A piecewise linear model for trade sign inference

We use transaction level data for twelve stocks with large market capitalization on the Australian Stock Exchange to develop an empirical model for trade sign (trade initiator) inference. The new model is a piecewise linear parameterization of the model proposed recently in Ref. [1]. The space of the predictor variables is partitioned into six regions. Signs of individual trades within the regions are inferred according to simple and interpretable rules. Across the 12 stocks the new model achieves an average out-of-sample classification accuracy of 74.38% (SD=4.25%), which is 2.98% above the corresponding accuracy reported in Ref. [1]. Two of the model's regions, together accounting for 16.79% of the total number of daily trades, have each an average classification accuracy exceeding 91.50%. The results indicate a strong dependence between the predictor variables and the trade sign, and provide evidence for an endogenous component in the order flow. An interpretation of the trade sign classification accuracy within the model's regions offers new insights into a relationship between two regularities observed in the markets with a limit order book, competition for order execution and transaction cost minimization.Order submission, Trade classification, Piecewise linear, Competition for order execution, Transaction cost minimization

A local non-parametric model for trade sign inference

We investigate a regularity in market order submission strategies for twelve stocks with large market capitalization on the Australian Stock Exchange. The regularity is evidenced by a predictable relationship between the trade sign (trade initiator), size of the trade, and the contents of the limit order book before the trade. We demonstrate this predictability by developing an empirical inference model to classify trades into buyer-initiated and seller-initiated. The model employs a local non-parametric method, k-nearest-neighbor, which in the past was used successfully for chaotic time series prediction. The k-nearest- neighbor with three predictor variables achieves an average out-of- sample classification accuracy of 71.40%, compared to 63.32% for the linear logistic regression with seven predictor variables. The result suggests that a non-linear approach may produce a more parsimonious trade sign inference model with a higher out-of-sample classification accuracy. Furthermore, for most of our stocks the observed regularity in market order submissions seems to have a memory of at least 30 trading days.Order submission, Trade classification, K-nearest-neighbor, Non-linear, Memory

Corporate Cultures in Global Interaction: A Management Guide

Over the past few years, “globalization” has become a catchword. It reflects a widening horizon, particularly in the business world. Transactions are taking on international dimensions, the networks of relationships in business, politics and society are growing ever more complex. The problem that emerges is this: heterogeneity leads to complexity, and complexity leads to heterogeneity

Bike share in Greater Manchester

Active travel offers cities opportunities to address vital challenges such as health, air quality and congestion. Bike share is increasingly evident in cities across the globe, whether in the more conventional docked form found in, for example, London and Paris or the newer dockless technology facilitated through smartphone apps. Such systems offer people a bike to use without the hassle of ownership or storage and, if they have their own bike, access to a bike to make journeys when they do not have it with them. They therefore promise to make cycling an option for a wider population and for more journeys. They offer to complete the elusive ‘last mile’ that can make public transport difficult and to help to make cycling a visible and attractive option for many. This report provides new evidence of experiences and perceptions of bike share in Greater Manchester. It explores whether, to what extent and in what forms bike share can contribute to an overall increase in the number of people cycling, the number of journeys they make and the health and environmental benefits that follow

Dynamic stereo microscopy for studying particle sedimentation

We demonstrate a new method for measuring the sedimentation of a single colloidal bead by using a combination of optical tweezers and a stereo microscope based on a spatial light modulator. We use optical tweezers to raise a micron-sized silica bead to a fixed height and then release it to observe its 3D motion while it sediments under gravity. This experimental procedure provides two independent measurements of bead diameter and a measure of Faxén’s correction, where the motion changes due to presence of the boundary

Integration von Mitarbeitern als Konsumenten in Nachhaltigkeitsinnovationsprozesse : Erprobung eines neuen Forschungsansatzes im Rahmen eines BMBF-Verbundprojekts

Die Integration von Konsumentinnen und Konsumenten in Nachhaltigkeitsinnovationsprozesse hat das Potenzial, diese Innovationen erfolgreicher zu machen. Allerdings ist die Einbindung besonders innovativer Konsumenten, sogenannter Lead User, vielfach sehr aufwendig, wenn diese außerhalb der Unternehmen identifiziert und für die Mitarbeit gewonnen werden müssen. In diesem Beitrag wird mit der Integration von (nachhaltigkeitsorientierten) Mitarbeitern in ihrer Konsumentenrolle ein neuer Ansatz präsentiert. Dieser steht im Mittelpunkt des vom BMBF (Bundesministerium für Bildung und Forschung) finanzierten Verbundprojekts IMKoN (Integration von Mitarbeitern als Konsumenten in Nachhaltigkeitsinnovationsprozesse). Als erstes Teilergebnis werden hier Chancen und Risiken des IMKoN-Ansatzes am Beispiel einer Bestandsaufnahme bei acht Unternehmen unterschiedlicher Größe aus verschiedenen Branchen diskutiert.BMBF, 01UT1423A, Integration von Mitarbeitern als Konsumenten in Nachhaltigkeitsinnovationsprozesse, Teilprojekt 1: Open Innovation, Soziale Innovationen, Koordination (IMKoN

The LGBT Special Emphasis Program of the US Department of Agriculture’s Natural Resources Conservation Service

The Natural Resource Conservation Service (NRCS) is a branch of the US Department of Agriculture (USDA). The NRCS assists farmers and forest landowners to protect the soil, water, and other natural resources on their land. Located in almost every county within the United States, the NRCS employs almost 12,000 people. The NRCS is an equal opportunity employer and provider. Within the NRCS and other agencies of the USDA, Special Emphasis Programs (SEPs) have become established as a component of the Equal Employment Opportunity Program. SEPs were set up to address the unique concerns of members of certain groups in achieving diversity, inclusion, and equal opportunity. An LGBT SEP has been established to address the concerns of LGBT employees within the NRCS, as well as the concerns of LGBT individuals applying for NRCS programs. The specific purpose of the LGBT program is to provide LGBT awareness and education to NRCS employees and partners while focusing on such issues as employment, retention, promotion, training, career development, and advancement opportunities affecting NRCS employees and program applicants. Some goals of the program are to ensure LGBT individuals receive equal treatment in all aspects of employment, to encourage the participation of LGBT populations in all NRCS programs, and to educate all NRCS employees by raising the level of awareness of LGBT workplace issues and concerns

Computational Models for Stock Market Order Submissions

The motivation for the research presented in this thesis stems from the recent availability of high frequency limit order book data, relative scarcity of studies employing such data, economic significance of transaction costs management, and a perceived potential of data mining for uncovering patterns and relationships not identified by the traditional top-down modelling approach. We analyse and build computational models for order submissions on the Australian Stock Exchange, an order-driven market with a public electronic limit order book. The focus of the thesis is on the trade implementation problem faced by a trader who wants to transact a buy or sell order of a certain size. We use two approaches to build our models, top-down and bottom-up. The traditional, top-down approach is applied to develop an optimal order submission plan for an order which is too large to be traded immediately without a prohibitive price impact. We present an optimisation framework and some solutions for non-stationary and non-linear price impact and price impact risk. We find that our proposed transaction costs model produces fairly good forecasts of the variance of the execution shortfall. The second, bottom-up, or data mining, approach is employed for trade sign inference, where trade sign is defined as the side which initiates both a trade and the market order that triggered the trade. We are interested in an endogenous component of the order flow, as evidenced by the predictable relationship between trade sign and the variables used to infer it. We want to discover the rules which govern the trade sign, and establish a connection between them and two empirically observed regularities in market order submissions, competition for order execution and transaction cost minimisation. To achieve the above aims we first use exploratory analysis of trade and limit order book data. In particular, we conduct unsupervised clustering with the self-organising map technique. The visualisation of the transformed data reveals that buyer-initiated and seller-initiated trades form two distinct clusters. We then propose a local non-parametric trade sign inference model based on the k-nearest-neighbour classifier. The best k-nearest-neighbour classifier constructed by us requires only three predictor variables and achieves an average out-of-sample accuracy of 71.40% (SD=4.01%)1, across all of the tested stocks. The best set of predictor variables found for the non-parametric model is subsequently used to develop a piecewise linear trade sign model. That model proves superior to the k-nearest-neighbour classifier, and achieves an average out-of-sample classification accuracy of 74.38% (SD=4.25%). The result is statistically significant, after adjusting for multiple comparisons. The overall classification performance of the piecewise linear model indicates a strong dependence between trade sign and the three predictor variables, and provides evidence for the endogenous component in the order flow. Moreover, the rules for trade sign classification derived from the structure of the piecewise linear model reflect the two regularities observed in market order submissions, competition for order execution and transaction cost minimisation, and offer new insights into the relationship between them. The obtained results confirm the applicability and relevance of data mining for the analysis and modelling of stock market order submissions

Optimization of Indium Bump Morphology for Improved Flip Chip Devices

Flip-chip hybridization, also known as bump bonding, is a packaging technique for microelectronic devices that directly connects an active element or detector to a substrate readout face-to-face, eliminating the need for wire bonding. In order to make conductive links between the two parts, a solder material is used between the bond pads on each side. Solder bumps, composed of indium metal, are typically deposited by thermal evaporation onto the active regions of the device and substrate. While indium bump technology has been a part of the electronic interconnect process field for many years and has been extensively employed in the infrared imager industry, obtaining a reliable, high-yield process for high-density patterns of bumps can be quite difficult. Under the right conditions, a moderate hydrogen plasma exposure can raise the temperature of the indium bump to the point where it can flow. This flow can result in a desirable shape where indium will efficiently wet the metal contact pad to provide good electrical contact to the underlying readout or imager circuit. However, it is extremely important to carefully control this process as the intensity of the hydrogen plasma treatment dramatically affects the indium bump morphology. To ensure the fine-tuning of this reflow process, it is necessary to have realtime feedback on the status of the bumps. With an appropriately placed viewport in a plasma chamber, one can image a small field (a square of approximately 5 millimeters on each side) of the bumps (10-20 microns in size) during the hydrogen plasma reflow process. By monitoring the shape of the bumps in real time using a video camera mounted to a telescoping 12 magnifying zoom lens and associated optical elements, an engineer can precisely determine when the reflow of the bumps has occurred, and can shut off the plasma before evaporation or de-wetting takes place

Analysis of Adolescent Malnutrition on Oral Health: A Systematic Review

This review aimed to explore the relationship between malnutrition and oral health in adolescents. A literature search in PubMed, Dentistry & Oral Sciences Source, Scopus, and Web of Science was conducted on September 13th, 2023. Peer-reviewed articles written in English and published from 2013 containing information on the negative impact of adolescent malnutrition on oral health were considered eligible for review. From the 594 studies obtained from the literature search, 88 studies were included