Narrow Results

The aim of long-term mine planning (LTMP) is two-fold: to maximize the net present value of profits (NPV) and determine how ores are sequentially processed over the lifetime. This scheduling task is computationally complex as it is rife with variables, constraints, periods, uncertainties, and unique operations. In this paper, we present trends in the literature in the recent decade. One trend is the shift from deterministic toward stochastic problems as they reflect real-world complexities. A complexity of growing concern is also in sustainable mine planning. Another trend is the shift from traditional operational research solutions — relying on exact or (meta) heuristic methods — toward hybrid methods. They are compared through the scope of the problem formulation and discussed via solution quality, efficiency, and gaps. We finally conclude with opportunities to incorporate artificial intelligence (AI)-based methods due to paucity, multiple operational uncertainties simultaneously, sustainability indicator quantification, and benchmark instances.

We describe a hybrid pattern-matching algorithm that works on both regular and indeterminate strings. This algorithm is inspired by the recently proposed hybrid algorithm FJS and its indeterminate successor. However, as discussed in this paper, because of the special properties of indeterminate strings, it is not straightforward to directly migrate FJS to an indeterminate version. Our new algorithm combines two fast pattern-matching algorithms, ShiftAnd and BMS (the Sunday variant of the Boyer-Moore algorithm), and is highly adaptive to the nature of the text being processed. It avoids using the border array, therefore avoids some of the cases that are awkward for indeterminate strings. Although not always the fastest in individual test cases, our new algorithm is superior in overall performance to its two component algorithms — perhaps a general advantage of hybrid algorithms.

The Kyoto Protocol was the outcome of many years of multilateral negotiation and political compromise with the ultimate aim of reducing the risk of dangerous climate change. Unfortunately, most of the countries that ratified the Kyoto Protocol have not taken effective action to curb greenhouse gas emissions, with many Kyoto countries not looking likely to reach their targets. There is also a lack of enthusiasm from major developing countries to take on the binding targets that form the basis of the Kyoto Protocol Approach. This has raised serious doubts about the viability of the Kyoto policy of committing countries to targets and timetables especially as a model for the current negotiations. As the science becomes more compelling that action is needed to curb greenhouse gas emissions, countries are beginning to look for more sustainable alternatives for the period beyond 2012. This paper outlines the key features that are needed in a new climate change framework beyond Kyoto drawing on lessons from monetary history. Using the analogy to the way modern central banks run monetary policy, it outlines an alternative to the Kyoto Protocol, which is a system of national climate policies coordinated around a common global price for carbon.

The status of conventional baryon flux-tubes and hybrid baryons is reviewed. Recent surprises are that a model prediction indicates that hybrid baryons are very weakly produced in glue-rich Ψ decays, and an analysis of electro-production data concludes that the Roper resonance is not a hybrid baryon. The baryon decay flux-tube overlap has been calculated in the flux-tube model, and is discussed here. The behavior of the overlap follows naïve expectations.

Belle and BaBar have recently reported observations of meson states that decay to final states containing a c and -quark but with properties that do not match well to those expected for any of the as-yet unassigned charmonium meson states. Among the suggested interpretations for these states are meson-antimeson molecular states, diquark-antidiquark tetraquarks and/or -gluon hybrid mesona. I briefly review what is experimentally known about these states and comment on the status of recent attempts to classify them.

In this paper, a hybrid lattice Boltzmann flux solver (LBFS) is proposed for simulation of 3D integrated hypersonic fluid-thermal-structural problems. In the solver, the macroscopic 3D Navier–Stokes equations and structural heat transfer equation are discretized by the finite volume method and the numerical fluxes at the cell interface are reconstructed by the local solution of Boltzmann equation. To compute the numerical fluxes, two lattice velocity models are introduced. One is the D1Q4 discrete velocity model for calculating the inviscid flux across the cell interface of N–S equations, and the other one is the D3Q6 model for evaluating the flux of structural energy equation. Furthermore, a new dual-thermal-resistance model is proposed to calculate the thermal properties on the fluid-structure interface. To validate the accuracy and stability of the present solver, applications for hypersonic fluid-thermal-structural analysis are demonstrated on aerodynamically heated blunt cone body at $\mathrm{\text{Ma}}=10.6$. Numerical results showed that the present solver can predict accurately the thermal properties of hypersonic fluid-thermal-structural problems and offer the potential for significant improvements in predicting fluid-structural-thermal problems of long-endurance high speed vehicles.

Optical properties of PMMA/boehmite nano-hybrid synthesized directly from their relevant precursors via facile aqueous one-pot process is studied. Silane coupling agent was applied to make compatible chemically the organic–inorganic counterparts. The chemical bond of SiOAl as the main link between organic and inorganic moieties was confirmed by Fourier transforms infrared (FTIR) spectra. The chemical bond of Si–O–Al as the main link between organic and inorganic moieties. Determining the refractive and absorption index, real and imaginary parts of dielectric constant, energy loss function, and their distribution for PMMA/boehmite nano-hybrid as a function of boehmite loading was assessed in the range of 12–120 THz using FTIR data and Kramers–Kronig relations. The inorganic/organic weight ratio varied from 0.7% to 5% and its impact on optical features was evaluated. It was demonstrated that the optical features of hybrids are frequency-dependent and less affected from boehmite incorporation.

Multi-label learning (MLL) problems abound in many areas, including text categorization, protein function classification, and semantic annotation of multimedia. Issues that severely limit the applicability of many current machine learning approaches to MLL are the large-scale problem, which have a strong impact on the computational complexity of learning. These problems are especially pronounced for approaches that transform MLL problems into a set of binary classification problems for which Support Vector Machines (SVMs) are used. On the other hand, the most efficient approaches to MLL, based on decision trees, have clearly lower predictive performance. We propose a hybrid decision tree architecture, where the leaves do not give multi-label predictions directly, but rather utilize local SVM-based classifiers giving multi-label predictions. A binary relevance architecture is employed in the leaves, where a binary SVM classifier is built for each of the labels relevant to that particular leaf. We use a broad range of multi-label datasets with a variety of evaluation measures to evaluate the proposed method against related and state-of-the-art methods, both in terms of predictive performance and time complexity. Our hybrid architecture on almost every large classification problem outperforms the competing approaches in terms of the predictive performance, while its computational efficiency is significantly improved as a result of the integrated decision tree.

Single sample face recognition (SSFR) is a challenging research problem in which only one face image per person is available for training. Moreover, the face image may have different pose, expression, illumination, occlusion etc. rendering this problem more complex. Several methods have been suggested by various researchers in literature to solve SSFR. Here, we provide a comprehensive review of the methods proposed in the last decade for solving SSFR problem and introduce a novel taxonomy for the same. We divide SSFR methods broadly into five categories viz. (i) feature based, (ii) virtual sample generation based, (iii) generic database based, (iv) Hybrid and (v) other methods. We have also briefly reviewed the face databases used for evaluating single sample face recognition methods. Furthermore, the performance of the methods has been analyzed in terms of classification accuracy as given in literature. At last, we also suggest some future direction to the researchers and practitioners working in this fascinating research area.

This paper presents a hybrid approach towards accomplishing the task of range image segmentation. It tends to combine region growing and one-dimensional orthogonal polynomial fitting techniques for the detection of edges in range images. Edges detected by such a method possesses good localisation property. This aids to steer the region growing process towards accomplishing accurate border partitioning. In addition, the incorporation of region growing process eliminates internal micro edges and provides for missing border reconstruction as it is able to detect weak edges. It is believed that the edge and segmentation maps produced, when conveyed to the higher level recognition process, may prove valuable not only for CAD based modeling purposes but may also aid to provide descriptive syntax to the object identification process.

Spin Orbit Torque Magnetic Random Access Memory (SOT–)MRAM is gaining interest as it eradicates several limitations posed by its predecessor Spin Transfer Torque (STT-)MRAM, yet inherits all its advantages. This work explores in detail, the suitability of SOT–MRAM implemented caches in different levels of memory hierarchy in comparison to conventional SRAM technology, over several performance parameters like area, energy consumption and execution time for an embedded benchmark suite. Our circuit-level analysis shows that SOT–MRAM outperforms SRAM for caches ($>128$ KB), and only lags in area and read-access energy for smaller caches. A typical 512 KB SOT–MRAM cache improves area by 1%, read/write latency by 33/38%, and leakage by over 99% than that of SRAM memory technology. The architecture-level analysis confirms that on average SOT–MRAM is energy efficient by 74% in L1, 97.2% in L2 and 89.3% in both (i.e., L1 + L2) implementations against SRAM, for a 22nm technology node. We also estimate that SOT–MRAM only solution offers $\sim 68.8$% energy savings and $\sim 79.5$% better EDP than Hybrid (L1-SRAM and L2-SOT) memory hierarchy for multi-core ARM processors.

The past few years have witnessed the great success of a new family of paradigms, social tagging networks, which allows users to freely associate social tags to items and efficiently manage them. Thus it provides us a promising way to effectively find useful and interesting information. In this paper, we consider two typical roles of social tags: (i) an accessorial tool helping users organize items; (ii) a bridge that connects users and items. We then propose a hybrid algorithm to integrate the two different roles to obtain better recommendation performance. Experimental results on a real-world data set, Del.icio.us, shows that it can significantly enhance both the algorithmic accuracy and diversity.

This paper proposes a new hybrid encoding of finite linear CSP to SAT which integrates order and log encodings. The former maintains bound consistency by unit propagation and works well for constraints consisting of small/middle sized arity and variable domains. The latter generates smaller CNF and works well for constraints consisting of larger sized arity and variable domains but its performance is not good in general because more inference steps are required to ripple carries. This paper describes the first attempt of hybridizing the order and log encodings without channeling constraints. Each variable is encoded by either the order encoding or the log encoding, and each constraint can contain both types of variables. Using the CSP solver competition benchmark consisting of 1458 instances, we made a comparison between the order, log and proposed hybrid encodings. As a result, the hybrid encoding solves the largest number of instances with the shortest CPU time. We also made a comparison with the four state-of-the-art CSP and SMT solvers Mistral, Opturion CPX, Yices, and z3. In this comparison, the hybrid encoding also shows the best performance. Furthermore, we found that the hybrid encoding is especially superior than other solvers for instances containing disjunctive constraints and global constraints — it indeed solves more instances than the virtual best solver consisting of those four state-of-the-art systems.

In this paper, four three-node triangular finite element models which can readily be incorporated into the standard finite element program framework are devised via a multi-field variational functional for the bounded plane Helmholtz problem. In the models, boundary and domain fields are independently assumed. The former is constructed by nodal interpolation and the latter comprises nonsingular solutions of the Helmholtz equation. The equality of the two fields are enforced along the element boundary. Among the four devised models, the most accurate one is 1/3 to 1/2 less erroneous than the conventional single-field model in most examples.

Recently, on the internet, the level of image and video forgery has augmented due to the augmentation in the malware, which has facilitated user (anyone) to upload, download, or share objects online comprising audio, images, or video. Recently, Convolution Neural Network (CNN) has turn into a de-facto technique for classification of multi-dimensional data and it renders standard and also highly effectual network layer arrangements. But these architectures are limited by the speed due to massive number of calculations needed for training in addition to testing the network and also, it might render less accuracy. To trounce these issues, this paper proposed to ameliorate the image and video forgery detection’s efficiency utilizing hybrid CNN. Initially, the intensive along with incremental learning phase is carried out. After that, the hybrid CNN is implemented to detect the image together with video forgery. The developed system was tested on images together with videos for different kinds of forgeries, and it was observed that the proposed work obtains more than 98% accuracy for both testing as well as validation sets.

Micro-electro-discharge drilling ($\mu$EDD) is a type of non-traditional machining process used for drilling micro-holes of desired dimensions with a high aspect ratio. But, there are no such research works that could have explained the desired accurate circular shape of micro-holes. The need for a more advanced hybrid machining process to improve the overall efficiency in terms of mainly desired circular shape and radial overcut is evolved. In this research work, an electromagnetic field force-assisted micro-EDM process has been carried out on Inconel 800 with a copper tool of 450$\mu$m. Experimental results showed that measured metal removal rate and tool wear rate decreased for ascending values of magnetic flux density, peak current and gap voltage, whereas circularity increases linearly with an increase in magnetic flux density and also the effects of magnetic field on circularity of micro-holes on Inconel 800 are more predominant than other parameters.

This review article presents the concept of photorefractive beam coupling in hybrid photorefractive cells comprising a liquid crystal layer adjacent to inorganic photorefractive windows. The roles of the liquid crystal layer and the photorefractive windows, as well as the overall advantages of this architecture, are explained. The mechanism involved and the required window parameters to break the local alignment symmetry in order to achieve Bragg-matched two-beam coupling in a liquid crystal layer are discussed.

Traffic accident reconstruction is a reverse dynamic problem, which requires hundreds of iterations to reconstruct the whole process of accident. However, in current pedestrian-vehicle accident reconstructions, it is difficult to quickly establish a pedestrian model based on specific cases, and it is hard to solve the contradiction between calculation accuracy and calculation time. In this paper, a personalized pedestrian customization method is proposed. First, the pedestrian structure is divided into independent modules according to obvious bony markers. For each independent module, multi-body (MB) model and finite element (FE) model are established, respectively. Then the appropriate modules are selected to form the whole hybrid pedestrian model. This method can customize the structure of pedestrian model according to the injury characteristics of pedestrians in specific accidents, and customize the parameters of pedestrian model according to the height and weight of pedestrians. The impact simulation tests are carried out on hybrid pedestrian models to verify the reliability of the models. The proposed method can effectively improve the modeling efficiency of pedestrian models and the reconstruction quality of pedestrian traffic accidents.

Hybrid light emitting nanoparticles with diameter range from 2 to 4 nm were prepared via grafting organic-conjugated chains directly onto an inorganic rigid cage polyhedral oligomeric silsesquioxanes (POSS). The unique properties of these particles show evidence of quantum confinement effect on the conjugated short chains by two barriers of POSS cage and alkyl chains. The confinement effects are revealed in five aspects. First, the UV and PL spectra redshift as increasing the length of conjugated chains. This phenomenon can be considered as size effect. Second, PL spectra of these nanoparticles in solid film blueshift from that in most of organic solvents, which can be considered as limited intra- or inter-molecular interactions existed within the nanoparticles. Third, the Raman bands of the conjugated chains in these nanoparticles are redshifted and broadening with respect to their bulk counterparts. The systematic peak shifting and broadening of the Raman bands provided additional confirmation that the conjugated chains in hybrid nanoparticles at bulk state are isolated without any π–π stacking. Fourth, TEM and SEM images showed the particle size in a range of 2–4 nm and the nanoparticles in bulk state are noncrystalline materials. Lastly, the PL spectrum of the nanoparticle at low temperature was studied and found no change in PL position and intensity as temperature increasing from 4 K to 150 K.

The study in this paper investigates the strategies used by collaborators in an effort to integrate knowledge in the context of a multi-institutional environment. By approaching a startup engaged in the digital marketplace, this study aims to provide empirical evidence on the adoption of virtual workplace in the context of competing institutional logics. The theoretical model is built using the perspective of institutional logic and knowledge approach. Our findings suggest that collaborators in an effort to integrate their colleagues’ knowledge use hybrid strategies — segmentation and combination. The level of skill in segmentation and combination strategies depends on the level of experience and knowledge of collaborators outside of their specialty. The study in this paper contributes in two directions. First, collaborative networks through online collaboration resulted in knowledge integration can be developed with hybrid actor roles and skills. Second, this paper provides empirical evidence on the vertical relationship between institutions, organisations, and individuals in institutional theory and the emphasis on the micro-institutional level.