The detection of photovoltaic panels from images is an important field, as it leverages the possibility of forecasting and planning green energy production by assessing the level of energy autonomy for communities. Many existing approaches for detecting photovoltaic panels are based on machine learning; however, they require large annotated datasets and extensive training, and the results are not always accurate or explainable. This paper proposes an automatic approach that can detect photovoltaic panels conforming to a properly formed significant range of colours extracted according to the given conditions of light exposure in the analysed images. The significant range of colours was automatically formed from an annotated dataset of images, and consisted of the most frequent panel colours differing from the colours of surrounding parts. Such colours were then used to detect panels in other images by analysing panel colours and reckoning the pixel density and comparable levels of light. The results produced by our approach were more precise than others in the previous literature, as our tool accurately reveals the contours of panels notwithstanding their shape or the colours of surrounding objects and the environment.

A high level of data integrity is a strong requirement in systems where the life of people depends on accurate and timely responses. In healthcare emergency response systems, a centralized authority that handles data related to occurring events is prone to challenges, such as, e.g., disputes over event timestamps and data authenticity. To address both the potential lack of trust among collaborating parties and the inability of an authority to clearly certify events by itself, this paper proposes a blockchain-based framework designed to provide proof of integrity and authenticity of data in healthcare emergency response systems. The proposed solution integrates blockchain technology to certify the accuracy of events throughout their incident lifecycle. Critical events are timestamped and hashed using SHA-256; then, such hashes are stored immutably on an EVM-compatible blockchain via smart contracts. The system combines blockchain technology with cloud storage to ensure scalability, security, and transparency. Blockchain technology provides the advantage of eliminating a trusted server, providing timestamping and reducing costs by forgoing such a service. The experimental results, using publicly available incident data, demonstrated the feasibility and effectiveness of this approach. The system provides a cost-effective, scalable solution for managing incident data while keeping a proof of their integrity. The proposed blockchain-based framework offers a reliable, transparent mechanism for certifying incident-related data. This fosters trust among healthcare emergency response system actors.

When developing a distributed application, several issues need to be handled, and software components should include some mechanisms to make their execution resilient when network faults, delays, or tampering occur. For example, synchronous calls represent a too-tight connection between a client requesting a service and the service itself, whereby potential network delays or temporary server overloads would keep the client side hanging, exposing it to a domino effect. The proposed approach assists developers in dealing with such issues by providing an automatic tool that enhances a distributed application using simple blocking calls and makes it robust in the face of adverse events. The proposed devised solution consists in automatically identifying the parts of the application that connect to remote services using simple synchronous calls and substituting them with a generated customized snippet of code that handles potential network delays or faults. To accurately perform the proposed transformation, the devised tool finds application code statements that are data-dependent on the results of the original synchronous calls. Then, for the dependent statements, a solution involving guarding code, proper synchronization, and timeouts is injected. We experimented with the analysis and transformation of several applications and report a meaningful example, together with the analysis of the results achieved.

The increasing adoption of renewable energy sources introduces significant variability in power generation, requiring effective strategies to ensure maintain grid stability. Incentive-based demand response programs provide a practical solution for balancing supply and demand, however disputes may arise over energy data integrity. The existing solutions frequently rely on centralized authorities, exposing a single point of failure, or high costs and privacy limitation of recording granular data on-chain. To address this challenge, we propose a decentralized framework that separates cloud storage from integrity certification. This system employs a community aggregator to collect high-frequency energy measurements, store the raw data in the cloud, while anchors unique cryptographic hashes for batch of raw data to a public blockchain. This process creates an auditable and tamper-evident record of data. By recording only hashes on chain, our approach achieves privacy and scalability. Evaluation using a real-world Australian dataset confirms that the system enables transparent dispute resolution, with blockchain transaction costs consistently representing less than 0.10% of the total incentives awarded to participants.