Tool for fast assessment of stormwater flood volumes for urban catchment: A machine learning approach.

Specific flood volume is an important criterion for evaluating the performance of sewer networks. Currently, mechanistic models - MCMs (e.g., SWMM) are usually used for its prediction, but they require the collection of detailed information about the characteristics of the catchment and sewer network, which can be difficult to obtain, and the process of model calibration is a complex task. This paper presents a methodology for developing simulators to predict specific flood volume using machine learning methods (DNN - Deep Neural Network, GAM - Generalized Additive Model). The results of Sobol index calculations using the GSA method were used to select the ML model as an alternative to the MCM model. It was shown that the DNN model can be used for flood prediction, for which high agreement was obtained between the results of GSA calculations for rainfall data, catchment and sewer network characteristics, and calibrated SWMM parameters describing land use and sewer retention. Regression relationships (polynomials and exponential functions) were determined between Sobol indices (retention depth of impervious area, correction factor of impervious area, Manning's roughness coefficient of sewers) and sewer network characteristics (unit density of sewers, retention factor - the downstream and upstream of retention ratio) obtaining R2 = 0. 55-0.78. The feasibility of predicting sewer network flooding and modernization with the DNN model using a limited range of input data compared to the SWMM was shown. The developed model can be applied to the management of urban catchments with limited access to data and at the stage of urban planning.

Ultrasound tomography enhancement by signal feature extraction with modular machine learning method.

Robust and reliable diagnostic methods are desired in various types of industries. This article presents a novel approach to object detection in industrial or general ultrasound tomography. The key idea is to analyze the time-dependent ultrasonic signal recorded by three independent transducers of an experimental system. It focuses on finding common or related characteristics of these signals using custom-designed deep neural network models. In principle, models use convolution layers to extract common features of signals, which are passed to dense layers responsible for predicting the number of objects or their locations and sizes. Predicting the number and properties of objects are characterized by a high value of the coefficient of determination R2 = 99.8% and R2 = 98.4%, respectively. The proposed solution can result in a reliable and low-cost method of object detection for various industry sectors.

Rapid Method of Wastewater Classification by Electronic Nose for Performance Evaluation of Bioreactors with Activated Sludge.

Currently, e-noses are used for measuring odorous compounds at wastewater treatment plants. These devices mimic the mammalian olfactory sense, comprising an array of multiple non-specific gas sensors. An array of sensors creates a unique set of signals called a "gas fingerprint", which enables it to differentiate between the analyzed samples of gas mixtures. However, appropriate advanced analyses of multidimensional data need to be conducted for this purpose. The failures of the wastewater treatment process are directly connected to the odor nuisance of bioreactors and are reflected in the level of pollution indicators. Thus, it can be assumed that using the appropriately selected methods of data analysis from a gas sensors array, it will be possible to distinguish and classify the operating states of bioreactors (i.e., phases of normal operation), as well as the occurrence of malfunction. This work focuses on developing a complete protocol for analyzing and interpreting multidimensional data from a gas sensor array measuring the properties of the air headspace in a bioreactor. These methods include dimensionality reduction and visualization in two-dimensional space using the principal component analysis (PCA) method, application of data clustering using an unsupervised method by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, and at the last stage, application of extra trees as a supervised machine learning method to achieve the best possible accuracy and precision in data classification.

Linking Metallic Micronutrients and Toxic Xenobiotics to Atherosclerosis and Fatty Liver Disease-Postmortem ICP-MS Analysis of Selected Human Tissues.

Dyslipidaemia is a disorder of the lipid metabolism, caused mainly by poor eating habits. The most severe consequence of an inappropriate diet is the development of atherosclerosis and hepatic steatosis. It is generally believed that a change in nutrition, and increased physical activity can eliminate these health problems. The contemporary research and therapies used to treat dyslipidemia mainly focus on lowering the triglyceride and cholesterol levels. However, disturbances in trace element homeostasis or the accumulation of toxic elements can also affect physiological processes, and be involved in the development of metabolically mediated diseases. The present study aimed to determine the mineral profiles of liver and brain tissues collected at autopsy (n = 39) in groups of people with hepatic steatosis (n = 5), atherosclerosis (n = 9), hepatic steatosis, and atherosclerosis (n = 16), and others without the selected disorders (n = 9). Concentrations of 51 elements were analysed via inductively coupled plasma mass spectrometry (ICP-MS) after the initial wet mineralisation of the samples with nitric acid. The results obtained allow us to conclude that the hepatic steatosis group suffers from a deficiency of important trace elements, such as copper, zinc, and molybdenum (p < 0.05), whereas the group with atherosclerosis is characterised by elevated levels of cadmium in the liver tissue (p = 0.01). Analysing the mean values of the element concentrations measured in 11 brain areas, statistically significant higher levels of calcium and copper (p < 0.001) were found in the atherosclerosis group, compared to the hepatic steatosis group, confirming the involvement of these elements in the pathogenesis of atherosclerosis. In addition, an accumulation of cadmium, lead, titanium, and strontium in the brain tissue was observed in the atherosclerosis group. While the accumulation of individual elements differs in different parts of the brain, the differences in the cadmium content (p < 0.05) between the study groups apply to the whole brain, except for the nucleus accumbens septi area, where a statistically significant titanium accumulation occurs in the atherosclerosis and steatosis groups, compared to the others (p < 0.05). In addition, the disruption of elemental homeostasis in the brain of a single case with bipolar disorder, and a case with hip replacement was observed. Our results confirm the involvement of chemical elements in the pathogenesis of selected metabolic diseases, and the need for further studies in larger populations.

Assessment of the Concentration of 51 Elements in the Liver and in Various Parts of the Human Brain-Profiling of the Mineral Status.

The anthropogenic environment and diet introduce many metals into the human body, both essential and toxic. Absorption leads to systemic exposure and accumulation in body fluids and tissues. Both excess and deficiency of trace elements are health hazards. The primary aim of the present study was to evaluate the concentration of 51 elements in liver samples and 11 selected brain regions obtained at post-mortem examination from a population of adults living in south-eastern Poland (n = 15). A total of 180 analyses were performed by inductively coupled plasma mass spectrometry in two independent replicates. The collected data show very high individual variability in the content of the investigated elements. Macroelements such as sodium, magnesium, phosphorus, potassium, calcium, iron, and zinc occurred in the highest concentrations and with the greatest statistically significant variations. Although the elemental content of the brain and liver differed significantly, the strongest positive correlation between liver and polus frontalis was observed for the essential element selenium (0.9338) and the strongest negative one for manganese (-0.4316) and lanthanum (-0.5110). The brain areas studied have different requirements for phosphorus, manganese, iron, and molybdenum. In addition, males had a significantly (p < 0.05) higher brain content of lanthanides and actinides than females. The results of this study show that the inhabitants of south-eastern Poland are exposed to a fairly uniform accumulation of aluminum and vanadium in the brain, which have the highest affinity to the thalamus dorsalis. This result proves that there is environmental exposure to these elements.

Application of Machine Learning Algorithms to the Discretization Problem in Wearable Electrical Tomography Imaging for Bladder Tracking.

The article presents the implementation of artificial intelligence algorithms for the problem of discretization in Electrical Impedance Tomography (EIT) adapted for urinary tract monitoring. The primary objective of discretization is to create a finite element mesh (FEM) classifier that will separate the inclusion elements from the background. In general, the classifier is designed to detect the area of elements belonging to an inclusion revealing the shape of that object. We show the adaptation of supervised learning methods such as logistic regression, decision trees, linear and quadratic discriminant analysis to the problem of tracking the urinary bladder using EIT. Our study focuses on developing and comparing various algorithms for discretization, which perfectly supplement methods for an inverse problem. The innovation of the presented solutions lies in the originally adapted algorithms for EIT allowing for the tracking of the bladder. We claim that a robust measurement solution with sensors and statistical methods can track the placement and shape change of the bladder, leading to effective information about the studied object. This article also shows the developed device, its functions and working principle. The development of such a device and accompanying information technology came about in response to particularly strong market demand for modern technical solutions for urinary tract rehabilitation.

Application of Machine Learning Methods for an Analysis of E-Nose Multidimensional Signals in Wastewater Treatment.

The work represents a successful attempt to combine a gas sensors array with instrumentation (hardware), and machine learning methods as the basis for creating numerical codes (software), together constituting an electronic nose, to correct the classification of the various stages of the wastewater treatment process. To evaluate the multidimensional measurement derived from the gas sensors array, dimensionality reduction was performed using the t-SNE method, which (unlike the commonly used PCA method) preserves the local structure of the data by minimizing the Kullback-Leibler divergence between the two distributions with respect to the location of points on the map. The k-median method was used to evaluate the discretization potential of the collected multidimensional data. It showed that observations from different stages of the wastewater treatment process have varying chemical fingerprints. In the final stage of data analysis, a supervised machine learning method, in the form of a random forest, was used to classify observations based on the measurements from the sensors array. The quality of the resulting model was assessed based on several measures commonly used in classification tasks. All the measures used confirmed that the classification model perfectly assigned classes to the observations from the test set, which also confirmed the absence of model overfitting.

Bioaccumulation Capacity of Onion (Allium cepa L.) Tested with Heavy Metals in Biofortification.

On a worldwide scale, A. cepa is among the most commonly consumed vegetables. In Europe, the leading onion producers are Russia, the Netherlands, Spain, Poland and Germany. In this study, the bioaccumulation of heavy metals (Cr, Cu, Zn, Ni, Fe, Mn, Co, Sr, Cd and Pb) by Allium cepa L. plants was followed under hydroponic conditions. The heavy metals were applied at six concentrations (0, 25, 50, 100, 200 and 400 mg L-1) over three weeks. The quantitative analysis of selected heavy metals in plant tissues (bulbs, roots and assimilation leaves) was performed using atomic absorption spectrometry with flame atomization (F-AAS). The accumulation of metal ions was strongly dependent on their concentrations in the solution and the analyzed parts of plants. The highest accumulation of metal ions was confirmed for the roots and ranged from 8.48 to 5912.34 µg g-1 DW (dry weight). All parts of A. cepa were characterized by the high accumulation of Mn2+. The lowest accumulation was confirmed for Co2+ in the roots, Pb2+ in the assimilation leaves and Cu2+ in the bulbs of onion. Moreover, the study showed that the highest concentrations of heavy metals decreased the growth of bulbs and even caused them to die off. In contrast, lower concentrations of some elements stimulated plant development.

Multi-Elemental Analysis of Human Optic Chiasm-A New Perspective to Reveal the Pathomechanism of Nerve Fibers' Degeneration.

The effect of metals on the functioning of the human eye is multifactorial and includes enzyme activity modulation, trace metal metabolic pathways changes, and cytotoxic activity. Functional dysfunctions appear mostly as a result of the accumulation of toxic xenobiotic metals or disturbances of micronutrients' homeostasis. So far, the affinity of selected metals to eye tissues, i.e., the cornea, choroid, lens, and anterior chamber fluid, has been most studied. However, it is known that many eye symptoms are related to damage to the optic nerve. In order to fill this gap, the aim of the study is to perform a multi-element analysis of tissue collected postmortem from optic chiasm and optic nerves. A total of 178 samples from 107 subjects were tested. The concentrations of 51 elements were quantified by inductively coupled plasma mass spectrometry (ICP-MS) after the wet-mineralization step. In terms of elemental composition, the optic chiasm is dominated by two trace elements, i.e., iron (Fe) and zinc (Zn), besides macro-elements Ca, K, Na, P, and Mg. The subjects formed a homogeneous cluster (over 70% subjects) with the highest accumulation of aluminum (Al). The remaining two departing clusters were characterized by an increased content of most of the elements, including toxic elements such as bismuth (Bi), uranium (U), lead (Pb), chromium (Cr), and cadmium (Cd). Changes in elemental composition with age were analyzed statistically for the selected groups, i.e., females, males, and subjects with alcohol use disorder (AUD) and without AUD. A tendency of women to lose Se, Cu, Zn, Fe with age was observed, and a disturbed Ca/Mg, Na/K ratio in subjects with AUD. Although the observed trends were not statistically significant, they shed new light on the risks and possible pathologies associated with metal neurotoxicity in the visual tract.

Chronic Alcohol Abuse Alters Hepatic Trace Element Concentrations-Metallomic Study of Hepatic Elemental Composition by Means of ICP-OES.

Trace element accumulation varies in different human tissues. Distribution of several elements was found to be disrupted in the case of excessive alcohol consumption, causing negative effects and exacerbation of pathological processes in the liver. In this study, we analyzed the levels and interactions between seven trace elements including calcium (Ca), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), potassium (K), and magnesium (Mg), manganese (Mn), sodium (Na), zinc (Zn), and selenium (Se) in individuals with alcohol-use disorder (AUD) and patients without AUD (control group). The liver samples were collected during autopsy from 39 individuals with AUD and 45 control subjects. Elemental composition inductively coupled plasma optical emission spectrometry (ICP-OES) after wet mineralization by nitric acid was applied for the evaluation of the samples. Positive correlations dominated in the AUD group, mainly in relation to Mg, which strongly positively correlated with Ca, Mn, Fe; K correlated with Mn and Zn, and Cu positively correlated with K and Zn. The strongest positive correlation in the AUD group was observed for the Mg-Mn pair (r = 0.87). Significant statistical differences (p < 0.05) between the groups concerned the average concentration of Co, Cu, Mn, and Mg, which were lower in the AUD group, and Fe, the level of which was significantly higher in the AUD group compared to the control group. Evaluation of the chronic alcohol consumption effect on the accumulation of trace elements in the liver allows a better understanding of the pathological processes taking place in this organ.

Hemodialysis Effect on the Composition of the Eye Fluid of Cataract Patients.

Numerous reports have proven that dialysis patients experience disturbances in the levels of elements in biological fluids. Disturbances in the homeostasis of essential elements or the appearance of highly toxic elements are serious problems also in clinical ophthalmology. The purpose of this study was to investigate the influence of hemodialysis (HD) on the elemental composition of anterior chamber aqueous humor (AH) in patients undergoing cataract surgery. The study involved 22 patients. The control group enrolled 16 patients (age 75.68 ± 9.67, female 54.55%, male 45.45%) with cataract and normal kidney function (control), and the second group included six patients (age 70.33 ± 12.74, female 33.33%, male 66.67%) with cataract undergoing HD treatment. The elements quantification was established using an inductively coupled plasma optical emission spectrometer (ICP-MS). In the eye fluid of dialysis patients, there were increased levels of manganese (Mn) and mercury (Hg) and decreased levels of vanadium (V) and zinc (Zn). In addition, a statistically significant increase in the Hg/Zn and Hg/selenium (Se) ratios and a lowering of the iron (Fe)/Mn ratio were observed in the studied group in comparison to the control. The obtained results indicated the need for Zn and Se supplementation in order to eliminate the hazards caused by Hg toxicity. A lower level of V in the eye fluid of dialysis patients may have a positive effect on maintaining a calcium and phosphorus homeostasis. Our study gives a deep insight into changes of elements concentrations in AH induced by HD.

The Multi-Elemental Composition of the Aqueous Humor of Patients Undergoing Cataract Surgery, Suffering from Coexisting Diabetes, Hypertension, or Diabetic Retinopathy.

The aim of the study was the multi-elemental analysis of aqueous humor (AH) collected from patients undergoing cataract surgery. The study included: 16 patients with age-related macular degeneration AMD (99 controls), 10 patients with retinopathy (105 controls), 61 patients with hypertension (54 controls), and 33 patients with coexisting diabetes (82 controls). The control groups were recruited from patients with a lack of co-existing disease characterizing the specified studied group. The measurements were performed by the use of inductively coupled plasma optical emission spectrometry (ICP-OES). The statistical analysis was carried out using non-parametric testing (Mann-Whitney U). The level of significance was set at p = 0.05. The data obtained revealed substantial variations in elemental composition between the test groups in comparison to the controls. However, the significant variations concerned only a few elements. The phosphorous (P) level and the ratio of P/Ca were significant in retinopathy and diabetes, whereas cobalt (0.091 ± 0.107 mg/L vs. 0.031 ± 0.075 mg/L; p = 0.004) was significant in AMD. In co-existing hypertension, the levels of tin (0.293 ± 0.409 mg/L vs. 0.152 ± 0.3 mg/L; p = 0.031), titanium (0.096 ± 0.059 mg/L vs. 0.152 ± 0.192 mg/L; p = 0.045), and ruthenium (0.035 ± 0.109 mg/L vs. 0.002 ± 0.007 mg/L; p = 0.006) varied in comparison to the controls. The study revealed inter-elemental interactions. The correlation matrices demonstrated the domination of the positive correlations, whereas negative correlations mainly concerned sodium.

Teicoplanin-Modified HPLC Column as a Source of Experimental Parameters for Prediction of the Anticonvulsant Activity of 1,2,4-Triazole-3-Thiones by the Regression Models.

The cell membrane is a complex system that consists of lipids, proteins, polysaccharides, and amphiphilic phospholipids. It plays an important role in ADME processes that are responsible for the final pharmaceutical effects of xenobiotics (bioavailability, activity). To study drug-membrane interaction at the molecular level, several high-performance liquid chromatography (HPLC) membrane model systems have been proposed which are mimicking mainly its lipid character. The aim of this work was to study interactions of new synthesized antiepileptic compounds of 4-alkyl-5-(3-chlorophenyl)-2,4-dihydro-3H-1,2,4-triazole-3-thione derivatives with Chirobiotic column containing glycoprotein ligand attached to the silica matrix. The affinity of the analytes to immobilized glycoprotein ligand was examined chromatographically in reversed-phase mode. The thermodynamics of interactions between bioactive compounds and teicoplanin was studied in terms of the van't Hoff linear relationship ln k vs. 1/T in the range of 5-45 °C. Change in enthalpy (ΔH°), change in entropy (ΔS°) and change in Gibbs free energy (ΔG°) were estimated utilizing graphical extrapolation and interpolation methods. The density functional theory (DFT) approach and docking simulations were used to get the molecular interpretation and prove the obtained experimental results. Cross-correlations of chromatographic and thermodynamic parameters with non-empirical topological and quantum chemical indices suggest that the polarizability of analytes appears to be responsible for the interactions of the tested molecules with teicoplanin and, ultimately, their retention on the column. Experimental and theoretical parameters were subjected to statistical analysis using regression models. Partial least squares (PLS) regression model showed the usefulness of the experimentally measured parameter φ0 (MeOH) to discriminate between anticonvulsant active and inactive 1,2,4-triazole-3-thione derivatives. Obtained results point out the usefulness of interaction of potential anticonvulsants with glycoprotein class of compounds to anticipate their activity.

Soft Sensor Application in Identification of the Activated Sludge Bulking Considering the Technological and Economical Aspects of Smart Systems Functioning.

The paper presented the methodology for the construction of a soft sensor used for activated sludge bulking identification. Devising such solutions fits within the current trends and development of a smart system and infrastructure within smart cities. In order to optimize the selection of the data-mining method depending on the data collected within a wastewater treatment plant (WWTP), a number of methods were considered, including: artificial neural networks, support vector machines, random forests, boosted trees, and logistic regression. The analysis conducted sought the combinations of independent variables for which the devised soft sensor is characterized with high accuracy and at a relatively low cost of determination. With the measurement results pertaining to the quantity and quality of wastewater as well as the temperature in the activated sludge chambers, a good fit can be achieved with the boosted trees method. In order to simplify the selection of an optimal method for the identification of activated sludge bulking depending on the model requirements and the data collected within the WWTP, an original system of weight estimation was proposed, enabling a reduction in the number of independent variables in a model-quantity and quality of wastewater, operational parameters, and the cost of conducting measurements.

Correlations Between Trace Elements in Selected Locations of the Human Brain in Individuals with Alcohol Use Disorder.

Trace element distribution varies in different locations of the human brain. Several elements were found to cause various negative effects, such as neurodegeneration. In this paper, we analyzed the interactions between seven trace elements: zinc (Zn), selenium (Se), manganese (Mg), iron (Fe), copper (Cu), chromium (Cr) and cobalt (Co) in individuals with alcohol use disorder (AUD) and individuals without (control group). Brain tissue samples from 31 individuals with AUD and 31 control subjects were harvested. Inductively coupled plasma optical emission spectrometry was used for trace element determination. In the control group, there were several positive correlations between Cr, Cu, Fe and Mn. In the AUD group, positive correlations between Co and Cr, Cu, Fe, Mn, Zn were found. The majority of correlations between Zn and other elements are positive. In the studied group, Mn had strong positive correlations with Co, Cr, Cu and Fe. The strongest positive correlation found between average element concentration was between Cu and Cr. The knowledge of kinetics and metabolism of trace elements as well as the impact of alcohol on these processes is essential for understanding the pathological processes and functioning of human brain tissue.

Levels of Trace Elements in the Aqueous Humor of Cataract Patients Measured by the Inductively Coupled Plasma Optical Emission Spectrometry.

Trace elements play an important role in the pathogenesis of several serious ophthalmological disorders, such as glaucoma, age-related macular degeneration (AMD), diabetic retinopathy, cataract, etc. This study aimed to measure alterations of chemical elements' (67) levels in the aqueous humor of patients undergoing cataract surgery. The pilot study included 115 patients, (age 74 ± 7.27, female 64.35%, male 35.65%). The aqueous levels of elements were measured by the use of the inductively coupled plasma optical emission spectrometry (ICP-OES), quality controlled with certified standards. The classification of elements based on their concentration was achieved by hierarchical cluster analysis. This is the first screening study that quantifies over 60 elements which are present in the fluid from the anterior chamber of the eye of cataract patients. The obtained results can be suitable for understanding and identifying the causes that may play a role in the initiation and progression of lens opacity.

Analysis of the Occurrence of Thermal Bridges in Several Variants of Connections of the Wall and the Ground Floor in Construction Technology with the Use of a Hemp-lime Composite.

This article analyses the connection of the two types of floors on the ground (floors on joists and self-supporting floors), with the external wall made of a hemp-lime composite for the occurrence of thermal bridges. Several factors that may affect the heat transfer in the junction were taken into account: the level of the floor on the ground, the wall thickness, the thermal conductivity of the composite, and the location of the timber frame construction. The technology of using hemp and lime is relatively new, and there is a lack of such analyses in the literature. The two-dimensional (2D) heat-transfer in the described construction joints was analyzed based on the finite-element method with the use of the THERM 7.4 software. The results were presented as averaged and linear thermal transmittance coefficients dependent on the above mentioned factors. The possibility of surface condensation was also checked. The differences in the values of the thermal transmittance of the junction between the two variants of ground floors reached around 0.13%-1.67% and the values of linear thermal transmittance factor reached approximately 2.43%-10.13%. The junctions with the highest floor level showed a decrease in the thermal transmittance value by about 3.00%-5.77% and in the linear thermal transmittance, by about 21.98%-53.83%, compared to the junctions with the lowest floor level. Calculations showed that almost all analyzed junctions are free from surface condensation causing mould growth, because the minimum temperature factors f0.25 were higher than 0.78 (except for junctions with the lowered floor levels). The junction with a floor on the timber joists showed better thermal parameters than the junction with a self-supporting floor in each of the analyzed variants. By increasing the level of floor insulation, it is possible to limit the thermal bridges and improve the thermal properties of the junction.

HPLC-DAD Determination of Nitrite and Nitrate in Human Saliva Utilizing a Phosphatidylcholine Column.

The aim of this research was to optimize the separation and quantitative determination of nitrites and nitrates in human saliva. HPLC with UV absorption (HPLC/DAD) using a phosphatidylcholine column (IAM.PC.DD2 Regis HPLC) was applied in this assay. Nitrates were detected directly by their absorbance at 210 nm, whereas nitrites were detected after oxidation to nitrates by potassium permanganate at acidic conditions. The kinetics of the permanganate-nitrite reaction was measured chromatographically. The calibration graph for nitrates was linear in the range of 0.5-35 µg mL-1 with a correlation coefficient of 0.9999. The limit of detection was 4.56 ng mL-1. The calibration graph for nitrites (after oxidation to nitrates) was linear in the range of 0.5-15 µg mL-1 with a correlation coefficient of 0.9972. The limit of detection was 4.21 ng mL-1. The nitrate concentrations in the saliva samples were found in the range of 8.98-18.52 µg mL-1, whereas nitrite was in the range of 3.50-5.34 µg mL-1.

A Noninvasive TDR Sensor to Measure the Moisture Content of Rigid Porous Materials.

The article presents the potential application of the time domain reflectometry (TDR) technique to measure moisture transport in unsaturated porous materials. The research of the capillary uptake phenomenon in a sample of autoclaved aerated concrete (AAC) was conducted using a TDR sensor with the modified construction for non-invasive testing. In the paper the basic principles of the TDR method as a technique applied in metrology, and its potential for measurement of moisture in porous materials, including soils and porous building materials are presented. The second part of the article presents the experiment of capillary rise process in the AAC sample. Application of the custom sensor required its individual calibration, thus a unique model of regression between the readouts of apparent permittivity of the tested material and its moisture was developed. During the experiment moisture content was monitored in the sample exposed to water influence. Monitoring was conducted using the modified TDR sensor. The process was additionally measured using the standard frequency domain (FD) capacitive sensor in order to compare the readouts with traditional techniques of moisture detection. The uncertainty for testing AAC moisture, was expressed as RMSE (0.013 cm³/cm³) and expanded uncertainty (0.01⁻0.02 cm³/cm³ depending on moisture) was established along with calibration of the applied sensor. The obtained values are comparable to, or even better than, the features of the traditional invasive sensors utilizing universal calibration models. Both, the TDR and capacitive (FD) sensor enabled monitoring of capillary uptake phenomenon progress. It was noticed that at the end of the experiment the TDR readouts were 4.4% underestimated and the FD readouts were overestimated for 12.6% comparing to the reference gravimetric evaluation.

Mature landfill leachate utilization using a cost-effective hybrid method.

The main goal of this study was to investigate the co-digestion of sewage sludge and mature landfill leachate pretreated through hydrodynamic cavitation. The process efficiency was analyzed from the aspects of organics removal, biogas production, kinetics and digestate quality. Energy efficiency of the overall treatment was evaluated as well. A dose of hydrodynamically cavitated leachate of 5% v/v was used as a substrate for co-digestion with sewage sludge at a hydraulic retention time of 20 days. This improved the kinetics, biogas production and corresponding yields, as well as digestate quality. Fourier transform infrared photoacoustic spectroscopy analysis seemed to indicate that no new toxic compounds were formed with co-digestion. The investigated two-stage treatment is recommended as a safe and cost-effective method of utilizing mature leachate.