74 research outputs found
Project Play: An exploration of how to combine architecture and play
No full textEverything started with my fascination of Pokémon-Go; the cultural moment when the building environment was used differently than intended; massive groups gathered around in public parks and kids invading your backyard. I wanted to understand the behavior of people and the new ways of interaction between space and humans. Literature research helped defining different fields of game-theory and architectural space, and most importantly the relationship between those. It gave the knowledge needed to do observations and make new conclusions. The results were descriptions of spaces where the situation of the Pokémon-GO effect occurred: playgrounds. These playgrounds were spaces where the players could be free and use their imagination to build their own world, where humans can be playful. With my design I intent to have the same methodology as these playgrounds.Architecture, Urbanism and Building Sciences | Explorela
FIGURE 1. Aristolochia thotteaeformis T.V in Aristolochia thotteaeformis, a distinct new species from southern Vietnam
No full textFIGURE 1. Aristolochia thotteaeformis T.V.Do & Luu sp.nov. A. Young branches; B. Cymose inflorescence in axillary and perianth straight in pre-anthesis; C. Lateral view of opened flower showing a distinct stipe (indicated by an arrow); D. Longitudinal section showing inner surface of perianth; E. Frontal view of opened flower; F. Close up of a 6-lobed gynostemium; G. Shape of capsule; H. Abaxial surface of seed showing densely circular warts. Drawn by Quyet Huu Nguyen & Truong Van Do.Published as part of Luu, Hong Truong, Nguyen, Tran Quoc Trung, Nguyen, Quoc Dat, Nguyen, Thanh Trung, Nguyen, Thanh Trung & Do, Truong Van, 2022, Aristolochia thotteaeformis, a distinct new species from southern Vietnam, pp. 167-176 in Phytotaxa 547 (2) on page 170, DOI: 10.11646/phytotaxa.547.2.4, http://zenodo.org/record/657144
INTELLIGENT CONTROL SYSTEMS SUPPORTING ENVIRONMENTAL MONITORING: A TECHNICAL FRAMEWORK FOR EVIDENCE BASED ENVIRONMENTAL GOVERNANCE
No full textEnvironmental monitoring is increasingly expected to do more than “observe” ecological conditions: it must also sustain lawful enforcement, support defensible administrative decisions, and enable credible public accountability. Yet many monitoring programs still rely on fragmented sampling routines, manual calibration cycles, and opaque data handling practices that can undermine evidentiary reliability—especially when measurements are challenged by regulated entities, communities, or courts. This paper develops an interdisciplinary framework that connects intelligent control systems with environmental governance needs. We conceptualize monitoring as a socio technical “evidence infrastructure” and show how control oriented functions—state estimation, adaptive sampling, fault detection, and disturbance rejection—can be designed to improve data continuity, uncertainty management, traceability, and responsiveness. Drawing on literature in wireless sensor networks, environmental sensor networks, industrial control, and governance by disclosure, we propose a reference architecture that integrates sensor/edge layers with an auditable data pipeline and governance aligned performance indicators. A simulation based case study for urban air quality monitoring illustrates how adaptive sampling and estimation can reduce missingness, shorten detection delay for exceedance events, and improve robustness to sensor drift while maintaining energy constraints. The discussion translates technical design choices into legal policy implications, including chain of custody practices, transparency and contestability, cybersecurity requirements, and institutional capacity building. The paper contributes a practical roadmap for agencies seeking to modernize monitoring under budgetary pressure without weakening evidentiary standards
Superresolution recurrent convolutional neural networks for learning with multi-resolution whole slide images
No full textThis file was last viewed in Microsoft Edge.A recurrent convolutional neural network is supervised machine learning way to process images that has both properties of convolutional and recurrent networks. We propose Convolutional Neural Network (CNN) based approach and its advanced recurrent version (RCNN) to solve the problem of enhancing the resolution of images obtained from a low magnification scanner, also known as the image super-resolution (SR) problem. The given class of scanner produces microscopic images relatively fast and storage efficiently. However, those scanners generate comparatively low quality images than images from complex and sophisticated scanners and do not have the necessary resolution for diagnostic or clinical researches, therefore low resolutions scanners are not in demand. The motivation of this study is to determine whether an image with low resolution could be enhanced by applying deep learning framework such that it would serve the same diagnostic purpose as a high resolution image from expensive scanners or microscopes. We presented novel network design and complex loss function. We validate these resolution improvements with computational analysis to show an enhanced image give the same quantitative results. In summary, our extensive experiments demonstrate that this method indeed produces images which are same quality to images from high resolution scanners. This approach opens up new application possibilities for using low-resolution scanners not only in terms of cost but also in access and speed of scanning for both research and possible clinical use
Discrete Diffusion Language Model for Long Text Summarization
No full textWhile diffusion models excel at conditional generating high-quality images,
prior works in discrete diffusion models were not evaluated on conditional
long-text generation. In this work, we address the limitations of prior
discrete diffusion models for conditional long-text generation, particularly in
long sequence-to-sequence tasks such as abstractive summarization. Despite fast
decoding speeds compared to autoregressive methods, previous diffusion models
failed on the abstractive summarization task due to the incompatibility between
the backbone architectures and the random noising process. To overcome these
challenges, we introduce a novel semantic-aware noising process that enables
Transformer backbones to handle long sequences effectively. Additionally, we
propose CrossMamba, an adaptation of the Mamba model to the encoder-decoder
paradigm, which integrates seamlessly with the random absorbing noising
process. Our approaches achieve state-of-the-art performance on three benchmark
summarization datasets: Gigaword, CNN/DailyMail, and Arxiv, outperforming
existing discrete diffusion models on ROUGE metrics as well as possessing much
faster speed in inference compared to autoregressive models
“Adverse Drug Reaction Reporting” Knowledge, Attitude and Practices of Community Pharmacy Dispensers in Dar es salaam, Tanzania
No full textUnder reporting of adverse drug reactions (ADRs) by healthcare personnel is a common problem of many Pharmacovigilence programs. Lack of involvement of healthcare professionals such as pharmacists and other pharmaceutical dispensers has been cited as one of the reasons for under reporting. Pharmaceutical dispensers in the community pharmacies are in unique position by virtue of their training and profession to observe ADRs in patients, as many patients often try to avoid doctor consultation fees by visiting community pharmacies. The knowledge and ability of dispensers in Tanzanian community pharmacies to identify and report ADRs is however unknown. To determine the knowledge, attitude and practices of dispensers in community pharmacies in Dar es Salaam towards the ADRs reporting. A descriptive cross sectional survey was conducted involving 254 dispensers from selected retail pharmacies in Dar es Salaam region. SPSS version 16 was used for data entry, cleaning and subsequently analysis. Results: The majority of personnel working in community pharmacies are non pharmaceutical professionals i.e 52% were nurse assistants. Community dispensers have limited knowledge and practices with regard towards ADRs reporting. Only 13.8% of respondents had good ADRs reporting knowledge, while only 8.7% had ever submitted ADRs reports to the relevant authorities. There was a significant difference in the level of knowledge with regard to ADRs reporting between Pharmaceutical professionals (i.e Pharmacists, Pharmaceutical technicians and pharmaceutical assistants) and non Pharmaceutical professionals (P value = 0.000). The knowledge levels correlated positively with profession and attendance of continuous professional education courses (CPE). The majority of dispensers (68.9%) however had a positive attitude towards ADRs reporting. Community pharmacies dispensers in Dar es Salaam have limited knowledge and experience with regard to ADRs reporting. Thus community pharmacies in Dar es Salaam cannot presently act as centres to collect data on ADRs effectively. The staffing of community pharmacies with unqualified pharmaceutical professionals is the main reason for the lack of knowledge, thus sincere and sustained efforts should be made by the Government through its National Regulatory Authorities and Schools of Pharmaceutical Sciences to ensure that there is an increased output of vii pharmaceutical professionals in Tanzania, ADRs reporting forms and guidelines are available in community pharmacies and that continuous professional education is provided to in-service pharmaceutical professionals to improve their ADRs reporting capabilities
HOMOE: A Memory-Based and Composition-Aware Framework for Zero-Shot Learning with Hopfield Network and Soft Mixture of Experts
No full textCompositional Zero-Shot Learning (CZSL) has emerged as an essential paradigm
in machine learning, aiming to overcome the constraints of traditional
zero-shot learning by incorporating compositional thinking into its
methodology. Conventional zero-shot learning has difficulty managing unfamiliar
combinations of seen and unseen classes because it depends on pre-defined class
embeddings. In contrast, Compositional Zero-Shot Learning uses the inherent
hierarchies and structural connections among classes, creating new class
representations by combining attributes, components, or other semantic
elements. In our paper, we propose a novel framework that for the first time
combines the Modern Hopfield Network with a Mixture of Experts (HOMOE) to
classify the compositions of previously unseen objects. Specifically, the
Modern Hopfield Network creates a memory that stores label prototypes and
identifies relevant labels for a given input image. Following this, the Mixture
of Expert models integrates the image with the fitting prototype to produce the
final composition classification. Our approach achieves SOTA performance on
several benchmarks, including MIT-States and UT-Zappos. We also examine how
each component contributes to improved generalization
Finite prism method based topology optimization of beam cross section for buckling load maximization
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