The use of hybrid-power sources for improved performance with pulsed loads

Electrical characteristics of hybrid power sources consisting of Li-ion cells and double layer capacitors were studied at 25 C and {minus}20 C. The cells were initially evaluated for pulse performance and then measured in hybrid modes of operation. Cells manufactured by Panasonic delivered pulses up to 3A and cells from A and T delivered 4A at 25 C before cell capacity dropped. Measured cell resistances were 0.15 ohms and 0.12 ohms, respectively. These measurements were repeated at {minus}20 C. Direct coupling of the cells and capacitors (dumb hybrid) extended the pulse limits to 5.6A using the Panasonic cells and 9A for the A and T cells. Operation in a smart hybrid mode using uncoupled cell/capacitor discharge allowed full cell capacity usage at 25 C and showed a factor of 5 improvement in delivered capacity at {minus}20 C

Analysis of a Lithium/Thionyl Chloride Battery under Moderate-Rate Discharge

A one-dimensional mathematical model of a spirally wound lithium/thionyl chloride primary battery is developed and used for parameter estimation and design studies. The model formulation is based on the fundamental conservation laws using porous electrode theory and concentrated solution theory. The model is used to estimate the transference number, the diffusion coefficient, and the kinetic parameters for the reactions at the anode and the cathode as a function of temperature. These parameters are obtained by fitting the simulated capacity and average cell voltage to experimental data over a wide range of temperatures (–55 to 49°C) and discharge loads (10-250 ). The experiments were performed on D-sized, cathode-limited, spirally wound lithium/thionyl chloride cells. The model is also used to study the effect of cathode thickness on the cell capacity as a function of temperature, and it was found that the optimum thickness for the cathode-limited design is temperature and load dependent

Electrochemical characteristics of lithium-ion cells

The authors describe below the electrochemical performance characteristics, including charge-discharge characteristics at different rates, of cylindrical 18650 (18 mm diameter, 65 mm high) and prismatic lithium ion cells at ambient and sub-ambient temperatures. Ragone plots of power and energy data for these cells are compared and indicate that at room temperature the prismatic lithium ion cells (approx. 500 mAh) exhibit higher specific power and power density than the 18650 cells (approx. 1,100 mAhr). The cell impedance was measured between 35 C and {minus}40 C at three open circuit voltages: 4.1 v (fully charged), 3.6 v (partially discharged), and 3.1 v (almost completely discharged). Over the temperature range from 35 C to {minus}20 C, the cell impedance is nearly constant for both cell types and increases by 2 to 3 times at {minus}40 C. The impedance doesn`t vary significantly with open circuit voltage (OCV). These cells show very little voltage drop at room temperature for current pulses up to 1 A. The charge-discharge characteristics of the cells are being studied at different rates as a function of temperature to compute the power, energy, and capacity outputs. This will not only broaden the database on lithium ion cells, but will also allow us to evaluate the suitability of the cells as power sources for low temperature applications. Other electrochemical characteristics of these cells including pulse response are being evaluated. Impedance measurements of the cells under load are planned to make meaningful correlations between the voltage drop and the current pulse amplitude

Corrosion of current-collector materials in Li-ion cells

The primary current-collector materials being used in lithium-ion cells are susceptible to environmental degradation: aluminum to pitting corrosion and copper to environmentally assisted cracking. Pitting occurs at the highly oxidizing potentials associated with the positive-electrode charge condition. However, the pitting mechanism is more complex than that typically observed in aqueous systems in that the pits are filled with a mixed metal/oxide product and exist as mounds or nodules on the surface. Electrochemical impedance was shown to be an effective analytical tool for quantification and verification of visual observations and trends. Two fluorocarbon-based coatings were shown to improve the resistance of Al to localized pitting. Finally, environmental cracking of copper can occur at or near the lithium potential and only if specific metallurgical conditions exist (work hardening and large grain size)

Computational Fluid Dynamics Modeling of a Lithium/Thionyl Chloride Battery with Electrolyte Flow

A two-dimensional model is developed to simulate discharge of a lithium/thionyl chloride primary battery. As in earlier one-dimensional models, the model accounts for transport of species and charge, and electrode porosity variations and electrolyte flow induced by the volume reduction caused by electrochemical reactions. Numerical simulations are performed using a finite volume method of computational fluid dynamics. The predicted discharge curves for various temperatures show good agreement with published experimental data, and are essentially identical to results published for one-dimensional models. The detailed two-dimensional flow simulations show that the electrolyte is replenished from the cell head space predominantly through the separator into the front of the cathode during most parts of the discharge, especially for higher cell temperatures

Detection and isolation of black hole attack in mobile ad hoc networks: A review

© 2020 SPIE. Mobile Ad hoc Network or MANET is a wireless network that allows communication between the nodes that are in range of each other and are self-configuring. The distributed administration and dynamic nature of MANET makes it vulnerable to many kind of security attacks. One such attack is Black hole attack which is a well known security threat. A node drops all packets which it should forward, by claiming that it has the shortest path to the destination. Intrusion Detection system identifies the unauthorized users in the system. An IDS collects and analyses audit data to detect unauthorized users of computer systems. This paper aims in identifying Black-Hole attack against AODV with Intrusion Detection System, to analyze the attack and find its countermeasure

Phase 1/2 study of the selective TRK inhibitor larotrectinib, in pediatric patients with cancer

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How useful is Active Learning for Image-based Plant Phenotyping?

Deep learning models have been successfully deployed for a diverse array of image-based plant phenotyping applications including disease detection and classification. However, successful deployment of supervised deep learning models requires large amount of labeled data, which is a significant challenge in plant science (and most biological) domains due to the inherent complexity. Specifically, data annotation is costly, laborious, time consuming and needs domain expertise for phenotyping tasks, especially for diseases. To overcome this challenge, active learning algorithms have been proposed that reduce the amount of labeling needed by deep learning models to achieve good predictive performance. Active learning methods adaptively select samples to annotate using an acquisition function to achieve maximum (classification) performance under a fixed labeling budget. We report the performance of four different active learning methods, (1) Deep Bayesian Active Learning (DBAL), (2) Entropy, (3) Least Confidence, and (4) Coreset, with conventional random sampling-based annotation for two different image-based classification datasets. The first image dataset consists of soybean [Glycine max L. (Merr.)] leaves belonging to eight different soybean stresses and a healthy class, and the second consists of nine different weed species from the field. For a fixed labeling budget, we observed that the classification performance of deep learning models with active learning-based acquisition strategies is better than random sampling-based acquisition for both datasets. The integration of active learning strategies for data annotation can help mitigate labelling challenges in the plant sciences applications particularly where deep domain knowledge is required

A pilot clinical study of Δ9-tetrahydrocannabinol in patients with recurrent glioblastoma multiforme

Δ9-Tetrahydrocannabinol (THC) and other cannabinoids inhibit tumour growth and angiogenesis in animal models, so their potential application as antitumoral drugs has been suggested. However, the antitumoral effect of cannabinoids has never been tested in humans. Here we report the first clinical study aimed at assessing cannabinoid antitumoral action, specifically a pilot phase I trial in which nine patients with recurrent glioblastoma multiforme were administered THC intratumoraly. The patients had previously failed standard therapy (surgery and radiotherapy) and had clear evidence of tumour progression. The primary end point of the study was to determine the safety of intracranial THC administration. We also evaluated THC action on the length of survival and various tumour-cell parameters. A dose escalation regimen for THC administration was assessed. Cannabinoid delivery was safe and could be achieved without overt psychoactive effects. Median survival of the cohort from the beginning of cannabinoid administration was 24 weeks (95% confidence interval: 15–33). Δ9-Tetrahydrocannabinol inhibited tumour-cell proliferation in vitro and decreased tumour-cell Ki67 immunostaining when administered to two patients. The fair safety profile of THC, together with its possible antiproliferative action on tumour cells reported here and in other studies, may set the basis for future trials aimed at evaluating the potential antitumoral activity of cannabinoids