures was even more significant, with 2,085 and 852 premature deaths from ship-related PM2.5 and O3, respectively. In 2030, we projected the future ship emissions with and without an ECA, using two possible land scenarios. With an ECA, we predicted 76% reductions in SO2 and 13% reductions in NO x from the shipping sector. Assuming constant land emissions from 2015 in 2030 (2030 Constant scenario), we found that an ECA could avoid 811 PM2.5-related and 108 O3-related deaths from chronic exposures. Using 2030 Projected scenario for land emissions, we found that an ECA would avoid 1,194 PM2.5-related and 160 O3-related premature deaths in 2030. In both scenarios, implementing an ECA resulted in 30% fewer PM2.5-related premature deaths and 10% fewer O3-related premature deaths, illustrating the importance of reducing ship emissions.

Hub genes and key pathways of non-small lung cancer identified using bioinformatics.

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for ~80% of all lung cancer cases. The aim of the present study was to identify key genes and pathways in NSCLC, in order to improve understanding of the mechanism of lung cancer. The GSE33532 gene expression dataset, containing 20 normal and 80 NSCLC samples, was used. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to obtain the enrichment data of differently expressed genes (DEGs). Disease modules within NSCLC were constructed by Cytoscape, using protein-protein interaction (PPI) from the Search Tool for the Retrieval of Interacting Genes database. In addition, the Kaplan Meier plotter KMplot was used to assess the top hub genes in the PPI network. As a result, 1,795 genes were identified in NSCLC; 729 were upregulated and 1,066 were downregulated. The results of the GO analysis indicated that the upregulated DEGs were significantly enriched in 'biological processes' (BP), including 'cell cycle and nuclear division'; the downregulated DEGs were also significantly enriched in BP, including 'response to wounding', 'anatomical structure morphogenesis' and 'response to stimulus'. Upregulated DEGs were also enriched in 'cell cycle', 'DNA replication' and the 'tumor protein 53 signaling pathway', while the downregulated DEGs were also enriched in 'complement and coagulation cascades', 'malaria' and 'cell adhesion molecules'. The top 9 hub genes were cyclin-dependent kinase 9 (CDK1), polo-like kinase 1, aurora kinase B, cell division cycle 20, baculoviral initiator of apoptosis repeat containing 5, mitotic checkpoint serine/threonine kinase B, proliferating cell nuclear antigen (PCNA), centromere protein A and MAD2 mitotic arrest deficient-like 1, and the KMplot results revealed that the high expression levels of these genes resulted in significantly low survival rates, compared with low expression samples (P<0.05), with the exception of PCNA and CDK1. In the pathway crosstalk analysis, 26 nodes and 41 interactions were divided into two groups: One module of the two groups primarily included 'metabolism of amino acid' and the other primarily contained 'tumor necrosis signaling' pathways. In conclusion, the present study assisted in improving the understanding of the molecular mechanisms underlying NSCLC development, and the results may help the understanding of the biological mechanism of NSCLC.

Design, Fabrication, and Implementation of an Array-Type MEMS Piezoresistive Intelligent Pressure Sensor System.

To meet the radiosonde requirement of high sensitivity and linearity, this study designs and implements a monolithically integrated array-type piezoresistive intelligent pressure sensor system which is made up of two groups of four pressure sensors with the pressure range of 0⁻50 kPa and 0⁻100 kPa respectively. First, theoretical models and ANSYS (version 14.5, Canonsburg, PA, USA) finite element method (FEM) are adopted to optimize the parameters of array sensor structure. Combing with FEM stress distribution results, the size and material characteristics of the array-type sensor are determined according to the analysis of the sensitivity and the ratio of signal to noise (SNR). Based on the optimized parameters, the manufacture and packaging of array-type sensor chips are then realized by using the standard complementary metal-oxide-semiconductor (CMOS) and microelectromechanical system (MEMS) process. Furthermore, an intelligent acquisition and processing system for pressure and temperature signals is achieved. The S3C2440A microprocessor (Samsung, Seoul, Korea) is regarded as the core part which can be applied to collect and process data. In particular, digital signal storage, display and transmission are realized by the application of a graphical user interface (GUI) written in QT/E. Besides, for the sake of compensating the temperature drift and nonlinear error, the data fusion technique is proposed based on a wavelet neural network improved by genetic algorithm (GA-WNN) for average measuring signal. The GA-WNN model is implemented in hardware by using a S3C2440A microprocessor. Finally, the results of calibration and test experiments achieved with the temperature ranges from -20 to 20 °C show that: (1) the nonlinear error and the sensitivity of the array-type pressure sensor are 8330 × 10-4 and 0.052 mV/V/kPa in the range of 0⁻50 kPa, respectively; (2) the nonlinear error and the sensitivity are 8129 × 10-4 and 0.020 mV/V/kPa in the range of 50⁻100 kPa, respectively; (3) the overall error of the intelligent pressure sensor system is maintained at ±0.252% within the hybrid composite range (0⁻100 kPa). The involved results indicate that the developed array-type composite pressure sensor has good performance, which can provide a useful reference for the development of multi-range MEMS piezoresistive pressure sensor.

Monte Carlo study of the influence of energy spectra, mesh size, high Z element on dose and PVDR based on 1-D and 3-D heterogeneous mouse head phantom for Microbeam Radiation Therapy.

PURPOSE: To evaluate the influence of energy spectra, mesh sizes, high Z element on dose and PVDR in Microbeam Radiation Therapy (MRT) based on 1-D analogy-mouse-head-model (1-D MHM) and 3-D voxel-mouse-head-phantom (3-D VMHP) by Monte Carlo simulation. METHODS: A Microbeam-Array-Source-Model was implemented into EGSnrc/DOSXYZnrc. The microbeam size is assumed to be 25µm, 50µm or 75µm in thickness and fixed 1mm in height with 200µmc-t-c. The influence of the energy spectra of ID17@ESRF and BMIT@CLS were investigated. The mesh size was optimized. PVDR in 1-D MHM and 3-D VMHP was compared with the homogeneous water phantom. The arc influence of 3-D VMHP filled with water (3-D VMHWP) was compared with the rectangle phantom. RESULTS: PVDR of the lower BMIT@CLS spectrum is 2.4times that of ID17@ESRF for lower valley dose. The optimized mesh is 5µm for 25µm, and 10µm for 50µm and 75µm microbeams with 200µmc-t-c. A 500µm skull layer could make PVDR difference up to 62.5% for 1-D MHM. However this influence is limited (<5%) for the farther homogeneous media (e.g. 600µm). The peak dose uniformity of 3-D VMHP at the same depth could be up to 8% for 1.85mm×1mm irradiation field, whereas that of 3-D VMHWP is<1%. The high Z element makes the dose uniformity enhance in target. The surface arc could affect the superficial PVDR (from 44% to 21% in 0.2mm depth), whereas this influence is limited for the more depth (<1%). CONCLUSION: An accurate MRT dose calculation algorithm should include the influence of 3-D heterogeneous media.

Design Optimization and Fabrication of High-Sensitivity SOI Pressure Sensors with High Signal-to-Noise Ratios Based on Silicon Nanowire Piezoresistors.

In order to meet the requirement of high sensitivity and signal-to-noise ratios (SNR), this study develops and optimizes a piezoresistive pressure sensor by using double silicon nanowire (SiNW) as the piezoresistive sensing element. First of all, ANSYS finite element method and voltage noise models are adopted to optimize the sensor size and the sensor output (such as sensitivity, voltage noise and SNR). As a result, the sensor of the released double SiNW has 1.2 times more sensitivity than that of single SiNW sensor, which is consistent with the experimental result. Our result also displays that both the sensitivity and SNR are closely related to the geometry parameters of SiNW and its doping concentration. To achieve high performance, a p-type implantation of 5 × 10¹8 cm-3 and geometry of 10 µm long SiNW piezoresistor of 1400 nm × 100 nm cross area and 6 µm thick diaphragm of 200 µm × 200 µm are required. Then, the proposed SiNW pressure sensor is fabricated by using the standard complementary metal-oxide-semiconductor (CMOS) lithography process as well as wet-etch release process. This SiNW pressure sensor produces a change in the voltage output when the external pressure is applied. The involved experimental results show that the pressure sensor has a high sensitivity of 495 mV/V·MPa in the range of 0⁻100 kPa. Nevertheless, the performance of the pressure sensor is influenced by the temperature drift. Finally, for the sake of obtaining accurate and complete information over wide temperature and pressure ranges, the data fusion technique is proposed based on the back-propagation (BP) neural network, which is improved by the particle swarm optimization (PSO) algorithm. The particle swarm optimization⁻back-propagation (PSO⁻BP) model is implemented in hardware using a 32-bit STMicroelectronics (STM32) microcontroller. The results of calibration and test experiments clearly prove that the PSO⁻BP neural network can be effectively applied to minimize sensor errors derived from temperature drift.

High Efficiency Dye-sensitized Solar Cells Constructed with Composites of TiO2 and the Hot-bubbling Synthesized Ultra-Small SnO2 Nanocrystals.

An efficient photo-anode for the dye-sensitized solar cells (DSSCs) should have features of high loading of dye molecules, favorable band alignments and good efficiency in electron transport. Herein, the 3.4 nm-sized SnO2 nanocrystals (NCs) of high crystallinity, synthesized via the hot-bubbling method, were incorporated with the commercial TiO2 (P25) particles to fabricate the photo-anodes. The optimal percentage of the doped SnO2 NCs was found at ~7.5% (SnO2/TiO2, w/w), and the fabricated DSSC delivers a power conversion efficiency up to 6.7%, which is 1.52 times of the P25 based DSSCs. The ultra-small SnO2 NCs offer three benefits, (1) the incorporation of SnO2 NCs enlarges surface areas of the photo-anode films, and higher dye-loading amounts were achieved; (2) the high charge mobility provided by SnO2 was confirmed to accelerate the electron transport, and the photo-electron recombination was suppressed by the highly-crystallized NCs; (3) the conduction band minimum (CBM) of the SnO2 NCs was uplifted due to the quantum size effects, and this was found to alleviate the decrement in the open-circuit voltage. This work highlights great contributions of the SnO2 NCs to the improvement of the photovoltaic performances in the DSSCs.