Correlation-based damage detection for complicated truss bridges using multi-layer genetic algorithms

The study presents a multi-layer genetic algorithm (GA) approach using correlation-based methods to facilitate damage determination for through-truss bridge structures. To begin, the structure’s damage-suspicious elements are divided into several groups. In the first GA layer, the damage is initially optimised for all groups using correlation objective function. In the second layer, the groups are combined to larger groups and the optimisation starts over at the normalised point of the first layer result. Then the identification process repeats until reaching the final layer where one group includes all structural elements and only minor optimisations are required to fine tune the final result. Several damage scenarios on a complicated through-truss bridge example are nominated to address the proposed approach’s effectiveness. Structural modal strain energy has been employed as the variable vector in the correlation function for damage determination. Simulations and comparison with the traditional single-layer optimisation shows that the proposed approach is efficient and feasible for complicated truss bridge structures when the measurement noise is taken into account.

Genetic algorithms to schedule container transfers at multimodal terminals

Optimising the container transfer schedule at the multimodal terminals is known to be NP-hard, which implies that the best solution becomes computationally infeasible as problem sizes increase. Genetic Algorithm (GA) techniques are used to reduce container handling/transfer times and ships' time at the port by speeding up handling operations. The GA is chosen due to the relatively good results that have been reported even with the simplest GA implementations to obtain near-optimal solutions in reasonable time. Also discussed, is the application of the model to assess the consequences of increased scheduled throughput time as well as different strategies such as the alternative plant layouts, storage policies and number of yard machines. A real data set used for the solution and subsequent sensitivity analysis is applied to the alternative plant layouts, storage policies and number of yard machines.

Pretreatment of sugarcane bagasse by acid-catalysed process in aqueous ionic liquid solutions

A biomass pretreatment process was developed using acidified ionic liquid (IL) solutions containing 10-30% water. Pretreatment of sugarcane bagasse at 130°C for 30min by aqueous 1-butyl-3-methylimidazolium chloride (BMIMCl) solution containing 1.2% HCl resulted in a glucan digestibility of 94-100% after 72h of enzymatic hydrolysis. HCl was found to be a more effective catalyst than H(2)SO(4) or FeCl(3). Increasing acid concentration (from 0.4% to 1.2%) and reaction temperature (from 90 to 130°C) increased glucan digestibility. The glucan digestibility of solid residue obtained with the acidified BMIMCl solution that was re-used for three times was >97%. The addition of water to ILs for pretreatment could significantly reduce IL solvent costs and allow for increased biomass loadings, making the pretreatment by ILs a more economic proposition.

Comparative study on adsorption of two cationic dyes by milled sugarcane bagasse

Adsorptions of Rhodamine B (RhB) and Basic Blue 9 (BB9, also known as methylene blue) by sugarcane bagasse of different surface areas were compared in this study. There was a small gain in the amount of dye removed by increasing bagasse surface area from 0.57 m2/g to 1.81 m2/g. BB9 adsorption was less sensitive to surface area change than RhB adsorption. Adsorption capacity of 250 mg/L RhB on 1 g/L bagasse was 65.5 mg/g compared to a value of 30.7 mg/g obtained with BB9 under the same conditions. Increasing adsorption temperature (from 30 °C to 50 °C) while having no effect on RhB adsorption, slightly decreased BB9 adsorption by ~4%. The differences in adsorption performances between these dyes have been related to the molecular structure of the dyes and the surface chemistry of bagasse.

A penalty-based grouping genetic algorithm for multiple composite SaaS components clustering in Cloud

Software as a Service (SaaS) in Cloud is getting more and more significant among software users and providers recently. A SaaS that is delivered as composite application has many benefits including reduced delivery costs, flexible offers of the SaaS functions and decreased subscription cost for users. However, this approach has introduced a new problem in managing the resources allocated to the composite SaaS. The resource allocation that has been done at the initial stage may be overloaded or wasted due to the dynamic environment of a Cloud. A typical data center resource management usually triggers a placement reconfiguration for the SaaS in order to maintain its performance as well as to minimize the resource used. Existing approaches for this problem often ignore the underlying dependencies between SaaS components. In addition, the reconfiguration also has to comply with SaaS constraints in terms of its resource requirements, placement requirement as well as its SLA. To tackle the problem, this paper proposes a penalty-based Grouping Genetic Algorithm for multiple composite SaaS components clustering in Cloud. The main objective is to minimize the resource used by the SaaS by clustering its component without violating any constraint. Experimental results demonstrate the feasibility and the scalability of the proposed algorithm.

Energy-efficient virtual machine placement in data centers by genetic algorithm

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches to the virtual machine placement problem consider the energy consumption by physical machines in a data center only, but do not consider the energy consumption in communication network in the data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement in order to make the data center more energy-efficient. In this paper, we propose a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both the servers and the communication network in the data center. Experimental results show that the genetic algorithm performs well when tackling test problems of different kinds, and scales up well when the problem size increases.

Characterisation of sugarcane juice particles that influence the clarification process

Problems associated with processing whole sugarcane crop can be minimised by removing impurities during the clarification stage. As a first step, it is important to understand the colloidal chemistry of juice particles on a molecular level to assist development of strategies for effective clarification performance. This paper presents the composition and surface characteristics of colloidal particles originating from various juice types by using scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX), X-ray photoelectron spectroscopy(XPS) and zeta potential measurements. The composition and surface characteristics of colloidal juice particles are reported. The results indicate that there are three types of colloidal particles present, viz. an aluminosilicatecompound, silica and iron oxide, with the latter two being abundant. Proteins, polysaccharides and organic acids were identified on the surface of particles in juice. The overall particle charge varies from -2 mV to -6 mV. In comparison to juice expressed from burnt cane, the zeta potential values were more negative with juice particles originating from whole crop. This in part explains why these juices are difficult to clarify.

Adaptive sizing of populations and number of islands in distributed genetic algorithms

Deciding the appropriate population size and number of is- lands for distributed island-model genetic algorithms is often critical to the algorithm’s success. This paper outlines a method that automatically searches for good combinations of island population sizes and the number of islands. The method is based on a race between competing parameter sets, and collaborative seeding of new parameter sets. This method is applicable to any problem, and makes distributed genetic algorithms easier to use by reducing the number of user-set parameters. The experimental results show that the proposed method robustly and reliably finds population and islands settings that are comparable to those found with traditional trial-and-error approaches.

Dynamic optimization of migration topology in internet-based distributed genetic algorithms

Distributed Genetic Algorithms (DGAs) designed for the Internet have to take its high communication cost into consideration. For island model GAs, the migration topology has a major impact on DGA performance. This paper describes and evaluates an adaptive migration topology optimizer that keeps the communication load low while maintaining high solution quality. Experiments on benchmark problems show that the optimized topology outperforms static or random topologies of the same degree of connectivity. The applicability of the method on real-world problems is demonstrated on a hard optimization problem in VLSI design.

Effects of pH on pretreatment of sugarcane bagasse using aqueous imidazolium ionic liquids

Pretreatments of sugarcane bagasse for saccharification using different acid-catalysed imidazolium IL solutions (containing 20% water) at 130 °C for 30 min were investigated. At the same solution pH, pretreatment effectiveness in terms of glucan digestibility, delignification and xylan removal were similar for aqueous 1-butyl-3-methylimidazolium methane sulfonate (BMIMCH3SO3), 1-butyl-3-methylimidazolium methyl sulfate (BMIMCH3SO4), 1-ethyl-3-methylimidazolium chloride (EMIMCl) and 1-butyl-3-methylimidazolium chloride (BMIMCl). Decreasing solution pH of aqueous IL systems from 6.0 to 0.4 increased bagasse delignification and xylan removal, and as a result, improved glucan digestibility. The glucan digestibilities for bagasse samples pretreated by IL solutions with pH ≤ 0.9 were > 90% after 72 h of enzymatic hydrolysis. Without pH adjustment, the effectiveness of these aqueous IL solutions (except BMIMCH3SO3 because of its low natural pH of 0.9) to deconstruct the biomass was poor and the glucan digestibilities of pretreated bagasse samples were < 20%. These results show that pretreatment effectiveness of aqueous imidazolium ILs can simply be estimated from solution pH rather than hydrogen bond basicity (β) of the IL solution.

Caustic potash pulping study of water efficient Arudno donax and Sorghum bicolor; a comparison to sugarcane bagasse

Numerous crops grow in sugar regions that have the potential to increase the amount of biomass available to a small bagasse-based pulp factory. Arundo donax and Sorghum offer unique advantages to farmers compared to other agricultural crops. Sorghum bicolour requires only 1/3 of the water of sugarcane. Arundo donax is a very high yield crop, it can also grow with little water but it has the further advantage in that it is also highly stress tolerant, making it suitable for land which is unsuited to other crops. Pulps produced from these crops were benchmarked against sugarcane bagasse pulp. Arundo, sorghum and bagasse were pulped using KOH and anthraquinone to 20 Kappa number so as to produce a bleachable pulp. The unbleached sorghum pulp has better tensile strength properties than the unbleached Arundo pulp (43.8 Nm/g compared to 21.4 Nm/g) and the bleached sorghum pulp tensile strength was similar to bagasse (28.4 Nm/g). At 20 Kappa number, sorghum pulp had acceptable yield for a non-wood fibre (45% c.f. 55% for bagasse), Arundo donax pulp had low tensile strength, and relatively low yield (38.7%), even for an agricultural fibre and required severe cooking conditions to achieve similar delignification to sugarcane bagasse or sorghum. Sorghum and Arundo donax produced thicker handsheets than bagasse (>160 μm c.f. 122 μm for bagasse). In preliminary experiments sorghum and bagasse responded slightly better to Totally Chlorine Free bleaching (QPP), although none achieved a satisfactory brightness level and more optimisation is needed.

Damage diagnosis for complex steel truss bridges using multi-layer genetic algorithm

Considerate amount of research has proposed optimization-based approaches employing various vibration parameters for structural damage diagnosis. The damage detection by these methods is in fact a result of updating the analytical structural model in line with the current physical model. The feasibility of these approaches has been proven. But most of the verification has been done on simple structures, such as beams or plates. In the application on a complex structure, like steel truss bridges, a traditional optimization process will cost massive computational resources and lengthy convergence. This study presents a multi-layer genetic algorithm (ML-GA) to overcome the problem. Unlike the tedious convergence process in a conventional damage optimization process, in each layer, the proposed algorithm divides the GA’s population into groups with a less number of damage candidates; then, the converged population in each group evolves as an initial population of the next layer, where the groups merge to larger groups. In a damage detection process featuring ML-GA, as parallel computation can be implemented, the optimization performance and computational efficiency can be enhanced. In order to assess the proposed algorithm, the modal strain energy correlation (MSEC) has been considered as the objective function. Several damage scenarios of a complex steel truss bridge’s finite element model have been employed to evaluate the effectiveness and performance of ML-GA, against a conventional GA. In both single- and multiple damage scenarios, the analytical and experimental study shows that the MSEC index has achieved excellent damage indication and efficiency using the proposed ML-GA, whereas the conventional GA only converges at a local solution.

Genetic improvement of giant freshwater prawn in Vietnam

The giant freshwater prawn (Macrobrachium rosenbergii) or GFP is one of the most important freshwater crustacean species in the inland aquaculture sector of many tropical and subtropical countries. Since the 1990’s, there has been rapid global expansion of freshwater prawn farming, especially in Asian countries, with an average annual rate of increase of 48% between 1999 and 2001 (New, 2005). In Vietnam, GFP is cultured in a variety of culture systems, typically in integrated or rotational rice-prawn culture (Phuong et al., 2006) and has become one of the most common farmed aquatic species in the country, due to its ability to grow rapidly and to attract high market price and high demand. Despite potential for expanded production, sustainability of freshwater prawn farming in the region is currently threatened by low production efficiency and vulnerability of farmed stocks to disease. Commercial large scale and small scale GFP farms in Vietnam have experienced relatively low stock productivity, large size and weight variation, a low proportion of edible meat (large head to body ratio), scarcity of good quality seed stock. The current situation highlights the need for a systematic stock improvement program for GFP in Vietnam aimed at improving economically important traits in this species. This study reports on the breeding program for fast growth employing combined (between and within) family selection in giant freshwater prawn in Vietnam. The base population was synthesized using a complete diallel cross including 9 crosses from two local stocks (DN and MK strains) and a third exotic stock (Malaysian strain - MY). In the next three selection generations, matings were conducted between genetically unrelated brood stock to produce full-sib and (paternal) half-sib families. All families were produced and reared separately until juveniles in each family were tagged as a batch using visible implant elastomer (VIE) at a body size of approximately 2 g. After tags were verified, 60 to 120 juveniles chosen randomly from each family were released into two common earthen ponds of 3,500 m2 pond for a grow-out period of 16 to 18 weeks. Selection applied at harvest on body weight was a combined (between and within) family selection approach. 81, 89, 96 and 114 families were produced for the Selection line in the F0, F1, F2 and F3 generations, respectively. In addition to the Selection line, 17 to 42 families were produced for the Control group in each generation. Results reported here are based on a data set consisting of 18,387 body and 1,730 carcass records, as well as full pedigree information collected over four generations. Variance and covariance components were estimated by restricted maximum likelihood fitting a multi-trait animal model. Experiments assessed performance of VIE tags in juvenile GFP of different size classes and individuals tagged with different numbers of tags showed that juvenile GFP at 2 g were of suitable size for VIE tags with no negative effects evident on growth or survival. Tag retention rates were above 97.8% and tag readability rates were 100% with a correct assignment rate of 95% through to mature animal size of up to 170 g. Across generations, estimates of heritability for body traits (body weight, body length, cephalothorax length, abdominal length, cephalothorax width and abdominal width) and carcass weight traits (abdominal weight, skeleton-off weight and telson-off weight) were moderate and ranged from 0.14 to 0.19 and 0.17 to 0.21, respectively. Body trait heritabilities estimated for females were significantly higher than for males whereas carcass weight trait heritabilities estimated for females and males were not significantly different (P > 0.05). Maternal and common environmental effects for body traits accounted for 4 to 5% of the total variance and were greater in females (7 to 10%) than in males (4 to 5%). Genetic correlations among body traits were generally high in both sexes. Genetic correlations between body and carcass weight traits were also high in the mixed sexes. Average selection response (% per generation) for body weight (transformed to square root) estimated as the difference between the Selection and the Control group was 7.4% calculated from least squares means (LSMs), 7.0% from estimated breeding values (EBVs) and 4.4% calculated from EBVs between two consecutive generations. Favourable correlated selection responses (estimated from LSMs) were detected for other body traits (12.1%, 14.5%, 10.4%, 15.5% and 13.3% for body length, cephalothorax length, abdominal length, cephalothorax width and abdominal width, respectively) over three selection generations. Data in the second selection generation showed positive correlated responses for carcass weight traits (8.8%, 8.6% and 8.8% for abdominal weight, skeleton-off weight and telson-off weight, respectively). Data in the third selection generation showed that heritability for body traits were moderate and ranged from 0.06 to 0.11 and 0.11 to 0.22 at weeks 10 and 18, respectively. Body trait heritabilities estimated at week 10 were not significantly lower than at week 18. Genetic correlations between body traits within age and genetic correlations for body traits between ages were generally high. Overall our results suggest that growth rate responds well to the application of family selection and carcass weight traits can also be improved in parallel, using this approach. Moreover, selection for high growth rate in GFP can be undertaken successfully before full market size has been reached. The outcome of this study was production of an improved culture strain of GFP for the Vietnamese culture industry that will be trialed in real farm production environments to confirm the genetic gains identified in the experimental stock improvement program.