Chemical treatment of sludge: in-depth study on toxic metal removal efficiency, dewatering ability and fertilizing property preservation

The presence of toxic metals in municipal sewage sludge restricts the application of this biomass in agricultural area. A chemical leaching process using a combination of inorganic acid and two oxidants has been developed for sludge decontamination. The present study investigated the effects of the concentrations of sulphuric acid (H2SO4), hydrogen peroxide and ferric chloride on metal solubilization from sewage sludge, as well as preservation of fertilizing properties (nutrient content) and dewatering ability of the treated sludge. The analysis of the results from batch leaching tests has allowed to define the optimal conditions for the reagent concentrations, which are 56 kg Fe3+ tonne(-1) of dry sludge solids (tds), 8 kg H2O2 tds(-1), and enough H2SO4 to reach a pH between 2.0 and 2.5 but less than 142 kg H2SO4 tds(-1). Finally, under these conditions, oxidoreduction potential values are found to be between 450 and 475 mV.     

Symbolic interpretation of artificial neural networks

Hybrid intelligent systems that combine knowledge-based and artificial neural network systems typically have four phases, involving domain knowledge representation, mapping of this knowledge into an initial connectionist architecture, network training and rule extraction, respectively. The final phase is important because it can provide a trained connectionist architecture with explanation power and validate its output decisions. Moreover, it can be used to refine and maintain the initial knowledge acquired from domain experts. In this paper, we present three rule extraction techniques. The first technique extracts a set of binary rules from any type of neural network. The other two techniques are specific to feedforward networks, with a single hidden layer of sigmoidal units. Technique 2 extracts partial rules that represent the most important embedded knowledge with an adjustable level of detail, while the third technique provides a more comprehensive and universal approach. A rule-evaluation technique, which orders extracted rules based on three performance measures, is then proposed. The three techniques area applied to the iris and breast cancer data sets. The extracted rules are evaluated qualitatively and quantitatively, and are compared with those obtained by other approaches     

Analysis matching of 5 GHz operating planar antenna EM characteristics between rigorous raw model,simulation and measurements

Journal of Computational Electronics - One of the challenges in antenna analysis is achieving concordance between the results of a purely theoretical numerical method application and commercial...     

Flexible Vanadium Dioxide Photodetectors for Visible to Longwave Infrared Detection at Room Temperature

Flexible optoelectronics is a rapidly growing field, with a wide range of potential applications. From wearable sensors to bendable solar cells, curved displays, and curved focal plane arrays, the possibilities are endless. The criticality of flexible photodetectors for many of these applications is acknowledged, however, devices that are demonstrated thus far are limited in their spectral range. In this study, flexible photodetectors are demonstrated using a VO_x nanoparticle ink, with an extremely broad operating wavelength range of 0.4 to 20 µm. This ink is synthesized using a simple and scalable wet-chemical process. These photodetectors operate at room temperature and exhibit minimal variance in performance even when bent at angles of up to 100 ° at a bend radius of 6.4 mm. In addition, rigorous strain testing of 100 bend and release cycles revealed a photoresponse with a standard deviation of only 0.55%. This combination of mechanical flexibility, wide spectral response, and ease of fabrication makes these devices highly desirable for a wide range of applications, including low-cost wearable sensors and hyperspectral imaging systems.     

A systematic literature review for network intrusion detection system (IDS)

International Journal of Information Security - With the recent increase in internet usage, the number of important, sensitive, confidential individual and corporate data passing through internet...     

Preparation and characterization of silica nanoparticles as an efficient carrier for two bio-detergents based enzymes

Bio-detergents are new bio-friendly formulas that contain biobased ingredients, including enzymes. In the present study, alkaline protease and α-amylase were immobilized via physisorption onto silica nanoparticles (SNPs). The derivatized SNPs served as major components of a prepared bio-detergent. Alkaline protease was produced by the recombinant Bacillus subtilis cells that carry the protease genes on a multiple-copy plasmid, while α-amylase was commercially purchased. SNPs were prepared by the sol–gel method and well-characterized through the Brunauer–Emmett–Teller (BET) method, scanning electron microscopy (SEM), transmission electron microscopy (TEM), zeta potential, X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy. The adsorption capacity of the SNPs was determined via colorimetry through the adsorption of methylene blue dye (MB), with approximately 97% adsorption achieved under the conditions employed. The Langmuir isotherm well-described the adsorption of MB on SNPs. High immobilization yield for the enzymes was obtained, and the storage stability of SNP-alkaline protease and SNP-α-amylase was good, reaching 65% and 85% of their initial activities after 6 weeks of storage at 4°C, respectively. The immobilized enzymes could be reused for 7 cycles. Additionally, the immobilized enzymes retained residual activity to a greater extent than free enzymes in simulated basic detergent solutions. SNPs containing adsorbed alkaline protease and α-amylase were mixed with a basic detergent solution, and the washing efficiency of some proteinous and starchy stains was examined through Hunter Lab spectrophotometry. The latter experiments demonstrated that the immobilized enzymes performed well during the washing process.     

Machine-learning modeling of hourly potential thermal refuge area: A case study from the Sainte-Marguerite River (Quebec,Canada)

To understand the temporal and spatial variability of thermal refuges, this study focused on modeling potential thermal refuge area (PTRA) at a sub-daily time-step in two tributary confluences of the Sainte-Marguerite River (Canada) during the summers of 2020 and 2021. Aquatic ectotherm species, such as Atlantic salmon (Salmo salar), seek these refuges to avoid heat stress during high summer river temperatures. To investigate the temporal variability of these PTRA, we employed inverse weighted distance interpolation to delineate the hourly area available at both confluences. We then analyzed the impact of the atypical low flow conditions of summer 2021 on the diel cycle of PTRA extremes using the coefficient of variation and the generalized additive model (GAM). Finally, we used four supervised machine-learning regression models and three to five hydrometeorological predictors to estimate hourly PTRA availability: multivariate adaptive splines regression (MARS), GAM, support vector machine regression (SVM), and random forest regression (RF). The results showed that tree-based and kernel-based regression models, RF and SVM, outperformed GAM and MARS. RF had the highest accuracy at both sites, with a relative root mean square error and Nash–Sutcliffe efficiency coefficient (Nash) of 13% and 93%, respectively. Our study discovered that under warm conditions in August 2021, small perennial tributary inflows in combination with low mainstem discharge could create high and constant PTRA at confluences, potentially providing vital thermal refuges for cold-water taxa. These refuges may be especially important at the local level, within a specific stretch or section of the river. Given the decreasing availability of thermal refuges for salmonids, it is crucial to monitor stream temperatures at small spatial and temporal scales using data-driven techniques in order to understand stream temperature heterogeneity at tributary confluences.     

γ-irradiation hardness investigations of (PANI)1−x(Bi2Te3)x composites for thermistor applications

The (PANI)_1−x(Bi₂Te₃)_x composites were successfully synthesized. These composites have a simple physical combination of intrinsic conducting polymer and nanocrystal topological insulator materials which are promising new materials for unusual applications. The influence of γ-irradiation (100 kGy doses) on the performance of the (PANI)_1−x(Bi₂Te₃)_x composites was described by atypical techniques to assess the radiation hardness of the samples. X-ray diffraction and Raman spectroscopy measurements show the semi-crystalline phases, the other structural parameters, and the phonon modes. The compositional analysis and the morphological properties were investigated by energy-dispersive X-ray, while transmission electron microscopy imaging confirmed the morphology of the dopants of Bi₂Te₃ nanoparticles into the polyaniline (PANI) matrix, which shows the accumulation of the particles. Thermogravimetric analysis figured out the degradation behavior of the compositions. The representative electron spin resonance (ESR) signal exhibits a narrow single-line ESR spectrum for PANI/Bi₂Te₃ nanoparticle concentrations. (PANI)_1−x(Bi₂Te₃)_x illustrates unexpected ferromagnetic behavior that confirms the magnetic performance of the samples without introducing any magnetic dopants. The obtained materials showed promising performance for using such hybrid materials as a positive temperature coefficient thermistor.