Politica industriale e sviluppo sostenibile

Il libro riproduce ed amplia le relazioni presentate al workshop del 3 ottobre 2014 presso il Dipartimento di Economia dell’Università di Parma, ad opera di studiosi appartenenti all’Università di Ferrara, allo Iefe-Università Bocconi di Milano, all’Università di Modena, alla Scuola Sant’Anna di Pisa, nonché alla stessa Università di Parma. I cinque contributi qui presentati fotografano cinque diversi aspetti del rapporto fra politica industriale (più in generale crescita economica diretta dalle istituzioni pubbliche) e sviluppo sostenibile: a livello nazionale, il possibile trade-off fra i due obiettivi di politica industriale e di sostenibilità ambientale nel tentativo di gerarchizzare i “settori strategici”, e la necessità che questo trade-off sia parzialmente compensato a livello di sforzo innovativo (Di Tommaso e Tassinari); a livello internazionale, la possibilità che politiche industriali nazionali non operino all’interno di un gioco a somma zero, ma diano risultati favorevoli al raggiungimento di un bene pubblico globale quale il cambiamento climatico (Fabbri e Ninni); a livello di imprese, la tendenziale riduzione delle contraddizioni fra incentivi al loro operare e “impronta” ambientale, grazie agli accordi volontari e in particolare all’importante ruolo della certificazione (Frey); a livello di istituzioni, l’esistenza di tipologie diverse di obiettivi e di strumenti a livello nazionale e a livello locale, e l’analisi in un confronto tra paesi europei delle caratteristiche delle politiche ambientali impostate a livello sub-nazionale (Croci e Molteni); a livello di mercato del lavoro, l’effetto sul tessuto industriale delle politiche di aumento della flessibilità del lavoro nella singola impresa, come aspetto particolare di una ridiscussione più ampia del concetto di sostenibilità ambientale e dei suoi rapporti con la politica nei confronti delle imprese (Giovannetti)

Role of microstructure in the exploitation of self-healing potential in form-stable composite phase change materials based on immiscible alloys

Metallic Phase Change Materials (PCMs), based on solid-liquid transitions, represents one of the most promising technologies for efficient Thermal Energy Storage (TES), due to their superior thermal conductivity and energy storability per unit volume, but suffer of limited solutions for their handling at the molten state. The use of Miscibility Gap Alloys (MGAs) allows to manage PCM volume expansion and keep it confined when molten, preventing interaction with the environment. A relevant example is provided by the Al-Sn system, where Al covers the role of the high-temperature stable and highly thermal-conductive passive matrix and Sn the active PCM. The alloy can thus be considered a Composite PCM (C-PCM). The response fastness of these systems depends on their thermal diffusivity, subjected to abrupt variations under the presence of discontinuities and damages. In this sense, the authors investigated the possibility to employ molten Sn mobility in a potentially damaged C-PCM for self-healing purposes, aimed to restore, at least partially, the material continuity and thus its thermal diffusivity. Exudation heat treatments above the melting temperature of Sn were performed on sets of Al-40%wt. Sn metallic composites, produced either with powder metallurgy or liquid metal routes, in order to quantify and assess the mobility of the Sn under simulated operating conditions. Exudation tests assess Simple Mixed powders and liquid metal routes sample as the ones with the highest healing potential. Al dissolution and re-deposition was established by EDS analyses as one of the principal Sn mobility mechanisms. Laser Flash Analysis tests, as well as microstructural investigations, were performed on the samples before and after both healing-focused and simulated service heat treatments to evaluate the changes of thermal diffusivity. Healing-focused treatment at 250°C for 1 hour generally displayed a moderate thermal diffusivity recovery and simulated service by shorter cycles between 170°C and 270°C slightly reduce it. The beneficial role of healing focused heat treatments at 250°C for 1 hour suggests that the presence of fully molten Sn phase during service for relatively long time could be beneficial for functional healing. The requirements of suitable Al-Sn microstructures for self-healing purposes, granting at the same time the C-PCM functionalities, i.e., thermal energy storage and form-stability, were set

Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches

Background: To develop a pipeline for automatic extraction of quantitative metrics and radiomic features from lung computed tomography (CT) and develop artificial intelligence (AI) models supporting differential diagnosis between coronavirus disease 2019 (COVID-19) and other viral pneumonia (non-COVID-19). Methods: Chest CT of 1,031 patients (811 for model building; 220 as independent validation set (IVS) with positive swab for severe acute respiratory syndrome coronavirus-2 (647 COVID-19) or other respiratory viruses (384 non-COVID-19) were segmented automatically. A Gaussian model, based on the HU histogram distribution describing well-aerated and ill portions, was optimised to calculate quantitative metrics (QM, n = 20) in both lungs (2L) and four geometrical subdivisions (GS) (upper front, lower front, upper dorsal, lower dorsal; n = 80). Radiomic features (RF) of first (RF1, n = 18) and second (RF2, n = 120) order were extracted from 2L using PyRadiomics tool. Extracted metrics were used to develop four multilayer-perceptron classifiers, built with different combinations of QM and RF: Model1 (RF1-2L); Model2 (QM-2L, QM-GS); Model3 (RF1-2L, RF2-2L); Model4 (RF1-2L, QM-2L, GS-2L, RF2-2L). Results: The classifiers showed accuracy from 0.71 to 0.80 and area under the receiving operating characteristic curve (AUC) from 0.77 to 0.87 in differentiating COVID-19 versus non-COVID-19 pneumonia. Best results were associated with Model3 (AUC 0.867 ± 0.008) and Model4 (AUC 0.870 ± 0.011. For the IVS, the AUC values were 0.834 ± 0.008 for Model3 and 0.828 ± 0.011 for Model4. Conclusions: Four AI-based models for classifying patients as COVID-19 or non-COVID-19 viral pneumonia showed good diagnostic performances that could support clinical decisions

Enhancement of Magnetic Stability in Antiferromagnetic CoO Films by Adsorption of Organic Molecules

Antiferromagnets are a class of magnetic materials of great interest in spintronic devices because of their stability and ultrafast dynamics. When interfaced with an organic molecular layer, antiferromagnetic (AF) films are expected to form a spinterface that can allow fine control of specific AF properties. In this paper, we investigate spinterface effects on CoO, an AF oxide. To access the magnetic state of the antiferromagnet, we couple it to a ferromagnetic Co film via an exchange bias (EB) effect. In this way, the formation of a spinterface is detected through changes induced on the CoO/Co EB system. We demonstrate that C-60 and Gaq(3) adsorption on CoO shifts its blocking temperature; in turn, an increase in both the EB fields and the coercivities is observed on the EB-coupled Co layer. Ab initio calculations for the CoO/C-60 interface indicate that the molecular adsorption is responsible for a charge redistribution on the CoO layer that alters the occupation of the d orbitals of Co atoms and, to a smaller extent, the p orbitals of oxygen. As a result, the AF coupling between Co atoms in the CoO is enhanced. Considering the granular nature of CoO, a larger AF stability upon molecular adsorption is then associated with a larger number of AF grains that are stable upon reversal of the Co layer

Toll-like receptor 4 and high-mobility group box-1 are involved in ictogenesis and can be targeted to reduce seizures

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Colorectal Cancer Stage at Diagnosis Before vs During the COVID-19 Pandemic in Italy

IMPORTANCE Delays in screening programs and the reluctance of patients to seek medical attention because of the outbreak of SARS-CoV-2 could be associated with the risk of more advanced colorectal cancers at diagnosis. OBJECTIVE To evaluate whether the SARS-CoV-2 pandemic was associated with more advanced oncologic stage and change in clinical presentation for patients with colorectal cancer. DESIGN, SETTING, AND PARTICIPANTS This retrospective, multicenter cohort study included all 17 938 adult patients who underwent surgery for colorectal cancer from March 1, 2020, to December 31, 2021 (pandemic period), and from January 1, 2018, to February 29, 2020 (prepandemic period), in 81 participating centers in Italy, including tertiary centers and community hospitals. Follow-up was 30 days from surgery. EXPOSURES Any type of surgical procedure for colorectal cancer, including explorative surgery, palliative procedures, and atypical or segmental resections. MAIN OUTCOMES AND MEASURES The primary outcome was advanced stage of colorectal cancer at diagnosis. Secondary outcomes were distant metastasis, T4 stage, aggressive biology (defined as cancer with at least 1 of the following characteristics: signet ring cells, mucinous tumor, budding, lymphovascular invasion, perineural invasion, and lymphangitis), stenotic lesion, emergency surgery, and palliative surgery. The independent association between the pandemic period and the outcomes was assessed using multivariate random-effects logistic regression, with hospital as the cluster variable. RESULTS A total of 17 938 patients (10 007 men [55.8%]; mean [SD] age, 70.6 [12.2] years) underwent surgery for colorectal cancer: 7796 (43.5%) during the pandemic period and 10 142 (56.5%) during the prepandemic period. Logistic regression indicated that the pandemic period was significantly associated with an increased rate of advanced-stage colorectal cancer (odds ratio [OR], 1.07; 95%CI, 1.01-1.13; P = .03), aggressive biology (OR, 1.32; 95%CI, 1.15-1.53; P < .001), and stenotic lesions (OR, 1.15; 95%CI, 1.01-1.31; P = .03). CONCLUSIONS AND RELEVANCE This cohort study suggests a significant association between the SARS-CoV-2 pandemic and the risk of a more advanced oncologic stage at diagnosis among patients undergoing surgery for colorectal cancer and might indicate a potential reduction of survival for these patients

Modeling of Fibrin Gels Based on Confocal Microscopy and Light-Scattering Data

AbstractFibrin gels are biological networks that play a fundamental role in blood coagulation and other patho/physiological processes, such as thrombosis and cancer. Electron and confocal microscopies show a collection of fibers that are relatively monodisperse in diameter, not uniformly distributed, and connected at nodal points with a branching order of ∼3–4. Although in the confocal images the hydrated fibers appear to be quite straight (mass fractal dimension Dm = 1), for the overall system 1<Dm<2. Based on the confocal images, we developed a method to generate three-dimensional (3D) in silico gels made of cylindrical sticks of diameter d, density ρ, and average length 〈L〉, joined at randomly distributed nodal points. The resulting 3D network strikingly resembles real fibrin gels and can be sketched as an assembly of densely packed fractal blobs, i.e., regions of size ξ, where the fiber concentration is higher than average. The blobs are placed at a distance ξ0 between their centers of mass so that they are overlapped by a factor η = ξ/ξ0 and have Dm ∼1.2–1.6. The in silico gels’ structure is quantitatively analyzed by its 3D spatial correlation function g3D(r) and corresponding power spectrum I(q) = FFT3D[g3D(r)], from which ρ, d, Dm, η, and ξ0 can be extracted. In particular, ξ0 provides an excellent estimate of the gel mesh size. The in silico gels’ I(q) compares quite well with real gels’ elastic light-scattering measurements. We then derived an analytical form factor for accurately fitting the scattering data, which allowed us to directly recover the gels’ structural parameters