Sensor System Research Articles

Imipenem exposure influence the expression of quorum sensing receptor sdiA in Escherichia coli.

The increasing trend of carbapenem resistance amongst Escherichia coli poses a major public health crisis and requires active surveillance of resistance mechanisms to control the threat. Quorum Sensing system plays a role in bacterial resistance to antibiotics. Quorum Sensing is a cell-cell communication system where bacteria alter their gene expression in response to specific stimuli. Here, in this study we investigated the transcriptional response of quorum sensing receptor, sdiA in E.coli under sub-inhibitory concentration of carbapenem in presence of quorum sensing signal molecules. Two E.coli isolates harbouring blaNDM were subjected to treatment with 10% SDS for 20 consecutive days of which blaNDM encoding plasmid was successfully eliminated from one isolate. Both the wild type and the cured mutant were then allowed to grow under eight different inducing conditions and the transcriptional response of sdiA gene was studied by quantitative real time PCR method. We found different response levels of sdiA in wild type and cured mutant under exogenous AHL and imipenem and when co-cultured with P.aeruginosa under imipenem stress. This study highlighted that sub-inhibitory concentration of imipenem in combination with AHL is acting as signal to SdiA, a quorum sensing receptor in E.coli.

Electrochemical behavior and L-tyrosine sensing properties of nanostructured Cr, Sn and La-doped α-Fe2O3 interfaces

L-tyrosine (Tyr) is a promising biomarker for the diagnosis and monitoring of metabolic disorders and neurodegenerative diseases. This study reports on the electrochemical properties of α-Fe2O3 nanostructures doped with Cr, Sn and La, referred to as CrFeOx, SnFeOx and LaFeOx, respectively, and their application in the enzyme-free electrochemical Tyr sensors. These disposable sensors offer accurate Tyr concentration analysis at room temperature, addressing the limitations of current point-of-care diagnostic methods. The CrFeOx, SnFeOx, and LaFeOx nanostructures serve as selective agents for binding and recognizing Tyr, deposited onto disposable graphite pencil electrodes to form the electrochemical interface. The interfacial resistance, charge-transfer kinetics, mechanism, and reversibility are studied via extensive electrochemical measurements employing electro¬chemical impedance spectroscopy and cyclic voltammetry. Furthermore, differen¬tial pulse voltammetry demonstrates excellent Tyr sensing performance in the concen¬tration range of 0 to 80 μM with 2.65 µA µM-1 cm-2 sensitivity and 360 nM thres¬hold detection limit for the best-performing CrFeOx sensors. Hence, these α-Fe2O3-based sensor systems are practical and efficient for selective Tyr detection, offering potential advancements in personalized healthcare and early disease diagnosis.

Development of engineered Zn-MOF/g-C3N4 based photoelectrochemical system for real-time sensors and removal of naproxen in wastewater

Naproxen-contaminated water may lead to the accumulation of the drug in aquatic organisms and can pose high risks to an aquatic environment and human beings. Therefore, this work aimed to develop photoelectrochemical sensing and degradation of naproxen (NPX) using zinc-metal organic framework /graphitic carbon nitride thin film-based fluorine-doped tin oxide (Zn-MOF/g-C3N4/FTO) as anode material for the sensing and degradation of naproxen (NPX). The surface morphology, structure, surface property, surface area, optical property, photocurrent, and charge transfer kinetics abilities were studied using different techniques. The nanocomposites showed a superior photocurrent response (0.815 mA cm-2) compared to the original g-C3N4 (0.328 mA cm-2). The photo-anode made of Zn-MOF@g-C3N4/FTO displayed the highest photocurrent value, indicating that the alignment of the two semiconductor bands prevented the quick recombination of electron-hole pairs. Owing to these attractive features, the Zn-MOF/g-C3N4/FTO electrode was applied for photoelectrochemical detection of NPX using chronoamperometry. Interestingly, the nanocomposites-based FTO ascribed a lower detection limit (2.3 ng l-1) with a wide linear range concentration of NPX (0.5 to 200 µg l-1). Additionally, the analytical assessment of repeatability and reproducibility demonstrated robust performance, with commendable relative standard deviations (RSD%) of 2.54 % and 2.40 %, respectively. On the other hand, a remarkable degradation efficiency of 97.52 % was attained when employing a bias potential of 0.1 V during a 2 h photoelectrocatalytic oxidation of NPX. The degradation process was primarily driven by the active participation of holes and hydroxyl radicals in ring opening and subsequent cleavage of by-products. The notable effectiveness of this degradation can be attributed to the combined and synergistic effects of both electrochemical and photocatalytic degradation techniques. The current state demonstrates its effectiveness in the photoelectrochemical sensing and removal of NPX using MOF/g-C3N4 nanocomposites-based electrode materials in wastewater.

Vehicle flow indication and identification using FBG sensors

Abstract This paper proposes a vehicle flow monitoring system utilizing fiber Bragg grating (FBG) sensor technology. The system comprises a prototype road model along with a car with varying loads. The FBG sensor is embedded in the road surface for sensing the strain induced by the vehicle movement. The laboratory experiments were conducted using an embankment model equipped with FBG sensor to identify vehicle movement under static and movable loads. Under static condition, different loads were applied on the car and the corresponding wavelength shifts have been measured using FBG-interrogator. Under dynamic condition, the vehicle was moved with certain speed and the corresponding wavelength shifts were recorded over time. The sensitivity of FBG for vehicle load sensing has been obtained as 0.3 pm/N. The proposed vehicle flow sensor system offers good accuracy and sensitivity and has potential applications for real-time vehicle flow monitoring. The proposed sensor system can be used to analyze road surface irregularities, crucial for assessing heavy-vehicle fatigue.

Silver Nanoparticle-Decorated Cellulose Nanocrystal Reinforced Ionic Polymer Hydrogel With High Conductivity and Environmental Tolerance for Multifunctional Sensing and Emergency Alarm System.

Conductive hydrogels hold great promise for flexible electronics. However, the simultaneous achievement of satisfactory mechanical strength, outstanding environmental tolerance, high sensitivity, and multiple sensing applications in a single conductive hydrogel remains a significant challenge. Herein, ionic polymer-based hydrogels with a double conductive network consisting of [2-(methacryloyloxy)ethyl] trimethyl ammonium chloride (DMC), 2-hydroxyethyl acrylate (HEA) and silver nanoparticle decorated cellulose nanocrystal (CNC@Ag) are prepared by a facile one-pot method. The resultant hydrogel (CDH) exhibits high stretchability, satisfactory self-adhesion, excellent environment tolerance (from -60 to 60°C), long-term stability (60 days), effective UV-shielding, and strong antibacterial properties. Significantly, the CDH hydrogel displays high conductivity and rapid response due to its double conductive network of ionic polymer and CNC@Ag. Therefore, the CDH-assembled sensor can accurately detect signals from both strain and pressure deformations, exhibiting outstanding sensitivity and reliability for human motion detection, signal transmission, object recognition, and tactile sensing. More interestingly, collaborating with a development board, the CDH-based sensor can be developed as an emergency alarm to realize prompt alarms in dangerous situations. Overall, this work presents a strategy for the fabrication of conductive hydrogel with remarkable properties, making it possible for multifunctional sensing applications in wearable electronics.

Recent innovations in land capability classification for sustainable development: a brief overview.

Land is a finite resource that must be managed wisely to ensure its sustainability. Consequently, land evaluation has become essential. Identifying and utilizing productive capacity of land efficiently and profitably is crucial; otherwise, resource degradation can severely impact natural ecosystems and food production. Over the years, various methodologies have been employed to assess land resources, and one such method is the Land Capability Classification (LCC). LCC is a widely used and fundamental approach to land-use planning, traditionally assessing land based on its intrinsic qualities and climate. This study aims to highlight new approaches and developments in land evaluation techniques. It provides a brief overview of recent technological and scientific advancements integrated into land evaluation. The findings suggest that LCC alone is inadequate for precise land assessment, emphasizing the need to integrate new technologies. Remote Sensing (RS) and Geographic Information System (GIS) technologies are becoming increasingly significant. Integrating various software, decision-making systems, and mathematical models can also enhance the accuracy of land assessment results. The continuous advancement of GIS and remote sensing technologies is paving the way for new tools to facilitate natural resource mapping, appraisal, surveillance, and management. Utilizing these technologies for future projections will be highly beneficial in accurately assessing the long-term impacts of current land management practices.

Deep Learning Solution for Quality Control in a Die Casting Process

Industry 4.0 aims for a digital transformation of manufacturing and production systems, producing what is known as smart factories, where information coming from Cyber-Physical Systems (core elements in Industry 4.0) will be used in all the manufacturing stages to improve productivity. Cyber-physical systems through their control and sensor systems, provide a global view of the process, and generate large amounts of data that can be used for instance to produce datadriven models of the processes. However, having data is not enough, we must be able to store, visualize and analyze them, and to integrate induced knowledge in the whole production process. In this work, we present a solution to automate the quality control process of manufactured parts through image analysis. In particular, we present a Deep Learning solution to detect defects in manufactured parts from thermographic images of a die casting machine at an aluminum foundry.

A Multi-Active and Multi-Passive Sensor Fusion Algorithm for Multi-Target Tracking in Dense Group Clutter Environments

Multi-target tracking (MTT) of multi-active and multi-passive sensor (MAMPS) systems in dense group clutter environments is facing significant challenges in measurement fusion. Due to the difference in measurement information characteristics in MAMPS fusion, it is difficult to effectively correlate and fuse different types of sensors’ measurements, leading to difficulty in taking full advantage of various types of sensors to improve target tracking accuracy. To this end, we present a novel MAMPS fusion algorithm, which is based on centralized measurement association fusion (MAF) and distributed deep neural network (DNN) track fusion, named the MAMPS-MAF-DNN algorithm. Firstly, to reduce the impact of the dense group clutter, a clutter pre-processing algorithm is elaborated, which combines the advantages of the CFDP (cluster by finding density peaks) and double threshold screening algorithms. Then, for the single-active and multi-passive sensor (SAMPS) system, a centralized MAF algorithm based on angle information is developed, called the SAMPS-MAF algorithm. Finally, the SAMPS-MAF algorithm is extended to the MAMPS system within the DNN framework, and the complete MAMPS-MAF-DNN algorithm is proposed. Experimental results indicate that, compared to the existing MAF and covariance intersection (CI) fusion algorithms, the proposed MAMPS-MAF-DNN algorithm can fully combine the advantages of multi-active and multi-passive sensors, efficiently reduce the computational complexity, and obviously improve the tracking accuracy.

Development and Validation of Concept of Innovative Method of Computer-Aided Monitoring and Diagnostics of Machine Components

The monitoring and diagnostic system has been suggested as a non-destructive diagnostic method. The structure and operation of the suggested system can be described by the concept of digital shadow (DS). One of the main DS subsystems is a set of sensors properly placed on the monitored object and coupled with a discrete data processing model created in Matlab/Simulink. The discrete model, as another important DS subsystem of the monitored facility, transfers information about its technical condition to the operator based on data recorded by the sensor system. The digital monitoring model processes the recorded data in the form of the object’s response to actions caused by its operating conditions. This work formalized a mathematical model determining the coupling of the digital model with the sensors placed on the monitored object. The formulated method using DS, due to its sensitivity, enables the detection of the damage in the object at an early stage. The tests allowed for detecting the regularities enabling the determination of the area of damage to the shaft and its size.

Research on intelligent cutting control technology of transverse moving machine for large cross-section roadheader

With the continuous development of the coal mine industry in our country, the cross-section of the roadway is getting larger and larger. The widely used EBZ series roadheader cannot complete the roadway cutting operation with a large cross-section at one time, so it usually needs to move the equipment left and right to complete all its cutting tasks. This paper studies the intelligent control technology of large cross-section roadway cutting, which mainly solves the positioning and attitude determination technology of roadheader in the large cross-section, and offers an accurate shape-cutting control technology and a path planning technology using a lateral moving machine. This paper proposes to combine the signals of laser sensor and fiber-optic inertial navigation system to form a combined positioning and attitude determination system, so as to make use of the advantages and avoid the disadvantages of their respective systems, and improve the accuracy of roadheader positioning and attitude determination. A swing kinematics model of the cutting arm is established, based on which the precise motion control of the first roadway section cutting and the second roadway section cutting can be realized. A key parameter calculation model of roadheader lateral moving machine is put forward to calculate the operation parameters for roadheader lateral moving machine path planning. Finally, the experimental research on intelligent cutting control technology of large cross-section roadheader is carried out in 12307 intelligent heading face of Wangjialing Coal Mine. The results show that the positioning error of the combined positioning and attitude determination system proposed in this paper is between 50-90 mm in the X-axis direction and between 25-45 mm in the Y-axis direction. The average attitude measurement error of heading angle is between 0.5 and 1.5°. This makes the technology appropriate for engineering use. The studied cutting control system achieves a cutting error of 25-50 mm for the first section, and a cutting error of 40-90 mm for the complete section. The complete section cutting error meets the ±100 mm cutting error requirement required in engineering. The intelligent cutting control technology of the lateral moving machine of the large-section tunnel boring machine studied in this paper can realize accurate cutting of the large-section tunnel of 5.6 meters. The research results of this paper provide a reference for the intelligent construction of heading faces.

Quantitative analysis of pressure levels in manual lymphatic drainage across stages of breast cancer-related lymphedema: implications for optimized treatment protocols.

To quantify the pressure levels necessary for effective Manual Lymphatic Drainage (MLD) in managing Breast Cancer-Related Lymphedema (BCRL) across various stages, and to contribute to the development of standardized protocols for MLD therapy. The study included 42 patients with BCRL (Stages I-III) and 14 certified lymphedema therapists. Forearms and upper arm circumferences were measured pre and post a 21-day MLD intervention. A tactile sensor system recorded the applied pressure during treatment. The data were preprocessed and statistically analyzed to assess pressure patterns and their stage-specific impacts on lymphedema. The mean age of the patients was 52.4years, and that of the therapists was 39.1years. A statistically significant reduction in arm circumference was observed post-MLD treatment (P < 0.05). The pressure applied varied across stages: I forearm 16.5-20.1mmHg, I upper arm 16.1-20.7mmHg; II forearm 16.6-19.8mmHg, II upper arm 19.7-23.8mmHg; III forearm 29.3-34.3mmHg, III upper arm 29.7-34.3mmHg. No statistically significant difference was found between forearm and upper arm treatment pressures within Stages I (P = 0.283) and III (P = 0.08), while Stage II exhibited a significant difference (P < 0.001). Across the same treatment area, pressures for Stages I and II in the forearm were significantly lower than those in Stage III (P < 0.001). The treatment pressure differences between forearm stages I and II were not statistically significant (P > 0.05). Differences in upper arm treatment pressures across Stages I, II, and III were also statistically significant (P < 0.001). The study provides quantitative evidence on the pressure ranges needed for MLD across different stages of BCRL. It highlights the importance for stage-specific pressure adjustments to optimize treatment outcomes. These findings contribute to the existing body of knowledge on MLD and offer valuable data that could inform the development of rehabilitation technologies, including intelligent robots and visualization systems, as well as enhance therapist training programs.

Formulation of Envelope Correlation Coefficient for Multiple Sensors Based on Scattering Parameter

The Envelope Correlation Coefficient (ECC) is crucial for assessing multiple sensor systems in wireless communication, sensing, and microwave imaging. ECC measures signal similarity between sensors, with higher values (close to 1.00) indicating strong correlation. Efficient power transmission, reception, and multiband path transmission enhance sensor network performance by improving data rates and reliability through multiple frequency bands. This study uses scattering parameters to calculate ECC for four different sensors in a single arrangement. Most of the sensors exhibit higher correlation at f2 and f3, while f1 shows a low ECC.

Engineering a Graphene Quantum Dot‐Enhanced Surface Plasmon Resonance Sensor for Ultra‐Sensitive Detection of Hg2⁺ Ions

AbstractThe contamination of soil and water by heavy metals poses a significant environmental and public health concern worldwide. To address this issue, a novel graphene quantum dot (GQD)‐based surface plasmon resonance (SPR) sensor is developed for the detection of mercury ions (Hg2+), a notorious heavy metal pollutant. The thiol and amine‐functionalized GQDs (S,N‐GQDs), synthesized via pyrolysis of citric acid and L‐cysteine, are directly immobilized onto the SPR chip surface without prior pretreatment, demonstrating their potential as efficient sensing materials. The SPR sensor exhibits high sensitivity and selectivity toward Hg2+ ions, as confirmed by kinetic binding analysis and isotherm modeling. The Langmuir isotherm model, which accurately describes the interactions between Hg2+ and S,N‐GQDs, provides insights into the sensor's mechanism of action. Furthermore, the sensor demonstrates robustness and reusability, with recoveries ranging from 98% to 104% over multiple cycles of analysis. Given the presence of contaminants in tap water, the developed sensor system holds significant importance for environmental monitoring and public health protection, offering a rapid, accurate, and cost‐effective solution for detecting Hg2+ ions in such samples. Overall, this study represents a significant advancement in the field of heavy metal detection, with potential implications for addressing environmental pollution and ensuring water quality.

A sensing system and solving method for dynamic detection of relative pose of hydraulic support group

The precise detection of the pose of hydraulic support groups is crucial for the construction of intelligent coal mines. Addressing current challenges in comprehensive position sensing of support groups, such as one-sided descriptions, missing information, and limited detection capabilities, this paper, based on the concept of digital twin, proposes a sensing system and solving method for dynamic detection of the relative pose of hydraulic support group. Initially, a relative pose detection model, based on the six-point localization principle, is proposed by combining existing sensor information. Then, a sensing system is designed, incorporating a photoelectric detection device for monitoring key position parameters and virtual sensors for determining pose information. Consequently, a method for dynamically deriving relative positions, driven by the fusion of real-virtual iterative solving, real-virtual error feedback, and iterative condition optimization, is established. Finally, a relative pose dynamic detection system for a hydraulic support prototype is constructed in a laboratory environment. Experimental results demonstrate that the sensor system and the relative position detection method designed in this paper achieve a relative position accuracy of over 95.32 % for the hydraulic support, with a dynamic reconstruction time of less than 0.2 s. This verifies the rationality of the sensor system and the feasibility and accuracy of the dynamic deduction method. To sum up, this work provides a theoretical foundation and technical support for the autonomous perception of hydraulic support group, autonomous frame adjustment between frames, and overall straightness adjustment. It also lays the groundwork for further research and the advancement of information-physical systems in the fully-mechanized mining face.

Comprehensive Link-Level Simulator for Terahertz MIMO Integrated Sensing and Communication Systems With TDD Framework

Comprehensive Link-Level Simulator for Terahertz MIMO Integrated Sensing and Communication Systems With TDD Framework

Low-power and cost-effective readout circuit design for compact semiconductor gas sensor systems

This study introduces a novel readout circuit architecture that enhances semiconductor gas sensor systems by reducing power consumption, enabling miniaturization, and improving economic viability. Validated at the PCB level, the design shows strong commercial potential by addressing power efficiency and signal accuracy challenges. The technology is adaptable for applications in environmental monitoring, industrial safety, and medical diagnostics, where efficient and reliable gas sensing is essential.

A novel on-line dual sensing system for soil property measurement and mapping

Real-time assessment of within-field soil fertility variation is crucial for making informed and sustainable decisions, like crop nitrogen (N) fertilization. Two commonly used soil sensors i.e., visible-near-infrared (vis-NIR) spectroscopy and ion-selective electrodes (ISE) have been reported to successfully estimate various soil macronutrients and nitrate-N, respectively, however, their integrated use for mapping a full-scale soil fertility variation has not been taken into consideration to date. The aim of this study is to use an integrated on-line dual-sensor system of vis-NIRS and ISE to estimate and map within-field variation in multiple soil fertility parameters including pH, organic carbon (OC), extractable- phosphorus (P), potassium (K), calcium (Ca) and moisture content (MC) and nitrate-N. The sensing system was mounted to the three-point linkage of a tractor and surveyed two arable fields in Belgium. Partial least squares regression models for vis-NIRS sensor were calibrated and validated, while a linear regression model was established for validation of the ISE sensor. Geostatistical surface maps for on-line measured soil attributes were generated using the inverse distance weighting and ordinary kriging methods for ISE data and vis NIR, respectively. The linear correlation confirms a very high similarity between ISE-measured nitrate-N and laboratory-analyzed nitrate-N (coefficient of determination (R2) = 0.93). Besides, the vis-NIRS demonstrates very good prediction accuracies for all the fertility attributes with R2 = 0.70-0.78, root mean square error (RMSE) =0.52-2.46 %, residual of prediction deviation (RPD) = 1.89 – 2.13 and the ratio of the performance to interquartile distance (RPIQ) = 1.72 – 6.56. Validation between laboratory and on-line measured soil maps also shows quite comparable spatial distribution patterns. Therefore, the proposed dual on-line sensor system has a high potential to estimate and map within-field spatial fertility distributions including nitrate-N, offering a basis for sustainable management decisions for precision soil and crop production.

Call for Papers: Special Issue on Intelligent Sensor Systems for the IEEE Journal of Electron Devices

Call for Papers: Special Issue on Intelligent Sensor Systems for the IEEE Journal of Electron Devices

Stabilization of gold nanoparticles in a transparent polymer while maintaining the capabilities of a colorimetric glucose sensor

We suggest optical sensor systems based on the gold nanoparticles embedded within the polymethacrylate matrix for dual-signal spectrophotometric and colorimetric approaches can be used for the determination of glucose in the range of 0.1–0.8 mM and 0.3–0.8 mM with the detection limits of 0.06 mM and 0.1 mM, correspondingly. We studied the conditions of interaction between the gold nanoparticles, iodide and glucose by evaluating the influence of such factors as the contact time, pH and the analyte concentration upon the analytical signal.

Programmable DNA-based nanostructure sensors for mutation detection coupled with asymmetric amplification in precision genome profiling

We introduce a DNA-based nanostructure sensor for the rapid, precise identification of single-nucleotide polymorphisms (SNPs), a key factor in determining disease susceptibility and tailoring medical treatment. The proposed two-state sensor design facilitates multiplexed, sequence-specific detection of double-stranded genomic DNA (gDNA), overcoming traditional obstacles presented by base pairing interference. Utilizing detector strands with binding penalties, the sensor achieves high SNP specificity, discernible by distinct gel electrophoresis band positions. Coupled with asymmetric PCR (asy-PCR), our integrated system amplifies genetic profiling effectiveness, especially in clinical biopsies, enhancing gDNA detection sensitivity and specificity for concurrent multiple mutation identifications. This cost-effective method ($0.41 per test) streamlines mutation screening, enabling high-throughput analysis with limited equipment—requiring only a single staining agent and a unified electrophoresis apparatus. Validated clinically with 70 colorectal cancer samples, the system boasts a 98.57% sensitivity and 95.71% specificity for KRAS mutations, surpassing traditional PCR detection and uncovering low-frequency mutations untraceable by direct sequencing. Our findings suggest this asy-PCR enhanced DNA-based nanostructure sensor system has the potential to revolutionize genetic diagnostics, offering expansive applications in detecting viral variants and microbial strains, thus advancing personalized genetic testing.