The Work and Impact of Neighbourhood Development Plans post-adoption

Neighbourhood planning was introduced as a new level of participatory neighbourhood-scale planning in England under the 2011 Localism Act. Most of previous studies have focused on mainly the emergence of neighbourhood planning and the preparation of neighbourhood development plans. There are no in-depth academic accounts of neighbourhood development plans ‘post-adoption’, whilst development plans in England have considerable and practical power to influence and shape the growth of the real world. This thesis aims to identify and explore the work and impact of neighbourhood development plans after their adoption based on empirical evidence, tracing how the neighbourhood development plans are used within the planning system and contexts. To do this, multiple embedded case study design with the mixed quantitative and qualitative methods is employed for scrutinising four selected neighbourhood development plans. This thesis deploys and reworks the concept of the communicative work of development plans proposed by Healey (1993) as a conceptual and theoretical tool. This concept is extended to understand the nature and influence of a development plan within its continuing and interactive contexts and further reproduced by reflecting and adjusting to the particularities and attributes of neighbourhood development plans as a relatively new form of community-led plans. The findings highlight that neighbourhood development plans and neighbourhood planning groups themselves seek to remain actively involved in post-adoption planning processes, interacting constantly but sometimes in quite different ways with their external planning environments. The research provides deeper insight into the work of neighbourhood development plans and their interactive power and influence. In turn this insight can provide practical guidelines for those who produce or revise neighbourhood plans and those who support them

Static Detection of Design Patterns in Class Diagrams

Teaching Object-Oriented design on the class diagram level is often a cumbersome effort. Requiring the use of specific design patterns helps the students in structuring their design properly. However, checking whether students used the right design pattern can be a very time-intensive task due to the variety of possibilities of creating structure using design patterns on the high-level class diagrams. For the same reason, it is hard for students to check for themselves whether their solution fulfills the basic requirements that are required by the instructor with respect to the use of design patterns. Efficiency and the quality of design pattern education can be improved by automatic detection of design patterns in \textsc{UML} class diagrams. We introduce a new method to detect design patterns in class diagrams, together with a prototype of a tool that uses this new method. Using this tool, an instructor needs less effort to review solutions of design exercises since the tool can check the basic class requirements automatically. Consequently, an instructor can focus on the more high-level requirements that were set in the exercise and students can easier check for themselves whether their design satisfies the basic required properties on the pattern level. The method offers static decidability for those design patterns, that are identified by structural properties e.c. the names of the classes and their associations. It is non-duplicating. That is a specific occurrence of a design pattern is not reported multiple times. The method not only detects all 16 static Gang of Four design patterns without false positives or false negatives, but also it can detect redundant relations. Our tool contributes tto the quality and efficiency of design pattern education, both for students and instructors

Implicit Stacked Autoregressive Model for Video Prediction

Future frame prediction has been approached through two primary methods: autoregressive and non-autoregressive. Autoregressive methods rely on the Markov assumption and can achieve high accuracy in the early stages of prediction when errors are not yet accumulated. However, their performance tends to decline as the number of time steps increases. In contrast, non-autoregressive methods can achieve relatively high performance but lack correlation between predictions for each time step. In this paper, we propose an Implicit Stacked Autoregressive Model for Video Prediction (IAM4VP), which is an implicit video prediction model that applies a stacked autoregressive method. Like non-autoregressive methods, stacked autoregressive methods use the same observed frame to estimate all future frames. However, they use their own predictions as input, similar to autoregressive methods. As the number of time steps increases, predictions are sequentially stacked in the queue. To evaluate the effectiveness of IAM4VP, we conducted experiments on three common future frame prediction benchmark datasets and weather\&climate prediction benchmark datasets. The results demonstrate that our proposed model achieves state-of-the-art performance

The Effects of Operational Efficiency and Environmental Risk on the Adoption of Environmental Management Practices

Given that prior research has provided inconsistent findings on the relationship between financial performance and the adoption of environmental management practices (EMPs), we aim to resolve the inconsistency by positing that the firm may consider different components of financial performance when making decisions. Specifically, we maintain that operational efficiency, measured based on net profit margin, is a key determinant of a firm’s decision to adopt EMPs. Additionally, we aim to examine environmental risk as one contingency that moderates the relationship between operational efficiency and EMP adoption. Employing a firm-fixed effect model to examine the effects of various measures of financial performance, including the net profit margin, return on asset (ROA), return on equity (ROE), and asset turnover, on the adoption rates of EMPs by firms, we find that firms with higher operational efficiency measured based on net profit margin are more inclined to adopt EMPs, while measures such as ROA, ROE, and asset turnover do not demonstrate any substantial effect. This study also finds that while environmental risk increases the possibility of adopting EMPs, it weakens the impact of operational efficiency on the adoption rates of EMPs

RHINO: Rotated DETR with Dynamic Denoising via Hungarian Matching for Oriented Object Detection

With the publication of DINO, a variant of the Detection Transformer (DETR), Detection Transformers are breaking the record in the object detection benchmark with the merits of their end-to-end design and scalability. However, the extension of DETR to oriented object detection has not been thoroughly studied although more benefits from its end-to-end architecture are expected such as removing NMS and anchor-related costs. In this paper, we propose a first strong DINO-based baseline for oriented object detection. We found that straightforward employment of DETRs for oriented object detection does not guarantee non-duplicate prediction, and propose a simple cost to mitigate this. Furthermore, we introduce a $\textit{dynamic denoising}$ strategy that uses Hungarian matching to filter redundant noised queries and $\textit{query alignment}$ to preserve matching consistency between Transformer decoder layers. Our proposed model outperforms previous rotated DETRs and other counterparts, achieving state-of-the-art performance in DOTA-v1.0/v1.5/v2.0, and DIOR-R benchmarks.Comment: State-of-the-art Rotated Object Detector in DOTA v1.0/v1.5/v2.0 and DIOR-