Sustainability of technological innovation investiments. Photovoltaic panels case study

as photovoltaic panels. In the case study analysed are shown the benefits obtained from the investments of the central Italy after installing photovoltaic systems. The total expenditure for the electricity purchase is € 52.326, while the total benefit of the investment is € 18.789, equal in percentage to a 53% energy saving over a period of 20 years. The company expeniture in the absence of a photovoltaic system is equal to € 109.03, while in the presence of a plant, considering also all costs incurred for € 93.090, with a percentage of profit on the investment made equal to almost 15% in 20 years

From direct to digital survey. The Abbey of San Giovanni Battista in Lucoli (L' Aquila)

[EN] Lucoli is a scattered municipality in the area of L Aquila, in the Italian region of Abruzzo. In this place between the mountains of the conca Aquilana stands the Abbey of San Giovanni Battista, an important historic and religious site. Despite the damage suffered caused during the 2009 earthquake, the local people still use it and look at it as a symbol of community. With the aim of analyse and so mitigate the seismic vulnerability, the abbey has been the subject of an architectural survey with direct method in a first step, and then of digital laser scanning survey at a later stage, to integrate and verify the first.Brusaporci, S.; Ruggieri, A. (2022). From direct to digital survey. The Abbey of San Giovanni Battista in Lucoli (L' Aquila). EGE Revista de Expresión Gráfica en la Edificación. (17):56-71. https://doi.org/10.4995/ege.2022.1890256711

AUC-based Selective Classification

Selective classification (or classification with a reject option) pairs a classifier with a selection function to determine whether or not a prediction should be accepted. This framework trades off coverage (probability of accepting a prediction) with predictive performance, typically measured by distributive loss functions. In many application scenarios, such as credit scoring, performance is instead measured by ranking metrics, such as the Area Under the ROC Curve (AUC). We propose a model-agnostic approach to associate a selection function to a given probabilistic binary classifier. The approach is specifically targeted at optimizing the AUC. We provide both theoretical justifications and a novel algorithm, called AUCROSS, to achieve such a goal. Experiments show that our method succeeds in trading-off coverage for AUC, improving over existing selective classification methods targeted at optimizing accuracy

Three-dimension-printed custom-made prosthetic reconstructions: from revision surgery to oncologic reconstructions

Background The use of custom-made 3D-printed prostheses for reconstruction of severe bone defects in selected cases is increasing. The aims of this study were to evaluate (1) the feasibility of surgical reconstruction with these prostheses in oncologic and non-oncologic settings and (2) the functional results, complications, and outcomes at short-term follow-up. Methods We analyzed 13 prospectively collected patients treated between June 2016 and January 2018. Diagnoses were primary bone tumour (7 patients), metastasis (3 patients), and revision of total hip arthroplasty (3 patients). Pelvis was the most frequent site of reconstruction (7 cases). Functional results were assessed with MSTS score and complications according to Henderson et al. Statistical analysis was performed using Kaplan-Meier and log-rank test curves. Results At a mean follow-up of 13.7 months (range, 6 \u2013 26 months), all patients except one were alive. Oncologic outcomes show seven patients NED (no evidence of disease), one NED after treatment of metastasis, one patient died of disease, and another one was alive with disease. Overall survival was 100% and 80% at one and two years, respectively. Seven complications occurred in five patients (38.5%). Survival to all complications was 62% at two years of follow-up. Functional outcome was good or excellent in all cases with a mean score of 80.3%. Conclusion 3D-printed custom-made prostheses represent a promising reconstructive technique in musculoskeletal oncology and challenging revision surgery. Preliminary results were satisfactory. Further studies are needed to evaluate prosthetic design, fixation methods, and stability of the implants at long-ter

The layered structure of company share networks

We present a framework for the analysis of corporate governance problems using network science and graph algorithms on ownership networks. In such networks, nodes model companies/shareholders and edges model shares owned. Inspired by the widespread pyramidal organization of corporate groups of companies, we model ownership networks as layered graphs, and exploit the layered structure to design feasible and efficient solutions to three key problems of corporate governance. The first one is the long-standing problem of computing direct and indirect ownership (integrated ownership problem). The other two problems are introduced here: computing direct and indirect dividends (dividend problem), and computing the group of companies controlled by a parent shareholder (corporate group problem). We conduct an extensive empirical analysis of the Italian ownership network, which, with its 3.9M nodes, is 30× the largest network studied so far

Post-tsunami primary Scedosporium apiospermum osteomyelitis of the knee in an immunocompetent patient

SummaryScedosporium apiospermum is a filamentous fungus present in soil and polluted waters that may cause infection by direct inoculation. Osteomyelitis represents a challenge both for diagnosis and treatment. We report a case of post-tsunami primary S. apiospermum osteomyelitis of the knee in an immunocompetent patient

Near-field electrospinning of conjugated polymer light-emitting nanofibers

The authors report on the realization of ordered arrays of light-emitting conjugated polymer nanofibers by near-field electrospinning. The fibers, made by poly[2-methoxy-5-(2-ethylhexyloxy)-1,4-phenylenevinylene], have diameters of few hundreds of nanometers and emission peaked at 560 nm. The observed blue-shift compared to the emission from reference films is attributed to different polymer packing in the nanostructures. Optical confinement in the fibers is also analyzed through self-waveguided emission. These results open interesting perspectives for realizing complex and ordered architectures by light-emitting nanofibers, such as photonic circuits, and for the precise positioning and integration of conjugated polymer fibers into light-emitting devices.Comment: 11 pages, 6 figures Nanoscale, 201

Ataxia in children: early recognition and clinical evaluation

Background: Ataxia is a sign of different disorders involving any level of the nervous system and consisting of impaired coordination of movement and balance. It is mainly caused by dysfunction of the complex circuitry connecting the basal ganglia, cerebellum and cerebral cortex. A careful history, physical examination and some characteristic maneuvers are useful for the diagnosis of ataxia. Some of the causes of ataxia point toward a benign course, but some cases of ataxia can be severe and particularly frightening. Methods: Here, we describe the primary clinical ways of detecting ataxia, a sign not easily recognizable in children. We also report on the main disorders that cause ataxia in children. Results: The causal events are distinguished and reported according to the course of the disorder: acute, intermittent, chronic-non-progressive and chronic-progressive. Conclusions: Molecular research in the field of ataxia in children is rapidly expanding; on the contrary no similar results have been attained in the field of the treatment since most of the congenital forms remain fully untreatable. Rapid recognition and clinical evaluation of ataxia in children remains of great relevance for therapeutic results and prognostic counseling

Deep Neural Network Benchmarks for Selective Classification

With the increasing deployment of machine learning models in many socially-sensitive tasks, there is a growing demand for reliable and trustworthy predictions. One way to accomplish these requirements is to allow a model to abstain from making a prediction when there is a high risk of making an error. This requires adding a selection mechanism to the model, which selects those examples for which the model will provide a prediction. The selective classification framework aims to design a mechanism that balances the fraction of rejected predictions (i.e., the proportion of examples for which the model does not make a prediction) versus the improvement in predictive performance on the selected predictions. Multiple selective classification frameworks exist, most of which rely on deep neural network architectures. However, the empirical evaluation of the existing approaches is still limited to partial comparisons among methods and settings, providing practitioners with little insight into their relative merits. We fill this gap by benchmarking 18 baselines on a diverse set of 44 datasets that includes both image and tabular data. Moreover, there is a mix of binary and multiclass tasks. We evaluate these approaches using several criteria, including selective error rate, empirical coverage, distribution of rejected instance's classes, and performance on out-of-distribution instances. The results indicate that there is not a single clear winner among the surveyed baselines, and the best method depends on the users' objectives

Medium-Long-Term Clinical and Radiographic Outcomes of Minimally Invasive Distal Metatarsal Metaphyseal Osteotomy (DMMO) for Central Primary Metatarsalgia: Do Maestro Criteria Have a Predictive Value in the Preoperative Planning for This Percutaneous Technique?

The purpose of this prospective study was first to evaluate the safety and effectiveness of Minimally Invasive Distal Metatarsal Metaphyseal Osteotomy (DMMO) in treating central metatarsalgia, identifying possible contraindications. The second objective was to verify the potential of DMMO to restore a harmonious forefoot morphotype according to Maestro criteria. Methods. A consecutive series of patients with metatarsalgia was consecutively enrolled and treated by DMMO. According to Maestro criteria, preoperative planning was carried out by both clinical and radiological assessment. Patient demographic data, AOFAS scores, 17-FFI, MOXFQ, SF-36, VAS, and complications were recorded. Maestro parameters, relative morphotypes, and bone callus formation were assessed. Statistical analysis was carried out (p<0.05). Results. Ninety-three patients (93 feet) with a mean age of 62.4 (31-87) years were evaluated. At mean follow-up of 58.7 (36-96) months, all of the clinical scores improved significantly (p<0.0001). Most of the osteotomies (76.3%) had healed by 3-month follow-up, while ideal harmonious morphotype was restored only in a few feet (3.2%). Clinical and radiological outcomes were not different based on principal demographic parameters. Long-term complications were recorded in 12 cases (12.9%). Conclusion. DMMO is a safe and effective method for the treatment of metatarsalgia. Although Maestro criteria were useful to calculate the metatarsal bones to be shortened and a significant clinical improvement of all scores was achieved, the ideal harmonious morphotype was restored only in a few feet. Hence, our data show that Maestro criteria did not have a predictive value in clinical outcomes of DMM