Towards 2050 net zero carbon infrastructure:a critical review of key decarbonization challenges in the domestic heating sector in the UK

One of the most challenging sectors to meet “Net Zero emissions” target by 2050 in the UK is the domestic heating sector. This paper provides a comprehensive literature review of the main challenges of heating systems transition to low carbon technologies in which three distinct categories of challenges are discussed. The first challenge is of decarbonizing heat at the supply side, considering specifically the difficulties in integrating hydrogen as a low-carbon heating substitute to the dominant natural gas. The next challenge is of decarbonizing heat at the demand side, and research into the difficulties of retrofitting the existing UK housing stock, of digitalizing heating energy systems, as well as ensuring both retrofits and digitalization do not disproportionately affect vulnerable groups in society. The need for demonstrating innovative solutions to these challenges leads to the final focus, which is the challenge of modeling and demonstrating future energy systems heating scenarios. This work concludes with recommendations for the energy research community and policy makers to tackle urgent challenges facing the decarbonization of the UK heating sector.</p

A Soft Robot for Random Exploration of Terrestrial Environments

A swarm of randomly moving miniature robots is an effective solution for the exploration of unknown terrains. However, the deployment of a swarm of miniature robots poses two challenges: finding an adequate locomotion strategy for fast exploration and obstacles negotiation; and implementing simple design and control solutions suited for mass manufacturing. Here, we tackle these challenges by developing a new soft robot with a minimalistic design and a simple control strategy that can randomly propel itself above obstacles and roll on the ground upon landing. The robot is equipped with two propellers that are periodically activated to jump, a soft cage that protects the robot from impacts and allows to passively roll on the ground, and a passive self-righting mechanism for repetitive jumps. The minimalistic control and design reduce the complexity of the mechanics and electronics and are instrumental to the production of a large number of robots. In the paper, the key design aspects of the robot are discussed, the locomotion of a single prototype is experimentally characterized, and improvements of the system for future swarm operations are discussed

Education Sector Workforce Planning: Practitioner level

This Practitioner level module is designed to ensure that Department of Foreign Affairs and Trade (DFAT) staff members who engage with and lead policy dialogue with international and domestic partners are informed about education workforce planning cycles, key challenges related to managing workforce supply and demand, as well as initiatives that can improve workforce quality. It is recommended that staff complete the Education Sector Workforce Planning: Foundation level module as background information to this Practitioner level module

Deep Risk Prediction and Embedding of Patient Data: Application to Acute Gastrointestinal Bleeding

Acute gastrointestinal bleeding is a common and costly condition, accounting for over 2.2 million hospital days and 19.2 billion dollars of medical charges annually. Risk stratification is a critical part of initial assessment of patients with acute gastrointestinal bleeding. Although all national and international guidelines recommend the use of risk-assessment scoring systems, they are not commonly used in practice, have sub-optimal performance, may be applied incorrectly, and are not easily updated. With the advent of widespread electronic health record adoption, longitudinal clinical data captured during the clinical encounter is now available. However, this data is often noisy, sparse, and heterogeneous. Unsupervised machine learning algorithms may be able to identify structure within electronic health record data while accounting for key issues with the data generation process: measurements missing-not-at-random and information captured in unstructured clinical note text. Deep learning tools can create electronic health record-based models that perform better than clinical risk scores for gastrointestinal bleeding and are well-suited for learning from new data. Furthermore, these models can be used to predict risk trajectories over time, leveraging the longitudinal nature of the electronic health record. The foundation of creating relevant tools is the definition of a relevant outcome measure; in acute gastrointestinal bleeding, a composite outcome of red blood cell transfusion, hemostatic intervention, and all-cause 30-day mortality is a relevant, actionable outcome that reflects the need for hospital-based intervention. However, epidemiological trends may affect the relevance and effectiveness of the outcome measure when applied across multiple settings and patient populations. Understanding the trends in practice, potential areas of disparities, and value proposition for using risk stratification in patients presenting to the Emergency Department with acute gastrointestinal bleeding is important in understanding how to best implement a robust, generalizable risk stratification tool. Key findings include a decrease in the rate of red blood cell transfusion since 2014 and disparities in access to upper endoscopy for patients with upper gastrointestinal bleeding by race/ethnicity across urban and rural hospitals. Projected accumulated savings of consistent implementation of risk stratification tools for upper gastrointestinal bleeding total approximately $1 billion 5 years after implementation. Most current risk scores were designed for use based on the location of the bleeding source: upper or lower gastrointestinal tract. However, the location of the bleeding source is not always clear at presentation. I develop and validate electronic health record based deep learning and machine learning tools for patients presenting with symptoms of acute gastrointestinal bleeding (e.g., hematemesis, melena, hematochezia), which is more relevant and useful in clinical practice. I show that they outperform leading clinical risk scores for upper and lower gastrointestinal bleeding, the Glasgow Blatchford Score and the Oakland score. While the best performing gradient boosted decision tree model has equivalent overall performance to the fully connected feedforward neural network model, at the very low risk threshold of 99% sensitivity the deep learning model identifies more very low risk patients. Using another deep learning model that can model longitudinal risk, the long-short-term memory recurrent neural network, need for transfusion of red blood cells can be predicted at every 4-hour interval in the first 24 hours of intensive care unit stay for high risk patients with acute gastrointestinal bleeding. Finally, for implementation it is important to find patients with symptoms of acute gastrointestinal bleeding in real time and characterize patients by risk using available data in the electronic health record. A decision rule-based electronic health record phenotype has equivalent performance as measured by positive predictive value compared to deep learning and natural language processing-based models, and after live implementation appears to have increased the use of the Acute Gastrointestinal Bleeding Clinical Care pathway. Patients with acute gastrointestinal bleeding but with other groups of disease concepts can be differentiated by directly mapping unstructured clinical text to a common ontology and treating the vector of concepts as signals on a knowledge graph; these patients can be differentiated using unbalanced diffusion earth mover’s distances on the graph. For electronic health record data with data missing not at random, MURAL, an unsupervised random forest-based method, handles data with missing values and generates visualizations that characterize patients with gastrointestinal bleeding. This thesis forms a basis for understanding the potential for machine learning and deep learning tools to characterize risk for patients with acute gastrointestinal bleeding. In the future, these tools may be critical in implementing integrated risk assessment to keep low risk patients out of the hospital and guide resuscitation and timely endoscopic procedures for patients at higher risk for clinical decompensation

The Resiliency of Highly Mobile Military Children: Implications for Military and Education Leadership

The life of a highly mobile child brings educational and social challenges. Highly mobile children who move at least four to five times during their PK-12 grade years generally experience greater difficulty in their social interactions and education than children with a more stable living experience. One specific group of highly mobile children, military children, face these challenges and more, due somewhat to the unique culture of the military. Military children are called upon to move across state lines and/or international borders and typically face multiple school absences and stress related to deployments of their active duty parent(s). There is a lack of research, generally, on the lives of highly mobile military children and, particularly, research that incorporates their own testimony. This qualitative study was conducted to gain a better understanding of the experiences of highly mobile military children and the strategies they claim to have developed to cope with the consequences of multiple moves. A total of 25 young adults who were highly mobile during their PK-12 grade years were interviewed to gain their perspective. Grounded theory was used to analyze the findings that emerged inductively from their interviews. The unit of analysis was the highly mobile military child; however, some parents were interviewed to provide contextual information about the experiences of their children. The participants’ ability to successfully navigate multiple moves showed that the interrelatedness of having a strong supportive family, being part of a military community that created a sense of belonging, having the benefit of culturally sensitive educators, and having a combination of formal and informal support structures helped these participants build resiliency and the human and social capital needed to navigate the multiple moves they experienced in their PK-12 grade years. This study responds to the gap in knowledge about the experiences of highly mobile military children by providing their perspective. This study better informs the community that works to support these children, including parents, school educators, and counselors, and it provides important knowledge to better support future generations of highly mobile military children

Ultralightweight Spatial-Spectral Feature Cooperation Network for Change Detection in Remote Sensing Images

Copyright © The Author(s) 2023. Deep convolutional neural networks (CNNs) have achieved much success in remote sensing image change detection (CD) but still suffer from two main problems. First, the existing multiscale feature fusion methods often use redundant feature extraction and fusion strategies, which often lead to high computational costs and memory usage. Second, the regular attention mechanism in CD is difficult to model spatial–spectral features and generate 3-D attention weights at the same time, ignoring the cooperation between spatial features and spectral features. To address the above issues, an efficient ultralightweight spatial–spectral feature cooperation network (USSFC-Net) is proposed for CD in this article. The proposed USSFC-Net has two main advantages. First, a multiscale decoupled convolution (MSDConv) is designed, which is clearly different from the popular atrous spatial pyramid pooling (ASPP) module and its variants since it can flexibly capture the multiscale features of changed objects using cyclic multiscale convolution. Meanwhile, the design of MSDConv can greatly reduce the number of parameters and computational redundancy. Second, an efficient spatial–spectral feature cooperation (SSFC) strategy is introduced to obtain richer features. The SSFC differs from the existing 2-D attention mechanisms since it learns 3-D spatial–spectral attention weights without adding any parameters. The experiments on three datasets for remote sensing image CD demonstrate that the proposed USSFC-Net achieves better CD accuracy than most CNNs-based methods and requires lower computational costs and fewer parameters, even it is superior to some Transformer-based methods. The code is available at https://github.com/SUST-reynole/USSFC-Net .10.13039/501100017596-Natural Science Basic Research Program of Shaanxi (Grant Number: 2021JC-47 and 2022JQ-592); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62271296, 62201452 and 62201334); 10.13039/501100015401-Key Research and Development Program of Shaanxi Province (Grant Number: 2022GY-436, 2021ZDLGY08-07 and 2021GY-181); Shaanxi Joint Laboratory of Artificial Intelligence (Grant Number: 2020SS-03); Scientific Research Program Funded by the Shaanxi Provincial Education Department (Grant Number: 22JK0568)