Catalytically Active Bacterial Nanocellulose‐Based Ultrafiltration Membrane

Large quantities of highly toxic organic dyes in industrial wastewater is a persistent challenge in wastewater treatment processes. Here, for highly efficient wastewater treatment, a novel membrane based on bacterial nanocellulose (BNC) loaded with graphene oxide (GO) and palladium (Pd) nanoparticles is demonstrated. This Pd/GO/BNC membrane is realized through the in situ incorporation of GO flakes into BNC matrix during its growth followed by the in situ formation of palladium nanoparticles. The Pd/GO/BNC membrane exhibits highly efficient methylene orange (MO) degradation during filtration (up to 99.3% over a wide range of MO concentrations, pH, and multiple cycles of reuse). Multiple contaminants (a cocktail of 4‐nitrophenol, methylene blue, and rhodamine 6G) can also be effectively treated by Pd/GO/BNC membrane simultaneously during filtration. Furthermore, the Pd/GO/BNC membrane demonstrates stable flux (33.1 L m^?2 h^?1) under 58 psi over long duration. The novel and robust membrane demonstrated here is highly scalable and holds a great promise for wastewater treatment.     

Metal–Organic Framework Encapsulation for the Preservation and Photothermal Enhancement of Enzyme Activity

Interfacing biomolecules with functional materials is a key strategy toward achieving externally‐triggered biological function. The rational integration of functional proteins, such as enzymes, with plasmonic nanostructures that exhibit unique optical properties such as photothermal effect provides a means to externally control the enzyme activity. However, due to the labile nature of enzymes, the photothermal effect of plasmonic nanostructures is mostly utilized for the enhancement of the biocatalytic activity of thermophilic enzymes. In order to extend and utilize the photothermal effect to a broader class of enzymes, a means to stabilize the immobilized active protein is essential. Inspired by biomineralization for the encapsulation of soft tissue within protective exteriors in nature, metal–organic framework is utilized to stabilize the enzyme. This strategy provides an effective route to enhance and externally modulate the biocatalytic activity of enzymes bound to functional nanostructures over a broad range of operating environments that are otherwise hostile to the biomolecules.     

A relational hierarchical model for decision-theoretic assistance

Building intelligent assistants has been a long-cherished goal of AI, and many were built and fine-tuned to specific application domains. In recent work, a domain-independent decision-theoretic model of assistance was proposed, where the task is to infer the user??s goal and take actions that minimize the expected cost of the user??s policy. In this paper, we extend this work to domains where the user??s policies have rich relational and hierarchical structure. Our results indicate that relational hierarchies allow succinct encoding of prior knowledge for the assistant, which in turn enables the assistant to start helping the user after a relatively small amount of experience.     

A chevron specimen for the measurement of mixed-mode interface toughness of wafer bonds

Accurate characterization of interfacial adhesion is essential for the development of reliable wafer bonding processes. In most applications in which wafers are bonded, the interface experiences a combination of shear and normal loading (i.e., mixed-mode loading). When characterizing the fracture properties of a bond, it is important to measure the interface toughness under similar mixed-mode conditions. In the current work, a chevron test specimen composed of bonded cantilever layers of dissimilar thicknesses is analyzed, and the dependence of the mode mixity at the interface is determined as function of the layer thickness ratio. This test geometry is well-suited for the measurement of bonds between typical semiconductor substrates that range in thickness from 0.1 to 1 mm. A nominal specimen geometry with a total layer thickness of 0.5-2 mm and in-plane dimensions of 10 × 10 mm is analyzed using a 3-D finite element (FE) model in combination with the virtual crack closure technique. It is demonstrated that the phase angle (i.e. the degree of mode-mixity) at the interface can be varied from 0° to 35° by changing the layer thickness ratio from 1 to 0.1. The FE results have been fitted to an expression that allows the interface toughness to be easily calculated from experimental data.     

Comment on ‘Stability analysis and controller synthesis for discrete linear time-delay systems with state saturation nonlinearities’

A recently reported paper (Ji, X., Liu, T., Sun, Y., and Su, H. (2011), ‘Stability analysis and controller synthesis for discrete linear time-delay systems with state saturation nonlinearities’, International Journal of Systems Science, 42, 397–406) for the global asymptotic stability analysis and controller synthesis for a class of discrete linear time delay systems employing state saturation nonlinearities is reviewed. It is claimed in Ji, Liu, Sun and Su (2011) that a previous approach by Kandanvli and Kar (Kandanvli, V.K.R and Kar, H. (2009), ‘Robust stability of discrete-time state-delayed systems with saturation nonlinearities: Linear matrix inequality approach’, Signal Processing, 89, 161–173) is recovered from their approach as a special case. It is shown that this claim is not justified.     

On Exact Learning of Unordered Tree Patterns

Tree patterns are natural candidates for representing rules and hypotheses in many tasks such as information extraction and symbolic mathematics. A tree pattern is a tree with labeled nodes where some of the leaves may be labeled with variables, whereas a tree instance has no variables. A tree pattern matches an instance if there is a consistent substitution for the variables that allows a mapping of subtrees to matching subtrees of the instance. A finite union of tree patterns is called a forest. In this paper, we study the learnability of tree patterns from queries when the subtrees are unordered. The learnability is determined by the semantics of matching as defined by the types of mappings from the pattern subtrees to the instance subtrees. We first show that unordered tree patterns and forests are not exactly learnable from equivalence and subset queries when the mapping between subtrees is one-to-one onto, regardless of the computational power of the learner. Tree and forest patterns are learnable from equivalence and membership queries for the one-to-one into mapping. Finally, we connect the problem of learning tree patterns to inductive logic programming by describing a class of tree patterns called Clausal trees that includes non-recursive single-predicate Horn clauses and show that this class is learnable from equivalence and membership queries.     

LEARNING TO SOLVE PROBLEMS FROM EXERCISES

It is a common observation that learning easier skills makes it possible to learn the more difficult skills. This fact is routinely exploited by parents, teachers, textbook writers, and coaches. From driving, to music, to science, there hardly exists a complex skill that is not learned by gradations. Natarajan's model of “learning from exercises” captures this kind of learning of efficient problem solving skills using practice problems or exercises ( Natarajan 1989 ). The exercises are intermediate subproblems that occur in solving the main problems and span all levels of difficulty. The learner iteratively bootstraps what is learned from simpler exercises to generalize techniques for solving more complex exercises. In this paper, we extend Natarajan's framework to the problem reduction setting where problems are solved by reducing them to simpler problems. We theoretically characterize the conditions under which efficient learning from exercises is possible. We demonstrate the generality of our framework with successful implementations in the Eight Puzzle, symbolic integration, and simulated robot planning domains illustrating three different representations of control knowledge, namely, macro‐operators, control rules, and decision lists. The results show that the learning rates for the exercises framework are competitive with those for learning from problems solved by the teacher.     

Structured machine learning: the next ten years

The field of inductive logic programming (ILP) has made steady progress, since the first ILP workshop in 1991, based on a balance of developments in theory, implementations and applications. More recently there has been an increased emphasis on Probabilistic ILP and the related fields of Statistical Relational Learning (SRL) and Structured Prediction. The goal of the current paper is to consider these emerging trends and chart out the strategic directions and open problems for the broader area of structured machine learning for the next 10 years.     

Depression and enhancement of baroreceptor pressor response in cats after intracerebroventricular injection of noradrenergic blocking agents: dependence on supracollicular areas of the brain

The alpha-adrenergic blocking drugs, phentolamine and Hydergine, both act centrally at different sites to depress and enhance the pressor and sympathetic nerve response to decreased baroreceptor afferent input in anesthetized cats. Depression of the rise in blood pressure and sympathetic nerve discharge during bilateral carotid occlusion (BCO) followed injection of the agents into the 4th cerebral ventricle when the brain was intact but not when connections were interrupted at the midcollicular level by transection or lesion. Enhancement of responses occurred when drug distribution was confined to the brain rostral to the midcollicular level via injection into the 3rd cerebral ventricle with the cerebral aqueduct cannulated. Both agents decreased resting blood pressure and Hydergine decreased heart rate in intact and decerebrate preparations but not in 3rd ventricle-cerebral aqueduct experiments. We found that pretreatment with the noradrenergic precursor. L-dopa consistently prevented depression by phentolamine but was less effective against Hydergine. The results indicate that mechanisms which enhance and suppress the baroreceptor pressor response are normally operative in anesthetized cats and, furthermore, that neural pathways mediating the effects are ones connecting the caudal brainstem with supracollicular levels of the brain. It is further suggested that the pathways may be noradrenergic.