ESBMC-Jimple: Verifying Kotlin Programs via Jimple Intermediate Representation

In this work, we describe and evaluate the first model checker for verifying Kotlin programs through the Jimple intermediate representation. The verifier, named ESBMC-Jimple, is built on top of the Efficient SMT-based Context-Bounded Model Checker (ESBMC). It uses the Soot framework to obtain the Jimple IR, representing a simplified version of the Kotlin source code, containing a maximum of three operands per instruction. ESBMC-Jimple processes Kotlin source code together with a model of the standard Kotlin libraries and checks a set of safety properties. Experimental results show that ESBMC-Jimple can correctly verify a set of Kotlin benchmarks from the literature and that it is competitive with state-of-theart Java bytecode verifiers. A demonstration is available at https://youtu.be/J6WhNfXvJNc

Differences in walking football initiation and maintenance influences across respondent characteristics: a cross-sectional survey in 50-75 year -old adults

Despite health benefits gained from physical activity and sport participation, older adults are less likely to be active. This study investigates what influences 50–75-year-olds (N=439) to initiate and maintain walking football, across gender, socioeconomic status, number of health conditions and physical activity level. It also considers relationships between participant characteristics and influences, and intentions to play after a forced break (COVID-19). Results of a UK online cross-sectional survey found those with two or more health conditions rated social influences significantly higher in initiation and maintenance, than participants with no health conditions. Multiple regression analysis found a positive walking football culture and perceived use of maintenance resources contributed significantly to intentions to return to play after COVID-19 restrictions eased. Practitioners should consider providing opportunities for social connection, foster a positive walking football culture, and encourage players to utilise maintenance resources (e.g., scheduling sessions) in older adult walking football sessions.Keywords: behaviour change, soccer, physical activity, survey research, older adults<br/

Effect of Temperature on the Preferential Intergranular Oxidation Susceptibility of Thermally-Treated Alloy 600

Thermally-treated Alloy 600 coupons were tested in pressurized water reactor simulated primary water at 320 °C and in high-pressure superheated steam at 400 °C to study the effect of a “semi-continuous” network of intergranular carbides on the stress corrosion cracking “precursors events”: preferential intergranular oxidation (PIO) and diffusion-induced grain boundary migration (DIGM). The intergranular carbides were partially consumed due to the environmental exposure, whereby the Cr released from the carbides contributed to the formation of the external oxide layer that limited both PIO and DIGM. The results were compared with previous studies in which similar coupons were exposed to H2-steam

Customized Multi-Energy Pricing in Smart Grids: A Bilevel and Evolutionary Computation Approach

This paper proposes a customized energy pricing scheme for energy retailers in multi-energy (i.e., electricity and natural gas) retail markets. Microgrids with distributed energy resources (DERs) and demand response (DR) programs are considered on the demand side. We adopt a bilevel single-leader multi-follower model to analyze the customized multi-energy pricing decisions where the retailer’s profit maximization problem is formulated at the upper level, and the microgrids’ operation costs minimization problems are considered at the lower level. A particle swarm optimization (PSO) based evolutionary solution approach is developed to solve the proposed bilevel decision-making problem. Through a numerical case study, we demonstrate the feasibility and effectiveness of the proposed bilevel model and the solution algorithm. We reveal that the proposed customized pricing scheme could offer differentiated optimal pricing decisions to various microgrids characterized by their energy conversion efficiencies

IDENTIFYING THE BEST METHODS TO REPORT ON THE POST-VACCINATION SURVEILLANCE OF PHYSICAL SIDE-EFFECTS OF THE COVID-19 VACCINES: A SYSTEMATIC REVIEW

Introduction: On the 11th of March 2020, the World Health Organisation declared COVID-19 to be a global pandemic. This had led to the development of many novel vaccines for which effective post-vaccination surveillance is essential. This study aims to synthesise post-vaccination surveillance of physical side-effects to learn the best method that can be applied to the surveillance of novel COVID-19 vaccines. Methods: A full systematic search was performed on four databases following the PRISMA guidelines. Following the inclusion and exclusion criteria, and hand-searching the eligible papers, a total of seven studies were included in this review. The risk of bias within studies and their quality was assessed using the Critical Appraisal for Public Health Checklist.Results: The seven studies included were from six countries representing both active and passive surveillance systems. The results showed reliability and reproducibility between data across countries. They also provided a good framework for how post-vaccination surveillance can be performed in low-income countries. Conclusions: The reporting of post-vaccine side-effects is essential. Active and passive systems were essential to encourage reporting of AEFIs and should be encouraged globally. A combination of active and passive surveillance should be used to monitor adverse events relating to the novel COVID-19 vaccines

Hybrid Autoregressive Inference for Scalable Multi-hop Explanation Regeneration

Regenerating natural language explanations in the scientific domain has been proposed as a benchmark to evaluate complex multi-hop and explainable inference. In this context, large language models can achieve state-of-the-art performance when employed as cross-encoder architectures and fine-tuned on human-annotated explanations. However, while much attention has been devoted to the quality of the explanations, the problem of performing inference efficiently is largely under-studied. Cross-encoders, in fact, are intrinsically not scalable, possessing limited applicability to real world scenarios that require inference on massive facts banks.To enable complex multi-hop reasoning at scale, this paper focuses on bi-encoder architectures, investigating the problem of scientific explanation regeneration at the intersection of dense and sparse models. Specifically, we present SCAR (for Scalable Autoregressive Inference), a hybrid framework that iteratively combines a Transformer-based bi-encoder with a sparse model of explanatory power, designed to leverage explicit inference patterns in the explanations. Our experiments demonstrate that the hybrid framework significantly outperforms previous sparse models, achieving performance comparable with that of state-of-the-art cross-encoders while being ≈ 50 times faster and scalable to corpora of millions of facts. Further analyses on semantic drift and multi-hop question answering reveal that the proposed hybridisation boosts the quality of the most challenging explanations, contributing to improved performance on downstream inference tasks

Understanding the Effects of Zn Injection and OLNC-Treatment on 316 Stainless Steel Oxide under Simulated BWR Conditions

This study investigates the mechanism of incorporation of metal cations (Zn and Co) into the 316 stainless steel (SS) oxide under hydrogenated water chemistry (HWC) and On-Line NobleChemTM (OLNC). Coupons of 316 SS were exposed to simulated boiling water reactor (BWR) conditions (pure water, H:O molar ratio ~8) at 288 °C and then analysed via surface characterisation techniques. Specifically, coupons were initially exposed under HWC conditions for 500 h, then subjected for 200 h to the OLNCtreatment, and then a final exposure under HWC conditions for 500 h, all with simultaneous monitoring of the electrochemical corrosion potential. These exposures were performed both with and without Zn and or Co injection. It was found that Zn decreased the oxide thickness of the inner oxide layer and decreased the size of the crystallites on the outer layer. Addition of 0.2 ppb of Co in the water chemistry containing 10 ppb Zn did not appear to influence the oxide morphology nor its composition. Hard X-ray XPS analysis showed that Zn did not completely suppress Co incorporation in the outer oxide layer which was still found in concentrations up to 0.6 at. % on the surface of the oxide under Zn addition conditions

The TRANSCEND University Consortium: Integrated Waste Management

TRANSCEND (Transformative Science and Engineering for Nuclear Decommissioning) is a collaborative research consortium comprising 11 universities and 8 industry partners. The £9.4 million research program, funded primarily by the Engineering and Physical Sciences (EPSRC) Research Council of the UK, incorporates &gt;40 projects in total, which will address some of the key challenges within the areas of nuclear decommissioning and waste management; including mobilization, processing, packaging, storage, transport and final disposal. This paper will outline a summary of the current progress and impact of Theme 1 - Integrated Waste Management. This theme focuses on underpinning science and engineering in areas of relevance to hazard reduction and decommissioning, where the three key work package objectives are: (1) New materials and methods for effluent decontamination; (2) Modelling and experiments for understanding pond and silo sludge/slurry behavior; (3) Innovative wasteform materials. In total, this theme has 15 different projects, delivered through both postdoctoral and PhD researchers, all with specific industry supervision from our partners, led by the NNL. The following provides a review of the project summaries to date, and their critical impact