Effect of Engineering Properties of Soil on Pavement Failures in Ogbagi - Akoko Area, Southwestern, Nigeria

Geotechnical properties of lateritic soil from 8 failed and 2 un –failed sections of road along Ogbagi and Arigidi Akoko, Southwestern Nigeria were investigated to determine the causes of the road failure. Tests carried out were natural moisture content, specific gravity, grain size distribution, atterberg limits, linear shrinkage, compaction and California bearing ratio. From the test results, natural moisture content ranged between 6.75 and 25.5 %, specific gravity (2.68 and 2.76), linear shrinkage (5.7 and 11.4 %) , maximum dry density (1483 and 1780 kg/m3), optimum moisture content (13.5 and 26.0 %), CBR (14 and 31%), liquid limits (31.1 and 53.5 %), plastic limits (0 and 28.2 %) and plasticity index (0 and 29.5 %). Factors responsible for the failure of the pavement are stress induced associated with high swelling of the soil due to ingress of water through joints or cracks, poor engineering properties of the soil due to high percentage of fine materials or clayey nature of topsoil/sub-grade soil below the pavement, poor compaction and drainage systems of the road. Keywords: Atterberg limits, Engineering properties, lateritic soil, pavement failures, grain siz

Assessment of the Dimensionless Groups-Based Scale-Up of Gas–Solid Fluidized Beds

The most common scale-up approach for gas–solids fluidized beds is based on matching the governing dimensionless parameters. In the literature, this approach has been validated only by means of measuring global parameters between different sizes of fluidized beds. However, such global measurements are not sufficient to depict all the interplaying hydrodynamic phenomena and hence verify the scale-up relationships. Therefore, to assess this approach, an advanced gas–solids optical probe and pressure transducer measurement techniques have been applied to quantify local hydrodynamic parameters in two different sized fluidized beds. Four different sets of experimental conditions were designed and conducted to examine the assessment of the scaling approach with matched and mismatched dimensionless groups between the two beds. The results indicated that the reported dimensionless groups are not adequate for achieving similarity between the two gas–solids fluidized beds in terms of solids holdup, gas holdup, particle velocity, mass flux, and pressure fluctuation. This finding demonstrates the importance of local measurements of the hydrodynamic parameters of fluidized beds in order to evaluate scale-up relationships. Finally, the results further advance the understanding of the gas–solids fluidized beds and present deeper insight into their solids dynamics

Dynamic resource allocation for opportunistic software-defined IoT networks: stochastic optimization framework

Several wireless technologies have recently emerged to enable efficient and scalable internet-of-things (IoT) networking. Cognitive radio (CR) technology, enabled by software-defined radios, is considered one of the main IoT-enabling technologies that can provide opportunistic wireless access to a large number of connected IoT devices. An important challenge in this domain is how to dynamically enable IoT transmissions while achieving efficient spectrum usage with a minimum total power consumption under interference and traffic demand uncertainty. Toward this end, we propose a dynamic bandwidth/channel/power allocation algorithm that aims at maximizing the overall network’s throughput while selecting the set of power resulting in the minimum total transmission power. This problem can be formulated as a two-stage binary linear stochastic programming. Because the interference over different channels is a continuous random variable and noting that the interference statistics are highly correlated, a suboptimal sampling solution is proposed. Our proposed algorithm is an adaptive algorithm that is to be periodically conducted over time to consider the changes of the channel and interference conditions. Numerical results indicate that our proposed algorithm significantly increases the number of simultaneous IoT transmissions compared to a typical algorithm, and hence, the achieved throughput is improved

Prognostic Value of Computed Tomography : Measured Parameters of Body Composition in Primary Operable Gastrointestinal Cancers

Professor Graeme Murray, Department of Pathology, University of Aberdeen provided us access to the colorectal cancer pathology databases from which the colorectal component of the research was based. Conflict of interest There are no conflicts of interest.Peer reviewedPublisher PD

Subdiffusion and weak ergodicity breaking in the presence of a reactive boundary

We derive the boundary condition for a subdiffusive particle interacting with a reactive boundary with finite reaction rate. Molecular crowding conditions, that are found to cause subdiffusion of larger molecules in biological cells, are shown to effect long-tailed distributions with identical exponent for both the unbinding times from the boundary to the bulk and the rebinding times from the bulk. This causes a weak ergodicity breaking: typically, an individual particle either stays bound or remains in the bulk for very long times. We discuss why this may be beneficial for in vivo gene regulation by DNA-binding proteins, whose typical concentrations are nanomolarComment: 4 pages, 1 figure, REVTeX4, accepted to Phys Rev Lett, some typos correcte

The lifestyle habits and wellbeing of physicians in Bahrain: a cross-sectional study.

BACKGROUND: Lifestyle habits of physicians are of paramount importance both because they influence the physician\u27s own health and because these habits have been shown to affect patients\u27 care. There is limited information on physician health and lifestyle habits in Bahrain. METHODS: In a cross-sectional study design, an anonymous self-administered questionnaire that assesses wellbeing and lifestyle habits was distributed to a random sample of 175 out of 320 primary health care physicians in Bahrain. Descriptive analyses were performed, and the variables were cross-tabulated using SPSS version 20.0. RESULTS: 152 physicians agreed to participate in the study. Respondents were 67.1 % female with a mean age of 45 (SD = 10). The majority were of Bahraini nationality. The most prevalent reported health conditions were hyperlipidaemia (25.5 %), hypertension (20.3 %), and diabetes (11.0 %). Only 29.6 % of physicians reported performing ≥ 30 min of exercise in a usual week. Of physicians exercising ≥ 30 min weekly, only 13 % exercised ≥ 5 days weekly. 98.0 % report never drinking, 1.3 % report previously drinking, and 0.7 % report drinking less than once weekly. The average body mass index (BMI) was 27.8 (SD = 5), with 39 % of physicians being overweight and 33 % obese. BMI was directly associated with sleep time (P0.027, r(2) = 0.034), age (P CONCLUSIONS: There is a clear pattern of unfavourable lifestyle habits and obesity among primary health care physicians in Bahrain. We encourage institutions and public health sectors to be more proactive in assisting physicians to attain healthier lifestyles

Elastomer-based visuotactile sensor for normality of robotic manufacturing systems

Modern aircrafts require the assembly of thousands of components with high accuracy and reliability. The normality of drilled holes is a critical geometrical tolerance that is required to be achieved in order to realize an efficient assembly process. Failure to achieve the required tolerance leads to structures prone to fatigue problems and assembly errors. Elastomer-based tactile sensors have been used to support robots in acquiring useful physical interaction information with the environments. However, current tactile sensors have not yet been developed to support robotic machining in achieving the tight tolerances of aerospace structures. In this paper, a novel elastomer-based tactile sensor was developed for cobot machining. Three commercial silicon-based elastomer materials were characterised using mechanical testing in order to select a material with the best deformability. A Finite element model was developed to simulate the deformation of the tactile sensor upon interacting with surfaces with different normalities. Additive manufacturing was employed to fabricate the tactile sensor mould, which was chemically etched to improve the surface quality. The tactile sensor was obtained by directly casting and curing the optimum elastomer material onto the additively manufactured mould. A machine learning approach was used to train the simulated and experimental data obtained from the sensor. The capability of the developed vision tactile sensor was evaluated using real-world experiments with various inclination angles, and achieved a mean perpendicularity tolerance of 0.34°. The developed sensor opens a new perspective on low-cost precision cobot machining

Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous and a non-invasive BP measurement system is proposed using Photoplethysmogram (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo pre-processing and feature extraction steps. Time, frequency and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for Systolic BP (SBP) and Diastolic BP (DBP) estimation individually. Gaussian Process Regression (GPR) along with ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.Comment: Accepted for publication in Sensor, 14 Figures, 14 Table

Temperature Effects on Growth of the Biocontrol Agent Pantoea Agglomerans (an Oval Isolate From Iraqi Soils)

The growth response of the biocontrol agent Pantoea agglomerans to changes in temperature was determined in vitro in nutrient yeast extract-sucrose medium. The minimum temperature at which P. agglomerans was able to grow was 4°C and the maximum temperature was 42°C. This study defines the range of environmental condition (Temperature) over which the bacteria may be developed for biocontrol of postharvest diseases