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Discrete ConvolutionalFixedSum
Get PDFThe ConvolutionalFixedSum (CFS) algorithm provides a generalization of the Unifast and RandFixedSum algorithms enabling the generation of vectors of uniformly sampled random values that sum to a specified total, while respecting upper and lower constraints on each individual element. ConvolutionalFixedSum provides a foundational technique that is used in the generation of synthetic tasks sets that underpin the performance evaluation of real-time scheduling algorithms and schedulability analysis techniques. The CFS algorithm generates continuous values; however, some use cases, for example considering message and packet scheduling on communications networks, require solutions that are constrained to discrete values. Adapting the CFS algorithm to the discrete case is non-trivial, with simple rounding to the nearest lattice point producing a non-uniform distribution. In this paper, we present the Discrete-ConvolutionalFixedSum (DCFS) algorithm, which solves this problem
Smoking cessation after referral from hospital to community stop smoking services: an observational study
Get PDFIntroduction: In England, acute National Health Service (NHS) hospitals routinely ask patients about smoking status on admission, offering in-hospital treatment for tobacco dependence and support for quitting postdischarge. Referring patients to community stop smoking services (CSSS), which offer behavioural and pharmacological support postdischarge, is a key strategy for this continued support. This study investigated the patient flows from hospital to CSSS and the subsequent quitting outcomes.
Methods: This study was part of an evaluation of a hospital tobacco dependence treatment service in South Yorkshire, England. The primary data source was electronic record data from one CSSS that received hospital referrals. Data were from July 2021 to March 2023, covering the initial phase of hospital service implementation. We described patient flows from hospital referral through to the 4-week self-reported quitting outcomes recorded by the CSSS. Generalised linear models explored associations between 4-week abstinence and patient characteristics including demographics, socioeconomic status, nicotine dependence and health factors.
Results: Of 3223 hospital referrals, 72.0% (2322) could be contacted by the CSSS, 52.5% (1692) then registered, 41.4% (1333) made a CSSS-supported quit attempt and 25.3% (815) self-reported abstinence from smoking 4 weeks later. The analysis highlighted lower quitting success for people receiving free NHS prescriptions—an indicator of health and/or socioeconomic vulnerability (OR 0.54, 95% CI 0.32 to 0.90) and with high nicotine dependence (OR 0.57, 95% CI 0.37 to 0.87). Higher quitting success was found for people who reported having cancer (OR 2.26, 95% CI 1.18 to 4.32), but otherwise, there were no significant influences of the health factors investigated.
Conclusions: The substantial drop-out between hospital referral to CSSS and receiving their support for quit attempts is a key area for hospital and CSSS service improvement. The strong quitting success among people with cancer underscores the potential benefits of improved care transfer for vulnerable patient groups
Proposals for sustainable welfare policies
Get PDFSustainable welfare can be defined as welfare or social policy systems that support the satisfaction of human needs within planetary boundaries and in a post-growth context.
Following a brief introduction to the concept of sustainable welfare and thinking around the mutual dependency between welfare states and economic growth, this chapter discusses policy proposals for sustainable welfare that have been presented in the literature so far. Key policy proposals covered in this chapter include: a prioritisation of ecological and social objectives in policy making and at the organisational level; greater redistribution; decoupling of work and welfare through Universal Basic Services and Universal Basic Income or Income Guarantees; and working time reduction.
The discussion section briefly reviews work on the political feasibility of these policy proposals
Analysis and implications of a negative parameter in Tikhonov regularisation
Get PDFThe application of Tikhonov regularisation to the least squares (LS) problem arises frequently in machine learning, for example, in regression and the calculation of the excess risk (out-of-sample prediction error) from a given set of noisy observations. It requires the minimisation with respect to x of a function f(x, λ), where λ is the regularisation parameter. If λ≥0, there exists an optimal value λopt of λ such that the vector x(λopt) that minimises f(x, λ) is numerically stable and its error with respect to x(0) is small. It has been claimed that λopt may be negative, and the aim of this article is the analysis of the consequences of this condition. It is shown theoretically that the condition λ < 0 yields a family of solutions x(λ), each of whose members has a large error and is unstable. Furthermore, the L-curve, which is a method for the calculation of the value of λopt, yields a good result for λ≥0, and it also shows that λ < 0 yields unsatisfactory solutions. The L-curve implies, therefore, that λopt≥0, which is in accord with the theoretical analysis. Examples of LS problems that consider λ < 0 and λ≥0 are shown, and the unsatisfactory results for λ < 0 are evident
Contrasting Seasonal Variation of Photosynthesis in Evergreen and Deciduous Tree Species From a Tropical Forest
Get PDFOne‐Size‐Fits‐All: A Universal Binding Site for Single‐Layer Metal Cluster Self‐Assembly
Get PDF2D metal clusters maximize atom–surface interactions, making them highly attractive for energy and electronic technologies. However, their fabrication remains extremely challenging because they are thermodynamically unstable. Current methods are limited to element-specific binding sites or confinement of metals between layers, with no universal strategy achieved to date. Here, a general approach is presented that uses vacancy defects as universal binding sites to fabricate single-layer metal clusters (SLMC). It is demonstrated that the density of these vacancies governs metal atom diffusion and bonding to the surface, overriding the metal's physicochemical properties. Crucially, the reactivity of vacancy sites must be preserved prior to metal deposition to enable SLMC formation. This strategy is demonstrated across 21 elements and their mixtures, yielding SLMC with areal densities up to 4.3 atoms∙nm⁻2, without heteroatom doping, while maintaining high thermal, environmental, and electrochemical stability. These findings provide a universal strategy for stabilizing SLMC, eliminating the need for element-specific synthesis and metal confinement protocols and offering a strategy for efficiently utilizing metals
Assessing the resilience of urban truck transport networks under the COVID-19 pandemic: A case study of China
Get PDFThe COVID-19 pandemic underscores the critical role of urban truck transport networks in maintaining essential supply chains amidst crises. However, existing research on the resilience of these networks is limited, often relying on aggregated socio-economic data to provide an overview of broader trends. This study addresses the gap by utilizing a large-scale GPS dataset of individual trucks from Chinese cities, enabling a micro-level analysis of urban truck transport network resilience under pandemic. First, we examine how pandemic-induced disruptions reshaped the spatial distribution of freight activities, identifying demand pattern shifts in the city. Second, we introduce two key resilience metrics—handling efficiency and transport efficiency—to determine whether truck transport networks could adapt to fluctuating demand and maintain reliable service across regions, freight hubs, and industry-specific supply chains. The findings reveal significant variations in network resilience across cities, with suburban hubs and critical industries demonstrating higher adaptability, while urban hubs and non-essential sectors faced greater challenges. By providing a micro-level perspective and quantifiable resilience metrics, this study advances the understanding of urban logistics resilience and provides practical insights for policymakers to enhance supply chain reliability in the face of future disruptions
Awakening the sense of the possible: the Symptoms Clinic as liminal affective technology
Get PDFPersistent (‘medically unexplained’) physical symptoms (PPS) that are disproportionate to detectable disease are common in all clinical settings, with significant impacts in terms of quality of life and cost to health services and society. In the absence of an orthodox biomedical explanation, PPS are often attributed to psychological causes and associated with significant stigma. Emerging neuroscientific approaches to symptom explanation imply causal complexity – involving factors across biological, psychological, and social systems – which exceeds what a conventional diagnostic consultation is designed to address. A successful clinical model needs to be able to open, but also contain, a discursive space for the type of complexity that is relevant to PPS. In this paper we present the Symptoms Clinic Intervention (SCI) as a new model of consultation for patients with PPS. While the SCI was developed in the context of a system broadly organised by the norms of biomedicine we argue that, in its operation, it deviates from such norms in significant and instructive ways. Drawing on causal dispositionalism and on liminality theory, we offer an account of the efficacy of the SCI focused on its ability to shift problematic dispositions. We propose that a carefully crafted experience of liminality can catalyse change by shifting hardened dispositions even in the context of a relatively brief and time-limited intervention such as the SCI. Importantly, this shift refers not only to dispositions in and of the patient, but also to the dispositions of the medical system and of the clinician as its operator and representative
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