AI-Based Metamaterial Design.

The use of metamaterials in various devices has revolutionized applications in optics, healthcare, acoustics, and power systems. Advancements in these fields demand novel or superior metamaterials that can demonstrate targeted control of electromagnetic, mechanical, and thermal properties of matter. Traditional design systems and methods often require manual manipulations which is time-consuming and resource intensive. The integration of artificial intelligence (AI) in optimizing metamaterial design can be employed to explore variant disciplines and address bottlenecks in design. AI-based metamaterial design can also enable the development of novel metamaterials by optimizing design parameters that cannot be achieved using traditional methods. The application of AI can be leveraged to accelerate the analysis of vast data sets as well as to better utilize limited data sets via generative models. This review covers the transformative impact of AI and AI-based metamaterial design for optics, acoustics, healthcare, and power systems. The current challenges, emerging fields, future directions, and bottlenecks within each domain are discussed.

Deep learning-augmented T-junction droplet generation.

Droplet generation technology has become increasingly important in a wide range of applications, including biotechnology and chemical synthesis. T-junction channels are commonly used for droplet generation due to their integration capability of a larger number of droplet generators in a compact space. In this study, a finite element analysis (FEA) approach is employed to simulate droplet production and its dynamic regimes in a T-junction configuration and collect data for post-processing analysis. Next, image analysis was performed to calculate the droplet length and determine the droplet generation regime. Furthermore, machine learning (ML) and deep learning (DL) algorithms were applied to estimate outputs through examination of input parameters within the simulation range. At the end, a graphical user interface (GUI) was developed for estimation of the droplet characteristics based on inputs, enabling the users to preselect their designs with comparable microfluidic configurations within the studied range.

Apple crown and collar canker and necrosis caused by Cytospora balanejica sp. nov. in Iran.

Apple is the most important fruit tree in West Azarbaijan province of Iran. In a survey of apple orchards, a disease with crown and collar canker and necrosis symptoms was observed in three young apple orchards in Urmia, affecting 15% and 1% of 'Red Delicious' and 'Golden Delicious' cultivars, respectively. A fungus with typical characteristics of the asexual morph of Cytospora was regularly isolated from the diseased tissues. Morphological characteristics and phylogenetic analyses inferred from the combined dataset of the ITS-rDNA, parts of LSU, tef1-α, rpb2, and act1 genes revealed that the isolates represent a new species of Cytospora, described herein as Cytospora balanejica sp. nov.. The pathogenicity of all isolates was confirmed on apple cv. 'Red Delicious' based on Koch's postulates. Also, the reaction of 12 other apple cultivars was assessed against five selected isolates with the highest virulence. The results showed that except for cv. 'Braeburn', which did not produce any symptoms of the disease, the other 11 cultivars showed characteristic disease symptoms including sunken and discolored bark and wood. The mean length of the discolored area was different among the 11 so-called susceptible cultivars, hence cvs. 'M4' and 'Golden Delicious' showed the highest and the lowest lesion length, respectively. Moreover, the aggressiveness of the five tested isolates was different, and the isolates BA 2-4 and BA 3-1 had the highest and lowest aggressiveness, respectively. Based on our observations on the potential ability of the fungus to cause disease on young and actively growing apple trees, it will be a serious threat to apple cultivation and industry.

Machine learning-augmented fluid dynamics simulations for micromixer educational module.

Micromixers play an imperative role in chemical and biomedical systems. Designing compact micromixers for laminar flows owning a low Reynolds number is more challenging than flows with higher turbulence. Machine learning models can enable the optimization of the designs and capabilities of microfluidic systems by receiving input from a training library and producing algorithms that can predict the outcomes prior to the fabrication process to minimize development cost and time. Here, an educational interactive microfluidic module is developed to enable the design of compact and efficient micromixers at low Reynolds regimes for Newtonian and non-Newtonian fluids. The optimization of Newtonian fluids designs was based on a machine learning model, which was trained by simulating and calculating the mixing index of 1890 different micromixer designs. This approach utilized a combination of six design parameters and the results as an input data set to a two-layer deep neural network with 100 nodes in each hidden layer. A trained model was achieved with R2 = 0.9543 that can be used to predict the mixing index and find the optimal parameters needed to design micromixers. Non-Newtonian fluid cases were also optimized using 56700 simulated designs with eight varying input parameters, reduced to 1890 designs, and then trained using the same deep neural network used for Newtonian fluids to obtain R2 = 0.9063. The framework was subsequently used as an interactive educational module, demonstrating a well-structured integration of technology-based modules such as using artificial intelligence in the engineering curriculum, which can highly contribute to engineering education.

Microneedle arrays integrated with microfluidic systems: Emerging applications and fluid flow modeling.

Microneedle arrays are patches of needles at micro- and nano-scale, which are competent and versatile technologies that have been merged with microfluidic systems to construct more capable devices for biomedical applications, such as drug delivery, wound healing, biosensing, and sampling body fluids. In this paper, several designs and applications are reviewed. In addition, modeling approaches used in microneedle designs for fluid flow and mass transfer are discussed, and the challenges are highlighted.

Novel species of Alternaria section Nimbya from Iran as revealed by morphological and molecular data.

In this study, the diversity of Alternaria species in section Nimbya associated with symptomatic plants in the Cyperaceae and Juncaceae families was assessed. Multilocus sequence analyses of the rDNA internal transcribed spacer (ITS) region and parts of Alternaria major allergen (Alt a 1), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), second largest subunit of RNA polymerase II (RPB2), and translation elongation factor 1-alpha (TEF1) genes revealed the presence of two previously known species, A. scirpivora and A. caricicola, and three new species, which are described here as A. cypericola, sp. nov., A. heyranica, sp. nov., and A. junci-acuti, sp. nov. These new species were characterized morphologically with respect to the dimensions of conidia, the number of pseudosepta in mature conidia, and the type of conidium apical beak. According to the results of phylogenetic analyses, the presence of long, filiform true beak is not a reliable morphological indicator for grouping species in sections Alternantherae and Nimbya and phylogenetic species recognition should be used. All identified species were described, illustrated, and their morphology and phylogenetic relationships with other species in Alternaria section Nimbya were discussed.

Exogenous ß-aminobutyric acid application attenuates Aspergillus decay, minimizes aflatoxin B1 accumulation, and maintains nutritional quality in fresh-in-hull pistachio kernels.

BACKGROUND: Pistachio fruits suffer from postharvest decay, caused by Aspergillus flavus. This results in aflatoxin B1 (AFB1 ) accumulation in kernels, which is hazardous for human health due to its carcinogenic activity. In this study, the mechanism used by exogenous ß-aminobutyric acid (BABA) treatment for attenuating Aspergillus decay, minimizing aflatoxin B1 (AFB1 ) accumulation, and maintaining nutritional quality in fresh-in-hull pistachio kernels, infected by A. flavus during storage at 25 °C for 18 days, was investigated. RESULT: Results of an in vivo assay showed that the spore germination and germ tube elongation of A. flavus was repressed by BABA treatment at 7.5 mM. Aspergillus decay accompanied by AFB1 accumulation was also minimized in fresh-in-hull pistachio kernels treated with BABA at 7.5 mM and infected by A. flavus. Fresh-in-hull pistachio kernels, infected by A. flavus, treated with BABA at 7.5 mM, also exhibited higher phenol and flavonoid accumulation and 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging capacity accompanied by higher phenylalanine ammonia lyase (PAL) enzyme activity. CONCLUSION: Promoting phenylpropanoid pathway activity with higher PAL enzyme activity in fresh-in-hull pistachio kernels treated with BABA may not only reduce Aspergillus decay in kernels by cell wall fortification but also may be favorable for maintaining the kernels' nutritional quality through its effects on ROS scavenging capacity. As oxidative stress, represented by ROS accumulation, is responsible for A. flavus growth and AFB1 accumulation, higher phenol and flavonoid accumulation in fresh-in-hull pistachio kernels treated with BABA may be beneficial for attenuating Aspergillus decay and minimizing AFB1 accumulation. © 2019 Society of Chemical Industry.