Observations and modeling of the surface seiches of Lake Tahoe, USA

A rich array of spatially complex surface seiche modes exists in lakes. While the amplitude of these oscillations is often small, knowledge of their spatio-temporal characteristics is valuable for understanding when they might be of localized hydrodynamic importance. The expression and impact of these basin-scale barotropic oscillations in Lake Tahoe are evaluated using a finite-element numerical model and a distributed network of ten high-frequency nearshore monitoring stations. Model-predicted nodal distributions and periodicities are confirmed using the presence/absence of spectral power in measured pressure signals, and using coherence/phasing analysis of pressure signals from stations on common and opposing antinodes. Surface seiches in Lake Tahoe have complex nodal distributions despite the relative simplicity of the basin morphometry. Seiche amplitudes are magnified on shallow shelves, where they occasionally exceed 5 cm; elsewhere, amplitudes rarely exceed 1 cm. There is generally little coherence between surface seiching and littoral water quality. However, pressure–temperature coherence at shelf sites suggests potential seiche-driven pumping. Main-basin seiche signals are present in attached marinas, wetlands, and bays, implying reversing flows between the lake and these water bodies. On the shallow sill connecting Emerald Bay to Lake Tahoe, the fundamental main-basin seiche combines with a zeroth-mode harbor seiche to dominate the cross-sill flow signal, and to drive associated temperature fluctuations. Results highlight the importance of a thorough descriptive understanding of the resonant barotropic oscillations in any lake basin in a variety of research and management contexts, even when the magnitude of these oscillations tends to be small

Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia

Event-based models (EBM) are a class of disease progression models that can be used to estimate temporal ordering of neuropathological changes from cross-sectional data. Current EBMs only handle scalar biomarkers, such as regional volumes, as inputs. However, regional aggregates are a crude summary of the underlying high-resolution images, potentially limiting the accuracy of EBM. Therefore, we propose a novel method that exploits high-dimensional voxel-wise imaging biomarkers: n-dimensional discriminative EBM (nDEBM). nDEBM is based on an insight that mixture modeling, which is a key element of conventional EBMs, can be replaced by a more scalable semi-supervised support vector machine (SVM) approach. This SVM is used to estimate the degree of abnormality of each region which is then used to obtain subject-specific disease progression patterns. These patterns are in turn used for estimating the mean ordering by fitting a generalized Mallows model. In order to validate the biomarker ordering obtained using nDEBM, we also present a framework for Simulation of Imaging Biomarkers' Temporal Evolution (SImBioTE) that mimics neurodegeneration in brain regions. SImBioTE trains variational auto-encoders (VAE) in different brain regions independently to simulate images at varying stages of disease progression. We also validate nDEBM clinically using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). In both experiments, nDEBM using high-dimensional features gave better performance than state-of-the-art EBM methods using regional volume biomarkers. This suggests that nDEBM is a promising approach for disease progression modeling.Comment: IPMI 201

Modeling the impact of the oil sector on the economy of sultanate of Oman

This study constructs and analyses a simple macroeconomic model that specifically tailored to model the impact of oil sector on the economy of Sultanate of Oman. The constructed model of the study measures the impact of oil sector on the Oman economy for the last three decades and also provides some forecasting for the major macroeconomics indicators related to the Oman economy. Model simulations indicate that the oil sector has large and positive impact on Oman gross domestic product and its influence spills over to all other non-oil sectors of Oman economy. The study found that largest influence of oil was on the gas sector and the least economic sector influenced by oil was agricultural sector. The findings of the study suggest that Oman economy is far from being diversified and that the proposed model helps the policy makers in Oman to identify and forecast the impact of oil on other components of the Oman economy

The Characterisation of the Strength Development of A Cement-Stabilised Soft Soil Treated with Two Different Types of Fly Ashes

There are several problems associated with soft soils such as the low strength, high compressibility and the sensitivity with the changes in the water content. In order to mitigate such undesirable properties, soft soils are often improved and stabilised either mechanically chemically. However, chemical stabilisation is the most effective technique to improve the geotechnical properties of the soft soil. This study aims to improve the properties of a soft soil regarding the consistency and compressive strength by using a small amount of cement (5% OPC by the dry weight of the treated soil). Then two different types of fly ash were examined for pozzolanic activation of the cement treated soil. These fly ashes were pulverised fuel ash (PFA) and palm oil fuel ash (POFA). Initially, trial specimens containing 5% OPC with 5% of PFA or POFA were prepared for unconfined compressive strength testing (UCS) conducted at 7 days of curing. These trial specimens were manufactured to indicate with which type of fly ash the future research should be based on. The results of UCS test revealed that PFA indicated higher strength than that for POFA after 7 days of curing. Thus PFA was considered in this study as a pozzolanic activator for further experimental works. Additionally, the cement-stabilised soil (CSS) mixture was mixed with PFA with different proportions where OPC was kept as 5% and PFA was varied from 5–15% by the dry weight of the stabilised soil. The improvement levels in the stabilised soil were evaluated dependent on the results of UCS test conducted on specimens of CSS treated with different percentages of PFA and subjected to two different periods of curing (7 and 28 days). The effect of PFA on the compaction parameters (maximum dry density (MDD), optimum moisture content (OMC)) and Atterberg limits (liquid limit (LL), plastic limit (PL), along with the plasticity index (PI)) of the CSS soil was also explored in this study. The plasticity characteristic of the treated soil was found to decrease with continuous increments of PFA. The PI decreased from 20.3 for the untreated soil to 13.75 for the cement stabilised soil treated with 10% PFA. The optimised mixture in this research was found to be (soil + 5% OPC + 10% PFA) which increased the UCS of the soil from 134kPa for the virgin soil (VS) and 732kPa for the soil treated with only 5% OPC cured for 28 days to 946kPa at an equivalent 28 days of curing

Dexamethasone treatment of pregnant F0 mice leads to parent of origin-specific changes in placental function of the F2 generation.

Dexamethasone treatment of F0 pregnant rodents alters F1 placental function and adult cardiometabolic phenotype. The adult phenotype is transmitted to the F2 generation without further intervention, but whether F2 placental function is altered by F0 dexamethasone treatment remains unknown. In the present study, F0 mice were untreated or received dexamethasone (0.2µgg(-1)day(-1), s.c.) over Days 11-15 or 14-18 of pregnancy (term Day 21). Depending on the period of F0 dexamethasone treatment, F1 offspring were lighter at birth or grew more slowly until weaning (P<0.05). Glucose tolerance (1gkg(-1), i.p.) of adult F1 males was abnormal. Mating F1 males exposed prenatally to dexamethasone with untreated females had no effect on F2 placental function on Day 19 of pregnancy. In contrast, when F1 females were mated with untreated males, F2 placental clearance of the amino acid analogue (14)C-methylaminoisobutyric acid was increased by 75% on Day 19 specifically in dams prenatally exposed to dexamethasone on Days 14-18 (P<0.05). Maternal plasma corticosterone was also increased, but F2 placental Slc38a4 expression was decreased in these dams (P<0.05). F0 dexamethasone treatment had no effect on F2 fetal or placental weights, regardless of lineage. Therefore, the effects of F0 dexamethasone exposure are transmitted intergenerationally to the F2 placenta via the maternal, but not paternal, line.This is the accepted manuscript. The final version is available at http://dx.doi.org/10.1071/RD14285

Removal of Nitrogen Compounds from Industrial Wastewater Using Sequencing Batch Reactor: The Effects of React Time

This study was performed to optimise the react time (RT) and study its effects on the removal rates of nitrogen compounds in a sequencing batch reactor (SBR) treating synthetic industrial wastewater. The results showed that increasing the RT from 4 h to 10, 16 and 22 h significantly improved the nitrogen compounds’ removal efficiency, it was increased from 69.5% to 95%, 75.7 to 97% and from 54.2 to 80.1% for NH3-N, NO3-N and NO2-N respectively. The results obtained from this study showed that the RT of 22 h was the optimum for nitrogen compounds removal efficiency

A review of the mosquito species (Diptera: Culicidae) of Bangladesh

© The Author(s). 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. The attached file is the published version of the article

The Development of a Low Carbon Cementitious Material Produced from Cement, Ground Granulated Blast Furnace Slag and High Calcium Fly Ash

This research represents experimental work for investigation of the influence of utilising Ground Granulated Blast Furnace Slag (GGBS) and High Calcium Fly Ash (HCFA) as a partial replacement for Ordinary Portland Cement (OPC) and produce a low carbon cementitious material with comparable compressive strength to OPC. Firstly, GGBS was used as a partial replacement to OPC to produce a binary blended cementitious material (BBCM); the replacements were 0, 10, 15, 20, 25, 30, 35, 40, 45 and 50% by the dry mass of OPC. The optimum BBCM was mixed with HCFA to produce a ternary blended cementitious material (TBCM). The replacements were 0, 10, 15, 20, 25, 30, 35, 40, 45 and 50% by the dry mass of BBCM. The compressive strength at ages of 7 and 28 days was utilised for assessing the performance of the test specimens in comparison to the reference mixture using 100% OPC as a binder. The results showed that the optimum BBCM was the mix produced from 25% GGBS and 75% OPC with compressive strength of 32.2 MPa at the age of 28 days. In addition, the results of the TBCM have shown that the addition of 10, 15, 20 and 25% of HCFA to the optimum BBCM improved the compressive strength by 22.7, 11.3, 5.2 and 2.1% respectively at 28 days. However, the replacement of optimum BBCM with more than 25% HCFA have showed a gradual drop in the compressive strength in comparison to the control mix. TBCM with 25% HCFA was considered to be the optimum as it showed better compressive strength than the control mix and at the same time reduced the amount of cement to 56%. Reducing the cement content to 56% will contribute to decrease the cost of construction materials, provide better compressive strength and also reduce the CO2 emissions into the atmosphere

A Statistical Model for the Geotechnical Parameters of Cement-Stabilised Hightown's Soft Soil: A Case Study of Liverpool, UK

This study investigates the effect of two important parameters (length of curing period and percentage of the added binder) on the strength of soil treated with OPC. An intermediate plasticity silty clayey soil with medium organic content was used in this study. This soft soil was treated with different percentages of a commercially available cement type 32.5-N. laboratory experiments were carried out on the soil treated with 0, 1.5, 3, 6, 9, and 12% OPC by the dry weight to determine the effect of OPC on the compaction parameters, consistency limits, and the compressive strength .Unconfined compressive strength test (UCS) was carried out on cement treated specimens after exposing them to different curing periods (1, 3, 7, 14, 28, and 90 days). The results of UCS test were used to develop a non-linear multiple regression model to find the relationship between the predicted and the measured maximum compressive strength of treated soil (qu). The results indicated that there were a significant improvement in the index of plasticity (IP) with treating by OPC; IP was decreased from 21 to 14.1 by using 12% of OPC; this percentage was enough to increase the unconfined compressive strength of the treated soil up to 1362kPa after 90 days of curing. With respect to the statistical model of the predicted qu, the results showed that the regression coefficients (R2) was equal to 0.8534 which indicates a good reproducibility for the constructed model

Optimization Based Hybrid Congestion Alleviation for 6LoWPAN Networks

The IPv6 over Low-Power Wireless Personal Area Network (6LoWPAN) protocol stack is a key part of the Internet of Things (IoT) where the 6LoWPAN motes will account for the majority of the IoT ‘things’. In 6LoWPAN networks, heavy network traffic causes congestion which significantly effects the network performance and the quality of service (QoS) metrics. Generally, two main strategies are used to control and alleviate congestion in 6LoWPAN networks: resource control and traffic control. All the existing work of congestion control in 6LoWPAN networks use one of these. In this paper, we propose a novel congestion control algorithm called optimization based hybrid congestion alleviation (OHCA) which combines both strategies into a hybrid solution. OHCA utilizes the positive aspects of each strategy and efficiently uses the network resources. The proposed algorithm uses a multi-attribute optimization methodology called grey relational analysis for resource control by combining three routing metrics (buffer occupancy, expected transmission count and queuing delay) and forwarding packets through non-congested parents. Also, OHCA uses optimization theory and Network Utility Maximization (NUM) framework to achieve traffic control when the non-congested parent is not available where the optimal nodes’ sending rate are computed by using Lagrange multipliers and KKT conditions. The proposed algorithm is aware of node priorities and application priorities to support the IoT application requirements where the applications’ sending rate allocation is modelled as a constrained optimization problem. OHCA has been tested and evaluated through simulation by using Contiki OS and compared with comparative algorithms. Simulation results show that OHCA improves performance in the presence of congestion by an overall average of 28.36%, 28.02%, 48.07%, 31.97% and 90.35% in terms of throughput, weighted fairness index, end-to-end delay, energy consumption and buffer dropped packets as compared to DCCC6 and QU-RPL