Determination of the heat capacity of posistors

A method is proposed for determining the heat capacity of posistors. The temperature dependence of the heat capacity is considered.     

Empirical relationships between AIRSAR backscatter and LiDAR-derived forest biomass, Queensland, Australia

To evaluate the use of multi-frequency, polarimetric Synthetic Aperture Radar (SAR) data for quantifying the above ground biomass (AGB) of open forests and woodlands, NASA JPL AIRSAR (POLSAR) data were acquired over a 37 × 60 km area west of Injune, central Queensland, Australia. From field measurements recorded within 32 50 × 50 m plots, AGB was estimated by applying species-specific allometric equations to stand measurements. AGB was then scaled-up to the larger area using relationships established with Light Detection and Ranging (LiDAR) data acquired over 150 (10 columns, 15 rows) 500 × 150 m cells (or Primary Sampling Units, PSUs) spaced 4 × 4 km apart in the north- and east-west directions. Large-scale (1 : 4000) stereo aerial photographs were also acquired for each PSU to assess species composition. Based on the LiDAR extrapolations, the median AGB for the PSU grid was 82 Mg ha^− 1 (maximum 164 Mg ha^− 1), with the higher levels associated with forests containing a high proportion of Angophora and Callitris species. Empirical relationships between AGB and SAR backscatter confirmed that C-, L- and P-band saturated at different levels and revealed a greater strength in the relationship at higher incidence angles and a larger dynamic range and consistency of relationships at HV polarizations. A higher level of saturation (above ∼50 Mg ha^− 1) was observed at C-band HV compared to that reported for closed forests which was attributable to a link between foliage projected cover (FPC) and AGB. The study concludes that L-band HV backscatter data acquired at incidence angles approaching or exceeding 45° are best suited for estimating the AGB up to the saturation level of ∼80-85 Mg ha^− 1. For regional mapping of biomass below the level of saturation, the use of the Japanese Space Exploration Agency (JAXA) Advanced Land Observing Satellite (ALOS) Phase Arrayed L-band SAR (PALSAR) is advocated.     

Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images

Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer diagnosis (ADL-BCD) model using digital mammograms. The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms. The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation. In addition, Deep Convolutional Neural Network based Residual Network (ResNet 34) is applied for feature extraction purposes. Specifically, a hyper parameter tuning process using chimp optimization algorithm (COA) is applied to tune the parameters involved in ResNet 34 model. The wavelet neural network (WNN) is used for the classification of digital mammograms for the detection of breast cancer. The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures. The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.     

Sea-level rise and its possible impacts given a 'beyond 4°C world' in the twenty-first century

The range of future climate-induced sea-level rise remains highly uncertain with continued concern that large increases in the twenty-first century cannot be ruled out. The biggest source of uncertainty is the response of the large ice sheets of Greenland and west Antarctica. Based on our analysis, a pragmatic estimate of sea-level rise by 2100, for a temperature rise of 4°C or more over the same time frame, is between 0.5 m and 2 m--the probability of rises at the high end is judged to be very low, but of unquantifiable probability. However, if realized, an indicative analysis shows that the impact potential is severe, with the real risk of the forced displacement of up to 187 million people over the century (up to 2.4% of global population). This is potentially avoidable by widespread upgrade of protection, albeit rather costly with up to 0.02 per cent of global domestic product needed, and much higher in certain nations. The likelihood of protection being successfully implemented varies between regions, and is lowest in small islands, Africa and parts of Asia, and hence these regions are the most likely to see coastal abandonment. To respond to these challenges, a multi-track approach is required, which would also be appropriate if a temperature rise of less than 4°C was expected. Firstly, we should monitor sea level to detect any significant accelerations in the rate of rise in a timely manner. Secondly, we need to improve our understanding of the climate-induced processes that could contribute to rapid sea-level rise, especially the role of the two major ice sheets, to produce better models that quantify the likely future rise more precisely. Finally, responses need to be carefully considered via a combination of climate mitigation to reduce the rise and adaptation for the residual rise in sea level. In particular, long-term strategic adaptation plans for the full range of possible sea-level rise (and other change) need to be widely developed.     

Excitonic Mechanism of Local Phase Transformations by Optical Pumping

Transformations of cooperative electronic states by impacts of optical pumping and/or electrostatic doping is a new mainstream in physics of correlated systems. Here we present a semi-phenomenological modeling of spatio-temporal effects in a system where the light absorption goes through a channel creating the excitons—intra-molecular ones or bound electron–hole pairs—and finally the condensate of optical excitons feeds and stimulates phase transformations. Interacting with a near-critical order parameter and deformations, the excitons are subject to self-trapping. That locally enhances their density which can surpass a critical value to trigger the phase transformation, even if the mean density is below the required threshold. The model can be used e.g. as a simplified version of optically induced neutral-ionic transitions in organic chain compounds.     

Biology and architecture: An ongoing hybridization of scientific knowledge and design practice by six architectural offices in France

As a highly interdisciplinary field, architecture is being influenced by many subjects of natural and social sciences. Biology despite being apparently distant from architecture is currently a scientific field blending into design practices, which have evolved and shifted towards a new hybrid framework. In this article, we present an emerging design field of what we categorize as biomimetic architecture pioneering by six architectural offices in France. We observe the impact of scientific researches on design processes and practices through six case studies led by these offices, which can be seen through the actors who involved in various types of interdisciplinary collaborations, through the competencies of the architect himself, and through new sources of ideas coming from biological sciences and related fields. We propose to use a classification of theoretical uses in modeling practice to better understand the role that biological knowledge plays in architectural design practices. Finally, the result of this analysis shows that the use of biology taking place in a design space has external purposes, which transform it to produce engineering devices or urban schemes rather than architectural projects. It also shows that biology in architectural design induces other kinds of non-biological knowledge, is not strictly theoretical and could be obsolete or approximate. These findings lead to an epistemological discussion concerning the confusion between biological ‘knowledge’ and architectural design ‘know-how’.     

CO2-efflux measurements for evaluating source zone natural attenuation rates in a petroleum hydrocarbon contaminated aquifer

In order to gain regulatory approval for source zone natural attenuation (SZNA) at hydrocarbon-contaminated sites, knowledge regarding the extent of the contamination, its tendency to spread, and its longevity is required. However, reliable quantification of biodegradation rates, an important component of SZNA, remains a challenge. If the rate of CO(2) gas generation associated with contaminant degradation can be determined, it may be used as a proxy for the overall rate of subsurface biodegradation. Here, the CO(2)-efflux at the ground surface is measured using a dynamic closed chamber (DCC) method to evaluate whether this technique can be used to assess the areal extent of the contaminant source zone and the depth-integrated rate of contaminant mineralization. To this end, a field test was conducted at the Bemidji, MN, crude oil spill site. Results indicate that at the Bemidji site the CO(2)-efflux method is able to both delineate the source zone and distinguish between the rates of natural soil respiration and contaminant mineralization. The average CO(2)-efflux associated with contaminant degradation in the source zone is estimated at 2.6 μmol m(-2) s(-1), corresponding to a total petroleum hydrocarbon mineralization rate (expressed as C(10)H(22)) of 3.3 g m(-2) day(-1).     

The Role of Melatonin on Behavioral Changes and Concomitant Oxidative Stress in icvAβ1-42 Rat Model with Pinealectomy

One of the pathological hallmarks of Alzheimer’s disease (AD) associated with its progression that contributes to β-amyloid (Aβ) generation is oxidative stress (OS). Clinical data suggest that melatonin is a potent antioxidant that might be effective in the adjunctive therapy of this neurodegenerative disease. The present study aimed to explore the role of melatonin on behavioral changes and markers of OS in three rat models, namely, pinealectomy (pin) model of melatonin deficit, intracerebroventricular (icv)Aβ_1-42 model of AD, and combination of both pin and Aβ_1-42 model (pin+icvAβ_1-42). The chronic injection with vehicle/melatonin (50 mg/kg, i.p. for 40 days) started on the same day of sham/pin and icv vehicle/Aβ_1-42 infusion procedures. Anxiety in the open field and the elevated plus-maze test and cognitive responses in the object recognition test were tested between the 30th–35th day after the surgical procedures. Markers of OS in the frontal cortex (FC) and hippocampus were detected by the ELISA method. Melatonin treatment corrected the exacerbated anxiety response only in the pin+icvAβ_1-42 model while it alleviated the cognitive impairment in the three models. Pinealectomy disturbed the antioxidant system via enhanced SOD activity and decreased GSH levels both in the FC and hippocampus. The Aβ_1-42 model decreased the SOD activity in the FC and elevated the MDA level in the two brain structures. The pin+icvAβ_1-42 model impaired the antioxidant system and elevated lipid peroxidation. Melatonin supplementation restored only the elevated MDA level of icvAβ_1-42 and pin+icvAβ_1-42 model in the hippocampus. In conclusion, our study reveals that the pin+icvAβ_1-42 rat model triggers more pronounced anxiety and alterations in markers of OS that may be associated with melatonin deficit concomitant to icvAβ_1-42-induced AD pathology.     

Computer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm

Computer-aided diagnosis (CAD) models exploit artificial intelligence (AI) for chest X-ray (CXR) examination to identify the presence of tuberculosis (TB) and can improve the feasibility and performance of CXR for TB screening and triage. At the same time, CXR interpretation is a time-consuming and subjective process. Furthermore, high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis. Therefore, computer-aided diagnosis (CAD) models using machine learning (ML) and deep learning (DL) can be designed for screening TB accurately. With this motivation, this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification (WSODTL-TBC) model on Chest X-rays (CXR). The presented WSODTL-TBC model aims to detect and classify TB on CXR images. Primarily, the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image segmentation. Besides, a pre-trained residual network with a two-dimensional convolutional neural network (2D-CNN) model is applied to extract feature vectors. In addition, the WSO algorithm with long short-term memory (LSTM) model was employed for identifying and classifying TB, where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology, showing the novelty of the work. The performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset, and the outcomes were investigated in many aspects. The experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.