Abstract

Abstract
Memory safety issues in C are the origin of various vulnerabilities that can compromise a program's correctness or safety from attacks. We propose a different approach to tackle memory safety, the replication of Rust's Mid-level Intermediate Representation (MIR) Borrow Checker, through the usage of static analysis and successive source-to-source code transformations, to be composed upstream of the compiler, thus ensuring maximal compatibility with most build systems. This allows us to approximate a subset of C to Rust's core concepts, applying the memory safety guarantees of the rustc compiler to C. In this work, we present a survey of Rust's efforts towards ensuring memory safety, and describe the theoretical basis for a C borrow checker, alongside a proof-of-concept that was developed to demonstrate its potential. This prototype correctly identified violations of the ownership and aliasing rules, and accurately reported each error with a level of detail comparable to that of the rustc compiler.

Abstract

Abstract
The United Nations has issued a warning over the limited time for climate disaster prevention. In the last two decades, several countries have set targets to reduce fossil fuel usage and greenhouse gas emissions. These goals are tracked through the adoption of energy systems that prioritise efficiency and low-carbon alternatives, in alignment with the Sustainable Development Goals outlined by the United Nations. In the winemaking sector, the wine produced in the European Union comprised 65 % of the worldwide total from 2014 to 2018, with vineyards making up 4.7 % of its farms in 2020. Electricity is the primary source of energy used in vineries, accounting for around 90 % of the total energy consumption. The energy consumption associated with winemaking is mostly attributed to two key processes: fermentation, which accounts for 45 % to 90 % of the entire energy consumption, and bottling and storage, which contribute around 18 % of the overall energy consumption. The aim of this article is to provide an integrated review of energy efficiency in wineries through examining 144 academic publications. The selected publications cover various aspects, including sustainable energy utilisation in the wine industry, thermal performance analysis of buildings, energy efficiency assessment of systems and technologies, and the integration of renewable energy sources. A link has been established between the geographic distribution of academic publications and wine -producing countries. In relation to European publications, it is observed that research funding is associated with the energy directives of the European Union. It can also be concluded that wine customers are pushing for environmentally friendly practices. However, not everyone in the winemaking sector is moving in the same direction or at the same pace. To identify areas for improvement, winemakers must have supporting tools to manage energy use. Systems optimisation, monitoring, and accounting can be used to decrease energy consumption in winemaking processes or equipment. Progresses on sustainable energy use through greater energy efficiency and share of renewable energies in the wineries can contribute to the reduction of greenhouse gas emissions, and consequently, brings the wine industry closer to climate neutrality.

Abstract
Artificial intelligence (AI) is a strategy for global economic development due to its economic potential. However, the need for more transparency in AI applications generates mistrust because of the complexity of the algorithms. AI has transformed the service industry along with the development and challenge of human-AI interactions. This interaction can elicit negative feelings in consumers, creating communities to voice their disapproval and hatred of brands. Research in this area needs to be improved, and this study aims to understand the negative feelings that result from human-AI interaction in online communities (Reddit). Using sentiment analysis techniques and a qualitative approach, we aimed to identify the predominant negative emotions generated by this interaction. This study also hopes to understand the emotional effects of this interaction better, thus filling in a gap in the literature. The insights obtained can help develop more effective interaction strategies between humans and AI that can benefit brands and society. The results show a sizable presence of negative feelings such as hate anger and frustration. It is, therefore, essential to understand the negative interactions between consumers, brands and AI and the need to develop strategies to mitigate these feelings. Contributions from the academic and corporate fields emphasise the importance of monitoring feelings and promoting more positive interactions between brands and consumers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
The paper presents a modification of the Magnitude Optimum Multiple Integration (MOMI) method process non-parametric data in the frequency domain instead of the time domain The required frequency data are obtained directly from the filtered amplitude -shifted process step response and have been shown to be relatively insensitive to normally distributed process noise. All calculations, including the calculation of the PID controller parameters, are performed analytically. The closed loop responses to tested processes with added normally distributed noise were relatively fast with small or no overshoot, all according to the Magnitude Optimum (MO) method. The proposed method is not limited to open loop step responses or to the PID controller structure.