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Air pollution in urban areas is a huge concern that demands an efficient air quality control to ensure health quality standards. The hotspots can be located by increasing spatial distribution of ambient air quality monitoring for which the low-cost sensors can be used. However, it is well-known that many factors influence their results. For low-cost Particulate Matter (PM) sensors, high relative humidity can have a significant impact on data quality. In order to eliminate or reduce the impact of high relative humidity on the results obtained from low-cost PM sensors, a low-cost dryer was developed and its effectiveness was investigated. For this purpose, a test chamber was designed, and low-cost PM sensors as well as professional reference devices were installed. A vaporizer regulated the humid conditions in the test chamber. The low-cost dryer heated the sample air with a manually adjustable intensity depending on the voltage. Different voltages were tested to find the optimum one with least energy consumption and maximum drying efficiency. The low-cost PM sensors with and without the low-cost dryer were compared. The experimental results verified that using the low-cost dryer reduced the influence of relative humidity on the low-cost PM sensor results., Bundesministerium für Bildung und Forschung

The use of lead in manufacturing has decreased significantly over the last few decades. However, previous widespread use of lead-containing products and their incorrect disposal has resulted in environmental contamination. Accumulation of harmful quantities of lead pose a threat to all living organisms, through inhalation, ingestion, or direct contact, resulting in lead poisoning. This study utilized synthetic biology principles to develop plasmid-based whole-cell bacterial biosensors for detection of lead. The genetic element of the lead biosensor construct consists of pbrR, which encodes the regulatory protein, together with its divergent promoter region and a promoterless gfp. GFP expression is controlled by PbrR in response to the presence of lead. The lead biosensor genetic element was cloned onto a low-copy number broad host range plasmid, which can stably exist in a range of laboratory and environmental isolates, including Pseudomonas, Shewanella, and Enterobacter. The biosensors constructed were found to be sensitive, rapid, and specific and could, as such, serve as monitoring tools for lead-contaminated water.

Designing energy-efficient cognitive radio sensor networks is important to intelligently use battery energy and to maximize the sensor network life. In this paper, the problem of determining the power allocation that maximizes the energy-efficiency of cognitive radio-based wireless sensor networks is formed as a constrained optimization problem, where the objective function is the ratio of network throughput and the network power. The proposed constrained optimization problem belongs to a class of nonlinear fractional programming problems. Charnes-Cooper Transformation is used to transform the nonlinear fractional problem into an equivalent concave optimization problem. The structure of the power allocation policy for the transformed concave problem is found to be of a water-filling type. The problem is also transformed into a parametric form for which a ε-optimal iterative solution exists. The convergence of the iterative algorithms is proven, and numerical solutions are presented. The iterative solutions are compared with the optimal solution obtained from the transformed concave problem, and the effects of different system parameters (interference threshold level, the number of primary users and secondary sensor nodes) on the performance of the proposed algorithms are investigated.

Actigraphs for personalized health and fitness monitoring is a trending niche market and fit aptly in the Internet of Medical Things (IoMT) paradigm. Conventionally, actigraphy is acquired and digitized using standard low pass filtering and quantization techniques. High sampling frequencies and quantization resolution of various actigraphs can lead to memory leakage and unwanted battery usage. Our systematic investigation on different types of actigraphy signals yields that lower levels of quantization are sufficient for acquiring and storing vital movement information while ensuring an increase in SNR, higher space savings, and in faster time. The objective of this study is to propose a low-level signal encoding method which could improve data acquisition and storage in actigraphs, as well as enhance signal clarity for pattern classification. To further verify this study, we have used a machine learning approach which suggests that signal encoding also improves pattern recognition accuracy. Our experiments indicate that signal encoding at the source results in an increase in SNR (signal-to-noise ratio) by at least 50&ndash, 90%, coupled with a bit rate reduction by 50&ndash, 80%, and an overall space savings in the range of 68&ndash, 92%, depending on the type of actigraph and application used in our study. Consistent improvements by lowering the quantization factor also indicates that a 3-bit encoding of actigraphy data retains most prominent movement information, and also results in an increase of the pattern recognition accuracy by at least 10%.

Surface Electromyography (sEMG) is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems are: (1) they are not able to provide real-time monitoring, (2) they suffer from long processing time and low speed, (3) they are not effective for wireless healthcare systems because they consume huge power. In this work, we present an analog-based Compressed Sensing (CS) architecture, which consists of three novel algorithms for design and implementation of wearable wireless sEMG bio-sensor. At the transmitter side, two new algorithms are presented in order to apply the analog-CS theory before Analog to Digital Converter (ADC). At the receiver side, a robust reconstruction algorithm based on a combination of ℓ1-ℓ1-optimization and Block Sparse Bayesian Learning (BSBL) framework is presented to reconstruct the original bio-signals from the compressed bio-signals. The proposed architecture allows reducing the sampling rate to 25% of Nyquist Rate (NR). In addition, the proposed architecture reduces the power consumption to 40%, Percentage Residual Difference (PRD) to 24%, Root Mean Squared Error (RMSE) to 2%, and the computation time from 22 s to 9.01 s, which provide good background for establishing wearable wireless healthcare systems. The proposed architecture achieves robust performance in low Signal-to-Noise Ratio (SNR) for the reconstruction process.

This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC) between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs) based on Canonical Correlation Analysis (CCA) to recognize 40 targets of steady-state visual evoked potential (SSVEP), providing an accuracy (ACC) of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly (p < 0.01) improved for most of the subjects (ACC ¿ 74.79%), when compared with other BCIs based on Common Spatial Pattern, Filter Bank - Common Spatial Pattern, and Riemannian Geometry., This research was partially supported by the Spanish Government, Ministry of Economy and Competitiveness (grants NeuroMOD, DPI2015-68664-C4-1-R, and ESSENTIAL, DPI2015-72638-EXP). Authors would like to thank CNPq (304192/2016-3), CAPES (88887.095626/2015-01), and FAPES (72982608) from Brazil, Emerging Leaders in the America’s Program (ELAP, Canada), and SENESCYT (Ecuador) for supporting this research

This paper presents the design, modeling and control of a fully actuated aerial robot for infrastructure contact inspection as well as its localization system. Health assessment of transport infrastructure involves measurements with sensors in contact with the bridge and tunnel surfaces and the installation of monitoring sensing devices at specific points. The design of the aerial robot presented in the paper includes a 3DoF lightweight arm with a sensorized passive joint which can measure the contact force to regulate the force applied with the sensor on the structure. The aerial platform has been designed with tilted propellers to be fully actuated, achieving independent attitude and position control. It also mounts a &ldquo, docking gear&rdquo, to establish full contact with the infrastructure during the inspection, minimizing the measurement errors derived from the motion of the aerial platform and allowing full contact with the surface regardless of its condition (smooth, rough, ...). The localization system of the aerial robot uses multi-sensor fusion of the measurements of a topographic laser sensor on the ground and a tracking camera and inertial sensors on-board the aerial robot, to be able to fly under the bridge deck or close to the bridge pillars where GNSS satellite signals are not available. The paper also presents the modeling and control of the aerial robot. Validation experiments of the localization system and the control system, and with the aerial robot inspecting a real bridge are also included.

PTDC/QEQ- QFI/1971/2014 POCI- 01-0145-FEDER-016387 Different alkali deep eutectic solvents (DES), such as LiI:nEG, NaI:nEG, and KI:nEG, have been tested as electrolytes for dye sensitized solar cells (DSSCs). These DSSCs were prepared using pure DES or, alternatively, DES combined with different amounts of iodine (I2). The most important parameters, such as open circuit voltage (VOC), short circuit current density (JSC), fill factor (FF), and the overall conversion efficiency (η), were evaluated. Some DES seem to be promising candidates for DSSC applications, since they present higher VOC (up to 140 mV), similar FF values but less current density values, when compared with a reference electrolyte in the same experimental conditions. Additionally, electrochemical impedance spectroscopy (EIS) has been performed to elucidate the charge transfer and transport processes that occur in DSSCs. The values of different resistance (Ω·cm2) phenomena and recombination/relaxation time (s) for each process have been calculated. The best-performance was obtained for DES-based electrolyte, KI:EG (containing 0.5 mol% I2) showing an efficiency of 2.3%. The efficiency of this DES-based electrolyte is comparable to other literature systems, but the device stability is higher (only after seven months the performance of the device drop to 60%). publishersversion published

Heart failure (HF) is a major health care issue, and the incidence of HF is only expected to grow further. Due to the frequent hospitalizations, HF places a major burden on the available hospital and healthcare resources. In the future, HF care should not only be organized solely at the clinical ward and outpatient clinics, but remote monitoring strategies are urgently needed to guide, monitor, and treat chronic HF patients remotely from their homes as well. The intuitiveness and relatively low costs of non-invasive remote monitoring tools make them an appealing and emerging concept for developing new medical apps and devices. The recent COVID-19 pandemic and the associated transition of patient care outside the hospital will boost the development of remote monitoring tools, and many strategies will be reinvented with modern tools. However, it is important to look carefully at the inconsistencies that have been reported in non-invasive remote monitoring effectiveness. With this review, we provide an up-to-date overview of the available evidence on non-invasive remote monitoring in chronic HF patients and provide future perspectives that may significantly benefit the broader group of HF patients.

Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: 76.37% and 74.03% for V-A on CASE, and 70.29% and 68.15% for V-A on MERCA. Our findings show: (1) instance segment lengths between 1&ndash, 4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (&le, 64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance.

The removal of Anit-Inflammatory drugs, namely; Acetaminophen (ACTP), from wastewater by bulk liquid membrane (BLM) process using Aliquat 336 (QCl) as a carrier was investigated. The effects of several parameters on the extraction efficiency were studied in this research, such as the initial feed phase concentration (10-50) ppm of ACTP, stripping phase (NaCl) concentration (0.3,0.5,0.7 M), temperature (30-50oC), the volume ratio of feed phase to membrane phase (200-400ml/80ml), agitation speed of the feed phase (75-125 rpm), membrane stirring speed (0, 100, 150 rpm), carrier concentration (1, 5, 9 wt%), the pH of feed (2, 4, 6, 8, 10), and solvent type (CCl4 and n-Heptane). The study shows that high extraction efficiency for ACTP of about 97% was achieved by a bulk liquid membrane at 50 ppm initial concentration of feed; stirring speed of feed phase 130 rpm; stirring speed of membrane phase 100rpm; 0.5 M NaCl concentration; carrier concentration 1wt%; volume ratio of 200ml feed:80ml membrane; feed pH of ACTP is 6, and 50˚C. The transport kinetics was evaluated using a kinetic model with two consecutive first-order irreversible reactions. The kinetics of (ACTP) transport by bulk liquid membrane was investigated at the best experimental conditions. The activation energy values of the extraction and stripping processes were 1.733 and 1.826 kJ.mol−1. The activation energy confirms that the transport process from solutions is controlled by diffusion.

The moisture sorption isotherms of Mefenamic acid tablets were investigated by measuring the experimental equilibrium moisture content (EMC) using the static method of saturated salt solutions at three temperatures (25, 35, and 45°C) and water activity range from 0.056 to 0.8434. The results showed that EMC increased when relative humidity increased and the sorption capacity decreased, the tablets became less hygroscopic and more stable when the temperature increased at constant water activity. The sorption curves had a sigmoid shape, type II according to Brunauer’s classification. The hysteresis effect was significant along with the whole sorption process. The results were fitted to three models: Oswin, Smith, and Guggenhein - Anderson and de Boer. According to the fitting results, the GAB model was the most appropriate model to describe the sorption behavior of Mefenamic acid; it had a regression coefficient range (0.9803-0.994), %E (0.69-4.06), and low values of SEE (0.85-2.2). The monolayer moisture content was calculated using the GAB model and it was concluded that the tablets should be stored at moisture content equal or slightly higher than (0.2046, 0.1843, and 0.1437 %) for desorption and (0.2073, 0.1269, and 0.1452 %) for adsorption for the three temperatures.

This research investigates the use of scale-space theory to detect individual trees in orchards from very-high resolution (VHR) satellite images. Trees are characterized by blobs, for example, bell-shaped surfaces. Their modeling requires the identification of local maxima in Gaussian scale space, whereas location of the maxima in the scale direction provides information about the tree size. A two-step procedure relates the detected blobs to tree objects in the field. First, a Gaussian blob model identifies tree crowns in Gaussian scale space. Second, an improved tree crown model modifies this model in the scale direction. The procedures are tested on the following three representative cases: an area with vitellaria trees in Mali, an orchard with walnut trees in Iran, and one case with oil palm trees in Indonesia. The results show that the refined Gaussian blob model improves upon the traditional Gaussian blob model by effectively discriminating between false and correct detections and accurately identifying size and position of trees. A comparison with existing methods shows an improvement of 10&ndash, 20% in true positive detections. We conclude that the presented two-step modeling procedure of tree crowns using Gaussian scale space is useful to automatically detect individual trees from VHR satellite images for at least three representative cases.

We report on the first implementation of a terahertz (THz) source based on a Josephson flux-flow oscillator (FFO) that radiates to open space. The excellent performance of this source and its maturity for practical applications has been demonstrated by the spectroscopy of gas absorption. To study the radiated power, we used a bolometric detection method and additionally calibrated the power by means of pumping the superconductor&ndash, insulator&ndash, superconductor (SIS) junction, integrated on a single chip with the FFO. For calibration, we developed a program using the SIS-detected power calculations in accordance with the Tien and Gordon model. The power emitted to open space is estimated to be from fractions of &micro, W to several &micro, W in the wide region from 0.25 THz up to 0.75 THz for different designs, with a maximum power of 3.3 &micro, W at 0.34 THz. Next, we used a gas cell and a heterodyne superconducting integrated receiver to trace the absorption lines of water and ammonia with a spectral resolution better than 100 kHz. Our experiment for gas absorption is the first demonstration of the applicability of the FFO as an external active source for different tasks, such as THz spectroscopy, near-field THz imaging and microscopy.

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43&ndash, 99% sensitivity and 48&ndash, 98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.

We report the synthesis of α-Ag2S nanoparticles (NPs) by one-step laser ablation of a silver target in aqueous solution of thiourea (Tu, CH4N2S) mixed with cationic cetyltrimethylammonium bromide (CTAB) as surfactant. The effect of the CTAB surfactant on the structural, morphological, optical, and elemental composition of Ag2S NPs was evaluated using X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and UV–vis spectroscopy. The optical absorption decreased and the optical energy gap of α-Ag2S increased from 1.5 to 2 eV after the CTAB surfactant was added to the Tu solution. XRD studies revealed that the synthesized Ag2S NPs were polycrystalline with a monoclinic structure and that crystallinity of the nanoparticles was improved after adding CTAB. Raman studies revealed the presence of peaks related to Ag–S bonds (Ag modes) and the longitudinal optical phonon 2LO mode. Scanning electron microscopy investigations confirmed the production of monodisperse Ag2S NPs when using the CTAB surfactant. The optoelectronic properties of α-Ag2S/p-Si photodetector, such as current–voltage characteristics and responsivity in the dark and under illumination, were also improved after using the CTAB surfactant. The responsivity of the photodetector increases from 0.64 to 1.85 A/W at 510 nm after adding CTAB. The energy band diagram of the α-Ag2S/p-Si photodetector under illumination was constructed. The fabricated photodetectors exhibited reasonable stability after three weeks of storage under ambient conditions with a responsivity of 70% of the initial value.

Gait quality characteristics obtained from accelerometry during daily life are predictive of falls in older people but it is unclear how they relate to fall risk. Our aim was to test whether these gait quality characteristics are associated with the severity of fall risk. We collected one week of trunk accelerometry data from 279 older people (aged 65–95 years; 69.5% female). We used linear regression to investigate the association between six daily-life gait quality characteristics and categorized physiological fall risk (QuickScreen). Logarithmic rate of divergence in the vertical (VT) and anteroposterior (AP) direction were significantly associated with the level of fall risk after correction for walking speed (both p < 0.01). Sample entropy in VT and the mediolateral direction and the gait quality composite were not significantly associated with the level of fall risk. We found significant differences between the high fall risk group and the very low- and low-risk groups, the moderate- and very low-risk and the moderate and low-risk groups for logarithmic rate of divergence in VT and AP (all p ≤ 0.01). We conclude that logarithmic rate of divergence in VT and AP are associated with fall risk, making them feasible to assess the physiological fall risk in older people.

Electric permeabilization of cell membranes is the main mechanism of irreversible electroporation (IRE), an ablation technique for treatment of unresectable cancers, but the pulses also induce a significant temperature increase in the treated volume. To investigate the therapeutically thermal contribution, a preclinical setup is required to apply IRE at desired temperatures while maintaining stable temperatures. This study&rsquo, s aim was to develop and test an electroporation device capable of maintaining a pre-specified stable and spatially homogeneous temperatures and electric field in a tumor cell suspension for several clinical-IRE-settings. A hydraulically controllable heat exchange electroporation device (HyCHEED) was developed and validated at 37 &deg, C and 46 &deg, C. Through plate electrodes, HyCHEED achieved both a homogeneous electric field and homogenous-stable temperatures, IRE heat was removed through hydraulic cooling. IRE was applied to 300 &mu, L of pancreatic carcinoma cell suspension (Mia PaCa-2), after which cell viability and specific conductivity were determined. HyCHEED maintained stable temperatures within &plusmn, 1.5 &deg, C with respect to the target temperature for multiple IRE-settings at the selected temperature levels. An increase of cell death and specific conductivity, including post-treatment, was found to depend on electric-field strength and temperature. HyCHEED is capable of maintaining stable temperatures during IRE-experiments. This provides an excellent basis to assess the contribution of thermal effects to IRE and other bio-electromagnetic techniques.

Neuromuscular disorders cause progressive muscular weakness, which limits upper extremity mobility and performance during activities of daily life. A dynamic arm support can improve mobility and quality of life. However, their use is often discontinued over time for unclear reasons. This study aimed to evaluate whether users of dynamic arm supports demonstrate and perceive quantifiable mobility benefits over a period of two months. Nine users of dynamic arm supports were included in this observational study. They had different neuromuscular disorders and collectively used four different arm supports. They were observed for three consecutive weeks during which they were equipped with a multi-sensor network of accelerometers to assess the actual use of the arm support and they were asked to provide self-reports on the perceived benefits of the devices. Benefits were experienced mainly during anti-gravity activities and the measured use did not change over time. The self-reports provided contextual information in domains such as participation to social life, in addition to the sensor system. However self-reports overestimated the actual use by up to three-fold compared to the accelerometer measures. A combination of objective and subjective methods is recommended for meaningful and quantifiable mobility benefits during activities of daily life.

Early Barrett’s neoplasia are often missed due to subtle visual features and inexperience of the non-expert endoscopist with such lesions. While promising results have been reported on the automated detection of this type of early cancer in still endoscopic images, video-based detection using the temporal domain is still open. The temporally stable nature of video data in endoscopic examinations enables to develop a framework that can diagnose the imaged tissue class over time, thereby yielding a more robust and improved model for spatial predictions. We show that the introduction of Recurrent Neural Network nodes offers a more stable and accurate model for tissue classification, compared to classification on individual images. We have developed a customized Resnet18 feature extractor with four types of classifiers: Fully Connected (FC), Fully Connected with an averaging filter (FC Avg (n = 5)), Long Short Term Memory (LSTM) and a Gated Recurrent Unit (GRU). Experimental results are based on 82 pullback videos of the esophagus with 46 high-grade dysplasia patients. Our results demonstrate that the LSTM classifier outperforms the FC, FC Avg (n = 5) and GRU classifier with an average accuracy of 85.9% compared to 82.2%, 83.0% and 85.6%, respectively. The benefit of our novel implementation for endoscopic tissue classification is the inclusion of spatio-temporal information for improved and robust decision making, and it is the first step towards full temporal learning of esophageal cancer detection in endoscopic video.

2D materials such as hexagonal boron nitride (h-BN) are widely used to decouple organic molecules from metal substrates. Nevertheless, there are also indications in the literature for a significant hybridization, which results in a perturbation of the intrinsic molecular properties. In this work we study the electronic and optical properties as well as the lateral structure of tetraphenyldibenzoperiflanthene (DBP) on Ni(111) with and without an atomically thin h-BN interlayer to investigate its possible decoupling effect. To this end, we use in situ differential reflectance spectroscopy as an established method to distinguish between hybridized and decoupled molecules. By inserting an h-BN interlayer we fabricate a buried interface and show that the DBP molecules are well decoupled from the Ni(111) surface. Furthermore, a highly ordered DBP monolayer is obtained on h-BN/Ni(111) by depositing the molecules at a substrate temperature of 170 °C. The structural results are obtained by quantitative low-energy electron diffraction and low-temperature scanning tunneling microscopy. Finally, the investigation of the valence band structure by ultraviolet photoelectron spectroscopy shows that the low work function of h-BN/Ni(111) further decreases after the DBP deposition. For this reason, the h-BN-passivated Ni(111) surface may serve as potential n-type contact for future molecular electronic devices.

3D printing of soft EMG sensing structures enables the creation of personalized sensing structures that can be potentially integrated in prosthetic, assistive and other devices. We developed and characterized flexible carbon-black doped TPU-based sEMG sensing structures. The structures are directly 3D-printed without the need for an additional post-processing step using a low-cost, consumer grade multi-material FDM printer. A comparison between the gold standard Ag/AgCl gel electrodes and the 3D-printed EMG electrodes with a comparable contact area shows that there is no significant difference in the EMG signals&rsquo, amplitude. The sensors are capable of distinguishing a variable level of muscle activity of the biceps brachii. Furthermore, as a proof of principle, sEMG data of a 3D-printed 8-electrode band are analyzed using a patten recognition algorithm to recognize hand gestures. This work shows that 3D-printed sEMG electrodes have great potential in practical applications.

The potential application of granules of brick waste (GBW) as a low-cost sorbent for removal of Ni+2ions from aqueous solutions has been studied. The properties of GBW were determined through several tests such as X-Ray diffraction (XRD), Energy dispersive X-ray (EDX), Scanning electron microscopy (SEM), and BET surface area. In batch tests, the influence of several operating parameters including contact time, initial concentration, agitation speed, and the dose of GBW was investigated. The best values of these parameters that provided maximum removal efficiency of nickel (39.4%) were 1.5 hr, 50 mg/L, 250 rpm, and 1.8 g/100mL, respectively. The adsorption data obtained by batch experiments subjected to the Three isotherm models called Langmuir, Freundlich and Elovich, The results showed that the Freundlich isotherm model described well the sorption data (R2=0.9176) in comparison with other models. The kinetic data were analyzed using two kinetic models called pseudo-first-order and pseudo-second-order. The pseudo-first-order kinetic model was found to agree well with the experimental data.

Microsoft&rsquo, s HoloLens, a mixed-reality headset, provides, besides holograms, rich position data of the head, which can be used to quantify what the wearer is doing (e.g., walking) and to parameterize such acts (e.g., speed). The aim of the current study is to determine test-retest reliability, concurrent validity, and face validity of HoloLens 1 for quantifying spatiotemporal gait parameters. This was done in a group of 23 healthy young adults (mean age 21 years) walking at slow, comfortable, and fast speeds, as well as in a group of 24 people with Parkinson&rsquo, s disease (mean age 67 years) walking at comfortable speed. Walking was concurrently measured with HoloLens 1 and a previously validated markerless reference motion-registration system. We comprehensively evaluated HoloLens 1 for parameterizing walking (i.e., walking speed, step length and cadence) in terms of test-retest reliability (i.e., consistency over repetitions) and concurrent validity (i.e., between-systems agreement), using the intraclass correlation coefficient (ICC) and Bland&ndash, Altman&rsquo, s bias and limits of agreement. Test-retest reliability and between-systems agreement were excellent for walking speed (ICC &ge, 0.861), step length (ICC &ge, 0.884), and cadence (ICC &ge, 0.765), with narrower between-systems than over-repetitions limits of agreement. Face validity was demonstrated with significantly different walking speeds, step lengths and cadences over walking-speed conditions. To conclude, walking speed, step length, and cadence can be reliably and validly quantified from the position data of the wearable HoloLens 1 measurement system, not only for a broad range of speeds in healthy young adults, but also for self-selected comfortable speed in people with Parkinson&rsquo, s disease.

A temperature-sensitive paint (TSP) using a chameleon luminophore [ Tb 0 . 99 Eu 0 . 01 ( hfa ) 3 ( dpbp ) ] n is proposed. The chameleon luminophore was dispersed in isobutyl methacrylate polymer in a toluene solvent to fix it on a sample coupon. Temperature and pressure sensitivities of the chameleon luminophore-based TSP were measured using a spectrofluorophotometer. The emission for each wavelength was confirmed to be dependent on the temperature and pressure. The temperature and pressure sensitivities of the TSP were 0.81&ndash, 2.8%/K and 0.08&ndash, 0.12%/kPa, respectively. Higher temperature sensitivity can be obtained using the ratio of emissions from the two lanthanide ions, Tb III and Eu III . The temperature sensitivity when using the ratio of the emission intensities at 616 nm derived from Eu III and at 545 nm derived from Tb III was 3.2%/K, which was the highest value in the present study. In addition, the pressure sensitivity for the case using the ratio of the emission intensities at 616 and 545 nm was 4 . 8 &times, 10 &minus, 2 % /kPa. Higher temperature sensitivity and lower pressure sensitivity than that with a single wavelength can be achieved using the ratio of the emission intensities at the two peak wavelengths derived from Tb III and Eu III .

The types of blue ceramics are monotonous, mainly alumina and zirconia ceramics, and their colors are not pure, with some green tones (&minus, a* value is not close to 0). In this paper, aluminate blue ceramics (LaMgAl11&minus, xCoxO19) doped with Co were prepared by the high temperature solid-phase reaction method, which enriched the blue ceramics system. The effect of Co content on the color of ceramics was studied, and the optimal doping amount of Co was found. X = 1.0 is the bluest color of the material (&minus, b* = 35.36), and there is almost no noise effect (&minus, a* = &minus, 2.71). By studying the effect of temperature on the system, it is found that the color effect is best when the temperature reaches 1450 &deg, C. When the temperature exceeds 1450 &deg, C, it can only promote the synthesis of LaMgAl11O19 phase, and has no effect on the color of ceramics samples. Based on the material&rsquo, s pure and bright colors, and good color stability at room temperature, it has great potential in the decoration industry, such as the preparation of jewelry or building decoration materials.

Abstract There is a substantial loss of value in bovine leather every year due to a leather quality defect known as “looseness”. Data show that 7% of domestic hide production is affected to some degree, with a loss of $35 m in export returns. This investigation is devoted to gaining a better understanding of tight and loose wet blue leather based on vibrational spectroscopy observations of its structural variations caused by physical and chemical changes that also affect the tensile and tear strength. Several regions from the wet blue leather were selected for analysis. Samples of wet blue bovine leather were collected and studied in the sliced form using Raman spectroscopy (using 532 nm excitation laser) and Attenuated Total Reflectance - Fourier Transform InfraRed (ATR-FTIR) spectroscopy. The purpose of this study was to use ATR-FTIR and Raman spectra to classify distal axilla (DA) and official sampling position (OSP) leather samples and then employ univariate or multivariate analysis or both. For univariate analysis, the 1448 cm− 1 (CH2 deformation) band and the 1669 cm− 1 (Amide I) band were used for evaluating the lipid-to-protein ratio from OSP and DA Raman and IR spectra as indicators of leather quality. Curve-fitting by the sums-of-Gaussians method was used to calculate the peak area ratios of 1448 and 1669 cm− 1 band. The ratio values obtained for DA and OSP are 0.57 ± 0.099, 0.73 ± 0.063 for Raman and 0.40 ± 0.06 and 0.50 ± 0.09 for ATR-FTIR. The results provide significant insight into how these regions can be classified. Further, to identify the spectral changes in the secondary structures of collagen, the Amide I region (1600–1700 cm− 1) was investigated and curve-fitted-area ratios were calculated. The 1648:1681 cm− 1 (non-reducing: reducing collagen types) band area ratios were used for Raman and 1632:1650 cm− 1 (triple helix: α-like helix collagen) for IR. The ratios show a significant difference between the two classes. To support this qualitative analysis, logistic regression was performed on the univariate data to classify the samples quantitatively into one of the two groups. Accuracy for Raman data was 90% and for ATR-FTIR data 100%. Both Raman and ATR-FTIR complemented each other very well in differentiating the two groups. As a comparison, and to reconfirm the classification, multivariate analysis was performed using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The results obtained indicate good classification between the two leather groups based on protein and lipid content. Principal component score 2 (PC2) distinguishes OSP and DA by symmetrically grouping samples at positive and negative extremes. The study demonstrates an excellent model for wider research on vibrational spectroscopy for early and rapid diagnosis of leather quality. Graphical abstract

In this work, Hydroxyapatite (HAp) was sintered by microwave and compared with the sintered samples by conventional method. The power levels of microwave were 450, 600, 900 W and the sintering temperatures were in the range from 750 to 1000 ?C without isothermal holding. It was shown that the using of higher power level at the same heating temperature increases the density of sintered samples. Samples sintered in microwave at 1000 ?C by 900 W power reached a higher density of 95 % compared to the conventional process (85 %), however in the case of using 450 W power, the densification trend was almost similar to the conventional one. HAp to TCP phase transformation was not detected at 1000 ?C; however, the preferred growth of (211) planes corresponded to circular morphology was observed. Microstructural observations showed larger grains (500 nm) and porosities (300 nm) in the traditional sintered samples compared to the samples, which were sintered by microwave (200 nm and 100 nm, respectively).

The effect of CaO on cordierite formation from kaolin-MgO-CaO powder mixtures, milled for 5 h and reaction sintered for 2 h in the temperature range 900-1400?C, was investigated. Phases formed in the developed materials were characterized by x-ray powder diffraction method (XRD) and Raman spectroscopy. Non-isothermal differential thermal analysis (DTA) and thermogravimetric (TG) experiments were performed from room temperature to 1400?C, at heating rates from 20 to 40?C/min. Activation energies were determined using Kissinger method. It was found that sintering the stoichiometric kaolin-magnesia mixture led to the nucleation and growth of monolithic cordierite; while cordierite along with anorthite were present in the other two samples where 4 or 8 wt% of CaO was added. The increase in CaO decreased cordierite formation temperature and increased the activation energy, which ranged from 445 to 619 kJ/mol for ?-cordierite and from 604 to 1335 kJ/mol for ?-cordierite.

CMC-NH4CH3CO2 complexes were characterized via theoretical and experimental approaches using molecular dynamics (MD) calculation, Fourier transform infrared spectrometry (FTIR), X-ray diffraction (XRD), and electrical impedance spectroscopy (EIS) analysis. These analyses successfully disclosed the structural and ion conduction properties of the bio-polymer electrolytes (BPE) system. The FTIR analysis further revealed that an interaction exists between the carboxylate anion group (COO–) from CMC and the H+ substructure of NH4CH3CO2. The ionic conductivity value at ambient temperature was found to achieve an optimum value of 5.07× 10–6 S/cm for a system containing 10 wt% NH4CH3CO2. The ionic conductivity improvement was demonstrated via the increment on the amorphous phase of the BPEs system as shown in the XRD analysis upon the inclusion of NH4CH3CO2. Based on the IR-deconvolution approach, the mobility (µ) and diffusion coefficient (D) were found to influence the ionic conductivity and aligned with the theoretical molecular dynamic (MD) calculation. To evaluate the potential application of the CMC-NH4CH3CO2, an electrical double-layer capacitor (EDLC) was fabricated from the BPE and tested using cyclic voltammetry (CV), and charge-discharge (GCD) for 300 cycles and the BPE exhibited a specific of capacitance~ 2.4 F/g.

Sweet cherries (Prunus avium L.) were vacuum dried at different temperatures in the range between 50 and 70oC and different pressures between 20 and 200 mbar. Seven mathematical models (Henderson-Pabis, Modified Henderson-Pabis, Simplified Fick’s diffusion, Peleg, Logarithmic, Two term and Midilli et al.) were used for description of the vacuum drying process and the Midilli et al. model was selected as the most suitable with the highest mean value of coefficient of determination (R2=0.9985) and the lowest mean values of the average absolute relative deviation (AARD=0.90 %), root mean square error (RMSE=0.0061) and the reduced chi-square (χ2=0.0001). Seven textural properties (shear force, penetration force, hardness, springiness, cohesiveness, gumminess and chewiness) were investigated in all dried sweet cherry samples. The results indicated that the pressure influenced the textural properties of sweet cherries during vacuum drying since the minimum values of all investigated texture properties were obtained in samples dried at the pressure of 200 mbar, while the maximum values were obtained at 20 and 65 mbar. It also was noticed that the temperature influenced the textural properties in the temperature range investigated, but not as significantly as it was the case of the pressure influence.

Multiferroic systems are attractive to the researches worldwide due to diversity of existing applications, as well as possible novel ones. In order to contribute to understanding of the processes that take place within the structure of such a system, we subjected it to mechanochemical activation and thermal treatment. Powdery mixtures of iron and barium titanate in a mass ratio of 30% Fe and 70% BaTiO3 were activated in a planetary ball mill for time duration of 30 to 300 min and subsequently sintered at 1200°C in the atmosphere of air. During the activation the system undergoes structural phase transitions, whereby the content of iron and its oxides changes. The highest Fe content was observed in the sample activated for 270 min, with local maxima in crystallite size and microstrain values and a minimum in dislocation density. The complex dielectric permittivity changes in the applied radio frequency field, rangingfrom 176.9 pF/m in thesample activated for 90 min to 918.1 pF/m in the sample activated for 180 min. As the frequency of the field increases, an exponential decrease in the magnetic with a simultaneous increase in the electrical energy losses is noticeable. The system exhibits ferromagnetic resonance, whereby longer activation in the mill shifts the resonant frequency to higher values. Negative electrical resistance was observed in all analyzed samples. The activation time changes both the demagnetization temperature and the Curie temperature of the samples undergoing heating and cooling cycles in the external permanent magnetic field. Curie temperature is the highest in the sample activated for 240 min. Thermal treatment increases the initial magnetization of all samples, with the most pronounced increase of ~95% in the sample activated for 300 min. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. OI 172057]

Due to the presence of bioactive compounds, fruits are an essential part of people&rsquo, s healthy diet. However, endogenous ethylene produced by climacteric fruits and exogenous ethylene in the microenvironment could play a pivotal role in the physiological and metabolic activities, leading to quality losses during storage or shelf life. Moreover, due to the variety of fruits and complex scenarios, different ethylene control strategies need to be adapted to improve the marketability of fruits and maintain their high quality. Therefore, this study proposed an ethylene dynamic monitoring based on multi-strategies control to reduce the post-harvest quality loss of fruits, which was evaluated here for blueberries, sweet cherries, and apples. The results showed that the ethylene dynamic monitoring had rapid static/dynamic response speed (2 ppm/s) and accurately monitoring of ethylene content (99% accuracy). In addition, the quality parameters evolution (firmness, soluble solids contents, weight loss rate, and chromatic aberration) showed that the ethylene multi-strategies control could effectively reduce the quality loss of fruits studied, which showed great potential in improving the quality management of fruits in the supply chain.

Indoor navigation systems offer many application possibilities for people who need information about the scenery and the possible fixed and mobile obstacles placed along the paths. In these systems, the main factors considered for their construction and evaluation are the level of accuracy and the delivery time of the information. However, it is necessary to notice obstacles placed above the user&rsquo, s waistline to avoid accidents and collisions. In this paper, different methodologies are associated to define a hybrid navigation model called iterative pedestrian dead reckoning (i-PDR). i-PDR combines the PDR algorithm with a Kalman linear filter to correct the location, reducing the system&rsquo, s margin of error iteratively. Obstacle perception was addressed through the use of stereo vision combined with a musical sounding scheme and spoken instructions that covered an angle of 120 degrees in front of the user. The results obtained in the margin of error and the maximum processing time are 0.70 m and 0.09 s, respectively, with obstacles at ground level and suspended with an accuracy equivalent to 90%.

The present research was conducted to synthesis Y-Zeolite by sol-gel technique using MWCNT (multiwalled carbon nanotubes) as crystallization medium to get a narrow range of particle size distribution with small average size compared with ordinary methods. The phase pattern, chemical structure, particle size, and surface area were detected by XRD, FTIR, BET and AFM, respectively. Results shown that the average size of Zeolite with and without using MWCNT were (92.39) nm and (55.17) nm respectively .Particle size range reduced from (150-55) nm to (130-30) nm. The surface area enhanced to be (558) m2/g with slightly large pore volume (0.231) km3/g was obtained. Meanwhile, degree of crystallization decreased to 120%.

Simultaneous determination of linear and angular positions of rotating objects is a challenging task for traditional sensor applications and a very limited set of solutions is available. The paper presents a novel approach of replacing a set of traditional linear and rotational sensors by a small set of image sensors. While the camera&rsquo, s angle of view can be a limiting factor in the tracking of multiple objects, the presented approach allows for a network of image sensors to extend the covered area. Furthermore, rich image data allows for the application of different data processing algorithms to effectively and accurately determine the object&rsquo, s position. The proposed solution thus provides a set of smart visual encoders emulated by an image sensor or a network of image sensors for more demanding spatially distributed tasks. As a proof of concept, we present the results of the experiment in the target application, where a&nbsp, 1.6 MP image sensor was used to obtain sub-degree angular resolution at 600 rpm and thus exceeding the design parameters and requirements. The solution allows for a compact, cost-effective, and robust integration into the final product.

The main objective of a Cooperative Multiple-Input Multiple-Output (CMIMO) system is to improve network throughput and network coverage and save energy. By grouping wireless devices as virtual multi-antenna nodes, it can thus simulate the functions of multi-antenna systems. A Space-Time Block Code (STBC) was proposed to utilize the spatial diversity of MIMO systems to improve the diversity gain and coding gain. In this paper, we proposed a cooperative strategy based on STBC and CMIMO, which is referred to as Space-Time Block Coded Cooperative Multiple-Input Multiple-Output (STBC-CMIMO) to inherit the advantages from both STBC and CMIMO. The theoretical performance analysis for the proposed STBC-CMIMO is presented. The performance advantages of the STBC-CMIMO are also shown by simulations. In the simulations, it is demonstrated that STBC-CMIMO can obtain significant performance compared with the existing CMIMO system.

In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field.

Food security has become an increasingly important challenge for all countries globally, particularly as the world population continues to grow and arable lands are diminishing due to urbanization. Water scarcity and lack of labor add extra negative influence on traditional agriculture and food production. The problem is getting worse in countries with arid lands and harsh climate, which exacerbates the food gap in these countries. Therefore, smart and practical solutions to promote cultivation and combat food production challenges are highly needed. As a controllable environment, greenhouses are the perfect environment to improve crops&rsquo, production and quality in harsh climate regions. Monitoring and controlling greenhouse microclimate is a real problem where growers have to deal with various parameters to ensure the optimal growth of crops. This paper shows our research in which we established a multi-tier cloud-based Internet of things (IoT) platform to enhance the greenhouse microclimate. As a case study, we applied the IoT platform on cucumber cultivation in a soilless medium inside a commercial-sized greenhouse. The established platform connected all sensors, controllers, and actuators placed in the greenhouse to provide long-distance communication to monitor, control, and manage the greenhouse. The implemented platform increased the cucumber yield and enhanced its quality. Moreover, the platform improved water use efficiency and decreased consumption of electrical energy. Based on the positive impact on water use efficiency and enhancement on cucumber fruit yield and quality, the established platform seems quite suitable for the soilless greenhouse cultivation in arid regions.

Oceanic eddy is a common natural phenomenon that has large influence on human activities, and the measurement and detection of offshore eddies are significant for oceanographic research. The previous classical detecting methods, such as the Okubo&ndash, Weiss algorithm (OW), vector geometry algorithm (VG), and winding angles algorithm (WA), not only depend on expert&rsquo, s experiences to set an accurate threshold, but also need heavy calculations for large detection regions. Differently from the previous works, this paper proposes a deep eddy detection neural network with pixel segmentation skeleton on high frequency radar (HFR) data, namely, the deep eddy detection network (DEDNet). An offshore eddy detection dataset is firstly constructed, which has origins from the sea surface current data measured by two HFR systems on the South China Sea. Then, a spatial globally optimum and strong detail-distinguishing pixel segmentation network is presented to automatically detect and localize offshore eddies in a flow chart. An eddy detection network based on fully convolutional networks (FCN) is also presented for comparison with DEDNet. Experimental results show that DEDNet performs better than the FCN-based eddy detection network and is competitive with the classical statistics-based methods.

Unlike the fixed power grid cooperative networks, which are mainly based on the reception reliability parameter while choosing the best relay, the wireless-powered cooperative communication network (WPCCN) and in addition to the reception reliability the transmission requirement consideration is important for relay selection schemes. Hence, enabling efficient transmission techniques that address high attenuation of radio frequency (RF) signals according to the distance without increasing the total transmission power is an open issue worth studying. In this relation, a multiantennas power beacon (PB) that assists wireless-powered cooperative communication network (PB-WPCCN) is studied in this paper. The communication between source and destination is achieved with the aid of multiple relays, where both the source and the multiple relays need to harvest energy from the PB in the first place to enable their transmission functionalities. A novel relay selection scheme is proposed, named as two-round relay selection (2-RRS), where a group of relays that successfully decode the source information is selected in the first round selection. In the second round, the optimal relay is selected to forward the recorded information to the destination. The proposed 2-RRS scheme is compared with two existing relay selection schemes, i.e., partial relay selection (PRS) and opportunistic relay selection (ORS). The analytical closed-form expressions of outage probability and average system throughput are derived and validated by numerical simulation. The comparison results between different relay selection schemes show: (I) The superiority of the proposed 2-RRS scheme as it achieves around 17% better throughput compared to the conventional ORS scheme and 40% better than the PRS scheme, particularly when PB transmit power is 10 dB, (II) The proposed 2-RRS scheme guarantees the lowest outage probability, especially when the PB is equipped with multiantennas and performs beamforming technique, (III) The optimal localisation of the PB between the source and N relays depends on the adopted relay selection scheme, (IV) The exhaustive search of the maximum system throughput value shows that the proposed 2-RRS scheme required shorter energy harvesting time compared to other schemes. The increase in energy harvesting time and number of relays do not necessarily reflect positively on the system throughput performance, hence tradeoffs should be taken into consideration.

Power-line communication (PLC) networks have been increasingly used for constructing industrial IoT (internet of things) and home networking systems due to their low-cost installation and broad coverage feature. To guarantee the transmission reliability, ARQ (automatic repeat request) scheme is introduced into the link layer of reliable PLC networks, which allows the retransmission of a data frame several times so that it has a higher probability to be correctly received. However, current studies of performance analysis for PLC MAC (medium access control) protocol (i.e., IEEE 1901) do not take into account of the impact of ARQ scheme. To resolve this problem, we propose an analytical model to investigate the MAC performance of IEEE 1901 protocol for reliable PLC networks with ARQ scheme. In the modeling process, we first establish a PLC channel model to reflect the impacts of PLC channel types (containing Rayleigh fading and Log-normal fading), additive non-Gaussian noise feature and ARQ scheme on data transmission at link layer. Next, we employ Renewal theory and Queueing dynamics to capture the transmission attempt behavior of executing IEEE 1901 protocol in the unsaturated environment with finite transit buffer size. On the basis of combining these two models, we derive the closed-form expressions of 1901 MAC metrics considering the influence of the ARQ scheme. Furthermore, we prove that the proposed analytical model has the convergence property. Finally, we evaluate the MAC performance of 1901 protocol for reliable PLC networks with ARQ scheme and verify the proposed analytical model.

Vehicle-to-everything (V2X) communication is seen as one of the main enabling technologies for automated vehicles. Collective perception is especially promising, as it allows connected traffic participants to &ldquo, see through the eyes of others&rdquo, by sharing sensor-detected objects via V2X communication. Its benefit is typically assessed in terms of the increased object update rate, redundancy, and awareness. To determine the safety improvement thanks to collective perception, the authors introduce new metrics, which quantify the environmental risk awareness of the traffic participants. The performance of the V2X service is then analyzed with the help of the test platform TEPLITS, using real traffic traces from German highways, amounting to over 100 h of total driving time. The results in the considered scenarios clearly show that collective perception not only contributes to the accuracy and integrity of the vehicles&rsquo, environmental perception, but also that a V2X market penetration of at least 25% is necessary to increase traffic safety from a &ldquo, risk of serious traffic accidents&rdquo, to a &ldquo, residual hypothetical risk of collisions without minor injuries&rdquo, for traffic participants equipped with non-redundant 360&deg, sensor systems. These results support the ongoing worldwide standardization efforts of the collective perception service.

The elevated concentrations of various trace metals beyond existing guideline recommendations in water bodies have promoted research on the development of various electrochemical nanosensors for the trace metals’ early detection. Inspired by the exciting physical and chemical properties of nanomaterials, advanced functional nanocomposites with improved sensitivity, sensitivity and stability, amongst other performance parameters, have been synthesized, characterized, and applied on the detection of various trace metals in water matrices. Nanocomposites have been perceived as a solution to address a critical challenge of distinct nanomaterials that are limited by agglomerations, structure stacking leading to aggregations, low conductivity, and limited porous structure for electrolyte access, amongst others. In the past few years, much effort has been dedicated to the development of various nanocomposites such as; electrochemical nanosensors for the detection of trace metals in water matrices. Herein, the recent progress on the development of nanocomposites classified according to their structure as carbon nanocomposites, metallic nanocomposites, and metal oxide/hydroxide nanocomposites is summarized, alongside their application as electrochemical nanosensors for trace metals detection in water matrices. Some perspectives on the development of smart electrochemical nanosensors are also introduced.

Target detection in hyperspectral imagery (HSI) aims at extracting target components of interest from hundreds of narrow contiguous spectral bands, where the prior target information plays a vital role. However, the limitation of the previous methods is that only single-layer detection is carried out, which is not sufficient to discriminate the target parts from complex background spectra accurately. In this paper, we introduce a hierarchical structure to the traditional algorithm matched filter (MF). Because of the advantages of MF in target separation performance, that is, the background components are suppressed while preserving the targets, the detection result of MF is used to further suppress the background components in a cyclic iterative manner. In each iteration, the average output of the previous iteration is used as a suppression criterion to distinguish these pixels judged as backgrounds in the current iteration. To better stand out the target spectra from the background clutter, HSI spectral input and the given target spectrum are whitened and then used to construct the MF in the current iteration. Finally, we provide the corresponding proofs for the convergence of the output and suppression criterion. Experimental results on three classical hyperspectral datasets confirm that the proposed method performs better than some traditional and recently proposed methods.

Background: Performance of wrist actigraphy in assessing sleep not only depends on the sensor technology of the actigraph hardware but also on the attributes of the interpretative algorithm (IA). The objective of our research was to improve assessment of sleep quality, relative to existing IAs, through development of a novel IA using deep learning methods, utilizing as input activity count and heart rate variability (HRV) metrics of different window length (number of epochs of data). Methods: Simultaneously recorded polysomnography (PSG) and wrist actigraphy data of 222 participants were utilized. Classic deep learning models were applied to: (a) activity count alone (without HRV), (b) activity count + HRV (30-s window), (c) activity count + HRV (3-min window), and (d) activity count + HRV (5-min window) to ascertain the best set of inputs. A novel deep learning model (Haghayegh Algorithm, HA), founded on best set of inputs, was developed, and its sleep scoring performance was then compared with the most popular University of California San Diego (UCSD) and Actiwatch proprietary IAs. Results: Activity count combined with HRV metrics calculated per 5-min window produced highest agreement with PSG. HA showed 84.5% accuracy (5.3&ndash, 6.2% higher than comparator IAs), 89.5% sensitivity (6.2% higher than UCSD IA and 6% lower than Actiwatch proprietary IA), 70.0% specificity (8.2&ndash, 34.3% higher than comparator IAs), and 58.7% Kappa agreement (16&ndash, 23% higher than comparator IAs) in detecting sleep epochs. HA did not differ significantly from PSG in deriving sleep parameters&mdash, sleep efficiency, total sleep time, sleep onset latency, and wake after sleep onset, moreover, bias and mean absolute error of the HA model in estimating them was less than the comparator IAs. HA showed, respectively, 40.9% and 54.0% Kappa agreement with PSG in detecting rapid and non-rapid eye movement (REM and NREM) epochs. Conclusions: The HA model simultaneously incorporating activity count and HRV metrics calculated per 5-min window demonstrates significantly better sleep scoring performance than existing popular IAs.

Accurate and fast path calculation is essential for applications such as vehicle navigation systems and transportation network routing. Although many shortest path algorithms for restricted search areas have been developed in the past ten years to speed up the efficiency of path query, the performance including the practicability still needs to be improved. To settle this problem, this paper proposes a new method of calculating statistical parameters based on a unidirectional road network model that is more in line with the real world and a path planning algorithm for dynamically restricted search areas that constructs virtual boundaries at a lower confidence level. We conducted a detailed experiment on the proposed algorithm with the real road network in Zhengzhou. As the experiment shows, compared with the existing algorithms, the proposed algorithm improves the search performance significantly in the condition of optimal path under the premise of ensuring the optimal path solution.

Due to the unique properties of polymer composites, these materials are used in many industries, including shipbuilding (hulls of boats, yachts, motorboats, cutters, ship and cooling doors, pontoons and floats, torpedo tubes and missiles, protective shields, antenna masts, radar shields, and antennas, etc.). Modern measurement methods and tools allow to determine the properties of the composite material, already during its design. The article presents the use of the method of acoustic emission and Kolmogorov&ndash, Sinai (K-S) metric entropy to determine the mechanical properties of composites. The tested materials were polyester-glass laminate without additives and with a 10% content of polyester-glass waste. The changes taking place in the composite material during loading were visualized using a piezoelectric sensor used in the acoustic emission method. Thanks to the analysis of the RMS parameter (root mean square of the acoustic emission signal), it is possible to determine the range of stresses at which significant changes occur in the material in terms of its use as a construction material. In the K-S entropy method, an important measuring tool is the extensometer, namely the displacement sensor built into it. The results obtained during the static tensile test with the use of an extensometer allow them to be used to calculate the K-S metric entropy. Many materials, including composite materials, do not have a yield point. In principle, there are no methods for determining the transition of a material from elastic to plastic phase. The authors showed that, with the use of a modern testing machine and very high-quality instrumentation to record measurement data using the Kolmogorov&ndash, Sinai (K-S) metric entropy method and the acoustic emission (AE) method, it is possible to determine the material transition from elastic to plastic phase. Determining the yield strength of composite materials is extremely important information when designing a structure.

Arc length is a crucial parameter of the manual metal arc (MMA) welding process, as it influences the arc voltage and the resulting welded joint. In the MMA method, the process&rsquo, stability is mainly controlled by the skills of a welder. According to that, giving the feedback about the arc length as well as the welding speed to the welder is a valuable property at the stage of weld training and in the production of welded elements. The proposed solution is based on the application of relatively cheap Complementary Metal Oxide Semiconductor (CMOS) cameras to track the welding electrode tip and to estimate the geometrical properties of welding arc. All measured parameters are varying during welding. To validate the results of image processing, arc voltage was measured as a reference value describing in some part the process stability.

Soft magnetic wires and microwires are currently used for the cores of magnetic sensors. Due to their low demagnetization, they contribute to the high sensitivity and the high spatial resolution of fluxgates, Giant Magnetoimpedance (GMI), and inductive sensors. The arrays of nanowires can be prepared by electrodeposition into predefined pores of a nanoporous polycarbonate membrane. While high coercivity arrays with square loops are convenient for information storage and for bistable sensors such as proximity switches, low coercivity cores are needed for linear sensors. We show that coercivity can be controlled by the geometry of the array: increasing the diameter of nanowires (20 &micro, m in length) from 30 nm to 200 nm reduced the coercivity by a factor of 10, while the corresponding decrease in the apparent permeability was only 5-fold. Finite element simulation of nanowire arrays is important for sensor development, but it is computationally demanding. While an array of 2000 wires can be still modelled in 3D, this is impossible for real arrays containing millions of wires. We have developed an equivalent 2D model, which allows us to solve these large arrays with acceptable accuracy. Using this tool, we have shown that as a core of magnetic sensors, nanowires are efficiently employed only together with microcoils with diameter comparable to the nanowire length.