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Brain disorders have been a health challenge and is increasing over the years. Early diagnosis and interventions are considered essential strategies to treat patients at risk of brain disease. Physical exercise has shown to be beneficial for patients with brain diseases. A type of exercise intervention known as whole-body vibration (WBV) exercise gained increasing interest. During WBV, mechanical vibrations, produced by a vibrating platform are transmitted, to the body. The purpose of the current review was to summarize the effects of WBV exercise on brain function and behavior in experimental studies with animal models. Searches were performed in EMBASE, PubMed, Scopus and Web of Science including publications from 1960 to July 2021, using the keywords "whole body vibration" AND (animal or mice or mouse or rat or rodent). From 1284 hits, 20 papers were selected. Rats were the main animal model used (75%) followed by mice (20%) and porcine model (5%), 16 studies used males species and 4 females. The risk of bias, accessed with the SYRCLE Risk of Bias tool, indicated that none of the studies fulfilled all methodological criteria, resulting in possible bias. Despite heterogeneity, the results suggest beneficial effects of WBV exercise on brain functioning, mainly related to motor performance, coordination, behavioral control, neuronal plasticity and synapse function. In conclusion, the findings observed in animal studies justifies continued clinical research regarding the effectiveness and potential of WBV for the treatment of various types of brain disorders such as trauma, developmental disorders, neurogenetic diseases and other neurological diseases.

Reduction in the number of lanes (lane drop) is common on motorways due to road design, incidents, or road maintenance, and it can be an active bottleneck if the traffic demand is high. If congestion occurs, the lane drop capacity will decrease 10–20%. In order to avoid capacity drop, this study analysed the reason and proposed lane-changing advisory control on the merge lane to distribute lane-changing using Cooperative Intelligent Transport Systems (C-ITS) technology. Further, this study used hyper-heuristic optimisation to obtain the lane-changing advisory proportion of each segment upstream of lane drop. Conditions of different traffic demands were analysed using the microscopic traffic simulation software AIMSUN and its Application Program Interface (API) function. Results indicated that the proposed lane-changing advisory strategy could reduce traffic congestion and obviously improve traffic efficiency. This study also analysed the effects with different proportions of connected vehicles and found that if the connected vehicle ratio is less than 20%, the lane-changing advisory has little impact on the lane drop performance, and that if the penetration rate is more than 50%, the gain in performance is marginal.

The Dynamic Vehicle Routing Problem (DVRP) is a complex variation of classical Vehicle Routing Problem (VRP). The aim of DVRP is to find a set of routes to serve multiple customers at minimal total travelling cost while the travelling time between point to point may vary during the process because of factors like traffic congestion. To effectively handle DVRP, a good algorithm should be able to adjust itself to the changes and continuously search for the best solution under dynamic environments. Because of this dynamic nature of DVRP, evolutionary algorithms (EAs) appear highly appropriate for DVRP as they search in a parallel manner with a population of solutions. Solutions scattered over the search space can better capture the dynamic changes. Solutions for new changes are not built from scratch as they can inherit problem-specific knowledge from parent solutions. However, the performance of EA is highly dependent on the utilised configuration. To address this issue, we propose a self-adaptive EA for DVRP. The proposed EA evolves a set of configurations including parameter values, operator types, combination of operators and order of operator invocation. The configurations are then encoded into DVRP solutions. So the search can use different configuration during a search process to effectively handle the dynamic changes and guide the search process towards promising areas. Two well known routing problems with traffic congestion, vehicle routing and the travelling salesman, were used to evaluate the performance of the proposed EA. The results demonstrate that under same conditions on both problems the proposed self-adaptive EA is better than standard EA and other algorithms from literature.

The harmony search algorithm has been proven to be an effective optimization method for solving diverse optimization problems. However, due to its slow convergence, the performance of HSA over constrained optimization problems is not very competitive. Therefore, many researchers have hybridized HSA with local search algorithms. However, it’s very difficult to known in advance which local search should be hybridized with HSA as it depends heavily on the problem characteristics. The question is how to design an effective selection mechanism to adaptively select a suitable local search to be combined with HSA during the search process. Therefore, this work proposes an adaptive HSA that embeds an adaptive selection mechanism to adaptively select a suitable local search algorithm to be applied. This work hybridizes HSA with five local search algorithms: hill climbing, simulated annealing, record to record, reactive tabu search and great deluge. We use the Solomon’s vehicle routing problem with time windows benchmark to examine the effectiveness of the proposed algorithm. The obtained results are compared with basic HSA, the local search algorithms and existing methods. The results demonstrate that the proposed adaptive HSA achieves very good results compared other methods. This demonstrates that the selection mechanism can effectively assist HSA to adaptively select a suitable local search during the problem solving process.

Traffic signals play a significant role in the urban transportation system. They control the movement of traffic on urban streets by determining the appropriate signal timing settings. Due to the stochastic nature of the traffic flow, deciding on the best signal timing settings is a computationally complex problem, with the result that traditional analytical methods have been found to be inadequate in dealing with real world scenarios. This issue has already been tackled using computational intelligence algorithms such as the genetic algorithm (GA). However, despite good results, GA may experience slow convergence, especially when dealing with constrained optimisation problems. To address this issue, we propose an adaptive memetic algorithm (MA) for optimising signal timings in real world urban road networks using traffic volumes derived from induction loop detectors. The proposed MA combines the strengths of GA with the exploitation power of a local search algorithm, in an adaptive manner, so as to accelerate the search process and generate high quality solutions. In this work, we propose two important techniques for improving the performance of a traditional MA. First, we use a systematic neighbourhood based simple descent algorithm as a local search to effectively exploit the search space around GA solutions. Second, to achieve a proper balance between the exploration of GA and the local search algorithm, we propose an indicator scheme to control the local search application based on the diversity and the quality of the search process. The proposed MA was tested in two different case studies for the cities of Brisbane, Australia, and Plock, Poland, using the well-known microscopic traffic simulator, AIMSUN. Results demonstrate that our MA is better than GA and traditional fixed-time traffic signal settings.

Article free to read on journal website The traffic in a weaving segment is subject to lane-changing turbulence in excess of that normally present on basic motorway segments. Empirical studies have observed a lane-changing concentration problem, as traffic flow increases, which can cause flow break down and congestion. This paper focuses on the lane-changing concentration problem in weaving segments. A Cooperative Intelligent Transport System (C-ITS) advisory has been shown to alleviate such a lane-changing concentration problem. The advisory aims to distribute the lane-changing along the weaving segment. Unlike previous methods in the literature, where weaving vehicles are assigned according to fixed distributions, this paper proposes an algorithm to optimize the lane-changing distribution. The proposed optimization algorithm was developed based on particle swarm optimization. The optimized lane-changing distribution for a one-sided motorway weaving segment using microscopic simulation has been evaluated. The initial results show that the proposed algorithm could be used as a successful optimization technique for the lane- changing concentration problem.

Educational timetabling is an ongoing challenging administrative task that is required in most academic institutions. This is mainly due to a large number of constraints and requirements that have to be satisfied. Educational timetabling problems have been classified as NP-hard problems and can be divided into three types: exam timetabling, course timetabling and high school timetabling. The domain of high school timetabling is not well developed when compared to other fields of educational timetabling such as university exam timetabling and course timetabling. As the evolution of the educational systems are continuous, new challenges often arise, requiring new models and solution methodologies. Over the years, a number of methodologies have been developed to address high school timetabling problems. However, there are no comparative studies or rigorous analysis of these methodologies. This survey paper aims to provide a scientific review of high school timetabling. The paper presents a categorisation of the methodologies conducted in recent years based on chronology, category and application (dataset). We first present comparative studies on the success of proposed methodologies. The components and mechanisms of different methodologies are analysed and compared. We also discuss their performance, advantages, disadvantages and potential for improvement. Methodology wise, a shift of popularity from meta-heuristic to mathematical optimisation is observed in recent years. Another observation is that more researchers are opting for XHSTT formatted datasets as a testbed for their algorithms. Finally, we outline the industrial perspective, trends and future direction in high school timetabling optimisation problems.

Deep Belief Networks (DBN) have become a powerful tools to deal with a wide range of applications. On complex tasks like image reconstruction, DBN’s performance is highly sensitive to parameter settings. Manually trying out different parameters is tedious and time consuming however often required in practice as there are not many better options. This work proposes an evolutionary hyper-heuristic framework for automatic parameter optimisation of DBN. The hyper-heuristic framework introduced here is the first of its kind in this domain. It involves a high level strategy and a pool of evolutionary operators such as crossover and mutation to generates DBN parameter settings by perturbing or modifying the current setting of a DBN. Providing a large set of operators could be beneficial to form a more effective high level strategy, but in the same time would increase the search space hence make it more difficulty to form a good strategy. To address this issue, a non-parametric statistical test is introduced to identify a subset of effective operators for different phases of the hyper-heuristic search. Three well-known image reconstruction datasets were used to evaluate the performance of the proposed framework. The results reveal that the proposed hyper-heuristic framework is very competitive when compared to the state of art methods.

The heterogeneous fleet vehicle routing problem with two-dimensional loading constraints (2L- HFVRP) is a complex variant of the classical vehicle routing problem. 2L-HFVRP seeks for minimal cost set of routes to serve a set of customers using a fleet of vehicles of different capacities, fixed and variable operating costs, different dimensions, and restricted loading constraints. To effectively deal with the 2L-HFVRP, we propose a two-stage method that successively calls the routing stage and the packing stage. For the routing stage, we propose an adaptive memetic approach that integrates new multi-parent crossover operators with multi-local search algorithms in an adaptive manner. A time-varying fitness function is proposed to avoid prematurity and improve search performance. An adaptive quality-and-diversity selection mechanism is devised to control the application of the memetic operators and the local search algorithms. In the packing stage, five heuristics are adopted and hybridised to perform the packing process. Experiments on a set of 36 2L-HFVRP benchmark instances demonstrate that the proposed method provides highly competitive results in comparison with state-of-the-art algorithms. In particular, the proposed method obtains the best results for several instances.

It has been proven that the hybridization of the harmony search algorithm with a local search algorithm (LS) is essential to compensate for the inadequacy of its exploitation. However, the success of this hybridization relies on the achievement of a proper balance between HSA exploration and LS exploitation. The question is can we obtain this balance by adaptively selecting (i) HSA parameter values, (ii) LS and its parameters and (iii) the LS neighborhood structures? To address these issues, this work proposes a meta-harmony search algorithm (meta-HSA) that uses two HSA algorithms, an HSA-optimizer and HSA-solver. The HSA-optimizer will adaptively adjust the components and the configurations of the HSA-solver based on the search status. The HSA-solver, which is a hybridization of HSA and LS, takes the configuration generated by the HSA-optimizer as input and then solves the given problem instance. That is, the HSA-optimizer operates on the components and the configurations of the HSA-solver, while the HSA-solver operates directly on the given problem instance (the solution search space). The proposed meta-HSA was applied to Solomon's vehicle routing problem with time windows benchmark to verify its effectiveness compared with standard HSA and the state-of-the art methods. The results of the comparison confirmed that the meta-HSA produces competitive results with respect to the other methods. Therefore, we can conclude that the meta-optimization technique does assist the hybrid HSA in obtaining the appropriate selection of its components and configurations during the search process. This demonstrates that the meta-HSA can provide a proper balance between exploration and exploitation by adaptively selecting (i) HSA parameter values, (ii) LS and its parameters and (iii) the LS neighborhood structures. Moreover, the meta-HSA optimizer decreases the effort exerted by the user in tuning these components and configurations.

Local search algorithms have been successfully used for many combinatorial optimisation problems. The choice of the most suitable local search algorithm is, however, a challenging task as their performance is highly dependent on the problem characteristic. In addition, most of these algorithms require users to select appropriate internal neighbourhood structures to obtain desirable performance. No single local search algorithm can consistently perform well with a fixed setting, for different types of problems or even different instances of the same problem. To address this issue, we propose a hyper-heuristic framework which incorporates multiple local search algorithms and a pool of neighbourhood structures. This framework is novel in three respects. Firstly, a two-stage hyper-heuristic structure is designed to control the selection of a local search algorithm and its internal operators. Secondly, we propose an adaptive ranking mechanism to choose the most appropriate neighbourhood structures for the current local search algorithm. The proposed mechanism uses the entropy to evaluates the contribution of the local search in terms of quality and diversity. It adaptively adjusts the pool of candidate neighbourhood structures. Thirdly, we use a population of solutions within the proposed framework to effectively navigate different areas in the solutions search space and share solutions with local search algorithms. To ensure different solutions is allocated in different regions of the search space, we propose a distance-based strategy for population updating process that allowing solutions to share local search algorithms. We have evaluated the performance of the proposed framework using two challenging optimisation problems: Multi-Capacity Bin Packing benchmark instances and Google Machine Reassignment benchmark instances. The results show the effectiveness of the proposed framework, which outperformed state-of-the-art algorithms on several problem instances.

Landing aircraft safely is an important operation that air traffic controllers have to deal with on a daily basis. For each arriving aircraft a runway and a landing time must be allocated. If these allocations can be done in an efficient way, it could give the airport a competitive advantage. The Aircraft Landing Problem (ALP) aims to minimize the deviation from a preferred target time of each aircraft. It is an NP-hard problem, meaning that we may have to resort to heuristic methods as exact methods may not be suitable, especially as the problem size increases. This paper proposes an iterated local search (ILS) algorithm for the ALP. ILS is a single solution based search methodology that successively invokes a local search procedure to find a local optimum solution. A perturbation operator is used to modify the current solution in order to escape from the local optimum and to provide a new solution for the local search procedure. As different problems and/or instances have different characteristics, the success of the ILS is highly dependent on the local search, the perturbation operator(s) and the perturbation strength. To address these issues, we utilize four perturbation operators and a time varying perturbation strength which changes as the algorithm progresses. A variable neighborhood descent algorithm is used as our local search. The proposed ILS generates high quality solutions for the ALP benchmark instances taken from the scientific literature, demonstrating its efficiency in terms of both solution quality and computational time. Moreover, the proposed ILS produces new best results for some instances.

6-Methyl-1-({[(2E)-2-methyl-3-phenyl-prop-2-en-1-yl]oxy}methyl)-1,2,3,4-tetra-hydro quinazoline-2,4-dione was prepared via treatment of silylated 6-methylquinazoline-2,4-dione with bis-[(E)-2-methyl-3-phenylallyloxy]methane. FT-IR and FT-Raman spectra were recorded and analyzed. The vibrational wavenumbers were computed using DFT methods and are assigned with the help of potential energy distribution method. The first hyperpolarizability, infrared intensities and Raman activities also reported. The geometrical parameters of the title compound obtained from XRD studies are in agreement with the calculated (B3LYP) values. The stability of the molecule arising from hyper-conjugative interaction and charge delocalization has been analyzed using NBO analysis. The HOMO and LUMO analysis are used to determine the charge transfer within the molecule. MEP was performed by the B3LYP method and from the MEP it is evident that the negative charge covers the CO group and the positive region is over the phenyl ring and NH group.

The FT-IR and FT-Raman spectra of 1-[(Cyclopropylmethoxy)methyl]-5-ethyl-6-(4-methylbenzyl)-1,2,3,4-tetrahydropyrimidine-2,4-dione were recorded. In this work, experimental and theoretical study on the molecular structure and vibrational wavenumbers of the title compound are presented. The vibrational wavenumbers were obtained theoretically at the DFT level and were compared with the experimental results. The study is extended to calculate the HOMO-LUMO energy gap, NBO, mapped molecular electrostatic potential and first hyperpolarizability. The calculated first hyperpolarizability of the title compound is 9.15 times that of urea and hence the title compound and the series of compounds it represents are attractive candidates for further studies in non linear optical applications. In the title compound, the HOMO of pi nature is delocalized over the phenyl ring while the LUMO is located over the pyrimidine ring. The inter-molecular hydrogen bonding at O-7 and N-1-H-25 positions in each monomer give rise to a C-2-symmetry dimer which is predicted to be about 10 kcal mol(-1) more stable than the monomeric form. (C) 2014 Elsevier B.V. All rights reserved.

Many optimisation problems are dynamic in the sense that changes occur during the optimisation process, and therefore are more challenging than the stationary problems. To solve dynamic optimisation problems, the proposed approaches should not only attempt to seek the global optima but be able to also keep track of changes in the track record of landscape solutions. In this research work, one of the most recent new population-based meta-heuristic optimisation technique called a harmony search algorithm for dynamic optimization problems is investigated. This technique mimics the musical process when a musician attempts to find a state of harmony. In order to cope with a dynamic behaviour, the proposed harmony search algorithm was hybridised with a (i) random immigrant, (ii) memory mechanism and (iii) memory based immigrant scheme. The performance of the proposed harmony search is verified by using the well-known dynamic test problem called the Moving Peak Benchmark (MPB) with a variety of peaks. The empirical results demonstrate that the proposed algorithm is able to obtain competitive results, but not the best for most of the cases, when compared to the best known results in the scientific literature published so far.

Feature selection problems (FS) can be defined as the process of eliminating redundant features while avoiding information loss. Due to that fact that FS is an NP-hard problem, heuristic and meta-heuristic approaches have been widely used by researchers. In this work, we proposed an Exponential Monte-Carlo algorithm (EMC-FS) for the feature selection problem. EMC-FS is a meta-heuristic approach which is quite similar to a simulated annealing algorithm. The difference is that no cooling schedule is required. Improved solutions are accepted and worse solutions are adaptively accepted based on the quality of the trial solution, the search time and the number of consecutive non-improving iterations. We have evaluated our approach against the latest methodologies in the literature on standard benchmark problems. The quality of the obtained subset of features has also been evaluated in terms of the number of generated rules (descriptive patterns) and classification accuracy. Our research demonstrates that our approach produces some of the best known results.

Nonionic polymeric surfactants were prepared by acrylated of poly (ethylene glycol) (PEG) esterfied with oleic or palmitic acid. The unique structural features of these polymeric surfactants were investigated by using Fourier transformer infrared spectroscopy (FTIR) and X-ray diffraction (XRD). The surface tension at different temperatures of these compounds was investigated. The surface parameters including critical micelle concentration (CMC), maximum surface excess (Γmax), minimum surface area (Amin), efficiency (Pc20) and effectiveness (πCMC) were studied and calculated. The biological activity of these prepared compounds was evaluated and determined via the inhibition zone diameter against different microorganisms. From the outcomes, it was noticed that these compounds possess superior surface properties and perfect antimicrobial action.

In this work, we propose a variant of the honey-bee mating optimization algorithm for solving educational timetabling problems. The honey-bee algorithm is a nature inspired algorithm which simulates the process of real honey-bees mating. The performance of the proposed algorithm is tested over two benchmark problems; exam (Carter’s un-capacitated datasets) and course (Socha datasets) timetabling problems. We chose these two datasets as they have been widely studied in the literature and we would also like to evaluate our algorithm across two different, yet related, domains. Results demonstrate that the performance of the honey-bee mating optimization algorithm is comparable with the results of other approaches in the scientific literature. Indeed, the proposed approach obtains best results compared with other approaches on some instances, indicating that the honey-bee mating optimization algorithm is a promising approach in solving educational timetabling problems.

In the present study, we evaluated the sensitivity of different Escherichia coli strains to Cumene hydroperoxide (CHP) treatment under distinct conditions of Fe2+ availability. Our results showed that the pretreatment with an iron chelator (dipyridyl) protects all the tested strains against CHP toxic effects, but it was not sufficient to abolish the CHP induced mutagenesis. On the other hand, simultaneous pretreatment with both dipyridyl and neocuproine (copper chelator) leads to a complete protection against CHP mutagenic effects. Our data suggest the participation of copper ion in the CHP mutagenesis induced in E. coli.

We studied the participation of the stress-inducible systems, as the OxyR, SoxRS and SOS regulons in the protection of Escherichia coli cells against lethal effects of cumene hydroperoxide (CHP). Moreover, we evaluated the participation of BER and NER in the repair of the DNA damage produced by CHP. Our results suggest that the hypersensitivity observed in the oxyR mutants to the lethal effect of CHP does not appear to be due to SOS inducing DNA lesions, but rather to cell membrane damage. On the other hand, DNA damage induced by CHP appears to be repaired by enzymes involved in BER and NER pathways. In this case, Fpg protein and UvrABC complex could be involved cooperatively in the elimination of a specific DNA lesion. Finally, we have detected the requirement for the uvrA gene function in SOS induction by CHP treatment.

Pretreatment with 2.5 mM H2O2 protects bacterial cells against cumene hydroperoxide killing. This response is independent of the OxyR system, but possibly involves the participation of Ahp protein, since ahp mutants are not protected. Treatment of bacterial cells with high H2O2 concentrations caused an alteration on the electrophoretic profile of the smaller subunit (22-kDa) of Ahp. This alteration does not require novel gene products and is not dependent on the OxyR protein. In this way, we propose that the modification of the 22-kDa subunit of Ahp by high H2O2 concentration may be responsible for the protection against the lethal effects of cumene hydroperoxide.

Cross-adaptive response is defined as the capacity of cells to become resistant to a lethal agent when pretreated with a different lethal substance. In the present paper, the cross-adaptive response between hydrogen peroxide and N -methyl- N ′-nitro- N -nitrosoguanidine (MNNG) was studied in Escherichia coli repair mutants. Our results suggest that high doses of H 2 O 2 induces protection against the lethal effects of MNNG in wild-type strain, ada , ogt , ada-ogt , aidB and alkA mutants. On the other hand, the MNNG induced mutagenesis is reduced by H 2 O 2 pretreatment in wild-type and ogt mutant strains, but not in ada mutant. Furthermore, the protecting effect induced by H 2 O 2 is time dependent: it decreases 15 min after the pretreatment and, after 30 min, is almost abolished. This reduction in the protecting effect is followed by an augmentation in the mutation frequency when MNNG is added 30 min after H 2 O 2 pretreatment. This cross-adaptive response may be due to a modification of the MNNG alkylation pattern in the oxidized DNA.