The Biodegradation of Methyl Tert-Butyl Ether (MTBE) by Indigenous Bacillus cereus Strain RJ1 Isolated From Soil

Methyl Tert-Butyl Ether (MTBE) is an oxygenated organic compound extensively substituted for lead in gasoline worldwide. MTBE can affect human and environment. In this research, biodegradation capability of MTBE by identified indigenous Bacillus cereus strain RJ1 was studied. Obtained results showed that biological removal of MTBE with 200 mg/L samples in 28°C is 27.5% while in 37°C within 120 days reaches to 34%. In addition, biodegradation of Bacillus cereus RJ1 in 500 mg/L and 1,000 mg/L is 28% and 23.9% in 28°C, respectively. Therefore, this bacterium could clean up MTBE from the environment.     

Acoustic Emission Methodology to Evaluate the Fracture Toughness in Heat Treated AISI D2 Tool Steel

In this article, fracture toughness behavior of tool steel was investigated using Acoustic Emission (AE) monitoring. Fracture toughness (K _IC) values of a specific tool steel was determined by applying various approaches based on conventional AE parameters, such as Acoustic Emission Cumulative Count (AECC), Acoustic Emission Energy Rate (AEER), and the combination of mechanical characteristics and AE information called sentry function. The critical fracture toughness values during crack propagation were achieved by means of relationship between the integral of the sentry function and cumulative fracture toughness (KICUM). Specimens were selected from AISI D2 cold-work tool steel and were heat treated at four different tempering conditions (300, 450, 525, and 575?°C). The results achieved through AE approaches were then compared with a methodology proposed by compact specimen testing according to ASTM standard E399. It was concluded that AE information was an efficient method to investigate fracture characteristics.     

Variation in aphid alarm pheromone content among glandular and eglandular-hairedMedicago accessions

Pea (Acyrthosiphon pisum Harris) and blue alfalfa aphid (A. kondoi Shinji) deterrency in alfalfa (Medicago saliva L.) may result from incorporating higher levels of the aphid alarm pheromone,(E)--farnesene relative to(E)--caryophyllene. We evaluated five eglandular and two glandular-haired alfalfa accessions for differences in(E)--farnesene and(E)--caryophyllene content under glasshouse conditions using supercritical fluid extraction and gas chromatography. In addition, pea and blue alfalfa aphid olfactory behavioral tests were conducted uponMedicago species containing different ratios of(E)--famesene relative to(E)--caryophyllene. No differences in(E)--caryophyllene content were observed among the seven entries (=0.42 ng/g plant material). Significant differences (P 0.05) among entries were observed for(E)--famesene content, with KS94GH6 exhibiting the highest (1.18 ng/g), and CUF 101 the lowest levels (0.35 ng/g). Elite tetraploid sources possessed significantly lower levels (=0.42 ng/g) of(E)--farnesene than did wild and cultivated diploid accessions (=0.83 ng/g). Olfactory behavioral tests for both the pea and blue alfalfa aphids demonstrated KS94GH6 repelled aphids while cultivated alfalfa types attracted aphids in each case. Previously demonstrated aphid resistance in diploid KS94GH6 may result from superior(E)-- farnesene levels, but(E)--farnesene is probably not a factor in cultivated alfalfa resistance. Finally, accession KS94GH6 could act as an excellent germplasm resource for the incorporation of higher(E)--farnesene levels into cultivated alfalfa.Research supported by the New Mexico State University Agricultural Experiment Station.     

A hybrid machine learning approach for detecting unprecedented DDoS attacks

Service availability plays a vital role on computer networks, against which Distributed Denial of Service (DDoS) attacks are an increasingly growing threat each year. Machine learning (ML) is a promising approach widely used for DDoS detection, which obtains satisfactory results for pre-known attacks. However, they are almost incapable of detecting unknown malicious traffic. This paper proposes a novel method combining both supervised and unsupervised algorithms. First, a clustering algorithm separates the anomalous traffic from the normal data using several flow-based features. Then, using certain statistical measures, a classification algorithm is used to label the clusters. Employing a big data processing framework, we evaluate the proposed method by training on the CICIDS2017 dataset and testing on a different set of attacks provided in the more up-to-date CICDDoS2019. The results demonstrate that the Positive Likelihood Ratio (LR+) of our method is approximately 198% higher than the ML classification algorithms.

     

A Wirelessly Controlled Smart Bandage with 3D‐Printed Miniaturized Needle Arrays

Chronic wounds are one of the most devastating complications of diabetes and are the leading cause of nontraumatic limb amputation. Despite the progress in identifying factors and promising in vitro results for the treatment of chronic wounds, their clinical translation is limited. Given the range of disruptive processes necessary for wound healing, different pharmacological agents are needed at different stages of tissue regeneration. This requires the development of wearable devices that can deliver agents to critical layers of the wound bed in a minimally invasive fashion. Here, for the first time, a programmable platform is engineered that is capable of actively delivering a variety of drugs with independent temporal profiles through miniaturized needles into deeper layers of the wound bed. The delivery of vascular endothelial growth factor (VEGF) through the miniaturized needle arrays demonstrates that, in addition to the selection of suitable therapeutics, the delivery method and their spatial distribution within the wound bed is equally important. Administration of VEGF to chronic dermal wounds of diabetic mice using the programmable platform shows a significant increase in wound closure, re‐epithelialization, angiogenesis, and hair growth when compared to standard topical delivery of therapeutics.     

Dissolution enhancement of gliclazide using in situ micronization by solvent change method

Gliclazide (GL) is a second-generation sulphonylurea, widely used for the treatment of non-insulin dependent diabetes mellitus. The low water-solubility of GL leads to a low dissolution rate and variable bioavailability. The aim of this study was to enhance the dissolution rate of GL by the preparation of micron-sized particles using a solvent change method. The in situ micronization process was carried out using solvent change method in the presence of HPMC or Brij 35 (0.05 or 0.1 g) as stabilizing agents. GL (0.5 or 1 g) was dissolved in acetone and the stabilizing agent in water (as non-solvent). The non-solvent was poured rapidly into the drug solution under stirring at 26,000 rpm by an ultra-homogenizer, and the resultant was freeze-dried. The crystalline shape of GL changed from rod-shape to diamond- or cube-shape. The FTIR and DSC results showed no interaction between the drug and the stabilizers. Presence of sharp peaks in the XRD diffractograms of microcrystals with 10 times smaller height than untreated crystals indicates that a crystalline habit modification has occurred in the microcrystals without any polymorphic changes. The particle size was reduced about 50 times and the dissolution efficiency of GL at 15 min (DE₁₅%) was increased about 4 times.     

Quantitative comparison of volatile compounds among sevenMedicago spp. accessions

Previous research identified the presence of unique semivolatile compounds in alfalfa weevil (Hypera postica Gyllenhal) (AW) -resistantMedicago accessions. These compounds may impart repellency to AW in alfalfa (Medicago sativa L.). The objective of our study was to analyze within- and between-population variation for four of these unique semivolatile compounds across eglandular and glandular-hairedMedicago accessions. SevenMedicago accessions were evaluated under greenhouse conditions in an experiment arranged as randomized complete blocks with four replicates. Volatile compounds were extracted via supercritical fluid extraction and then quantitatively analyzed for levels of 2,7-dimethyl, 2,6-octadienol;cis-geraniol; 1,(E)-8,(Z)-10-tetradecatriene; and 1-hepten-3-ol. Significant difference (P<0.05) among entries were observed for 2,7-dimethyl,2,6-octadienol;cis-geraniol; 1, and (E)-8,(Z)-10-tetradecatriene. These compounds were found only in glandularhaired accessions. There were no significant differences in levels of 1-hepten-3-ol. Our findings demonstrate that the genetic incorporation of 2,7-dimethyl, 2,6-octadienol;cis-geraniol; 1, and (E)-8,(Z)-10-tetradecatriene into cultivated alfalfa will require interspecific crossing. Alfalfa weevil behavioral tests using these compounds must be conducted prior to such crosses in order to ascertain the efficacy of repellency by these compounds.     

A novel ultrasonic assisted-reverse micelle procedure to synthesize Eu-MOF nanostructure with high sono/sonophotocatalytic activity: a systematic study for brilliant green dye removal

Journal of Materials Science: Materials in Electronics - In recent years, one of the growing environmental concerns has been contamination of water sources by dyes. For solving this problem,...     

Interpretable machine learning learns complex interactions of urban features to understand socio-economic inequality

Inequality in cities is a phenomenon arising from the complex interactions among urban systems and population activities. Conventional statistics and mathematical models like multiple regression models require assumptions of feature interactions with specified mathematical forms that may fail to fully capture complex interactions of heterogeneous urban components, creating challenges in systematically assessing socio-economic inequality in cities. To overcome the limitations of these conventional mathematical models, in this work, we propose an interpretable machine learning model to capture the complex interactions of urban variables and the main interaction effects on socio-economic statuses. We extract urban features from high-resolution anonymized mobile phone data with billions of activity records related to people and facilities in 47 US metropolitan areas and predict the attributes of urban areas from six income and race groups. We show that socio-economic inequality in cities can be effectively measured by the predictability of trained machine learning models in controlled experiments. We also examine the tradeoff between spatial resolution, sample size, and model accuracy; test the presence of influential features; and measure the transferability of the trained models to identify the optimal values for controlled factors. The results show that metropolitan areas share similar patterns of inequality, which could be moderated by improved polycentric facility distribution and road density. The generality of associated factors and transferability of machine learning models can help bridge data gaps between cities and inform about inequality alleviation strategies. Despite similarities, 50% to 90% of variations among cities are still present, which shows the need for localized policies for inequality alleviation and mitigation. Our study shows that machine learning models could be an effective approach to examine inequality, which opens avenues for more data-centric and complexity-informed planning, design, policymaking, and engineering toward equitable cities.     

CO2 utilization for methanol production; Optimal pathways with minimum GHG reduction cost

Mitigating CO₂ emissions from industries and other sectors of our economy is a critical component of building a sustainable economy. This paper investigates two different methanol synthesis routes based on CO₂ utilization (CO₂ capture and utilization [CCU], and tri-reforming of methane [TRM]), and compares the results with the conventional methanol production using natural gas as the feedstock (NG-MeOH). A comprehensive techno-economic analysis (TEA) model that includes the findings of the life cycle assessment (LCA) models of methanol production using various CO₂ utilization pathways is conducted. Economic analysis is conducted by developing a cost model that is connected to the simulation models for each production route. Compared to the conventional process (with a GHG emission of 0.6 kg CO₂/kg MeOH), the lifecycle GHG reduction of 1.75 and 0.41 kg CO₂/kg MeOH are achievable in the CCU and TRM pathways, respectively. Furthermore, the results indicate that, under current market conditions and hydrogen production costs, methanol production via CO₂ hydrogenation will result in a cost approximately three times higher than that of the conventional process. The integrated TEA–LCA model shows that this increased cost of production equates to a required life cycle GHG reduction credit of $279 to $422 per tonne of CO₂ utilized, depending on construction material and selected pathway. Additionally, when compared to the CO₂ hydrogenation route, the tri-reforming process (TRM-MeOH) can result in a 42% cost savings. Furthermore, a minimum financial support of $56 per tonne utilized CO₂ will be required to make the TRM-MeOH process economically viable.