Synthesis and Evaluation of New Anionic Gemini Dispersants as Oil Dispersants to Treat Crude Oil Spill Pollution

Floating petroleum oil spills on the marine ecosystem are generally associated with an escalation of serious environmental problems. This research aimed to synthesis five water-soluble anionic Gemini dispersants to minimize the effect of floating petroleum oil spills on the marine ecosystem. Referring to the spacer type, these dispersant are classified into two categories: the first category is the three acyl amine Gemini dispersants of N,N′- diacyl, (distearoyl-, dipalmitoy-, and dimyristoyl-) N,N-′ sodium 2-hydroxy 3-propane sulfonate propane 1,3 diamine, denoted S, P, and M, and the second category is two alkyl amine Gemini dispersants of N,N′dialkyl -(dicetyl-, and dilauryl-) N,N′-sodium 2-hydroxy 3-propane sulfonate propane 1,3 diamine, denoted C and L. Fourier transform infrared and ¹H NMR spectroscopy, gel permeation chromatography, and elemental analysis were used to verify the chemical formula of the synthesized dispersants. The effectiveness of the synthesized dispersants was investigated as regards their dynamic interfacial tension, carbon–chain length, and hydrophilic–lipophilic balance. The dispersion efficiency of the synthesized dispersant was detected qualitatively and quantitatively using the screen test and Warren Spring Laboratory methods, respectively. Experimental outcomes showed that the maximum dispersion efficiency of 94.7 wt% was achieved by dispersant C at a dispersant\oil ratio of 1:20 at 25°C.     

Correlation analysis and MLP/CMLP for optimum variables to predict orientation and tilt angles in intelligent solar tracking systems

Different solar tracking variables have been employed to build intelligent solar tracking systems without considering the dominant and optimum ones. Thus, several low performance intelligent solar tracking systems have been designed and implemented due to the inappropriate combination of solar tracking variables and intelligent predictors to drive the solar trackers. This research aims to investigate and evaluate the most effective and dominant variables on dual‐ and single‐axis solar trackers and to find the appropriate combination of solar variables and intelligent predictors. The optimum variables will be found by using correlation results between different variables and both orientation and tilt angles. Then, to use the selected variables to develop different intelligent solar trackers. The results revealed that month, day, and time are the most effective variables for horizontal single‐axis and dual‐axis solar tracking systems. Using these variables in cascade multilayer perceptron (CMLP) and multilayer perceptron (MLP) produced high performance. These predictors could predict both orientation and tilt angles efficiently. It is found that day variable is very effective to increase the performance of solar trackers although day variable is neither correlated nor significant with both orientation and tilt angles. Linear regression predicted less than 70% of the given data in most cases, whereas nonlinear models could predict the optimum orientation and tilt angles. In single‐axis tracker, month, day, and time variables achieved prediction rates of 96.85% and 96.83% for three hidden layers of MLP and CMLP, respectively, whereas the MSE are 0.0025 and 0.0008, respectively. In dual‐axis solar tracker, MLP and CMLP predicted 96.68% and 97.98 % respectively, with MSE of 0.0007 for both.     

Fault Tolerant Suffix Trees

Classical algorithms and data structures assume that the underlying memory is reliable, and the data remain safe during or after processing. However, the assumption is perilous as several studies have shown that large and inexpensive memories are vulnerable to bit flips. Thus, the correctness of output of a classical algorithm can be threatened by a few memory faults. Fault tolerant data structures and resilient algorithms are developed to tolerate a limited number of faults and provide a correct output based on the uncorrupted part of the data. Suf- fix tree is one of the important data structures that has widespread applications including substring search, super string problem and data compression. The fault tolerant version of the suffix tree presented in the literature uses complex techniques of encodable and decodable error-correcting codes, blocked data structures and fault-resistant tries. In this work, we use the natural approach of data replication to develop a fault tolerant suffix tree based on the faulty memory random access machine model. The proposed data structure stores copies of the indices to sustain memory faults injected by an adversary. We develop a resilient version of the Ukkonen’s algorithm for constructing the fault tolerant suffix tree and derive an upper bound on the number of corrupt suffixes.     

Improving security in NoSQL document databases through model-driven modernization

Knowledge and Information Systems - NoSQL technologies have become a common component in many information systems and software applications. These technologies are focused on performance, enabling...     

Inhibition of acrylamide formation in potato strip by ultrasonic-treated methylcellulose batter

The ultrasonic (US)-treated batter mixtures of commercial methylcellulose (MC) as the fried potato strips' coating layer were demonstrated. The layer formed a rapid gel that absorbs the substrate's water content and related functional groups that contribute to acrylamide formation reduction. Different concentrations of MC (0.5-2.0 w/v %) mixtures were subjected to US at 20 W to evaluate coating layers' effect prior to the deep-frying process. At 1% w/v concentration batter mixture, the USMC layer reduced the highest acrylamide value. For comparison purposes, the differences in the acrylamide content of the USMC, the non-USMC and the uncoated samples were 64.7 ± 5.04, 156.4 ± 7.44 and 344.1 ± 5.39 ng g^-1, respectively. The results were supported with lower oil uptake and water retention of the USMC compared with the non-USMC with the values of 2.2 g/100g and 28.6 g/100g with 5.1 g/100g and 47 g/100g, respectively.     

Effect of pre-gelatinised high-amylose maize starch combined with Ca2+ -induced setting of alginate on the physicochemical and sensory properties of rice flour noodles

This study explored the effect of pre-gelatinised high-amylose maize starch combined with Ca²⁺ -induced setting of alginate on the physicochemical and sensory properties of rice flour noodles. Rice flour noodles were prepared by partially substituting rice flour with pre-gelatinised high-amylose maize starch in ratios of 5, 10 and 15% (wt/wt), combined with sodium alginate at 1% (wt/wt). The noodles were then immersed in a calcium chloride solution to set the alginate. Results indicated that the hydrocolloids significantly (P < 0.05) increased noodle tensile strength, hardness, water absorption and optimum cooking time while cooking loss was significantly (P < 0.05) reduced. SEM micrographs showed a denser, compact noodle structure with an increase in the matrix's continuity as a result of hydrocolloids addition. Formulated noodles also scored above average (5.91) sensory acceptability on the 7 points hedonic scale. Overall, results proved promising for the preparation of rice flour noodles with enhanced properties.     

Institutional stakeholder collaborations (ISCs): a conceptual framework for housing research

Journal of Housing and the Built Environment - Housing development calls for collaboration through a multipronged and concerted effort from all stakeholders to acquire resources within...     

Should Google Scholar be used for benchmarking against the professoriate in education?

Scientometrics - In the neoliberal environment of contemporary academia, an individual’s research rankings and outputs can shape their career security and progression. When applying for...     

Discrete Jaya with refraction learning and three mutation methods for the permutation flow shop scheduling problem

The Permutation Flow Shop Scheduling Problem (PFSSP) is an interesting scheduling problem that has many real-world applications. It has been widely used as a benchmark to prove the efficiency of many discrete optimization algorithms. The DJaya algorithm is a discrete variation of the Jaya algorithm that has been recently proposed for solving discrete real-world problems. However, DJaya may get stuck in a local optima because of some limitations in its optimization operators. In this paper, we propose a new discrete optimization algorithm called Discrete Jaya with Refraction Learning and Three Mutation Methods (DJRL3M) for solving the PFSSP. DJRL3M incorporates five modifications into DJaya. First, it utilizes Refraction Learning (RL), which is a special type of opposition learning, to generate a diverse initial population of solutions. Second, it uses three mutation methods to explore the search space of a problem: DJaya mutation, highly disruptive polynomial mutation and Pitch Adjustment mutation. Third, it employs RL at each iteration to generate the opposite solutions of the best and worst solutions in an attempt to jump out local optima. Fourth, it uses the abandon method at the end of each iteration to discard a predefined percentage of the worst solutions and generate new random solutions. Finally, it uses the smallest position value to determine the correct values of the decision variables in a given candidate solution. The performance of DJRL3M was evaluated and compared with six well-recognized optimization algorithms [(New Cuckoo Search (NCS) (Wang et al. in SC 21:4297–4307, 2017), DJaya (Gao et al. in ITC 49:1944–1955, 2018), Hybrid Harmony Search (HHS) (Zhao et al. in EAAI 65:178-199, 2017), Modified Genetic algorithm (MGA) (Mumtaz et al. in: Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, 2018), Generalised Accelerations for Insertion-based Heuristics (GAIbH) (Fernandez-Viagas et al. in EJOR 282:858–872, 2020), Memetic algorithm with novel semi-constructive crossover and mutation operators (MASC) (Kurdi in ASC 94:106548, 2020)] using a set of Taillard’s benchmark instances. The experimental and statistical results show that DJRL3M obtains better performance than the performance of NCS, DJaya, HHS and MGA and exhibits competitive performance compared to the performance of MASC and GAIbH.