ABSTRACT In this paper, a novel swarm optimization approach, namely sparrow search algorithm (SSA), is proposed inspired by the group wisdom, foraging and anti-predation behaviours of sparrows. Experiments on 19 benchmark functions are conducted to test the performance of the SSA and its performance is compared with other algorithms such as grey wolf optimizer (GWO), gravitational search algorithm (GSA), and particle swarm optimization (PSO). Simulation results show that the proposed SSA is superior over GWO, PSO and GSA in terms of accuracy, convergence speed, stability and robustness. Finally, the effectiveness of the proposed SSA is demonstrated in two practical engineering examples.
Home healthcare has become more and more central in the last decades, due to the advantages it can bring to both healthcare institutions and patients. Planning activities in this context, however, presents significant challenges related to route planning and mutual synchronization of caregivers.In this paper we propose a new compact model for the combined optimization of scheduling (of the activities) and routing (of the caregivers) characterized by fewer variables and constraints when compared with the models previously available in the literature. The new model is solved by a constraint programming solver and compared experimentally with the exact and metaheuristic approaches available in the literature on the common datasets adopted by the community. The results show that the new model provides improved lower bounds for the vast majority of the instances, while producing at the same time high quality heuristic solutions, comparable to those of tailored metaheuristics, for small/medium size instances.
In this paper, a particular form of practical ℎ-observers for piecewise continuous Lipschitz, one-sided piecewise continuous Lipschitz systems and quasi-one-sided piecewise continuous Lipschitz systems is extended to nonlinear non-autonomous dynamical systems with disturbances. With the notion of practical ℎ-stable functions, the obtained state estimates are used for an eventual feedback control, and the practical separation principle is tackled. An example is given to show the applicability of the main result.
The growing emphasis on power quality has posed significant challenges for distribution system operators (DSOs). Among these challenges, short-term voltage fluctuations, specifically voltage sag, have drawn considerable attention. In this study, three concepts of average edge (AE), lower average edge (LAE), and upper average edge (UAE) based on the electrical connection matrix and voltage-magnitude sensitivity matrix are defined and used as the partitioning first level. At the second level, a kernel smoothing function is employed to refine the zoning process. Subsequently, strategic locations within each zone are identified: the vertex and middle buses. These carefully selected buses serve as installation points for dynamic voltage restorers (DVRs). In response, this study proposes a novel solution by partitioning the distribution network into distinct zones. The focus lies in developing a two-level offline partitioning approach for active distribution networks (ADNs) that incorporate photovoltaic (PV) systems. To evaluate the effectiveness of the proposed method, numerical studies were conducted on modified IEEE 33-bus, IEEE 69-bus, and Iranian 95-bus systems, with simulations performed using MATLAB/Simulink. The proposed method provides good performance and fast calculation speed for distribution network partitioning, as confirmed by the results. Test results show improved bus voltage with PV unit integration. Additionally, power loss in the IEEE 33-bus, IEEE 69-bus, and Iranian 95-bus networks decreased by 47.73 kW, 56.87 kW, and 69.63 kW, respectively. Furthermore, the voltage profile improved from 0.75 p.u. to 0.928 p.u. during a voltage sag in the IEEE 33-bus system, and in steady state, the voltage increased from 0.933 to 0.959 p.u.
In this study, we employed a novel approach to improve the serotonin-responsive ssDNA-wrapped single-walled carbon nanotube (ssDNA-SWCNT) nanosensors, combining directed evolution and machine learning-based prediction. Our iterative optimization process is aimed at the sensitivity and selectivity of ssDNA-SWCNT nanosensors. In the three rounds for higher serotonin sensitivity, we substantially improved sensitivity, achieving a remarkable 2.5-fold enhancement in fluorescence response compared to the original sequence. Following this, we directed our efforts towards selectivity for serotonin over dopamine in the two rounds. Despite the structural similarity between these neurotransmitters, we achieved a 1.6-fold increase in selectivity. This innovative methodology, offering high-throughput screening of mutated sequences, marks a significant advancement in biosensor development. The top-performing nanosensors, N2-1 (sensitivity) and L1-14 (selectivity) present promising reference sequences for future studies involving serotonin detection.
Ramzi A. Abd Alsaheb, Mohammed A. Atyia, Jaafar Kamil Abdullah
et al.
Succinic acid is an essential base ingredient for manufacturing various industrial chemicals. Succinic acid has been acknowledged as one of the most significant bio based building block chemicals. Rapid demand for succinic acid has been noticed in the last 10 years. The production methods and mechanisms developed. Hence, these techniques and operations need to be revised. Recently, an omnibus rule for developing succinic acid is to find renewable carbohydrate Feedstocks. The sustainability of the resource is crucial to disintegrate the massive use of petroleum based-production. Accordingly, systematically reviewing the latest findings of bacterial production and related fermentation methods is critical. Therefore, this paper aims to study the latest research and assess the findings statistically comprehensively. The current review attempt is a step toward comprehending all the conditions surrounding succinic acid production from raw materials, microorganisms, and fermentation methods.
Chemical engineering, Engineering (General). Civil engineering (General)
A manufacturing method is proposed for carbon based composite double polymer compliant electrode. The stiffness of this compliant electrode is changed by adjusting the mass fraction of carbon black and the ratios between Ecoflex20 and RT625. Tensile machine is used to test its ductility and hardness. The conductivity is measured through the source table. Finally, it is printed on the dielectric elastomers (DE) film, and the high-voltage amplifier is used for dielectric elastomers actuators (DEAs) dynamics testing. The results show that the compliant electrode has high tensile properties (>200%), low stiffness (<300 kPa) and well conductivity (0.049 3 S/cm). It is proved that the DEAs displacement output is up to 1.189 mm by this compliant electrode under dynamic response, which is 1.64 times and 1.32 times of the same type. Moreover, this formula extends the curing time of the original compliant electrode ink. It can provide a reference for the production of compliant electrode and DEAs in the future.