Journal of Sensors
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Acceptance rate12%
Submission to final decision129 days
Acceptance to publication27 days
CiteScore2.600
Journal Citation Indicator0.440
Impact Factor1.9

Disposable Screen-Printed Microchip Based on Nanoparticles Sensitive Membrane for Potentiometric Determination of Lead

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Journal of Sensors publishes research focused on all aspects of sensors, from their theory and design, to the applications of complete sensing devices.

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Chief Editor, Professor Harith Ahmad, is currently the director of the Photonics Research Center, University of Malaya, Malaysia. His current research is in the exploration of various 2D and 3D nanomaterials for optoelectronics applications.

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Research Article

Research on the Factors Influencing the Seismic Performance of Grouting Sleeve Assembled Double-Column Piers

The present study investigated the influence of key design parameters on the seismic performance of prefabricated precast assembled piers’ connection parts to better adapt to the industrialized construction of prefabricated precast assembled pier connected using grouting sleeves. Relying on a prefabricated assembled bridge in the actual project, the ABAQUS software was used to establish a refined solid finite element model of prefabricated assembled piers connected with grouting sleeves. Numerical simulation analysis was conducted for the piers with low circumferential reciprocating loading. The seismic performance of the prefabricated assembled piers was evaluated in terms of hysteresis characteristics, dissipation characteristics, and damage development. The effects of the length of the grouting sleeve and the diameter of the longitudinal reinforcement on the seismic performance were also investigated. The maximum error between the numerical simulation results and the test results was 5.7%, and the plastic region of the precast assembled pier obtained from the numerical simulation was consistent with the test results, indicating that the numerical simulation method is accurate and reliable. When the length of the grouting sleeve increased from 0.6 to 1.2 m, the yield load, peak load, and dissipation of energy of prefabricated assembled piers increased by 11.6%, 10.9%, and 11.4%, respectively; no significant change in residual displacement; ductility coefficient decreased by a small amount. When the longitudinal reinforcement diameter increased from 20 to 50 mm, prefabricated assembled piers yield load, peak load, and dissipation increased by 99.6%, 89.3%, and 218.9%, respectively, whereas the residual displacement increased by 137.3%, and the ductility coefficient decreased more. Increasing the length of the grouting sleeve or increasing the diameter of longitudinal reinforcement improved the stiffness of the piers, causing the piers to displace less and damage less under the same force, but the residual displacement would increase.

Research Article

Monitoring Analysis of Urban Subsidence in Northern Henan Province Based on TS-InSAR Technology

The protracted and pervasive incidence of land subsidence emerges as a pivotal factor exerting a substantial impact on the sustainable development of urban landscapes. A nuanced comprehension of the spatiotemporal evolution characteristics of land subsidence within the Northern Henan Plain assumes paramount significance in the context of mitigating potential urban geological disasters. This study endeavors to redress the deficiency in information concerning the temporal and spatial evolution characteristics of enduring deformation in cities within the northern plain of Henan Province. To this end, the authors leveraged Sentinel-1A radar data processed through persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technology to elucidate the distribution patterns of ground deformation and temporal evolution characteristics within the expansive 24-scene coverage research area. Empirical findings illuminate conspicuous surface deformation in Anyang, Puyang, and Hebi throughout the monitoring period. Spatially, land subsidence in the study area predominantly clusters in the suburban peripheries of the cities, with Hebi and Puyang registering a maximum subsidence rate exceeding 25 mm per annum. Temporally, land subsidence manifests predominantly during autumn and winter, whereas spring and summer display relatively stable land subsidence interspersed with a slight ground uplift. In order to rectify the spatial disparities observed between leveling data and PS-InSAR monitoring data, this experiment employed an averaging procedure on the PS-InSAR monitoring data, subsequently subjecting it to comparative analysis with the leveling data. Additionally, through the integration of the singular spectrum analysis (SSA) method and the time series deformation model, this study aspires to attain a comprehensive understanding of the temporal dynamics manifested in the PS-InSAR monitoring outcomes, while concurrently elucidating the factors influencing the observed deformations. Ultimately, this analysis discloses that the monitoring outcomes derived via PS-InSAR technology exhibit a root mean square error of ±12.9 mm and a standard deviation of ±13.31 mm. These statistical metrics furnish valuable insights into the precision and consistency of the PS-InSAR monitoring data. Drawing upon a comparative scrutiny of on-site data and historical remote sensing imagery within the study area, it has been discerned that excessive groundwater extraction and expansive surface engineering initiatives stand as the principal instigators of land subsidence in the research domain. Consequently, this experiment assumes the role of a salient reference for the mitigation of urban ground subsidence within the study area.

Research Article

Efficacy of Multiseason Sentinel-2 Imagery for Classifying and Mapping Grassland Condition

Assessing the condition of ecosystems is imperative for understanding their degree of degradation and managing their conservation. The increasing availability of remote sensing products offers unprecedented opportunities for mapping vegetation with high detail and accuracy. However, mapping complex ecosystems, like grasslands, remains challenging due to their heterogeneity in vegetation composition and structure. Furthermore, degraded ecosystems affected by invasive vegetation present different condition levels within vegetation classes, limiting the accuracy of classifications and condition assessment. Here, we evaluated the capacity of Sentinel-2 multispectral time series imagery as an input for classifying different levels of cover within a vegetation class to detect the subtle differences needed to assess the condition of degraded ecosystems. Our study was conducted in the iron-grasslands of South Australia, a perennial tussock grassland dominated by iron-grasses (Lomandra spp.) and severely affected by invasive annual grasses. We developed random forest models to discriminate classes defined by the cover of iron-grasses, wild oats (Avena barbata), and woodland (training points = 250). We tested the importance of data seasonality, spatial resolution, spectral bands, and vegetation indices. The combination of spatial, temporal, and spectral detail produced the best classification results. Random forest classifications performed best at 10 m resolution, suggesting that detailed resolution outweighs spectral detail for discriminating vegetation patterns in systems with high spatial heterogeneity. The model at 10 m resolution combining all periods and all variables (spectral bands and vegetation indices) produced a mean kappa coefficient of 56% and a mean overall accuracy of 67%. The dry season imagery and vegetation indices emerged as the most informative, suggesting that vegetation classes presented different phenological properties critical for their discrimination. Our study contributes to mapping complex ecosystems, facilitating the discrimination of different levels of condition in grasslands degraded by invasive species, and thus benefits the conservation of native grasslands and other ecosystems.

Research Article

A Feasibility Study on the Efficacy of Functional Near-Infrared Spectrometry (fNIRS) to Measure Prefrontal Activation in Paediatric HIV

Human immunodeficiency virus (HIV) infection is associated with disturbed neurotransmission and aberrant cortical networks. Although advances in the imaging of brain microarchitecture following neuroHIV has added to our knowledge of structural and functional changes associated with HIV, no data exists on paediatric HIV using optical neuroimaging techniques. This study investigated the feasibility of optical neuroimaging in paediatric HIV using functional near-infrared spectrometry (fNIRS). We measured prefrontal brain activation while participants executed a sustained attention task. We specifically tested whether patients living with HIV and study controls could perform the study protocol and whether we could measure the typical fNIRS haemodynamic response associated with neuronal activity. Eighteen participants (10 HIV participants, mean age: 13.9, SD = 1.66 years; 8 controls, mean age: 14.8, SD = 1.28 years), matched for sex, grade, and socio-economic status, were included in the study. All participants completed the Stroop colour word test (SCWT). Oxygenated haemoglobin concentration and the deoxygenated haemoglobin signal were recorded from the dorsolateral prefrontal cortex and the frontopolar area (FA) using fNIRS. The control group performed significantly better in terms of reaction time on the congruent and incongruent condition (congruent: t (16) = −3.36, : incongruent: ). A pooled group analysis of the sample indicated significant activation in the DLPF and FA to the congruent condition of the SCWT (). Although cortical activation was noted in the DLPF and the FA in each of the groups when analysed independently, this neural activation did not reach statistical significance. The results show promise that fNIRS techniques are feasible for assessing prefrontal cortical activity in paediatric HIV. Future studies should seek to reduce the signal-to-noise ratio and consider inter-individual variability when measuring prefrontal activation in paediatric samples.

Research Article

Wrist EMG Monitoring Using Neural Networks Techniques

In rehabilitation, the correct performance of the exercises the specialist prescribes wrist movement is crucial. However, this may have the disadvantage of the patient’s subjectivity. Moreover, recent studies show that feedback through electrostimulation devices is beneficial during the process that leads to neuromotor rehabilitation. Besides, the electromyographic (EMG) signals give information about the actual degree of rehabilitation. This work examines whether temporal features can be used to classify wrist movements using back-propagation artificial neural networks and superficial EMG (sEMG) signals. The data for the evaluation were based on the information acquired from sEMG signals of two forearm muscles: the flexor carpi ulnaris (FCU) and the brachioradialis (B). These sEMG signals were analyzed to find the most critical parameters for classifying the wrist’s movement and to configure a multilayer perceptron (MLP) capable of classifying such movements.

Research Article

Fault Feature Extraction Method of Rolling Bearing Based on IAFD and TKEO

The study of bearing fault feature extraction using adaptive Fourier decomposition (AFD) holds significant practical importance. However, AFD is constrained by its reliance on prior knowledge for determining decomposition levels, which can result in either underdecomposition or overdecomposition based on a single indicator. Consequently, an improved adaptive Fourier decomposition (IAFD) is proposed. First, a combined weight index called SP is constructed, and the whale optimization algorithm is employed to optimize the SP weight parameter. Second, the IAFD decomposition levels can be adaptively determined using the optimized SP. Finally, a feature extraction method-based IAFD and Teager–Kaiser energy operator is applied in rolling bearing fault diagnosis. Case studies on the Case Western Reserve University and self-made KUST-SY datasets validate the effectiveness of the proposed method.

Journal of Sensors
 Journal metrics
See full report
Acceptance rate12%
Submission to final decision129 days
Acceptance to publication27 days
CiteScore2.600
Journal Citation Indicator0.440
Impact Factor1.9
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