From Meso to Macro: Controlling Hierarchical Porosity in Supraparticle Powders

A drying droplet containing colloidal particles can consolidate into a spherical assembly called a supraparticle. Such supraparticles are inherently porous due to the spaces between the constituent primary particles. Here, the emergent, hierarchical porosity in spray-dried supraparticles is tailored via three distinct strategies acting at different length scales. First, mesopores (<10 nm) are introduced via the primary particles. Second, the interstitial pores are tuned from the meso- (35 nm) to the macro scale (250 nm) by controlling the primary particle size. Third, defined macropores (>100 nm) are introduced via templating polymer particles, which can be selectively removed by calcination. Combining all three strategies creates hierarchical supraparticles with fully tailored pore size distributions. Moreover, another level of the hierarchy is added by fabricating supra-supraparticles, using the supraparticles themselves as building blocks, which provide additional pores with micrometer dimensions. The interconnectivity of the pore networks within all supraparticle types is investigated via detailed textural and tomographic analysis. This work provides a versatile toolbox for designing porous materials with precisely tunable, hierarchical porosity from the meso- (3 nm) to the macroscale (≈10 µm) that can be utilized for applications in catalysis, chromatography, or adsorption.     

Analyzing the influence of changes in land use and management practices on the lag time of peak flows for tropical watersheds of Ethiopia

Changes in peak flow response time governed by rainfall and physical configurations of watersheds have been the topic of many studies, but other factors are also important. We aimed to analyze the dynamics in the lag time (T_L) of peak flow for a tropical watershed (Kecha) and its sub-watersheds (Dokmit, Zenjero Maderia, and Wotit Minch) in Ethiopia, as influenced by land-use changes from 1982 to 2017 and the implementation of soil and water conservation (SWC) practices since 2011. Layered maps of land use, soil type, and SWC practices were used to determine the curve number, and the Natural Resource Conservation Service hydrologic model was used to estimate T_L. We compared the estimated values against measured median values of T_L for 30 rainfall–runoff events. The estimated T_L of 1982, 2005, and 2017 varied from 9 to 19 min, 8 to 18 min, and 10 to 22 min, respectively, in the Dokmit, Zenjero Maderia, and Wotit Minch sub-watersheds. The smallest (8 min) and greatest (22 min) values of T_L were observed during 2005 and 2017, respectively. These results are likely attributable to the increased amount of cultivated area at the expense of bushland and forest in 2005 as compared to 1982, and the implementation of SWC practices in 2011–2017. Dokmit had the lowest T_L values of the three sub-watersheds, most likely because of its greater coverage of grazing and cultivated lands and degraded Nitic Luvisol. The variation in T_L values among the sub-watersheds was related to changes in both land use and SWC practices but also to the presence of degraded areas inherited from past human activities. Overall, such spatiotemporal flow response time analysis can provide useful information for the proper design of sustainable development strategies for particular niches in tropical regions of Ethiopia and elsewhere.     

Sea Turtle Foraging Optimization-Based Controller Placement with Blockchain-Assisted Intrusion Detection in Software-Defined Networks

Software-defined networking (SDN) algorithms are gaining increasing interest and are making networks flexible and agile. The basic idea of SDN is to move the control planes to more than one server’s named controllers and limit the data planes to numerous sending network components, enabling flexible and dynamic network management. A distinctive characteristic of SDN is that it can logically centralize the control plane by utilizing many physical controllers. The deployment of the controller—that is, the controller placement problem (CPP)—becomes a vital model challenge. Through the advancements of blockchain technology, data integrity between nodes can be enhanced with no requirement for a trusted third party. Using the latest developments in blockchain technology, this article designs a novel sea turtle foraging optimization algorithm for the controller placement problem (STFOA-CPP) with blockchain-based intrusion detection in an SDN environment. The major intention of the STFOA-CPP technique is the maximization of lifetime, network connectivity, and load balancing with the minimization of latency. In addition, the STFOA-CPP technique is based on the sea turtles’ food-searching characteristics of tracking the odour path of dimethyl sulphide (DMS) released from food sources. Moreover, the presented STFOA-CPP technique can adapt with the controller’s count mandated and the shift to controller mapping to variable network traffic. Finally, the blockchain can inspect the data integrity, determine significantly malicious input, and improve the robust nature of developing a trust relationship between several nodes in the SDN. To demonstrate the improved performance of the STFOA-CPP algorithm, a wide-ranging experimental analysis was carried out. The extensive comparison study highlighted the improved outcomes of the STFOA-CPP technique over other recent approaches.     

Enhancing CNN for Forensics Age Estimation Using CGAN and Pseudo-Labelling

Age estimation using forensics odontology is an important process in identifying victims in criminal or mass disaster cases. Traditionally, this process is done manually by human expert. However, the speed and accuracy may vary depending on the expertise level of the human expert and other human factors such as level of fatigue and attentiveness. To improve the recognition speed and consistency, researchers have proposed automated age estimation using deep learning techniques such as Convolutional Neural Network (CNN). CNN requires many training images to obtain high percentage of recognition accuracy. Unfortunately, it is very difficult to get large number of samples of dental images for training the CNN due to the need to comply to privacy acts. A promising solution to this problem is a technique called Generative Adversarial Network (GAN). GAN is a technique that can generate synthetic images that has similar statistics as the training set. A variation of GAN called Conditional GAN (CGAN) enables the generation of the synthetic images to be controlled more precisely such that only the specified type of images will be generated. This paper proposes a CGAN for generating new dental images to increase the number of images available for training a CNN model to perform age estimation. We also propose a pseudo-labelling technique to label the generated images with proper age and gender. We used the combination of real and generated images to train Dental Age and Sex Net (DASNET), which is a CNN model for dental age estimation. Based on the experiment conducted, the accuracy, coefficient of determination (R2) and Absolute Error (AE) of DASNET have improved to 87%, 0.85 and 1.18 years respectively as opposed to 74%, 0.72 and 3.45 years when DASNET is trained using real, but smaller number of images.     

Hill Matrix and Radix-64 Bit Algorithm to Preserve Data Confidentiality

There are many cloud data security techniques and algorithms available that can be used to detect attacks on cloud data, but these techniques and algorithms cannot be used to protect data from an attacker. Cloud cryptography is the best way to transmit data in a secure and reliable format. Various researchers have developed various mechanisms to transfer data securely, which can convert data from readable to unreadable, but these algorithms are not sufficient to provide complete data security. Each algorithm has some data security issues. If some effective data protection techniques are used, the attacker will not be able to decipher the encrypted data, and even if the attacker tries to tamper with the data, the attacker will not have access to the original data. In this paper, various data security techniques are developed, which can be used to protect the data from attackers completely. First, a customized American Standard Code for Information Interchange (ASCII) table is developed. The value of each Index is defined in a customized ASCII table. When an attacker tries to decrypt the data, the attacker always tries to apply the predefined ASCII table on the Ciphertext, which in a way, can be helpful for the attacker to decrypt the data. After that, a radix 64-bit encryption mechanism is used, with the help of which the number of cipher data is doubled from the original data. When the number of cipher values is double the original data, the attacker tries to decrypt each value. Instead of getting the original data, the attacker gets such data that has no relation to the original data. After that, a Hill Matrix algorithm is created, with the help of which a key is generated that is used in the exact plain text for which it is created, and this Key cannot be used in any other plain text. The boundaries of each Hill text work up to that text. The techniques used in this paper are compared with those used in various papers and discussed that how far the current algorithm is better than all other algorithms. Then, the Kasiski test is used to verify the validity of the proposed algorithm and found that, if the proposed algorithm is used for data encryption, so an attacker cannot break the proposed algorithm security using any technique or algorithm.     

Garden cress gum and maltodextrin as microencapsulation coats for entrapment of garden cress phenolic-rich extract: improved thermal stability,storage stability,antioxidant and antibacterial activities

Food Science and Biotechnology - The obtained garden cress 6-day sprouts phenolic-rich extract (GCSP) contained efficient health-promoting antioxidant-phenolic compounds. To improve the stability,...     

Novel chaos game optimization tuned-fractional-order PID fractional-order PI controller for load–frequency control of interconnected power systems

In this work, chaos game optimization (CGO), a robust optimization approach, is employed for efficient design of a novel cascade controller for four test systems with interconnected power systems (IPSs) to tackle load–frequency control (LFC) difficulties. The CGO method is based on chaos theory principles, in which the structure of fractals is seen via the chaotic game principle and the fractals’ self-similarity characteristics are considered. CGO is applied in LFC studies as a novel application, which reveals further research gaps to be filled. For practical implementation, it is also highly desirable to keep the controller structure simple. Accordingly, in this paper, a CGO-based controller of fractional-order (FO) proportional–integral–derivative–FO proportional–integral (FOPID–FOPI) controller is proposed, and the integral time multiplied absolute error performance function is used. Initially, the proposed CGO-based FOPID–FOPI controller is tested with and without the nonlinearity of the governor dead band for a two-area two-source model of a non-reheat unit. This is a common test system in the literature. A two-area multi-unit system with reheater–hydro–gas in both areas is implemented. To further generalize the advantages of the proposed scheme, a model of a three-area hydrothermal IPS including generation rate constraint nonlinearity is employed. For each test system, comparisons with relevant existing studies are performed. These demonstrate the superiority of the proposed scheme in reducing settling time, and frequency and tie-line power deviations.