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A simple solution-phase route to synthesize mesoporous anatase TiO2 nanocrystallites at room temperature was presented. TiO2 nanocrystallites were prepared via a unique hydrolysis of titanium complexes in the presence of H3BO3. The mean size of the as-obtained TiO2 nanocrystallite was in about 10 nm. They possess large surface area of 48.6 m2 g−1 and the dominant pore size of 4.3 nm. The effect of aging time on crystallinity was studied. For crystallization occurred at room temperature, the longer the aging time is, the greater the crystallinity is.

Decorative materials, including bamboo timber, have been proposed to exploit their superhydrophobic and self-cleaning properties, but a comprehensive appraisal of their environmental adaptability is still deficient. In this paper, a robust and durable superhydrophobic surface was formed on bamboo timber substrate through a process combining chemical solution deposition and chemical modification. The superhydrophobic surface resulted from micro-nanoscale binary-structured TiO 2 films and the assembly of low-surface-energy fluorinated components, which exhibited a water contact angle of 163±1° and a sliding angle of 3±1°. The surface maintained superhydrophobicity after mechanical abrasion against 1500 mesh SiC sandpaper for 800 mm at the applied pressure of 1.2 kPa, indicating good mechanical stability. Moreover, the superhydrophobic surface exhibited good chemical stability against both acidic and basic aqueous solutions (e.g., simulated acid rain). After exposure to atmosphere for more than 180 days, the obtained surface still maintained a contact angle of 155±2° and a sliding angle of 6±2°, revealing good long-term stability. In addition, the as-prepared superhydrophobic surface exhibited almost complete wet self-cleaning of dirt particles with water droplets. It is believed that the method presented in this study can provide a straightforward and effective route to fabricate a large-area, mechanically robust, anticorrosive and self-cleaning superhydrophobic surface on woody materials for a great number of potential applications.

Vehicle scheduling plays a profound role in public transit planning. Traditional approaches for the Vehicle Scheduling Problem (VSP) are based on a set of predetermined trips in a given timetable. Each trip contains a departure point/time and an arrival point/time whilst the trip time (i.e. the time duration of a trip) is fixed. Based on fixed durations, the resulting schedule is hard to comply with in practice due to the variability of traffic and driving conditions. To enhance the robustness of the schedule to be compiled, the VSP based on stochastic trip times instead of fixed ones is studied. The trip times follow the probability distributions obtained from the data captured by Automatic Vehicle Locating (AVL) systems. A network flow model featuring the stochastic trips is devised to better represent this problem, meanwhile the compatibility of any pair of trips is redefined based on trip time distributions instead of fixed values as traditionally done. A novel probabilistic model of the VSP is proposed with the objectives of minimizing the total cost and maximizing the on-time performance. Experiments show that the probabilistic model may lead to more robust schedules without increasing fleet size.

A simple and reliable method for fast extraction and sensitive detection of ethyl carbamate (EC) in rice wine and fruit brandy based on the integration of molecularly imprinted polymers and surface-enhanced Raman spectroscopy (MIPs–SERS) was developed. Molecularly imprinted polymer microspheres were synthesized as artificial antibodies towards EC. Adsorption tests and thermogravimetric analysis validated the specific selectivity and stability of MIPs respectively. The synthesized MIPs was used as sorbents in solid-phase extraction (SPE) and could selectively separate and enrich EC from rice wine and fruit brandy samples with little interference. A silver dendrite nanostructure was employed as a SERS-active substrate for the enhancement of Raman signals. Spectra processed by principal component analysis (PCA) can clearly differentiate Raman signatures of wine samples with various EC contents. A partial least square (PLS) regression model demonstrated a good fitting effect (the correlation coefficient of calibration and prediction were 0.9614 and 0.9456, and 0.9559 and 0.9393 respectively for rice wine and fruit brandy) between the predicted and the reference data of EC in rice wine and fruit brandy samples. Our results show that the high selectivity of MIPs and the fingerprint Raman identification can be integrated into a novel nano-biosensor for fast and efficient detection of hazardous substances (EC) in complex rice wine and fruit brandy samples.

Carbon-based aerogel fabricated from waste biomass is a potential absorbent material for solving organic pollution. Herein, the lightweight, hydrophobic and porous carbon aerogels (CAs) have been synthesized through freezing-drying and post-pyrolysis by using waste newspaper as the only raw materials. The as-prepared CAs exhibited a low density of 18.5 mg cm(-3) and excellent hydrophobicity with a water contact angle of 132° and selective absorption for organic reagents. The absorption capacity of CA for organic compounds can be 29-51 times its own weight. Moreover, three methods (e.g., squeezing, combustion, and distillation) can be employed to recycle CA and harvest organic pollutants. Combined with waste biomass as raw materials, green and facile fabrication process, excellent hydrophobicity and oleophilicity, CA used as an absorbent material has great potential in application of organic pollutant solvents absorption and environmental protection.

Skin lesion border irregularity is considered an important clinical feature for the early diagnosis of melanoma, representing the B feature in the ABCD rule. In this article we propose an automated approach for skin lesion border irregularity detection. The approach involves extracting the skin lesion from the image, detecting the skin lesion border, measuring the border irregularity, training a Convolutional Neural Network and Gaussian naive Bayes ensemble, to the automatic detection of border irregularity, which results in an objective decision on whether the skin lesion border is considered regular or irregular. The approach achieves outstanding results, obtaining an accuracy, sensitivity, specificity, and F-score of 93.6%, 100%, 92.5% and 96.1%, respectively.

Asymmetry, color variegation and diameter are considered strong indicators of malignant melanoma. The subjectivity inherent in the first two features and the fact that 10% of melanomas tend to be missed in the early diagnosis due to having a diameter less than 6mm, deem it necessary to develop an objective computer vision system to evaluate these criteria and aid in the early detection of melanoma which could eventually lead to a higher 5-year survival rate. This paper proposes an approach for evaluating the three criteria objectively, whereby we develop a measure to find asymmetry with the aid of a decision tree which we train on the extracted asymmetry measures and then use to predict the asymmetry of new skin lesion images. A range of colors that demonstrate the suspicious colors for the color variegation feature have been derived, and Feret’s diameter has been utilized to find the diameter of the skin lesion. The decision tree is 80% accurate in determining the asymmetry of skin lesions, and the number of suspicious colors and diameter values are objectively identified.

The antifungal activity of mesoporous TiO2 films modified with [M(NH3)4]2+ (M = Cu, Zn) was evaluated using bamboo as a substrate. Mesoporous anatase TiO2 films were facilely deposited on the bamboo surface (TB) by a hydrothermal method. [M(NH3)4]2+ ions were almost entirely embedded into the pores of the TiO2 films through capillary attraction. Subsequently, antifungal experiments on Trichoderma viride and Penicillium citrinum were performed on potato dextrose agar plates containing the [M(NH3)4]2+ modified TB (MTB) in the dark under 85% relative humidity for four weeks. Compared to original bamboo, TB, and [M(NH3)4]2+/bamboo, all the MTB samples exhibited an excellent antifungal efficiency, and the growth of fungi could be inhibited for at least four weeks, meeting the requirements of the China national standard (GB/T 18,261–2013) and demonstrating the superiority of the anti-mildew technology for practical applications.

The physicochemical properties of broad bean paste koji made from extruded and traditional steam-cooked raw materials were investigated by scanning electron micrographs (SEM), confocal laser scanning micrographs (CLSM), circular dichroism (CD) and Molecular weight (Mw) analysis. The results showed that the protease activity of koji made from the extruded materials was higher than that of the traditional cooking method. A network porous structure in the extruded samples was observed by SEM. Furthermore, the CLSM results showed that the starch of the extrudate was embedded well in its protein network. CD spectroscopy implied that the α-helix decreased while the β-turn and random coil increased after both treatments. Mw analysis determined that the starch molecules were degraded during extrusion, the proportion of micromolecules was more in the extruded samples than in the steam-cooked samples. However, the chain length distribution of starch exhibited no significant difference between the extruded and steam-cooked broad beans.

Flame retardant and visible light-activated Fe-doped TiO

Permeability has been proven useful and important in the application and basic research of biomaterials, such as anti-mildew, dyeing, or other impregnated modification, especially in the study of bamboo. However, the traditional methods of improving bamboo’s permeability are chemical treatments and destructive physical treatments. This study proposed an innovative way to produce more porous bamboo with effective penetration via the vacuum freeze-dried method. The greatest advantage of this method is that the original form of bamboo was preserved according to the three-phase principle of water. From scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP), the pore characteristics and microstructure of moso bamboo were characterized, the porosity of bamboo increased to 73%, a microporous bamboo was formed, and the liquid penetration of bamboo improved noticeably. Meanwhile, the vacuum freeze-dried method turned bamboo into a mould-preservation biomaterial that effectively removed starch grains. Moreover, testing the mechanical properties showed that the vacuum freeze-dried method did not have a noticeable impact on bamboo’s mechanical properties, although it had a remarkable impact during later-stage processing and utilization. More importantly, this work provided a good example with which to expand high-value applications of bamboo resources.

The purpose of this study is to evaluate the influence of media treatments on color changes, dimensional stability, and cracking behavior of bamboo scrimber, which has many applications in construction. The bamboo scrimber specimens comprised bamboo bundles with a low molecular weight phenol formaldehyde resin which were manufactured via a cold-in and cold-out hot-pressing process. Small clear specimens were sampled and treated under ten different conditions: water immersion at 25°C, 63°C, and 100°C; oil immersion at 100°C and 150°C; air drying at 63°C, 100°C, and 150°C; and infrared drying at 100°C and 150°C. Then, tests were conducted to determine the color changes, dimensional stability, and cracking behavior of the bamboo scrimbers. The results showed that the bamboo scrimber specimens became darker after all treatment conditions, especially the oil treatments and infrared drying. The color of the oil-treated bamboo scrimbers was found to be more homogenous than the others. The dimensional stability of the bamboo scrimbers was more or less influenced by the water treatments, air drying, and infrared drying, and the oil-treated bamboo scrimbers were relatively stable. Moreover, during the 4-hour treatments, cracks were found in the bamboo scrimbers after air drying at 150°C or infrared drying at 100°C or 150°C.

Magnetic γ-Fe2O3 nanoparticles were successfully deposited on the surface of the bamboo via a coprecipitation process at room temperature. Spherical-like magnetic γ-Fe2O3 nanoparticles with a diameter of about 17 nm displayed well superparamagnetic behavior and were chemically bonded to the bamboo surface through the combination of hydrogen groups. With further modification by 1H,1H,2H,2H-perfluorodecyltri ethoxysilane (FAS-17), magnetic γ-Fe2O3/bamboo composites (MBCs) expressed superhydrophobic performances to not only water but also common liquids like coffee, milk, ink, tea, and coke. When immersed into the corrosive solutions including strong acid (pH = 1), heavy alkaline (pH = 14), and salt with high concentration (5 M) for 24 h, superhydrophobic magnetic γ-Fe2O3/bamboo composites (SMBC) still remained magnetism as well as superhydrophobicity. Also, under harsh conditions like boiled at 100 °C for 4 h, frozen at −40 °C for 24 h, SMBCs were kept a robust magnetism and superhydrophobicity. Additionally, SMBC was a typical ferromagnet and exhibited some microwave absorbabilities. Keywords: γ-Fe2O3, Magnetic, Superhydrophobicity, Microwave absorption, Bamboo

Functional bamboo surfaces with reversibly tunable wettability have become much sought after because of their usefulness in sustainable material protection strategies and industrial applications. In this paper, the hybrid ZnO/bamboo surfaces with reversibly light-switchable wettability between superhydrophobicity and superhydrophilicity were successfully prepared via a hydrothermal method at low temperature. The bamboo substrates served as adhesion, and the well-aligned ZnO nanosheet arrays (WZNA) were deposited on the bamboo surfaces after a hydrothermal process. A subsequent chemical treatment with octadecyltrichlorosilane (OTS) led to a superhydrophobic surface with a water contact angle (WCA) up to 153°. Under UV irradiation, the WCA decreased gradually, and the surface eventually became superhydrophilic because of hydroxyl absorption on the ZnO surfaces. The wetting behavior of the WZNA can be reversibly switched between superhydrophilic and superhydrophobic via alternation of UV exposure for 12 h and dark storage for 10 days.

Silver nanoparticles (Ag NPs) were successfully in situ deposited onto the surface of the bamboo timber through a simple silver mirror reaction. Scanning electron microscopy (SEM) images showed that the surface of the bamboo timber was densely covered with the uniform Ag NPs, which made the intrinsic insulating bamboo timber conductive. With further modification by fluoroalkylsilane (FAS), the Ag NPs-covered bamboo timber showed superhydrophobicity with the water contact angle (WCA) of 155°. Simultaneously, the modified bamboo timber displayed a durable and robust superhydrophobic property even under corrosive solutions including acidic, alkali and NaCl solutions with different molar concentrations. Especially in harsh conditions of boiling water or intense water stirring, the modified bamboo timber remained superhydrophobicity and high conductivity.

This paper presents a state transition based formal framework for a new search method, called Evolutionary Ruin and Stochastic Recreate, which tries to learn and adapt to the changing environments during the search process. It improves the performance of the original Ruin and Recreate principle by embedding an additional phase of Evolutionary Ruin to mimic the survival-of-the-fittest mechanism within single solutions. This method executes a cycle of Solution Decomposition, Evolutionary Ruin, Stochastic Recreate and Solution Acceptance until a certain stopping condition is met. The Solution Decomposition phase first uses some problem-specific knowledge to decompose a complete solution into its components and assigns a score to each component. The Evolutionary Ruin phase then employs two evolutionary operators (namely Selection and Mutation) to destroy a certain fraction of the solution, and the next Stochastic Recreate phase repairs the “broken” solution. Last, the Solution Acceptance phase selects a specific strategy to determine the probability of accepting the newly generated solution. Hence, optimisation is achieved by an iterative process of component evaluation, solution disruption and stochastic constructive repair. From the state transitions point of view, this paper presents a probabilistic model and implements a Markov chain analysis on some theoretical properties of the approach. Unlike the theoretical work on genetic algorithm and simulated annealing which are based on state transitions within the space of complete assignments, our model is based on state transitions within the space of partial assignments. The exam timetabling problems are used to test the performance in solving real-world hard problems.

To determine the hydrothermal growth conditions for ZnO nanocrystals on the surface of bamboo and to further develop new ZnO/bamboo hybrid materials, ZnO nanostructured materials with six morphologies, namely, spheroidal nanoparticles, nanoparticles, nanoneedles, nanograsses, nanowall networks, and nanoorchids, were successfully grown on the surface of bamboo via a low-temperature hydrothermal method based on sol–gel-prepared ZnO seed layers. The effects of temperature, time, molar ratio (zinc acetate/sodium hydroxide), and zinc salt precursor (zinc nitrate, zinc sulfate, zinc chloride, and zinc acetylacetonate) on the ZnO morphologies were comprehensively studied. Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and energy dispersive spectrometry (EDS) were employed to characterize the morphology, chemical structure, and crystalline structure of the ZnO nanomaterials deposited on the bamboo surface. The ZnO nanostructure morphology was most sensitive to temperature and could be controlled by the reaction temperature. All of the as-prepared ZnO nanostructured materials were wurtzite. ZnO nanomaterials were bonded through the interaction between the hydroxyl moiety of the bamboo surface and the corresponding ZnO. A possible hydrothermal fabrication mechanism was also proposed. Further research is required to assess the improvements in the properties of bamboo from the hydrothermal deposition of nanomaterials at the bamboo surface.

A facile method for fabricating superhydrophobic bamboo timber based on an anatase TiO2 film for acid rain protection and flame retardancy is described in the present work. Bamboo timber with a maximal water contact angle of 154° has been prepared by the hydrothermal deposition of anatase TiO2 nanoparticles and further modified with octadecyltrichlorosilane (OTS). The geometric microstructure of the anatase TiO2 nanoparticles and the chemical composition of the superhydrophobic coating were analyzed by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA). The wetting behavior of the bamboo timber samples was investigated by water contact angle (WCA) measurements. The results indicated that strong hydrogen bonds were formed between the amorphous TiO2 and the hydroxide radicals of the bamboo timber surfaces, and the strong interaction contributed to the heat stability enhancement of the TiO2/bamboo timber composites. Moreover, the diverse performance of the superhydrophobic bamboo timber has been evaluated as well. The treated bamboo timber exhibited outstanding superhydrophobicity, excellent waterproofing durability, acid rain resistance, and flame retardancy, offering a potential opportunity to accelerate the large-scale production of superhydrophobic woody material for new industrial applications.

Problems with rehydration and palatability are considered as unacceptable quality characteristics for the noodles produced using high-strength extrusion technique. Thus, the aim of this study was to solve these problems by designing a novel method to create a porous structure for the high-strength extruded noodles (HENs). The quality indices of HENs were significantly improved after adding to them thermostable α-amylase (TαA) at 0.05% to 0.10%. The microstructure graphs showed that a well-developed porous structures was successfully created throughout noodle strands. This indicated that the TαA has effectively worked on starch granules in spite of the high-strength performance of the extrusion process. MALLS-GFC, X-ray diffraction, and differential scanning calorimeter investigations showed that the appearance of a porous structure was mainly attributed to the internal collapse of α-1,4-glycosidic bonds and the dissolution of water-soluble degradation products, such as dextrin and oligosaccharides. Moreover, the slight inhibited effect of excess TαA on the starch gelatinization was because of the fact that the high enzyme concentration might cause TαA to adhere or overlay on it.The rehydration and palatability properties of HENs were greatly improved by creating a well-developed or honeycomb-like porous structure using TαA at low concentration. The findings of this study could be applied to enhance the quality characteristics of HENs and to encourage the research and development in the noodle industry.

Enclosures (fenced, grazing or clipping) within a certain period of years are the most common tools for restoration of degraded grasslands in temperate regions. Short-term enclosures can improve biodiversity and productivity by effectively relieving grazing pressure, while long-term enclosures can reduce species diversity. We therefore carried out a field experiment to investigate the specific causes of the reduced species diversity in Hulunbeier grassland of northern China. After eight years of enclosure, the significantly increased soil available nitrogen (AN) and available phosphorus (AvP) in enclosure community reduced nitrogen (N) limitation but most vegetation was still N limited. Many environmental factors led to decreased species richness, but increased soil AN and decreased light intensity at the community bottom were the most significant ones. Community density decreased independently of soil nutrition but significantly related to decreased species richness. Density of dominant canopy species increased, while dominant understory species decreased during assemblage-level thinning; therefore, the random-loss hypothesis was not supported. The dominant understory species responded to lower light availability by increasing their height, leaf area, and chlorophyll content. Moreover, our results were expected to provide some specific guidance for the restoration mode selection of degraded grasslands in northern China.

Bamboo remains a vital component of modern-day society; however, its use is severely limited in certain applications because of its hydrophilic and oleophilic properties. In this work, we present a method to render bamboo surfaces superamphiphobic by combining control of ZnO nanostructures and fluoropolymer deposition while maintaining their corrosion resistance. Large-scale rose-like ZnO nanoflowers (RZN) were planted on the bamboo surface by a hydrothermal method. After fluoroalkylsilane (FAS) film deposition to lower the surface energy, the resulting surface showed superamphiphobicity toward water, oil, and even certain corrosive liquids, including salt solutions and acidic and basic solutions at all pH values. The as-prepared superamphiphobic bamboo surface was durable and maintained its superhydrophobic property with water contact angles >150° when stored under ambient condition for two months or immersed in a hydrochloric acid solution of pH 1 and a sodium hydroxide solution of pH 14 for 3 h at 50 °C.

A novel recyclable photocatalyst was fabricated by hydrothermal method to immobilize the cross-linked ZnO nanowalls on the bamboo surface. The resultant samples were characterized by using scanning electron microscopy (SEM), X-ray diffraction (XRD), energy dispersive spectroscopy (EDS), and Fourier transformation infrared (FTIR) techniques. FTIR spectra demonstrated that the cross-linked wurtzite ZnO nanowalls and bamboo surface were interconnected with each other by hydrogen bonds. Meanwhile, the cross-linked ZnO nanowalls modified bamboo (CZNB) presented a superior photocatalytic ability and could be recycled at least 3 times with a photocatalytic efficiency up to 70%. The current research provides a new opportunity for the development of a portable and recycled biomass-based photocatalysts which can be an efficiently degraded pollutant solution and reused several times.

The successful usage of fuzzy systems can be seen in many application domains owing to their capabilities to model complex systems by exploiting knowledge of domain experts. Their accuracy and performance are, however, primarily dependent on the design of its membership functions and control rules. The commonly employed technique to design membership functions is to exploit the knowledge of domain experts. However, in certain application domains, the knowledge of domain experts are limited and therefore, cannot be relied upon. Alternatively, optimization techniques such as genetic algorithms are utilized to optimize the various design parameters of fuzzy systems. In this paper, we report a case study of optimizing the membership functions of a fuzzy system using genetic algorithm, which is an important part of our recently developed cloud elasticity framework. This work aims to improve the overall performance of the framework. Results obtained from this research work demonstrate performance improvement in comparison with our previous experimental settings.

This paper describes a Variable Neighbourhood Search (VNS) combined with Metropolis-Hastings acceptance to tackle a highly constrained workforce scheduling problem typical of field service operations (FSO) companies. A refined greedy algorithm is firstly designed to create an initial solution which meets all hard constraints and satisfies some of the soft constraints. The VNS is then used to swap out less promising combinations, continually moving towards a optimal solution until meeting finishing requirements, which are either a satisfactory mean fitness set as a parameter, or a time allowance of one hour. The results of this approach are promising when compared to the stand-alone greedy algorithm, and have showed an average of 10.3% increase in fitness when parameterized with expected demand data and real employee data.

This paper presents an adaptive evolutionary approach incorporating a hybrid genetic algorithm (GA) for public transport crew scheduling problems, which are well-known to be NP-hard. To ensure the search efficiency, a suitable chromosome representation has to be determined first. Unlike a canonical GA for crew scheduling where the chromosome length is fixed, the chromosome length in the proposed approach may vary adaptively during the iterative process, and its initial value is elaborately designated as the lower bound of the number of shifts to be used in an unachievable optimal solution. Next, the hybrid GA with such a short chromosome length is employed to find a feasible schedule. During the GA process, the adaptation on chromosome lengths is achieved by genetic operations of crossover and mutation with removal and replenishment strategies aided by a simple greedy algorithm. If a feasible schedule cannot be found when the GA’s termination condition is met, the GA will restart with one more gene added. The above process is repeated until a feasible solution is found. Computational experiments based on 11 real-world crew scheduling problems in China show that, compared to a fuzzy GA known to be well performed for crew scheduling, better solutions are found for all the testing problems. Moreover, the algorithm works fast, has achieved results close to the lower bounds obtained by a standard linear programming solver in terms of the number of shifts, and has much potential for future developments.

Cloud elasticity augments applications to dynamically adapt to changes in demand by acquiring or releasing computational resources on the fly. Recently, we developed a framework for cloud elasticity utilizing multiple feedback controllers simultaneously, wherein, each controller determines the scaling action with different intensity, and the selection of an appropriate controller is realized with a fuzzy inference system. In this paper, we aim to identify the similarities between cloud elasticity and action selection mechanism in the animal brain. We treat each controller in our previous framework as an action, and propose a novel bioinspired, soft switching approach. The proposed methodology integrates a basal ganglia computational model as an action selection mechanism. Initial experimental results demonstrate the improved potential of the basal ganglia-based approach by enhancing the overall system performance and stability.

For the purpose of investigating the effect of enzyme concentration (EC), barrel temperature (BT), moisture content (MC), and screw speed (SS) on processing parameters (product temperature, die pressure and special mechanical energy (SME)) and product responses (extent of gelatinization (GE), retention rate of total phenolic content (TPC-RR)), rice flour extruded with thermostable α-amylase was analyzed by response surface methodology. Stepwise regression models were computed to generate response surface and contour plots, revealing that both TPC-RR and GE increased as increasing MC while expressed different sensitivities to BT during enzymatic extrusion. Phenolics preservation was benefited from low SME. According to multiple-factor optimization, the conditions required to obtain the target SME (10kJ/kg), GE (100%) and TPC-RR (85%) were: EC=1.37‰, BT=93.01°C, MC=44.30%, and SS=171.66rpm, with the actual values (9.49kJ/kg, 99.96% and 87.10%, respectively) showing a good fit to the predicted values.

As a kind of magnesium rich material, magnesium olivine has been proved to be a kind of artificial wetland substrate, which has a good effect on phosphorus removal. Therefore, it selected firing of the magnesium olivine under the 800℃, 1000℃, 1600℃ as the research object. The characteristics of phosphorus removal were investigated and compared. The experimental results show that the adsorption isotherm equation of Langmuir can well describe the adsorption process of phosphate on the magnesium olivine at different temperatures. Its phosphate saturated adsorption capacity was 195.65mg·kg -1 , 169.49 mg·kg -1 and 145.43 mg·kg -1 . At the same time, all kinds of magnesium olivine phosphorus desorption degree is low. It is proved that the performance of the magnesium olivine with low temperature is better. It is more suitable to be used as artificial wetland substrate.

We present a hybrid approach of goal programming and meta-heuristic search to find compromise solutions for a difficult employee scheduling problem, i.e. nurse rostering with many hard and soft constraints. By employing a goal programming model with different parameter settings in its objective function, we can easily obtain a coarse solution where only the system constraints (i.e. hard constraints) are satisfied and an ideal objective-value vector where each single goal (i.e. each soft constraint) reaches its optimal value. The coarse solution is generally unusable in practise, but it can act as an initial point for the subsequent meta-heuristic search to speed up the convergence. Also, the ideal objective-value vector is, of course, usually unachievable, but it can help a multi-criteria search method (i.e. compromise programming) to evaluate the fitness of obtained solutions more efficiently. By incorporating three distance metrics with changing weight vectors, we propose a new time-predefined meta-heuristic approach, which we call the falling tide algorithm, and apply it under a multi-objective framework to find various compromise solutions. By this approach, not only can we achieve a trade off between the computational time and the solution quality, but also we can achieve a trade off between the conflicting objectives to enable better decision-making.

A hyper-heuristic often represents a heuristic search method that operates over a space of heuristic rules. It can be thought of as a high level search methodology to choose lower level heuristics. Nearly 200 papers on hyper-heuristics have recently appeared in the literature. A common theme in this body of literature is an attempt to solve the problems in hand in the following way: at each decision point, first employ the chosen heuristic(s) to generate a solution, then calculate the objective value of the solution by taking into account all the constraints involved. However, empirical studies from our previous research have revealed that, under many circumstances, there is no need to carry out this costly 2-stage determination and evaluation at all times. This is because many problems in the real world are highly constrained with the characteristic that the regions of feasible solutions are rather scattered and small. Motivated by this observation and with the aim of making the hyper-heuristic search more efficient and more effective, this paper investigates two fundamentally different data mining techniques, namely artificial neural networks and binary logistic regression. By learning from examples, these techniques are able to find global patterns hidden in large data sets and achieve the goal of appropriately classifying the data. With the trained classification rules or estimated parameters, the performance (i.e. the worth of acceptance or rejection) of a resulting solution during the hyper-heuristic search can be predicted without the need to undertake the computationally expensive 2-stage of determination and calculation. We evaluate our approaches on the solutions (i.e. the sequences of heuristic rules) generated by a graph-based hyper-heuristic proposed for exam timetabling problems. Time complexity analysis demonstrates that the neural network and the logistic regression method can speed up the search significantly. We believe that our work sheds light on the development of more advanced knowledge-based decision support systems.

Numerous papers based on various search methods across a wide variety of applications have appeared in the literature over recent years. Most of these methods apply the following same approach to address the problems at hand: at each iteration of the search, they first apply their search methods to generate new solutions, then they calculate the objective values (or costs) by taking some constraints into account, and finally they use some strategies to determine the acceptance or rejection of these solutions based upon the calculated objective values. However, the premise of this paper is that calculating the exact objective value of every resulting solution is not a must, particularly for highly constrained problems where such a calculation is costly and the feasible regions are small and disconnected. Furthermore, we believe that for newly-generated solutions, evaluating the quality purely by their objective values is sometimes not the most efficient approach. In many combinatorial problems, there are poor-cost solutions where possibly just one component is misplaced and all others work well. Although these poor-cost solutions can be the intermediate states towards the search of a high quality solution, any cost-oriented criteria for solution acceptance would deem them as inferior and consequently probably suggest a rejection. To address the above issues, we propose a pattern recognition-based framework with the target of designing more intelligent and more flexible search systems. The role of pattern recognition is to classify the quality of resulting solutions, based on the solution structure rather than the solution cost. Hence, the general contributions of this work are in the line of "insights" and recommendations. Two real-world cases of the assignment problem, i.e. the hospital personnel scheduling and educational timetabling, are used as the case studies. For each case, we apply neural networks as the tool for pattern recognition. In addition, we present our theoretical and experimental results in terms of runtime speedup.

A facile method to synthesize ZnO-TiO2-layered double-nanostructures with the average thickness of 20 &mu, m on a bamboo substrate was proposed to improve the antifungal and flame-retardant properties. The cross-linked wurtzite ZnO nanostructures with an average thickness of approximately 0.14 &mu, m were uniformly distributed on the anatase TiO2 surface. The energy-dispersive X-ray spectroscopy (EDS) confirmed that the ZnO-TiO2 coating on bamboo was a layered double nanostructure. During a two-month antifungal test conducted in an outdoor environment, the fungi began to grow after one week on pristine bamboo and three weeks on ZnO-bamboo and TiO2-bamboo. Furthermore, there was an infected area of 100% after four weeks for pristine bamboo and six weeks for ZnO-bamboo, while there was an infected area of 43% after eight weeks for TiO2-bamboo. By comparison, there was no visible fungal growth on ZnO-TiO2-bamboo until the end of the test. The electron spin resonance (ESR) technique has demonstrated that the reactive oxygen species (ROS) of &bull, O2&minus, and &bull, OH were produced from the ZnO-TiO2 surface under visible light irradiation (&lambda, &gt, 420 nm). This large quantity of &bull, compared to &bull, OH is considered to be mainly responsible for the inactivation of fungi. Additionally, the limiting oxygen index has increased from 25.6% to 30.2% after being covered with a ZnO-TiO2 coating, which revealed a significant enhancement of its flame-retardant property.

Bamboo is susceptible to mould and attack by fungi because of its high content of starch and sugar. To make bamboo-based outdoor materials, a new type of bamboo timber with improved mould resistance and antibacterial activity, coated with reduced graphene oxide and nanocrystal ZnO (abbreviated as RGO@ZnOBT), was fabricated by a two-step dip-dry and hydrothermal process. A possible synthesis mechanism for RGO@ZnOBT was investigated by X-ray diffraction, scanning electron microscopy, transmission electron microscope, energy-dispersal X-ray analysis, X-ray photoelectron spectroscopy and Fourier-transform infrared spectroscopy. According to the China standard test method, the Aspergillus niger mould resistance of RGO@ZnOBT is grade 2, whereas the Trichoderma viride and Penicillium citrinum mould resistance of RGO@ZnOBT is grade 0, both of which are better than the grade 4 of original bamboo timber. The Escherichia coli resistance test showed that the antibacterial circle of RGO@ZnOBT is 3 mm, which is significantly higher than that of original bamboo timber (0 mm). The antibacterial activity of treated bamboo is significantly improved compared with that of untreated bamboo.

This paper presents a hybrid multi-objective model that combines integer programming (IP) and variable neighbourhood search (VNS) to deal with highly-constrained nurse rostering problems in modern hospital environments. An IP is first used to solve the subproblem which includes the full set of hard constraints and a subset of soft constrains. A basic VNS then follows as a postprocessing procedure to further improve the IP’s resulting solutions. The satisfaction of the excluded constraints from the preceding IP model is the major focus of the VNS. Very promising results are reported compared with a commercial genetic algorithm and a hybrid VNS approach on real instances arising in a Dutch hospital. The comparison results demonstrate that our hybrid approach combines the advantages of both the IP and the VNS to beat other approaches in solving this type of problems. We also believe that the proposed methodology can be applied to other resource allocation problems with a large number of constraints.

In this paper, we propose a search technique for nurse scheduling, which deals with it as a multi-objective problem. For each nurse, we first randomly generate a set of legal shift patterns which satisfy all shift-related hard constraints. We then employ an adaptive heuristic to quickly find a solution with the least number of violations on the coverage-related hard constraint by assigning one of the available shift patterns of each nurse. Next, we apply a coverage repairing procedure to make the resulting solution feasible, by adding/removing any under-covered/over-covered shifts. Finally, to satisfy the soft constraints (or preferences), we present a simulated annealing based search method with the following two options: one with a weighted-sum evaluation function which encourages moves towards users’ predefined preferences, and another one with a domination-based evaluation function which encourages moves towards a more diversified approximated Pareto set. Computational results demonstrate that the proposed technique is applicable to modern hospital environments.

In this paper we present a novel Case-Based Reasoning (CBR) system called CABAROST (CAsed-BAsed ROSTering) which was developed for personnel scheduling problems. CBR is used to capture and store examples of personnel manager behaviour which are then used to solve future problems. Previous examples of constraint violations in schedules and the repairs that were used to solve the violations are stored as cases. The sequence in which violations are repaired can have a great impact on schedule quality. A novel memetic algorithm is proposed which evolves good quality sequences of repairs generated by CABAROST. The algorithm was tested on instances of the real-world nurse rostering problem at the Queens Medical Centre NHS Trust in Nottingham.

This paper presents a Multi-Objective Simulated Annealing (MOSA) approach for noncyclic bus driver rostering. A heuristic is first devised to construct an initial solution. Next, a SA-based feasibility repairing algorithm is designed to make the solution feasible. Finally, a SA-based non-dominated solution generating algorithm is devised to find the Pareto front based on the feasible solution. Differing from previous work on the problem, the MOSA provides two options to handle user preferences: one with a weighted-sum evaluation function encouraging moves towards users’ predefined preferences, and another with a domination-based evaluation function encouraging moves towards a more diversified Pareto set. Moreover, the MOSA employs three strategies, i.e. incremental evaluation, neighbourhood pruning and biased elite solution restart strategy, to make the search more efficient and effective. Experiments show that the MOSA can produce a large number of solutions that reconcile contradictory objectives rapidly, and the strategies can enhance the computational efficiency and search capability.

Portfolio optimization is one of the most important problems in the finance field. The traditional mean-variance model has its drawbacks since it fails to take the market uncertainty into account. In this work, we investigate a two-stage stochastic portfolio optimization model with a comprehensive set of real world trading constraints in order to capture the market uncertainties in terms of future asset prices. A hybrid approach, which integrates genetic algorithm (GA) and a linear programming (LP) solver is proposed in order to solve the model, where GA is used to search for the assets selection heuristically and the LP solver solves the corresponding sub-problems of weight allocation optimally. Scenarios are generated to capture uncertain prices of assets for five benchmark market instances. The computational results indicate that the proposed hybrid algorithm can obtain very promising solutions. Possible future research directions are also discussed.

Clinical Decision Support (CDS) aids in early diagnosis of liver cancer, a potentially fatal disease prevalent in both developed and developing countries. Our research aims to develop a robust and intelligent clinical decision support framework for disease management of cancer based on legacy Ultrasound (US) image data collected during various stages of liver cancer. The proposed intelligent CDS framework will automate real-time image enhancement, segmentation, disease classification and progression in order to enable efficient diagnosis of cancer patients at early stages. The CDS framework is inspired by the human interpretation of US images from the image acquisition stage to cancer progression prediction. Specifically, the proposed framework is composed of a number of stages where images are first acquired from an imaging source and pre-processed before running through an image enhancement algorithm. The detection of cancer and its segmentation is considered as the second stage in which different image segmentation techniques are utilized to partition and extract objects from the enhanced image. The third stage involves disease classification of segmented objects, in which the meanings of an investigated object are matched with the disease dictionary defined by physicians and radiologists. In the final stage; cancer progression, an array of US images is used to evaluate and predict the future stages of the disease. For experiment purposes, we applied the framework and classifiers to liver cancer dataset for 200 patients. Class distributions are 120 benign and 80 malignant in this dataset.

This paper reports an evolutionary meta-heuristic incorporating fuzzy evaluation for some large-scale set covering problems originating from the public transport industry. First, five factors characterized by fuzzy membership functions are aggregated to evaluate the structure and generally the goodness of a column. This evaluation function is incorporated into a refined greedy algorithm to make column selection in the process of constructing a solution. Secondly, a self-evolving algorithm is designed to guide the constructing heuristic to build an initial solution and then improve it. In each generation an unfit portion of the working solution is removed. Broken solutions are repaired by the constructing heuristic until stopping conditions are reached. Orthogonal experimental design is used to set the system parameters efficiently, by making a small number of trials. Computational results are presented and compared with a mathematical programming method and a GA-based heuristic.