102128 research outputs found
Sort by
A stochastic electric vehicle routing problem under uncertain energy consumption
Full text linkThe increasing adoption of Electric Vehicles (EVs) for service and goods distribution operations has led to the emergence of Electric Vehicle Routing Problems (EVRPs), a class of vehicle routing problems addressing the unique challenges posed by the limited driving range and recharging needs of EVs. While the majority of EVRP variants have considered deterministic energy consumption, this paper focuses on the Stochastic Electric Vehicle Routing Problem with a Threshold
recourse policy (SEVRP-T), where the uncertainty in energy consumption is considered, and a recourse policy is employed to ensure that EVs recharge at Charging Stations (CSs) whenever their State of Charge (SoC) falls below a specified threshold. We formulate the SEVRP-T as a two-stage stochastic mixed-integer second-order cone model, where the first stage determines the sequences of customers to be visited, and the second stage incorporates charging activities. The objective is
to minimize the expected total duration of the routes, composed by travel times and recharging operations. To cope with the computational complexity of the model, we propose a heuristic based on an Iterated Local Search (ILS) procedure coupled with a Set Partitioning problem. To further speed up the heuristic, we develop two lower bounds on the corresponding first-stage customer sequences. Furthermore, to handle a large number of energy consumption scenarios, we employ
a scenario reduction technique. Extensive computational experiments are conducted to validate
the effectiveness of the proposed solution strategy and to assess the importance of considering the stochastic nature of the energy consumption. The research presented in this paper contributes to the growing body of literature on EVRP and provides insights into managing the operational deployment of EVs in logistics activities under uncertainty
Self-standing bearing capacity of symmetric circular masonry arches at finite friction: Technical handbook of physical states
Full text linkThe present contribution concerns the issue of finite friction, in ruling self-standing bearing capacity and collapse modes of (symmetric circular) continuous masonry arches, with ideal inherent radial stereotomy. With primary reference, and as an enhancement, to classical “Couplet-Heyman problem”, of least-thickness form optimization, in the realm of purely-rotational collapse solutions, finite (Coulomb) friction is herein set, and thoroughly explored, in implying the possible appearance of sliding activation. The configuration of uniform (vertical) self-weight distribution is considered, herein for the true Milankovitch-like distribution accounting for the real centres of gravity of the ideal wedge-shaped chunks of the arch. The mechanical problem is analyzed, through a full analytical approach, by deriving all physical domains, and explicitly separating safe vs. collapse states of the arch. Outcomes are eventually validated by a separate dedicated Complementarity Problem/Mathematical Programming (CP/MP) numerical implementation, by fully consistent and illustrative results. Diverse key aspects are newly outlined, specifically for the representation of the characteristic solution variables as a function of friction and geometrical parameters, namely: (a) two- and three-dimensional state maps are analytically elucidated, specifically at variable arch opening; (b) underlying numerical data are thoroughly evaluated and reported in handbook tables; (c) catalogue arrays of arch geometries and collapse modes are systematically formed. The analytical-numerical achievements shall allow for a full understanding of the problem at hand, and synoptically form a technical compendium, in the Mechanics (statics) of masonry arches, and specific related role of finite friction, in providing crucial self-bearing structural capacity
Laser powder bed fusion of atomized industrial waste-derived Inconel 725 alloy powders: A machine learning-assisted process optimization
Full text linkAmong nickel-based superalloys, Inconel® 725 (IN725) stands out for its excellent strength and corrosion resistance. Despite this, its application in additive manufacturing remains largely unexplored. This study investigates laser powder bed fusion of metals (PBF-LB/M) applied to IN725 powder derived from recycled industrial waste, addressing sustainability and process optimization goals. Using the design of experiments approach, the laser power–scan speed process parameter space was explored. Gaussian process regression models were developed to predict surface roughness, relative density, and microhardness. Both direct process parameters and volumetric energy density were evaluated as model inputs to assess predictive performance. The findings established a broad optimal process window for manufacturing high-quality IN725 parts using PBF-LB/M. Specifically, an optimal combination of 99.99% relative density, 7.3 μm roughness, and 311 HV microhardness was achieved by processing the powder at 250 W and 1,500 mm/s. By demonstrating the feasibility of using recycled IN725 powder, this study contributes to the development of sustainable manufacturing practices and supports wider adoption of PBF-LB/M in oil and gas, marine, and chemical processing industries, where IN725 is widely employed
Effects of hypoxic training interventions on cardiometabolic health of adults with overweight and obesity: A systematic review and meta-analysis
Full text linkObesity rates have surpassed underweight globally, increasing the burden of cardiometabolic complications on healthcare systems. Hypoxic training has emerged as a potential intervention to improve cardiometabolic health in adults with obesity, but evidence remains inconclusive. This systematic review and meta-analysis evaluated whether hypoxic training is more effective than normoxic training in this context. A systematic search of PubMed, Web of Science, and Cochrane Library (up to June 2025) identified randomised controlled trials comparing hypoxic and normoxic training in adults with overweight or obesity. Outcomes included glucose homeostasis, lipid profile, and blood pressure. Subgroup, moderation, and sensitivity analyses were also conducted to explore sources of heterogeneity and assess the robustness of findings. Of 1815 studies screened, 9 (278 participants) met the criteria. Meta-analysis results demonstrated no significant differences between hypoxic and normoxic training for fasting glucose (p = 0.118) or fasting insulin (p = 0.415), with substantial heterogeneity observed across studies (I2 = 60%-77%). Similarly, lipid profile markers and blood pressure showed no significant between-group differences (all p > 0.05), also with moderate to high heterogeneity. Subgroup and moderation analyses partially explained this variability, suggesting greater fasting glucose reductions with shorter and lower-intensity hypoxic interventions. Hypoxic training did not outperform normoxic training in improving cardiometabolic outcomes. However, the considerable variability in intervention duration, hypoxic dose, and exercise intensity across studies limits the certainty of these findings. Well-designed, adequately powered trials are needed to determine whether specific hypoxic training protocols or participant characteristics may modulate efficacy in adults with overweight or obesity
Hybrid Organic-Inorganic Sol-Gel-Based Textile Finishing for Real-Time Sweat pH Monitoring in Wearable Health Systems
No full textFlexible wearable sweat sensors are advancing health monitoring by enabling continuous, non-invasive analysis of biomarkers. Advances in chemistry, materials science, and electronics have enabled the development of skinconformal devices with significant potential in diagnostics, athletic performance, and personalized care. Cellulosic textiles are optimal substrates due to their softness, breathability, and compatibility with functional coatings. This study reports the development of a durable, pH-responsive smart textile obtained by covalently grafting a halochromic dye onto cotton through sol-gel chemistry. Litmus, a naturally derived, non-toxic halochromic dye, was chemically functionalized via epoxy ring-opening of 3-glycidoxypropyltrimethoxysilane to produce a hybrid silane-dye molecule. The functionalized dye was placed onto cotton using a paddry-cure sol-gel process optimized for coating stability and laundering durability. Morphological and spectroscopic analyses implied uniform deposition and chemical integrity. FTIR and UV-vis spectroscopy verified the formation of siloxane linkages and the retention of the dye’s pH responsiveness within the physiological range (pH 4–8). SEM imaging revealed a continuous xerogel layer, indicating robust chemical anchoring. Colorimetric measurements demonstrated a pronounced
and reversible chromatic transition, with ΔE values exceeding 20 units
between acidic and slightly basic condition. The obtained coating maintained its pH responsiveness after a one washing cycle and demonstrated high repeatability, with colour deviation of less than 5% across five pH cycles. These findings highlight the potential of this halochromic textile platform for reliable, reusable, and wearable sensing applications
Bayesian generation of synthetic datasets for machine-learning tasks: a performance study
Full text linkPerforming Machine Learning (ML) tasks on large-scale datasets, as well as simply storing them for subsequent analysis or for long-term archival, require large computational power. The described approach builds on the technique known as "Bayesian Generation" to produce synthetic datasets in such a way that the probability dis tribution in the source dataset is maintained as much as possible in the new synthetic ones, even if they are much smaller than the original (large) dataset. In fact, this study investigates the impact of generating smaller synthetic datasets for training ML models in place of the original dataset, adopting a twofold perspective. Firstly, the impact on the effectiveness of ML models trained on these smaller synthetic datasets is assessed. Secondly, the amount of computational resources required to generate the synthetic datasets, train ML models on them, and perform the testing phase is measured. Specifically, both execution time and main memory usage are taken into account. Finally, this research work shows that the loss in terms of effectiveness remains consistently limited and stable, and it identifies the scenarios and ML techniques for which incorporating the generation of small syn thetic datasets into the ML pipeline can be beneficial for practical deployment in environments with constrained computational resources, such as mobile or industrial devices
There is more social in semantics! A brief commentary and reanalysis of Balgova et al. (2024)
No full textBalgova et al. (2024) recently conducted a large-scale meta-analysis on mentalizing and on semantic cognition, to investigate the degree to which the neural correlates of these two processes are overlapping. The study found consistent neural overlap between the two processes, especially in the bilateral anterior temporal lobe (ATL) and the left temporoparietal junction (TPJ), although they also identified many areas of activation specific to mentalizing. Although we agree with their general conclusion, we investigated to what extent the semantic dataset was actually devoid of social content, and if not, how this would change the results. After careful screening and categorization of the “semantic” material, we found experiments that contained elements of social mentalizing (N = 36) and social action observation (N = 16), apart from nonsocial semantics (N = 46). ALE analyses on the social mentalizing and nonsocial semantic subsets from the original “semantic” full dataset, confirmed that semantic brain areas are activated when processing both social mentalizing and nonsocial semantic content, while mentalizing brain areas are uniquely activated when processing social mentalizing content. Specifically, semantic and mentalizing content activated the left inferior frontal gyrus (IFG), left middle temporal gyrus (MTG) and posterior medial frontal cortex (pmFC); and also the left ventral temporal lobe, supporting the graded multimodal hub model of semantic cognition. Critically, as we claimed, mentalizing content uniquely activated the temporal pole (TP), medial prefrontal cortex (mPFC), although activation in the left TPJ was also shared with semantic processes. We conclude that a more careful distinction between social and nonsocial datasets guarantees more sensitive and valid analyses
Persistent specialization and growth: The Italian land reform
No full textThe impact of land redistribution on structural transformation is ambiguous. While large landowners may hinder industrialization by restricting access to education, larger farm scale can facilitate mechanization and productivity growth. This study uses novel fine-grained data to examine the long-term effects of the 1950 Italian land reform, which redistributed land from
large landowners to landless farmers. Employing two difference-in-differences strategies, we find that the reform significantly slowed industrialization in affected municipalities, which, fifty years after the reform, exhibited an agricultural employment share approximately 70% higher
than the estimated counterfactual scenario. Reductions in agglomeration and occupational mobility emerge as key mechanisms, while education seemingly played a limited role. Finally, we show that the reform significantly hindered the overall economic growth of affected municipalities between 1970 and 2000