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The main goal of brain cancer surgery is to perform an accurate resection of the tumor, preserving as much normal brain tissue as possible for the patient. The development of a non-contact and label-free method to provide reliable support for tumor resection in real-time during neurosurgical procedures is a current clinical need. Hyperspectral imaging is a non-contact, non-ionizing, and label-free imaging modality that can assist surgeons during this challenging task without using any contrast agent. In this work, we present a deep learning-based framework for processing hyperspectral images of in vivo human brain tissue. The proposed framework was evaluated by our human image database, which includes 26 in vivo hyperspectral cubes from 16 different patients, among which 258,810 pixels were labeled. The proposed framework is able to generate a thematic map where the parenchymal area of the brain is delineated and the location of the tumor is identified, providing guidance to the operating surgeon for a successful and precise tumor resection. The deep learning pipeline achieves an overall accuracy of 80% for multiclass classification, improving the results obtained with traditional support vector machine (SVM)-based approaches. In addition, an aid visualization system is presented, where the final thematic map can be adjusted by the operating surgeon to find the optimal classification threshold for the current situation during the surgical procedure.

Non-revenue water (NRW) is a persistent global problem generated in municipal systems, causing millions of cubic meters of water to be wasted per year, becoming a financial obstacle for water and sanitation companies, and generating economic losses. The main objective of this study is to determine the NRW in the supply system of the Tulcán canton from information provided by the Empresa Pública Municipal de Agua Potable y Alcantarillado de Tulcán (EPMAPA-T) during the period 2013 to 2020, where the NRW, the Physical Performance Indicator (PPI), which measures the technical and operational efficiency of the water distribution system, and the Return Flow Coefficient, a fraction of the produced water that returns to the sewer system, were analyzed. The estimation of Non-Revenue Water (NRW) and the Physical Performance Indicator (PPI) was based on the comparison of volumetric records, including billed drinking water, water output from the Water Treatment Plant (WTP), and influent to the Sewage Treatment Plants (STPs). They are in a range of 37% to 43% and 57% to 63%, respectively, and with a return coefficient of 0.93. In conclusion, an average NRW of 41% was presented, indicating that not all the produced water enters the sewer system, reflecting a total loss of $ 509,802.69 during the period from 2013 to 2020. In Ecuador, studies on NRW are still incipient but strategic; this work has shown that almost half of the drinking water produced does not generate income, highlighting the magnitude of its losses and cost.

For many years, a major question in cancer genomics has been the identification of those variations that can have a functional role in cancer, and distinguish from the majority of genomic changes that have no functional consequences. This is particularly challenging when considering complex chromosomal rearrangements, often composed of multiple DNA breaks, resulting in difficulties in classifying and interpreting them functionally. Despite recent efforts towards classifying structural variants (SVs), more robust statistical frames are needed to better classify these variants and isolate those that derive from specific molecular mechanisms. We present a new statistical approach to analyze SVs patterns from 2392 tumor samples from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium and identify significant recurrence, which can inform relevant mechanisms involved in the biology of tumors. The method is based on recursive KDE clustering of 152,926 SVs, randomization methods, graph mining techniques and statistical measures. The proposed methodology was able not only to identify complex patterns across different cancer types but also to prove them as not random occurrences. Furthermore, a new class of pattern that was not previously described has been identified.

Currently, intraoperative guidance tools used for brain tumor resection assistance during surgery have several limitations. Hyperspectral (HS) imaging is arising as a novel imaging technique that could offer new capabilities to delineate brain tumor tissue in surgical-time. However, the HS acquisition systems have some limitations regarding spatial and spectral resolution depending on the spectral range to be captured. Image fusion techniques combine information from different sensors to obtain an HS cube with improved spatial and spectral resolution. This paper describes the contributions to HS image fusion using two push-broom HS cameras, covering the visual and near-infrared (VNIR) [400–1000 nm] and near-infrared (NIR) [900–1700 nm] spectral ranges, which are integrated into an intraoperative HS acquisition system developed to delineate brain tumor tissue during neurosurgical procedures. Both HS images were registered using intensity-based and feature-based techniques with different geometric transformations to perform the HS image fusion, obtaining an HS cube with wide spectral range [435–1638 nm]. Four HS datasets were captured to verify the image registration and the fusion process. Moreover, segmentation and classification methods were evaluated to compare the performance results between the use of the VNIR and NIR data, independently, with respect to the fused data. The results reveal that the proposed methodology for fusing VNIR–NIR data improves the classification results up to 21% of accuracy with respect to the use of each data modality independently, depending on the targeted classification problem.

Brain surgery is one of the most common and effective treatments for brain tumour. However, neurosurgeons face the challenge of determining the boundaries of the tumour to achieve maximum resection, while avoiding damage to normal tissue that may cause neurological sequelae to patients. Hyperspectral (HS) imaging (HSI) has shown remarkable results as a diagnostic tool for tumour detection in different medical applications. In this work, we demonstrate, with a robust k-fold cross-validation approach, that HSI combined with the proposed processing framework is a promising intraoperative tool for in-vivo identification and delineation of brain tumours, including both primary (high-grade and low-grade) and secondary tumours. Analysis of the in-vivo brain database, consisting of 61 HS images from 34 different patients, achieve a highest median macro F1-Score result of 70.2 ± 7.9% on the test set using both spectral and spatial information. Here, we provide a benchmark based on machine learning for further developments in the field of in-vivo brain tumour detection and delineation using hyperspectral imaging to be used as a real-time decision support tool during neurosurgical workflows.

This study describes the implementation of Internet of Things (IoT) sensors in flow meters installed in drinking water systems and in fog catchers built in low-income, high-altitude communities in the Andes region of Ecuador, taking studies at the University de las Fuerzas Armadas ESPE as our reference. The influence and management of these intelligent sensors are analyzed, as well as a basic review of the materials and methods used in their implementation. The importance of validating the accuracy and reliability of IoT sensors compared to professional devices is highlighted, especially in mountain areas with difficult access. Additionally, the cost–benefit of using IoT sensors in fog catchers and drinking water distribution networks is mentioned, which depends on several factors such as the scale of the project, the objectives to be achieved concerning monitoring, and the available resources. Finally, it is highlighted that using Internet of Things (IoT) sensors in construction and water collection systems has proven beneficial in detecting possible effects on its operation and determining consumption and supply flows for a given population.

Magenta Cherry or Eugenia (Syzygium paniculatum Gaertn) is an underutilized berry species with an interesting source of functional components. This study aimed to evaluate these berries’ morphometric, nutritional, and phytochemical characteristics at two ripening stages, CM: consumer maturity (CM) and OM: over-maturity. Morphometric analysis revealed size and weight parameters comparable to commercial berries such as blueberries. Fresh fruits were processed into pulverized material, and in this, a proximate analysis was evaluated, showing high moisture content (88.9%), dietary fiber (3.56%), and protein (0.63%), with negligible fat, indicating suitability for low-calorie diets. Phytochemical screening by HPLC identified gallic acid, chlorogenic acid, hydroxycinnamic acid, ferulic acid, quercetin, rutin, and condensed tannins. Ethanol extracts showed stronger bioactive profiles than aqueous extracts, with significant antioxidant capacity (up to 803.40 µmol Trolox/g via Ferric Reducing Antioxidant Power (FRAP assay). Fourier-transform infrared spectroscopy (FTIR) and Raman spectroscopic analyses established structural transformations of hydroxyl, carbonyl, and aromatic groups associated with ripening. These changes were supported by observed variations in anthocyanin and flavonoid contents, both higher at the CM stage. A notable pigment loss in OM fruits could be attributed to pH changes, oxidative degradation, enzymatic activity loss, and biotic stressors. Antioxidant assays (DPPH, ABTS, and FRAP) confirmed higher radical scavenging activity in CM-stage berries. Elemental analysis identified minerals such as potassium, calcium, magnesium, iron, and zinc, although in moderate concentrations. In summary, Syzygium paniculatum Gaertn fruit demonstrates considerable potential as a source of natural antioxidants and bioactive compounds. These findings advocate for greater exploration and sustainable use of this native berry species in functional food systems.

Ceramic-reinforced slabs were widely used in Spain during the second half of the 20th century, especially for industrial buildings. This solution was popular due to the lack of materials at that time, as it requires almost no concrete and low ratios of reinforcement. In this study, we present and discuss the results of a real load-bearing test of a real ceramic-reinforced slab, which was loaded and reloaded cyclically for a duration of one week in order to describe any damage under a high-demand loading series. Due to the design of these slabs, the structural response is based more on shear than on bending due to the low levels of concrete and the geometry and location of re-bars. The low ratio of concrete makes these slabs ideal for short-span structures, mainly combined with steel or RC frames. The slab which was analyzed in this study covers a span of 4.88 m between two steel I-beams (IPN400), and corresponds to a building from the mid-1960s in the city of Igualada (Barcelona, Spain). A load-bearing test was carried out up to 7.50 kN/m2 by using two-story sacks full of sand. The supporting steel beams were propped up in order to avoid any interference in the results of the test; without the shoring of the steel structure, deflections would come from the combination of the ceramic slab together with the steel profiles. A process of loading and unloading was repeated for a duration of six days in order to describe the cyclic response of the slab under high levels of loading. Finally, vibration analysis of the slab was also done; the higher the load applied, the higher the fundamental frequency of the cross section, which is more comfortable in terms of serviceability.

Modern applications demand resources at an unprecedented level. In this sense, data-centers are required to scale efficiently to cope with such demand. Resource disaggregation has the potential to improve resource-efficiency by allowing the deployment of workloads in more flexible ways. Therefore, the industry is shifting towards disaggregated architectures, which enables new ways to structure hardware resources in data centers. However, determining the best performing resource provisioning is a complicated task. The optimality of resource allocation in a disaggregated data center depends on its topology and the workload collocation. This paper presents DRMaestro, a framework to orchestrate disaggregated resources transparently from the applications. DRMaestro uses a novel flow-network model to determine the optimal placement in multiple phases while employing best-efforts on preventing workload performance interference. We first evaluate the impact of disaggregation regarding the additional network requirements under higher network load. The results show that for some applications the impact is minimal, but other ones can suffer up to 80% slowdown in the data transfer part. After that, we evaluate DRMaestro via a real prototype on Kubernetes and a trace-driven simulation. The results show that DRMaestro can reduce the total job makespan with a speedup of up to ≈1.20x and decrease the QoS violation up to ≈2.64x comparing with another orchestrator that does not support resource disaggregation.