112819 research outputs found
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Statistical analysis of experimental crossing speed on vertical traffic calming devices: influence of vehicle category and road safety implications
No full textThe growing number of accidents involving pedestrians every year represents a critical issue for road safety in urban areas. These accidents are often caused by excessive speed and risky behaviour by the drivers of the vehicles involved. In this context, traffic calming devices, especially raised pedestrian crossings (RPCs), are installed to reduce the transit speed of vehicles and allow pedestrians to cross safely. This study aims to determine whether the transit speeds of vehicles on a traffic calming device depend on the category of the vehicle itself. To this end, an experimental data collection was conducted on an RPC case study, considering different collection days, in various time slots. Statistical tests, including ANOVA (F-test), Student’s t-test, and Tukey’s correction, were carried out on these data, showing a significant correlation between the crossing speed of the traffic calming device and the vehicle category. This study, therefore, lays the foundations for urban planning dedicated to pedestrian safety, enabling the adaptation of traffic calming device characteristics according to the vehicle fleet, maximizing their effectiveness
Enhancing interpretability and automation in data-driven energy modelling: an analytical approach to change-point regression models
Full text linkReliable, automated Measurement & Verification (M&V) and portfolio-scale energy analytics need models that are both accurate and interpretable. Current practice often relies on change-point regressions whose balance points are found via grid search or optimisation. As an alternative, an analytical formulation for simplified and automated identification of three-parameter heating (3PH), three-parameter cooling (3PC), and five-parameter (5P) models as defined in ASHRAE Guideline 14:2023 is proposed in this paper, with the goal of preserving interpretability also in more sophisticated workflows at the state of the art, which can use this novel formulation at different temporal scales (monthly, daily, and hourly). Standardised test datasets (39 in total) for 3PH, 3PC, and 5P models' testing and the Inverse Modelling Toolkit (IMT) have been used, showing comparable results in the large majority of cases and minor discrepancies in the others. The total batch runtime has been markedly reduced compared to the original implementation. Moreover, datasets from prior studies have been employed to evaluate applicability in real-world scenarios, demonstrating analogous results in this instance as well. While the current formulation is tested with monthly and daily interval data, its incorporation in hourly and sub-hourly resolution modelling workflows can promote further research developments in the area of interpretable data-driven analytics towards the “digital twins” paradigm, where interpretability of machine learning techniques and physical interpretation of underlying parameters is relevant to deliver effective and trusted solutions. Open-source code and datasets are made available to encourage further research on robust, transparent, and scalable data-driven energy modelling methodologies based on M&V principles. In this regard, additional efforts may be pursued to expand the concepts presented for the analytical formulation's development to encompass various automated processes with different objective functions (e.g., lasso, elastic net regression, etc.), model formulations, and constraints (e.g., physics-based interpretation of slopes and change points)
Partial Cubes and Fibonacci Dimension: Insights and Perspectives
No full textA partial cube is a graph G that can be isometrically embedded into a hypercube Qk, with the minimum of such k called the isometric dimension, idim(G), of G. A Fibonacci cubeΓk excludes strings containing 11 from the vertices. Any partial cube G embeds into some Γd, defining Fibonacci dimension, fdim(G), as the minimum of such d. It holds idim(G)≤fdim(G)≤2·idim(G)-1. While idim(G) is computable in polynomial time, check whether idim(G)=fdim(G) is NP-complete. We survey the properties of partial cubes and Generalized Fibonacci Cubes and present a new family of graphs G for which idim(G)=fdim(G). We conclude with some open problems
Boosting oxygen evolution reaction activity descriptors in LaNiO3 perovskite oxide via one-pot synthesis for alkaline electrolysis
No full textPerovskite oxides have emerged as promising alternatives to platinum group metal based state-of-the-art electrocatalysts for alkaline oxygen evolution reaction, such as RuO2 and IrO2. Specifically, LaNiO3 is a highly efficient OER electrocatalyst in alkaline environment, nevertheless, its performance is strongly dependent on the preparation conditions. In this work, a one-pot solution combustion synthesis (SCS) is presented as a straightforward method to enhance LaNiO3 OER descriptors. The process involves a clever combination of low-cost, easily available fuels: glycine (Gly) and citric acid monohydrate (CAM). The SCS Gly +CAM LaNiO3 is compared to other LaNiO3 samples obtained via sustainable wet-chemistry techniques like coprecipitations and other SCS methods. Although all samples show comparable XRD patterns, the SCS Gly +CAM route ensures superior textural properties and optimal Ni3+/Ni2+ratio, resulting in higher intrinsic OER activity. SCS Gly +CAM displays the lowest overpotential, 377 ±7 mV, further decreasing to 350 ±5 mV after 25 cycles, due to surface reconstruction. Over 25 cycles, SCS Gly +CAM LaNiO3 outperformes the state-of-the-art RuO2. To the best of our knowledge, the Gly +CAM combination has not been explored for LaNiO3 and presents a cost-effective alternative for scaled-up production of high-performance OER electrocatalysts
Cobalt oxide synthesis via flame spray pyrolysis for enhanced oxygen evolution reaction activity
Full text linkInteractions between drivers and road infrastructure characteristics: combining OBD and geographic data to classify driver behaviors with feed-forward neural network
No full textDriver 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
Weight-reducing treatments are associated with an improvement in depression, functional health status, and quality of life: A meta-analysis of randomized controlled trials
Full text linkAim: 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
Two-tier screening approach for liver fibrosis stratification in outpatients with type 2 diabetes mellitus: A multicenter cross-sectional study
No full textBackground: To examine the prevalence and severity of metabolic dysfunction-associated steatotic liver disease (MASLD) in outpatients with type 2 diabetes mellitus (T2DM), and to assess the effectiveness of the EASL-EASD-EASO algorithm for liver fibrosis screening. Methods: We retrospectively enrolled 1203 Italian older outpatients with T2DM who underwent vibration-controlled transient elastography (VCTE) with liver stiffness measurement (LSM) and controlled attenuation parameter (CAP) assessment. MASLD was defined as CAP ≥248 dB/m. Significant liver fibrosis was defined as LSM ≥8 kPa, compensated advanced chronic liver disease (cACLD) as LSM ≥10 kPa, and clinically significant portal hypertension (CSPH) as LSM ≥25 kPa or LSM ≥20 kPa and platelet count <150 000/mm3. FIB-4 index was calculated in all participants. Results: The prevalence rates of MASLD, significant liver fibrosis, cACLD, and CSPH were 71.3%, 21.1%, 11.7% and 1.7%, respectively. A 2-tier screening strategy for liver fibrosis using the FIB-4 index and VCTE showed that among patients with a normal FIB-4 index, 629 (83.3%) had LSM <8 kPa and 126 (16.7%) had LSM≥8 kPa. Sensitivity, specificity, NPV, and PPV of the FIB-4 index for detecting LSM≥8 kPa were 50.4%, 66.3%, 83.3% and 28.6%, respectively. Increased body weight (adjusted-OR 3.34, 95%CI 1.75–6.39) and elevated ALT levels (adjusted-OR 1.54, 95%CI 1.01–2.36) were the strongest predictors of significant liver fibrosis. Conclusions: MASLD and significant liver fibrosis are common in older patients with T2DM. Fibrosis risk stratification using FIB-4, followed by VCTE, is a good strategy in real-world settings. However, relying solely on FIB-4 may fail to identify some patients with advanced disease, particularly those with increased body weight and elevated serum aminotransferase levels
