The geothermal occurrence of Kapistri, Ierapetra area, Crete

Η γεωθερμική εμφάνιση εντοπίζεται κοντά στο χωριό Καπίστρι, στην ευρύτερη περιοχή της πόλης της Ιεράπετρας στο νομό Λασηθίου της Κρήτης. Σε μερικές υδρογεωτρήσεις μετρήθηκαν θερμοκρασίες περί τους 25οC σε βάθος 100 – 150 μ. από την επιφάνεια. Η γεωθερμική βαθμίδα που υπολογίστηκε είναι διπλάσια της κανονικής.H Περιοχή καλύπτεται από πλακώδεις ασβεστολίθους της αυτόχθονης σειράς της Κρήτης πάνω στην οποία είναι επωθημένες μονάδες της φυλλιτικής – χαλαζιτικής σειράς και των ζωνών Πίνδου και Τριπόλεως. Εντός των ασβεστολίθων έχουν διεισδύσει μαγματικά πετρώματα (γρανιτοειδή) που έχουν προκαλέσει μεταμόρφωση επαφής και ρηγμάτωση κοντά στο Καπίστρι. Έντονη τεκτονική δραστηριότητα παρατηρείται στην ευρύτερη λεκάνη Ιεράπετρας με κύριες διευθύνσεις των ρηγμάτων Β – Ν, Α – Δ, ΒΔ – ΝΑ και ΒΑ – ΝΔ.Η αυξημένη γεωθερμική βαθμίδα, η έντονη ρηγμάτωση της περιοχής και η γεωχημική ένδειξη για την παρουσία νερού που κυκλoφορεί σε μεγάλο βάθος δείχνουν την πιθανή ανάπτυξη ενός βαθύτερου γεωθερμικού πεδίου.The geothermal occurence is located close to the Kapistri village, Ierapetra town, prefecture of Lassithi. In some water wells temperature of about 25oC in a depth of 100 - 150 m below surface, were measured. The calculated geothermal gradient is thus double in size compared to normal gradient.The geological environment is composed of platy limestones of the autochthonous series of Crete on which units of phyllite – quartzite series as well as Pindos and Tripoli zones are overthrusted. Granite intrusion occurs in the carbonates with distinct contact metamorphism, in the Kapistri area. Intense tectonic activity is observed in the wider area of the Ierapetra graben with main fault direction N – S, E –W, NW – SE, and NE – SW.The elevated geothermal gradient, the intense faulting of the area and the existence of deep circulated water indicates the development of a deeper geothermal field

Are Trolley Buses in Athens and Piraeus technically efficient?

Reliable measures of technical efficiency are of great interest because they can assist in addressing important issues. For instance, inefficient operation of firms - in the sense that if a firm is inefficient it does not produce at minimum cost - could lead to higher prices which could drive costumers to substitute away toward some other product or service. The purpose of this paper is the estimation of technical efficiency of Trolley Buses of Athens & Piraeus Area (T.B.A.P.A.), for each one of its twenty (20) lines for the year 2003. We apply the methodological framework of Stochastic Frontier Analysis (S.F.A.), by using the Cobb- Douglas specification of the production function. The dependent variable is the total kilometers that are covered by the vehicles of each line, while the independent variables include the fleet of the vehicles used, labor expanded and energy expanded. The data set consists of the monthly observations of the twenty (20) lines of the APTB. The results are compared to those from Data Envelopment Analysis (D.E.A.), a particularly widely used approach for efficiency measurement in the literature. Findings suggest that most lines were highly efficient, since technical efficiency ranged between 97% and 100%. The results obtained by means of the SFA approach are, in general terms, consistent with the DEA findings, despite the fact that DEA usually cannot discriminate between inefficiency and noise and tends to provide overestimated results

Fibrinolytics and intraventricular hemorrhage: a systematic review and meta-analysis

Intraventricular hemorrhage (IVH) is an independent poor prognostic factor in subarachnoid and intra-parenchymal hemorrhage. The use of intraventricular fibrinolytics (IVF) has long been debated, and its exact effects on outcomes are unknown. A systematic review and meta-analysis were performed in accordance with the PRISMA guidelines to assess the impact of IVF after non-traumatic IVH on mortality, functional outcome, intracranial bleeding, ventriculitis, time until clearance of third and fourth ventricles, obstruction of external ventricular drains (EVD), and shunt dependency. Nineteen studies were included in the meta-analysis, totaling 1020 patients. IVF was associated with lower mortality (relative risk [RR] 0.58; 95% confidence interval [CI] 0.47-0.72), fewer EVD obstructions (RR 0.41; 95% CI 0.22-0.74), and a shorter time until clearance of the ventricles (median difference [MD] - 4.05 days; 95% CI - 5.52 to - 2.57). There was no difference in good functional outcome, RR 1.41 (95% CI 0.98-2.03), or shunt dependency, RR 0.93 (95% CI 0.70-1.22). Correction for publication bias predicted an increased risk of intracranial bleeding, RR 1.67 (95% CI 1.01-2.74) and a lower risk of ventriculitis, RR 0.68 (95% CI 0.45-1.03) in IVH patients treated with IVF. IVF was associated with improved survival, faster clearance of blood from the ventricles and fewer drain obstructions, but further research is warranted to elucidate the effects on ventriculitis, long-term functional outcomes, and re-hemorrhage.Scientific Assessment and Innovation in Neurosurgical Treatment Strategie

Surgery vs. biopsy in the treatment of butterfly glioblastoma: a systematic review and meta-analysis

: Butterfly glioblastomas (bGBM) are grade IV gliomas that spread to bilateral hemispheres by infiltrating the corpus callosum. Data on the effect of surgery are limited to small case series. The aim of this meta-analysis was to compare resection vs. biopsy in terms of survival outcomes and postoperative complications. A systematic review of the literature was conducted using PubMed, EMBASE, and Cochrane databases through March 2021 in accordance with the PRISMA checklist. Pooled hazard ratios were calculated and meta-analyzed in a random-effects model including assessment of heterogeneity. Out of 3367 articles, seven studies were included with 293 patients. Surgical resection was significantly associated with longer overall survival (HR 0.39, 95%CI 0.2-0.55) than biopsy. Low heterogeneity was observed (I2: 0%). In further analysis, the effect persisted in extent of resection subgroups of both ≥80% and <80%. No statistically significant difference between surgery and biopsy was detected in terms of postoperative complications, although these were numerically larger for surgery. In patients with bGBM, surgical resection was associated with longer survival prospects compared with biopsy

Preoperative Brain Tumor Imaging:Models and Software for Segmentation and Standardized Reporting

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16-54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5-15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports

Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16–54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5–15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports.publishedVersio

Automatic assessment of glioma burden: A deep learning algorithm for fully automated volumetric and bi-dimensional measurement

Background Longitudinal measurement of glioma burden with MRI is the basis for treatment response assessment. In this study, we developed a deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hyperintensity and contrast-enhancing tumor, quantitating tumor volumes as well as the product of maximum bidimensional diameters according to the Response Assessment in Neuro-Oncology (RANO) criteria (AutoRANO). Methods Two cohorts of patients were used for this study. One consisted of 843 preoperative MRIs from 843 patients with low- or high-grade gliomas from 4 institutions and the second consisted of 713 longitudinal postoperative MRI visits from 54 patients with newly diagnosed glioblastomas (each with 2 pretreatment “baseline” MRIs) from 1 institution. Results The automatically generated FLAIR hyperintensity volume, contrast-enhancing tumor volume, and AutoRANO were highly repeatable for the double-baseline visits, with an intraclass correlation coefficient (ICC) of 0.986, 0.991, and 0.977, respectively, on the cohort of postoperative GBM patients. Furthermore, there was high agreement between manually and automatically measured tumor volumes, with ICC values of 0.915, 0.924, and 0.965 for preoperative FLAIR hyperintensity, postoperative FLAIR hyperintensity, and postoperative contrast-enhancing tumor volumes, respectively. Lastly, the ICCs for comparing manually and automatically derived longitudinal changes in tumor burden were 0.917, 0.966, and 0.850 for FLAIR hyperintensity volume, contrast-enhancing tumor volume, and RANO measures, respectively. Conclusions Our automated algorithm demonstrates potential utility for evaluating tumor burden in complex posttreatment settings, although further validation in multicenter clinical trials will be needed prior to widespread implementation

A general framework for coupled hydro-mechanical modelling of rainfall-induced instability in unsaturated slopes with multivariate random fields

An accurate estimation of rainfall-induced instability of slopes for extremely nonhomogeneous materials such as lignite mine spoils is a major challenge. This paper investigates the stability of nonhomogeneous soil slopes with respect to slip surface development, size of sliding volume, and determination of safety factor. Specified dependent random variables are cross-correlated using a multivariate Gaussian copula, the use of which provides a faster and more accurate representation of the inter-dependent properties of randomly-distributed soil. A Monte-Carlo simulation is used to generate a series of multivariate random fields for slopes. These are then implemented in Abaqus and analysed under constant rainfall conditions using a fully coupled hydro-elasto-plastic model. The resulting stress, strain, pore pressure, and displacement data are further processed in MATLAB to evaluate critical slip surfaces and safety factors. Results indicate that the factor of safety in a homogenous case is overestimated compared to the nonhomogeneous condition, while the sliding volume is underestimated. Moreover, the factor of safety decreases as the rainfall simulation continues and the probability of failure increases to nearly 100% after 10 days of rainfall. The framework developed in this paper can provide guidance for conducting relatively inexpensive probabilistic analyses

Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16–54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5–15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports