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The convergence of complex networks, IoT, various services, the enormous amount of data over the network, and 5G have brought challenges to the telecom industry for enhanced service delivery and network maintenance and monitoring. With numerous Netrounds probes and test agents generating massive volumes of data, the knowledge produced is underutilized due to the manual search for insight within the data. With the help of Netrounds APIs and machine learning, the automation of Netrounds metrics data aimed to predict the network performance degradation and anomaly detection ahead of time. The automation was expected to provide meaningful data insight and minimize the violation of SLAs in terms of delay and packet loss. These are one of the primary objectives of business continuity management to handle threats and risks for various network and information systems. Automating Netrounds’ open and programable APIs through Python fed data to an automatic machine learning model (supervised learning for prediction and unsupervised learning for anomaly detection). The analytics were used to predict network behavior, anomaly detection, and maintenance of the SLA threshold.

This study reports a three-year monitoring campaign of 62 volatile organic compounds (VOCs) of increasing concern in urban atmospheres near petrochemical industrial areas. A total of 770 air samples were taken during the sampling campaigns conducted monthly between January 2019 and December 2021. The analytical method applied involved a 14-day passive sampling in Carbopack X tubes followed by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). To gain insight into the correlations between VOCs, multivariate data analysis was used to assess the similarities and differences between datasets. Special attention was paid to benzene, 1,3-butadiene and 1,2-dichloroethane, because they are carcinogenic and are produced, handled and stored in the petrochemical industrial areas studied. The compounds found in the highest concentrations were alkanes, which were 21-66% of the total. Aromatic hydrocarbons and solvents were also detected at all the sampling sites, with BTEX and ethanol as the most prevalent compounds. Various concentration peaks of up to 4.8 mu g m(-3) for benzene and up to 4.0 mu g m(-3) for 1,3-butadiene were detected during the monitoring campaigns performed. Nevertheless, the average concentrations of these two compounds were always below the values set in the current air quality regulations, with maximum values of 1.3 mu g m(-3) for benzene and 1.5 mu g m(-3) for 1,3-butadiene near the North industrial park of Tarragona. Multivariate analysis results revealed four different patterns at the reference sites and discriminated between the sampling sites close to the petrochemical areas evaluated. The autumn-winter months, with thermal inversion phenomena and prevailing north and north-westerly winds, were the ones with the highest concentration level

This review intends to illustrate the fruitful collaboration between chemometrics and cultural heritage science. It showcases past achievements, aiming to inspire numerous cultural heritage researchers who have yet to incorporate multivariate techniques in analysing their research findings. The goal is to practically discuss applications with examples extracted from the literature. These range from the most basic early applications, influenced by the limited laboratory equipment available at those times, to the more contemporary research endeavours. Recently, numerous new analytical instrumentations have gained widespread adoption, some of which were previously inconceivable for field use or analysing micro-samples, characteristics needed to work with cultural heritage. Chemometrics serves as the binding element in this process. It handles the multivariate data generated by analytical instruments, even from multiple instruments used to characterize the same sample and yields easily interpretable graphs that encapsulate all the information considered simultaneously. This review will delve into the challenges of achieving commendable results and constructing effective descriptive or predictive models. Additionally, it will offer essential theoretical insights crucial for a comprehensive grasp of the fundamental algorithms employed. We have explored fundamental qualitative and quantitative models capable of addressing most issues encountered in studying a historical or artistic artifact. Additionally, focus was directed towards data pre-processing, at times essential for enhancing model outcomes.

Seafood plays an important role in diet because of its health benefits. However, the fact that chemical compounds such as high production volume chemicals may be present in seafood means that its consumption can be a potential risk for population. To assess the occurrence of HPVs and estimate the exposure and risk associated with their consumption, specimens of the most consumed seafood species in Catalonia and the Canary Islands, Spain, were collected and analysed. Results showed higher levels of HPVs in samples from Catalonia and a prevalence of phthalate esters and benzenesulfonamides over the other target compounds in samples from both locations. Multivariate analysis showed spatial differences between the mean concentration profiles of HPVs for the samples from Catalonia and the Canary Islands. Exposures were higher for the samples from Catalonia, although the intake of HPVs via seafood was not of any real concern in either of the locations.Copyright © 2023. Published by Elsevier Ltd.

Systematic errors in the calix [4] pyrrole-based potentiometric detection of creatinine have been observed in heavy smokers. This work further characterizes the interactions between the nicotinium cation and the cavitand as well as the resulting interference produced during the potentiometric detection. It is found that the nicotinium cation binds the electronic rich aromatic cavity defined by the pyrrole rings of the receptor's cone conformation with an estimated binding constant higher than 10−4 M−1 in methylene chloride. On the other hand, the creatininium cation is preferentially included in the hydrophobic aromatic cavity of the ionophore by establishing hydrogen bond interactions with the pyrrole NHs groups. Potentiometric calibrations confirmed the detection of the nicotinium cation at neutral and acidic pH, respectively. Due to the lower pka of creatinine, a methodology to quantify creatinine in presence of nicotine by using an array of three sensors at two pH values is proposed. A partial least squares regression was performed and reported recoveries of 103% with a standard deviation of 20%. The improved determination of creatinine was therefore discussed. This approach represents a step forward in the development of effective approaches to improve the measurement of creatinine in decentralized settings.

© 2020 Wiley-VCH GmbH A paper-based potentiometric sensor was constructed and characterized for the detection of glucose in saliva. Dilution of the samples was optimized to afford the optimum experimental conditions of measurements. The performance allows for detecting abnormal high glucose concentrations observed in diabetes patients. Repeatability data were presented and the performance of the sensor compared to literature examples showing suitable characteristics at a much lower cost. The validation in real saliva samples was performed against a commercial colorimetric kit showing that glucose could be effectively determined in the 4–10 mM range based on the comparison with a reference method.

© 2020 Elsevier B.V. This work reports the simple and inexpensive fabrication of homemade paper-based carbon-printed electrodes (HP C-PEs), aiming to produce an alternative way to generate electrochemical biosensors to all and promoting their wide use. This is especially important in times of pandemics, considering the excellent features of electrochemical biosensing, which may ensure portability, low-cost and quick responses. HP C-PEs were fabricated using a standard cellulose filter paper that was first modified with wax, to make it hydrophobic. Then, the electrodes were manually printed on top of this cellulose/wax substrate. The electrodes were designed by having standard configurations for potentiometric and electrochemical readings, combining two or three electrodes. In general, both electrode systems showed excellent electrochemical and mechanical features, which were better in specific cases than commercial devices. The 3-electrode system displayed high current levels with low peak-to-peak potential separation, yielding highly stable signals after consecutive electrode bending that corresponded to high active areas. The possibility of modifying the devices with polymers produced in-situ was also explored and proven successful, providing also advantageous features when compared to other devices. The 2-electrode system was also proven highly stable and capable of subsequent use in potentiometric sensing development. Overall, the fabrication process of the 2- and 3-electode systems described herein may be employed in laboratories to produce successful electrochemical biosensors, with the final devices displaying excellent electrochemical and mechanical features. This procedure offers the advantages of being simple and inexpensive, when compared to other commercial

© 2020, © 2020 Taylor & Francis Group, LLC. The role of hydrogen peroxide in a wide range of biological processes has led to a steady increase in research into hydrogen peroxide determination in recent years, and conducting polymers have attracted much interest in electrochemistry as promising materials in this area. We present an overview of electrochemical devices for hydrogen peroxide determination using conducting polymers, either as a target or as a byproduct of redox reactions. We describe different combinations of electrode modifications through the incorporation of conducting polymers as the main component along with other materials or nanomaterials. We critically compare the analytical performances cited and highlight some of the future challenges for the feasible application of such devices.

© 2020 Elsevier B.V. Biosensors for the detection of pathogenic bacteria in food are a promising alternative to conventional methods of analysis. This review focuses on the electrochemical biosensors reported in recent years for use with food samples. It highlights the performance parameters of these sensors, and provides a critical discussion of current and future trends, including future commercialization.

The miniaturisation of analytical devices, reduction of analytical data acquisition time, or the reduction of waste generation throughout the analytical process are important requirements of modern analytical chemistry, and in particular of green analytical chemistry. Green analytical chemistry has fostered the development of a new generation of miniaturized near-infrared spectroscopy (NIR) spectrometric systems. However, one of the drawbacks of these systems is the need for a compromise between the performance parameters (accuracy and sensitivity) and the aforementioned requirements of green analytical chemistry. In this paper, we evaluated the capabilities of two recently developed portable NIR instruments (SCiO and NeoSpectra) to achieve a rapid, simple and low-cost quantitative determination of commercial milk macronutrients. Commercial milk samples from Italy, Switzerland and Spain were chosen, covering the maximum range of variability in protein, carbohydrate and fat content, and multivariate calibration was used to correlate the recorded spectra with the macronutrient content of milk. Both SCiO and NeoSpectra can provide a fast and reliable analysis of fats in commercial milk, and they are able to correctly classify milk according to fat level. SCiO can also provide predictions of protein content and classification according to presence or absence of lactose.