Modelling and simulating ‘informal urbanization’: An integrated agent-based and cellular automata model of urban residential growth in Ghana

The global urban population is expected to grow by 2.5 billion over the next three decades, and 90% of this growth will occur in African and Asian countries. Urban expansion in these regions is often characterised by ‘informal urbanization’ whereby households self-build without planning permission in contexts of ambiguous, insecure or disputed property rights. Despite the scale of informal urbanization, it has received little attention from scholars working in the domains of urban analytics and city science. Towards addressing this gap, we introduce TI-City, an urban growth model designed to predict the locations, legal status and socio-economic status of future residential developments in an African city. In a bottom-up approach, we use agent-based and cellular automata modelling techniques to predict the geospatial behaviour of key urban development actors, including households, real estate developers and government. We apply the model to the city-region of Accra, Ghana, drawing on local data collection, including a household survey, to parameterise the model. Using a multi-spatial-scale validation technique, we compare TI-City’s ability to simulate historically observed built-up patterns with SLEUTH, a highly popular urban growth model. Results show that TI-City outperforms SLEUTH at each scale, suggesting the model could offer a valuable decision support tool in similar city contexts.

Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity

Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R1 and R2*, proton density PD, and magnetization transfer saturation MTsat) that are sensitive to microstructural tissue properties such as iron and myelin content. Their capability to reveal microstructural brain differences, however, is tightly bound to controlling random noise and artefacts (e.g. caused by head motion) in the signal. Here, we introduced a method to estimate the local error of PD, R1, and MTsat maps that captures both noise and artefacts on a routine basis without requiring additional data. To investigate the method's sensitivity to random noise, we calculated the model-based signal-to-noise ratio (mSNR) and showed in measurements and simulations that it correlated linearly with an experimental raw-image-based SNR map. We found that the mSNR varied with MPM protocols, magnetic field strength (3T vs. 7T) and MPM parameters: it halved from PD to R1 and decreased from PD to MT_sat by a factor of 3-4. Exploring the artefact-sensitivity of the error maps, we generated robust MPM parameters using two successive acquisitions of each contrast and the acquisition-specific errors to down-weight erroneous regions. The resulting robust MPM parameters showed reduced variability at the group level as compared to their single-repeat or averaged counterparts. The error and mSNR maps may better inform power-calculations by accounting for local data quality variations across measurements. Code to compute the mSNR maps and robustly combined MPM maps is available in the open-source hMRI toolbox.

Privacy-Preserving Federated Learning Using Homomorphic Encryption

Federated learning (FL) is a machine learning technique that enables distributed devices to train a learning model collaboratively without sharing their local data. FL-based systems can achieve much stronger privacy preservation since the distributed devices deliver only local model parameters trained with local data to a centralized server. However, there exists a possibility that a centralized server or attackers infer/extract sensitive private information using the structure and parameters of local learning models. We propose employing homomorphic encryption (HE) scheme that can directly perform arithmetic operations on ciphertexts without decryption to protect the model parameters. Using the HE scheme, the proposed privacy-preserving federated learning (PPFL) algorithm enables the centralized server to aggregate encrypted local model parameters without decryption. Furthermore, the proposed algorithm allows each node to use a different HE private key in the same FL-based system using a distributed cryptosystem. The performance analysis and evaluation of the proposed PPFL algorithm are conducted in various cloud computing-based FL service scenarios.

A Federated Graph Neural Network Framework for Privacy-Preserving Personalization

Graph neural network (GNN) is effective in modeling high-order interactions and has been widely used in various personalized applications such as recommendation. However, mainstream personalization methods rely on centralized GNN learning on global graphs, which have considerable privacy risks due to the privacy-sensitive nature of user data. Here, we present a federated GNN framework named FedGNN for both effective and privacy-preserving personalization. Through a privacy-preserving model update method, we can collaboratively train GNN models based on decentralized graphs inferred from local data. To further exploit graph information beyond local interactions, we introduce a privacy-preserving graph expansion protocol to incorporate high-order information under privacy protection. Experimental results on six datasets for personalization in different scenarios show that FedGNN achieves 4.0%~9.6% lower errors than the state-of-the-art federated personalization methods under good privacy protection. FedGNN provides a novel direction to mining decentralized graph data in a privacy-preserving manner for responsible and intelligent personalization.

Design of Resources Allocation in 6G Cybertwin Technology Using the Fuzzy Neuro Model in Healthcare Systems

In 6G edge communication networks, the machine learning models play a major role in enabling intelligent decision-making in case of optimal resource allocation in case of the healthcare system. However, it causes a bottleneck, in the form of sophisticated memory calculations, between the hidden layers and the cost of communication between the edge devices/edge nodes and the cloud centres, while transmitting the data from the healthcare management system to the cloud centre via edge nodes. In order to reduce these hurdles, it is important to share workloads to further eliminate the problems related to complicated memory calculations and transmission costs. The effort aims mainly to reduce storage costs and cloud computing associated with neural networks as the complexity of the computations increases with increasing numbers of hidden layers. This study modifies federated teaching to function with distributed assignment resource settings as a distributed deep learning model. It improves the capacity to learn from the data and assigns an ideal workload depending on the limited available resources, slow network connection, and more edge devices. Current network status can be sent to the cloud centre by the edge devices and edge nodes autonomously using cybertwin, meaning that local data are often updated to calculate global data. The simulation shows how effective resource management and allocation is better than standard approaches. It is seen from the results that the proposed method achieves higher resource utilization and success rate than existing methods. Index Terms are fuzzy, healthcare, bioinformatics, 6G wireless communication, cybertwin, machine learning, neural network, and edge.

Weight-to-weight conversion factors for benthic macrofauna: recent measurements from the Baltic and the North seas

The availability of standardised biomass data is essential for studying population dynamics, energy flows, fisheries and food web interactions. To make the estimates of biomass consistent, weight-to-weight conversion factors are often used, for example to translate more widely available measurements of wet weights into required dry weights and ash-free dry weight metrics. However, for many species and groups the widely applicable freely available conversion factors have until now remained very rough approximations with high degree of taxonomic generalisation. To close up this gap, here for the first time we publish the most detailed and statically robust list of ratios of wet weight (WW), dry weight (DW) and ash-free dry weight (AFDW). The dataset includes over 17 000 records of single measurements for 497 taxa. Along with aggregated calculations, enclosed reference information with sampling dates and geographical coordinates the dataset provides a broad opportunity for reuse and repurposing. It empowers the future user to do targeted sub-selections of data to best combine them with their own local data, instead of only having a single value of conversion factor per region. The dataset can thereby be used to quantify natural variability and uncertainty. The dataset is available via an unrestricted repository from https://doi.org/10.12754/data-2021-0002-01 (Gogina et al., 2021).

Quality improvement project to improve patient outcomes by reducing door to CT and door to needle time and increasing appropriate referrals for endovascular thrombectomy.

This paper describes a stroke quality improvement (QI) project in a primary stroke centre in a 431-bed hospital serving a local population of 114 000 people. Approximately 170 acute strokes are treated each year in a seven-bed stroke unit managed by three geriatricians with a subspecialty interest in stroke. 24-hour CT radiology service is available. Endovascular thrombectomy (EVT) is performed by neuro-interventional radiology at one of two comprehensive stroke centres located 90–120 min away.In 2018, as part of a national collaborative QI initiative a new national thrombectomy referral pathway was introduced with an aim that all eligible patients be referred for EVT. This initiative included maximising timely access to CT and thrombolysis. Review of local data highlighted significant deficits in these areas.A local QI team convened and a multidisciplinary approach was employed to map the existing process for CT access and time to thrombolysis decision.We describe how focused timesaving interventions such as; new emergency and radiology department ‘pre-alerts’, dedicated acute stroke pagers, new ‘FAST’ registration by clerical staff, new CT ordering codes and new ‘FAST packs’ (including tissue plasminogen activator, paper National Institute of Health Stroke Scale scoring tools, consent forms and EVT patient selection tools) were created and incorporated into a multidisciplinary detailed clinical stroke care pathway.We describe how we achieved our SMART aims; to reduce our door to CT time and to reduce our door to needle time to the national target of less than 30 min. A third aim was to increase the number of patients referred for EVT from our centre.This project is an accurate description of how a multidisciplinary approach combined with teamwork and effective communication can create sustainable improved patient care and is generalisable to all institutions that require timely referral to external centres for EVT.

Report from the 13th International Conference on Researching and Applying Metaphor: "Metaphorical Creativity in a Multilingual World" (Hamar, Norway, 18–21 June 2020)

Report from the 13th International Conference on Researching and Applying Metaphor: Metaphorical Creativity in a Multilingual World (Hamar, Norway, 18–21 June 2020)The RaAM 2020 conference on metaphor research was held online on 18–21 June 2020, hosted by the Inland Norway University of Applied Sciences (INN) in Hamar, Norway. The aim was to exchange ideas and research findings of historians, culture studies specialists, and cognitive linguists from all around the world. The theme of the event was Metaphorical Creativity in a Multilingual World, including the following areas: multimodal metaphor, metaphor in spoken discourse, metaphor in gesture, metaphor in cross-cultural communication, metaphor and translation, metaphor and film, metaphor in education. Among the large group of researchers, specialists from Lithuania and Latvia presented their findings in metaphor research based on local data (Lithuanian media, posters, advertisements and billboards, film translation into Lithuanian). Sprawozdanie z trzynastej międzynarodowej konferencji dotyczącej badania i zastosowania metafory pt. Metaphorical Creativity in a Multilingual World (Hamar, Norwegia, 18–21 czerwca 2020)Wirtualna konferencja naukowa RaAM 2020 poświęcona badaniom nad metaforą odbyła się w dniach 18–21 czerwca 2020 roku w Inland Norway University of Applied Sciences (INN) w Hamarze w Norwegii. Celem spotkania była wymiana myśli i wyników badań naukowych historyków, kulturoznawców i językoznawców kognitywnych z całego świata. Tematem konferencji była kreatywność metaforyczna w wielojęzycznym świecie i obejmował on następujące obszary: metafora multimodalna, metafora w dyskursie mówionym, metafora w gestach, metafora w komunikacji międzykulturowej, metafora i przekład, metafora i film, metafora w edukacji. Wśród licznych badaczy byli również specjaliści z Litwy i Łotwy, którzy zaprezentowali wyniki badań nad metaforą na podstawie danych ze źródeł krajowych (media litewskie, plakaty, reklamy i bilbordy, tłumaczenie filmów na język litewski).

VADAF: Visualization for Abnormal Client Detection and Analysis in Federated Learning

Federated Learning (FL) provides a powerful solution to distributed machine learning on a large corpus of decentralized data. It ensures privacy and security by performing computation on devices (which we refer to as clients) based on local data to improve the shared global model. However, the inaccessibility of the data and the invisibility of the computation make it challenging to interpret and analyze the training process, especially to distinguish potential client anomalies. Identifying these anomalies can help experts diagnose and improve FL models. For this reason, we propose a visual analytics system, VADAF, to depict the training dynamics and facilitate analyzing potential client anomalies. Specifically, we design a visualization scheme that supports massive training dynamics in the FL environment. Moreover, we introduce an anomaly detection method to detect potential client anomalies, which are further analyzed based on both the client model’s visual and objective estimation. Three case studies have demonstrated the effectiveness of our system in understanding the FL training process and supporting abnormal client detection and analysis.