Matheuristics: using mathematics for heuristic design

Matheuristics are heuristic algorithms based on mathematical tools such as the ones provided by mathematical programming, that are structurally general enough to be applied to different problems with little adaptations to their abstract structure. The result can be metaheuristic hybrids having components derived from the mathematical model of the problems of interest, but the mathematical techniques themselves can define general heuristic solution frameworks. In this paper, we focus our attention on mathematical programming and its contributions to developing effective heuristics. We briefly describe the mathematical tools available and then some matheuristic approaches, reporting some representative examples from the literature. We also take the opportunity to provide some ideas for possible future development

Understanding the Origin and Mixing of Deep Fluids in Shallow Aquifers and Possible Implications for Crustal Deformation Studies: San Vittorino Plain, Central Apennines

Expanding knowledge about the origin and mixing of deep fluids and the water–rock–gas interactions in aquifer systems can represent an improvement in the comprehension of crustal deformation processes. An analysis of the deep and meteoric fluid contributions to a regional groundwater circulation model in an active seismic area has been carried out. We performed two hydrogeochemical screenings of 15 springs in the San Vittorino Plain (central Italy). Furthermore, we updated the San Vittorino Plain structural setting with a new geological map and cross-sections, highlighting how and where the aquifers are intersected by faults. The application of Na-Li geothermometers, coupled with trace element and gas analyses, agrees in attributing the highest temperatures (>150 °C), the greatest enrichments in Li (124.3 ppb) and Cs (>5 ppb), and traces of mantle-derived He (1–2%) to springs located in correspondence with high-angle faults (i.e., S5, S11, S13, and S15). This evidence points out the role of faults acting as vehicles for deep fluids into regional carbonate aquifers. These results highlight the criteria for identifying the most suitable sites for monitoring variations in groundwater geochemistry due to the uprising of deep fluids modulated by fault activity to be further correlated with crustal deformation and possibly with seismicit

Artificial Intelligence Application to Screen Abdominal Aortic Aneurysm Using Computed tomography Angiography

The aim of our study is to validate a totally automated deep learning (DL)-based segmentation pipeline to screen abdominal aortic aneurysms (AAA) in computed tomography angiography (CTA) scans. We retrospectively evaluated 73 thoraco-abdominal CTAs (48 AAA and 25 control CTA) by means of a DL-based segmentation pipeline built on a 2.5D convolutional neural network (CNN) architecture to segment lumen and thrombus of the aorta. The maximum aortic diameter of the abdominal tract was compared using a threshold value (30 mm). Blinded manual measurements from a radiologist were done in order to create a true comparison. The screening pipeline was tested on 48 patients with aneurysm and 25 without aneurysm. The average diameter manually measured was 51.1 ± 14.4 mm for patients with aneurysms and 21.7 ± 3.6 mm for patients without aneurysms. The pipeline correctly classified 47 AAA out of 48 and 24 control patients out of 25 with 97% accuracy, 98% sensitivity, and 96% specificity. The automated pipeline of aneurysm measurements in the abdominal tract reported a median error with regard to the maximum abdominal diameter measurement of 1.3 mm. Our approach allowed for the maximum diameter of 51.2 ± 14.3 mm in patients with aneurysm and 22.0 ± 4.0 mm in patients without an aneurysm. The DL-based screening for AAA is a feasible and accurate method, calling for further validation using a larger pool of diagnostic images towards its clinical use

Broadband enhancement of light-matter interaction in photonic crystal cavities integrating site-controlled quantum dots

The fabrication of integrated quantum dot (QD)-optical microcavity systems is a requisite step for the realization of a wide range of nanophotonic experiments (and applications) that exploit the ability of QDs to emit nonclassical light, e.g., single photons. Thanks to their similar to 20-nm positioning accuracy and to their proven potential for single-photon operation, the QDs obtained by spatially selective hydrogen irradiation of dilute-nitride semiconductors-such as Ga(AsN) and Ga(PN)-are uniquely suited for integration with photonic nanodevices. In the present work, we demonstrate the ability to deterministically integrate single, site-controlled Ga(AsN)/Ga(AsN):H QDs within a photonic crystal (PhC) cavity. The properties of the fabricated QD-PhC cavity systems are then probed by photon correlation-providing clear evidence of single-photon emission-and time-resolved microphotoluminescence spectroscopy. Detailed information on the dynamics of our integrated nanodevices can be inferred by comparing these experiments to the solutions of a rate-equations system, developed by taking into account all the main processes leading to the capture, relaxation, and recombination of carriers in and out of the QD. This allows us to follow the evolution of the relevant recombination rates in our system for varying energy detuning, Delta E, between the QD and the PhC cavity. When the QD exciton transition is nearly resonant with the cavity mode, a large (>tenfold) enhancement of the spontaneous emission rate is observed, in substantial agreement with Jaynes-Cummings (JC) theory. For intermediate detunings (Delta E similar to 1.5-3.5 meV), on the other hand, the observed enhancement is significantly larger than that predicted by JC theory, due to the important role played by acoustic phonons in mediating the QD-PhC cavity coupling in a solid-state environment. Apart from its fundamental interest, the observation of such phonon-mediated, broadband enhancement of light-matter interaction significantly relaxes the requirements for the realization of a large variety of cavity QED-based experiments and applications. These include many photonic devices for which the use of site-controlled Ga(AsN)/Ga(AsN):H QDs would be inherently advantageous, such as those based on the coupling between more than one QD and a single cavity mode (e.g., few-QD nanolasers and QD solids)

How the First Year of the COVID-19 Pandemic Impacted Patients’ Hospital Admission and Care in the Vascular Surgery Divisions of the Southern Regions of the Italian Peninsula

Background: To investigate the effects of the COVID-19 lockdowns on the vasculopathic population. Methods: The Divisions of Vascular Surgery of the southern Italian peninsula joined this multicenter retrospective study. Each received a 13-point questionnaire investigating the hospitalization rate of vascular patients in the first 11 months of the COVID-19 pandemic and in the preceding 11 months. Results: 27 out of 29 Centers were enrolled. April-December 2020 (7092 patients) vs. 2019 (9161 patients): post-EVAR surveillance, hospitalization for Rutherford category 3 peripheral arterial disease, and asymptomatic carotid stenosis revascularization significantly decreased (1484 (16.2%) vs. 1014 (14.3%), p = 0.0009; 1401 (15.29%) vs. 959 (13.52%), p = 0.0006; and 1558 (17.01%) vs. 934 (13.17%), p < 0.0001, respectively), while admissions for revascularization or major amputations for chronic limb-threatening ischemia and urgent revascularization for symptomatic carotid stenosis significantly increased (1204 (16.98%) vs. 1245 (13.59%), p < 0.0001; 355 (5.01%) vs. 358 (3.91%), p = 0.0007; and 153 (2.16%) vs. 140 (1.53%), p = 0.0009, respectively). Conclusions: The suspension of elective procedures during the COVID-19 pandemic caused a significant reduction in post-EVAR surveillance, and in the hospitalization of asymptomatic carotid stenosis revascularization and Rutherford 3 peripheral arterial disease. Consequentially, we observed a significant increase in admissions for urgent revascularization for symptomatic carotid stenosis, as well as for revascularization or major amputations for chronic limb-threatening ischemia

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available

Association of kidney disease measures with risk of renal function worsening in patients with type 1 diabetes

Background: Albuminuria has been classically considered a marker of kidney damage progression in diabetic patients and it is routinely assessed to monitor kidney function. However, the role of a mild GFR reduction on the development of stage 653 CKD has been less explored in type 1 diabetes mellitus (T1DM) patients. Aim of the present study was to evaluate the prognostic role of kidney disease measures, namely albuminuria and reduced GFR, on the development of stage 653 CKD in a large cohort of patients affected by T1DM. Methods: A total of 4284 patients affected by T1DM followed-up at 76 diabetes centers participating to the Italian Association of Clinical Diabetologists (Associazione Medici Diabetologi, AMD) initiative constitutes the study population. Urinary albumin excretion (ACR) and estimated GFR (eGFR) were retrieved and analyzed. The incidence of stage 653 CKD (eGFR < 60 mL/min/1.73 m2) or eGFR reduction > 30% from baseline was evaluated. Results: The mean estimated GFR was 98 \ub1 17 mL/min/1.73m2 and the proportion of patients with albuminuria was 15.3% (n = 654) at baseline. About 8% (n = 337) of patients developed one of the two renal endpoints during the 4-year follow-up period. Age, albuminuria (micro or macro) and baseline eGFR < 90 ml/min/m2 were independent risk factors for stage 653 CKD and renal function worsening. When compared to patients with eGFR > 90 ml/min/1.73m2 and normoalbuminuria, those with albuminuria at baseline had a 1.69 greater risk of reaching stage 3 CKD, while patients with mild eGFR reduction (i.e. eGFR between 90 and 60 mL/min/1.73 m2) show a 3.81 greater risk that rose to 8.24 for those patients with albuminuria and mild eGFR reduction at baseline. Conclusions: Albuminuria and eGFR reduction represent independent risk factors for incident stage 653 CKD in T1DM patients. The simultaneous occurrence of reduced eGFR and albuminuria have a synergistic effect on renal function worsening