Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework

The number of people with dementia (PwD) is increasing dramatically. PwD exhibit impairments of reasoning, memory, and thought that require some form of self‐management intervention to support the completion of everyday activities while maintaining a level of independence. To address this need, efforts have been directed to the development of assistive technology solutions, which may provide an opportunity to alleviate the burden faced by the PwD and their carers. Nevertheless, uptake of such solutions has been limited. It is therefore necessary to use classifiers to discriminate between adopters and nonadopters of these technologies in order to avoid cost overruns and potential negative effects on quality of life. As multiple classification algorithms have been developed, choosing the most suitable classifier has become a critical step in technology adoption. To select the most appropriate classifier, a set of criteria from various domains need to be taken into account by decision makers. In addition, it is crucial to define the most appropriate multicriteria decision‐making approach for the modelling of technology adoption. Considering the above‐mentioned aspects, this paper presents the integration of a five‐phase methodology based on the Fuzzy Analytic Hierarchy Process and the Technique for Order of Preference by Similarity to Ideal Solution to determine the most suitable classifier for supporting assistive technology adoption studies. Fuzzy Analytic Hierarchy Process is used to determine the relative weights of criteria and subcriteria under uncertainty and Technique for Order of Preference by Similarity to Ideal Solution is applied to rank the classifier alternatives. A case study considering a mobile‐based self‐management and reminding solution for PwD is described to validate the proposed approach. The results revealed that the best classifier was k‐nearest‐neighbour with a closeness coefficient of 0.804, and the most important criterion when selecting classifiers is scalability. The paper also discusses the strengths and weaknesses of each algorithm that should be addressed in future research

Gravity duals for logarithmic conformal field theories

Logarithmic conformal field theories with vanishing central charge describe systems with quenched disorder, percolation or dilute self-avoiding polymers. In these theories the energy momentum tensor acquires a logarithmic partner. In this talk we address the construction of possible gravity duals for these logarithmic conformal field theories and present two viable candidates for such duals, namely theories of massive gravity in three dimensions at a chiral point.Comment: 15 pages, 1 figure, invited plenary talk at the First Mediterranean Conference on Classical and Quantum Gravity, v2: published version, corrected typo in left eq. (5

Generalizing Galileons

The Galileons are a set of terms within four-dimensional effective field theories, obeying symmetries that can be derived from the dynamics of a 3+1-dimensional flat brane embedded in a 5-dimensional Minkowski Bulk. These theories have some intriguing properties, including freedom from ghosts and a non-renormalization theorem that hints at possible applications in both particle physics and cosmology. In this brief review article, we will summarize our attempts over the last year to extend the Galileon idea in two important ways. We will discuss the effective field theory construction arising from co-dimension greater than one flat branes embedded in a flat background - the multiGalileons - and we will then describe symmetric covariant versions of the Galileons, more suitable for general cosmological applications. While all these Galileons can be thought of as interesting four-dimensional field theories in their own rights, the work described here may also make it easier to embed them into string theory, with its multiple extra dimensions and more general gravitational backgrounds.Comment: 16 pages; invited brief review article for a special issue of Classical and Quantum Gravity. Submitted to CQ

Thermodynamic analysis of black hole solutions in gravitating nonlinear electrodynamics

We perform a general study of the thermodynamic properties of static electrically charged black hole solutions of nonlinear electrodynamics minimally coupled to gravitation in three space dimensions. The Lagrangian densities governing the dynamics of these models in flat space are defined as arbitrary functions of the gauge field invariants, constrained by some requirements for physical admissibility. The exhaustive classification of these theories in flat space, in terms of the behaviour of the Lagrangian densities in vacuum and on the boundary of their domain of definition, defines twelve families of admissible models. When these models are coupled to gravity, the flat space classification leads to a complete characterization of the associated sets of gravitating electrostatic spherically symmetric solutions by their central and asymptotic behaviours. We focus on nine of these families, which support asymptotically Schwarzschild-like black hole configurations, for which the thermodynamic analysis is possible and pertinent. In this way, the thermodynamic laws are extended to the sets of black hole solutions of these families, for which the generic behaviours of the relevant state variables are classified and thoroughly analyzed in terms of the aforementioned boundary properties of the Lagrangians. Moreover, we find universal scaling laws (which hold and are the same for all the black hole solutions of models belonging to any of the nine families) running the thermodynamic variables with the electric charge and the horizon radius. These scale transformations form a one-parameter multiplicative group, leading to universal "renormalization group"-like first-order differential equations. The beams of characteristics of these equations generate the full set of black hole states associated to any of these gravitating nonlinear electrodynamics...Comment: 51 single column pages, 19 postscript figures, 2 tables, GRG tex style; minor corrections added; final version appearing in General Relativity and Gravitatio

Patient-specific mental rehearsal with three-dimensional models before low anterior resection: randomized clinical trial

Background It was hypothesized that preparing for a surgical procedure, taking into account individual patient characteristics, may facilitate the procedure and improve surgical quality. The aim of this study was to compare different case-specific, preoperative mental rehearsal methods before minimally invasive rectal cancer surgery. Methods In this RCT, patients were allocated in a 1 : 1 : 1 : 1 ratio to four groups: systematic mental rehearsal (SMR) using MRI scans; SMR and three-dimensional (3D) virtual models; SMR and synthetic 3D printed models; and routine practice (control group). Surgeons operating on all but the control group underwent mental rehearsal with the visual aids, including axial MRI scans of the pelvis, interactive 3D virtual models reconstructed from axial MRIs, and synthetic models, manufactured by 3D printing. Operations were video-recorded and assessed by two experts blinded to allocation using two validated scores, the Competency Assessment Tool (CAT) and Objective Clinical Human Reliability Analysis (OCHRA). The primary outcome of the study was surgical performance, measured by the CAT. Results Forty-nine patients were randomized and allocated to the four groups. There were 12 participants in each of the control, MRI and SMR, and virtual and SMR groups, whereas the SMR using physical models and simulation group included 13. No difference was observed between groups in median CAT scores (control 30.50, MRI 34.25, virtual 31.75, physical 34.00; P = 0.748, partial η2 0.200, pη2 =0.052–0.088). Time spent not performing dissection was significantly shorter for the SMR with MRI group than for the control (57.5 versus 42 respectively; P < 0.001, pη2 =0.212). Conclusion Mental rehearsal did not affect CAT and OCHRA scores of consultant surgeons. Reference number: ISRCTN 75603704 (https://www.isrctn.com)

What kind of illness is anorexia nervosa? Revisited: some preliminary thoughts to finding a cure.

Two decades have elapsed since our publication of 'What kind of illness is anorexia nervosa?'. The question remains whether our understanding of anorexia nervosa and its treatment thereof has evolved over this time. The verdict is disappointing at best. Our current gold standard treatments remain over-valued and clinical outcomes are modest at best. Those in our field are haunted by the constant reminder that anorexia nervosa carries the highest mortality rate of any psychiatric disorder. This cannot continue and demands immediate action. In this essay, we tackle the myths that bedevil our field and explore a deeper phenotyping of anorexia nervosa. We argue that we can no longer declare agnostic views of the disorder or conceive treatments that are "brainless": it is incumbent upon us to challenge the prevailing zeitgeist and reconceptualise anorexia nervosa. Here we provide a roadmap for the future

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

Background: The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. Materials and methods: We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. Results: A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. Conclusion: AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice

A Multispecialty Evaluation of Thiel Cadavers for Surgical Training

Background: Changes in UK legislation allow for surgical procedures to be performed on cadavers. The aim of this study was to assess Thiel cadavers as high-fidelity simulators and to examine their suitability for surgical training. Methods: Surgeons from various specialties were invited to attend a 1 day dissection workshop using Thiel cadavers. The surgeons completed a baseline questionnaire on cadaveric simulation. At the end of the workshop, they completed a similar questionnaire based on their experience with Thiel cadavers. Comparing the answers in the pre- and post-workshop questionnaires assessed whether using Thiel cadavers had changed the surgeons’ opinions of cadaveric simulation. Results: According to the 27 participants, simulation is important for surgical training and a full-procedure model is beneficial for all levels of training. Currently, there is dissatisfaction with existing models and a need for high-fidelity alternatives. After the workshop, surgeons concluded that Thiel cadavers are suitable for surgical simulation (p = 0.015). Thiel were found to be realistic (p < 0.001) to have reduced odour (p = 0.002) and be more cost-effective (p = 0.003). Ethical constraints were considered to be small. Conclusion: Thiel cadavers are suitable for training in most surgical specialties