ALBERT

Description: This research presents a machine vision approach to detect lesions in liver ultrasound as well as resolving some issues in ultrasound such as artifacts, speckle noise, and blurring effect. The anisotropic diffusion is modified using the edge preservation conditions which found better than traditional ones in quantitative evolution. To dig for more potential information, a learnable super-resolution (SR) is embedded into the deep CNN. The feature is fused using Gabor Wavelet Transform (GWT) and Local Binary Pattern (LBP) with a pre-trained deep CNN model. Moreover, we propose a Bayes rule-based informative patch selection approach to reduce the processing time with the selective image patches and design an algorithm to mark the lesion region from identified ultrasound image patches. To train this model, standard data ensures promising resolution. The testing phase considers generalized data with a varying resolution and test the performance of the model. Exploring cross-validation, it finds that a 5-fold strategy can successfully eradicate the overfitting problem. Experiment data are collected using 298 consecutive ultrasounds comprising 15,296 image patches. This proposed feature fusion technique confirms satisfactory performance compared to the current relevant works with an accuracy of 98.40%.

Description: Cybersecurity issues constitute a key concern of today’s technology-based economies. Cybersecurity has become a core need for providing a sustainable and safe society to online users in cyberspace. Considering the rapid increase of technological implementations, it has turned into a global necessity in the attempt to adapt security countermeasures, whether direct or indirect, and prevent systems from cyberthreats. Identifying, characterizing, and classifying such threats and their sources is required for a sustainable cyber-ecosystem. This paper focuses on the cybersecurity of smart grids and the emerging trends such as using blockchain in the Internet of Things (IoT). The cybersecurity of emerging technologies such as smart cities is also discussed. In addition, associated solutions based on artificial intelligence and machine learning frameworks to prevent cyber-risks are also discussed. Our review will serve as a reference for policy-makers from the industry, government, and the cybersecurity research community.

Description: The seismic hazard of vulnerable regions warrants the investigation of new technologies, such as base level isolation by lead rubber bearing (LRB) devices, that can help to mitigate structural damage on seismic prone buildings. The behavior adopting such technologies can be dynamically observed in simulated environments and thus serves as a valuable metric for their feasibility. LRB base isolators were incorporated into the design of 16 model buildings to better understand how they affected a building’s seismic response while also providing information on the structural parameters. A total of 12 different types of bearing systems were tested in base isolated (BI) buildings against conventional fixed-base (FB) isolated buildings. The system of each model high-rise building was represented by the finite element package. Static as well as dynamic analysis were conducted using response spectrum analysis (RSA) based on the seismic excitation to determine the influence of the model type in the aseismic design and the alteration in superstructure behavior. The study reveals that the LRB isolators can potentially diminish respective story accelerations, story inertia, and base shear. Use of LRB isolators in BI buildings resulted in a 10–20% reduction in base shear compared to FB buildings. A notable lateral shift of superstructure is offered by LRB-induced flexibility. The reduction of story acceleration for response spectrum varies 30% on lower stories up to 70% on upper stories. The LRB systems with higher characteristic strength and relatively less isolation periods shows better productivity to minimize displacements in the bearing face for dropping structural shift. However, the LRB with comparatively lower characteristic strength and high isolation periods shows the most efficiency in controlling base shear, offering least story accelerations and consenting lower story inertia forces.

Description: A shape memory alloy (SMA) can remember its original shape and recover from strain due to loading once it is exposed to heat (shape memory effect). SMAs also exhibit elastic response to applied stress above the characteristic temperature at which transformation to austenite is completed (pseudoelasticity or superelasticity). Shape memory effect and pseudoelasticity of SMAs have been addressed by several microscopic thermodynamic and macroscopic phenomenological models using different modeling approaches. The Tanaka and Liang-Rogers models are two of the most widely used macroscopic phenomenological constitutive models for describing SMA behavior. In this paper, we performed sensitivity and uncertainty analysis using Sobol and extended Fourier Amplitude Sensitivity Testing (eFAST) methods for the Tanaka and Liang-Rogers models at different operating temperatures and loading conditions. The stress-dependent and average sensitivity indices have been analyzed and are presented for determining the most influential parameters for these models. The results show that variability is primarily caused by a change in operating temperature and loading conditions. Both models appear to be influenced by the uncertainty in elastic modulus of the material significantly. The analyses presented in this paper aim to provide a better insight for designing applications using SMAs by increasing the understanding of these models’ sensitivity to the input parameters and the cause of output variability due to uncertainty in the same input parameters.

Description: Application of conventional synthetic pesticides and agrochemicals has boosted the yield and productivity of crops by reducing pest infestation and promoting crop growth yet increasing reliance on many of these products poses serious environmental threats. This has led to growing interest in obtaining more environmentally friendly alternatives to conventional pesticides and agrochemicals. Allelochemicals produced by plants, fungi, and microbes offer options for developing novel natural product-based pesticides and agrochemicals that are effective but with lower environmental half-lives. Here, we review the current state of knowledge about the potential use of juglone (5-hydroxy-1,4-naphthoquinone), the allelochemical produced by black walnut trees (Juglans nigra), which has been investigated for applications across a range of different agricultural purposes. We then offer our perspective on what opportunities and challenges exist for harnessing juglone as a component of sustainable agriculture.

Description: A deeper understanding of the effects of experimental warming and elevated ozone (O3) concentration on carbon dioxide (CO2) fluxes is imperative for reducing potential CO2 emissions in agroecosystems, but are less understood particularly in rotational wheat (Triticum aestivum)—soybean (Glycine max) croplands. In order to understand such effects on CO2 fluxes from winter wheat-soybean rotation, a field experiment was conducted by using the open-top chamber (OTCs) during the growing seasons of 2012 and 2013 at an agro-ecological station in southeast China. The experimental treatments included the control (CK), experimental warming (T, crop canopy temperature increased by ~2 °C), elevated O3 concentration (O, O3 concentration about 100 ppb) along with temperature enhancement (OT, elevated ~2 °C temperature plus 100 ppb O3). The results showed that warming significantly increased the mean CO2 fluxes (MCF) and the cumulative amount of CO2 (CAC) from soil and soil-crop systems, while elevated O3 and warming enhancement (OT) significantly reduced MCF and CAC. Besides, warming significantly reduced the biomass of winter-wheat, but it insignificantly decreased the biomass of soybean in the harvest period. The O and OT treatments significantly reduced the biomass of winter-wheat and soybean cropping systems in the harvest time. Both warming and elevated O3 concentration decreased the temperature sensitivity coefficients (Q10) in soil respiration during the experimental period. Overall, our results indicate that elevated O3 concentration compensates the effect of warming on CO2 emission to some extents, which has a positive feedback impact on the climate system.

Description: Shape memory alloys (SMAs) are classified as smart materials due to their capacity to display shape memory effect and pseudoelasticity with changing temperature and loading conditions. The thermomechanical behavior of SMAs has been simulated by several constitutive models that adopted microscopic thermodynamic or macroscopic phenomenological approaches. The Ivshin–Pence model is one of the most popular SMA macroscopic phenomenological constitutive models. The construction of the model requires involvement of parameters that possess inherent uncertainty. Under varying operating temperatures and loading conditions, the uncertainty in these parameters propagates and, therefore, affects the predictive power of the model. The propagation of uncertainty while using this model in real-life applications can result in performance discrepancies or failure at extreme conditions. In this study, we employed a probabilistic approach to perform the sensitivity and uncertainty analysis of the Ivshin–Pence model. Sobol and extended Fourier Amplitude Sensitivity Testing (eFAST) methods were used to perform the sensitivity analysis for simulated isothermal loading/unloading at various operating temperatures. It is evident that the model’s prediction of the SMA stress–strain curves varies due to the change in operating temperature and loading condition. The average and stress-dependent sensitivity indices present the most influential parameters at several temperatures.

Description: Natural product-based herbicides could be the effective alternatives to synthetic chemical herbicides for eco-friendly weed management. This research, therefore, was conducted to identify the phytotoxic properties of Parthenium hysterophorus L., Cleome rutidosperma DC. and Borreria alata (Aubl.) DC. with a view to introducing them as a tool for natural herbicide development. The methanol extracts of these plants were examined on the germination and growth of Zea mays L., Oryza sativa L., Abelmoschus esculentus (L.) Moench and Amaranthus gangeticus L., Oryza sativa f. Spontanea Roshev. (Weedy rice), Echinochloa colona (L.) Link., Euphorbia hirta L., and Ageratum conyzoides L. under laboratory and glasshouse conditions. A complete randomized design (CRD) with five replications and randomized complete block design (RCBD) with four replications were laid out for laboratory and glasshouse experiments, respectively. In the laboratory experiment, three plant extracts of 0, 6.25, 12.5, 50, and 100 g L−1 were tested on survival rate, hypocotyl, and radicle length of eight test plant species. No seed germination of A. conzyoides, E. hirta, and A. gangeticus were recorded when P. hysterophorus extract was applied at 50 g L−1. C. rutidosperma had the same effect on those plants at 100 g L−1. In the glasshouse, similar extracts and concentrations used in thelaboratory experiments were sprayed on at the 2–3 leaf stage for grasses and 4–6 for the broadleaf species. Tested plants were less sensitive to C. rutidosperma and B. alata compared to P. hysterophorus extract. Among the weeds and crops, A. conyzoides, E. hirta, A. esculentus and A. gangeticus were mostly inhibited by P. hysterophorus extract at 100 g L−1. Based on these results, P. hysterophorus was the most phytotoxic among the tested plant extracts and could be used for developing a new natural herbicide for green agriculture.