Different Levels Of Interference Research Articles

The Blasingame method considering the effect of inter-well interference in a circular homogeneous closed reservoir

The Blasingame method is a valuable tool for analysing the diminishing production challenge in the presence of variable bottomhole pressure and diverse production conditions. It is essential to highlight that the Blasingame method views the closed reservoir as the outer boundary condition for its model. This paper introduces the concept of the equivalent well control radius and formulates the Blasingame method based on the fluid flow characteristics inherent in twin-well production, taking into account the influence of inter-well interference. This study provides an in-depth analysis of production variations in reservoirs with inter-well interference by examining Blasingame type-curve definitions of the dimensionless parameters. Additionally, it investigates key factors impacting production, including neighbouring wells’ production, distance, and opening time: variations in all three parameters lead to different levels of interference between wells, which manifests itself in the regularised production integral derivative curve with different lengths and heights of convexity. Numerical simulations employing orthogonality are conducted to validate the reliability of this method. The results of these simulations offer theoretical support for the precise evaluation of reservoirs facing similar challenges.

Transient aerodynamics of a square cylinder under downburst-like accelerating flows reproduced in a multiple-fan wind tunnel

Aerodynamic and pressure coefficients of a sectional model of a sharp-edged square cylinder, at zero incidence angle, are experimentally evaluated in accelerating flow conditions through the use of a multiple-fan wind tunnel. Different unsteady flows are reproduced, characterized by flow parameters which dictate the initial and target velocities, as well as the flow acceleration and deceleration, and the temporal spacing between the ramp-up and the ramp-down. The accelerating turbulent flows reproduced during the campaign are consistent with those typical of full-scale downbursts.The ensemble mean of the mean drag coefficient in unsteady conditions reveals to be either comparable or definitely lower than the one relevant to the steady case. The drop seems to increase for higher levels of the acceleration and to be enhanced for the ramp-up conditions, while the ramp-down case suggests a lower level of reduction. This non-symmetric pattern appears to be mitigated when the starting wind velocity increases, reflecting a higher initial Reynolds number. Same comments, but with greater discrepancies compared to the steady conditions, might be addressed for the standard deviation of the lift coefficient connected with vortex shedding. Both these findings point out the presence of fluid-memory effects associated with the acceleration, which might affect the development of vortex shedding and the consequent bluff-body aerodynamics in transient conditions. This is also confirmed by the analyses of the mean pressure distributions. In particular, the detailed scrutiny of specific pressure coefficients reflects their different level of interference with the wake and the model edge.

Interference-aware opportunistic job placement for shared distributed deep learning clusters

Distributed deep learning frameworks facilitate large deep learning workloads. These frameworks support sharing one GPU device among multiple jobs to improve resource utilization. Modern deep learning training jobs consume a large amount of GPU memory. Despite that, sharing GPU memory among jobs is still possible because a training job has iterative steps that its memory usage fluctuates over time. However, resource sharing also introduces the risk of job performance degradation. Co-located jobs sharing a GPU device may suffer from different levels of interference, mainly caused by memory oversharing. How to improve resource utilization while maintaining good job performance is a novel challenge for job placement strategies. This paper studies the job placement problem. We propose an opportunistic memory sharing model to describe the time-varying job memory requirements. Based on this model, we introduce an Opportunistic Job Placement Problem (OJPP) for shared GPU clusters that seek job placement configurations using a minimum number of GPU devices and guarantee user-defined performance requirements at the same time. We propose a greedy algorithm and a heuristic algorithm with computational complexities of O(nlog⁡n) and O(n2log⁡n), respectively, to solve the problem. We also propose an online adjustment algorithm with the computational complexity of O(nlog⁡n) to perform updates to job placement configurations in runtime. A machine-learning-based interference prediction method is used to prepare accurate interference estimations. Extensive experiments are conducted on a GPU cluster to verify the correctness and effectiveness of our algorithms. Compared with standalone training jobs on dedicated clusters, the proposed approach reduces resource consumption by 46% in a shared cluster, while guaranteeing over 92.97% of the job performance, in terms of average job completion time.

Multi-scale split dual calibration network with periodic information for interpretable fault diagnosis of rotating machinery

Conventional intelligent fault diagnosis algorithms based on signal processing and pattern recognition have high demands on expert experience and poor generalization performance, which may not have good fault diagnosis performance in complex industrial fields. Meanwhile, the data acquisition system may suffer from cyber attacks when collecting vibration signals. The vibration signal has a very low signal-to-noise ratio (SNR), which seriously affects the accuracy of fault diagnosis. Aiming at the problem of fault diagnosis under low SNR, a new fault diagnosis framework based on a Multi-scale Split Dual Calibration Network with Periodic Information (PI-MSDCN) is proposed in this paper. In the fault diagnosis framework, a periodic block is constructed to automatically learn the periodic information of vibration signals through the neural network. The learned periodic information and raw vibration signal are used as the input data of MSDCN. Specifically, MSDCN uses convolution kernels of different sizes for different channels of input features to generate multi-scale features, and obtain mixed domain attention features for features with different scales respectively. Then, the attention feature is used as the threshold to remove the redundant information in the multi-scale feature adaptively. Finally, in order to calibrate the contribution of different scale features to fault diagnosis, the mixed-domain attention coefficients are applied to the corresponding features to obtain richer multi-scale attention features. The experimental studies under different levels of interference are performed to demonstrate the average accuracy of the proposed method is 92.91% (±5.08%), which is superior to other existing results in literature.

Toward Hybrid Backscatter-Aided Wireless-Powered Internet of Things Networks: Cooperation and Coexistence Scenarios

The emerging hybrid backscatter and energy harvesting devices have been regarded as a promising scheme for green Internet-of-Things (IoT). In the interwoven primary and secondary wireless powered IoT networks, we develop a novel hybrid scheme that integrates the backscatter communication and the harvest-then-transmit (HTT) protocol. In order to mitigate the adverse effect on the primary user (PU), the secondary users (SUs) are classified into ‘Cooperation Scenario’ and ‘Coexistence Scenario’ based on their different levels of interference to the PU. In ‘Cooperation Scenario’, we propose a cooperation protocol where the SUs operate in the backscatter mode so as to relay information to the PU. Therefore, the SUs are rewarded for harvesting energy and obtaining the spectrum access time from the primary system. Also, in ‘Coexistence Scenario’, a coexistence strategy is developed to enable the SUs to operate in either ambient backscatter or energy harvesting mode during the channel busy time. When the primary channel becomes idle, the SUs are capable of active transmission by using the harvested energy. For each scenario, we investigate the sum-throughput maximization problem of the secondary system. Employing the Lambert W function and the block coordinate descent method, the optimal time allocation can be obtained via the Lagrangian dual method. Numerical results validate that the proposed hybrid backscatter and HTT scheme improves the performance of the secondary system evidently compared with benchmark methods.

ANN based approach for selective detection of breath acetone by using hybrid GO-FET sensor array

This research used hybrid graphene oxide (GO) field effect transistors (FETs) based sensor array to design an electronic nose (e-nose) for identifying exhaled breath acetone to diagnose diabetes mellitus through noninvasive route. Six back gated FET sensors were fabricated with hybrid channel of GO, WO3 and noble metals (Au, Pd and Pt) nanoparticles. The experiment was carried out by using four distinct forms of synthetic breath, each with a different level of interference. Linear discriminant analysis (LDA) and artificial neural networks (ANN) were utilized to classify and analyze the sensor response vector. In contrast, partial least square (PLS) and multiple linear regression (MLR) were used to evaluate the exact acetone concentration in synthetic breath. First, LDA was used to lower the dimensionality of the response vector, which was then provided as an input to the ANN model. ANN was performed with ten perceptrons model in the hidden layer and highest accuracy of 99.1% was achieved. Additionally, by using the loading plot of PLS, three sensors (Pt/WO3/GO, Pd/WO3/GO, and WO3/GO) had the ample use to predict the concentration of breath acetone. Moreover, the MLR approach with correlation coefficient (R2) of 0.9572 and root mean square error (RMSE) of 5.63% were used for obtaining the exact concentration of acetone. Consequently, e-nose with matrix of hybrid GO-FET sensors and pattern recognition algorithms (LDA, ANN, PLS and MLR) exhibited considerable ability in selective detection of acetone in synthetic breath.

Robust gas recognition with mixed interference using a spiking neural network

Spiking neural networks (SNNs) have attracted significant interest owing to their high computing efficiency. However, few studies have focused on the robustness of SNNs and their application to electronic noses for gas recognition under strong interference. The goal of this study was to explore the robustness of a SNN for gas recognition under mixed interference. Data on mixed gases with different levels of interference were simulated by fitting experimental data. Two layers of a SNN based on leaky integrate-and-fire (LIF) neurons were constructed and the network was trained solely on datasets of pure targeted gases. Testing was then performed using data with mixed interference. The SNN achieved superior performance compared to other algorithms and remained 100% accurate for gas recognition up to a 10% interference ratio. The interval distance of spiking times between classes represents the robust capacity of the SNN according to the algorithm of the LIF neurons. SNNs have excellent capacity to maximize the differences between data of different classes and are promising candidates for electronic noses.

Can the interference effect in multiplication fact retrieval be modulated by an arithmetic training? An fMRI study

Single-digit multiplications are thought to be associated with different levels of interference because they show different degrees of feature overlap (i.e., digits) with previously learnt problems. Recent behavioral and neuroimaging studies provided evidence for this interference effect and showed that individual differences in arithmetic fact retrieval are related to differences in sensitivity to interference (STI). The present study investigated whether and to what extent competence-related differences in STI and its neurophysiological correlates can be modulated by a multiplication facts training.Participants were 23 adults with high and 23 adults with low arithmetic competencies who underwent a five-day multiplication facts training in which they intensively practiced sets of low- and high-interfering multiplication problems. In a functional magnetic resonance imaging (fMRI) test session after the training, participants worked on a multiplication verification task that comprised trained and untrained problems.Analyses of the behavioral data revealed an interference effect only in the low competence group, which could be reduced but not resolved by training. On the neural level, competence-related differences in the interference effect were observed in the left supramarginal gyrus (SMG), showing activation differences between low- and high-interfering problems only in the low competent group. These findings support the idea that individuals’ low arithmetic skills are related to the development of insufficient memory representations because of STI. Further, our results indicate that a short training by drill (i.e., learning associations between operands and solutions) was not fully effective to resolve existing interference effects in arithmetic fact knowledge.

Exoantigens of Paracoccidioides spp. Promote Proliferation and Modulation of Human and Mouse Pulmonary Fibroblasts.

Paracoccidioidomycosis (PCM) is a systemic granulomatous fungal infection caused by thermally dimorphic fungi of the genus Paracoccidioides. Endemic in Latin America, PCM presents with high incidence in Brazil, Colombia, and Venezuela, especially among rural workers. The main clinical types are acute/subacute (AF) form and chronic form (CF). Even after effective antifungal treatment, patients with CF usually present sequelae, such as pulmonary fibrosis. In general, pulmonary fibrosis is associated with dysregulation wound healing and abnormal fibroblast activation. Although fibrogenesis is recognized as an early process in PCM, its mechanisms remain unknown. In the current study, we addressed the role of Paracoccidioides spp. exoantigens in pulmonary fibroblast proliferation and responsiveness. Human pulmonary fibroblasts (MRC-5) and pulmonary fibroblasts isolated from BALB/c mice were cultivated with 2.5, 5, 10, 100, and 250 µg/ml of exoantigens produced from P. brasiliensis (Pb18 and Pb326) and P. lutzii (Pb01, Pb8334, and Pb66) isolates. Purified gp43, the immunodominant protein of P. brasiliensis exoantigens, was also evaluated at concentrations of 5 and 10 µg/ml. After 24 h, proliferation and production of cytokines and growth factors by pulmonary fibroblasts were evaluated. Each exoantigen concentration promoted a different level of interference of the pulmonary fibroblasts. In general, exoantigens induced significant proliferation of both murine and human pulmonary fibroblasts (p < 0.05). All concentrations of exoantigens promoted decreased levels of IL-6 (p < 0.05) and VEGF (p < 0.05) in murine fibroblasts. Interestingly, decreased levels of bFGF (p < 0.05) and increased levels of TGF-β1 (p < 0.05) and pro-collagen I (p < 0.05) were observed in human fibroblasts. The gp43 protein induced increased TGF-β1 production by human cells (p = 0.02). In conclusion, our findings showed for the first time that components of P. brasiliensis and P. lutzii interfered in fibrogenesis by directly acting on the biology of pulmonary fibroblasts.

Contribution to mechanical fasteners for composite structures – An automated industrial approach

Current fastening technologies implemented in the composite structures of car body applications show various disadvantages compared to an interference-fit joint. For example, adhesive bonding requires clean and pre-treated surfaces and metallic fasteners are prone to galvanic corrosion. This work investigates the process of an interference-fit joint (connection) comprising a fiber-reinforced thermoplastic insert pressed into a pre-drilled hole in a polymeric composite structure. With the insert being oversized and the hole undersized, interface pressure is generated. Pressure in radial direction is leading to friction which in turn is giving the insert resistance against pull out. The connection is functionally evaluated by comparing both the pullout force curves and the injection force curves for three different levels of interference. Major target is a prediction of the resulting pull out force based on the injection process. Furthermore, a process capability evaluation of the connection is presented.Since the environmental conditions of a car manufacturer’s body shop require highly automated processes, the connection should be implemented in a fully automated manner. The process is conducted by a robot controlled device fulfilling relevant targets of process reliability, capability and reproducibility. Additionally, the whole equipment is designed with regards to body shop facilities allowing to transfer the test equipment into a production environment.

Experimental Interference Robustness Evaluation of IEEE 802.15.4-2015 OQPSK-DSSS and SUN-OFDM Physical Layers for Industrial Communications

In this paper, we experimentally evaluate and compare the robustness against interference of the OQPSK-DSSS (Offset Quadrature Phase Shift Keying-Direct Sequence Spread Spectrum) and the SUN-OFDM (Smart Utility Network-Orthogonal Frequency Division Multiplexing) physical layers, as defined in the IEEE 802.15.4-2015 standard. The objective of this study is to provide a comprehensive analysis of the impact that different levels of interference produce on these modulations, in terms of the resulting PDR (Packet Delivery Ratio) and depending on the length of the packet being transmitted. The results show that the SUN-OFDM physical layer provides significant benefits compared to the ubiquitous OQPSK-DSSS in terms of interference robustness, regardless of the interference type and the packet length. Overall, this demonstrates the suitability of choosing the SUN-OFDM physical layer when deploying low-power wireless networks in industrial scenarios, especially taking into consideration the possibility of trading-off robustness and spectrum efficiency depending on the application requirements.

Neural muscle activation detection: A deep learning approach using surface electromyography

The timing of muscles activation which is a key parameter in determining plenty of medical conditions can be greatly assessed by the surface EMG signal which inherently carries an immense amount of information. Many techniques for measuring muscle activity detection exist in the literature. However, due to the complex nature of the EMG signal as well as the interference from other muscles that is observed during the measurement of the EMG signal, the accuracy of these techniques is compromised. In this paper, we introduce the neural muscle activation detection (NMAD) framework that detects the muscle activation based on deep learning. The main motivation behind using deep learning is to allow the neural network to detect based on the appropriate signal features instead of depending on certain assumptions. Not only the presented approach significantly improves the accuracy of timing detection, but because of the training nature, it can adapt to operate under different levels of interference and signal-to-noise ratio.

Enhanced time‐slotted channel hopping

AbstractWith the development of technology for the Internet of Things (IoT), a large number of mobile, intelligent sensors are being connected to the Internet. The IoT is a heterogeneous environment. In industrial environments, wireless technologies coexist on the same frequency and generate different kinds of traffic. The use of different wireless technologies that are not designed to be compatible with each other causes an external interference problem, which degrades the system performance. The medium access control (MAC) sublayer protocols must be carefully designed to overcome such a problem. The time‐slotted channel‐hopping (TSCH) mode, which is dedicated for industrial applications, can mitigate such a problem because it uses the channel‐hopping technique. Because these channels are subjected to different levels of interference, this may greatly degrade the system performance when wireless sensors with mobility devices hop blindly from one channel to another. This paper improves the reliability of communications of the TSCH MAC sublayer protocol, proposed by the IEEE 802.15.4e standard, in wireless sensor networks, by applying a new, dynamic blacklisting technique. The technique selects and uses only high‐quality channels and blacklists those with low quality. The proposed solution has been simulated using Network Simulator 3. The results show significant improvements in terms of throughput, energy, and reliability.

Sense-and-Predict: Harnessing Spatial Interference Correlation for Cognitive Radio Networks

Cognitive radio (CR) is a key enabler realizing future networks to achieve higher spectral efficiency by allowing spectrum sharing between different wireless networks. It is important to explore whether spectrum access opportunities are available, but conventional CR based on transmitter (TX) sensing cannot be used to this end because the paired receiver (RX) may experience different levels of interference, according to the extent of their separation, blockages, and beam directions. To address this problem, this paper proposes a novel form of medium access control (MAC) termed sense-and-predict (SaP), whereby each secondary TX predicts the interference level at the RX based on the sensed interference at the TX; this can be quantified in terms of a spatial interference correlation between the two locations. Using stochastic geometry, the spatial interference correlation can be expressed in the form of a conditional coverage probability, such that the signal-to-interference ratio (SIR) at the RX is no less than a predetermined threshold given the sensed interference at the TX, defined as an opportunistic probability (OP). The secondary TX randomly accesses the spectrum depending on OP. We optimize the SaP framework to maximize the area spectral efficiencies (ASEs) of secondary networks while guaranteeing the service quality of the primary networks. Testbed experiments using USRP and MATLAB simulations show that SaP affords higher ASEs compared with CR without prediction.

Interference Precancellation for Resource Management in Heterogeneous Cellular Networks

Network densification by installing more cellular stations (cells) with smaller coverage is promising to improve wireless capacity to meet the explosive growth of cellular traffic. The macrocellular base station and smaller cells with different coverage form heterogeneous cellular networks. Typically, the smaller cells operate in the same spectrum as the macrocells. Most of the existing interference mitigation approaches either rely on resource isolation or power auto-configuration. In cellular networks, the data to all cells and the mobile stations (MSs) are originated from the core cellular network. We take advantage of this characteristic and propose a technique called interference precancellation . If the exact interferer to an MS is identified, the victim cell that serves the victim MS transmits the interference precanceled signal, which is the signal intended for the victim MS subtracts the interference signal. The interference precanceled signal and the interference signal scramble at the victim MS and became the intended signal. With interference precancellation, the interferer and the victim cell can utilize the same set of resources and thus the capacity is further improved. However, some MSs are interference-free, and for MSs whose exact interferers cannot be determined, the MSs still require resource isolation. Therefore, we propose an algorithm for resource management with interference precancellation (RMIP) that jointly considers MSs experiencing a different level of interference. Through experiments on GNU Radio/USRP, we show that the known interference signal can be precanceled and the combination of the interference signal and the precanceled signal becomes the intended signal. Simulation results show that if all exact interferers are identified, RMIP algorithm achieves higher throughput compared with pure resource isolation.

Explaining the variability in the response of annual eusocial insects to mass‐flowering events

Empirical studies of annual eusocial insects in agricultural landscapes report contrasting findings with regard to colony responses to mass-flowering of crops such as oilseed rape. In particular, total sexual production is often unaffected by such events, whereas worker number responds with a prominent increase. To resolve these conflicting observations, we model-using an established approach-the expected change in worker and sexual numbers in response to an increased worker productivity induced by mass-flowering events at different times of the season. We find that the predicted response pattern is mainly shaped by the degree to which individual worker productivity is reduced by an increasing number of workers in the colony. Different environmental conditions and colony characteristics result in different levels of interference of workers, for example, during foraging or nest construction. Reduction in individual productivity is low, when worker interference is negligible ("weak limitation") and high when an increasing number of workers substantially decreases per-capita efficiency ("strong limitation"). For weak limitation, any mass-flowering event that ends before the production of sexuals starts has a strong multiplicative impact on both worker and sexual numbers. The magnitude of the effect is quite independent of the precise timing of such an event. After the onset of sexual production, mass-flowering has a weaker effect, as the added resource supply is only linearly transferred into production of additional sexuals. For colonies under strong limitation, the predicted impact of mass-flowering events is generally weaker, especially on the production of sexuals, and the timing of mass-flowering events becomes more influential: Production of sexuals profits more from late than from early mass-flowering events. Consequently, early mass-flowering events are predicted to have a prominent effect on worker numbers but a negligible one on the output of sexuals. The model presented provides a mechanistic explanation of why increased worker abundances do not necessarily translate into increased production of sexuals. The model is also applicable to other eusocial insects such as paper wasps whenever brief pulses of massive resource availability shortly elevate resource intake rates above the "normal" levels.

Development of immunodetection system for botulinum neurotoxin serotype E.

Background & objectives:Botulism, a potentially fatal paralytic illness, is caused by the botulinum neurotoxins (BoNTs) secreted by Clostridium botulinum. It is an obligate anaerobic, Gram-positive, spore-forming bacterium. BoNTs are classified into seven serotypes based on the serological properties. Among these seven serotypes, A, B, E and, rarely, F are responsible for human botulism. The present study was undertaken to develop an enzyme-linked immunosorbent assay (ELISA)-based detection system for the detection of BoNT/E.Methods:The synthetic gene coding the light chain of BoNT serotype E (BoNT/E LC) was constructed using the polymerase chain reaction primer overlapping method, cloned into pQE30UA vector and then transformed into Escherichia coli M15 host cells. Recombinant protein expression was optimized using different concentrations of isopropyl-β-D-1-thiogalactopyranoside (IPTG), different temperature and the rBoNT/E LC protein was purified in native conditions using affinity column chromatography. The purified recombinant protein was checked by sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) and further confirmed by western blot and matrix-assisted laser desorption ionization-tandem time-of-flight (MALDI-TOF). Polyclonal antibodies were generated against rBoNT/E LC using Freund's adjuvant in BALB/c mice and rabbit. Sandwich ELISA was optimized for the detection of rBoNT/E LC and native crude BoNT/E, and food matrix interference was tested. The developed antibodies were further evaluated for their specificity/cross-reactivity with BoNT serotypes and other bacterial toxins.Results:BoNT/E LC was successfully cloned, and the maximum expression was achieved in 16 h of post-induction using 0.5 mM IPTG concentration at 25°C. Polyclonal antibodies were generated in BALB/c mice and rabbit and the antibody titre was raised up to 128,000 after the 2nd booster dose. The developed polyclonal antibodies were highly specific and sensitive with a detection limit about 50 ng/ml for rBoNT/E LC and 2.5×103 MLD50 of native crude BoNT/E at a dilution of 1:3000 of mouse (capturing) and rabbit (revealing) antibodies. Further, different liquid, semisolid and solid food matrices were tested, and rBoNT/E LC was detected in almost all food samples, but different levels of interference were detected in different food matrices.Interpretation & conclusions:There is no immune detection system available commercially in India to detect botulism. The developed system might be useful for the detection of botulinum toxin in food and clinical samples. Further work is in progress.

Incorporating container location dispersion into evaluating GCR performance at a transhipment terminal

ABSTRACTThe degree of container dispersion at a transhipment terminal is measured by an index termed container location dispersion, which represents the range, both horizontal and vertical, of all containers for ship loading that are scattered in the yard. First, this paper identifies the impacts of container location dispersion on loading performance with insufficient or sufficient equipment deployment in real-life settings at a terminal. Then, we investigate those scenarios with different levels of interference. This research aims to explore the relationship between the container location dispersion and Gross Crane Rate (GCR) stability at a transhipment terminal and proposes a discrete-event driven simulation model for this purpose. All data in our experiments are extracted from the terminal operation system at a real-life container terminal. It is concluded from a series of experiments that the container location dispersion well captures the overall performance of container terminal handling and can be used for yard template optimization and management. Implications of this investigation are discussed in the context of the yard template design and improvement in the overall performance of a container transhipment terminal.

Metabolic Interference of sod gene mutations on catalase activity in Escherichia coli exposed to Gramoxone® (paraquat) herbicide

Herbicides are continuously used to minimize the loss of crop productivity in agricultural environments. They can, however, cause damage by inhibiting the growth of microbiota via oxidative stress, due to the increased production of reactive oxygen species (ROS). Cellular responses to ROS involve the action of enzymes, including superoxide dismutase (SOD) and catalase (CAT). The objective of this study was to evaluate adaptive responses in Escherichia coli K-12 to paraquat, the active ingredient in the herbicide Gramoxone®. Mutant bacterial strains carrying deletions in genes encoding Mn-SOD (sodA) and Fe-SOD (sodB) were used and resulted in distinct levels of hydrogen peroxide production, interference in malondialdehyde, and viability. Mutations also resulted in different levels of interference with the activity of CAT isoenzymes and in the inactivation of Cu/Zn-SOD activity. These mutations may be responsible for metabolic differences among the evaluated strains, resulting in different patterns of antioxidative responses, depending on mutation background. While damage to the ΔsodB strain was minor at late log phase, the reverse was true at mid log phase for the ΔsodA strain. These results demonstrate the important role of these genes in defense against oxidative stress in different periods of growth. Furthermore, the lack of Cu/Zn-SOD activity in both mutant strains indicated that common metal cofactors likely interfere in SOD activity regulation. These results also indicate that E. coli K-12, a classical non-environmental strain, constitutes a model of phenotypic plasticity for adaptation to a redox-cycling herbicide through redundancy of different isoforms of SOD and CAT enzymes.

Numerical investigation into the influence of cubicle positioning in large-scale explosive arena trials

In arena blast testing, a common and economical practice employed is to distribute several targets radially around a central charge. However, if these targets are positioned too proximally, reflections and diffractions of blast waves off neighbouring cubicles can affect the nature of expected blast loading. Computational fluid dynamics software has been used through an extensive series of simulations to identify the levels of interference in incident pressure–time histories with and without an obstructing target present. The data were post-processed to identify the Cartesian co-ordinates in which different levels of interference in peak incident overpressure and incident positive phase impulse were achieved. The results indicated that in all cases, there was a greater interference in peak incident overpressure than incident positive phase impulse values directly proximal to the target but, at greater separations, significant differences in incident positive phase impulse existed where peak incident overpressure had returned to free-field equivalent magnitudes. When compared with the established ‘rules of thumb’ for cubicle placement, for targets at different stand-off ranges, an angle of 45° to the rear cubicle still holds some practical relevance, although it is too acute to cover all interference effects. For targets positioned at the same stand-off range, a separation distance of two cubicle widths is generally too conservative and, in many cases, more cubicles can be positioned around the charge. A bespoke recommendation table has been presented for targets at stand-off ranges between 15 and 50 m to allow users to identify the minimum distance from a target at which obstructed-field peak incident overpressure and incident positive phase impulse values differ negligibly from free-field equivalents.