Pure CuBi2O4 Photoelectrodes with Increased Stability by Rapid Thermal Processing of Bi2O3/CuO Grown by Pulsed Laser Deposition

A new method for enhancing the charge separation and photo‐electrochemical stability of CuBi₂O₄ photoelectrodes by sequentially depositing Bi₂O₃ and CuO layers on fluorine‐doped tin oxide substrates with pulsed laser deposition (PLD), followed by rapid thermal processing (RTP), resulting in phase‐pure, highly crystalline films after 10 min at 650 °C, is reported. Conventional furnace annealing of similar films for 72 h at 500 °C do not result in phase‐pure CuBi₂O₄. The combined PLD and RTP approach allow excellent control of the Bi:Cu stoichiometry and results in photoelectrodes with superior electronic properties compared to photoelectrodes fabricated through spray pyrolysis. The low photocurrents of the CuBi₂O₄ photocathodes fabricated through PLD/RTP in this study are primarily attributed to their low specific surface area, lack of CuO impurities, and limited, slow charge transport in the undoped films. Bare (without protection layers) CuBi₂O₄ photoelectrodes made with PLD/RTP shows a photocurrent decrease of only 26% after 5 h, which represents the highest stability reported to date for this material. The PLD/RTP fabrication approach offers new possibilities of fabricating complex metal oxides photoelectrodes with a high degree of crystallinity and good electronic properties at higher temperatures than the thermal stability of glass‐based transparent conductive substrates would allow.     

A multi-layer sampler for the study of detailed hydrochemical profiles in groundwater

A modular multi-layer sampler was developed and utilized for sampling undisturbed groundwater chemical profiles. Sampling at 3 cm depth intervals is based on the dialysis-cell method and has no depth limitations. The sampler may be used for water quality monitoring and research, both in existing or specially drilled screened wells     

Peak management

Peaks occur when a firm accustomed to operating under market constraint conditions occasionally encounters peaks in market demand that temporarily exceed the firm's supply capacity. This paper defines the demand peak, classifies peak management (PM) conditions and prescribes a framework for PM. The Theoryof-Constraints (TOC) is expanded to handle the PM concept, introducing the two-policy concept: one policy for peak periods and another for non-peak periods. The paper outlines techniques to elevate the firm's peak performance by loading during the peak period. Distinct pricing policies for peak and offpeak periods are presented and a comprehensive methodology is suggested to deal with the PM problem, including labour policy, outsourcing policy and pricing policy. The paper demonstrates the methodology and techniques in a broad range of real-life management situations. Under TOC principles, the present analysis shows the different decision rules for the two periods and their substantial impact on the firm's overall strategy. Finally, the paper discusses the ramifications of operating under two distinct sets of rules, as well as the rules for making the transitions between an off-peak and a peak period.     

Tracking Twin Boundary Jerky Motion at Nanometer and Microsecond Scales

The jerky motion of twin boundaries in the ferromagnetic shape memory alloy Ni-Mn-Ga is studied by simultaneous measurements of stress and magnetic emissions (ME). A careful design of the experimental conditions results in an approximately linear relationship between the measured ME voltage and the nm-scale volumes exhibiting twinning transformation during microsecond-scale abrupt “avalanche” events. This study shows that the same distributions of ME avalanches, related to features of jerky twin boundary motion, are found both during and between stress drop events. Maximum likelihood analysis of statistical distributions of several variables reveals a good fit to power laws truncated by exponential functions. Interestingly, the characteristic cutoffs described by the exponential functions are in the middle of the distribution range. Further, the cutoff values can be related to the physical characteristics of the studied problem. Particularly, the cutoff of amplitudes of ME avalanches matches the value predicted by high rate magnetic pulse tests performed under much larger driving force values. This observation implies that avalanches during slow rate twin boundary motion and velocity changes observed by high rate tests represent the same behavior and can be described by the same theory.     

Combinatorial Optimization and Performance Analysis of a Multi-arm Cartesian Robotic Fruit Harvester—Extensions of Graph Coloring

A mobile melon robotic harvester consisting of multiple Cartesian manipulators, each with three degrees of freedom, is being developed. In order to design an optimal robot in terms of number of arms, manipulator capabilities, and robot speed, a method of allocating the fruits to be picked by each manipulator in a way that yields the maximum harvest has been developed. Such a method has already been devised for a multi-arm robot with 2DOF each. The maximum robotic harvesting problem was shown there to be an example of the maximum k-colorable subgraph problem (MKCSP) on an interval graph. However, for manipulators with 3DOF, the additional longitudinal motion results in variable intervals. To overcome this issue, we devise a new model based on the color-dependent interval graph (CDIG). This enables the harvest by multiple robotic arms to be modeled as a modified version of the MKCSP. Based on previous research, we develop a greedy algorithm that solves the problem in polynomial time, and prove its optimality using induction. As with the multi-arm 2DOF robot, when simulated numerous times on a field of randomly distributed fruits, the algorithm yields a nearly identical percentage of fruit harvested for given robot parameters. The results of the probabilistic analysis developed for the 2DOF robot was modified to yield a formula for the expected harvest ratio of the 3DOF robot. The significance of this method is that it enables selecting the most efficient actuators, number of manipulators, and robot forward velocity for maximal robotic fruit harvest.     

Self‐Assembling,Bioinspired Wax Crystalline Surfaces with Time‐Dependent Wettability

One of the most fascinating properties of materials in nature is the superhydrophobic and self‐cleaning capabilities of different plant surfaces. This is usually achieved by the hydrophobic cuticles that are made of cutin and contain wax crystals both within them and on their surfaces. Here, bioinspired n‐hexatriacontane wax films are deposited via thermal evaporation and it is shown that the surface evolves in time via self‐assembly. This leads to a dramatic change in the wetting properties with a transition from hydrophobic to superhydrophobic characteristics, which takes place within several days at room temperature. This phenomenon is investigated and strain‐induced recrystallization is proposed to be the mechanism for it. This work could become the basis for the inspiration and production of tuned, time‐dependant, temperature‐sensitive, variable‐wettability surfaces.     

Sleeved co-clustering of lagged data

The paper focuses on mining clusters that are characterized by a lagged relationship between the data objects. We call such clusters lagged co-clusters. A lagged co-cluster of a matrix is a submatrix determined by a subset of rows and their corresponding lag over a subset of columns. Extracting such subsets may reveal an underlying governing regulatory mechanism. Such a regulatory mechanism is quite common in real-life settings. It appears in a variety of fields: meteorology, seismic activity, stock market behavior, neuronal brain activity, river flow, and navigation, but a limited list of examples. Mining such lagged co-clusters not only helps in understanding the relationship between objects in the domain, but assists in forecasting their future behavior. For most interesting variants of this problem, finding an optimal lagged co-cluster is NP-complete problem. We present a polynomial-time Monte-Carlo algorithm for mining lagged co-clusters. We prove that, with fixed probability, the algorithm mines a lagged co-cluster which encompasses the optimal lagged co-cluster by a maximum 2 ratio columns overhead and completely no rows overhead. Moreover, the algorithm handles noise, anti-correlations, missing values, and overlapping patterns. The algorithm is extensively evaluated using both artificial and real-world test environments. The first enable the evaluation of specific, isolated properties of the algorithm. The latter (river flow and topographic data) enable the evaluation of the algorithm to efficiently mine relevant and coherent lagged co-clusters in environments that are temporal, i.e., time reading data and non-temporal.     

From data to knowledge: The interaction between data management systems in educational institutions and the delivery of quality education

No school is an island; it is a part of a continuum or a pipeline of institutions which together form an educational pipeline through which groups of students pass. To turn a body of data into useful information for knowledge-based decision-making at any level, data must be collected, organised, analysed and reflected upon. The purpose of this paper is to discuss how schools and other educational institutions can not only collect better data but learn how to transform that data so that the information held within can be effectively shared among all stakeholders. This process will help to ensure that the school and the entire education system provide a more seamless and effective educational pipeline for students, and ultimately improve the quality of education delivered in the country as a whole.