Exploiting Magnetic Resonance Angiography Imaging Improves Model Estimation of BOLD Signal

The change of BOLD signal relies heavily upon the resting blood volume fraction () associated with regional vasculature. However, existing hemodynamic data assimilation studies pretermit such concern. They simply assign the value in a physiologically plausible range to get over ill-conditioning of the assimilation problem and fail to explore actual . Such performance might lead to unreliable model estimation. In this work, we present the first exploration of the influence of on fMRI data assimilation, where actual within a given cortical area was calibrated by an MR angiography experiment and then was augmented into the assimilation scheme. We have investigated the impact of on single-region data assimilation and multi-region data assimilation (dynamic cause modeling, DCM) in a classical flashing checkerboard experiment. Results show that the employment of an assumed in fMRI data assimilation is only suitable for fMRI signal reconstruction and activation detection grounded on this signal, and not suitable for estimation of unobserved states and effective connectivity study. We thereby argue that introducing physically realistic in the assimilation process may provide more reliable estimation of physiological information, which contributes to a better understanding of the underlying hemodynamic processes. Such an effort is valuable and should be well appreciated

The public health significance of latrines discharging to groundwater used for drinking.

Faecal contamination of groundwater from pit latrines is widely perceived as a major threat to the safety of drinking water for several billion people in rural and peri-urban areas worldwide. On the floodplains of the Ganges-Brahmaputra-Meghna delta in Bangladesh, we constructed latrines and monitored piezometer nests monthly for two years. We detected faecal coliforms (FC) in 3.3-23.3% of samples at four sites. We differentiate a near-field, characterised by high concentrations and frequent, persistent and contiguous contamination in all directions, and a far-field characterised by rare, impersistent, discontinuous low-level detections in variable directions. Far-field FC concentrations at four sites exceeded 0 and 10 cfu/100 ml in 2.4-9.6% and 0.2-2.3% of sampling events respectively. The lesser contamination of in-situ groundwater compared to water at the point-of-collection from domestic wells, which itself is less contaminated than at the point-of-consumption, demonstrates the importance of recontamination in the well-pump system. We present a conceptual model comprising four sub-pathways: the latrine-aquifer interface (near-field); groundwater flowing from latrine to well (far-field); the well-pump system; and post-collection handling and storage. Applying a hypothetical dose-response model suggests that 1-2% of the diarrhoeal disease burden from drinking water is derived from the aquifer, 29% from the well-pump system, and 70% from post-collection handling. The important implications are (i) that leakage from pit latrines is a minor contributor to faecal contamination of drinking water in alluvial-deltaic terrains; (ii) fears of increased groundwater pollution should not constrain expanding latrine coverage, and (iii) that more attention should be given to reducing contamination around the well-head

A Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computing

Mobile edge computing (MEC) has shown tremendous potential as a means for computationally intensive mobile applications by partially or entirely offloading computations to a nearby server to minimize the energy consumption of user equipment (UE). However, the task of selecting an optimal set of components to offload considering the amount of data transfer as well as the latency in communication is a complex problem. In this paper, we propose a novel energy-efficient deep learning based offloading scheme (EEDOS) to train a deep learning based smart decision-making algorithm that selects an optimal set of application components based on remaining energy of UEs, energy consumption by application components, network conditions, computational load, amount of data transfer, and delays in communication. We formulate the cost function involving all aforementioned factors, obtain the cost for all possible combinations of component offloading policies, select the optimal policies over an exhaustive dataset, and train a deep learning network as an alternative for the extensive computations involved. Simulation results show that our proposed model is promising in terms of accuracy and energy consumption of UEs

Smart Application Division and Time Allocation Policy for Computational Offloading in Wireless Powered Mobile Edge Computing

Limited battery life and poor computational resources of mobile terminals are challenging problems for the present and future computation-intensive mobile applications. Wireless powered mobile edge computing is one of the solutions, in which wireless energy transfer technology and cloud server’s capabilities are brought to the edge of cellular networks. In wireless powered mobile edge computing systems, the mobile terminals charge their batteries through radio frequency signals and offload their applications to the nearby hybrid access point in the same time slot to minimize their energy consumption and ensure uninterrupted connectivity with hybrid access point. However, the smart division of application into subtasks as well as intelligent partitioning of time slot for harvesting energy and offloading data is a complex problem. In this paper, we propose a novel deep-learning-based offloading and time allocation policy (DOTP) for training a deep neural network that divides the computation application into optimal number of subtasks, decides for the subtasks to be offloaded or executed locally (offloading policy), and divides the time slot for data offloading and energy harvesting (time allocation policy). DOTP takes into account the current battery level, energy consumption, and time delay of mobile terminal. A comprehensive cost function is formulated, which uses all the aforementioned metrics to calculate the cost for all number of subtasks. We propose an algorithm that selects the optimal number of subtasks, partial offloading policy, and time allocation policy to generate a huge dataset for training a deep neural network and hence avoid huge computational overhead in partial offloading. Simulation results are compared with the benchmark schemes of total offloading, local execution, and partial offloading. It is evident from the results that the proposed algorithm outperforms the other schemes in terms of battery life, time delay, and energy consumption, with 75% accuracy of the trained deep neural network. The achieved decrease in total energy consumption of mobile terminal through DOTP is 45.74%, 36.69%, and 30.59% as compared to total offloading, partial offloading, and local offloading schemes, respectively

Wireless Powered Mobile Edge Computing Systems: Simultaneous Time Allocation and Offloading Policies

To improve the computational power and limited battery capacity of mobile devices (MDs), wireless powered mobile edge computing (MEC) systems are gaining much importance. In this paper, we consider a wireless powered MEC system composed of one MD and a hybrid access point (HAP) attached to MEC. Our objective is to achieve a joint time allocation and offloading policy simultaneously. We propose a cost function that considers both the energy consumption and the time delay of an MD. The proposed algorithm, joint time allocation and offload policy (JTAOP), is used to train a neural network for reducing the complexity of our algorithm that depends on the resolution of time and the number of components in a task. The numerical results are compared with three benchmark schemes, namely, total local computation, total offloading and partial offloading. Simulations show that the proposed algorithm performs better in producing the minimum cost and energy consumption as compared to the considered benchmark schemes

Effects of quantum gravity on the inflationary parameters and thermodynamics of the early universe

The effects of generalized uncertainty principle (GUP) on the inflationary dynamics and the thermodynamics of the early universe are studied. Using the GUP approach, the tensorial and scalar density fluctuations in the inflation era are evaluated and compared with the standard case. We find a good agreement with the Wilkinson Microwave Anisotropy Probe data. Assuming that a quantum gas of scalar particles is confined within a thin layer near the apparent horizon of the Friedmann-Lemaitre-Robertson-Walker universe which satisfies the boundary condition, the number and entropy densities and the free energy arising form the quantum states are calculated using the GUP approach. A qualitative estimation for effects of the quantum gravity on all these thermodynamic quantities is introduced.Comment: 15 graghes, 7 figures with 17 eps graph

Cotangent bundle quantization: Entangling of metric and magnetic field

For manifolds $\cal M$ of noncompact type endowed with an affine connection (for example, the Levi-Civita connection) and a closed 2-form (magnetic field) we define a Hilbert algebra structure in the space $L^2(T^*\cal M)$ and construct an irreducible representation of this algebra in $L^2(\cal M)$. This algebra is automatically extended to polynomial in momenta functions and distributions. Under some natural conditions this algebra is unique. The non-commutative product over $T^*\cal M$ is given by an explicit integral formula. This product is exact (not formal) and is expressed in invariant geometrical terms. Our analysis reveals this product has a front, which is described in terms of geodesic triangles in $\cal M$. The quantization of $\delta$-functions induces a family of symplectic reflections in $T^*\cal M$ and generates a magneto-geodesic connection $\Gamma$ on $T^*\cal M$. This symplectic connection entangles, on the phase space level, the original affine structure on $\cal M$ and the magnetic field. In the classical approximation, the $\hbar^2$-part of the quantum product contains the Ricci curvature of $\Gamma$ and a magneto-geodesic coupling tensor.Comment: Latex, 38 pages, 5 figures, minor correction

The comparative study of the streptococcinum, Hepar sulfur, Rosmarinus officinalis and erythromycin effects on cultured rainbow trout (Oncorhynchus mykiss) with experimental streptococcusis.

The comparative study of the streptococcinum, Hepar sulfur, Rosmarinus officinalis and erythromycin effects on cultured rainbow trout (Oncorhynchus mykiss) with experimental streptococcosis Homeopathy is one of alternative medicines that is very useful for soul and body diseases with accurate prescription. The goal of this study was “survey of the effects of homeopathic remedies. There is not any more research about homeopathy on aquatics in the world especially in Iran, thus Some research about the effects of homeopathy on aquatics is needed. In this study, the effects of streptococcinum, Hepar sulfure, Rosmarinus officinalis(homeopathic remedies) and erythromycin in cultured rainbow trout, with experimental streptococcosis, also the mortality, were compared. There was 6 treatment and 2 reviews in 300 liter tanks that each of treatment contained 40 juvenile rainbow trout with 25±5 gr arrange weight. Treatment 1: contained of erythromycin. Treatment 2: Streptococcinum C30. Treatment 3: Rosmarinus officinalis Q. Treatment 4: Hepar sulfur C30 . Treatment 5:(control treatment )without any injection and any therapy. Treatment 6: (positive control treatment) with injection but without any therapy. Daily estimation of the water temperature, oxygen, pH and salinity and some other chemical factors. Tretment 1 had significance with the other treatments. Survival percent in treatments and their analysis showed that treatment 4(Hep-s) had significance (p<0.05) with treatment 3(Ros-off) and its survival percent is more than the other homeopathic remedies. Erythromycin is chemical drug and has many side effects but Hep-s has not any side effect and is an natural remedy for Streptococcosis in homeopathy. Thus we offer the Hep-s to cure the streptococcosis but some research with disk diffusion test about the different doses of Hep-s is needed. Daily survey of clinical symptoms such as hemorrhages in the external organs, Under eyes, under skin, under fins and gills, hemorrhages and exophthalmos, were the most symptoms. Important pathological symptoms were: necrosis, hyperplasia and melanosis in branches, liver and kidneys, hemorrhages in heart, kidneys and in visceral tissues. According to the survival results, there was significant difference between the treatment 1 and the other treatments (p<0.05). Also there was significant difference between treatment 3, treatment 4 and treatment 2(p<0.05), this difference is due to the high dose(Q) of R.officinalis, while the two other homeopathic remedies were in a moderated dose (C30), 30× 100 diluted dose. Survival percent of treatment 4(Hep-s) was more than the other homeopathic remedies and was related to erythromycin. Erythromycin is chemical medicine and has many side effects, while Hepar sulfur has not any side effects if the prescription would be accurate. Hep-s is suggested with disk diffusion tests for relief the symptoms of streptococcosis

Effect of Topological Defects on Buckling Behavior of Single-walled Carbon Nanotube

Molecular dynamic simulation method has been employed to consider the critical buckling force, pressure, and strain of pristine and defected single-walled carbon nanotube (SWCNT) under axial compression. Effects of length, radius, chirality, Stone–Wales (SW) defect, and single vacancy (SV) defect on buckling behavior of SWCNTs have been studied. Obtained results indicate that axial stability of SWCNT reduces significantly due to topological defects. Critical buckling strain is more susceptible to defects than critical buckling force. Both SW and SV defects decrease the buckling mode of SWCNT. Comparative approach of this study leads to more reliable design of nanostructures

Feasibility study of portable technology for weight loss and HbA1c control in type 2 diabetes

Background The study investigated the feasibility of conducting a future Randomised Controlled Trial (RCT) of a mobile health (mHealth) intervention for weight loss and HbA1c reduction in Type 2 Diabetes Mellitus (T2DM). Methods The intervention was a small wearable mHealth device used over 12 weeks by overweight people with T2DM with the intent to lose weight and reduce their HbA1c level. A 4 week maintenance period using the device followed. The device records physical activity level and information about food consumption, and provides motivational feedback based on energy balance. Twenty-seven participants were randomised to receive no intervention; intervention alone; or intervention plus weekly motivational support. All participants received advice on diet and exercise at the start of the study. Weight and HbA1c levels were recorded at baseline and weeks 6, 12, and 16. Qualitative interviews were conducted with participants who received the intervention to explore their experiences of using the device and involvement in the study including the training received. Results Overall the device was perceived to be well-liked, acceptable, motivational and easy to use by participants. Some logistical changes were required during the feasibility study, including shortening of the study duration and relaxation of participant inclusion criteria. Descriptive statistics of weight and HbA1c data showed promising trends of weight loss and HbA1c reduction in both intervention groups, although this should be interpreted with caution. Conclusions A number of methodological recommendations for a future RCT emerged from the current feasibility study. The mHealth device was acceptable and promising for helping individuals with T2DM to reduce their HbA1c and lose weight. Devices with similar features should be tested further in larger studies which follow these methodological recommendations