A Compact PV Panel Model for Cyber-Physical Systems in Smart Cities

One of the ambitious goals of the ‘‘Smart city’’ paradigm is to design zero-energy buildings. Buildings can be considered as connected cyber-physical systems that require the construction of sound methodologies inherited from the Electronic Design Automation (EDA) research. In particular, aiming at autonomous buildings, the effective design of renewable energy sources is a key aspect for which such methodologies have to be developed. In this work, we propose a modeling strategy for the early estimation of the performance of photovoltaic (PV) arrays. Although a plethora of PV panel models there exists, most of these models suffer from accuracy/complexity tradeoffs. On one hand, building fast models forces to ignore either the correlation between temperature and irradiance, or the topology of panels, thus yielding inaccurate estimations. On the other, more accurate models are time consuming and require costly measurements or circuit analysis, that cannot be extracted from the sole datasheet. This paper proposes a compact semi-empirical model, suitable for real time simulation and built solely from information derived from the PV panel datasheet. The model is built by empirically fitting an expression of the panel operating point as a function of both irradiance and temperature, and of the adopted PV system topology. The accuracy and effectiveness of the proposed model have been validated w.r.t. the production traces of the PV systems of a real world industrial building

Manufacturing as a Data-Driven Practice: Methodologies, Technologies, and Tools

n recent years, the introduction and exploitation of innovative information technologies in industrial contexts have led to the continuous growth of digital shop floor envi- ronments. The new Industry-4.0 model allows smart factories to become very advanced IT industries, generating an ever- increasing amount of valuable data. As a consequence, the neces- sity of powerful and reliable software architectures is becoming prominent along with data-driven methodologies to extract useful and hidden knowledge supporting the decision making process. This paper discusses the latest software technologies needed to collect, manage and elaborate all data generated through innovative IoT architectures deployed over the production line, with the aim of extracting useful knowledge for the orchestration of high-level control services that can generate added business value. This survey covers the entire data life-cycle in manufacturing environments, discussing key functional and methodological aspects along with a rich and properly classified set of technologies and tools, useful to add intelligence to data-driven services. Therefore, it serves both as a first guided step towards the rich landscape of literature for readers approaching this field, and as a global yet detailed overview of the current state-of-the-art in the Industry 4.0 domain for experts. As a case study, we discuss in detail the deployment of the proposed solutions for two research project demonstrators, showing their ability to mitigate manufacturing line interruptions and reduce the corresponding impacts and costs

A Comparison Analysis of BLE-Based Algorithms for Localization in Industrial Environments

Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoor localization converting the received signal strength indication (RSSI) to distance information. In this work, we study the effectiveness of proximity beacons for accurately locating objects within a manufacturing plant by performing extensive experiments in a real industrial environment. To this purpose, we compare localization algorithms based either on trilateration or environment fingerprinting combined with a machine-learning based regressor (k-nearest neighbors, support-vector machines, or multi-layer perceptron). Each algorithm is analyzed in two different types of industrial environments. For each environment, various configurations are explored, where a configuration is characterized by the number of beacons per square meter and the density of fingerprint points. In addition, the fingerprinting approach is based on a preliminary site characterization; it may lead to location errors in the presence of environment variations (e.g., movements of large objects). For this reason, the robustness of fingerprinting algorithms against such variations is also assessed. Our results show that fingerprint solutions outperform trilateration, showing also a good resilience to environmental variations. Given the similar error obtained by all three fingerprint approaches, we conclude that k-NN is the preferable algorithm due to its simple deployment and low number of hyper-parameters

Design of a multi-signature ensemble classifier predicting neuroblastoma patients' outcome

<p>Abstract</p> <p>Background</p> <p>Neuroblastoma is the most common pediatric solid tumor of the sympathetic nervous system. Development of improved predictive tools for patients stratification is a crucial requirement for neuroblastoma therapy. Several studies utilized gene expression-based signatures to stratify neuroblastoma patients and demonstrated a clear advantage of adding genomic analysis to risk assessment. There is little overlapping among signatures and merging their prognostic potential would be advantageous. Here, we describe a new strategy to merge published neuroblastoma related gene signatures into a single, highly accurate, Multi-Signature Ensemble (MuSE)-classifier of neuroblastoma (NB) patients outcome.</p> <p>Methods</p> <p>Gene expression profiles of 182 neuroblastoma tumors, subdivided into three independent datasets, were used in the various phases of development and validation of neuroblastoma NB-MuSE-classifier. Thirty three signatures were evaluated for patients' outcome prediction using 22 classification algorithms each and generating 726 classifiers and prediction results. The best-performing algorithm for each signature was selected, validated on an independent dataset and the 20 signatures performing with an accuracy > = 80% were retained.</p> <p>Results</p> <p>We combined the 20 predictions associated to the corresponding signatures through the selection of the best performing algorithm into a single outcome predictor. The best performance was obtained by the Decision Table algorithm that produced the NB-MuSE-classifier characterized by an external validation accuracy of 94%. Kaplan-Meier curves and log-rank test demonstrated that patients with good and poor outcome prediction by the NB-MuSE-classifier have a significantly different survival (p < 0.0001). Survival curves constructed on subgroups of patients divided on the bases of known prognostic marker suggested an excellent stratification of localized and stage 4s tumors but more data are needed to prove this point.</p> <p>Conclusions</p> <p>The NB-MuSE-classifier is based on an ensemble approach that merges twenty heterogeneous, neuroblastoma-related gene signatures to blend their discriminating power, rather than numeric values, into a single, highly accurate patients' outcome predictor. The novelty of our approach derives from the way to integrate the gene expression signatures, by optimally associating them with a single paradigm ultimately integrated into a single classifier. This model can be exported to other types of cancer and to diseases for which dedicated databases exist.</p

Functional genomic analysis of frataxin deficiency reveals tissue-specific alterations and identifies the PPARγ pathway as a therapeutic target in Friedreich’s ataxia

Friedreich’s ataxia (FRDA), the most common inherited ataxia, is characterized by focal neurodegeneration, diabetes mellitus and life-threatening cardiomyopathy. Frataxin, which is significantly reduced in patients with this recessive disorder, is a mitochondrial iron-binding protein, but how its deficiency leads to neurodegeneration and metabolic derangements is not known. We performed microarray analysis of heart and skeletal muscle in a mouse model of frataxin deficiency, and found molecular evidence of increased lipogenesis in skeletal muscle, and alteration of fiber-type composition in heart, consistent with insulin resistance and cardiomyopathy, respectively. Since the peroxisome proliferator-activated receptor gamma (PPARγ) pathway is known to regulate both processes, we hypothesized that dysregulation of this pathway could play a key role in frataxin deficiency. We confirmed this by showing a coordinate dysregulation of the PPARγ coactivator Pgc1a and transcription factor Srebp1 in cellular and animal models of frataxin deficiency, and in cells from FRDA patients, who have marked insulin resistance. Finally, we show that genetic modulation of the PPARγ pathway affects frataxin levels in vitro, supporting PPARγ as a novel therapeutic target in FRDA

Assessment of copy number variations in 120 patients with Poland syndrome

Poland Syndrome (PS) is a rare congenital disorder presenting with agenesis/hypoplasia of the pectoralis major muscle variably associated with thoracic and/or upper limb anomalies. Most cases are sporadic, but familial recurrence, with different inheritance patterns, has been observed. The genetic etiology of PS remains unknown. Karyotyping and array-comparative genomic hybridization (CGH) analyses can identify genomic imbalances that can clarify the genetic etiology of congenital and neurodevelopmental disorders. We previously reported a chromosome 11 deletion in twin girls with pectoralis muscle hypoplasia and skeletal anomalies, and a chromosome six deletion in a patient presenting a complex phenotype that included pectoralis muscle hypoplasia. However, the contribution of genomic imbalances to PS remains largely unknown

Co-occurrence of Beckwith-Wiedemann syndrome and pseudohypoparathyroidism type 1B: coincidence or common molecular mechanism?

Imprinting disorders are congenital diseases caused by dysregulation of genomic imprinting, affecting growth, neurocognitive development, metabolism and cancer predisposition. Overlapping clinical features are often observed among this group of diseases. In rare cases, two fully expressed imprinting disorders may coexist in the same patient. A dozen cases of this type have been reported so far. Most of them are represented by individuals affected by Beckwith–Wiedemann spectrum (BWSp) and Transient Neonatal Diabetes Mellitus (TNDM) or BWSp and Pseudo-hypoparathyroidism type 1B (PHP1B). All these patients displayed Multilocus imprinting disturbances (MLID). Here, we report the first case of co-occurrence of BWS and PHP1B in the same individual in absence of MLID. Genome-wide methylation and SNP-array analyses demonstrated loss of methylation of the KCNQ1OT1:TSS-DMR on chromosome 11p15.5 as molecular cause of BWSp, and upd(20)pat as cause of PHP1B. The absence of MLID and the heterodisomy of chromosome 20 suggests that BWSp and PHP1B arose through distinct and independent mechanism in our patient. However, we cannot exclude that the rare combination of the epigenetic defect on chromosome 11 and the UPD on chromosome 20 may originate from a common so far undetermined predisposing molecular lesion. A better comprehension of the molecular mechanisms underlying the co-occurrence of two imprinting disorders will improve genetic counselling and estimate of familial recurrence risk of these rare cases. Furthermore, our study also supports the importance of multilocus molecular testing for revealing MLID as well as complex cases of imprinting disorders

PGC-1alpha Down-Regulation Affects the Antioxidant Response in Friedreich's Ataxia

BACKGROUND: Cells from individuals with Friedreich's ataxia (FRDA) show reduced activities of antioxidant enzymes and cannot up-regulate their expression when exposed to oxidative stress. This blunted antioxidant response may play a central role in the pathogenesis. We previously reported that Peroxisome Proliferator Activated Receptor Gamma (PPARgamma) Coactivator 1-alpha (PGC-1alpha), a transcriptional master regulator of mitochondrial biogenesis and antioxidant responses, is down-regulated in most cell types from FRDA patients and animal models. METHODOLOGY/PRINCIPAL FINDINGS: We used primary fibroblasts from FRDA patients and the knock in-knock out animal model for the disease (KIKO mouse) to determine basal superoxide dismutase 2 (SOD2) levels and the response to oxidative stress induced by the addition of hydrogen peroxide. We measured the same parameters after pharmacological stimulation of PGC-1alpha. Compared to control cells, PGC-1alpha and SOD2 levels were decreased in FRDA cells and did not change after addition of hydrogen peroxide. PGC-1alpha direct silencing with siRNA in control fibroblasts led to a similar loss of SOD2 response to oxidative stress as observed in FRDA fibroblasts. PGC-1alpha activation with the PPARgamma agonist (Pioglitazone) or with a cAMP-dependent protein kinase (AMPK) agonist (AICAR) restored normal SOD2 induction. Treatment of the KIKO mice with Pioglitazone significantly up-regulates SOD2 in cerebellum and spinal cord. CONCLUSIONS/SIGNIFICANCE: PGC-1alpha down-regulation is likely to contribute to the blunted antioxidant response observed in cells from FRDA patients. This response can be restored by AMPK and PPARgamma agonists, suggesting a potential therapeutic approach for FRDA.Journal ArticleResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe

Fatality rate and predictors of mortality in an Italian cohort of hospitalized COVID-19 patients

Clinical features and natural history of coronavirus disease 2019 (COVID-19) differ widely among different countries and during different phases of the pandemia. Here, we aimed to evaluate the case fatality rate (CFR) and to identify predictors of mortality in a cohort of COVID-19 patients admitted to three hospitals of Northern Italy between March 1 and April 28, 2020. All these patients had a confirmed diagnosis of SARS-CoV-2 infection by molecular methods. During the study period 504/1697 patients died; thus, overall CFR was 29.7%. We looked for predictors of mortality in a subgroup of 486 patients (239 males, 59%; median age 71 years) for whom sufficient clinical data were available at data cut-off. Among the demographic and clinical variables considered, age, a diagnosis of cancer, obesity and current smoking independently predicted mortality. When laboratory data were added to the model in a further subgroup of patients, age, the diagnosis of cancer, and the baseline PaO2/FiO2 ratio were identified as independent predictors of mortality. In conclusion, the CFR of hospitalized patients in Northern Italy during the ascending phase of the COVID-19 pandemic approached 30%. The identification of mortality predictors might contribute to better stratification of individual patient risk