High-level Decision Research Articles

Exploring new trends of sport business: Japanese companies’ investment in ownership of foreign football clubs

ABSTRACTThis study seeks to understand the reasons for Japanese companies acquiring foreign football clubs and the perceived returns on this investment. There has been, in the last decade, a process of ‘Asianisation’ of football. Japan, one of the most mature football markets in Asia, has been a big contributor to this process. Asian investment in foreign football club ownership has been increasing, however there seems to be a gap in the knowledge regarding Japanese investment. Put simply, the reason for running a conventional business is to make a profit. However, according to the literature, acquiring a football club is likely to produce limited return. Owning football clubs can, for instance, be a way of expanding a brand and gaining brand value and recognition; creating a bridge to connect to overseas markets; giving the owner notoriety and celebrity-status; attending to political, business or personal interests. The data for this research was obtained by document analysis and semi-structured interviews, involving five high-level decision-makers in five companies which own foreign football clubs. This study finds the reasons for Japanese investment were: nurturing players to sell at a profit, developing Japanese football, developing local football and community, internationalising a brand, personal ambitions and personal and business connections. In addition, all except one company had low or no returns.

China\u2019s practice to prevent and control COVID-19 in the context of large population movement

BackgroundThe emerging infectious disease, coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), poses a serious threat in China and worldwide. Challenged by this serious situation, China has taken many measures to contain its transmission. This study aims to systematically review and record these special and effective practices, in hope of benefiting for fighting against the ongoing worldwide pandemic.MethodsThe measures taken by the governments was tracked and sorted on a daily basis from the websites of governmental authorities (e.g. National Health Commission of the People’s Republic of China). And the measures were reviewed and summarized by categorizations, figures and tables, showing an ever-changing process of combating with an emerging infectious disease. The population shift levels, daily local new diagnosed cases, daily mortality and daily local new cured cases were used for measuring the effect of the measures.ResultsThe practices could be categorized into active case surveillance, rapid case diagnosis and management, strict follow-up and quarantine of persons with close contacts, and issuance of guidance to help the public understand and adhere to control measures, plus prompt and effective high-level policy decision, complete activation of the public health system, and full involvement of the society. Along with the measures, the population shift levels, daily local new diagnosed cases, and mortality were decreased, and the daily local new cured cases were increased in China.ConclusionsChina’s practices are effective in controlling transmission of SARS-CoV-2. Considering newly occurred situations (e.g. imported cases, work resumption), the control measures may be adjusted.

Volume IV. The DUNE far detector single-phase technology

The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay—these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. DUNE is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. Central to achieving DUNE's physics program is a far detector that combines the many tens-of-kiloton fiducial mass necessary for rare event searches with sub-centimeter spatial resolution in its ability to image those events, allowing identification of the physics signatures among the numerous backgrounds. In the single-phase liquid argon time-projection chamber (LArTPC) technology, ionization charges drift horizontally in the liquid argon under the influence of an electric field towards a vertical anode, where they are read out with fine granularity. A photon detection system supplements the TPC, directly enhancing physics capabilities for all three DUNE physics drivers and opening up prospects for further physics explorations. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. Volume IV presents an overview of the basic operating principles of a single-phase LArTPC, followed by a description of the DUNE implementation. Each of the subsystems is described in detail, connecting the high-level design requirements and decisions to the overriding physics goals of DUNE.

General-purpose hierarchical optimisation of machine learning pipelines with grammatical evolution

This paper introduces Hierarchical Machine Learning Optimisation (HML-Opt), an AutoML framework that is based on probabilistic grammatical evolution. HML-Opt has been designed to provide a flexible framework where a researcher can define the space of possible pipelines to solve a specific machine learning problem, which can range from high-level decisions about representation and features to low-level hyper-parameter values. The evaluation of HML-Opt is presented via two different case studies, both of which demonstrate that it is competitive with existing AutoML tools on a variety of benchmarks. Furthermore, HML-Opt can be applied to novel problems, such as knowledge extraction from natural language text, whereas other techniques are insufficiently flexible to capture the complexity of these scenarios. The source code for HML-Opt is available online for the research community.

Toward Campus Mail Delivery Using BDI

Autonomous systems developed with the Belief-Desire-Intention (BDI) architecture are usually mostly implemented in simulated environments. In this project we sought to build a BDI agent for use in the real world for campus mail delivery in the tunnel system at Carleton University. Ideally, the robot should receive a delivery order via a mobile application, pick up the mail at a station, navigate the tunnels to the destination station, and notify the recipient. We linked the Robot Operating System (ROS) with a BDI reasoning system to achieve a subset of the required use cases. ROS handles the low-level sensing and actuation, while the BDI reasoning system handles the high-level reasoning and decision making. Sensory data is orchestrated and sent from ROS to the reasoning system as perceptions. These perceptions are then deliberated upon, and an action string is sent back to ROS for interpretation and driving of the necessary actuator for the action to be performed. In this paper we present our current implementation, which closes the loop on the hardware-software integration, and implements a subset of the use cases required for the full system.

ARCog: An Aerial Robotics Cognitive Architecture

SUMMARYEfficient algorithm integration is a key issue in aerial robotics. However, only a few integration solutions rely on a cognitive approach. Cognitive approaches break down complex problems into independent units that may deal with progressively lower-level data interfaces, all the way down to sensors and actuators. A cognitive architecture defines information flow among units to produce emergent intelligent behavior. Despite the improvements in autonomous decision-making, several key issues remain open. One of these issues is the selection, coordination, and decision-making related to the several specialized tasks required for fulfilling mission objectives. This work addresses decision-making for the cognitive unmanned-aerial-vehicle architecture coined as ARCog. The proposed architecture lays the groundwork for the development of a software platform aligned with the requirements of the state-of-the-art technology in the field. The system is designed to provide high-level decision-making. Experiments prove that ARCog works correctly in its target scenario.

Calibrated Low-Order Transient Thermal and Flow Models for Robust Test Facility Design

This paper describes an upgrade to high temperature operation of the Engine Component AeroThermal (ECAT) facility, an established engine-parts facility at the University of Oxford. The facility is used for high-TRL research and development, new technology demonstration, and for component validation (typically large civil-engine HP NGVs). In current operation the facility allows Reynolds number, Mach number, and coolant-to-mainstream pressure ratio to be matched to engine conditions. Rich-burn or lean-burn temperature, swirl and turbulence profiles can also be simulated. The upgrade will increase the maximum inlet temperature to 600 K, allowing coolant-to-mainstream temperature ratio to be matched to engine conditions. This will allow direct validation of temperature ratio scaling methods in addition to providing a test bed in which all important non-dimensional parameters for aero-thermal behaviour are exactly matched. To accurately predict the operating conditions of the upgraded facility, a low order transient thermal model was developed in which the air delivery system and working section are modelled as a series of distributed thermal masses. Nusselt number correlations were used to calculate convective heat transfer to and from the fluid in the pipes and working section. The correlation was tuned and validated with experimental results taken from tests conducted in the existing facility. This modelling exercise informed a number of high-level facility design decisions, and provides an accurate estimate of the running conditions of the upgraded facility. We present detailed results from the low-order modelling, and discuss the key design decisions. We also present a discussion of challenges in the mechanical design of the working section, which is complicated by transient thermal stress induced in the working section components during facility start-up. The high-temperature core is unusually high-TRL for a research organisation, and we hope both the development and methodology will be of interest to engine designers and the research community.

A System-of-Systems Approach to the Strategic Feasibility of Modular Vehicle Fleets

The value proposition for ground vehicle modularity in the U.S. Army and other services has been a topic of continuing debate. Studies to date have largely focused on individual system elements such as manufacturing or maintenance, lacking a holistic perspective of the implications of modularity for the entire fleet operation and life-cycle. The U.S. Army Science and Technology community has demonstrated the technical feasibility of large-scale, transformative ground vehicle modularity, but the business case for modularity remains incomplete. There are multiple criteria tradeoffs between modular and mission-specific (conventional) vehicle platforms, such as total life-cycle cost, mission utility, personnel requirements, and fleet adaptability. This paper presents a system-of-systems framework to address these tradeoffs to support high-level decisions on the strategic feasibility of ground vehicle modularity. We demonstrate this framework with a notional example and an application to the Joint Tactical Transport System (JTTS), a U.S. Army Tank Automotive Research, Development and Engineering Center demonstrator program. Under certain modeling assumptions with regards to the operation of a modular fleet, results for the JTTS study indicate that modularity can lead to significant cost savings at the expense of increased personnel requirements.

Hızlı Büyüyen KOBİ lerde İç Girişimcilik: Ankara İlinde Bir Araştırma (Internal Entrepreneurship in Fast Growing SME s: A Research in Ankara Province)

Purpose – Since today's economy is global, dynamic and competitive, the survival and growth of firms are considered as necessities. Growth requires many middle and high level decisions for SMEs. Growth in SMEs is a phenomenon that covers all middle and senior managers. Entrepreneurs in SMEs; it is a decision mechanism that directly affects growth without realizing it or being aware of it. It is believed that the level of internal entrepreneurship in SMEs directly affects the decision making process of the firm and enables rapid growth. From this point of view, internal entrepreneurship will be able to provide support to government agencies and financial institutions in the development of policies that encourage firm growth, particularly in the development of SME growth strategies. With this in mind, the aim of this study is to determine the differences between the level of internal entrepreneurship of medium and high level officials of fast-growing SMEs and non-fast-growing SMEs. Design/methodology/approach – For the purpose of the study, a survey was conducted in Ankara with KOSGEB registered SMEs. Internal entrepreneurship levels were determined by examining the rapid growth of 429 firms. Internal entrepreneurship levels of fast-growing firms and non-fast-growing firms were compared. The t-test was used for analysis. Findings – As a result of the analysis, it is seen that the level of innovation and proactive behavior of fast growing SMEs is higher than those that do not grow fast. Discussion – Within the scope of the research, innovation and proactive behavior development in SMEs will support rapid growth. This will provide an effective increase in employment and an advantage in global competition.

A Connected Autonomous Vehicle Testbed: Capabilities, Experimental Processes and Lessons Learned

VENTURER was one of the first three UK government funded research and innovation projects on Connected Autonomous Vehicles (CAVs) and was conducted predominantly in the South West region of the country. A series of increasingly complex scenarios conducted in an urban setting were used to: (i) evaluate the technology created as a part of the project; (ii) systematically assess participant responses to CAVs and; (iii) inform the development of potential insurance models and legal frameworks. Developing this understanding contributed key steps towards facilitating the deployment of CAVs on UK roads. This paper aims to describe the VENTURER Project trials, their objectives and detail some of the key technologies used. Importantly we aim to introduce some informative challenges that were overcame and the subsequent project and technological lessons learned in a hope to help others plan and execute future CAV research. The project successfully integrated several technologies crucial to CAV development. These included, a Decision Making System using behaviour trees to make high level decisions; A pilot-control system to smoothly and comfortably turn plans into throttle and steering actuation; Sensing and perception systems to make sense of raw sensor data; Inter-CAV Wireless communication capable of demonstrating vehicle-to-vehicle communication of potential hazards. The closely coupled technology integration, testing and participant-focused trial schedule led to a greatly improved understanding of the engineering and societal barriers that CAV development faces. From a behavioural standpoint the importance of reliability and repeatability far outweighs a need for novel trajectories, while the sensor-to-perception capabilities are critical, the process of verification and validation is extremely time consuming. Additionally, the added capabilities that can be leveraged from inter-CAV communications shows the potential for improved road safety that could result. Importantly, to effectively conduct human factors experiments in the CAV sector under consistent and repeatable conditions, one needs to define a scripted and stable set of scenarios that uses reliable equipment and a controllable environmental setting. This requirement can often be at odds with making significant technology developments, and if both are part of a project’s goals then they may need to be separated from each other.

EVER-EST: The Platform Allowing Scientists to Cross-Fertilize and Cross-Validate Data

Over recent decades large amounts of data about our Planet have become available. If this information could be easily discoverable, accessible and properly exploited, preserved and shared, it would potentially represent a wealth of information for a whole spectrum of stakeholders: from scientists and researchers to the highest level of decision and policy makers. By creating a Virtual Research Environment (VRE) using a service oriented architecture (SOA) tailored to the needs of Earth Science (ES) communities, the EVER-EST ( http://ever-est.eu ) project provides a range of both generic and domain specific data analysis and management services to support a dynamic approach to collaborative research. EVER-EST provides the means to overcome existing barriers to sharing of Earth Science data and information allowing research teams to discover, access, share and process heterogeneous data, algorithms, results and experiences within and across their communities, including those domains beyond Earth Science. The main objective of this paper is to present the EVER-EST platform in all its components describing the most relevant use cases implemented by the Virtual Research Communities (VRCs) involved in the project.

A cognitive decomposition to empirically study human performance in control room environments

Abstract Monitoring tasks in control room environments require operators to perform various mental and physical sub-tasks in series and simultaneously over long periods of time with minimal error. These tasks vary in cognitive complexity, ranging from low-level sensory processing to high-level decision making. Cognitive load, a measure of the effort required by the working memory, can serve as an indicator of tasks that may have higher risk of error. Task decomposition models for cognitive complexity can be combined with objective and subjective measures of workload to measure human performance in response to control room stimuli. In this study, we demonstrate the effectiveness of a cognitive task analysis approach to structure the design of experiments for the purpose of evaluating human performance in control room simulated use activities. Participants completed monitoring tasks in a simulated unmanned aerial vehicle (UAV) control room that required the completion of tasks ranging in cognitive complexity. Performance measures taken during the study were used to validate the breakdown of tasks complexity, and to identify potential sources of human error in workstation monitoring tasks. These findings can be linked to design specifications for workstation optimization. Results indicated that the task breakdown appropriately represented the use-case scenario, and the classification model adequately captured differences in cognitive workload experienced by participants. This research has broad implications on complex system design validation, providing a structure to achieve cognitive depth for the evaluation of human performance and subsequent design risk mitigation.

A three-dimensional mapping and virtual reality-based human–robot interaction for collaborative space exploration

Human–robot interaction is a vital part of human–robot collaborative space exploration, which bridges the high-level decision and path planning intelligence of human and the accurate sensing and modelling ability of the robot. However, most conventional human–robot interaction approaches rely on video streams for the operator to understand the robot’s surrounding, which lacks situational awareness and force the operator to be stressed and fatigued. This research aims to improve efficiency and promote the natural level of interaction for human–robot collaboration. We present a human–robot interaction method based on real-time mapping and online virtual reality visualization, which is implemented and verified for rescue robotics. At the robot side, a dense point cloud map is built in real-time by LiDAR-IMU tightly fusion; the resulting map is further transformed into three-dimensional normal distributions transform representation. Wireless communication is employed to transmit the three-dimensional normal distributions transform map to the remote control station in an incremental manner. At the remote control station, the received map is rendered in virtual reality using parameterized ellipsoid cells. The operator controls the robot with three modes. In complex areas, the operator can use interactive devices to give low-level motion commands. In the less unstructured region, the operator can specify a path or even a target point. Afterwards, the robot follows the path or navigates to the target point autonomously. In other words, these two modes rely more on the robot’s autonomy. By virtue of virtual reality visualization, the operator can have a more comprehensive understanding of the space to be explored. In this case, the high-level decision and path planning intelligence of human and the accurate sensing and modelling ability of the robot can be well integrated as a whole. Although the method is proposed for rescue robots, it can also be used in other out-of-sight teleoperation-based human–robot collaboration systems, including but not limited to manufacturing, space, undersea, surgery, agriculture and military operations.

On the role of route choice modeling in transit sketchy design

• The impact of route choice modeling on transit system performances is examined. • Optimal user costs are affected by route choice modeling, but agency costs are not. • The system design is often insensitive to route choice modeling. • Details of route choice are generally negligible, yet notable exceptions exist. Sketchy design models are used to examine the fundamental tradeoff in transit systems and to guide high-level decisions. Traditionally, passengers’ route choice is greatly simplified in such models. This study aims to understand whether this simplification would compromise qualitatively the results expected from the sketchy models. To this end, three transit systems, which all offer competitive alternative routes, are analyzed using the continuous approximation approach. We test what the impact on transit system performance (e.g. optimal designs and system costs) would be if travelers somehow split between these routes, rather than concentrate on the “best” one. A random utility model is employed to enable a probabilistic assignment of passengers to different routes according to the “perceived” utility. Analytical methods are then developed to estimate the aggregate share of each route in each system, based on which the user cost is obtained. Numerical results show that, while stochastic route choice modestly increases the optimal user cost, it has a negligible effect on the agency cost. Furthermore, the actual system design is largely insensitive to route choice modeling. Thus, while the simplest deterministic route choice assumption may not be valid in all systems, transit planners can safely ignore route choice details in most cases, at least for the purpose of strategic planning.

CAREER DECISION-MAKING ATTRIBUTION AND SELF EFFICACY: THE MODERATING ROLE OF EMOTIONAL INTELLIGENCE

It is known that gifted high-school students tend to have difficulty in choosing career due to their wide-range interests and capabilities. In order to successfully making an appropriate career choice, having a high level of career decision making self-efficacy (CDMSE) is an important precondition. CDMSE is the belief in one’s ability to successfully complete the task necessary to make career-related decisions. Of several factors known to be affecting CDMSE, previous study has shown that career decision-making attribution (CDMA) could affect students CDMSE. However, the CDMA effect on CDMSE in gifted student is seemed to be related to personal trait of students, namely emotional intelligence (EI). Therefore, this study aims to examine the moderating role of EI on the impact of gifted high-school student CDMA on CDMSE. Subjects in this study were gifted high school students (N = 165; 52.12% males; M-age = 16.20 years old) in Jakarta. The measurement tools used are CDMSE Scale Short-Form, Assessment of Attribution for Career Decision Making, and Trait Emotional Intelligence Questionnaire Short-Form (TEIQue-SF) adapted into Indonesian language. The data were analyzed using simple regression analysis with Hayes PROCESS model. It was found that there is a direct effect of CDMA on CDMSE with (F (1,163) = 10.6661, p = .0033 < .001), in which CDMA serves as a predictor of CDMSE. We also found that EI can serve as a predictor of CDMSE (F (1,163) = 10.6661, p = .0007<.001. However, EI did not moderate the CDMA-CDMSE relation. Discussion, limitations, and suggestions for further research are listed.

A practical method of accurate troubleshooting mechanism in the decision objective teaching type on the integrated landing system

An aircraft is a transportation system that works in conditions according to the standards. When an aircraft technician is aware of a system failure or damage, he must make the right decision to overcome the problems that occur. A relatively high level of accuracy decision making based is a necessary treat. This condition must be anticipated in aircraft vocational education. An integrated landing system is one of the subsystems on an aircraft that requires quick and precise handling if there are problems. A practical method that accommodates various concepts in troubleshooting is proposed to facilitate aircraft technicians’ competence. This method performs standardization of troubleshooting steps in a particular algorithm so that it is more systematic. The appropriate validation and verification steps follow this troubleshooting mechanism to ensure that the system is up to standard.

INNOVATIONS IN IRRIGATION SYSTEMS IN AFRICA†

INNOVATIONS IN IRRIGATION SYSTEMS IN AFRICA^†

The Adoption of Innovations Zero Burning Land Cultivation by The Community in Rokan Hilir Regency

This research aims to know what the extent to which the community adopted the innovation of processing land without burning and what stages of cultivation without burning by people of Rokan Hilir Riau Province. The research method used was survey method and in-depth analysis using questionnaires with primary and secondary data. The respondents in this research as many as 15 people consisting of a society that has already adopted the innovation of processing land without burning and the Government as well as Society of Fire Care (SFC) in the village of Rantau Bais Sub-district of Tanah Putih and Labuhan Tangga Kecil village Sub-district Of Bangko Regency of Rokan Hilir. The determination of community respondents used snowball sampling and purposive sampling for institutional respondents. The analysis of the data used for the purposes of the adoption process innovation to cultivate land without burning, namely Scales Likerts analysis and descriptive analysis is used to find out land processing system without burning have done by the community. Research results showed that land without burning processing system which conducted the community consist of several stages in the form of clearing or logging and spraying, and buildup and allowed to decay naturally. The rate of adoption of innovation of processing land without burning in the Rokan Hilir has thehigh categorywith value score 3.52. The high adoption process innovation of processing land without burning is due to the high level of knowledge, decision and implementation that also have been good. The decision to adopt was high because the society doing processing in terms of plant maintaining plants and plantations.

An Ant Colony Optimization approach for symbolic regression using Straight Line Programs. Application to energy consumption modelling

The increase of energy consumption and their direct effects on pollution and global warming have motivated governments to develop new strategies to promote a better usage of energy. One of the most important aspects related to energy efficiency is the need for a suitable model of energy consumption that can be used to make predictions or to aid experts in high level decision making processes. Symbolic regression techniques can be used to discover an energy consumption model that fits these purposes. Traditionally, the problem of symbolic regression has been solved by using genetic programming approaches to find the algebraic expression that best fits the regression problem data, where each expression is encoded as a tree structure. In previous works, we found that a different approach using Straight Line Programs as a representation technique could provide promising results for symbolic regression, although the size of the resulting algebraic expression might be increased when compared to the traditional approach. This work proposes an Ant Colony Optimization algorithm for Straight Line Programs to solve the problem, and makes a study to compare the approach with traditional genetic programming in a real energy consumption modelling problem.

Ready or Not, Patients Will Present: Improving Urban Pandemic Preparedness.

Over the past century, society has achieved great gains in medicine, public health, and health-care infrastructure, particularly in the areas of vaccines, antibiotics, sanitation, intensive care and medical technology. Still, despite these developments, infectious diseases are emerging at unprecedented rates around the globe. Large urban centers are particularly vulnerable to communicable disease events, and must have well-prepared response systems, including on the front-line level. In November 2018, the United States' largest municipal health-care delivery system, New York City Health + Hospitals, hosted a half-day executive-level pandemic response workshop, which sought to illustrate the complexity of preparing for, responding to, and recovering from modern-day infectious diseases impacting urban environments. Attendees were subjected to a condensed, plausible, pandemic influenza scenario and asked to simulate the high-level strategic decisions made by leaders by internal (eg, Chief Medical Officer, Chief Nursing Officer, and Legal Affairs) and external (eg, city, state, and federal public health and emergency management entities) partners across an integrated system of acute, postacute, and ambulatory sites, challenging players to question their assumptions about managing the consequences of a highly pathogenic pandemic.