Dysprosium doped copper oxide (Cu1-xDyxO) nanoparticles enabled bifunctional electrode for overall water splitting

The production of hydrogen, a favourable alternative to an unsustainable fossil fuel remains as a significant hurdle with the pertaining challenge in the design of proficient, highly productive and sustainable electrocatalyst for both oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). Herein, the dysprosium (Dy) doped copper oxide (Cu_1-xDy_xO) nanoparticles were synthesized via solution combustion technique and utilized as a non-noble metal based bi-functional electrocatalyst for overall water splitting. Due to the improved surface to volume ratio and conductivity, the optimized Cu_1-xDy_xO (x = 0.01, 0.02) electrocatalysts exhibited impressive HER and OER performance respectively in 1 M KOH delivering a current density of 10 mAcm^?2 at a potential of ?0.18 V vs RHE for HER and 1.53 V vs RHE for OER. Moreover, the Dy doped CuO electrocatalyst used as a bi-functional catalyst for overall water splitting achieved a potential of 1.56 V at a current density 10 mAcm^?2 and relatively high current density of 66 mAcm^?2 at a peak potential of 2 V. A long term stability of 24 h was achieved for a cell voltage of 2.2 V at a constant current density of 30 mAcm^?2 with only 10% of the initial current loss. This showcases the accumulative opportunity of dysprosium as a dopant in CuO nanoparticles for fabricating a highly effective and low-cost bi-functional electrocatalyst for overall water splitting.     

Discovery of Affinity-Based Probes for Btk Occupancy Assays

Bruton's tyrosine kinase (Btk) is an attractive target for the treatment of a wide array of B-cell malignancies and autoimmune diseases. Small-molecule covalent irreversible Btk inhibitors targeting Cys481 have been developed for the treatment of such diseases. In clinical trials, probe molecules are required in occupancy studies to measure the level of engagement of the protein by these covalent irreversible inhibitors. The result of this pharmacodynamic (PD) activity provides guidance for appropriate dosage selection to optimize inhibition of the drug target and correlation of target inhibition with disease treatment efficacy. This information is crucial for successful evaluation of drug candidates in clinical trials. Based on the pyridine carboxamide scaffold of a novel solvent-accessible pocket (SAP) series of covalent irreversible Btk inhibitors, we successfully developed a potent and selective affinity-based biotinylated probe 12 (2-[(4-{4-[5-(1-{5-[(3aS,4S,6aR)-2-oxo-hexahydro-1H-thieno[3,4-d]imidazol-4-yl]pentanamido}-3,6,9,12-tetraoxapentadecan-15-amido)pentanoyl]piperazine-1-carbonyl}phenyl)amino]-6-[1-(prop-2-enoyl)piperidin-4-yl]pyridine-3-carboxamide). Compound 12 has been used in Btk occupancy assays for preclinical studies to determine the therapeutic efficacy of Btk inhibition in two mouse lupus models driven by TLR7 activation and type I interferon.     

Protecting local access telecommunications networks: Toward a minimum-cost solution

The problem of designing fibre-optic networks for local-access telecommunications generates (at least) three non-trivial subproblems. In the first of these subproblems one must determine how many fibre-optic cables (fibres) are required at either end of a street. In the next subproblem a minimum-cost network must be designed to support the fibres. The network must also provide distinct paths from either end of the street to the central exchange(s). Finally, the fibre-optic cables must be placed in protective covers. These covers are available in a number of different sizes, allowing some flexibility when covering each section of the network. In this paper we describe a dynamic programming (DP) formulation for finding a minimum-cost (protective) covering for the network (the third of the subproblems). This problem is a generalised set covering problem with side constraints and is further complicated by the introduction of fixed and variable welding costs. The DP formulation selects covers along each arc (in the network), but cannot exactly model the fixed costs and so does not guarantee optimality. We also describe an integer programming (IP) formulation for assessing the quality of the DP solutions. The cost of the networks constructed by the IP model is less than those designed using the DP model, but the saving is not significant for the problems examined (less than 0.1%). This indicates that the DP model will generally give very good solutions. Furthermore, as the problem dimensions grow, DP gives significantly better solution times than IP.     

Life is pleasant--and memory helps to keep it that way!

People's recollections of the past are often positively biased. This bias has 2 causes. The 1st cause lies in people's perceptions of events. The authors review the results of several studies and present several new comparative analyses of these studies, all of which indicate that people perceive events in their lives to more often be pleasant than unpleasant. A 2nd cause is the fading affect bias: The affect associated with unpleasant events fades faster than the affect associated with pleasant events. The authors review the results of several studies documenting this bias and present evidence indicating that dysphoria (mild depression) disrupts such bias. Taken together, this evidence suggests that autobiographical memory represents an important exception to the theoretical claim that bad is stronger than good. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Formation of a geometric pattern with a mobile wireless sensor network

Mobile wireless sensor networks (MWSNs) will enable information systems to gather detailed information about the environment on an unprecedented scale. These self‐organizing, distributed networks of sensors, processors, and actuators that are capable of movement have a broad range of potential applications, including military reconnaissance, surveillance, planetary exploration, and geophysical mapping. In many of the foreseen applications, the MWSN will need to form a geometric pattern without assistance from the user. In military reconnaissance, for example, the nodes will be dropped onto the battlefield from a plane and land at random positions. The nodes will be expected to arrange themselves into a predetermined formation in order to perform a specific task. Thus, we present algorithms for forming a line, circle, and regular polygon from a given set of random positions. The algorithms are distributed and use no communication between the nodes to minimize energy consumption. Unlike past studies of geometric problems where algorithms are either tested in simulations where each node has global knowledge of all the other nodes or implemented on a small number of robots, the robustness of our algorithms has been studied with simulations that model the sensor system in detail. The simulations demonstrate that the algorithms are robust against random errors in the sensors and actuators. © 2004 Wiley Periodicals, Inc.     

Large deflection dynamics and control for planar continuum robots

This paper focuses on a class of robot manipulators termed "continuum" robots - robots that exhibit behavior similar to tentacles, trunks, and snakes. In previous work, we studied details of the mechanical design, kinematics, path-planning and small-deflection dynamics for continuum robots such as the Clemson "tentacle manipulator". In this paper, we discuss the dynamics of a planar continuum backbone section, incorporating a large-deflection dynamic model. Based on these dynamics, we formulate a vibration-damping setpoint controller, and include experimental results to illustrate the efficacy of the proposed controller.     

Situating natural language understanding within experience-based design

Building useful systems with an ability to understand "real" natural language input has long been an elusive goal for Artificial Intelligence. Well-known problems such as ambiguity, indirectness, and incompleteness of natural language inputs have thwarted efforts to build natural language interfaces to intelligent systems. In this article, we report on our work on a model of understanding natural language design specifications of physical devices such as simple electrical circuits. Our system, called KA, solves the classical problems of ambiguity, incompleteness and indirectness by exploiting the knowledge and problem-solving processes in the situation of designing simple physical devices. In addition, KA acquires its knowledge structures (apart from a basic ontology of devices) from the results of its problem-solving processes. Thus, KA can be bootstrapped to understand design specifications and user feedback about new devices using the knowledge structures it acquired from similar devices designed previously.In this paper, we report on three investigations in the KA project. Our first investigation demonstrates that KA can resolve ambiguities in design specifications as well as infer unarticulated requirements using the ontology, the knowledge structures, and the problem-solving processes provided by its design situation. The second investigation shows that KA's problem-solving capabilities help ascertain the relevance of indirect design specifications, and identify unspecified relations between detailed requirements. The third investigation demonstrates the extensibility of KA's theory of natural language understanding by showing that KA can interpret user feedback as well as design requirements. Our results demonstrate that situating language understanding in problem solving, such as device design in KA, provides effective solutions to unresolved problems in natural language processing.     

K-winner networks.

A special class of mutually inhibitory networks is analyzed, and parameters for reliable K-winner performance are presented. The network dynamics are modeled using interactive activation, and results are compared with the sigmoid model. For equal external inputs, network parameters that select the units with the larger initial activations (the network converges to the nearest stable state) are derived. Conversely, for equal initial activations, networks that select the units with larger external inputs (the network converges to the lowest energy stable state) are derived. When initial activations are mixed with external inputs, anomalous behavior results. These discrepancies are analyzed with several examples. Restrictions on initial states are derived which ensure accurate K-winner performance when unequal external inputs are used.     

Towards deterministic downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency

A deterministic approach for downscaling ～ 40 km resolution Soil Moisture and Ocean Salinity (SMOS) observations is developed from 1 km resolution MODerate resolution Imaging Spectroradiometer (MODIS) data. To account for the lower soil moisture sensitivity of MODIS surface temperature compared to that of L-band brightness temperature, the disaggregation scale is fixed to 10 times the spatial resolution of MODIS thermal data (10 km). Four different analytic downscaling relationships are derived from MODIS and physically-based model predictions of soil evaporative efficiency. The four downscaling algorithms differ with regards to i) the assumed relationship (linear or nonlinear) between soil evaporative efficiency and near-surface soil moisture, and ii) the scale at which soil parameters are available (40 km or 10 km). The 1 km resolution airborne L-band brightness temperature from the National Airborne Field Experiment 2006 (NAFE'06) are used to generate a time series of eleven clear sky 40 km by 60 km near-surface soil moisture observations to represent SMOS pixels across the three-week experiment. The overall root mean square difference between downscaled and observed soil moisture varies between 1.4% v/v and 1.8% v/v depending on the downscaling algorithm used, with soil moisture values ranging from 0 to 15% v/v. The accuracy and robustness of the downscaling algorithms are discussed in terms of their assumptions and applicability to SMOS.