Driven diffusive systems with mutually interactive Langmuir kinetics

We investigate the simple one-dimensional driven model, the totally asymmetric exclusion process, coupled to mutually interactive Langmuir kinetics. This model is motivated by recent studies on clustering of motor proteins on microtubules. In the proposed model, the attachment and detachment rates of a particle are modified depending upon the occupancy of neighbouring sites. We first obtain continuum mean-field equations and in certain limiting cases obtain analytic solutions. We show how mutual interactions increase (decrease) the effects of boundaries on the phase behavior of the model. We perform Monte Carlo simulations and demonstrate that our analytical approximations are in good agreement with the numerics over a wide range of model parameters. We present phase diagrams over a selective range of parameters.Comment: 9 pages, 8 Figure

Effects of light and drought stress on germination of Artemisia sieberi Besser

Preservation and development of plant cover are major factors in the management of range ecosystems. Artemisia sieberi is one of the native dominant species of vast areas in the Irano-Turanian bioclimatic region. This species is very tolerant to drought stress and grazing pressure. Therefore, it can be used to rehabilitate degraded rangelands of dry areas within this region. Understanding the germination characteristics of this species is important for the conduction of revegetation projects. Seeds of A. sieberi were collected randomly from 20 plants of existing vegetation along 6 kilometers transect at Isfahan Kolah Ghazi National Park located at 51°45'E and 35°15'N. To pass the dormant period, seeds were pre-chilled for nine days at 0 to 5°C. Two germination experiments were conducted in complete factorial block design. Moisture stress test was carried out with 0, -0.2, -0.4 and -0.6 MPa treatments using polyethylene glycol (PEG) solutions in Petri dishes. The light treatment test was conducted with 12 h light and dark versus a continual dark condition. Results indicated that, an increase in the drought level lead to a decrease in the percentage and rate of germination, plumule length and allometry ratios, whereas the radicle length increased. In comparison with the dark treatment, 12 h of light treatment increased the percentage of germination and radicle length, while allometry ratios and plumule length decreased. Continual dark treatment compared with the 12 h light and dark photoperiod significantly reduced radicle and increased plumule lengths. Drought tolerance of A. sieberi seeds during germination showed the high potential of this species for vegetation rehabilitation in dry regions.Key words: Artemisia sieberi, seed germination, drought tolerance, light treatments, Iran

Deep analysis of EIT dataset to classify apnea and non-apnea cases in neonatal patients

Electrical impedance tomography (EIT) is a non-invasive imaging modality that can provide information about dynamic volume changes in the lung. This type of image does not represent structural lung information but provides changes in regions over time. EIT raw datasets or boundary voltages are comprised of two components, termed real and imaginary parts, due to the nature of cell membranes of the lung tissue. In this paper, we present the first use of EIT boundary voltage data obtained from infants for the automatic detection of apnea using machine learning, and investigate which components contain the main features of apnea events. We selected 15 premature neonates with an episode of apnea in their breathing pattern and applied a hybrid classification model that combines two established methods; a pre-trained transfer learning method with a convolutional neural network with 50 layers deep (ResNet50) architecture, and a support vector machine (SVM) classifier. ResNet50 training was undertaken using an ImageNet dataset. The learnt parameters were fed into the SVM classifier to identify apnea and non-apnea cases from neonates' EIT datasets. The performance of our classification approach on the real part, the imaginary part and the absolute value of EIT boundary voltage datasets were investigated. We discovered that the imaginary component contained a larger proportion of apnea features

Deep Analysis of EIT Dataset to Classify Apnea and Non-apnea Cases in Neonatal Patients

Electrical impedance tomography (EIT) is a non-invasive imaging modality that can provide information about dynamic volume changes in the lung. This type of image does not represent structural lung information but provides changes in regions over time. EIT raw datasets or boundary voltages are comprised of two components, termed real and imaginary parts, due to the nature of cell membranes of the lung tissue. In this paper, we present the first use of EIT boundary voltage data obtained from infants for the automatic detection of apnea using machine learning, and investigate which components contain the main features of apnea events. We selected 15 premature neonates with an episode of apnea in their breathing pattern and applied a hybrid classification model that combines two established methods; a pre-trained transfer learning method with a convolutional neural network with 50 layers deep (ResNet50) architecture, and a support vector machine (SVM) classifier. ResNet50 training was undertaken using an ImageNet dataset. The learnt parameters were fed into the SVM classifier to identify apnea and non-apnea cases from neonates’ EIT datasets. The performance of our classification approach on the real part, the imaginary part and the absolute value of EIT boundary voltage datasets were investigated. We discovered that the imaginary component contained a larger proportion of apnea features

A Comparative Study of Field Gamma-ray Spectrometry by NaI(Tl) and HPGe Detectors in the South Caspian Region

Natural radionuclides present in soil as well as certain anthropogenic radionuclides released to the environment are the major contributors to terrestrial outdoor exposures. In the assessment of human exposures from environmental radioactivity, besides the conventional method of soil and vegetation sampling combined with laboratory based analyses of environmental media, the other choice would be field spectrometry which is a rapid, efficient and economical means of identification of radionuclides in the environment. Newly developed high resolution solid state gamma-ray detectors provide a state of art means for such a purpose. However, they are relatively expensive, may not provide the highest intrinsic efficiency possible and their use is complicated by the need for cryogenic cooling of the detector. Scintillation detector spectrometry systems are considered to be capable of yielding satisfactory results particularly for natural background measurements at a fraction of cost. This paper describes a comparative study on application of NaI(Tl) scintillation and HPGe solid state systems for in-situ measurements of 40K, 226Ra, 232Th and 137Cs soil inventories at selected regions on the south coast of Caspian Sea, along with the results from laboratory analyses of collected soil samples in the area. Based on in-situ measurement results and field experience, it is concluded that NaI(Tl) spectrometry system provide satisfactory results which might be even improved by incorporating special spectrum analysis techniques, is relatively less expensive and is operationally easier to carry out than either HPGe system or direct laboratory based analyses of soil samples

Energy efficiency in MAC 802.15.4 for wireless sensor networks

Recent technological advances in sensors, low power integrated circuits, and wireless communications have enabled the design of low-cost, lightweight, and intelligent physiological sensor nodes. The IEEE 802.15.4 is a new wireless personal area network designed for wireless monitoring and control applications. The fast progress of research on energy efficiency in wireless sensor networks, and the need to compare with the solutions adopted in the standards motivates the need for this work. In the analysis presented, the star network configuration of 802.15.4 standard at 868 MHz is considered for a Zigbee network. In this paper, we analyze the active duration of the superframe and entered the sleep mode status inside this period. It happens when sensors do not have any data to send. The nonpersistent CSMA uses the adaptive backoff exponent. This method helps the network to be reliable under traffic changes due to save the energy consumption. The introduction of sleep state has shown incredible reduction of the power consumption in all network load changes

Adaptive data collection algorithm for wireless sensor networks

Periodical Data collection from unreachable remote terrain and then transmit information to a base station is one of the targeted application of sensor networks. The energy restriction of battery powered sensor nodes is a big challenge for this network as it is difficult or in some cases not feasible to change the power supply of motes. Therefore, in order to keep the networks operating for long time, efficient utilization of energy is considered with highest priority. In this paper we propose TA-PDC-MAC protocol - a traffic adaptive periodic data collection MAC which is designed in a TDMA fashion. This design is efficient in the ways that it assigns the time slots for nodes’ activity due to their sampling rates in a collision avoidance manner. This ensures minimal consumption of network energy and makes a longer network lifetime, as well as it provides small end-to-end delay and packet loss ratio. Simulation results show that our protocol demonstrates up to 35% better performance than that of most recent protocol that proposed for this kind of application, in respect of energy consumption. Comparative analysis and simulation show that TA-PDC-MAC considerably gives a good compromise between energy efficiency and latency and packet loss rate

Trade-off between energy consumption and target delay for wireless sensor network

Wireless sensor networks (WSN) consists of unattended sensors with limited storage, energy (battery power) and computational and communication capabilities. Since battery power is the most crucial resource for sensor nodes and delay time is a critical metric for certain WSN applications, data diffusion between source sensors and sink should be done in an energy efficient and timely manner. We characterize the trade off between the energy consumption and source to sink delay in order to extend the operation of individual sensors and hence increase the lifetime of the WSN. To achieve this goal, the transmission range of sensors is first decomposes into certain ranges based on a minimal distance between consecutive forwarding sensors and then classifies these ranges due to Degree of Interest. It is also shown that the use of sensor nodes which lie on or closely to the shortest path between the source and the sink helps minimize these two metrics

Strain-driven criticality underlies nonlinear mechanics of fibrous networks

Networks with only central force interactions are floppy when their average connectivity is below an isostatic threshold. Although such networks are mechanically unstable, they can become rigid when strained. It was recently shown that the transition from floppy to rigid states as a function of simple shear strain is continuous, with hallmark signatures of criticality [Sharma et al., Nature Phys. 12, 584 (2016)]. The nonlinear mechanical response of collagen networks was shown to be quantitatively described within the framework of such mechanical critical phenomenon. Here, we provide a more quantitative characterization of critical behavior in subisostatic networks. Using finite-size scaling we demonstrate the divergence of strain fluctuations in the network at well-defined critical strain. We show that the characteristic strain corresponding to the onset of strain stiffening is distinct from but related to this critical strain in a way that depends on critical exponents. We confirm this prediction experimentally for collagen networks. Moreover, we find that the apparent critical exponents are largely independent of the spatial dimensionality. With subisostaticity as the only required condition, strain-driven criticality is expected to be a general feature of biologically relevant fibrous networks