Odour emission ability (OEA) and its application in assessing odour removal efficiency

Odourous emissions from sewer networks and wastewater treatment plants (WWTPs) can significantly impact a local population. Sampling techniques such as wind tunnels and flux hood chambers are traditionally used to collect area source samples for subsequent quantification of odour emission rates using dilution olfactometry, however these methods are unsuitable for assessing liquid samples from point sources due to the large liquid volumes required. To overcome this limitation, a gas phase sample preparation method was developed for assessing the total Odour Emission Ability (OEA) from a liquid sample. The method was validated using two volatile organic sulphur compounds (VOSCs), dimethyl-trisulphide (DMTS) and bismethylthiomethane (BMTM) that are frequently detected from sewers and WWTPs and are relatively stable compared with common VOSCs like mercaptan or methyl mercaptan. The recovery rates of DMTS and BMTM were quantified by injecting a known volume of a standard liquid sample into Tedlar bags using a static injection and a dynamic injection methodology. It was confirmed that both dynamic and static injection methods at ambient condition achieved high recovery rates with no need to consider increasing evaporation by elevating the temperature. This method can also be used to assess odour removal effectiveness of liquids by comparing the OEA before and after the treatment tests. Two application examples were presented.     

Effects of the heterogeneity of game complexity and user population in learning performance of business simulation games

Previous studies on business simulation games (BSGs) have concluded that improved performance may not be the primary benefit of using BSGs, due to mixed results between student performance and perceptions. Two relevant and insightful issues attract our attention, namely, the impacts of the heterogeneous student population and the different complexity levels of BSG software. To address these issues, the present study aims to understand the relationship between student profile/characteristics and performance in the classroom with BSG-facilitated learning. An in-depth case study is conducted on a general college course designed to teach three different complexities of BSGs to students enrolled in different majors. Four student profile factors are individually tested for differences in performance scores as evaluated by the teacher. Additionally, the influences of 11 student characteristics are assessed with regard their self-reported perceived learning performances. Regression analysis and ANOVA are used to investigate the impacts of heterogeneous users and game complexity on student performance. Based on the regression analyses of the data collected from 43 respondents who participated in the general course, the study concludes that knowledge and skill may influence the heterogeneous student population; moreover, student participation and tacit learning preference improve performance, and students with an auditory learning preference or high learning motivation may not perform well in classroom BSG learning. However, the low value of adjusted R square implies that more dimensions or variables are needed to increase the explaining power of the performance scores in the regression analyses. In contrast, heterogeneous BSG software with different complexity levels present different results. The current research contributes practical and incremental knowledge on the complexity of heterogeneous BSG software on performance scores and the perceived learning performance of heterogeneous student populations. With the research limitations acknowledged, a series of suggestions for teachers pertaining to appropriate applications of BSGs in classes is offered as well as recommendations to BSG providers. Nevertheless, in-depth analyses are required, preferably with larger student population samples, to further explore the insignificant relationship between student perceptions and attitude under nonlinear extended complexity.     

赣州市五大产业生产方程与组合预测

依据数学和经济学的理论方法，利用赣州市的历史统计数据，对赣州市的农业、工业、建筑业、运输业、商业分别建立生产方程，进而开展组合预测。     

Hybrid neural document clustering using guided self-organization and WordNet

Document clustering is text processing that groups documents with similar concepts. It's usually considered an unsupervised learning approach because there's no teacher to guide the training process, and topical information is often assumed to be unavailable. A guided approach to document clustering that integrates linguistic top-down knowledge from WordNet into text vector representations based on the extended significance vector weighting technique improves both classification accuracy and average quantization error. In our guided self-organization approach we integrate topical and semantic information from WordNet. Because a document-training set with preclassified information implies relationships between a word and its preference class, we propose a novel document vector representation approach to extract these relationships for document clustering. Furthermore, merging statistical methods, competitive neural models, and semantic relationships from symbolic Word-Net, our hybrid learning approach is robust and scales up to a real-world task of clustering 100,000 news documents.     

Anatomy of a portable digital mediaprocessor

Portable devices equipped with imaging, video, and audio functionality are proliferating rapidly, and manufacturers are shipping hundreds of millions of such devices. A general-purpose processor (GPP) typically consumes tens of watts to nearly a hundred watts, while a high-performance digital still cameras main processor consumes only hundreds of milliwatts to nearly half a watt. Designing a mediaprocessor with performance comparable to that of a GPP at a power budget two orders of magnitude lower and a cost more than an order of magnitude lower poses quite a challenge. To meet requirements and reduce overall system cost, mediaprocessor designers must integrate the device with an extensive set of peripherals. For performance, cost, and power reasons, application-specific integrated circuits have traditionally been the most popular choice for portable media systems. We discuss the architecture of the DM310, a highly integrated portable digital mediaprocessor, manufactured in a 0.13-micron process.     

Decreased Expression of Plakophilin-2 and αT-Catenin in Arrhythmogenic Right Ventricular Cardiomyopathy: Potential Markers for Diagnosis

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a hereditary disease of the heart muscle. Clinical challenges remain, however, in identifying patients with ARVC in the early or concealed stages with subtle clinical manifestations. Therefore, we wanted to identify potential targets by immunohistochemical (IHC) analysis in comparison with controls. Pathogenic mutations were identified in 11 of 37 autopsied patients with ARVC. As observed from IHC analysis of the RV, expression of αT-catenin and plakophilin-2 is significantly decreased in autopsied patients with ARVC as compared to controls, and the decreased expression is consistent in patients with and without pathogenic mutations. Furthermore, ARVC specimens demonstrated a reduced localization of αT-catenin, desmocollin-2, desmoglein-2, desmoplakin, and plakophilin-2 on intercalated discs. These findings have been validated by comparing RV specimens obtained via endomyocardial biopsy between patients with ARVC and those without. The pathogenic mutation was present in 3 of 5 clinical patients with ARVC. In HL-1 myocytes, siRNA was used to knockdown CTNNA3, and western blotting analysis demonstrated that the decline in αT-catenin expression was accompanied by a significant decline in the expression of plakophilin-2. The aforementioned effect was directed towards protein degradation rather than mRNA stability. Plakophilin-2 expression decreases concurrently with the decline in CTNNA3 expression. Therefore, the expression of αT-catenin and plakophilin-2 could be potential surrogates for the diagnosis of ARVC.     

HSPA4 Is a Biomarker of Placenta Accreta and Enhances the Angiogenesis Ability of Vessel Endothelial Cells

Placenta accreta spectrum (PAS) accounts for 7% of maternal mortality and is associated with intraoperative and postoperative morbidity caused by massive blood loss, infection, and adjacent organ damage. The aims of this study were to identify the protein biomarkers of PAS and to further explore their pathogenetic roles in PAS. For this purpose, we collected five placentas from pregnant subjects with PAS complications and another five placentas from normal pregnancy (NP) cases. Then, we enriched protein samples by specifically isolating the trophoblast villous, deeply invading into the uterine muscle layer in the PAS patients. Next, fluorescence-based two-dimensional difference gel electrophoresis (2D-DIGE) and MALDI-TOF/MS were used to identify the proteins differentially abundant between PAS and NP placenta tissues. As a result, nineteen spots were determined as differentially abundant proteins, ten and nine of which were more abundant in PAS and NP placenta tissues, respectively. Then, specific validation with western blot assay and immunohisto/cytochemistry (IHC) assay confirmed that heat shock 70 kDa protein 4 (HSPA4) and chorionic somatomammotropin hormone (CSH) were PAS protein biomarkers. Further tube formation assays demonstrated that HSPA4 promoted the in vitro angiogenesis ability of vessel endothelial cells, which is consistent with the in vivo scenario of PAS complications. In this study, we not only identified PAS protein biomarkers but also connected the promoted angiogenesis with placenta invasion, investigating the pathogenetic mechanism of PAS.     

SURFACE ROUGHNESS OF SPUTTERED SILICON. II. MODEL VERIFICATION

Experimental verification of the mathematical surface roughness model for sputtered silicon was performed. The beam shape and its significant level of intensity were determined first by measuring the topography of craters sputtered by focused ion beam (FIB). Then the beam function was generated for various combinations of beam parameters. The material function was developed both by theoretical and experimental analysis. These two functions were then used in the model to calculate the theoretical surface roughness. Microsurface analysis was formed by FIB sputtering of a (100) silicon wafer. The surface roughness at the bottom of the sputtered features was then measured using an atomic force microscope. The theoretical surface roughness was found to be within ±1 and ±5 nm of the measured surface roughness with the measurement uncertainty (standard deviation) of about ±0.36 and ±0.85 nm for R_a and R_t, respectively.     

A parallel genetic/neural network learning algorithm for MIMDshared memory machines

A new algorithm is presented for training of multilayer feedforward neural networks by integrating a genetic algorithm with an adaptive conjugate gradient neural network learning algorithm. The parallel hybrid learning algorithm has been implemented in C on an MIMD shared memory machine (Cray Y-MP8/864 supercomputer). It has been applied to two different domains, engineering design and image recognition. The performance of the algorithm has been evaluated by applying it to three examples. The superior convergence property of the parallel hybrid neural network learning algorithm presented in this paper is demonstrated.