Pedestrian crash severity prediction and contributory factors analysis by using machine learning methods

Pedestrians occupy a leading position among the most vulnerable road users. Each year about 270,000 pedestrians die due to road accidents, so this study aims to highlight the most influencing contributory factors and the most promising models to predict pedestrian crash severity. ISTAT data for the City of Rome (2013–2020) are used and different Machine Learning Methods are trained and tested, after balancing the data with oversampling techniques. In addition, analysis of the most influencing contributory factor is carried out, by using the ROC curve method, Variable Importance Analysis (VIP), and Support Vector Machine with a Linear Kernel. The findings suggest that the model with the best prediction performance is the Random Forest, followed by the Decision Tree and k-nearest neighbour algorithm. Regarding the analysis of contributory factors, the methods implemented highlight that the hour in which the accident occurs, pedestrian gender, and age seem to be the most critical factors that increase the severity of a pedestrian crash. There are also some limitations in this study: the first is connected to the black-box nature of these models; the second regards how these variables could influence positively or negatively the outcome

Biographies of Early Greek Lawgivers in the Suda

The Byzantine Suda provides biographical lemmata for the archaic Greek lawgivers Lycurgus, Zaleucus, Solon, Pittacus, and Dracon. This paper aims to analyse the main features of these entries, focusing on how individual legislators are characterized, connected to each other, and associated with other wise men and philosophers (e.g., the Seven Sages). The structural elements shared by these entries distin guish them from other lemmata in the Suda on historical figures and contribute to the distinctive nature of this group. For instance, of the three entries dedicated to Solon, the lemma Σ 776 Adler defines him as a ‘philosopher, legislator, and demagogue’ and provides essential information on his life, including his relationship with the tyrant Pisistratus, his exile, and his death. Likewise, Zaleucus is described as a ‘Pythagorean philosopher and lawgiver’ who died ‘fighting for his homeland’ (Ζ 12 Adler). As for Lycurgus of Sparta, while the lemma Λ 823 Adler is very concise, the following entry (Λ 824 Adler) provides many details concerning his biography and the excellent constitution he gave to the Spartans, emphasizing the direct relationship between Sparta’s political success and Lycurgus’ contribution. Thus, this paper thoroughly examines all the entries on the Greek lawgivers to better understand the details given in the Suda, and it also explores the relationship between these biographical headwords and the accounts of the same figures given by Plutarch, Diogenes Laertius, and other parallel sources

Quadrature rules for splines of high smoothness on uniformly refined triangles

In this paper, we identify families of quadrature rules that are exact for sufficiently smooth spline spaces on uniformly refined triangles in R2. Given any symmetric quadrature rule on a triangle T that is exact for polynomials of a specific degree d, we investigate if it remains exact for sufficiently smooth splines of the same degree d defined on the Clough-Tocher 3-split or the (uniform) Powell-Sabin 6-split of T. We show that this is always true for C(2r-1) splines having degree d = 3r on the former split or d = 2r on the latter split, for any positive integer r. Our analysis is based on the representation of the considered spline spaces in terms of suitable simplex splines

Weight-reducing treatments are associated with an improvement in depression, functional health status, and quality of life: A meta-analysis of randomized controlled trials

Aim: To assess whether there is a beneficial or detrimental effect of weight reduction on mental health. Materials and methods: Meta-analysis of randomized trials performed for weight loss, in which weight loss at endpoint was greater than 5% in the intervention arm and smaller than 5% in the control arm, obtained with any surgical, endoscopic, or EMA-approved pharmacological intervention. The endpoints were the incidence of overall and specific psychiatric adverse events. Results: Weight loss was associated with a reduced risk of major depression (MH-OR 0.45 95% CI [0.21, 0.94], I2 = 0), and overall depression (MH-OR 0.72 [0.54, 0.97]); in subgroup analyses, a weight loss greater than 10% was associated with a lower incidence of depression than smaller weight loss (p = 0.04), whereas no difference was found between different interventions. No difference was detected in the incidence of anxiety (MH-OR 1.04 [0.78, 1.39]), of serious (M-H, OR CI 1.07 [0.78, 1.47]) and overall (MH-OR 1.09 [0.89, 1.34]) psychiatric adverse events, suicidal ideation (M-H, OR 0.87 [0.44, 1.70]), or suicide (M-H, OR 0.87 [0.44, 1.70]). An improvement in functional health status was detected, either as SF-36 Mental (SMD-IV 0.45 [0.37, 0.52]) or SF-36 Physical function (SMD-IV 0.29 [0.14, 0.44]) or IWQOL Lite Physical function (MD-IV 3.96 [1.60, 6.32]). Conclusion: Weight-reducing treatments were associated with a beneficial effect on quality of life and functional health status and a reduced risk of depression, without any safety signal for serious or non-serious psychiatric adverse events

A new methodology for using hybrid configurational tools for local analysis of pedestrian flows in large areas

In recent years, the concept of sustainable mobility has been increasingly pursued; as a result, the need for effective planning of pedestrian infrastructure is highlighted. This research employs configurational analysis, specifically the spatial syntax methodology, to examine pedestrian flows in urban environments. Spatial analysis was integrated with demographic data to assess pedestrian movement patterns and network accessibility. The area in question falls within a district of the city of Rome, such as the Nomentano-Tiburtina. To improve predictive accuracy, an approach is proposed that studies the influence of neighboring municipalities on the estimation of pedestrian crowding. The results reveal that this consideration significantly influences the integration and estimates of pedestrian flow. This study offers valuable insights for urban planners seeking to optimize pedestrian networks and promote sustainable urban mobility

Interactions between drivers and road infrastructure characteristics: combining OBD and geographic data to classify driver behaviors with feed-forward neural network

Driver behavior is the set of actions that a road user undertakes during a driving task. There is a huge interest in studying driver behaviors evaluating fuel consumption and improving safety. Using in-vehicle sensors is a widely adopted methodology to fulfill these objectives. The large amount of data generated from multiple sensors opens the doors to machine learning and deep learning algorithms which are more adequate than other methodologies. In this paper, a Feed Forward Neural Network is trained and tested with OBD and geographic data to classify driver behaviors. Several datasets are analyzed but the most adequate has resulted in the DDD20 dataset. This contains a larger amount of data than the other one, with 51 h and 4000 km of total driving times and distances. After the selection of the dataset and the enrichment with geographical data, feature selection, and data labeling techniques and algorithms are implemented. The model shows a high level of accuracy (above 98%) for the three classes of driver behavior studied

Dancing with the algorithm: a framework to navigate knowledge and autonomy in AI-assisted managerial decisions

While artificial intelligence (AI) systems (assistive, human-in-the-loop decision support systems) increasingly participate in complex organizational judgments, their integration into knowledge processes raises fundamental challenges for autonomy, trust, and epistemic agency. This study aims to develop a dynamic, phase-specific framework that explains how autonomy evolves in relation to the data–information–knowledge–wisdom (DIKW) hierarchy and foundational knowledge management concepts during AI-assisted managerial decision-making. The study draws on 122 in-depth interviews with senior professionals across diverse sectors, complemented by two expert focus groups. Data were analyzed using the Gioia Methodology to support inductive theory development and generate a grounded conceptual framework. The authors develop the Human–AI autonomy loop (HAIL) framework, mapping decision-making to four recursive phases (frame, evaluate, commit, enact) and DIKW layers, each linked to distinct DIKW layers and autonomy configurations. Autonomy is a situated, distributed practice: managers preserve discretion through interpretive buffers, overrides and moral authorship. Trust in AI is recalibrated by phase, especially as decisions move from information to judgment. HAIL shows that autonomy is sustained through reflexive knowledge practices. This study advances the knowledge management literature by integrating autonomy, trust and epistemic agency into a unified framework of AI-assisted decision-making. It reinterprets the DIKW model not as a linear information hierarchy, but as a socio-technical terrain where knowledge becomes actionable only when embedded in situated judgment and ethical authorship. The HAIL framework offers both theoretical insights and practical guidance for preserving human discretion and organizational wisdom in AI-assisted environments