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Forests play a crucial role in maintaining ecological balance and biodiversity, making the accurate mapping of tree species and assessment of biodiversity indices essential for informed management decisions. This study introduces an innovative methodology that integrates EnMAP (Environmental Mapping and Analysis Program) hyperspectral data with Sentinel-2 multitemporal data to classify tree species in the biodiverse landscapes of Kampinos National Park and its surrounding regions in Poland.We extract essential vegetation indices such as NDVI, NDMI, SAVI, and EVI from Sentinel-2 data to assess forest health and dynamics. The Sentinel-2 data is upscaled from 10 m to 30 m to align with EnMAP’s spatial resolution, followed by precise co-registration of the images using QGIS. Utilizing a rich dataset from the National Forest Inventory, we extract spectral signatures of nine distinct tree species from both data sources. We employ five machine learning algorithms—Support Vector Machines (SVM), Random Forest (RF), CatBoost (CAT), Gradient Boosting Classifier (GBC), and XGBoost (XGB)—to enhance classification accuracy.Through iterative experimentation with data reduction techniques and algorithm tuning, we achieve optimal performance across needle-leaved and broad-leaved species. The resulting tree species maps are validated through quantitative accuracy assessments against mixed-species polygons from the National Forest Inventory and ground truthing in the Kampinos National Park. Achieving an overall accuracy of 85% to 93%, our study demonstrates the efficacy of this integrated approach in tree species mapping. Furthermore, the tree species maps serve as a foundation for deriving key biodiversity indices—species richness, Shannon-Wiener Diversity Index, Simpson’s Diversity Index, and a composite Biodiversity Index—providing insights into spatial biodiversity patterns and informing targeted conservation strategies. This study exemplifies the potential of combining advanced remote sensing techniques with field validation to enhance our understanding of forest ecosystems and guide sustainable management practices.

This research investigates the suitability of PRISMA and Sentinel-2 satellite imagery for retrieving topsoil properties such as Organic Matter (OM), Nitrogen (N), Phosphorus (P), Potassium (K), and pH in croplands using different Machine Learning (ML) algorithms and signal pre-treatments. Ninety-five soil samples were collected in Quzhou County, Northeast China. Satellite images captured soil reflectance data when bare soil was visible. For PRISMA data, a Linear Mixture Model (LMM) was used to separate soil and Photosynthetic Vegetation (PV) endmembers, excluding Non-Photosynthetic Vegetation (NPV) using Band Depth values at the 2100 nm absorption peak of cellulose. Sentinel-2 bare soil reflectance spectra were obtained using thresholds based on NDVI and NBR2 indices. Results showed PRISMA data provided slightly better accuracy in retrieving topsoil nutrients than Sentinel-2. While no optimal predictive algorithm was best, absorbance data proved more effective than reflectance. PRISMA results demonstrated potential for predicting soil nutrients in real scenarios.

In this paper we present an operational system for drought monitoring and early warning over South Africa-Lesotho. The indicators used for the alert issuance are based on meteorological, agricultural and hydrological indices, assembled by satellite and reanalysis raw data, to cover for all aspects of drought. The service is operationally updated every 10 days and has a spatial resolution of 10 Km. A severe drought period for South Africa covering the time period 2017–2020 was used to test the performance of the drought system. Our analysis indicated that the service is useful, providing sufficient warning on the onsets of and duration of droughts. Comparison of our results with a South African national drought service indicates a very good agreement in early warning messaging. The service is particularly user friendly and easily transferable to any region worldwide.

The planetary crisis regarding water resources means that new methods are needed to monitor large areas of water basins that are threatened by chemical and natural pollutants, together with climate change. With the aim to detect oil spill, we applied an algorithm, which could consistently and reliably confirm the presence of oil in four polluted lake waters analyzed. Combined algorithm application and metagenomic analysis from the spill areas that had been detected by the satellite identified drivers of the microbial response to oil pollution. Based on ortholog abundances, metabolic pathway reconstruction carried out in PICRUSt2 highlighted the degradative capacity of these microorganism. These microorganisms could be suitable candidates for treatment of crude oil, aromatic hydrocarbons and the desulfurization of persistent petroleum substances like dibenzothiophene.Environmental changes have been analysed with the combination of satellite monitoring and metagenomic in other studies. Red snow phenomenon, in Franz Josef Land's Arctic observed by satellite, is the result of a microbial succession dominated by Chlamydomonas nivalis, a unicellular, red-colored photosynthetic green algae. Similarly, satellite monitoring and metagenomic monitoring were used to assess the impact on coral reefs of a huge quantity of mud spill from iron ore mining on the Abrolhos Bank reef in Brazil.In our study, the combination of satellite sensing and metagenomics analyses offer useful tools for the real-time monitoring of water bodies threatened by oil spills, as well as for the design of recovery strategies based on the use of valuable hydrocarbonoclastic microorganisms.

Abstract The mammalian nervous system is made up of an extraordinary array of diverse cells that form intricate functional connections. The programs underlying cell lineage specification, identity and function of the neuronal subtypes are managed by regulatory proteins and RNAs, which coordinate the succession of steps in a stereotyped temporal order. In the central nervous system (CNS), motor neurons (MNs) are responsible for controlling essential functions such as movement, breathing, and swallowing by integrating signal transmission from the cortex, brainstem, and spinal cord (SC) towards peripheral muscles. A prime role in guiding the progression of progenitor cells towards the MN fate has been largely attributed to protein factors. More recently, the relevance of a class of regulatory RNAs abundantly expressed in the CNS - the long noncoding RNAs (lncRNAs) - has emerged overwhelmingly. LncRNA-driven gene expression control is key to regulating any step of MN differentiation and function, and its derangement profoundly impacts neuronal pathophysiology. Here, we uncover a novel function for the neuronal isoform of HOTAIRM1 (nHOTAIRM1), a lncRNA specifically expressed in the SC. Using a model system that recapitulates spinal MN (spMN) differentiation, we show that nHOTAIRM1 intervenes in the binary cell fate decision between MNs and interneurons, acting as a pro-MN factor. Furthermore, human iPSC-derived spMNs without nHOTAIRM1 display altered neurite outgrowth, with a significant reduction of both branch and junction numbers. Finally, the expression of genes essential for synaptic connectivity and neurotransmission is also profoundly impaired when nHOTAIRM1 is absent in spMNs. Mechanistically, nHOTAIRM1 establishes both direct and indirect interactions with a number of target genes in the cytoplasm, being a novel post-transcriptional regulator of MN biology. Overall, our results indicate that the lncRNA nHOTAIRM1 is essential for the specification of MN identity and the acquisition of proper morphology and synaptic activity of post-mitotic MNs.

Despite its high importance for crop yield prediction and monitoring, early-season crop mapping is severely hampered by the absence of timely ground truth. To cope with this issue, this study aims at evaluating the capability of PRISMA hyperspectral satellite images compared with Sentinel-2 multispectral imagery to produce early- and in-season crop maps using consolidated machine and deep learning algorithms. Results show that the accuracy of crop type classification using Sentinel-2 images is meaningfully poor compared with PRISMA (14% in overall accuracy (OA)). The 1D-CNN algorithm, with 89%, 91%, and 92% OA for winter, summer, and perennial cultivations, respectively, shows for the PRISMA images the highest accuracy in the in-season crop mapping and the fastest algorithm that achieves acceptable accuracy (OA 80%) for the winter, summer, and perennial cultivations early-season mapping using PRISMA images. Moreover, the 1D-CNN algorithm shows a limited reduction (6%) in performance, appearing to be the best algorithm for crop mapping within operational use in cross-farm applications. Machine/deep learning classification algorithms applied on the test fields cross-scene demonstrate that PRISMA hyperspectral time series images can provide good results for early- and in-season crop mapping.

Abstract Claude Debussy’s personal style is typically characterised as a departure from earlier diatonic tonality, including a greater variety of pitch-class materials organised in fragmented yet coherent compositions. Exploiting the music-theoretical interpretability of Discrete Fourier Transforms over pitch-class distributions, we performed a corpus study over Debussy’s solo-piano works in order to investigate the diachronic development of such stylistic features across the composer’s lifespan. We propose quantitative heuristics for the prevalence of different pitch-class prototypes, the fragmentation of a piece across different prototypes, as well as some aspect of the overall coherence of a piece. We found strong evidence for a decrease of diatonicity in favour of octatonicity, as well as for an increase of fragmentation accompanied by non-decreasing coherence. These results contribute to the understanding of the historical development of extended-tonal harmony, while representing a fertile testing ground for the interaction of computational corpus-based methods with traditional music analytical approaches.

Air pollution (AP) is a significant risk factor for public health, and its impact is becoming increasingly concerning in developing countries where it is causing a growing number of health issues. It is therefore essential to map and monitor AP sources in order to facilitate local action against them. This study aims at assessing the suitability of Sentinel-5 AP products based on Google Earth Engine (GEE) to monitor air pollutants, including CO, NO2, SO2, and O3 in Arak city, Iran from 2018 to 2019. Our process involved feeding satellite images to a cloud-free GEE platform that identified pollutant-affected areas monthly, seasonally, and annually. By coding in the JavaScript language in the GEE, four pollution parameters of Sentinel-5 satellite images were obtained. Following that, images with clouds were filtered by defining cloud filters, and average maps were extracted by defining average filters for both years. The employed model, which solely used Sentinel-5 AP products, was tested and assessed using ground data collected from the Environmental Organization of Central Province. Our findings revealed that annual CO, NO2, SO2, and O3 were estimated with RMSE of 0.13, 2.58, 4.62, and 2.36, respectively, for the year 2018. The annual CO, NO2, SO2, and O3 for the year 2019 were also calculated with RMSE of 0.17, 2.41, 4.31, and 4.6, respectively. The results demonstrated that seasonal AP was estimated with RMSE of 0.09, 5.39, 0.70, and 7.81 for CO, NO2, SO2, and O3, respectively, for the year 2018. Seasonal AP was also estimated with RMSE of 0.12, 4.99, 1.33, and 1.27 for CO, NO2, SO2, and O3, respectively, for the year 2019. The results of this study revealed that Sentinel-5 data combined with automated-based approaches, such as GEE, can perform better than traditional approaches (e.g., pollution measuring stations) for AP mapping and monitoring since they are capable of providing spatially distributed data that is sufficiently accurate.

Remote sensing techniques have become pivotal in monitoring algal blooms and population dynamics in freshwater bodies, particularly to assess the ecological risks associated with eutrophication. This study focuses on remote sensing methods for the analysis of 4 Italian lakes with diverse geological origins, leveraging water quality samples and data from the Sentinel-2 and Landsat 5.7–8 platforms. Chl-a, a well-correlated indicator of phytoplankton biomass abundance and eutrophication, was estimated using ordinary least squares linear regression to calibrate surface reflectance with chl-a concentrations. Temporal gaps between sample and image acquisition were considered, and atmospheric correction dedicated to water surfaces was implemented using ACOLITE and those specific to each satellite platform. The developed models achieved determination coefficients higher than 0.69 with mean square errors close to 3 mg/m3 for water bodies with low turbidity. Furthermore, the time series described by the models portray the seasonal variations in the lakes water bodies.

Wide field-of-view (FOV) sensors such as Sentinel-2 exhibit per-pixel view and illumination geometry variation that may influence the retrieval accuracy of essential crop biophysical and biochemical variables (BVs) for precision agriculture. However, this aspect is rarely studied in the existing literature. Hence, the current study aimed to evaluate the contribution of view and illumination geometries to the accuracy of retrieving Leaf Chlorophyll a and b (LCab), Canopy Chlorophyll Content (CCC), and Leaf Area Index (LAI) using the Random Forest (RF). The experiments were performed on various input variable scenarios where per-pixel geometric covariates, i.e. View and Sun Zenith Angles (VZA and SZA, respectively), and Relative Azimuth Angle (RAA), are excluded and included in spectral bands (SB) and spectral vegetation indices (SVIs), respectively, in two semi-arid areas. The results showed that spectral bands or vegetation indices combined with geometric covariates improved the R2 by 10–15% for LAI and 3–5% for CCC. In contrast, negligible improvements of 1–2% were achieved for LCab with cross-validation test data and independent held-out dataset, respectively. In agreement with previous studies, VZA and SZA were among the topmost influential variables in the RF models for estimating LAI, LCab, and CCC. Collectively, per-pixel geometric variables explained more than 30% of the variability in surface reflectance for all Sentinel-2 spectral bands (p

This contribution describes the creation of a support system for the operational activities of detailed perimeter of wooded areas attacked by insects/pathogens and/or forest fires, by means of a SAPR (Airborne System with Remote Piloting) with definition of the trajectory in automation via realtime recognition assisted by an airborne sensor and satellite system.

Assessing the performance of optical imaging systems is crucial to evaluate their capability to satisfy the product requirements for an Earth Observation (EO) mission. In particular, the evaluation of image quality is undoubtedly one of the most important, critical and problematic aspects of remote sensing. It involves not only pre-flight analyses, but also continuous monitoring throughout the operational lifetime of the observing system. The Ground Sampling Distance (GSD) of the imaging system is often the only parameter used to quantify its spatial resolution, i.e., its capability to resolve objects on the ground. In practice, this feature is also heavily influenced by other image quality parameters such as the image sharpness and Signal-to-Noise Ratio (SNR). However, these last two aspects are often analysed separately, using unrelated methodologies, complicating the image quality assessment and posing standardisation issues. To this end, we expanded the features of our Automatic Edge Method (AEM), which was originally developed to simplify and automate the estimate of sharpness metrics, to also extract the image SNR. In this paper we applied the AEM to a wide range of optical satellite images characterised by different GSD and Pixel Size (PS) with the objective to explore the nature of the relationship between the components of overall image quality (image sharpness, SNR) and product geometric resampling (expressed in terms of GSD/PS ratio). Our main objective is to quantify how the sharpness and the radiometric quality of an image product are affected by different product geometric resampling strategies, i.e., by distributing imagery with a PS larger or smaller than the GSD of the imaging system. The AEM allowed us to explore this relationship by relying on a vast amount of data points, which provide a robust statistical significance to the results expressed in terms of sharpness metrics and SNR means. The results indicate the existence of a direct relationship between the product geometric resampling and the overall image quality, and also highlight a good degree of correlation between the image sharpness and SNR.

The detection of burnt areas from satellite imagery is one of the most straightforward and useful applications of satellite remote sensing. In general, the approach relies on a change detection analysis applied on pre- and post-event images. This change detection analysis usually is carried out by comparing the values of specific spectral indices such as: NBR (normalised burn ratio), BAI (burn area index), MIRBI (mid-infrared burn index). However, some potential sources of error arise, particularly when near-real-time automated approaches are adopted. An automated approach is mandatory when the burnt area monitoring should operate systematically on a given area of large size (country). Potential sources of errors include but are not limited to clouds on the pre- or post-event images, clouds or topographic shadows, agricultural practices, image pixel size, level of damage, etc. Some authors have already noted differences between global databases of burnt areas based on satellite images. Sources of errors could be related to the spatial resolution of the images used, the land-cover mask adopted to avoid false alarms, and the quality of the cloud and shadow masks. This paper aims to compare different burnt areas datasets (EFFIS, ESACCI, Copernicus, FIRMS, etc.) with the objective to analyse their differences. The comparison is restricted to the Italian territory. Furthermore, the paper aims to identify the degree of approximation of these satellite-based datasets by relying on ground survey data as ground truth. To do so, ground survey data provided by CUFA (Comando Unità Forestali, Ambientali e Agroalimentari Carabinieri) and CFVA (Corpo Forestale e Vigilanza Ambientale Sardegna) were used. The results confirm the existence of significant differences between the datasets. The subsequent comparison with the ground surveys, which was conducted while also taking into account their own approximations, allowed us to identify the accuracy of the satellite-based datasets.

This study investigates the impact of landfills on surrounding vegetation in Naples, Italy, utilizing Sentinel-2 and Landsat 8 imagery processed with Sen2like. Seventeen landfill sites were studied with indices NDVI and GCI using four years of data, revealing significant vegetation growth reduction near two landfills compared to farther areas. Continuous monitoring is crucial, and satellite imagery offers an effective assessment tool. The study establishes a correlation between landfills and vegetation, underlining the need for ongoing monitoring and exploring additional factors influencing vegetation health within landfill environments. The findings contribute valuable insights to promote sustainable landfill management and protect surrounding ecosystems.

Accurate fuel mapping is crucial for effectively determining wildfire risk and implementing management strategies. The primary challenge in fuel type mapping lies in the need to develop accurate and efficient methods for identifying and categorizing the various combustible materials present in an area, often on a large scale. In response to this need, this paper presents a comprehensive approach that combines remote sensing data and Convolutional Neural Network (CNN) to discriminate between fire behavior fuel models. In particular, a CNN-based classification approach that leverages Sentinel-2 imagery is exploited to accurately classify fuel types into seven preliminary main classes (broadleaf, conifers, shrubs, grass, bare soil, urban areas, and water bodies). To further refine the fuel mapping results, subclasses were generated from the seven principles by using biomass and bioclimatic maps. These additional maps provide complementary information about vegetation density and climatic conditions, respectively. By incorporating this information, we align our fuel type classification with the widely used Standard Scott and Burgan (2005) fuel classification system. The results are highly promising, showcasing excellent CNN training performance with all three metrics—accuracy, recall, and F1 score—achieving an impressive 0.99%. Notably, the network exhibits exceptional accuracy in a test case conducted in the southern region of Sardinia, successfully identifying Burnable classes in previously unseen pixels: broadleaf at 0.99%, conifer at 0.79%, shrub at 0.76%, and grass at 0.84%. The proposed approach presents a valuable tool for enhancing fire management, contributing to more effective wildfire prevention and mitigation efforts. Thus, this tool could be leveraged by fire management agencies, policymakers, and researchers to improve the determination of wildfire risk and management.

The exacerbation of wildfires, attributed to the effects of climate change, presents substantial risks to ecological systems, infrastructure, and human well-being. In the context of the Sustainable Development Goals (SDGs), particularly those related to climate action, prioritizing the assessment and management of the occurrence and intensity of extensive wildfires is of utmost importance. In recent times, there has been a significant increase in the frequency and severity of widespread wildfires worldwide, affecting several locations, including Australia, Italy, and the United States of America. The presence of complex phenomena marked by limited predictability leads to significant negative impacts on biodiversity and human lives. The utilization of satellite-derived data with neural networks, such as convolutional neural networks (CNNs), is a potentially advantageous approach for augmenting the monitoring capabilities of wildfires. This research examines the generalization capability of four neural network models, namely the fully connected (FC), one-dimensional (1D) CNN, two-dimensional (2D) CNN, and three-dimensional (3D) CNN model. Each model’s performance, as measured by accuracy, recall, and F1 scores, is assessed through K-fold cross-validation. Subsequently, T-statistics and p-values are computed based on these metrics to conduct a statistical comparison among the different models, allowing us to quantify the degree of similarity or dissimilarity between them. By using training data from Australia and Sicily, the performances of the trained model are evaluated on the test dataset from Oregon. The results are promising, with cross-validation on the training dataset producing mean precision, recall, and F1 scores ranging between approximately 0.97 and 0.98. Especially, the fully connected model has superior generalization capabilities, whilst the 3D CNN offers more refined and less distorted classifications. However, certain issues, such as false fire detection and confusion between smoke and shadows, persist. The aforementioned methodologies offer significant perspectives on the capabilities of neural network technologies in supporting the detection and management of wildfires. These approaches address the crucial matter of domain transferability and the associated dependability of predictions in new regions. This study makes a valuable contribution to the ongoing efforts in climate change by assisting in monitoring and managing wildfires.

The noncarious cervical lesions (NCCLs) recognize an etiological framework of onset very different from the carious processes with etiology whose bacteria aggregated in a biofilm play a predominant role, leading in this way to the loss of the mineralized structure of the tooth. The pathological picture of the NCCLs, which manifests itself with a clinical picture of dental wear, differs from caries because it mainly recognizes a series of pathological processes, such as erosion, through the action of generally acidic chemical agents and abrasion, which is basically expressed through repeated mechanical trauma characteristic of tooth brushing. However, in the literature, there is no unanimous agreement in identifying only these two mechanisms, but there are some who propose a more marked role of anomalous occlusal loads, which would be unloaded on some teeth which, in addition to both erosive and abrasive mechanisms, would give rise to abfraction. Therefore, the aim of this review was to collect literature etio-pathological information and discuss the mechanisms underlying NCCLs.

The fracture of endodontic instruments inside the canal represents a problem that is not always easy to solve. The reutilization of endodontic instruments after sterilization procedures raises the question of how these processes affect their physical and mechanical properties. Alterations can involve the surface of the instruments, as well as their cutting effectiveness, shape and resistance to torsional and cyclic fatigue. The methodology adopted for this systematic review followed the PRISMA guidelines for systematic reviews. The following search terms were used in PubMed and Scopus: “endodontic sterilization”, “endodontic autoclave”, “cyclic fatigue”, “torsional”, “cutting efficiency”, “sterilization”, “surface characteristics” and “corrosion”. After the screening phase, the application of exclusion criteria and the removal of duplicates, 51 studies were identified and divided into four outcomes: cyclic fatigue; deformation and torsional fatigue; corrosion or surface alterations; and cutting efficiency. Our study of the scientific literature highlights disagreements between studies on these effects. After autoclaving, instruments exhibit a reduction in the cutting efficiency, but NiTi alloy instruments have an improved resistance to cyclic and torsional fatigue.

This article presents a discussion about the application of metaphors in the meaning of words as an original way of interpreting the world due to the ability of this linguistic resource to adapt to the multiplicity of being, with its different meanings. It was intended to define the concept of metaphor bringing the vision of Aristotle (1987), as well as delimiting the reflexes of the adoption of terms and words for figurative representation. The potential for cognitive abstraction of metaphors that arises from associations is evidenced and ends up enriching and valuing hermeneutic pluralism in everyday expression and also in scientific language.

Detecting RNA/RNA interactions in the context of a given cellular system is crucial to gain insights into the molecular mechanisms that stand beneath each specific RNA molecule. When it comes to non-protein coding RNA (ncRNAs), and especially to long noncoding RNAs (lncRNAs), the reliability of the RNA purification is dramatically dependent on their abundance. Exogenous methods, in which lncRNAs are in vitro transcribed and incubated with protein extracts or overexpressed by cell transfection, have been extensively used to overcome the problem of abundance. However, although useful to study the contribution of single RNA sub-modules to RNA/protein interactions, these exogenous practices might fail in revealing biologically meaningful contacts occurring in vivo and risk to generate non-physiological artifacts. Therefore, endogenous methods must be preferred, especially for the initial identification of partners specifically interacting with elected RNAs. Here, we apply an endogenous RNA pull-down to lncMN2-203, a neuron-specific lncRNA contributing to the robustness of motor neurons specification, through the interaction with miRNA-466i-5p. We show that both the yield of lncMN2-203 recovery and the specificity of its interaction with the miRNA dramatically increase in the presence of Dextran Sulfate Sodium (DSS) salt. This new set-up may represent a powerful means for improving the study of RNA-RNA interactions of biological significance, especially for those lncRNAs whose role as microRNA (miRNA) sponges or regulators of mRNA stability was demonstrated.

Head and neck squamous cell carcinomas (HNSCCs) are often diagnosed at advanced stages, incurring significant high mortality and morbidity. Several microRNAs (miRs) have been identified as pivotal players in the onset and advancement of HNSCCs, operating as either oncogenes or tumor suppressors. Distinctive miR patterns identified in tumor samples, as well as in serum, plasma, or saliva, from patients have significant clinical potential for use in the diagnosis and prognosis of HNSCCs and as potential therapeutic targets. The aim of this study was to identify previous systematic reviews with meta-analysis data and clinical trials that showed the most promising miRs in HNSCCs, enclosing them into a biomolecular signature to test the prognostic value on a cohort of HNSCC patients according to The Cancer Genome Atlas (TCGA). Three electronic databases (PubMed, Scopus, and Science Direct) and one registry (the Cochrane Library) were investigated, and a combination of keywords such as “signature microRNA OR miR” AND “HNSCC OR LSCC OR OSCC OR oral cancer” were searched. In total, 15 systematic literature reviews and 76 prognostic clinical reports were identified for the study design and inclusion process. All survival index data were extracted, and the three miRs (miR-21, miR-155, and miR-375) most investigated and presenting the largest number of patients included in the studies were selected in a molecular biosignature. The difference between high and low tissue expression levels of miR-21, miR-155, and miR-375 for OS had an HR = 1.28, with 95% CI: [0.95, 1.72]. In conclusion, the current evidence suggests that miRNAs have potential prognostic value to serve as screening tools for clinical practice in HNSCC follow-up and treatment. Further large-scale cohort studies focusing on these miRNAs are recommended to verify the clinical utility of these markers individually and/or in combination.

The development of unmanned aerial vehicles (UAV) and artificial intelligence (AI) techniques has drawn increasing interest and started a novel area of research applications [...]

Random walks (or Markov chains) are models extensively used in theoretical computer science. Several tools, including analysis of quantities such as hitting and mixing times, are helpful for devising randomized algorithms. A notable example is Schöning's algorithm for the satisfiability (SAT) problem. In this work, we use the density-matrix formalism to define a $\textit{quantum Markov chain}$ model which directly generalizes classical walks, and we show that a common tools such as hitting times can be computed with a similar formula as the one found in the classical theory, which we then apply to known quantum settings such as Grover's algorithm.

An implantable hemofilter for the treatment of kidney failure depends critically on the transport characteristics of the membrane and the biocompatibility of the membrane, cartridge, and blood conduits. A novel membrane with slit-shaped pores optimizes the trade-off between permeability and selectivity, enabling implanted therapy. Sustained (3-8) day function of an implanted parallel-plate hemofilter with minimal anticoagulation was achieved by considering biocompatibility at the subnanometer scale of chemical interactions and the millimeter scale of blood fluid dynamics. A total of 400 nm-thick polysilicon flat sheet membranes with 5-8 nm × 2 micron slit-shaped pores were surface-modified with polyethylene glycol. Hemofilter cartridge geometries were refined based on computational fluid dynamics models of blood flow. In an uncontrolled pilot study, silicon filters were implanted in six class A dogs. Cartridges were connected to the cardiovascular system by anastamoses to the aorta and inferior vena cava and filtrate was drained to collection pouches positioned in the peritoneum. Pain medicine and acetylsalicylic acid were administered twice daily until the hemofilters were harvested on postoperative days 3 (n = 2), 4 (n = 2), 5 (n = 1), and 8 (n = 1). No hemofilters were thrombosed. Animals treated for 5 and 8 days had microscopic fractures in the silicon nanopore membranes and 20-50 ml of transudative (albumin sieving coefficient θalb ~ 0.5 - 0.7) fluid in the collection pouches at the time of explant. Shorter experimental durations (3-4 days) resulted in filtration volumes similar to predictions based on mean arterial pressures and membrane hydraulic permeability and (θalb ~ 0.2 - 0.3), similar to preimplantation measurements. In conclusion, a detailed mechanistic and materials science attention to blood-material interactions allows implanted hemofilters to resist thrombosis. Additional testing is needed to determine optimal membrane characteristics and identify limiting factors in long-term implantation.