Explore the vast range of titles available.

This paper considers the problem of estimating the states in an unobservable power system, where the number of measurements is not sufficiently large for conventional state estimation. Existing methods are either based on pseudo-data that is inaccurate or depends on a large amount of data that is unavailable in current systems. This study proposes novel graph signal processing (GSP) methods to overcome the lack of information. To this end, first, the graph smoothness property of the states (i.e., voltages) is validated through empirical and theoretical analysis. Then, the regularized GSP weighted least squares (GSP-WLS) state estimator is developed by utilizing the state smoothness. In addition, a sensor placement strategy that aims to optimize the estimation performance of the GSP-WLS estimator is proposed. Simulation results on the IEEE 118-bus system show that the GSP methods reduce the estimation error magnitude by up to two orders of magnitude compared to existing methods, using only 70 sampled buses, and increase of up to 30% in the probability of bad data detection for the same probability of false alarms in unobservable systems The results conclude that the proposed methods enable an accurate state estimation, even when the system is unobservable, and significantly reduce the required measurement sensors.

In this paper, based on geometric singular perturbation analysis of a quasi-onedimensional Poisson-Nernst-Planck model for ionic flows, we study the problem of zero current condition for ionic flows through membrane channels with a simple profile of permanent charges. For ionic mixtures of multiple ion species, under an equal diffusion constant condition, Eisenberg, Liu, and Xu [Nonlinearity, 28 (2015), pp. 103-128] derived a system of two equations for determining the reversal potential and an equation for the reversal permanent charge. The equal diffusion constant condition is significantly degenerate from physical points of view. For unequal diffusion coefficients, the analysis becomes extremely challenging. This work will focus only on two ion species, one positively charged (cation) and one negatively charged (anion), with two arbitrary diffusion coefficients. Dependence of reversal potential on channel geometry and diffusion coefficients has been investigated experimentally, numerically, and analytically in simple setups, in many works. In this paper, we identify two governing equations for the zero current, which enable one to mathematically analyze how the reversal potential depends on the channel structure and diffusion coefficients. In particular, we are able to show, with a number of concrete results, that the possible different diffusion constants indeed make significant differences. The inclusion of channel structures is also far beyond the situation where the Goldman-Hodgkin-Katz (GHK) equation might be applicable. A comparison of our result with the GHK equation is provided. The dual problem of reversal permanent charges is briefly discussed too.

Topological signal processing is emerging alongside Graph Signal Processing (GSP) in various applications, incorporating higher-order connectivity structures—such as faces—in addition to nodes and edges, for enriched connectivity modeling. Rich point clouds acquired by multi-camera systems in Visual Simultaneous Localization and Mapping (V-SLAM) are typically processed using graph-based methods. In this work, we introduce a topological signal processing (TSP) framework that integrates texture information extracted from V-SLAM; we refer to this framework as TSP-SLAM. We show how TSP-SLAM enables the extension of graph-based point cloud processing to more advanced topological signal processing techniques. We demonstrate, on real stereo data, that TSP-SLAM enables a richer point cloud representation by associating signals not only with vertices but also with edges and faces of the mesh computed from the point cloud. Numerical results show that TSP-SLAM supports the design of topological filtering algorithms by exploiting the mapping between the 3D mesh faces, edges and vertices and their 2D image projections. These findings confirm the potential of TSP-SLAM for topological signal processing of point cloud data acquired in challenging V-SLAM environments.

The transition of the advertising market from traditional media to the internet has induced a proliferation of marketing agencies specialized in bidding in the auctions that are used to sell ad space on the web. We analyze how collusive bidding can emerge from bid delegation to a common marketing agency and how this can undermine the revenues and allocative efficiency of both the generalized second-price auction (GSP, used by Google, Microsoft Bing, and Yahoo!) and the Vickrey–Clarke–Groves (VCG) mechanism (used by Facebook). We find that despite its well-known susceptibility to collusion, the VCG mechanism outperforms the GSP auction in terms of both revenues and efficiency. This paper was accepted by Gabriel Weintraub, revenue management and market analytics .

We describe two auction forms for search engine advertising and present two simple theoretical results concerning i) the estimation of click-through rates and ii) how to adjust the auctions for broad match search. We also describe some of the practical issues involved in implementing a VCG auction.

Background In December 2019, the first case of COVID-19 was described in Wuhan, China, and by July 2022, there were already 540 million confirmed cases. Due to the rapid spread of the virus, the scientific community has made efforts to develop techniques for the viral classification of SARS-CoV-2. Results In this context, we developed a new proposal for gene sequence representation with Genomic Signal Processing techniques for the work presented in this paper. First, we applied the mapping approach to samples of six viral species of the Coronaviridae family, which belongs SARS-CoV-2 Virus. We then used the sequence downsized obtained by the method proposed in a deep learning architecture for viral classification, achieving an accuracy of 98.35%, 99.08%, and 99.69% for the 64, 128, and 256 sizes of the viral signatures, respectively, and obtaining 99.95% precision for the vectors with size 256. Conclusions The classification results obtained, in comparison to the results produced using other state-of-the-art representation techniques, demonstrate that the proposed mapping can provide a satisfactory performance result with low computational memory and processing time costs.

Background Social and Therapeutic Horticulture (STH) is a process where trained practitioners work with plants and people to improve an individual’s physical and psychological health, communication and thinking skills. Evidence suggests that STH can support individuals with mental ill-health, however, current commissioning of STH within mental health care is limited. This study aimed to understand the barriers to commissioning STH in mental health care and to identify potential solutions to barriers, to support more widespread availability of services.  Methods Individuals with a role in mental health care commissioning from across the UK were invited to take part in semi-structured interviews via zoom. Interviews explored factors influencing the mental health services they commission or refer to, their perception of the role of STH in mental health care and the barriers to commissioning STH, together with potential solutions to any barriers identified. Results Commissioners identified a lack of knowledge of STH and evidence of its effectiveness, and a culture which prioritises traditional medical models, as barriers to commissioning. Challenges for STH providers in responding to large-scale commissioning requirements were also highlighted as a barrier. Conclusions To upscale commissioning of STH in mental health care, STH interventions need to be embedded within NHS priorities and information on STH services and their effectiveness needs to be easily accessible to practitioners. The sector should also be supported in working collaboratively to enable commissioning of services at scale.

Smoking is a major risk factor for bladder cancer and postoperative complications. Therefore, urological guidelines strongly recommend smoking cessation. Notwithstanding, many patients continue to smoke beyond the time of diagnosis. By using the qualitative methodology, this study aimed to explore barriers, facilitators, and recommendations related to the intensive smoking cessation Gold Standard Programme (GSP) from the multi-perspective view of patients treated with transurethral resection of the bladder tumour (TURBT), their relatives, and clinicians. We conducted semi-structured individual interviews with eight patients, four relatives, and six clinicians in the urology setting. Data were analysed using the Framework Method. All participants perceived the GSP positively. Across the three groups, five categories emerged describing barriers and facilitators: perceptions of the GSP, pragmatic factors, health-related factors, psychological factors, and relational and communicative factors. Similarly, recommendations were represented in two categories: the GSP and pragmatic factors. While facilitators were relatively similar across the three groups, barriers were dissimilar or contradictory. The clinicians expressed the most challenges related to relational and communicative factors. The patients mainly had recommendations related to the GSP, while the clinicians’ recommendations focused on pragmatic factors for conducting the GSP. The potential involvement of relatives needs to be further investigated.

Background The widespread usage of Cap Analysis of Gene Expression (CAGE) has led to numerous breakthroughs in understanding the transcription mechanisms. Recent evidence in the literature, however, suggests that CAGE suffers from transcriptional and technical noise. Regardless of the sample quality, there is a significant number of CAGE peaks that are not associated with transcription initiation events. This type of signal is typically attributed to technical noise and more frequently to random five-prime capping or transcription bioproducts. Thus, the need for computational methods emerges, that can accurately increase the signal-to-noise ratio in CAGE data, resulting in error-free transcription start site (TSS) annotation and quantification of regulatory region usage. In this study, we present DeepTSS, a novel computational method for processing CAGE samples, that combines genomic signal processing (GSP), structural DNA features, evolutionary conservation evidence and raw DNA sequence with Deep Learning (DL) to provide single-nucleotide TSS predictions with unprecedented levels of performance. Results To evaluate DeepTSS, we utilized experimental data, protein-coding gene annotations and computationally-derived genome segmentations by chromatin states. DeepTSS was found to outperform existing algorithms on all benchmarks, achieving 98% precision and 96% sensitivity (accuracy 95.4%) on the protein-coding gene strategy, with 96.66% of its positive predictions overlapping active chromatin, 98.27% and 92.04% co-localized with at least one transcription factor and H3K4me3 peak. Conclusions CAGE is a key protocol in deciphering the language of transcription, however, as every experimental protocol, it suffers from biological and technical noise that can severely affect downstream analyses. DeepTSS is a novel DL-based method for effectively removing noisy CAGE signal. In contrast to existing software, DeepTSS does not require feature selection since the embedded convolutional layers can readily identify patterns and only utilize the important ones for the classification task. This study highlights the key role that DL can play in Molecular Biology, by removing the inherent flaws of experimental protocols, that form the backbone of contemporary research. Here, we show how DeepTSS can unleash the full potential of an already popular and mature method such as CAGE, and push the boundaries of coding and non-coding gene expression regulator research even further.