4,318 research outputs found
Implementing screening and brief Interventions for excessive alcohol consumption in primary health care
O consumo de bebidas alcoólicas é um dos principais fatores de risco da morbilidade e mortalidade prematura a nÃvel mundial. As pessoas que consomem este género de bebidas têm um risco aumentado de vir a desenvolver mais de 200 problemas de saúde diferentes. A maioria do impacto do consumo de álcool na saúde humana é determinado por duas dimensões: o volume total de álcool consumido e o padrão de consumo. Existem várias medidas com comprovada eficácia que podem ser empregues para reduzir o risco associado ao consumo de álcool, entre as quais se encontra a deteção precoce e intervenção breve ao nÃvel dos Cuidados de Saúde Primários. A maioria dos profissionais de saúde neste nÃvel de cuidados considera o consumo de álcool como um importante problema de saúde e manifesta o seu apoio a medidas que visem reduzir o seu impacto. No entanto, poucos são os profissionais dos Cuidados de Saúde Primários que de forma sistemática identificam e aconselham os seus doentes relativamente aos seus hábitos etÃlicos. Como tal, o objetivo geral desta tese foi investigar como implementar a deteção precoce e intervenção breve no consumo excessivo de álcool nos Cuidados de Saúde Primários.
Foi realizada uma revisão sistemática das barreiras e facilitadores à implementação da deteção precoce e intervenção breve no consumo excessivo de álcool nos Cuidados de Saúde Primários. As barreiras e facilitadores identificados nesta revisão foram analisados à luz da teoria de modificação comportamental para compreender a ligação destes fatores aos determinantes da mudança de comportamento, e para identificar as estratégias conceptualmente mais eficazes para abordar as barreiras e facilitadores à mudança de comportamento dos profissionais dos Cuidados de Saúde Primários no sentido de aumentar as taxas de deteção precoce e intervenção breve no consumo excessivo de álcool. Esta metodologia foi utilizada para desenhar um programa de implementação com base em pressupostos teóricos que foi testado num estudo experimental randomizado e controlado em clusters.
Esta tese identificou diversas barreiras à implementação, ligadas a todos os domÃnios teóricos da mudança comportamental. As barreiras mais frequentemente mencionadas pelos profissionais foram: preocupação sobre as suas competências e eficácia para realizar a deteção precoce e intervenção breve; falta de conhecimento especÃfico sobre o consumo de álcool; falta de tempo; falta de materiais; falta de apoio; e atitudes para com o doente com consumos excessivos de álcool. Esta tese mostrou também a existência de dois grupos distintos de médicos de famÃlia com base nas suas atitudes para com estes doentes, um com atitudes mais positivas, o outro com atitudes mais negativas. Esta tese mostrou ainda que um programa de implementação da deteção precoce e intervenção breve, desenhado com base em pressupostos teóricos de modificação comportamental, adaptado à s barreiras e facilitadores da implementação, aumenta de forma significativa as taxas de identificação precoce dos consumos de álcool.
Esta tese contribui para aumentar o conhecimento atual no sentido em que põe à disposição dos investigadores evidência prática sobre como abordar os fatores com influência na implementação da identificação precoce e intervenção breve para o consumo de álcool ao nÃvel dos Cuidados de Saúde Primários. Esta tese contribui também para um melhor entendimento dos mecanismos subjacentes à resistência e à mudança de comportamento dos profissionais dos Cuidados de Saúde Primários no que respeita à implementação da deteção precoce e intervenção breve do consumo de álcool. Os resultados desta tese poderão ser usados por investigadores e decisores polÃticos para desenhar novos programas de implementação tendo como objetivo modificar esta prática clÃnica ao nÃvel dos Cuidados de Saúde Primários.Alcohol use is among the leading risk factors for the global burden of disease and premature death. People who drink alcoholic beverages are at risk of developing more than 200 diseases and injury conditions. Most of the impact of alcohol consumption on human health and well-being is determined by two dimensions of drinking: the total volume of alcohol consumed and the pattern of drinking. Several effective strategies exist to reduce the harmful use of alcohol, which includes screening and brief interventions for excessive alcohol use in primary health care. The majority of primary health care providers agree that the excessive consumption of alcohol is an important health issue and express their support to policies for reducing the impact of alcohol on the health of their patients. Notwithstanding, implementation of screening and brief interventions is low at the primary health care level. Therefore, the overall aim of this thesis is to investigate how to implement screening and brief interventions for excessive alcohol consumption in primary health care.
This thesis reviewed the barriers of, and facilitators for, the implementation of alcohol screening and brief interventions in primary health care. Behaviour change theory was used to understand how these factors linked to the determinants of behaviour change and how they could be addressed in order to change primary health care providers’ behaviour, i.e. to increase the delivery of alcohol screening and brief interventions. A comprehensive theory-based implementation programme was designed and tested in a cluster randomized controlled trial.
This thesis identified several barriers to implementation which were mapped to all the theoretical domains of behaviour change. Primary health care providers concerns about their ability to deliver alcohol screening and brief interventions and to help patients to cut down, lack of alcohol-related knowledge, lack of time, lack of materials and support, and providers’ attitudes towards at-risk drinkers were among the most commonly cited barriers. This thesis found evidence that the attitudes of family physicians could be used to divide practitioners into two distinct groups, one with more positive and the other with more negative attitudes towards at-risk drinkers.
This thesis also found that a behaviour change theory-based programme, tailored to the barriers for, and facilitators of, the implementation of screening and brief intervention in primary health care is effective in increasing alcohol screening rates.
This thesis contributed to the evidence base by providing researchers with practical evidence on how to address the factors influencing the implementation of screening and brief interventions in primary health care. This thesis also provides researchers with insight into the behavioural mechanisms mediating primary health care providers’ decision to deliver alcohol screening and brief interventions. The results of this thesis could be used by researchers and policymakers to inform the design of novel theory-oriented interventions to support the implementation of alcohol screening and brief interventions in primary health care
A revisited branch-and-cut algorithm for large-scale orienteering problems
The orienteering problem is a route optimization problem which consists of finding a simple cycle that maximizes the total collected profit subject to a maximum distance limitation. In the last few decades, the occurrence of this problem in real-life applications has boosted the development of many heuristic algorithms to solve it. However, during the same period, not much research has been devoted to the field of exact algorithms for the orienteering problem. The aim of this work is to develop an exact method which is able to obtain the optimum in a wider set of instances than with previous methods, or to improve the lower and upper bounds in its disability.
We propose a revisited version of the branch-and-cut algorithm for the orienteering problem which includes new contributions in the separation algorithms of inequalities stemming from the cycle problem, in the separation loop, in the variables pricing, and in the calculation of the lower and upper bounds of the problem. Our proposal is compared to three state-of-the-art algorithms on 258 benchmark instances with up to 7397 nodes. The computational experiments show the relevance of the designed components where 18 new optima, 76 new best-known solutions and 85 new upper-bound values were obtained.The authors are partially supported by the projects BERC 2022-2025 (Basque Government) and by SEV-2017-0718 (Spanish Ministry of Science, Innovation and Universities). The first and third authors are partially supported by the grant PID2019-104933GB-I00 funded by MCIN/AEI/10.13039/ 501100011033 (Spanish Ministry of Science and Innovation). The first author is also supported by the grant BES-2015-072036 (Spanish Ministry of Economy and Competitiveness) and project ELKARTEK (Basque Government). The third author is supported by IT-1494-22 (Basque Government) and GIU20/054 (University of the Basque Country). The fourth author is also supported by IT-1504-22 (Basque Government) and the grants PID2019-104966GB-I00 and PID2019-106453GA-I00 funded by MCIN/AEI/10.13039/501100011033 (Spanish Ministry of Science and Innovation). We gratefully acknowledge the authors of the TSP solver Concorde for making their code available to the public, since it has been the working basis of our implementations. We also thank Prof. J.J. Salazar-Gonzalez who provided us with the codes used in Fischetti et al. (1998)
Towards Neuromorphic Gradient Descent: Exact Gradients and Low-Variance Online Estimates for Spiking Neural Networks
Spiking Neural Networks (SNNs) are biologically-plausible models that can run on low-powered non-Von Neumann neuromorphic hardware, positioning them as promising alternatives to conventional Deep Neural Networks (DNNs) for energy-efficient edge computing and robotics. Over the past few years, the Gradient Descent (GD) and Error Backpropagation (BP) algorithms used in DNNs have inspired various training methods for SNNs. However, the non-local and the reverse nature of BP, combined with the inherent non-differentiability of spikes, represent fundamental obstacles to computing gradients with SNNs directly on neuromorphic hardware. Therefore, novel approaches are required to overcome the limitations of GD and BP and enable online gradient computation on neuromorphic hardware.
In this thesis, I address the limitations of GD and BP with SNNs by proposing three algorithms. First, I extend a recent method that computes exact gradients with temporally-coded SNNs by relaxing the firing constraint of temporal coding and allowing multiple spikes per neuron. My proposed method generalizes the computation of exact gradients with SNNs and enhances the tradeoffs between performance and various other aspects of spiking neurons. Next, I introduce a novel alternative to BP that computes low-variance gradient estimates in a local and online manner. Compared to other alternatives to BP, the proposed method demonstrates an improved convergence rate and increased performance with DNNs. Finally, I combine these two methods and propose an algorithm that estimates gradients with SNNs in a manner that is compatible with the constraints of neuromorphic hardware. My empirical results demonstrate the effectiveness of the resulting algorithm in training SNNs without performing BP
Projection-free methods for solving smooth convex bilevel optimisation problems
When faced with multiple minima of an "inner-level" convex optimisation problem, the convex bilevel optimisation problem selects an optimal solution which also minimises an auxiliary "outer-level" convex objective of interest. Bilevel optimisation requires a different approach compared to single-level optimisation problems since the set of minimisers for the inner-level objective is not given explicitly. In this thesis, we propose new projection-free methods for convex bilevel optimisation which require only a linear optimisation oracle over the base domain. We provide convergence guarantees for both inner- and outer-level objectives that hold under our proposed projection-free methods. In particular, we highlight how our guarantees are affected by the presence or absence of an optimal dual solution. Lastly, we conduct numerical experiments that demonstrate the performance of the proposed methods
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Faster Discrete Convex Function Minimization with Predictions: The M-Convex Case
Recent years have seen a growing interest in accelerating optimization
algorithms with machine-learned predictions. Sakaue and Oki (NeurIPS 2022) have
developed a general framework that warm-starts the L-convex function
minimization method with predictions, revealing the idea's usefulness for
various discrete optimization problems. In this paper, we present a framework
for using predictions to accelerate M-convex function minimization, thus
complementing previous research and extending the range of discrete
optimization algorithms that can benefit from predictions. Our framework is
particularly effective for an important subclass called laminar convex
minimization, which appears in many operations research applications. Our
methods can improve time complexity bounds upon the best worst-case results by
using predictions and even have potential to go beyond a lower-bound result
Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics
We study a game between autobidding algorithms that compete in an online
advertising platform. Each autobidder is tasked with maximizing its
advertiser's total value over multiple rounds of a repeated auction, subject to
budget and/or return-on-investment constraints. We propose a gradient-based
learning algorithm that is guaranteed to satisfy all constraints and achieves
vanishing individual regret. Our algorithm uses only bandit feedback and can be
used with the first- or second-price auction, as well as with any
"intermediate" auction format. Our main result is that when these autobidders
play against each other, the resulting expected liquid welfare over all rounds
is at least half of the expected optimal liquid welfare achieved by any
allocation. This holds whether or not the bidding dynamics converges to an
equilibrium and regardless of the correlation structure between advertiser
valuations
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space
We consider the reinforcement learning (RL) problem with general utilities
which consists in maximizing a function of the state-action occupancy measure.
Beyond the standard cumulative reward RL setting, this problem includes as
particular cases constrained RL, pure exploration and learning from
demonstrations among others. For this problem, we propose a simpler single-loop
parameter-free normalized policy gradient algorithm. Implementing a recursive
momentum variance reduction mechanism, our algorithm achieves
and
sample complexities for -first-order stationarity and
-global optimality respectively, under adequate assumptions. We
further address the setting of large finite state action spaces via linear
function approximation of the occupancy measure and show a
sample complexity for a simple policy
gradient method with a linear regression subroutine.Comment: 48 pages, 2 figures, ICML 2023, this paper was initially submitted in
January 26th 202
Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning
We examine online safe multi-agent reinforcement learning using constrained
Markov games in which agents compete by maximizing their expected total rewards
under a constraint on expected total utilities. Our focus is confined to an
episodic two-player zero-sum constrained Markov game with independent
transition functions that are unknown to agents, adversarial reward functions,
and stochastic utility functions. For such a Markov game, we employ an approach
based on the occupancy measure to formulate it as an online constrained
saddle-point problem with an explicit constraint. We extend the Lagrange
multiplier method in constrained optimization to handle the constraint by
creating a generalized Lagrangian with minimax decision primal variables and a
dual variable. Next, we develop an upper confidence reinforcement learning
algorithm to solve this Lagrangian problem while balancing exploration and
exploitation. Our algorithm updates the minimax decision primal variables via
online mirror descent and the dual variable via projected gradient step and we
prove that it enjoys sublinear rate for
both regret and constraint violation after playing episodes of the game.
Here, is the horizon of each episode, and are the
state/action space sizes of the min-player and the max-player, respectively. To
the best of our knowledge, we provide the first provably efficient online safe
reinforcement learning algorithm in constrained Markov games.Comment: 59 pages, a full version of the main paper in the 5th Annual
Conference on Learning for Dynamics and Contro
Economic power dispatch solutions incorporating stochastic wind power generators by moth flow optimizer
Optimization encourages the economical and efficient operation of the electrical system. Most power system problems are nonlinear and nonconvex, and they frequently ask for the optimization of two or more diametrically opposed objectives. The numerical optimization revolution led to the introduction of numerous evolutionary algorithms (EAs). Most of these methods sidestep the problems of early convergence by searching the universe for the ideal. Because the field of EA is evolving, it may be necessary to reevaluate the usage of new algorithms to solve optimization problems involving power systems. The introduction of renewable energy sources into the smart grid of the present enables the emergence of novel optimization problems with an abundance of new variables. This study's primary purpose is to apply state-of-the-art variations of the differential evolution (DE) algorithm for single-objective optimization and selected evolutionary algorithms for multi-objective optimization issues in power systems. In this investigation, we employ the recently created metaheuristic algorithm known as the moth flow optimizer (MFO), which allows us to answer all five of the optimal power flow (OPF) difficulty objective functions: (1) reducing the cost of power generation (including stochastic solar and thermal power generation), (2) diminished power, (3) voltage variation, (4) emissions, and (5) reducing both the cost of power generating and emissions. Compared to the lowest figures provided by comparable approaches, MFO's cost of power production for IEEE-30 and IEEE-57 bus architectures is 31121.85 per hour, respectively. This results in hourly cost savings between 1.23 % and 1.92%. According to the facts, MFO is superior to the other algorithms and might be utilized to address the OPF problem
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