Background: There are major air pollution monitoring gaps in sub-Saharan Africa. Developing capacity in the region to conduct air monitoring in the region can help estimate exposure to air pollution for epidemiology research. The purpose of our study is to develop a land use regression (LUR) model using low-cost air quality sensors developed by a research group in Uganda (AirQo). Methods: Using these low-cost sensors, we collected continuous measurements of fine particulate matter (PM2.5) between May 1, 2019 and February 29, 2020 at 22 monitoring sites across urban municipalities of Uganda. We compared average monthly PM2.5 concentrations from the AirQo sensors with measurements from a BAM-1020 reference monitor operated at the US Embassy in Kampala. Monthly PM2.5 concentrations were used for LUR modeling. We used eight Machine Learning (ML) algorithms and ensemble modeling; using 10-fold cross validation and root mean squared error (RMSE) to evaluate model performance. Results: Monthly PM2.5 concentration was 60.2 µg/m³ (IQR: 45.4-73.0 µg/m³; median= 57.5 µg/m³). For the ML LUR models, RMSE values ranged between 5.43 µg/m³ - 15.43 µg/m³ and explained between 28% and 92% of monthly PM2.5 variability. Generalized additive models explained the largest amount of PM2.5 variability (R²=0.92) and produced the lowest RMSE (5.43 µg/m³) in the held-out test set. The most important predictors of monthly PM2.5 concentrations included monthly precipitation, major roadway density, population density, latitude, greenness, and percentage of households using solid fuels. Conclusion: To our knowledge, ours is the first study to model the spatial distribution of urban air pollution in sub-Saharan Africa using air monitors developed from the region itself. Non-parametric ML for LUR modeling performed with high accuracy for prediction of monthly PM2.5 levels. Our analysis suggests that locally produced low-cost air quality sensors can help build capacity to conduct air pollution epidemiology research in the region.

Achieving global goals on sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require, among others, instantaneous access to information on food quality at key points within agri-food systems. Although stationary methods are usually used to quantify grain quality (wet-lab chemistry, benchtop NIR spectrometer); these do not suit many required user-cases, such as stakeholders in decentralized agri-food-chains that are typical for emerging economies. Therefore, we explored new technologies and models that might aid these particular user-cases. For this purpose, we generated the NIR spectra of 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, sorghum) with a standard benchtop NIR Spectrometer (DS2500, FOSS) and a novel mobile NIR-based sensor (HL-EVT5, Hone). We explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra. We were able to build relevant calibrations out of both types of spectra. At the same time, ML-based methods enhanced the prediction capacity of calibration models compared to classical deterministic methods. We also documented that the prediction of grain protein content based on NIR spectra generated by a mobile sensor (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the findings of this study lay the foundations on which to expand the utilization of NIR spectroscopy applications for agricultural research and development.

Application of native PGPR as bio inoculant is an alternative sustainable agricultural practice to enhance crop productivity, grain quality, and soil fertility. In this view, a study was to examine the effect of either individual or consortium PGPR inoculation on growth, yield, and grain nutrient uptake of two teff varieties. The pot experiment was carried out in (CRD) three replication and 10 treatments. The PGPR inoculants used in this study were Pseudomonas fluorescens biotype G, Enterobacter cloacae ss disolvens, and Serratia marcescens ss marcescen and their consortium. Dukem and Magna varieties were used in this study. The results of the analysis of variance showed significant differences (P ≤ 0.001) among the treatment and most of the agronomic traits except number of fertile tillers and also significant different (P ≤ 0.01) for grain P and N uptake. The variety was significantly affected grain Mg, Zn and Fe uptake at 5 % probability level and did not significantly influence all agronomic traits of the two varieties. Furthermore, interaction effects of two factors (TM*VT) were significant differences (P ≤ 0.05) for plant height and panicle length. Individual treatments mean comparison results showed that inoculation of native PGPR consortium significantly affected most of the PGP traits at (P ≤ 0.05). The maximum traits like plant height (189cm), panicle length (66.7cm), shoot dry biomass (9.98g), root dry biomass (2.90g) and grain yield per plant (4.55g) were observed from Dz-01-196. It could be concluded that the consortium of native PGPR inoculants for plant growth, yield and grain nutrient uptake improvement performed better than their individual strain.

Currently, old-style personal medicare techniques rely mostly on traditional methods, such as cumbersome tools and complicated processes, which can be time-consuming and inconvenient in some circumstances. Furthermore, such old methods need the use of heavy equipment, blood draws, and traditional bench-top testing procedures. Invasive ways of acquiring test samples can potentially cause patients discomfort and anguish. Wearable sensors, on the other hand, may be attached to numerous body areas to capture diverse biochemical and physiological characteristics as a developing analytical tool. Physical, chemical, and biological data transferred via the skin is used to monitor health in various circumstances. Wearable sensors can assess the aberrant conditions of the physical or chemical components of the human body in real-time, exposing the body state in time, thanks to unintrusive sampling and high accuracy. Most commercially available wearable gadgets are mechanically hard components attached to bands and worn on the wrist, with form factors ultimately constrained by the size and weight of the batteries required for the power supply. Wearable gadgets with “skin-like” qualities are a new type of automation that is only starting to make its way out of research labs and into pre-commercial prototypes. In this paper, we studied the recent advancement in battery-powered wearable sensors established on optical phenomena and skin-like battery-free sensors which brings a breakthrough in wearable sensing automation.

MEMThe PdNi film hydrogen sensors with Wheatstone bridge structure were designed and fabricated by the micro-electro-mechanical system (MEMS) technology. The integrated sensors consisted of four PdNi alloy film resistors. The interval two of them were shielded with silicon nitride film and used as reference resistance, while the others were used for hydrogen sensing. The PdNi alloy films and SiN films were deposited by magnetron sputtering. The morphology and microstructure of the PdNi films were characterized with X-ray diffraction (XRD). The output resistance signal was converted to millivolt output voltage signal for easy data acquisition. Hydrogen (H2) sensing properties of PdNi film hydrogen sensor with Wheatstone bridge structure was investigated under different temperatures (30℃, 50℃ and 70℃) and H2 concentrations (from 10 ppm to 0.4%). The hydrogen sensor demonstrated good response at different hydrogen concentrations and high repeatability in cycle testing under 0.4% H2 concentration. Under 10ppm hydrogen, the PdNi film hydrogen sensor had evident and collectable output voltage of 600 μV.

The characteristics of biogenic aerosols in urban area were explored by determining the composition, temporal distribution of saccharides in PM_2.5 in Shanghai. The total saccharides showed a wide range of 15.2 ng/m³ to 1752.8 ng/m³, with the averaged concentrations were 169.8 ng/m³，300.5 ng/m³，288.4 ng/m³，688 ng/m³ in spring, summer, autumn, and winter, respectively. The concerned saccharides include anhydrosaccharides (levoglucosan and mannosan), which were higher in cold seasons due to the increased biomass burning, saccharide alcohols (mannitol, arabitol, sorbitol) and monosaccharides (fructose, glucose), which showed more abundant in warm seasons attributed to the biological emissions. By PMF analysis, four emission sources of saccharides were demonstrated, including biomass burning, fungal spores, soil suspension and plant pollens. Resolution of backward trajectory and fire points showed a process of high concentrations of levoglucosan. We found that concentrations of anhydrosaccharides showed relatively stable under different pollution levels while saccharide alcohols exhibited an obvious decrease, indicated that biomass burning was not the core reason of the heavy haze pollution, however, and high level PM_2.5 pollution might inhibit effects of biological activities.

This mini-review critically summarizes the knowledge of imprinted polymer-based electrochemical sensors, for the detection of pesticides, metal ions and waterborne pathogenic bacteria, focusing on the period the last 5 years (citation of 78 papers published in 2017-2021, ie 63% of total citations). MIP-based electrochemical sensors exhibit low limit of detection, high selectivity, high sensitivity and low cost. Herein, we focused on the timely topics of water pollution by organics, inorganics and micro-organisms represented by pesticides, metal ions and bacteria, respectively. We put the emphasis on the design of imprinted polymers and their composites and coatings by radical polymerization, oxidative polymerization of conjugated monomers or sol-gel chemistry. Whilst most imprinted polymers are used in conjunction with differential pulse or square wave voltammetry for sensing organics and metal ions, electrochemical impedance spectroscopy (EIS) appears as the chief technique for detecting bacteria. This successful combination of EIS and imprinting technology should be harnessed in the coming years in the case of bacteria. Interestingly, bacteria are not always probed by bacteria-imprinted polymers; we report here their detection by monitoring specific (macro)molecules that reflect bacterial activity, for example quorum sensing signaling molecules or flagella proteins. If much has been developed in the past decade with glassy carbon or gold electrodes, it is clear that carbon paste electrodes of imprinted polymers are more and more investigated due to their versatility. Shortlisted case studies were critically reviewed and discussed; clearly a plethora of tricky strategies of designing selective electrochemical sensors are offered to “Imprinters”. We anticipate this review will be of interest to experts and new comers in the field who are paying time and effort combining electrochemical sensors with MIP technology.

In this work, a tailored extreme learning machine (ELM) algorithm to enhance the overall robustness of gas analyzer based on the tunable diode laser absorption spectroscopy (TDLAS) method is presented. The ELM model is tailored through activation function selection, input weight and bias searching, and cross validation method to address the analyzer robustness issues for industrial process analysis field application. The two particular issues are the inaccurate gas concentration measurement caused by the process gas background components variation, and the inaccurate spectra shift calculation caused by spectral interference. By using our algorithm, the concentration error is reduced by one order of magnitude over a much larger stream pressure and component range compared with that obtained by classical least square (CLS) fitting methods based on reference curves. Additionally, it is shown that with our algorithm, the wavelength shift accuracy is improved to less than 1 count over 1000 counts spectra length. In order to test the viability of our algorithm, a trace ethylene (C₂H₄) TDLAS analyzer with coexisting methane was implemented, and its experimental measurements support analyzing robustness enhancement effect.

Activated carbons can be produced from biomass in a thermal process either in a direct carbonization-activation process or by first carbonizing the biomass and later on activating the biochars into activated carbons. The properties of the ACs are dependent on the type of process used for production. In this study, the properties of activated carbons produced in a one-stage and a two-stage process are considered. Activated carbons were produced by physical activation of two types of starting materials, bio chars produced from spruce and birch chips in a commercial carbonization plant and from the corresponding raw chips. The activated carbons produced were characterized regarding specific surfaces, pore volumes and pore size distributions. The un-activated bio chars had some degree of surface area 190 and 140 m²g^-1 for spruce and birch and pore volumes of 0.067 and 0.092 cm³g^-1. According to the results obtained, two slightly different types of activated carbons are produced depending if a one-stage or a two-stage carbonization and activation process is used. The ACs produced in the one-stage process had higher specific surface areas compared to the ones produced in a two-stage process (761-940 m²g^-1 vs. 540-650 m²g^-1) . In addition, total pore volumes were higher in one-stage process but development of micropores is greater compared to two-stage process. There was no significant difference in total carbon content between one-stage and two-stage process.

Indoor air quality and public health have always been an area of prime interest across the globe. The significance of low-cost air quality sensing and indoor public health practices spikes during the time of pandemic and epidemics when indoor air pollution becomes a threat to living beings especially human beings. Indoor diseases are hard to diagnose if they are due to the indoor environmental conditions. A major challenge was observed in establishing a baseline between the indoor air quality sensors and associated diseases. In this work, 10,000+ articles from top literature databases were reviewed using bibliometric analysis to formulate indoor air quality sensors and diseases correlation rubrics to critically review 500+ articles. A set of 200+ articles were selected based on for detailed study based on seven bibliometric indices for publications that used WHO, NIH, US EPA, CDC, and FDA defined principles. This review has been conducted to assist end-users, public health facilities, state agencies, researchers, scientists and air quality protection agencies.

Artificial reefs have been deployed in multiple regions of the world for different purposes including habitat restoration and protection, biodiversity and fish stock enhancement, fisheries management and recreation. Artificial reefs can be a valuable tool for ecosystem protection and rehabilitation, helping mitigate the effects of anthropogenic impacts that we face today. However, knowledge on artificial reefs is unevenly distributed worldwide, with some regions having much more quality information available and published (e.g. European Mediterranean Sea area), while others, for instance the North-East Atlantic area, do not. Here, we provide a characterization of purposely built artificial reefs in North-East Atlantic area based on all available literature (i.e. research papers and reports), highlighting the needs and gaps that are vital for establishing future perspectives for artificial reef deployment and research. In the North-East Atlantic area, sixty-one purposely built artificial reefs have been deployed since 1970, mostly between the years 1990-2009, with Spain being the country with the highest number of artificial reefs. The most reported purpose for their deployment is fisheries productivity and habitat/species protection, although, most artificial reefs are multipurpose in order to maximise the benefits of a given financial investment. The majority of artificial reefs were submerged at < 50 m, mainly between 10-20 m of depth. The most used designs were cubic blocks and complex designs made by an array of combined shapes, which mostly consist of concrete (79%). From all the analysed data on artificial reefs, 67% of the cases reported surveys to assess biodiversity after the deployment. However, in 26% of those cases, data was not available. When data was available, only 31% of cases reported long-term biomonitoring surveys (3 years or more). Based upon these findings, we noticed a general lack of scientifically robust data, including records of species and abundance of both fish and invertebrates, as well as macroalgae. Preventing an adequate determination of the best balance between shape, construction material and bio-colonization. Critiques and suggestions are discussed in the light of current available data in order to perform more efficient research, evaluation and functioning of future artificial reefs.

The green biomass of horticultural plants contains valuable secondary metabolites (SM) which can potentially be extracted and sold. When exposed to stress, plants accumulate higher amounts of these SMs, making the extraction and commercialization even more attractive. We evaluated the potential for accumulating of the flavones cynaroside and graveobioside A in leaves of two bell pepper cultivars (Mavras and Stayer) when exposed to salt stress (100 mM NaCl), UVA/B excitation (UVA 4-5 W/m²; UVB 10-14 W/m² for 3 hours per day) or a combination of both stressors. HPLC analyses proved the enhanced accumulation of both metabolites under stress conditions. Cynaroside accumulation is effectively triggered by high-UV stress, whereas graveobioside A contents increase under salt stress. Highest contents were observed in plants exposed to combined stress. Effects of stress on overall plant performance differed significantly between treatments, with least negative impact on aboveground biomass found for high-UV stressed plants. The usage of two non-destructive instruments (Dualex and Multiplex) allowed us to gain insights in ontogenetical effects at the leaf level and temporal development of SM contents over time. Indices provided by those devices correlate fairly with amounts detected via HPLC (Cynaroside: R2 = 0.46 – 0.66; Graveobioside A: R2 = 0.51 – 0.71). The concentrations of both metabolites tend to decrease at leaf level during the ontogenetical development even under stress conditions. High-UV stress is a promising tool for enriching plant leaves with valuable SM without major effects on plant biomass. All data is available online [1].

Although our emotional connection with the physical urban setting is often valued, it is rarely recognised or used as a resource to understand future actions in city planning. Yet, despite the importance of emotion, citizens’ emotions are typically seen as difficult to quantify and individualistic, even though knowledge about people’s response to space could help planners understand people’s behaviours and learn about how citizens use and live in the city. The study explores the relationship between the physical space and emotions through identifying the links between stress levels, and specific features of the urban environment. This study aims to show the potential of integrating the use of galvanic skin response (GSR) within urban spatial analysis and city planning, in order to address the relationship between emotions and urban spaces. This method involved participants using a (GSR) device linked to location data to measure participant’s emotional responses along a walking route in a city centre environment. Findings show correlations between characteristics of environment and stress levels, as well as how specific features of the city spaces such as road crossing create stress ‘hotspots’. We suggest that the data obtained could contribute to citizens creating their own information layer - an emotional layer- that could inform urban planning decision-making. The implications of this application of this method as an approach to public participation in urban planning are also discussed.

Atmospheric correction is a necessary step in image processing data and spectra recorded by spaceborne sensors for pure cloudless atmosphere, primarily in the visible and near-IR spectral range. We have present a fast and sufficiently accurate method of atmospheric correction based on the proposed analytical solutions describing with high accuracy the spectrum of outgoing radiation at the top boundary of the cloudless atmosphere. This technique includes the model of the atmosphere and its optical parameters that are important in terms of radiation transfer. The solution of the inverse problem for finding unknown parameters of the model is carried out by the method of non-linear least squares (Levenberg-Marquardt algorithm) for an individual selected pixel of the image (its spectrum), taking into account the adjacency effects. Using the found parameters of the atmosphere and the average surface albedo, assuming homogeneity of the atmosphere within a certain area of the hyperspectral image (or the whole frame), the spectral albedo at the Earth's surface is calculated for all other pixels. It is essential that the procedure of the numerical simulation with non-linear least squares of the direct transfer problem is based on using analytical solutions, which provides a very short calculation time of the atmospheric parameters (seconds or less) and the ability to perform atmospheric correction "on-fly." Testing methods of atmospheric correction was performed using the synthetic outgoing radiation spectra at the top of the atmosphere (TOA), obtained by numerical simulation in the LibRadTran code, as well as spectra of real space images of the Hyperion hyperspectrometer. A comparison with the results of atmospheric correction in module FLAASH of ENVI package has been performed. Finally, to validate our data obtained by the SHARK method, a comparative analysis with ground-based measurements of Radiometric Calibration Network (RadCalNet) was carried out.

Abstract: Nonlinear spectrum distortions are caused by the peculiarities of the operation of charge-coupled device elements (CCD), in which the signal exposition time (TINT) is one of the significant parameters. A change of TINT on a CCD leads to a nonlinear distortion of the resulting spectrum. Nonlinear distortion of the spectrum, in its turn, leads to errors in determining the central wavelength of Fiber Bragg Gratings (FBG) and spectrally sensitive sensors, which, in general, negatively affects the accuracy of measuring systems. The paper proposes an algorithm for correcting nonlinear distortions of the spectrum obtained on a spectrum analyzer using CCD as a receiver. It is shown that preliminary calibration of the optical spectrum analyzer with subsequent mathematical processing of the signal makes it possible to make corrections in the resulting spectrum, thereby leveling the errors caused by measurements at different TINT.

This paper consists of the design and implementation of a simple conditioning circuit to optimize the electronic nose performance, where a temperature modulation method was applied to the heating resistor to study the sensor’s response and confirm whether they are able to make the discrimination when exposed to different volatile organic compounds (VOC’s). This study was based on determining the efficiency of the gas sensors with the aim to perform an electronic nose, improving the sensitivity, selectivity and repeatability of the measuring system, selecting the type of modulation (e.g., pulse width modulation) for the analytes detection (i.e., Moscatel wine samples (2% of alcohol) and ethyl alcohol (70%)). The results demonstrated that by using temperature modulation technique to the heating resistors, it is possible to realize the discrimination of VOC’s in fast and easy way through a chemical sensors array. Therefore, a discrimination model based on principal component analysis (PCA) was implemented to each sensor, with data responses obtaining a variance of 94.5% and accuracy of 100%.

Parmigiano Reggiano cheese is one of the most appreciated and consumed food worldwide, especially in Italy, for its high content of nutrients and for its taste. However, these characteristics make this product subject to counterfeiting in different forms. In this study, a novel method based on an electronic nose has been developed in order to investigate the potentiality of this tool to distinguish rind percentage in grated Parmigiano Reggiano packages that should be lower than 18%. Different samples in terms of percentage, seasoning and rind working process were considered to tackle the problem at 360°. In parallel, GC-MS technique was used to give a name to the compounds that characterize Parmigiano and to relate them with sensors responses. Data analysis consisted of two stages: multivariate analysis (PLS) and classification made in a hierarchical way with PLS-DA ad ANNs. Results are promising in terms of correct classification of the samples. The classification rate is higher for ANNs than PLS-DA, reaching also 100% values.

Bio-separation of natural molecules as well as clinical compounds has been constantly developed in last decades. Several techniques are available but the majority of them presents drawbacks such us impossibility to be applied for industrial purposes. The main limitations for the scaling up are high costs and the fact that the devices work with microfluid dynamics. Nevertheless, magnetic bio-separation is considered the most prone to be used for large scale applications. Herein, we propose a simple magnetic separation method that is not based on microfluid dynamics, can work in a continuous- and high-flow rate and can be easily automated in order to be used for standard separation purposes. It is based on the use of an anisotropic flexible ferric magnetic strip, Teflon hoses and a pumping device. We show the modelling of the separation process along with an experimental test on iron oxide magnetic particles. The results showed that it is possible to remove, and separately collect, more than 92% of magnetic particles from a liquid solution of 100 ml in roughly 15 minutes.

Placing the needle inside the epidural space for locoregional anesthesia is a challenging procedure, which even today is left to the expertise of the operator. Recently, we have demonstrated that the use of optically sensorized needles significantly improve the effectiveness of such procedure. Here we propose an optimized configuration, where the optical fiber strain sensor is directly integrated inside the epidural catheter. The new design allows to solve the biocompatibility issues and increases the versatility of the former configuration. Through an in vivo study carried out on a porcine model we confirm the reliability of our approach, which also opens the way to catheter monitoring during its insertion inside biological spaces.

This review article is focused on the analysis of the state of the art of sensors for guided 9 ultrasonic waves for the detection and localization of impacts, therefore of interest for the structural 10 health monitoring (SHM). The recent developments in sensor technologies are then reported and 11 discussed through the many references in recent scientific literature. The physical phenomena re-12 lated to impact event and the main physical quantities are then introduced to discuss their im-13 portance in the development of the hardware and software components for SHM systems. An im-14 portant aspect of the article is the description of the different ultrasonic sensor technologies cur-15 rently present in the literature and what advantages and disadvantages they could bring, in relation 16 to the various phenomena investigated. In this context, the analysis of the front-end electronics is 17 deepened, the type of data transmission both in terms of wired and wireless technology and in terms 18 of online and offline signal processing. The integration aspects of sensors for the creation of net-19 works with autonomous nodes with the possibility of powering through energy harvesting devices 20 and the embedded processing capacity is also studied. Finally, the emerging sector of processing 21 techniques using deep learning and artificial intelligence concludes the review by indicating the 22 potential for the detection and autonomous characterization of the impacts.

This work addresses the problem of automatic head pose estimation and its application in 3D gaze estimation using low quality RGB--D sensors without any subject cooperation or manual intervention. The previous works on 3D head pose estimation using RGB--D sensors require either an offline step for supervised learning or 3D head model construction which may require manual intervention or subject cooperation for complete head model reconstruction. In this paper, we propose a 3D pose estimator based on low quality depth data, which is not limited by any of the aforementioned steps. Instead, the proposed technique relies on modeling the subject's face in 3--D rather than the complete head, which in turn, relaxes all of the constraints with the previous works. The proposed method is robust, highly accurate and fully automatic. Moreover, it does not need any offline step. Unlike some of the previous works, the method only uses depth data for pose estimation. The experimental results on the Biwi head pose database confirm the efficiency of our algorithm in handling large pose variations and partial occlusion. We also evaluate the performance of our algorithm on IDIAP database for 3D head pose and eye gaze estimation.

Bio-economy is a major area of the strategy that can afford the European Union to achieve growth: (i) smart, through the development of knowledge and innovation; and (ii) sustainable, based on a greener, more efficient economy in resource management. We believe that the progress of bio-economy cannot be achieved without the harnessing of intellectual capital. Our research aimed to emphasize the benefits of the dynamics of the intellectual capital growth on the evolution of the bio-economy. Thus, the information published by Eurostat (European Statistic Institute) during a period spanning seven years (2011-2018) was used to assess the influence exerted by the conduct of the harness of intellectual capital related to sustainability as well as for the reporting of indicators relevant to appreciating an economic progress and sustainability (renewable waste material, share of renewable energy and energy intensity of the economy). The ultimate goal was represented by the generation of a regression model to see what factor influences mostly the progress of the bio-economy at European and Romanian level. Significant dependency relationships were identified. The results remain robust even after the introduction of certain control variables, such as gross domestic product rate, food production, population growth, urbanization growth and inflation. Our paper sets out to contribute to expanding the specialty literature by highlighting the involvement of intellectual capital as a factor in optimizing sustainability growth and, at a methodological level, by using a multiple regression.

Reactive transport (RT) couples bio-geo-chemical reactions and transport. RT is important to understand numerous scientific questions and solve some engineering problems. RT is highly multidisciplinary, which hinders the development of a body of knowledge shared by RT modelers and developers. The goal of this paper is to review the basic conceptual issues shared by all RT problems, so as to facilitate advance along the current frontier: biochemical reactions. To this end, we review the basic equations to point that chemical systems are controlled by the set of equilibrium reactions, which are easy to model, but whose rate is controlled by mixing. Since mixing is not properly represented by the standard advection-dispersion equation (ADE), we conclude that this equation is poor for RT. This leads us to review alternative transport formulations, and the methods to solve RT problems using both the ADE and alternative equations. Since equilibrium is easy, difficulties arise for kinetic reactions, which is especially true for biochemistry, where numerous frontiers are open (how to represent microbial communities, impact of genomics, effect of biofilms on flow and transport, etc.). We conclude with the basic 10 issues that we consider fundamental for any conceptually sound RT effort.

Over a billion people around the world are disabled, among them, 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, poor environment, and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach in a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system's hardware and software design, construct their prototype implementations, and test them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than $80. Essentially, we provide designs of an inexpensive, miniature, green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach affords faster inference and decision-making using relatively low energy with smaller data sizes as well as faster communications for the edge, fog, and cloud computing.

Bio-surfactants are surface-active molecules which are produced by the wide range of microbes including bacteria, fungi, and yeast. This study was conducted to identify bio-surfactants by Bacillus subtilis combined with use of cheap substrates and industrial wastes (Mustard cake, Whey and Soya cake) which are found locally in Nepal. Bacillus subtilis, one of the most potential bio-surfactants producer; was isolated from soil sample of hydrocarbon contaminated site. Isolates were grown in a Minimal Salt Media (MSM) with 10% (v/v) mustard oil cake, whey and soya cake separately. The presence and potential of surfactant was determined by the oil spreading technique, emulsification index (%E24) and surface tension measurement. It was revealed that the surface tensions of cell free extract were 54.41, 60.02 and 56.64 mN/m for from mustard cake, whey and soya cake respectively as compared to distilled water (72.09) at 25oC. The emulsification index values are was found to be highest in engine oil from the bio-surfactant extracted from mustard cake, soya cake and whey respectively. Similarly, mustard oil showed the lowest value of emulsification index. The highest emulsification activity was shown in mustard oil i.e. 1.13 from the cell free extract from mustard oil and lowest in engine oil i.e., 0.07, by the extract from soya cake medium, when measured in spectrophotometer at 540 nm. In conclusion, strain of Bacillus subtilis was found to be the potential surface active agent producers on the mustard oil cake, which can be useful medium for various environmental, food and industrial processes.

We present the design and overall development of an eight degrees of freedom (DOF) based Bioinspired Quadruped Robot (BiQR). The robot is designed with a skeleton made of cedar wood. The wooden skeleton is based on exploring the potential of cedar wood to be a choice for legged robots’ design. With a total weight of 1.19 kg, the robot uses eight servo motors that run the position control. Relying on the inverse kinematics, the control design enables the robot to perform the walk gait-based locomotion in a controlled environment. The robot has two main aspects: 1) the initial wooden skeleton development of the robot showing it to be an acceptable choice for robot design, 2) the robot’s applicability as a low-cost legged platform to test the locomotion in a laboratory or a classroom setting.

Complementary Metal-Oxide-Semiconductor (CMOS) is a typical image sensor that has a wide range of applications. However, considering the limitations of the weather condition and hardware cost, it is hard to capture high-resolution images by CMOS sensor. Recently, Super-Resolution (SR) techniques for image restoration has been gaining attentions due to its excellent performance. Under the powerful learning ability, Generative Adversarial Networks (GANs) have been proved to achieve great success. In this paper, we propose the Advanced Generative Adversarial Networks (AGAN) to efficiently correct these issues; 1) we design a Laplacian pyramid framework as pre-trained module, which is beneficial to provide multi-scale features for our input. 2) at each feature block, a convolutional skip-connections network, which may contain some latent information, is significant for generative model to reconstruct a plausible-looking image; 3) considering that edge details usually play an important role in image generation, a novel perceptual loss function is defined to train and seek optimal parameters. It is effective to achieve excellent and compelling quality captured by CMOS sensor. Quantitative and qualitative evaluations have been demonstrated that our algorithm not only fully takes advantage of Convolutional Neural Networks (CNNs) to improve the image quality, but also performs better than previous GAN algorithms for super-resolution task.

The Needleman-Wunsch process is a classic tool in bioinformatics, being a dynamic programming algorithm that performs a pairwise alignment of two input biological sequences, either protein or nucleic acid. A distance matrix between the tokens used in the sequences is also required as input. The distance matrix is used to generate a positional pairwise similarity matrix between the input sequences, which is in turn used to generate a dynamic programming matrix. The best path through the dynamic programming matrix is navigated using a traceback procedure that maximises similarity, inserting gaps as necessary. Needleman-Wunsch can align both nucleic acids or proteins, which use alphabets of size 4 and 20 tokens respectively. It can also be applied to any other kind of sequence where distance matrices can be specified. Here, we apply it to chains of Pousseur’s Scambi electronic music fragments, of which there are 32, and which Pousseur categorised by their sonic properties, thus permitting the consecutive construction of distance, similarity and dynamic programming matrices. Traceback through the dynamic programming matrix thus produces contrapuntal duet compositions in which two Scambi chains are played in the maximally euphonious manner, providing also an illustration of the principles of biological sequence alignment in sound.

Delipidation is a method of defatting that is generally associated with the removal of residual lipids or lipid groups from matrices in which they are present in minute quantities. The bio-delipidation of protein-rich poultry slaughterhouse wastewater (PSW) pre-treated with a dissolved air flotation (DAF) system was developed using microbial lipases from bacterial strains isolated from the PSW. The efficacy of the bio-delipidation system was quantitatively characterised by comparing the quality parameters i.e., fats, oil and grease (FOGs), turbidity, total suspended solutes (TSS), total chemical oxygen demand (tCOD) and protein concentration of the DAF pre-treated PSW and bio-lipidized samples. As hypothesised, the bio-delipidation system was able to effectively reduce the levels of these quality parameters when crude lipases of Bacillus cereus AB1 (BF3) and Bacillus cereus CC-1 (B30) strains were used. Strain-dependent quality characteristics were also observed in bio-delipidized samples. The study successfully managed to complement physical reduction techniques (DAF) with biological strategies (bio-delipidation) for improved PSW quality, with potential industrial applications.

The demand for animal products is expected to continue to rise, which requires the development of efficient livestock farming systems. Environmental, societal and economic concerns regarding this industry are however accumulating, addressing the large resource demand, pollutants and greenhouse gas emissions and health concerns that the livestock industry is responsible for. Precision livestock farming systems allow the continuous automatic monitoring of various physiological, behavioural and phenotypic parameters of animals in order to increase productivity and animal welfare while controlling and minimizing the environmental impact. There is a high potential for digital farming to be the solution for responsibly and ethically feeding the growing and urbanizing population. However, many problems and concerns are still present in this developing industry and remain relatively unaddressed, starting with the ethical aspects in regard to the animal, including its objectification, human-animal relationships and welfare and ending with the societal implications of this digitalization. Concrete frameworks, inter-disciplinary studies and global legislation need to be put in place in order to ensure the safety and protection of the animals, farmer and society. Here, implications of digital farming for the animals, farmers, society and the planet are critically reviewed with the future outlook of digital farms.

Wearable cardiac sensors pave the way to advanced cardiac monitoring applications based on heart rate variability (HRV). In real-life settings, heart rate (HR) measurements are subject to mo-tion artifacts that can be timely removed from the recordings. This leads to frequent data loss in the HR signal, especially for commercial devices based on photoplethysmography (PPG). The cur-rent study had two main goals: (i) to provide a white-box quality index that estimates the amount of missing samples in any piece of HR signal; and (ii) to quantify the impact of data loss on feature extraction in a PPG-based HR signal. This was done by comparing real-life recordings from com-mercial sensors featuring both PPG (Empatica E4) and ECG (Zephyr BioHarness 3). After an out-lier rejection process, our quality index was used to isolate portions of ECG-based HR signal that could be used as benchmark, to validate the output of Empatica E4 at the signal level and at the feature level. Our results showed high accuracy for estimating the mean HR, poor accuracy for short-term HRV features and moderate accuracy for longer-term HRV features. Levels of error could be substantially reduced by using our quality index to identify time windows with few or no missing data.

Ever-increasing need for improving the livability of a city and improve outcomes for its residents, over the last decade, the adoption of technology to develop urbanised societies around the world has given rise to the need for developing smart cities. The speed at which the world population is growing, the use of Internet of Things in smart cities have really advanced the quality of life. One significant area of concern within the smart city framework is waste management. If the waste within a city is not adequately managed, then it leads to issues in the health of the citizens. Additionally, the waste management has such a high impact on the environmental footprint, hence the need to have a smart way of managing waste is of critical importance. Through our research, we analyse the challenges of waste management within a city to understand the impact of the problem on to the citizens and overall city operations. We then investigate ways in which we can solve these problems using the emerging technologies, such as the Internet of Things, to collect valuable data of large volumes arriving at an astronomical rate, then apply multi-agent deep reinforcement learning algorithms to harness the power of big data to extract meaningful information and actionable insights. We ingest data generated by our Internet of Things into our algorithm for three main purposes including providing the notifications to an external system, for example, a map navigation engine out of the scope for this project but a future extension for route optimisation and waste vehicle tracking; extracting and reporting the actionable insights from the underlying data; and consuming the extracted data for predictive forecasting to draw out the unknown patterns of waste fill levels within various geographical locations and again send out triggers and notification to external systems for example a waste collection authority who can efficiently schedule the waste collection vehicles and optimise the route. To achieve the above mentioned outcomes, we propose a framework that is agnostic of the hardware that it connects to and can effectively interface with a wide variety of hardware keeping a level of abstraction in the architecture.

The recent advancements in the field of communication have led data sharing to become an integral part of today's smart cities with the evolution of concepts such as the internet of vehicles (IoV) paradigm. As a part of IoV, Electric Vehicles (EVs) have recently gained momentum as authorities have started expanding their Low Emission Zones (LEZ) in an effort to build green cities with low carbon footprints. Energy is one of the key requirements of EVs not only to support the smooth and sustainable operation of EV itself but to also ensure connectivity between the vehicles and infrastructure with controlling devices like sensors and actuators installed within an EV. In this context, renewable energy sources (such as wind energy) dramatically play their parts in the automobile sector towards designing the energy harvesting electric vehicles (EH-EV) to pare the energy reliance on the national grid. In this article, a novel approach is presented to achieve electric generation due to vehicle mobility to support the communication primitives in electric vehicles which enables plenty of IoV use cases in the presence of surplus energy at hand. A small-scale wind turbine is designed to harness wind power for converting it into mechanical power. This power is then fed to the onboard DC generator to produce electrical energy. Furthermore, the acquired power is processed through a regulation circuitry to consequently achieve the desired power supply for the end load, i.e. the batteries installed. The suitable orientation for efficient power generation is proposed on ANSYS-based aerodynamics analysis. The voltages induced by DC generator at No-Load condition are 35V while at Full-Load 25V are generated at rated current of 6.9A, along with the generation of power at around 100W (at constant voltage) at the rated speed of 90 mph for nominal battery charging. Moreover, the acquired data can be monitored via an android application interface by using a Bluetooth module.

Reading is one of the essential processes during the maturation of an individual. It is estimated that 5-10% of school-age children are affected by dyslexia, the reading disorder characterised by difficulties in the accuracy or fluency of word recognition. There are many studies which have reported that colour overlays and background could improve the reading process, especially in children with reading disorders. As dyslexia has neurobiological origins, the aim of the present research was to understand the relationship between physiological parameters and colour modifications in the text and background during reading in children with and without dyslexia. We have measured differences in electroencephalography (EEG), heart rate variability (HRV), electrodermal activities (EDA), and eye movement of the 36 school-age children (18 with dyslexia and 18 of control group) during the reading performance in 13 combinations of background and overlay colours during the reading task. Our findings showed that the dyslexic children have longer reading duration, fixation count, fixation duration average, fixation duration total, and longer saccade count, saccade duration total, and saccade duration average while reading on white and coloured background/overlay. It was found that the turquoise, turquoise O, and yellow colours are beneficial for dyslexic readers, as they achieved the shortest time duration during the reading tasks when these colours were used. Also, dyslexic children have higher values of beta and the whole range of EEG while reading in particular colour (purple), as well as increasing theta range while reading on the purple overlay colour. We have observed no significant differences between HRV parameters on white colour, except for single colours (purple, turquoise overlay and yellow overlay) where the control group showed higher values for Mean HR, while dyslexic children scored higher with Mean RR. Regarding EDA measure we have found systematically lower values in children with dyslexia in comparison to the control group. Based on present results we can conclude that both colours (warm and cold background/overlays) are beneficial for both groups of readers and all sensor modalities could be used to better understand the neurophysiological origins in dyslexic children.

Gyroscopes are fascinating instruments with a history of about 200 years. When J.G.F. Bohnenberger presented his machine to his students in 1810 at the University of Tuebingen, Germany, nobody could have foreseen that this fascinating development would be used for complex orientation and positioning. At the University of Stuttgart, Germany, a collection of 160 exhibits is available and in transition for a sustainable future. Here, the systems are digitized in 2D, 2.5D and 3D and are made available for a world-wide community using OpenAccess platforms. The technologies being used are Computed Tomography, Computer Vision, Endoscopy and Photogrammetry. The workflows for combining voxel representations and colored point clouds are described, to create Digital Twins of the tangible assets. Advantages and disadvantages are discussed und work for near future is outlined in this new and challenging field of Tech Heritage digitization.

As the EU is moving towards a low carbon economy and seeks to further develop its renewable energy policy, this paper quantitatively investigates the impact of plausible energy market reforms from the perspective of bio-renewables. Employing a state-of-the-art biobased variant of a computable general equilibrium model, this study assesses the perceived medium-term benefits, risks and trade-offs which arise from an advanced biofuels plan, two exploratory scenarios of a more 'sustainable' conventional biofuels plan and a 'no-mandate' scenario. Consistent with more recent studies, none of the scenarios considered present significant challenges to EU food-security or agricultural land usage. An illustrative advanced biofuels plan simulation requires non-trivial public support to implement whilst a degree of competition for biomass with (high-value) advanced biomass material industries is observed. On the other hand, it significantly alleviates land use pressures, whilst lignocellulose biomass prices are not expected to increase to unsustainable levels. Clearly, these observations are subject to assumptions on technological change, sustainable biomass limits, expected trends in fossil fuel prices and EU access to third-country trade. With these same caveats in mind, the switch to increased bioethanol production does not result in significant market tensions in biomass markets.

The field of power harvesting has experienced significant growth over the past few years due to the ever-increasing desire to produce portable and wireless electronics with extended lifespans. The present work aims to introduce an approach to harvesting electrical energy from a mechanically excited piezoelectric element and investigates a power analytical model generated by a smart structure of type polyvinylidene fluoride(PVDF) that can be stuck onto fabrics and flexible substrates, although we report the effects of various substrates and investigates the sticking of these substrates on the characterization of the piezoelectric material.

Egyptian cotton is one of the most important commodities to the Egyptian economy and is renowned globally for its quality, which is currently graded by manual inspection. This has several drawbacks including significant labour requirement, low inspection efficiency, and influence from inspection conditions such as light and human subjectivity. This current work uses a low-cost colour vision system, combined with machine learning to predict the cotton lint grade of the cultivars Giza 86, 97, 90, 94 and 96. Unsupervised and supervised machine learning approaches were explored and compared. Three different supervised learning algorithms were evaluated: linear discriminant analysis, decision trees and ensemble modelling. The highest accuracy models (77.3-98.2%) used an ensemble modelling technique to classify samples within the Egyptian cotton grades: Fully Good, Good, Fully Good Fair, Good Fair and Fully Fair. The unsupervised learning technique k-means showed that human error is more likely to occur when classifying lint belonging to the higher quality grades and underlined the need for an intelligent system to replace manual inspection.

Although commercially-available low-cost air quality sensors have low accuracy, the sensor system are being used to collect the data for the regulation of PM2.5 emission caused by industrial activities or to estimate the personal exposure for PM2.5. In this work, to solve the accuracy problem of low-cost PM sensor, we developed a new PM2.5 calibration model by combining the deep neural network (DNN) optimized in calibration problem and a LSTM optimized in time-dependent characteristics. First, two datasets were generated to test the accuracy performance and generalization performance of the PM2.5 calibration machine learning (ML) model. The PM2.5 concentrations, temperature and humidity by low-cost sensor and gravimetric-based PM2.5 measuring instrument were sampled for a sufficiently long time. The proposed model was compared with benchmark (multiple linear regression model) and low-cost sensor results. For root mean square error (RMSE) for PM2.5 concentrations, the proposed model reduced 41-60% of error compared to the raw data of low-cost sensor, and reduced 30-51% of error compared to the benchmark model. R2 of ML model, MLR and raw data were 93, 80 and 59 %. Also, the developed model still showed consistent calibration performance when calibrated with new sensors in different locations. Low-cost sensors combined with ML model not only can improve the calibration performance of benchmark, but also can be applied to the sensor monitoring systems for various epidemiologic investigations and regulatory decisions.

Optical fiber sensors have become a widely-used tool to perform life-long monitoring of large structures. Different set-ups of sensors can be used to obtain information at different scopes. In this paper, the application of a portable Brillouin optical time domain analysis sensor applied to reinforced concrete beams is presented. The sensor is made up of a single-mode optical fiber applied to the structure that enables the measurement of tensile and compressive axial strain along the beams. The fiber is embedded into a concrete casting and fixed along the reinforcement bars so that a complete correlation can be obtained regarding strain and displacements in linear and non-linear flexural response. The use of four sensing fibers positioned along the beam's length at different vertical positions makes it possible to read the strain variation within the cross-section. In accordance with Bernoulli’s hypothesis, this allows curvature diagrams to be obtained across the structure. The measured curvature is used to set up the moment–curvature relationship, which highlights the possibility of the sensor monitoring structures continuously in space and time.

Lead, known as a metal with high neurotoxicity to children, cadmium, which is a carcinogenic and bioaccumulative contaminant, and arsenic, a class 1 carcinogenic according to the International Agency for Research on Cancer, are toxic elements (TEs) whose relevant route of exposure may be diet. We determined the bio-accessible fraction of lead, cadmium, and arsenic from the diet of preschool children from two day care centers (DCC). A cross-sectional study was conducted with 64 one–four-year-old children from two DCCs where the 24-h duplicate diet samples were collected. The diet samples were analyzed by ICP-MS for lead, cadmium, and arsenic total concentrations (n = 64) and their bio-accessibility were analyzed for a subsample (n = 10). The dietary intake (DI) mean for lead, cadmium, and arsenic were 0.18 ± 0.11 µg kg⁻¹ bw, 0.08 ± 0.04 µg kg⁻¹ bw, and 0.61 ± 0.41 µg kg⁻¹ bw, respectively. All DI calculated for TEs, considering total intake, were found lower than the tolerable limits (TL) (European Union, or World Health Organization, WHO, when applicable) except for one child’s Pb intake. Bio-accessibilities ranged between 0% to 93%, 0% to 103%, and 0% to 69%, for lead, cadmium, and arsenic, respectively. Although DI for TEs has been found lower than TL, these reference values have been recently decreased or withdrawn since it was for lead and arsenic whose TL were withdrawn by WHO.

The aim of the present study was to assess the efficacy of All Cosmos Industries (ACI) bio-organic and bio-chemical fertilizers and ACI N-Fixer (N-Bio Booster) on the paddy yields based on the field trial plots at Langkat, Medan, Indonesia. This application of ACI bio-organic fertilizer (NPK 5/5/5) and ACI bio-chemical (NPK 15/15/15) fertilizer and ACI N-Fixer tests were conducted at the paddy farm at Langkat from May-October 2018. This study employed a factorial randomized complete block design which consisted of two factors, namely: Factor I with four types of fertilizers while Factor II consisted of two paddy varieties (Inpari 30 and Inpari 32). Overall, the filled grains in the ACI treatments are significantly (P< 0.05) higher than those in the control treatments that used Normal Chemical Compound NPK. Overall, total weight per meter² (368-617g) in ACI treatments are also significantly (P< 0.05) higher than those (319-371g) in the control treatments. At harvesting time at 105 days after transplanting, significantly higher (P< 0.05) colony counts (13-15 x 10⁶ CFU/mL) (for ACI treatments), than those (8 x 10⁶ CFU/mL) in the controls positively indicated higher total yields of paddy grains per hectare. It was found that the application of ACI bio-organic and bio-chemical fertilizers and ACI N-Fixer can improve paddy yields of the two rice varieties, between 16.4-38.2% (up to 5.75 MT/ha), in the field trial plots at Langkat. These commercial fertilizers play an imperative role in refining the soil fertility and thereby can increase the yield of rice production. Therefore, it is highly recommended that ACI bio-organic and ACI bio-chemical fertilizers and ACI N-Fixer (N-Bio Booster) can be employed to increase the paddy yield in this region.

The excessive use of synthetic insecticides in modern agriculture has led to the contamination of the environment and the development of insect resistance. In this study, we evaluated the potential of tangerine (Citrus reticulata L.) peel essential oil (EO) as a natural insecticide against greenhouse whitefly (Trialeurodes vaporariorum W. (Homoptera: Aleyrodidae)), a common pest in greenhouse production. Petroleum ether (PET) and n-hexane (HEX) were used as solvents to extract essential oil (EO) from tangerine peels. The yield of EO was 1.59 % and 2.00 % (m/m) for PET and HEX, respectively. The insecticidal activity of EO was evaluated by measuring the mortality rate (MR) of greenhouse whiteflies at different time intervals. The results showed that PET and HEX extracts of tangerine EO effectively controlled the greenhouse whitefly. With both solvents, it was observed that with doses of 12.5% (v/v), similar results were achieved as with the positive control (corresponding to the commercial insecticide imidacloprid). Additionally, the FTIR analysis found that the EO contained d-limonene, which may be the source of its insecticidal properties. Therefore, tangerine peel essential oil is an excellent natural insecticide candidate for controlling greenhouse whiteflies effectively and sustainably.

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables to provide equipment state monitoring services and to generate decision-making for equipment operations. In this paper, we propose two machine learning models: 1) to forecast the amount of pentane (C5) content in the final product mixture; 2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach by using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models.

Within the Active Living and Well-Being Project (RRP3), funded by the Republic of Slovenia and the European Regional Development Fund Investing in Your Future programme, we aim to develop different approaches and prototypes to provide ICT solutions for the family in order to connect its members, communicate, promote quality family time, active life, a health-friendly lifestyle and well-being, and integrate various sensor and user-based data sources into a smart city ecosystem platform. A mixed methodology, combined qualitative and quantitative approaches, was selected to conduct a study. An online survey with a structured questionnaire as well as semi-structured interviews were performed. Through the analysis of the results we tried to establish a family-centred design approach that would be inclusive as much as possible, creating benefits for all generations in order to develop interactive prototypes that would allow us to further test and verify different use-case scenarios.

The fundamental question in the field of sensor research is new directions of scientific fields, which play a vital role in the progress of science and technology. This study confronts this question here by developing a bibliometric analysis, which endeavors to explain the evolution of sensor research and new technologies that are critical to science and society. The database of Scopus concerning scientific documents and patents is used for statistical and computational analyses in these topics. Results suggest that emerging technological trajectories in sensors are wireless sensor networks, wearable sensors and biosensors. Main characteristics of these growing research fields and technologies in sensors are described for fruitful implications of research and innovation policy directed to science advances and technological change in society.

The procedures for the extraction and separation of lipids and nutraceutics from microalgae using classic solvents have been used many times. However, these production methods usually require expensive and toxic solvents. Based on our studies involving the use of eco-sustainable methodologies and alternative solvents, we select ethanol (EtOH) and cyclopentyl methyl ether (CPME) for extracting bio-oil and lipids from algae. Different percentage of EtOH in CPME favors the production of an oil rich of SFA useful to production biofuel or rich of compounds bioactive. The proposed method for obtain a rich extract of saturated or unsaturated fatty acids from dry algal biomass is disclosed is eco-friendly and allows a good extraction yield. The method is compared both in extracted oil percentage yield and in extracted fatty acids selectivity to extraction by supercritical carbon dioxide.

Bio-economy is a major area of the strategy that must enable the European Union to achieve growth: smart, through the development of knowledge and innovation; and sustainable, based on a greener, more efficient economy in resource management. We believe that the progress of bio-economy cannot be achieved without the harnessing of intellectual capital. Our research aimed to emphasize the benefits of the dynamics of the intellectual capital growth on the evolution of the bio-economy. The aim of this analysis was to study the established link between the Energy Intensity of the Economy (EIE) and a number of factors that can measure the intellectual capital, such as: Market Capitalization of Bitcoin, Patent applications listed by European Patent Office and the Turnover from Innovation as a proportion of the total Turnover. The ultimate goal was represented by the generation of a regression model to see what factor influences mostly the progress of the bio-economy at European and Romanian level.

In this work a new pre-tuning multivariable PID controllers method for quadrotors is put forward. A procedure based on LQR/LQG theory is proposed for attitude and altitude control. With the aim of analyzing performance and robustness of the proposed method, a non-linear mathematical model of the DJI-F450 quadrotor is employed, where rotors dynamics, togheter with sensors drift/bias properties and noise characteristics of low-cost comercial sensors typically used in this type of applications (such as MARG with MEMS technology and LIDAR) are considered. In order to estimate the state vector and compensate bias/drift effects on rate gyros of the MARG, a combination of filtering and data fusion algorithms (Kalman filter and Madgwick algorithm for attitude estimation) are proposed and implemented. Performance and robutsness analysis of the control system is carried out by means of numerical simulations, which take into account the presence of uncertainty in the plant model and external disturbances. The obtained results show that the proposed pre-tuning method for multivariable PID controller is robust with respect to: a) parametric uncertainty in the plant model, b) disturbances acting at the plant input, c) sensors measurement and estimation errors.

Macromolecules with well-defined structures in terms of molar mass and monomer sequence became interesting building blocks for modern materials. The precision of the macromolecular structure makes fine-tuning of the properties of resulting materials possible. Conjugated macromolecules exhibit excellent optoelectronic properties that make them exceptional candidates for sensors construction. The importance of chain length and monomer sequence is particularly important in conjugated systems. The sequence lengths, monomer sequence and structural modification often influence the energy bang gap between HOMO and LUMO orbitals of the molecules that reflect in their properties. Moreover, the supramolecular aggregation that is often observed in oligo-conjugated systems is usually strongly affected by even minor structural changes that are used for sensor designs. This review summarizes the examples of well-defined conjugated macromolecules based on oligo(arylene ethynylene)s skeleton used for sensor applications. The sensing mechanisms and importance of uniformity of structure are discussed.

Pyrolysis has been applied in the human economy for many years, and it has become a significant alternative to the production of chemical compounds, including biofuels. The article focuses mostly on recent achievements in the technical and processing aspects of pyrolysis. This review provides an overview of the recent advanced pyrolysis technology used in gas, bio-oil, and biochar production. The key parameters to maximize the production of specific chemical compounds were discussed and considered during the construction of the reactors. The emphasis is put on optimizing the process parameters, technical requirements, and renewable energy use in the process and conception to improve the efficiency of product production. The application of pyrolysis gas, oil, and biochar as valuable chemical compounds are related to the intensifying effects of climate change, biofuel production, and waste management in accordance with the principles of sustainable development.

In order to design efficient and sustainable office spaces and to automate lighting, heating and air circulation in these facilities, solving the challenge of occupancy prediction is crucial. In office spaces where large areas need to be observed, multiple sensors must be used for full coverage. In these cases, it is normally important to keep the costs low, but also to make sure that the privacy of the people who use such environments are preserved. Low-cost and low-resolution heat (thermal) sensors can be very useful to build solutions that address these concerns. However, they are extremely sensitive to noise artifacts which might be caused by heat prints of the people who left the space or by other objects which are either using electricity or exposed to sunlight. There are some earlier solutions for occupancy prediction that employ low-resolution heat sensors, however, they have not addressed nor compensate for such heat artifacts. Therefore, in this paper, we present a low-cost and low-energy consuming smart space implementation to predict the number of people in the environment based on whether their activity is static or dynamic in time. We use a low-resolution (8×8) and non-intrusive heat sensor to collect data from an actual meeting room. We propose two novel workflows to predict the occupancy; one based on computer vision and one based on machine learning. Besides comparing the advantages and disadvantages of these different workflows, we use several state-of-the-art explainability methods in order to provide a detailed analysis of the algorithm parameters and how the image properties influence the resulting performance. Furthermore, we analyze noise resources which affect the heat sensor data. We hope that our analysis brings light into understanding how to handle very low-resolution heat images in these environments. The presented workflows could be used in various domains and applications other than smart offices, where occupancy prediction is essential, e.g., for elderly care.

The dependence on Unmanned Aerial Vehicles (UAVs) has dramatically increased in many sectors around the globe. UAVs are in high demand, and their technology is developing quickly due to their sophisticated ability to handle various issues. UAVs are capable of replacing labor-intensive tasks with conducive and safe regulation. Additional tools or sensors need to be added to the UAVs system to ensure the implementation of UAVs able to serve into industrial level. The paper aims to consolidate and present a thorough understanding of the various stages of image processing pipelines deployed in UAV applications, including image acquisition, preprocessing, feature extraction, object detection and tracking, and decision-making processes. Throughout this paper, several aspects were deliberate such as strengths, limitations, and performance metrics of existing approaches, this paper seeks to provide researchers, engineers, and practitioners with valuable insights into the challenges and opportunities of image processing systems for UAVs. Ultimately, the synthesis of this knowledge will contribute to enhancing the effective-ness, autonomy, and applicability of UAVs in diverse fields such as surveillance, agriculture, disaster management, and environmental monitoring.

Photoacoustic imaging is attracting a great deal of interest owing to its distinct advantages over other imaging techniques such as fluorescence or magnetic resonance image. Availability of photoacoustic probes for reactive oxygen and nitrogen species (ROS/RNS) could shed light on a plethora of biological processes mediated by these key intermediates. Tetramethylbenzidine (TMB) is a non-toxic and non-mutagenic colorless dye that develops a distinctive blue color upon oxidation. In this work we have investigated the potential of TMB as photoacoustic probe for ROS/RNS. Our results indicate that TMB reacts with hypochlorite, hydrogen peroxide, singlet oxygen and nitrogen dioxide to produce the blue oxidation product, while ROS such as the superoxide radical anion, sodium peroxide, hydroxyl radical or peroxynitrite yield a colorless oxidation product. TMB does not penetrate Escherichia coli cytoplasm but is capable of detecting singlet oxygen generated in its outer membrane.

We hypothesized possibility that inline registered reticulorumen pH can be as biomarker of cows reproduction and health status. Aim of this study was to evaluate the relationship of reticulorumen pH with biomarkers from automatic milking system (AMS) and some blood parameters and determinate reticulorumen pH as biomarker of cows reproduction and health status. According to cows reproductive status the cows were classified as belonging to the following four groups: 15-30 d. postpartum; 1-34 d. after insemination; 35 d. after insemination (non-pregnant); 35 d. after insemination (pregnant). According reticulorumen pH assay experimental animals were divided into four classes: 1) pH<6.22 (5.3% of cows), 2) pH - 6.22-6.42 (42.1% of cows), 3) pH - 6.42-6.62 (21.1% of cows), 4) pH >6.62 (10.5% of cows). Rumination time, body weight, milk yield, milk fat – protein ratio, milk lactose, milk somatic cell count (SCC), milk electrical conductivity of all quarters of udder were registered with the help of Lely Astronaut® A3 milking robots. The pH, temperature of the contents of cow reticulorumens and cow activity were measured using specific smaX-tec boluses. Blood gas parameters were analyzed using a blood gas analyzer (EPOC, Canada). We found that pregnant cows has higher reticulorumen pH during insemination time, comparing with non-pregnant. Cows with lower reticulorumen pH has lowest milk fat – protein ratio, and lactose concentration, and highest SCC. Cows with lowest reticulorumen pH has lowest blood pH. With increase reticulorumen pH, increases blood potasium and hematocrit, decreases CO2, saturation and sodium.

Wearable sensors have the potential to enable comprehensive patient characterization and optimized clinical intervention. Critical to realizing this vision is accurate estimation of biomechanical time-series in daily-life, including joint, segment, and muscle kinetics and kinematics, from wearable sensor data. The use of physical models for estimation of these quantities often requires many wearable devices making practical implementation more difficult. However, regression techniques may provide a viable alternative by allowing the use of a reduced number of sensors for estimating biomechanical time-series. Herein, we review 46 articles that used regression algorithms to estimate joint, segment, and muscle kinematics and kinetics. We present a high-level comparison of the many different techniques identified and discuss the implications of our findings concerning practical implementation and further improving estimation accuracy. In particular, we found that several studies report the incorporation of domain knowledge often yielded superior performance. Further, most models were trained on small datasets in which case nonparametric regression often performed best. No models were open-sourced, and most were subject-specific and not validated on impaired populations. Future research should focus on developing open-source algorithms using complementary physics-based and machine learning techniques that are validated in clinically impaired populations. This approach may further improve estimation performance and reduce barriers to clinical adoption.

Microbial growth has been of prime importance to the researchers in health and biotechnology industries. It has been known to be closely associated to the secretion of extracellular polymeric substances that help in the formation of colonies. Inter-microbial communication happens within such colonies by means of extracellular electron transfer mediated by the aforementioned polymeric substances. Conventionally, different phases of microbial growth are monitored with the aid of a traditional UV-Visible spectrophotometer by measuring the optical density of the liquid medium at 280 nm. In this paper, we have developed an alternative novel way to sense different growth phases employing electrochemical means i.e. two-terminal cyclic voltammetry. This cyclic voltammetry relies on the extracellular electron transfer mechanism taking place via the polymeric substances secreted by the microorganisms, measured by the temporal area changes in the current-voltage hysteresis curves in the inoculated nutrient broth. This work paves a new way to detect the biological activity in the medium, which can be directly correlated to the population of microorganisms. It would be of immense interest to scientists and researchers working in the field of microbiology as well as in development of biosensors, electrochemical sensors etc. which would be helpful in absence of traditional spectrophotometers.

In this study, effluent water was produced through Submerged Membrane Bio-Reactor (SMBR) process, which is a simple system and decomposes organic matter contained in wastewater with biological treatment process and performs solid-liquid separation, Especially, ozone oxidation treatment process is applied to effluent water containing fluorescent whitening agent, which is a trace pollutant which is not removed by biological treatment, and influences the quality of reused water. The concentration of COD in the SMBR was 449.3 mg/ℓ-COD, and the concentration of permeate water was 100.3 mg/ℓ-COD. The removal efficiency was about 70.1%. The amount of ozone re- quired for the removal of the fluorescent whitening agent in the permeated water in SMBR was 6.67 g-O3/min, and the amount of ozone required to remove COD relative to the permeate water was calculated to remove 0.997 mg-COD for 1 mg of O3.

In this paper, we investigate an animal-human cohabitation problem with the help of machine learning and fiber-wireless (FiWi) access networks integrating cloud and edge (fog) computing. We propose an early warning system which detects wild animals nearby road/rail with the help of wireless sensor networks and alerts passing vehicles of possible animal crossing. Additionally, we show that animals' detection at the earliest and the related processing, if possible, at sensors would reduce the energy consumption of edge devices and the end-to-end delay in notifying vehicles, as compared to the scenarios where raw sensed data needs to be transferred up the base stations or the cloud. At the same time, machine learning helps in classification of captured images at edge devices, and in predicting different time-varying traffic profiles-- distinguished by latency and bandwidth requirements-- at base stations, including animal appearance events at sensors, and allocating bandwidth in FiWi access networks accordingly. We compare three scenarios of processing data at sensor nodes, base stations and a hybrid case of processing sensed data at either sensors or at base stations, and showed that dynamic allocation of bandwidth in FiWi access networks and processing data at its origin leads to lowering the congestion of network traffic at base stations and reducing the average end-to-end delay.

Over the past decade, Deep Learning (DL) had been applied in a large number of optical sensors’ applications. DL algorithms can improve the accuracy and reduce the noise level in optical sensors. Optical sensors are considered as promise technology for the modern intelligent sensing platforms. These sensors are widely used to process monitoring, quality prediction, pollution, defense, security and many other applications. However, they suffer major challenges such as the large generated data and low processing speed for that data and moreover the much cost of these sensor. These challenges can be mitigated by integrating deep learning system with the optical sensor technologies. This paper presents recent studies that integrate DL algorithms with optical sensors applications. This paper also highlights several of DL algorithms directions that promise a considerable impact on use for optical sensor applications. Moreover, this study provides new directions for the future related research development.

Welding processes offer a unique capability with a wide range of applications in industries. In recent times, welding has established itself as a tool for large scale additive manufacturing. In general, the quality and repeatability assurance for welding and specifically for additive manufacturing necessitates integrating process monitoring techniques with existing welding and additive manufacturing processes. The process-specific signals such as welding current fluctuations, temperature, and acoustic, generated during the welding operations, make them a suitable candidate for digitization. This chapter comprehensively describes the process monitoring techniques relevant to welding and additive manufacturing. Firstly, various sensors used during welding are described for their construction and working. Subsequently, specific applications of the sensors in digitizing the welding processes are presented.

In the field of embedded systems, energy efficiency is a critical requirement, particularly for battery-powered devices. RISC-V processors have gained popularity due to their flexibility and open-source nature, making them an attractive choice for embedded applications. However, not all RISC-V processors are equally energy-efficient, and it is important to evaluate their performance in specific use cases. This paper evaluates the energy consumption and resource utilization of a new RISC-V processor, RisCO2, and four existing processors - Zero-riscy, Micro-riscy, Ri5cy, and CV32E40P - in a signal demodulation application for NDIR CO2 sensors. The processors were implemented in the PULPino SoC and synthesized using Vivado IDE. The processor named RisCO2 is based on the RV32E_Zfinx instruction set and was designed from scratch by the authors specifically for low-power signal processing applications such as signal demodulation in CO2 NDIR sensors. The other processors are Ri5cy, Micro-riscy, and Zero-riscy, developed by the PULP Platform team, and CV32E40P (derived from Ri5cy) from the OpenHW Group, all of them widely used in the RISC-V community. Our experiments showed that RisCO2 had the lowest energy consumption among the five processors, with a 53.5% reduction in energy consumption compared to CV32E40P and a 94.8% reduction compared to Micro-riscy. Additionally, RisCO2 had the lowest FPGA resource utilization compared to the best-performing processors, CV32E40P and Ri5cy, with a 46.1% and a 59% reduction in LUTs, respectively. Our findings suggest that RisCO2 is a highly energy-efficient RISC-V processor for NDIR CO2 sensors that require signal demodulation. The results also highlight the importance of evaluating processors in specific use cases to identify the most energy-efficient option. This paper provides valuable insights for designers of energy-efficient embedded systems using RISC-V processors.

Biosensors are essential tools which have been traditionally used to monitor environmental pollution, detect the presence of toxic elements and biohazardous bacteria or virus in organic matter and biomolecules for clinical diagnostics. In the last couple of decades, the scientific community has witnessed their widespread application in the fields of military, health care, industrial process control, environmental monitoring, food-quality control, and microbiology. Biosensor technology has greatly evolved from the in vitro studies based on the biosensing ability of organic beings to the highly sophisticated world of nanofabrication enabled miniaturized biosensors. The incorporation of nanotechnology in the vast field of biosensing has led to the development of novel sensors and sensing mechanisms, as well as an increase in the sensitivity and performance of the existing biosensors. Additionally, the nanoscale dimension further assists the development of sensors for rapid and simple detection in vivo as well as the ability to probe single-biomolecules and obtain critical information for their detection and analysis. However, the major drawbacks of this include, but are not limited to potential toxicities associated with the unavoidable release of nanoparticles into the environment, miniaturization induced unreliability, lack of automation, and difficulty of integrating the nanostructured-based biosensors as well as unreliable transduction signals from these devices. Although the field of biosensors is vast, we intend to explore various nanotechnology enabled biosensors as part of this review article and provide a brief description of their fundamental working principles and potential applications.

Recent advances in microscopy imaging and image analysis motivate more and more institutes world-wide to establish dedicated core-facilities for bio-image analysis. To maximize the benefits research groups at these institutes gain from their core-facilities, they should be established to fit well into their respective environment. In this article, we introduce common collaborator requests and corresponding potential services core-facilities can offer. We also discuss potential conflicts of interests between the targeted missions and implementations of services to guide decision makers and core-facility founders to circumvent common pitfalls.

Phononic crystals are periodic composite structures with specific resonant features that are gaining strength in the field as liquid sensors. The introduction of a structural defect in an otherwise periodic regular arrangement can generate a resonant mode, also called defect mode, inside the characteristic band gaps of phononic crystals. The morphology, as well as the frequency in which these defect modes appear, can give useful information on the composition and properties of an analyte. Currently, only gain, and frequency measurements are performed using phononic crystal sensors. Other measurements like the transient response have been implemented in resonant sensors such as quartz microbalances showing great results and proving to be a great complimentary measure to the gain and frequency measurements. In the present paper, a study of the feasibility of using the transient response as a measure to acquire additional information about the analyte is presented. Theoretical studies using the transmission line model were realized to show the impact of variations in the concentration of an analyte, in this case, lithium carbonate solutions, in the transient time of the system. Experimental realizations were also performed showing that the proposed measurement scheme presents significant changes in the resulting data, indicating the potential use of this measure in phononic crystal sensors. This proposed measure could be implemented as a stand-alone measure or as a compliment to current sensing modalities.

Efficient use of agricultural wastes and by-products, basically transforming waste materials into value added products, is considered as pivotal for an effective bioeconomy strategy for the rural development. To this scope, citrus waste management represents a major issue for citrus processors but, at the same time, it embodies a potentially unexploited resource for the rural sustainable development. This study focuses on analyzing the current management of citrus waste in South Italy and identifying the determinants and barriers that may affect an entrepreneur’s choice on the destination of citrus waste. Citrus processors’ preferences about the contract characteristics needed to take part in a co-investment scheme have been investigated. Both analyses are preliminary steps to design an innovative and sustainable citrus by-product supply chain. Results show that the distance between the citrus processors and the citrus by-products plant is one of the main criteria for choosing alternative valorization pathways. Moreover, the required guaranteed capital, the short duration of the contract, and low risk are contract scheme characteristics that improve entrepreneurs’ willingness to co-invest in the development of a citrus waste multifunctional plant.

In this paper, we propose an adaptive duty-cycled hybrid X-MAC (ADX-MAC) protocol for energy-efficient forest fire prediction. The X-MAC protocol acquires the additional environmental status collected by each forest fire monitoring sensor for a certain period. And, based on these values, the length of sleep interval of duty-cycle is changed to efficiently calculate the risk of occurrence of forest fire according to the mountain environment. The performance of the proposed ADX-MAC protocol was verified through experiments the proposed ADX-MAC protocol improves throughput by 19% and was more energy-efficient by 24% compared to X-MAC protocol. As the probability of forest fires increases, the length of the duty cycle is shortened, confirming that the forest fires are detected at a faster cycle.

The new climate law introduces a policy of sustainable construction, the assumption of which is the reduction of CO2 by the construction industry and the use of environmentally friendly materials, such as agricultural, mineral, and recycled waste, while limiting the consumption of natural resources. The article is a literature review that analyzes selected waste materials from various sectors of the economy that can be used as additives or partial substitutes for natural resources in the production of cement and in and cement building materials, the production of which reduces CO2 emissions, producing materials with high mechanical strength and environmentally friendly.

The utilisation of the industrial residual products to create new value-added materials and to reduce footprint is a modern challenge of science and industry. Development of the new multifunctional and bio-based composites is an excellent opportunity for complex utilisation of industrial residual products. The study describes the preparation and characterisation of the three-phases bio-based composites. The main components are bio-based binder made of peat, devulcanised crumb rubber (DCR) from used tires and part of the fly ash the cenosphere (CS). Three-phase composite prepared in the form of a block for investigation of the mechanical properties and density and a form of granules for determination of the water and oil products sorption was investigated. This work investigated the dependence of the properties on the main component DCR and CS fraction. Is found, that maximum compression strength (in block form) observed for composition without CS and DCR addition - 79.3 MPa, the second highest value of compression strength is 11.2 MPa for composition with 27.3 wt% of CS. For compositions with bio binder content from 17.4 to 55.8 wt% and with DCR contents in range from 11.0 to 62.0 wt% compression strength is in range 1.1 to 2.0 MPa. Liquid sorption analysis (water and diesel) showed that the maximum saturation of liquids in both cases is set after 35 minutes and ranges from 1.05 to 1.4 g·g -1 for water and 0.77 to 1.25 g·g-1 for diesel. It was noted that 90% of the maximum saturation with diesel fuel comes after 10 minutes and for water after 35 minutes.

We demonstrate a multi-purpose plasmonic sensor based on nanovoid array fabricated via inexpensive and highly reproducible direct femtosecond laser patterning of thin glass-supported Au films. The proposed nanovoid array exhibits near-IR surface plasmon (SP) resonances, which can be excited under normal incidence and optimised for specific application by tailoring array periodicity as well as nanovoid geometric shape. Fabricated SP sensor offers competitive sensitivity of about 1600 nm/RIU at a figure of merit of 12 in bulk refractive index tests, as well as allows for identification of gases and ultra-thin analyte layers making the sensor particularly useful for common bioassay experiments. Moreover, isolated nanovoids support strong electromagnetic field enhancement at lattice SP resonance wavelength, allowing for label-free molecular identification via surface-enhanced vibration spectroscopy.

Sensing Technology (ST) plays a key role in Structural Health Monitoring (SHM) systems. ST focuses on developing sensors, sensory systems or smart materials that monitor a wide variety of materials properties aiming to create smart structures and smart materials, using Embedded Sensors (ESs), and allowing continuous and permanent measurements of the structural integrity. The integration of ESs is limited to the processing technology to embed the sensor due to its high-temperature sensitivity and the possibility of damage during its insertion into the structure. In addition, the technological process selection is dependent on the base material composition, either metallic or composite parts. The selection of smart sensors or the technology underlying them is fundamental to the monitoring mode. This paper presents a critical review of the fundaments and applications of sensing technologies for SHM employing ESs, focusing on the actual developments and innovation of these, as well as analysing the challenges that these technologies present, to build a path that allows a connected world through distributed measurement systems.

Per- and polyfluoroalkyl substances (PFAS), a large group of developing contaminants, have recently become the subject of increased concern due to their potentially hazardous effects. They are classified as poisonous substances that can be found in a variety of aquatic situations. The widespread usage of PFAS across numerous industries has resulted in a high environmental and biological accumulation of the substance. Identification and elimination of PFAS from the environment is crucial since they are tenacious and have the potential to cause cancer. Traditional methods of PFAS content assessment, while useful in some cases, are often inadequate for continuing environmental control and monitoring. Within academia, there is a noticeable desire for rapid, cost-effective, durable, and readily transportable techniques targeted at detecting PFAS compounds in field settings. As a result, environmental labs and other governmental and non-governmental bodies may start testing more often as mandated by legislation. PFAS-detecting sensors, which offer an innovative solution that can be applied in situ and is affordable and simple to use, have emerged as a promising method for assessment based on the existing research. In addition, it may give administrators and users of water worldwide useful information they can use. This article provides a thorough summary of recent developments, limitations, and performance considerations in PFAS detection sensors. The Surface-enhanced Raman scattering (SERS) method has also been covered because of its excellent sensitivity and specificity for detecting pollutants, making it a potential solution for sensing PFAS. Intelligent sensing systems for PFAS detection should benefit from this research.

In recent years, several Ultrawideband (UWB) localization systems have already been proposed and evaluated for accurate position estimation of pedestrians. However, most of them are evaluated for a particular wearable sensor position; hence the accuracy obtained is subject to a given wearable sensor position. In this paper, we study the effects of body wearable sensor positions i.e., chest, arm, ankle, wrist, thigh, fore-head, hand, on the localization accuracy. The conclusion drawn is that the fore-head is the best, and the chest is the worst body sensor location for tracking a pedestrian. While the fore-head position is able to set an error lower than 0.35 m (90th percentile), the chest is able to set 4 m. The reason for such a contrast in the performance lies in the fact that in NLOS situations, the chest as an obstacle is larger in size and thickness than any other part of the human body, which the UWB signal needs to overcome to reach the target wearable sensor. And so, the large errors arise due to the signal arriving at the target wearable sensor from reflections of a nearby object or a wall in the environment.

A novel adaptive neural network-based fault-tolerant control scheme is proposed for six-degree freedom nonlinear helicopter dynamic. The proposed approach can detect and mitigate sensors' faults in real-time. An adaptive observer-based on neural network (NN) and extended Kalman filter (EKF) is designed, which incorporates the helicopter's dynamic model to detect faults in the navigation sensors. Based on the detected faults, an active fault-tolerant controller, including three loops of dynamic inversion, is designed to compensate for the occurred faults in real-time. The simulation results showed that the proposed approach is able to detect and mitigate different types of faults on the helicopter navigation sensors, and the helicopter tracks the desired trajectory without any interruption.

The authors of this paper use an original method of diatomaceous earth fractionation, which allows for obtaining a filler with a specific particle size distribution. The method makes it possible to separate small, disintegrated and broken diatom frustules from those which maintained their original form in diatomaceous earth. The study covers a range of tests conducted to prove that such a separated diatomic fraction shows features different from the base diatomite used as an epoxy resin filler. We have examined mechanical properties of a series of diatomite/resin composites considering the weight fraction of diatoms and the parameters of the composite production process. The studied composites of Epidian 601 epoxy resin cross-linked with amine-based curing agent Z-1 contained 0 to 70% vol. of diatoms or diatomaceous earth. Samples were produced by casting into silicone moulds in vacuum degassing conditions and, alternatively, without degassing. The results have shown that the size and morphology of the filler based on diatomaceous earth affects mechanical and rheological properties of systems based on epoxy resin.

The species and bio-availability of phosphorus (P) in primary, secondary and digested sludge were fractionated and further analyzed in this study. Results showed that inorganic P (IP) was the primary P fraction in the secondary sludge and digested sludge, in which non-apatite IP (NAIP) amounted to 91.6% and 69.3% of IP, respectively. Organic P (OP), accounting for about 71.7% of total P (TP), was the dominant P composition in primary sludge. The content of bio-available P was about 9.7, 43.4, 29.8 mg-P/g-TS in primary sludge, secondary sludge and digested sludge, respectively, suggesting secondary sludge is the optimal choice when land application of sewage sludge is taken into consideration, followed by digested sludge and primary sludge. Polyphosphate and orthophosphate, comprising approximately 54.3% and 89.2% of TP, was the dominant P species in the secondary sludge and digested sludge, respectively. Monoester-P (54.6% of TP in extract) and diester- P (24.1%) were identified as OP species in primary sludge by Phosphorus-31 nuclear magnetic resonance (31PNMR). The present results would be helpful for P recovery and recycle from sewage sludge in wastewater treatment plant.

Currently, large volumes of data are being collected on farms using multimodal sensor technol-ogies. These sensors measure the activity, housing conditions, feed intake, and health of farm animals. With traditional methods, the data from farm animals and their environment can be collected intermittently. However, with the advancement of wearable and non-invasive sensing tools, these measurements can be made in real-time for continuous quantitation relating to clinical biomarkers, resilience indicators, and behavioral predictors. The digital phenotyping of humans has drawn enormous attention recently due to its medical significance, but much research is still needed for the digital phenotyping of farm animals. Implications from human studies show great promise for the application of digital phenotyping technology in modern livestock farming, but these technologies must be directly applied to animals to understand their true capacities. Due to species-specific traits, certain technologies required to assess phenotypes need to be tailored ef-ficiently and accurately. Such devices allow for the collection of information that can better inform farmers on aspects of animal welfare and production that need improvement. By explicitly ad-dressing farm animals’ individual physiological and mental (affective states) needs, sensor-based digital phenotyping has the potential to serve as an effective intervention platform. Future re-search is warranted for the design and development of digital phenotyping technology platforms that create shared data standards, metrics, and repositories.

Background A body composition monitor (BCM) has a role not only in determining over-hydration (OH) but also as an aid to nutritional assessment. For dialysis patient-specific clinical applications of BCM, it is necessary to clarify the relationship between body composition parameters and OH in healthy Chinese individuals. Methods This cross-sectional study involved 314 healthy individuals with a mean age of 45.7±13.1 years. BCM measurements were performed while the subjects were fasting. Results The mean OH level was 0.379±0.81 L. Lean tissue index (LTI) and Lean tissue mass (LTM) were significantly higher in males (p<0.001), while fat tissue index (FTI) was significantly higher in females (p<0.001). In univariate correlation analysis, FTI, Fat, and ATM had a negative correlation with OH in females and all subjects (p<0.05), while LTM and BCM had a positive correlation in all subjects (p<0.05). There was a significant negative correlation between phase angle (PhA) and OH in males, females, and all subjects (r=-0.634, p<0.001; r=-0.666, p<0.001; r=-0.484, p<0.001, respectively). In multivariate linear regression analysis, PhA (b=-1.266, p<0.001), LTM (b=0.987, p<0.001), age (b=-0.307, p<0.001) were independent predictors of OH. Conclusions This study demonstrated that age, LTM and especially PhA, had important roles in predicting OH in healthy Chinese individuals. In the future, PhA may aid in clinical assessment by helping to titrate dry weight among hemodialysis patients with malnutrition.

3D object reconstruction from depth image streams using Kinect-style depth cameras has been extensively studied. In this paper, we propose an approach for accurate camera tracking and volumetric dense surface reconstruction assuming a known cuboid reference object is present in the scene. Our contribu¬tion is three-fold. (a) We maintain drift-free camera pose tracking by incorporating the 3D geometric constraints of the cuboid reference object into the image registration process. (b) We reformulate the problem of depth stream fusion as a binary classification problem, enabling high-fidelity surface reconstruction, especially in the con¬cave zones of objects. (c) We further present a surface denoising strategy to mitigate the topological inconsistency (e.g., holes and dangling triangles), which facilitates the generation of a noise-free triangle mesh. We extend our public dataset CU3D with several new image sequences, test our algorithm on these sequences and quantitatively compare them with other state-of-the-art algorithms. Both our dataset and our algorithm are available as open-source content at https://github.com/zhangxaochen/CuFusion for oth-er researchers to reproduce and verify our results.

Several studies have analyzed human gait data obtained from inertial gyroscope and accelerometer sensors mounted on different parts of the body. In this article, we take a step further in gait analysis and provide a methodology for predicting the movements of the missing parts of the legs. In particular, we propose a method, called GaIn, to control non-invasive, robotic, prosthetic legs. GaIn can infer the movements of both missing shanks and feet for humans suffering from double trans-femoral amputation using biologically inspired recurrent neural networks. Predictions are performed for casual walking related activities such as walking, taking stairs, and running based on thigh movement. In our experimental tests, GaIn achieved a 4.55 degree prediction error for shank movements on average. However, a patient's intention to stand up and sit down cannot be inferred from thigh movements. In fact, intention causes thigh movements while the shanks and feet remain roughly still. The GaIn system can be triggered by thigh muscle activities measured with electromyography (EMG) sensors to make robotic prosthetic legs perform standing up and sitting down actions. The GaIn system has low prediction latency and is fast and computationally inexpensive to be deployed on mobile platforms and portable devices.

: Microbial fuel cell (MFCs) devices utilise the metabolic processes of bacteria to generate electricity from various organic substrates. Due to the enormous amount of energy that organic waste produces, scientists are highly interested in advanced MFCs. MFCs serve as a multidisciplinary research platform at the engineering and natural sciences intersection. MFCs have various uses besides being used as energy sources, such as sensing capabilities. Despite showing considerable promise, only a few marine sediment MFCs have been deployed in real-world applications to supply current for low-power devices. It is now required to maintain track of the work being done by research groups worldwide and regularly compile significant discoveries due to the rising quantity of research publications. Review papers are a traditional beginning point for a literature review for new scholars. This review is a fast reckoner that directs readers to pertinent reviews and research publications detailing significant advances in microbial fuel cell research during the previous two decades. An overview of key advances in MFC research over the past two decades is provided as a quick reference in this review article. In addition, the report identifies research gaps that, if filled, could bring this technology closer to real-world use.

Immunoglobulin G (IgG), a type of antibody, represents approximately 75% of serum antibodies in humans, and is the most common type of antibody found in blood circulation Consequently, the development of simple, fast and reliable systems for IgG detection are of considerable interest which can be achieved using electrochemical sandwich-type immunosensors. In this study we have developed an immunosensor sub-strate using an inexpensive and very simple fabrication method based on ZnO nanorods obtained through the electrodeposition of ZnO. The ZnO nanorods were treated by electrodepositing a layer of reduced gra-phene oxide to ensure an easy immobilization of the antibodies. On this substrate, the sandwich configura-tion of the immunosensor was built through different incubation steps, that were all optimized. The im-munosensor is electrochemically active thanks to the presence of gold nanoparticles tagging the secondary antibody, therefore it has been used to measure the current density of the hydrogen development reaction which is indirectly linked to the concentration of H-IgG antigens. In this way the calibration curve was constructed obtaining a linear range of 1-100 ng / ml with a detection limit of few ng / mL and good sensi-tivity.

The impact of nanomaterials on lung fluids or on the plasma membrane of living cells has prompted researchers to examine the interactions between nanoparticles and lipid vesicles. Recent studies have shown that nanoparticle-lipid interaction leads to a broad range of structures including supported lipid bilayers (SLB), particles adsorbed at the surface or internalized inside vesicles, and mixed aggregates. Today, there is a need to have simple protocols that can readily assess the nature of structures obtained from particles and vesicles. Here we apply the method of continuous variation for measuring Job scattering plots and provide analytical expressions for the scattering intensity in various scenarios. The result that emerges from the comparison between modeling and experimental measurements is that electrostatics plays a key role in the association, but it is not sufficient to induce the formation of supported lipid bilayers.

It is widely accepted that plastic waste is one of our most urgent environmental concerns the world is currently facing. Plastic has contributed greatly to innovation in all fields, but the raw material for its production (fossil fuels), as well as the linear economy in which it is currently produced and used, makes the material problematic from a sustainability and human health perspectives. The emergence of bio-based plastics provides an opportunity to reduce dependency on fossil fuels and transition to a more circular plastics economy. For polyethylene terephthalate (PET), one of the most prevalent plastics in packaging and textiles, there exist two bio-based alternatives that are similar or superior in material property and recyclability. These are polyethylene furanoate (PEF) and polytrimethylene terephthalate (PTT). The overarching aim of this study was to examine the transition from fossil-based to renewable plastics, through the lens of PET upcycling into PEF and PTT. The process for the production of PEF and PTT from three feed streams (post-consumer PET waste, lignocellulosic biomass-derived cellulose and biodiesel-derived crude glycerol) was developed in the software SuperPro Designer and the economic viability assessed via a discounted cumulative cash flow (DCCF) analysis. This study represents a conceptual case study of the valorisation of existing plastic waste into new, recyclable bio-based plastics, for a more sustainable plastics production. A techno-economic analysis of the designed process revealed that the minimum selling price (MSP) of second generation-derived PEF and PTT is 3.13 USD/kg, and that utilities and the feedstock used for the production of 2,5-furandicarboxylic acid (FDCA) needed in PEF synthesis contributed the most to the process operating costs. The effect of recycling PEF and PTT through the process at three recycling rates (42%, 50% and 55%) was investigated and revealed that increased recycling could reduce the MSP of the 2G bio-plastics (by 48.5%) to 1.61 USD/kg. This demonstrates that increasing recycling rates would have a beneficial effect on the economic viability of the plastic biorefinery.

Smart devices such as smartphones, smartwatches, etc. are promising platforms that are being used for automatic recognition of human activities. However, it is difficult to accurately monitor complex human activities due to inter-class pattern similarity, which occurs when different human activities exhibit similar signal patterns or characteristics. Current smartphone-based recognition systems depend on the traditional sensors such as accelerometer and gyroscope, which are inbuilt in these devices. Therefore, apart from using information from the traditional sensors, these systems lack contextual information to support automatic activity recognition. In this article, we explore environment contexts such as illumination(light conditions) and noise level to support sensory data obtained from the traditional sensors using a hybrid of Convolutional Neural Networks and Long Short Time Memory(CNN_LSTM) learning models. The models performed sensor fusion by augmenting the low-level sensor signals with rich contextual data to improve recognition and generalisation ability of the proposed solution. Two sets of experiments were performed to validate the proposed solution. The first set of experiments used inertial sensing data whilst the second set of extensive experiments combined inertial signals with contextual information from environment sensing data. Obtained results demonstrate that contextual information such as environment noise level and illumination using hybrid deep learning models achieved better recognition accuracy than the traditional activity recognition models without contextual information.

— In a smart healthcare system," Human Activity Recognition (HAR)" is considered as an efficient approach in pervasive computing from activity sensor readings. The "Ambient Assisted Living (AAL)" in the home or community helps the people to provide independent care and enhanced living quality. However, many AAL models are restricted to multiple factors that include both the computational cost and system complexity. Moreover, the HAR concept has more relevance because of its applications, such as content-based video search, sports play analysis, crowd behavior prediction systems, patient monitoring systems, and surveillance systems. This paper attempts to implement the HAR system using a popular deep learning algorithm, namely "Recurrent Neural Network (RNN)" with the activity data collected from smart activity sensors over time, and it is publicly available in the "UC Irvine Machine Learning Repository (UCI)". The proposed model involves three processes: (1) data collection, (b) optimal feature learning, and (c) activity recognition. The data gathered from the benchmark repository was initially subjected to optimal feature selection that helped to select the most significant features. The proposed optimal feature selection method is based on a new meta-heuristic algorithm called "Colliding Bodies Optimization (CBO)". An objective function derived from the recognition accuracy has been used for accomplishing the optimal feature selection. The proposed model on the concerned benchmark dataset outperformed the conventional models with enhanced performance.

A micro Sagnac interferometer integration is proposed for electron cloud distributed sensors. The Sagnac interferometer consists of four microring probes integrated into a Sagnac loop. Each of the microring probes is embedded with the silver bars to form the plasmonic wave oscillation. At the center microrings, electrons are trapped and oscillated by the whispering gallery modes (WGMs), where the plasmonic antennas are established and applied for wireless fidelity (WiFi) and light fidelity (LiFi) transmissions for distributed sensors. The antenna gains are 2.59dB, 0.93dB, 1.75dB, and 1.16dB respectively for the four antennas formed at the center microrings. The polarized light of 1.50µm wavelength is fed into the interferometer input, which is polarized randomly into upstream and downstream directions. The polarization components can be obtained by the space-time modulation control. By controlling the electron cloud spin orientation, the space-time projection can be applied, and the ultra-high measurement resolution can be obtained in terms of fast switching time (change in phase). In manipulation, the applied stimuli are substituted by the change in input source power. The light input power variation causes a change in electron cloud density. Similarly, when the electron cloud is excited by the microscopic medium, which can be employed as the microscopic sensors. The WGM sensors have sensitivities of 1.35µm-2, 0.90µm-2, 0.97µm-2 and, 0.81µm-2, respectively. The WGMs behave as a four-point probe for the electron cloud distributed sensors, where the electron cloud sensitivities of 2.31 prads-1mm3 (electrons)-1, 2.27prads-1mm3 (electrons)-1, 2.22 prads-1mm3(electrons)-1, 2.38prads-1mm3(electrons)-1 are respectively obtained.

Environmental monitoring is an important topic and serves a vibrant scientific role by revealing long-term trends that can lead to new knowledge and understanding. Globally, there is active development of photonic sensors incorporating multidisciplinary research. The ultimate objective is to develop small, low-cost, sensitive, selective, quick, durable, remote-controllable sensors that are resistant to electromagnetic interference. Different photonic sensor designs and advances in photonic frameworks have shown the possibility to realize these capabilities. In this review paper, the latest developments in the field of optical waveguide and fiber-based sensors which can serve for environmental monitoring are discussed. Several important topics such as toxic gas, water quality, indoor environment, and natural disaster monitoring are reviewed.

Natural astaxanthin is a precious substance obtained from some organisms such as microalgae. This plant has many benefits for humans, so research into its cost-effective and economical production has recently increased. For this purpose, some methods such as the use of different culture media, gene engineering, different stresses, nanoparticles, bio acids, and phytohormones are important. Accordingly, this review study was conducted to demonstrate the effect of the factors mentioned above for the high production of astaxanthin in microalgae, especially Haematococcus pluvialis (H.p).

Carbon nanotubes have been attracting considerable interest among material scientists, physicists, chemists and engineers for almost 30 years. Owing to their high aspect ratio, coupled with remarkable mechanical, electronic and thermal properties, carbon nanotubes have found application in diverse fields. In this review, we will cover the work on carbon nanotubes used for sensing applications. In particular, we will see examples where carbon nanotubes act as main players in devices sensing biomolecules, gas, light or pressure changes. Furthermore, we will discuss how to improve the performance of carbon nanotube-based sensors after proper modification.

The article reviews the current status of label-free integrated optical sensors focusing on the evolution over the years of their analytical performance. At first a short introduction to the evanescent wave optics is provided followed by detailed description of the main categories of label-free optical sensors including sensors based on surface plasmon resonance (SPR), grating coupler, photonic crystals, ring resonators and interferometric transducers. After a short reference to SPR techniques, including localized SPR, integrated optical sensors i.e., grating-couplers, interferometers, photonic crystals, microring resonators are reviewed. For each type of sensor, the detection principle is first provided followed by description of the different transducer configurations so far developed and their performance as biosensors. Finally, a short discussion about the current limitations and future perspectives of integrated label-free biosensors optical sensors is provided

The aerospace industry demands high-performance materials that can withstand extreme conditions and maintain efficiency in a variety of applications. Bio-inspired polymer coatings have emerged as a promising approach to meet these demands, by drawing inspiration from natural systems to develop new coatings that exhibit enhanced properties, such as self-cleaning, anti-icing, thermal management, and corrosion resistance. In this review, recent developments in the field of bio-inspired polymer coatings for aerospace applications are presented, covering a range of coatings and their respective properties. We discuss the recent developments and applications of bio-inspired coatings, with a focus on their advantages and challenges in aerospace applications. We also highlight the potential for future research and development in this field, including the integration of advanced technologies such as nanotechnology and additive manufacturing. The review aims to provide insights into the current state-of-the-art of bio-inspired polymer coatings for aerospace applications and to inspire further research in this rapidly growing field.

The reliable online analysis of volatile compounds in exhaled breath remains a challenge as a plethora of molecules occur in different concentration ranges (i.e. ppt to %), and need to be detected against an extremely complex background matrix. While this complexity is commonly addressed by hyphenating a specific analytical technique with appropriate preconcentration and/or preseparation strategies prior to detection, we herein propose the combination of three analytical tools based on truly orthogonal measurement principles as an alternative solution: field-asymmetric ion mobility spectrometry (FAIMS), Fourier-transform infrared (FTIR) spectroscopy-based sensors utilizing substrate-integrated hollow waveguides (iHWG), and luminescence sensing (LS). These three tools have been integrated into a single compact analytical platform suitable for online exhaled breath analysis. The analytical performance of this prototype system was tested via artificial breath samples containing nitrogen (N2), oxygen (O2), carbon dioxide (CO2) and acetone as a model volatile organic compound (VOC) commonly present and detected in breath. Functionality of the combined system was demonstrated by detecting these analytes in their respectively breath-relevant concentration range and mutually independent of each other generating orthogonal yet correlated analytical signals. Finally, adaptation of the system towards the analysis of real breath samples during future studies is discussed.

The application of movement-detection sensors is crucial for understanding surface movement and tectonic activities. The development of modern sensors has been instrumental in earthquake monitoring, prediction, early warning, emergency commanding and communication, search and rescue, and life detection. There are numerous sensors currently being utilized in earthquake engineering and science. It is essential to review their mechanisms and working principles thoroughly. Hence, we have attempted to review the development and application of these sensors by classifying them based on the timeline of earthquakes, physical or chemical mechanisms of sensors, and location of sensor platforms. In this study, we analyzed all available sensor platforms that have been widely used in recent years, with satellites and UAVs being among the most used. The findings of our study will be useful for future earthquake response and relief efforts, as well as research aimed at reducing earthquake disaster risks.

Campylobacter infection affects more than 200,000 people every year in Europe and in the last four years trend shows an increase of campylobacteriosis. Main vehicle for transmission of the bacterium is contaminated food like meat, milk, fruit and vegetables. In this study, the aim was to find characteristic VOCs of C. jejuni in order to detect its presence with an array of MOX gas sensors. Using a starting concentration of 10³ CFU/mL, VOCs were analyzed using GC-MS with SPME technique at the initial time (T0) and after 20 hours (T20). It has been found that Campylobacter sample at T20 is characterized by a higher number of alcohol compounds that the one at T0 and this is due to sugar fermentation. Sensors results showed the ability of the system to follow bacteria curve growth from T0 to T20 using PCA. In particular, this results in a decrease of ΔR/R₀ value over time. For this reason, MOX sensors are a promising technology for the development of a rapid and sensitive system for C. jejuni.

The aim of this study was to investigate the effect of condensed tannin (CTs) with differing molecular weight on their capacity to modify the fatty acid profile in milk. Twenty multiparous crossbred lactating dairy goats were assigned in a randomized complete block design (RCBD), and were subjected to receive the dietary treatments as followings; T1: control (with no CTs supplementation), T2: supplemented with mangosteen peel in a concentrate as a source of low molecular weight CTs at level of 3.0 %DM of CTs equivalent, T3: supplemented with the same diet with T2 but added with polyethylene glycol (PEG, as tannin inactivator) as the control of T2, and T4: supplemented with quebracho CT extract (UNITAN ATO, Buenos Aires, Argentina; 75-77 % tannins) in a concentrate as a source of high molecular weight CTs at level of 3.0 %DM of CTs equivalent, and T5) supplemented with the same diet with T4 but added with PEG as the control of T4. No significant change was detected for feed intake and nutrient digestibility indicate that CTs at level of 3.0 %DM of diet did not showed the detrimental effect to feed intake and nutrient digestibility, however, ruminal fermentation parameters and milk yield and milk compositions did not affected by different source of CT inclusion.

Egyptian mongoose (Herpestes ichneumon) is a medium-size carnivore that in Europe is restricted to Iberia. The bio-ecology of this species remains to be elucidated in several dimensions, including gut microbiota that is nowadays recognized as a fundamental component of mammals. In this work, we investigated the gut microbiota of this herpestid by single-molecule real-time sequencing of twenty paired male (n=10) and female (n=10) intestinal samples. This culture-independent approach enabled microbial profiling based on 16S rDNA and investigation of taxonomical and functional features. The core gut microbiome of the adult subpopulation was dominated by Firmicutes, Fusobacteria, Actinobacteria, and Proteobacteria. Eight genera were uniquely found in adults and five in non-adults. When comparing gut bacterial communities across sex, four genera were exclusive of females and six uniquely found in males. Despite these compositional distinctions, alpha- and beta-diversity analyses showed no statistically significant differences across sex or between adult and non-adult specimens. However, males presented a significantly higher abundance of amino acid and citrate cycle metabolic pathways, compared to the significant overrepresentation in females of galactose’ metabolic pathways. Adults showed a significantly higher abundance of cationic antimicrobial peptide resistance pathways, while non-adults bared a significant overrepresentation of two-component systems associated with antibiotic synthesis, flagellin production and chemotaxis control. This study adds new insights into mongoose bio-ecology palette, highlighting taxonomical and functional microbiome dissimilarities across sex and age classes, possibly related to primary production resources and life-history traits that impact on behavior, diet and gut ecosystem.

Carboxylic acids such as acetic acid and propionic acid have been investigated as representative components for fast pyrolysis (FP) bio-oil upgrading. Selective catalytic conversion of carboxylic acids can enhance bio-refinery processing economics through catalyst preservation and process intensification. Various metal-doped molybdenum carbide bead catalysts have been synthesized and developed in this work. Our aim is to enable selective conversion of carboxylic acids. In the case of acetic acid conversion, calcium doped Mo₂C beads offer the highest yield of acetone ~96% at 450 °C among undoped and Ca or Ni doped catalysts. By comparing hot gas filter with and without Ca-Mo₂C catalyst tested with real FP vapors, the former showed a 36.7% reduction of acetic acid, a 37.5% reduction of small ketones in aqueous phase, and a ~50% reduction of methoxies (methoxy phenols and methoxy aromatics) in organic phase. The conversion resulted in the formation of more long chain chemicals in the organic phase, which are more amendable for downstream upgrading.

Highly sensitive multicomponent materials designed for the recognition of hazardous compounds request control over interfacial chemistry. The latter is key parameter in the construction of the sensing (macro)molecular architectures. In this work, multi-walled carbon nanotubes (in short, CNTs) were deposited on diazonium-modified flexible ITO electrodes prior to electropolymerization of pyrrole. This three step process, including diazonium electroreduction, deposition of CNTs and electropolymerization, provided adhesively bonded polypyrrole-wrapped CNT composite coatings on aminophenyl-modified flexible ITO sheets. The aminophenyl (AP) groups were attached to ITO by electroreduction of the in situ generated aminobenzenediazonium compound in aqueous, acidic medium. For the first time, polypyrrole (PPy) was electrodeposited in the presence of both benzenesulfonic acid (dopant) and ethylene glycol-bis(2-aminoethylether)-tetraacetic acid (EGTA) which acts as a chelator. The flexible electrodes were characterized by XPS, Raman and scanning electron microscopy (SEM) which provided strong supporting evidence for the wrapping of CNTs by the electrodeposited PPy. Indeed, the CNT average diameter increased from 182.6 nm to 274.8, 35.65.9 and 17520.1 after 1, 5 and 10 of electropolymerization of pyrrole. The PPy/CNT/NH2-ITO films generated by this strategy exhibit significantly improved stability and higher conductivity compared to a similar PPy coating without any embedded CNTs as assessed by from electrochemical impedance spectroscopy measurements. The potentiometric response was linear in the 10-83×10-7 mol.L-1 Pb(II) concentration range and the detection limit was 2.9×10-9 mol.L-1 at S/N=3. The EGTA was found to drastically improve selectivity for Pb(II) over Cu(II). To account for this improvement, density functional theory (DFT) was employed to calculate the EGTA-metal ion interaction energy which was found to be -374.6 and -116.4 kJ/mol for Pb(II) and Cu(II), respectively, considering solvation effects. This work demonstrates the power of a subtle combination of diazonium coupling agent, CNTs, chelators and conductive polymers to design high-performance electrochemical sensors for environmental applications.

Burning woody biomass for energy is gaining attention due to environmental issues associated with fossil fuels and carbon emission. The carbon released from burning wood is absorbed by plants and is carbon neutral. The purpose of this study was to investigate the combustion characteristics (heat calorific values and ash contents) of three timbers: Araucaria cunninghamii, Instia bijuga and Pometia pinnata and recommend for fuelwood. The test samples were sawdust particles (treatment) and solid woods (control) extracted from the heartwoods. The sawdust particles were oven-dried, sieved and pelletized into pellets using a hand-held pelletizing device, thus, forming cylindrical dimension (volume 1178.57 mm3, oven dry density 0.0008 g/mm3). While the solid woods were cubed and oven-dried (volume 1000.00 mm3, oven dry density 0.001 g/mm3). Prior to combustion in a semi-automatic bomb calorimeter, 90 test specimens (15 replicates per treatment and control per species) were conditioned to 14 % moisture content (at temperature 105 ºC) and weighed to a constant (unit) mass (1.0 g). The heat energy outputs and ash residues (of treatments) were analyzed statistically. The results indicated variability in heat energy outputs and ash residues between test specimens of the three species. Comparatively, the treatment specimens of A. cunninghamii produced higher calorific value (18.546 kJ/g) than the control (18.376 kJ/g) whilst the treatment specimens of I. bijuga and P. pinnata generated lower heat calorific values (17.124 kJ/g and 18.822 kJ/g) than the control (18.415 kJ/g and 20.659 kJ/g), respectively. According to ash content analysis, A. cunninghamii generated higher residues (6.3%) followed by P. pinnata (4.5%) and I. bijuga (2.8%). The treatment specimens of the three species could not meet the standard heat energy requirement (20.0 kJ/g) and thus, were unsuitable for fuelwood. However, the control specimens of P. pinnata generated equivalent heat energy (20.659 kJ/g) and could be a potential fuelwood.