Evaluating the functional performance of small-scale public demountable buildings

This thesis investigates the design, operation and use of contemporary demountable buildings, and explores how functional performance can be assessed in small-scale examples for public use alongside with their relationship to other design elements. The research focuses on three case studies that do not require a high-technology building environment or complex construction skills. Demountable buildings are defined as those that are transported in a number of parts for assembly on site. Contemporary demountable buildings respond to ecological issues, social impacts, technological innovation and economic demands. They can be used to measure a society’s development in environmental sustainability, innovation and economic growth through various forms. Small-scale demountable buildings fulfil many temporary habitation needs in diverse roles, such as non-emergency transitional housing, ephemeral exhibition buildings and seasonal entertainment facilities. The purpose of examining functional performance is to assess if, and how, the requirements of the design have been achieved. This enables project operators to address functional performance from a public perspective by reflecting on the scope and ambition of their projects. This thesis draws on existing literature to investigate previous and on-going research relating to demountable buildings, including classification, the construction process and project management. It also examines selected existing evaluation methods that cover principles, modelling and computer-based solutions from a wider research area, including Guidelines Developed by City Council and Culture Sectors; Assessment Methods in Humanitarian Response and Methods in Environmental Assessment. The research was conducted by combining both quantitative and qualitative research methods, including field research, case studies, interviews, questionnaires and group discussions. Fragmented narratives were transformed into structured evidence, identifying models of best performance in demountable buildings and developing a new method – the Evaluation Conceptual Model – for the effective evaluation and evidencing of the value of demountable buildings in the 21st century. Recommendations for adapting a suitable model to evaluate other design elements in demountable buildings and other types of moveable buildings in further research are suggested and the findings have been used to lay the foundations for a practical evaluation tool for the future

Proteomic analyses reveal distinct chromatin-associated and soluble transcription factor complexes.

The current knowledge on how transcription factors (TFs), the ultimate targets and executors of cellular signalling pathways, are regulated by protein-protein interactions remains limited. Here, we performed proteomics analyses of soluble and chromatin-associated complexes of 56 TFs, including the targets of many signalling pathways involved in development and cancer, and 37 members of the Forkhead box (FOX) TF family. Using tandem affinity purification followed by mass spectrometry (TAP/MS), we performed 214 purifications and identified 2,156 high-confident protein-protein interactions. We found that most TFs form very distinct protein complexes on and off chromatin. Using this data set, we categorized the transcription-related or unrelated regulators for general or specific TFs. Our study offers a valuable resource of protein-protein interaction networks for a large number of TFs and underscores the general principle that TFs form distinct location-specific protein complexes that are associated with the different regulation and diverse functions of these TFs

A snoRNA modulates mRNA 3' end processing and regulates the expression of a subset of mRNAs.

mRNA 3' end processing is an essential step in gene expression. It is well established that canonical eukaryotic pre-mRNA 3' processing is carried out within a macromolecular machinery consisting of dozens of trans-acting proteins. However, it is unknown whether RNAs play any role in this process. Unexpectedly, we found that a subset of small nucleolar RNAs (snoRNAs) are associated with the mammalian mRNA 3' processing complex. These snoRNAs primarily interact with Fip1, a component of cleavage and polyadenylation specificity factor (CPSF). We have functionally characterized one of these snoRNAs and our results demonstrated that the U/A-rich SNORD50A inhibits mRNA 3' processing by blocking the Fip1-poly(A) site (PAS) interaction. Consistently, SNORD50A depletion altered the Fip1-RNA interaction landscape and changed the alternative polyadenylation (APA) profiles and/or transcript levels of a subset of genes. Taken together, our data revealed a novel function for snoRNAs and provided the first evidence that non-coding RNAs may play an important role in regulating mRNA 3' processing

Towards Understanding the Capability of Large Language Models on Code Clone Detection: A Survey

Code cloning, the duplication of code fragments, is common in software development. While some reuse aids productivity, excessive cloning hurts maintainability and introduces bugs. Hence, automatic code clone detection is vital. Meanwhile, large language models (LLMs) possess diverse code-related knowledge, making them versatile for various software engineering challenges. However, LLMs' performance in code clone detection is unclear and needs more study for accurate assessment. In this paper, we provide the first comprehensive evaluation of LLMs for clone detection, covering different clone types, languages, and prompts. We find advanced LLMs excel in detecting complex semantic clones, surpassing existing methods. Adding intermediate reasoning steps via chain-of-thought prompts noticeably enhances performance. Additionally, representing code as vector embeddings, especially with text encoders, effectively aids clone detection.Lastly, the ability of LLMs to detect code clones differs among various programming languages. Our study suggests that LLMs have potential for clone detection due to their language capabilities, offering insights for developing robust LLM-based methods to enhance software engineering.Comment: 13 pages, 3 figure

A customised down-sampling machine learning approach for sepsis prediction

ObjectiveSepsis is a life-threatening condition in the ICU and requires treatment in time. Despite the accuracy of existing sepsis prediction models, insufficient focus on reducing alarms could worsen alarm fatigue and desensitisation in ICUs, potentially compromising patient safety. In this retrospective study, we aim to develop an accurate, robust, and readily deployable method in ICUs, only based on the vital signs and laboratory tests.MethodsOur method consists of a customised down-sampling process and a specific dynamic sliding window and XGBoost to offer sepsis prediction. The down-sampling process was applied to the retrospective data for training the XGBoost model. During the testing stage, the dynamic sliding window and the trained XGBoost were used to predict sepsis on the retrospective datasets, PhysioNet and FHC.ResultsWith the filtered data from PhysioNet, our method achieved 80.74% accuracy (77.90% sensitivity and 84.42% specificity) and 83.95% (84.82% sensitivity and 82.00% specificity) on the test set of PhysioNet-A and PhysioNet-B, respectively. The AUC score was 0.89 for both datasets. On the FHC dataset, our method achieved 92.38% accuracy (88.37% sensitivity and 95.16% specificity) and 0.98 AUC score on the test set of FHC.ConclusionOur results indicate that the down-sampling process and the dynamic sliding window with XGBoost brought robust and accurate performance to give sepsis prediction under various hospital settings. The localisation and robustness of our method can assist in sepsis diagnosis in different ICU settings

A TPA-TCN Prediction Model Applied In Photovoltaic Power Generation Field

To solve the problem of large fluctuation and instability of photovoltaic power generation, a deep learning prediction model (TPA-TCN) based on temporal pattern attention mechanism (TPA) and temporal convolutional network (TCN) is proposed, and then applied to photovoltaic power generation. First of all, the k-means clustering algorithm is used to cluster historical data to obtain three typical weather types, and the model is trained by dividing test sets according to the clustering results. After TPA is introduced into the TCN model, which can capture the influence of each variable on the predicted series of the model, help the model pay better attention to the key features in the time series, improve the model’s ability to understand the data, and thus efficiently and accurately predict the short-term photovoltaic power. Combined with the measured data, the experiment results show that the TPA-TCN model has good generalization ability and high precision in different weather types

Selective ion removal by capacitive deionization (CDI)-based technologies

Severe freshwater shortages and global pollution make selective removal of target ions from solutions of great significance for water purification and resource recovery. Capacitive deionization (CDI) removes charged ions and molecules from water by applying a low applied electric field across the electrodes and has received much attention due to its lower energy consumption and sustainability. Its application field has been expanding in the past few years. In this paper, we report an overview of the current status of selective ion removal in CDI. This paper also discusses the prospects of selective CDI, including desalination, water softening, heavy metal removal and recovery, nutrient removal, and other common ion removal techniques. The insights from this review will inform the implementation of CDI technology

Mindfulness practice for protecting mental health during the COVID-19 pandemic.

Emerging evidence shows that the coronavirus disease 2019 (COVID-19) pandemic is negatively affecting mental health around the globe. Interventions to alleviate the psychological impact of the pandemic are urgently needed. Whether mindfulness practice may protect against the harmful emotional effects of a pandemic crisis remains hitherto unknown. We investigated the influence of mindfulness training on mental health during the COVID-19 outbreak in China. We hypothesized that mindfulness practitioners might manifest less pandemic-related distress, depression, anxiety, and stress than non-practitioners and that more frequent practice would be associated with an improvement in mental health during the pandemic. Therefore, we assessed pandemic-related distress and symptoms of depression, anxiety, and stress, as well as the frequency of meditation practice at the peak of new infections (Feb 4-5; N = 673) and three weeks later (Feb 29-30; N = 521) in mindfulness practitioners via online questionnaires. Self-reported symptoms were also collected from non-practitioners at peak time only (N = 1550). We found lower scores of pandemic-related distress in mindfulness practitioners compared to non-practitioners. In general, older participants showed fewer symptoms of depression and anxiety. In younger practitioners, pandemic-related distress decreased from peak to follow-up. Importantly, increased mindfulness training during the preceding two weeks was associated with lower scores of depression and anxiety at both assessments. Likewise, practice frequency predicted individual improvement in scores of depression, anxiety, and stress at follow-up. Our results indicate that mindfulness meditation might be a viable low-cost intervention to mitigate the psychological impact of the COVID-19 crisis and future pandemics

Mindfulness practice for protecting mental health during the COVID-19 pandemic.

Emerging evidence shows that the coronavirus disease 2019 (COVID-19) pandemic is negatively affecting mental health around the globe. Interventions to alleviate the psychological impact of the pandemic are urgently needed. Whether mindfulness practice may protect against the harmful emotional effects of a pandemic crisis remains hitherto unknown. We investigated the influence of mindfulness training on mental health during the COVID-19 outbreak in China. We hypothesized that mindfulness practitioners might manifest less pandemic-related distress, depression, anxiety, and stress than non-practitioners and that more frequent practice would be associated with an improvement in mental health during the pandemic. Therefore, we assessed pandemic-related distress and symptoms of depression, anxiety, and stress, as well as the frequency of meditation practice at the peak of new infections (Feb 4-5; N = 673) and three weeks later (Feb 29-30; N = 521) in mindfulness practitioners via online questionnaires. Self-reported symptoms were also collected from non-practitioners at peak time only (N = 1550). We found lower scores of pandemic-related distress in mindfulness practitioners compared to non-practitioners. In general, older participants showed fewer symptoms of depression and anxiety. In younger practitioners, pandemic-related distress decreased from peak to follow-up. Importantly, increased mindfulness training during the preceding two weeks was associated with lower scores of depression and anxiety at both assessments. Likewise, practice frequency predicted individual improvement in scores of depression, anxiety, and stress at follow-up. Our results indicate that mindfulness meditation might be a viable low-cost intervention to mitigate the psychological impact of the COVID-19 crisis and future pandemics