Protocol for the cultural adaptation of pulmonary rehabilitation and subsequent testing in a randomised controlled feasibility trial for adults with chronic obstructive pulmonary disease in Sri Lanka

Introduction: International guidelines recommend pulmonary rehabilitation (PR) should be offered to adults living with chronic obstructive pulmonary disease (COPD), but PR availability is limited in Sri Lanka. Culturally appropriate PR needs to be designed and implemented in Sri Lanka. The study aims to adapt PR to the Sri Lankan context and determine the feasibility of conducting a future trial of the adapted PR in Sri Lanka. Methods and analysis: Eligible participants will be identified and will be invited to take part in the randomised controlled feasibility trial, which will be conducted in Central Chest Clinic, Colombo, Sri Lanka. A total of 50 participants will be recruited (anticipated from April 2021) to the trial and randomised (1:1) into one of two groups; control group receiving usual care or the intervention group receiving adapted PR. The trial intervention is a Sri Lankan-specific PR programme, which will consist of 12 sessions of exercise and health education, delivered over 6 weeks. Focus groups with adults living with COPD, caregivers and nurses and in-depth interviews with doctors and physiotherapist will be conducted to inform the Sri Lankan specific PR adaptations. After completion of PR, routine measures in both groups will be assessed by a blinded assessor. The primary outcome measure is feasibility, including assessing eligibility, uptake and completion. Qualitative evaluation of the trial using focus groups with participants and in-depth interviews with PR deliverers will be conducted to further determine feasibility and acceptability of PR, as well as the ability to run a larger future trial. Ethics and dissemination: Ethical approval was obtained from the ethics review committee of Faculty of Medical Sciences, University of Sri Jayewardenepura, Sri Lanka and University of Leicester, UK. The results of the trial will be disseminated through patient and public involvement events, local and international conference proceedings, and peer-reviewed journals. Trial registration number ISRCTN1336773

Search for jet extinction in the inclusive jet-pT spectrum from proton-proton collisions at s=8 TeV

Published by the American Physical Society under the terms of the Creative Commons Attribution 3.0 License. Further distribution of this work must maintain attribution to the author(s) and the published articles title, journal citation, and DOI.The first search at the LHC for the extinction of QCD jet production is presented, using data collected with the CMS detector corresponding to an integrated luminosity of 10.7  fb−1 of proton-proton collisions at a center-of-mass energy of 8 TeV. The extinction model studied in this analysis is motivated by the search for signatures of strong gravity at the TeV scale (terascale gravity) and assumes the existence of string couplings in the strong-coupling limit. In this limit, the string model predicts the suppression of all high-transverse-momentum standard model processes, including jet production, beyond a certain energy scale. To test this prediction, the measured transverse-momentum spectrum is compared to the theoretical prediction of the standard model. No significant deficit of events is found at high transverse momentum. A 95% confidence level lower limit of 3.3 TeV is set on the extinction mass scale

Searches for electroweak neutralino and chargino production in channels with Higgs, Z, and W bosons in pp collisions at 8 TeV

Searches for supersymmetry (SUSY) are presented based on the electroweak pair production of neutralinos and charginos, leading to decay channels with Higgs, Z, and W bosons and undetected lightest SUSY particles (LSPs). The data sample corresponds to an integrated luminosity of about 19.5 fb(-1) of proton-proton collisions at a center-of-mass energy of 8 TeV collected in 2012 with the CMS detector at the LHC. The main emphasis is neutralino pair production in which each neutralino decays either to a Higgs boson (h) and an LSP or to a Z boson and an LSP, leading to hh, hZ, and ZZ states with missing transverse energy (E-T(miss)). A second aspect is chargino-neutralino pair production, leading to hW states with E-T(miss). The decays of a Higgs boson to a bottom-quark pair, to a photon pair, and to final states with leptons are considered in conjunction with hadronic and leptonic decay modes of the Z and W bosons. No evidence is found for supersymmetric particles, and 95% confidence level upper limits are evaluated for the respective pair production cross sections and for neutralino and chargino mass values

Self-energized full-duplex UAV-assisted cooperative communication systems

In this paper, we propose a unified energy harvesting scheme using wireless power transfer (WPT), simultaneous wireless information and power transfer (SWIPT) and loop-back self-interference energy harvesting (SI-EH) enabled full-duplex (FD) cooperative communication system for unmanned aerial vehicles (UAVs). In contrast to traditional UAV assisted cooperative networks, here the UAV relies on alternative energy sources rather than pre-charged battery. Furthermore, the optimal time allocation for WPT and SWIPT scheme is obtained theoretically. Considering a delay-limited transmission mode, we derive an approximate close-form expression for the outage probability and the average throughput of the proposed system.This work was supported, in part, by sponsorship agreement in support of research by Ooredoo, Doha, Qatar, in part, by the Scheme for Promotion of Academic and Research Collaboration (SPARC), Ministry of Human Resource Development, India under the No. P145. The statements made herein are solely the responsibility of the authors.Scopu

Impacts of extreme climate conditions due to climate change on the energy system design and operation

Extreme climate events occur more frequently and stronger in the future due to climate change. Maintaining the energy security during extreme conditions is essential to reduce the impacts of extreme climate and avoid disasters. Resilient design of the energy system to resist against extreme climate events are investigated considering four scenarios, namely, typical demand (TD), extreme demand (ED), extreme renewable energy generation (ER) and, extreme demand and renewable generation (EDR). A regional climate model is used to develop the four scenarios with the assistance of a building simulation model. Subsequently, multi-energy hub is optimized for each scenario considering net present value (NPV) and grid integration (GI) level as the objective functions. A significant difference in objective function values is observed when analyzing the four scenarios. Similarly, a significant difference in the energy system design is observed when moving from one scenario to another. The results of the study reveal that a energy system design is strongly influenced by extreme climate scenario considered which will make the energy system to be a sub-optimal when operating at a different climatic condition with a significant performance gap. Therefore, improving the climate flexibility of energy systems is an essential task which is challenging at the early design process

Redefining energy system flexibility for distributed energy system design

A novel method is introduced in this study to consider flexibility taking into account both system design and operation strategy by using fuzzy logic. A stochastic optimization algorithm is introduced to optimize the system design and operation strategy of the energy system while considering the flexibility. GPU (Graphics Processing Unit)-accelerated computing is introduced to speed up the computation process when computing the expected values of the objective functions considering a pool up to 5832 scenarios. Subsequently, a Pareto optimization is conducted considering Net Present Value (NPV), Grid Integration (GI) level (which represents the autonomy level of the energy system) and system flexibility. The case study assesses an energy system design problem for the city of Lund in Sweden. According to the obtained NPV and GI Pareto front, a renewable energy penetration level covering more than 45% of the annual demand of the energy hub (an integrated energy system consisting of wind turbines, solar PV panels, internal combustion generator and a battery bank) can be achieved. However, the flexibility of the system notably decreases when the renewable energy penetration level exceeds above 30%. Furthermore, the results show that poor system flexibility notably increases the risk of higher-loss of load probability and operation cost. It is also shown that the utility grid acts as a virtual storage when integrating renewable energy sources. In this context, a grid dependency level of 25–30% (of the annual energy demand) is sufficient while reaching a renewable energy penetration level of 30% and maintaining the system flexibility

Integrating renewable energy technologies into distributed energy systems maintaining system flexibility

Flexibility of the energy system plays a vital role when integrating non-dispatchable renewable energy technologies. However, flexibility of the energy system has been often discussed only focusing on the operation of the energy system. This study extends the flexibility concept considering both design and operation of the energy system. In order to achieve this, pseudo chronological scenarios used for stochastic optimization is used to define system flexibility. Multiple criterions are considered when evaluating the flexibility of the system and fuzzy logic is used to consider the ambiguity in the assessment process when localizing into a specific application. Subsequently, multi objective optimization is conducted to design a multi-energy hub considering net present value (NPV), system flexibility and renewable energy generation. GPU-accelerated computing is introduced to speed up the computing when evaluating the objective functions for number of scenarios. Results of the study show that poor system flexibility can leads to poor utilization of renewable energy generated. More importantly, penetration levels of non-dispatchable renewable energy technologies notably reduce by 20-30% when considering the flexibility of the energy system which guarantees robust operation

Introducing reinforcement learning to the energy system design process

Design optimization of distributed energy systems has become an interest of a wider group of researchers due the capability of these systems to integrate non-dispatchable renewable energy technologies such as solar PV and wind. White box models, using linear and mixed integer linear programing techniques, are often used in their design. However, the increased complexity of energy flow (especially due to cyber-physical interactions) and uncertainties challenge the application of white box models. This is where data driven methodologies become effective, as they demonstrate higher flexibility to adapt to different environments, which enables their use for energy planning at regional and national scale. This study introduces a data driven approach based on reinforcement learning to design distributed energy systems. Two different neural network architectures are used in this work, i.e. a fully connected neural network and a convolutional neural network (CNN). The novel approach introduced is benchmarked using a grey box model based on fuzzy logic. The grey box model showed a better performance when optimizing simplified energy systems, however it fails to handle complex energy flows within the energy system. Reinforcement learning based on fully connected architecture outperformed the grey box model by improving the objective function values by 60%. Reinforcement learning based on CNN improved the objective function values further (by up to 20% when compared to a fully connected architecture). The results reveal that data-driven models are capable to conduct design optimization of complex energy systems. This opens a new pathway in designing distributed energy systems

Machine learning methods to assist energy system optimization

This study evaluates the potential of supervised and transfer learning techniques to assist energy system optimization. A surrogate model is developed with the support of a supervised learning technique (by using artificial neural network) in order to bypass computationally intensive Actual Engineering Model (AEM). Eight different neural network architectures are considered in the process of developing the surrogate model. Subsequently, a hybrid optimization algorithm (HOA) is developed combining Surrogate and AEM in order to speed up the optimization process while maintaining the accuracy. Pareto optimization is conducted considering Net Present Value and Grid Integration level as the objective functions. Transfer learning is used to adapt the surrogate model (trained using supervised learning technique) for different scenarios where solar energy potential, wind speed and energy demand are notably different. Results reveal that the surrogate model can reach to Pareto solutions with a higher accuracy when grid interactions are above 10% (with reasonable differences in the decision space variables). HOA can reach to Pareto solutions (similar to the solutions obtained using AEM) around 17 times faster than AEM. The Surrogate Models developed using Transfer Learning (SMTL) shows a similar capability. SMTL combined with the optimization algorithm can predict Pareto fronts efficiently even when there are significant changes in the initial conditions. Therefore, STML can be used along with the HOA, which reduces the computational time required for energy system optimization by 84%. Such a significant reduction in computational time enables the approach to be used for energy system optimization at regional or national scale