Light and shadows of smart contract development with LLMs

Smart contract development remains almost inaccessible to non-experts developers despite the growing adoption of blockchain technology across industries. This paper evaluates the potential of Large Language Models (LLMs) for automated smart contract generation from legal agreements. The work systematically assesses the capabilities of four leading commercial LLMs – gpt-4-turbo (OpenAI), claude-3.5-sonnet (Anthropic), mistral-large (MistralAI), and gemini-1.5-pro (Google) – across a diverse range of legal agreements with varying complexity. The evaluation framework consists of a in-depth evaluation of structured code patterns – typical to smart contracts – to provide nuanced insights into model performances. The results reveal a performance hierarchy with claude-3.5-sonnet and gpt-4-turbo consistently outperforming mistral-large and gemini-1.5-pro, particularly when handling complex agreements such as mortgage note agreement and property sales agreement. A nonlinear relationship has been observed between contract complexity and model performance, with even top-performing models showing significant degradation when processing intricate legal structures. Although achieving syntactic correctness has become increasingly feasible, ensuring functional completeness and security remains challenging, as evidenced by high-impact vulnerabilities detected across all generated smart contracts. This work contributes to the growing discourse on LLM applications in blockchain technology by providing empirical evidence of current capabilities and limitations, establishing a robust foundation for future research in AI-assisted smart contract development

Exploring the Potential of AR in Simulating Central Vision Loss: An Experimental Study with HoloLens2

Central vision loss (CVL) is an increasingly prevalent condition that significantly affects essential daily activities such as reading and visual search. This study simulates a central scotoma in a controlled experimental setting using the Microsoft HoloLens 2 augmented reality (AR) headset, enabling the investigation of compensatory mechanisms when the foveal region is compromised. Thirty-three participants performed a reading task (Task 1) and a visual search task (Task 2) under both normal vision and AR-simulated defect conditions, while eye-tracking data were recorded to analyze differences in visual behaviour and compensatory strategies. Key performance metrics included error rates, task completion times, and eye movement metrics such as the number and duration of fixations and saccades. Results showed that simulated central vision loss induced a more effortful visual exploration strategy, with an increased number of fixations and saccades, as well as longer total fixation duration, while saccadic timing remained stable. Specifically, the mean number and total duration of fixations during the reading task were approximately 14 and 8 s under normal vision, and 18 and 11 s under CVL condition, respectively. These findings demonstrate the potential of AR-based methods to replicate visual impairments in research settings. Future work could expand on the use of extended reality technologies for assistive and rehabilitative applications in individuals with central vision deficits

Valuing the contribution of green roofs to pluvial flood risk mitigation: A cost-benefit analysis

Cities in the 21st century face increasing pressures from population growth, urban sprawl, and emissions, while flood-related challenges exacerbated by climate change strongly intensify their vulnerabilities. Consequently, urban global change is becoming an urgent necessity globally. Nature-based solutions (NBS) have gained increasing attention as valuable sources of ecosystem services, which can also address these multiple societal challenges. Green roofs are widely used for stormwater management and treatment in compact urban environments. However, to date, most research has been conducted regarding green roof costs or flood risk mitigation benefits at a citywide scale; while local administrations need more evidence on the economic viability of green roofs that may increase the willingness to consider these nature-based solutions. Hence, the objective of this study is to develop and apply a spatially explicit assessment of flood risk mitigation impacts (biophysically – water depth), costs and benefits of NBS (economically – implementation costs, avoided damage costs, net present values and benefit-cost ratios) under current (2013) and future (2050; RCP 4.5) climate conditions – with a case study for green roofs in Rapallo (Italy). The spatial biophysical-economic approach integrates the InVEST Urban Flood Risk Mitigation model (spatial resolution: 5 m × 5 m), benefit transfer methods, and geographic information systems into a cost-benefit analysis. Results show that flood risks under current (2013) climate conditions imply significant building damage costs (~6.5 million €/yr for Rapallo), that these costs increase when considering future (2050) climate conditions (by about 7 %), and that NBS (green roofs) implementation can reduce these costs (by almost 90 %). Moreover, green roofs result to be economically viable from a flood mitigation perspective alone when considering Low NBS costs, while flood mitigation benefits contribute to, respectively, 87 % and 63 % of the green roof annual implementation costs when considering Medium and High NBS costs. Finally, results show that the economic viability of green roofs differs across neighbourhoods – hence allowing for the economic prioritization of green roof implementation across neighbourhoods. By quantitively assessing NBS impacts, costs, and benefits at the neighbourhood level, this study supports the decision on the most viable locations for the implementation of NBS for flood risk mitigation – highlighting the need for spatial assessment studies to support urban NBS development strategies

Diagnosis of Resting Tremor in Parkinson’s Disease Using Accelerometer and Gyroscope Sensors Built into a Smartwatch

Monitoring resting tremor in Parkinson’s disease (PD) can be performed using wearable technology and machine learning. Smartwatches offer a cost-effective and non-intrusive way to track tremors remotely. However, to ensure precise monitoring in free-living environments, optimized systems are needed. This chapter discuss about the performance of inertial sensors to identify resting tremors and its classification according to MDS-UPDRS III. Six PD patients wore a smartwatch on their wrists while performing different exercise based on MDS-UPDRS. During eight weeks, data from triaxial accelerometers and gyroscopes were collected simultaneously and analyzed using machine learning techniques. In tremor presence detection, using binary classification, the use of only accelerometer gives the best results in terms of accuracy (97%) and training time (47 s) compared accelerometer and gyroscope combined (96.4% and 67 s) and only gyroscope alone (93% and 59 s). In the MDS-UPDRS scale detection, using multi-class models, the best accuracy is offered by the combination of accelerometer and gyroscope (96.5%) but offers the worst training times (77 s), while accelerometer is slightly worse (96.1%) but require the less training time (57 s). These results show the performance and training times of Machine Learning models for the detection of resting tremor and prediction of the MDS-UPDRS assessment for the correct decision making of sensors and models to be used in future application developments. The results could be used to contribute to the development of reliable tremor monitoring systems using devices equipped with inertial sensors and Machine Learning algorithms

Enhancing Manufacturing Engineering Higher Education Through Mixed Reality and Gaussian Splatting: Preliminary Experimental Results

The integration of Mixed Reality (MR) technologies into higher manufacturing-engineering education can contribute to face the challenges of providing hands-on training with real manufacturing systems. This paper explores the potential of MR combined with Gaussian Splatting (GS) to create high-fidelity digital replicas of industrial machinery (e.g., lathes, milling machines, etc.), enhancing students’ understanding of manufacturing processes. GS is emerging as a breakthrough technique for real-time rendering of objects and environments. By delineating the scene as the realisation of an object in a defined temporal state, GS methodology represents a 3D high-fidelity digital scene as a collection of 3D Gaussian ellipsoids characterised by position, geometry, shape, colour and opacity. The integration of MR with GS allows trainees to engage with realistic virtual models, simulating a physical presence in a machining workshop. The capacity to digitally manipulate and analyse individual objects enhances the learning experience, addressing logistical and safety constraints by providing a risk-free and accessible training environment. A lathe is used as a case study, and the GS-based digital scene is compared with conventional CAD-based model in terms of qualitative performance

Energy Performance Certificates and Housing Transaction Prices: Empirical Evidence from Market Data Analysis

In recent years, the intersection between real estate market dynamics and environmental sustainability has gained increasing attention, particularly in light of the European Union’s regulatory efforts to decarbonize the building sector. Energy Performance Certificates (EPCs), initially conceived as informational tools, are now emerging as significant drivers of housing prices and investment decisions. This study investigates the impact of EPCs on real estate transaction prices within the urban context of Turin, Italy, with a specific focus on spatial effects often neglected in traditional analyses. Using a dataset of over 5,000 property listings from 2022–2023, a Spatial Error Model (SEM) is implemented to capture spatial autocorrelation and improve model accuracy. The results reveal a statistically and economically significant “green premium” for high-efficiency dwellings (EPC A and B–C) and a corresponding “brown discount” for inefficient properties (EPC F–G). These findings not only validate the role of EPCs in price formation but also align with the broader objectives of the EU Green Deal and the Green Asset Ratio (GAR), which increasingly integrates energy performance into financial valuation. The study offers novel insights into the spatial valuation of sustainability and suggests that market forces, coupled with regulatory instruments, are reshaping investment patterns in the built environment

A Systematic Literature Review on Disruptions in Construction Supply Chain: Some Stylized Trends

Construction supply chains (CSC) are intricate systems characterized by fragmentation and inefficiencies, which have been exacerbated by external disruptions such as the COVID-19 pandemic, natural disasters, and geopolitical instabilities. These challenges underscore the urgent need for resilient and adaptive supply chain strategies that integrate emerging technologies. However, existing research remains fragmented, lacking a comprehensive framework that aligns digital innovations with broader operational, economic, and stakeholder considerations. To bridge this gap, this study employs a systematic literature review (SLR) complemented by natural language processing (NLP) techniques to analyze existing knowledge on CSC disruptions and resilience-building strategies. A total of 63 articles were reviewed, with thematic clustering and topic modeling applied to uncover key challenges and opportunities. Findings reveal two primary clusters: macro-level strategies focused on systemic resilience and micro-level solutions addressing operational inefficiencies. The results indicate that while Industry 4.0 and modular construction methods offer promising solutions, their integration into CSC frameworks remains inconsistent. By combining qualitative SLR insights with quantitative NLP analysis, this study provides a holistic perspective on CSC disruptions and potential resilience strategies, offering valuable implications for both academia and industry. In particular, it might support practitioners in identifying suitable solutions for material tracking and in turn reducing inefficiencies. In addition, the issue of collaboration and information sharing is stressed in order to achieve a more aware decision-making process and reduce the level of uncertainty with a consequent higher resilience along the supply chain in the construction industry

Ammonia-hydrogen blends combustion in turbulent high temperature co-flow

In response to the stringent vehicle emission regulations, ammonia, with its potential as a carbon-free alternative fuel for reducing carbon emissions, faces application challenges due to higher ignition energy requirements and lower flame stability. Adding hydrogen is one of the effective ways to improve combustion performance of pure ammonia. This study focuses on the auto-ignition characteristics and jet flame stability of ammonia-hydrogen fuel blends under various conditions, such as different injection pressures, co-flow velocities, co-flow temperatures, and hydrogen blending ratios, employing a controllable active thermal atmosphere burner. Hydrogen addition increases flame brightness, area, and crinkly morphology due to enhanced NH2 production and higher combustion temperatures. The flame length increases together to the hydrogen ratio and the co-flow temperature, and it has been verified that it is primarily governed by jet momentum. Above 1073 K of co-flow temperature, the heat transfer becomes dominant for auto-ignition, reducing the effect of hydrogen presence. Combustion efficiency improves for higher co-flow temperatures, while hydrogen enhances propagation until a threshold is reached for XH2 = 20 %, beyond which a sensible increment in propagation cannot be detected. When the injection pressure augments, the flame is enlarged but auto-ignition can be hindered. Hydrogen addition reduces fluctuations, ensuring optimal stability for XH2 = 20 %

An Automated Diagram Generator of Reference Solutions for Modeling Educators

UML class diagrams are a relevant modeling language in Software Engineering education since they can be used to teach students how to visualize and display the different entities that compose a system, with their functionalities and relationships. The definition of modeling exercises and their evaluation can be time-consuming for educators due to the need to consider possible semantic variations and alternative representations of the same system requirements. To facilitate teachers in this process, we present TIGRE (auTomated dIagram Generator of REference solutions), an online editor for the definition of UML modeling exercises where teachers can define reference solutions in the form of both diagrams and detailed structures to be used for automated evaluation. The tool is enhanced by the interaction with recent Large Language Models for the automated generation of reference solutions starting from text, facilitating the creation of early drafts. A proof-of-concept case study has been performed by having TIGRE generate reference solutions for two exercises: most of the relevant concepts have been represented correctly, but issues emerged in the form of unnecessary classes being included and incorrect understanding of associations

KAN You See It? KANs and Sentinel for Effective and Explainable Crop Field Segmentation

Segmentation of crop fields is essential for enhancing agricultural productivity, monitoring crop health, and promoting sustainable practices. Deep learning models adopted for this task must ensure accurate and reliable predictions to avoid economic losses and environmental impact. The newly proposed Kolmogorov-Arnold networks (KANs) offer promising advancements in the performance of neural networks. This paper analyzes the integration of KAN layers into the U-Net architecture (U-KAN) to segment crop fields using Sentinel-2 and Sentinel-1 satellite images and provides an analysis of the performance and explainability of these networks. U-KAN performs comparable to or better than the full-convolutional U-Net in half of the GFLOPs. Furthermore, gradient-based explanation techniques show that U-KAN predictions are highly plausible and that the network has a very high ability to focus on the boundaries of cultivated areas rather than on the areas themselves. The per-channel relevance analysis also reveals which channels are critical for explaining model behavior and which have little to no impact, thus providing insights into the features the model relies on for the segmentation task