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Mobile calibration for bus-based urban sensing
Get PDFIn bus-based sensing, public transport serves as a mobile urban sensing platform. While offering much higher geographical coverage, the low-cost sensors mounted on vehicles can be less accurate and demand more frequent calibration, which may be challenging for large vehicle fleets. As calibration is performed by relating mobile sensor readings to those of fixed reference stations, the placement of reference stations is very important. In this work, we propose an algorithm for computing the optimal locations for reference stations to maximize the sensing coverage. Contrary to prior work, coverage is defined in terms of geographical area, extending a certain distance away from the route trajectory, which represents the actual sensing capacity of the vehicles. The proposed algorithm computes it using geographical set operations, such as spatial join and subtraction to compute the unique contribution of each bus route. We evaluate the approach using real bus trajectories from Manhattan, USA, and compare it with a random baseline and prior work. The results indicate that given the bus routes, a complete sensing coverage can be achieved using a single reference station with a maximum 2-hop calibration path
Monte-Carlo study of some robust estimators: the simple linear regression case
No full textIn this study, Least Trimmed Squares (LTS), Theil’s Pair-wise Median (Theil) and Bayesian estimation methods (BAYES) are compared relative to the OLSE via Monte-Carlo Simulation. Variance, Bias, Mean Square Error (MSE) and Relative Mean Square Error (RMSE) were calculated to evaluate the estimators’ performance. The Simple Linear Regression model is explored for the conditions in which the error term is assumed to be drawn from three error distributions: unit normal, lognormal and Cauchy. Theil’s non-parametric estimation procedure was found to have the strongest and most reliable performance. The subsequent-best results are acquired from LTS approach Though it was observed that the Bayesian estimators are affected by deviation of the dataset from normality, yet it is established from the results that the Bayesian estimators performed optimally more than all other competitors, even under non normal situations (especially under the standard lognormal distribution) in some cases, except whenever the error is drawn from a heavy tail distribution (Lognormal and Cauchy)..OLSE is most effective reliable as long as the normality assumptions preserv
MBE growth of Ga droplets with the size of wide distribution via an in-situ laser irradiation
No full textDroplet epitaxy is a very powerful technology for the fabrication of semiconductor quantum optoelectronic devices due to its offering all-preparation of quantum dots, nanowires and nanorings. As is known, the size control of the droplets is a key issue for the droplet epitaxy. Usually, the droplet size is adjusted by the growth temperature and growth rate based on the well-known MBE method, but it is still quite limited. In this work, we attempted to use an in situ laser irradiation to modify the Ga droplet size during the MBE growth. Two groups of samples were prepared: for all the samples, after the growth of a 300nm GaAs buffer layer on GaAs substrate, the total following deposition amount of Ga is 8ML with a single in-situ shot of laser irradiation. In contrast, for group A, the exposure was performed when the 8ML Ga deposition was completely finished, while for group B, the exposure was inserted in the middle of the deposition, i.e., when it reached 4ML. We carefully compared the effect of the insertion position of the exposure on the obtained droplet morphology with different pulse energies. The results show the droplet size can be strongly adjusted via the in-situ laser irradiation especially when it is inserted in the middle of the droplet growth. Finally, droplets have specific heights and widths with an extremely wide distribution ranging from 0.7nm to 97.3nm and 21.6nm to 396.5nm, respectively, are successfully realized
An analysis of digital strategy opportunities in a records management system
No full textDigital Technology has steered business development for years, with the World Wide Web and digital transforming both how humans and firms work and how they collaborate with firms and one another, thus making IT-business placement dominant to a firm’s success (Pratt, 2022). Digitalisation is in top effect; the dawn and abundant existence of Digital Technologies (DT) and the movement of technologically driven revolutions are holistically modifying organisations, the economy, and society (Stockinger 2020). The paper analyses the main challenge fostered by COVID-19 and how work-from-home measures were implemented. In addition, the investigative analysis revealed that the current manual records system at Anti-Allied Corporation leaned towards an insufficient system that is relied on for decision-making. Furthermore, this paper focuses on the strategic information system initiative that can be developed to improve records management and enhance productivity, performance, and business strategies
A blended graph-MCMC framework for carbon emission reduction in oil & gas supply chain
No full textAmidst growing global concerns about climate change and heightened environmental awareness, this scholarly paper introduces an innovative approach to addressing the imperative of carbon emissions reduction in the Oil & Gas sector. Leveraging the analytical power of Monte Carlo Markov Models (MCMMs), this study responds to the pressing need for emission mitigation strategies within an industry that significantly contributes to global carbon emissions. Recent empirical data underscores the urgency of this endeavour. In 2021, the Oil & Gas industry accounted for a substantial 45% of global energy-related emissions, emitting approximately 34 billion metric tons of CO2-equivalent. Projections paint a dire picture, indicating a potential 50% increase in emissions by 2050 without substantial intervention. To tackle this challenge, our research introduces a robust framework for modelling, simulating, and optimizing supply chain operations in the Oil & Gas sector. This framework encompasses dynamic variables encompassing exploration, extraction, refining, transportation, and distribution. Monte Carlo simulations yield probabilistic forecasts of carbon emissions, empowering decision-makers with critical information to make informed choices within the supply chain. A comprehensive case study demonstrates substantial reductions in emissions while preserving operational efficiency, highlighting the practical significance of emission reduction strategies in the Oil & Gas industry. This research underscores the urgent necessity of mitigating emissions within the sector, given its significant contribution to global carbon emissions, while also offering a promising path towards sustainability
Big Data innovation and implementation in projects teams: towards a SEM approach to conflict prevention
Get PDFPurpose: Despite an enormous body of literature on conflict management, intra-group conflicts vis-à-vis team performance, there is currently no study investigating the conflict prevention approach to handling innovation-induced conflicts that may hinder smooth implementation of big data technology in project teams. Design/methodology/approach: This study uses constructs from conflict theory, and team power relations to develop an explanatory framework. The study proceeded to formulate theoretical hypotheses from task-conflict, process-conflict, relationship and team power conflict. The hypotheses were tested using Partial Least Square Structural Equation Model (PLS-SEM) to understand key preventive measures that can encourage conflict prevention in project teams when implementing big data technology. Findings: Results from the structural model validated six out of seven theoretical hypotheses and identified Relationship Conflict Prevention as the most important factor for promoting smooth implementation of Big Data Analytics technology in project teams. This is followed by power-conflict prevention, prevention of task disputes and prevention of Process conflicts respectively. Results also show that relationship and power conflicts interact on the one hand, while task and relationship conflict prevention also interact on the other hand, thus, suggesting the prevention of one of the conflicts could minimise the outbreak of the other. Research limitations/implications: The study has been conducted within the context of big data adoption in a project-based work environment and the need to prevent innovation-induced conflicts in teams. Similarly, the research participants examined are stakeholders within UK projected-based organisations. Practical implications: The study urges organisations wishing to embrace big data innovation to evolve a multipronged approach for facilitating smooth implementation through prevention of conflicts among project frontlines. This study urges organisations to anticipate both subtle and overt frictions that can undermine relationships and team dynamics, effective task performance, derail processes and create unhealthy rivalry that undermines cooperation and collaboration in the team. Social implications: The study also addresses the uncertainty and disruption that big data technology presents to employees in teams and explore conflict prevention measure which can be used to mitigate such in project teams. Originality/value: The study proposes a Structural Model for establishing conflict prevention strategies in project teams through a multidimensional framework that combines constructs like team power conflict, process, relationship and task conflicts; to encourage Big Data implementation.</p
Big baths around turnovers: what happens if the former CEO stays on board?
Get PDFWe examine whether retaining the former CEO as a board member has an impact on big bath accounting around CEO turnovers. Early evidence shows that when a CEO turnover occurs, the new CEO uses big bath to shift the responsibility for low earnings toward the previous management. However, the former CEO is often retained. This event may restrict the new CEO’s ability to take a big bath. Using a hand-collected sample of CEO turnover events in US firms, we find that CEO turnover increases the probability of a big bath. However, retaining the CEO acts as a monitoring mechanism by reducing the probability of big baths, especially opportunistic ones. Our findings indicate that CEO retention could be a useful corporate governance mechanism that restricts new CEO’s opportunistic practices
Who deserves help and who is bad? race and class in 'doing' school exclusion
No full textThis article presents findings from qualitative research on school exclusion. The study was conducted in a Pupil Referral Unit (PRU), part of alternative education provision, in England. Mixed methods used included ethnographic approaches, drama-based group work, focus group discussions and interviews. Research participants were teenage boys (age 14 -16) and professionals including Teachers and Teaching Assistants (TAs). Data were analysed in multiple ways within a post structural framework, this included a participatory Data Sharing workshop with boys at the PRU and psycho social approaches. Intersectionality and post structural theories provide conceptual resources for the study. Key themes are: Situated context, boys’ creative practices through rap music and the phenomenon of parents sending their sons ‘back home’ outside the UK. The article highlights school exclusion as part of a wider global context of inequity and punishment in education. It offers nuanced insights from the raced and classed experience of exclusion
Revolutionizing higher education: unleashing the potential of large language models for strategic transformation
Get PDFThis paper investigates the transformative potential of Large Language Models (LLMs) within higher education, highlighting their capacity to reshape the academic landscape. By examining the complex impact of LLMs across critical areas of Higher Education Institutions (HEIs), including the role of HEIs as gatekeepers of knowledge, providers of credentials, research centres, incubators of innovation, drivers of social change and employers. In addition to academic integrity, the future of higher education, intellectual property, and public perception. The findings of this paper indicate that LLMs can empower transformation in HEIs by revolutionising various aspects of academia. The aim is to unveil the profound implications of integrating these cutting-edge technologies. The comprehensive study in this paper reveals the significant impacts and challenges associated with using LLMs in academic settings, which is achieved through a detailed analysis of current literature. The core findings suggest that LLMs hold the promise to trigger significant advancements in higher education. This paper also discusses the innovative potential of LLMs, and it outlines a path for their effective use in HEIs, emphasising the importance of a thoughtful approach to maximise their educational benefits. HEIs must address these challenges thoughtfully, ensuring that the integration of LLMs aligns with their fundamental objectives of promoting education, critical thinking, and personal growth
An investigation of antecedents and consequences of green value internalisation among sampled UK enterprises
Get PDFDespite the growing popularity of the concept of green value internalisation, research on how this concept is being accomplished at the enterprise level is still limited. The purpose of this study is to address this knowledge gap by drawing from previously known concepts of green value and value internalisation. It examines the antecedents and consequences of green value internalisation and evaluates how these environment-leaning approaches impact competitive advantage. This study uses the resource-based view and the stakeholder theory as theoretical lenses in linking green value internalisation to its antecedents and how these impact competitive advantage. A two-step approach involving a measurement model and a structural model was used to analyse survey data from 213 UK enterprises to validate the research hypotheses. Hypotheses testing shows that green value internalisation has a positive and significant impact on green criteria development. The results also show that external pressure positively and significantly affects green value internalisation. These findings extend prior knowledge by establishing the level of significance in the relationship among the antecedents and consequences in the research model. The research design for this study draws from a systematic literature review. The study offers rigorous empirical insights for implementing green value internalisation as a value-creating strategy. However, the antecedents and consequences examined in this study may not capture in detail all underlying constructs. Hence future studies should proffer valid and reliable instruments for these constructs. The findings provide managers from enterprises across a broad industry size range seeking to implement green value internalisation with resources for embedding an enterprise-level pro-environmental strategy
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