Detected Research Articles

Multiscale wildfire and smoke detection in complex drone forest environments based on YOLOv8

Global climate change has triggered frequent extreme weather events, leading to a significant increase in the frequency and intensity of forest fires. Traditional fire monitoring methods such as manual inspections, sensor technologies, and remote sensing satellites have limitations. With the advancement of drone technology and deep learning, using drones combined with artificial intelligence for fire monitoring has become mainstream. This paper proposes an improved YOLOv8-based model that incorporates local convolution instead of full convolution in the C2F module and integrates the EMA module to enhance the feature channel interaction modeling capability and contextual information utilization, thereby reducing model complexity and increasing efficiency. Additionally, in order to address the risk of false positives and missed detections caused by vegetation, terrain, and lighting changes in forests, we have introduced the AgentAttention module in the Backbone. This module combines Softmax and linear attention to optimize feature extraction, improving the model’s accuracy and robustness. Furthermore, in order to tackle the challenges of detecting flames and smoke at different scales and angles, we have designed the BiFormer module, which adaptively fuses global and local features, significantly enhancing the model’s multi-scale and multi-angle detection capability. Experimental results show that the improved model achieves Precision and Recall of 93.57% and 88.51%, respectively, representing improvements of 5.05% and 2.72% over the original model. It also optimizes FPS, GFLOPs, and Params by 14.3%, 25%, and 19.7%, respectively. This research has significant application prospects in forest fire early warning, emergency response, and loss reduction, while also providing strong technical support for forest resource protection and public safety.

Increasing the efficiency of bioreactor operation for cultivation of methane-oxidizing bacteria under conditions of decreasing carbon dioxide concentration in the cultural liquid

Objectives. The work set out to develop a bioreactor that incorporates a carbon dioxide removal unit within the apparatus gas phase, which is capable of operating without the need for supplementary compression apparatus. As part of testing the developed equipment in order to ascertain its capacity for enhanced biomass production, the principal fermentation system parameters that facilitate the optimal bioreactor productivity in conditions of carbon dioxide removal from the apparatus gas phase were identified.Methods. A series of tests were conducted on the fermentation unit with the objective of controlling the oxygen and carbon dioxide content in the gas phase of the bioreactor. This was achieved using an in-line gas analyzer fitted with electrochemical sensors. The oxygen and carbon dioxide content in the gas phase was determined by means of gas chromatography. The oxygen and natural gas flow rates were determined using a thermal electronic flow controller equipped with thermoresistive elements. The oxygen content of the cultural liquid was determined by means of an optical oxygen sensor with integrated transducer. The pH level in the bioreactor was monitored and maintained using an electrochemical pH sensor.Results. The efficacy of the newly devised jet-type bioreactor design, which permits the incorporation of a carbon dioxide removal unit into the fermentation system without requiring supplementary compression apparatus, was evaluated through experimentation. The system was tested with the carbon dioxide removal unit included in the design, resulting in a 64% increase in bioreactor productivity and a 18% reduction in oxygen consumption as a component of the gas supply.Conclusions. The operational parameters of a technological bioreactor that facilitate a stable continuous process of bacterial cultivation were identified.

Leveraging Federated Learning For Optimizing Fiber Optic Sensor Design For Biodetection

Leveraging Federated Learning For Optimizing Fiber Optic Sensor Design For Biodetection

Changes in sensor recorded activity patterns and neuropsychiatric symptoms after deep brain stimulation for Parkinson’s disease: 5 case reports

BackgroundEffects of subthalamic nucleus deep brain stimulation (STN-DBS) on neuropsychiatric symptoms of Parkinson’s disease (PD) remain debated. Sensor technology might help to objectively assess behavioural changes after STN-DBS.Case presentation5 PD patients were assessed 1 before and 5 months after STN-DBS with the Movement Disorders Society Unified Parkinson’s Disease Rating Scale part III in the medication ON (plus postoperatively stimulation ON) condition, the Montreal Cognitive Assessment, the Questionnaire for Impulsive-Compulsive Behaviors in Parkinson’s Disease Rating Scale present version, the Hospital Anxiety and Depression Scale and the Starkstein Apathy Scale. Steps taken per hour, nighttime spent in bed and time spent outside were monitored with a smartwatch and ambient sensors placed in patient homes for an average of 20 days pre- and postoperatively. Postoperative improvement in ICDs and concomitant anxious-depressive symptoms was observed in 3 patients and was accompanied by a decrease in steps taken per hour, as well as an increase in nighttime spent in bed. In the two patients without baseline ICDs, mild anxiety and apathy improved postoperatively, and no new neuropsychiatric symptoms occurred. Steps taken per hour did not decrease in these cases and nighttime spent in bed improved in one of the patients, but decreased in the other, who had experienced pain during OFF-phases at night before STN-DBS.ConclusionChanges in neuropsychiatric symptoms are associated with distinct activity patterns after STN-DBS, and wearable and ambient sensors may aid to capture those gradual shifts in behavior.

A New Low-Cost Piezoelectric Ceramic Strain Detection Method

With the rapid development of the electronic information industry, more and more attention has been paid to piezoelectric ceramic materials, but the electrical properties and characteristics of piezoelectric ceramic materials have problems such as high cost and inconvenient measurement. In this paper, a new method of electroinduced strain measurement of piezoelectric ceramics is proposed, and an innovative measuring device is constructed based on the working mode and testing principle of an optical displacement sensor and piezoelectric ceramics. An optical displacement measuring device with a simple structure, convenient operation, high measurement accuracy, and good cost benefit was designed and manufactured, and the electroinduced strain performance of piezoelectric ceramics was effectively measured. It is verified by experimental analysis that the device can accurately measure the axial displacement of various piezoelectric ceramics, the measurement accuracy is comparable to the existing equipment, the error is less than 10%, and has good stability and repeatability, which provides a reliable technical means for the performance measurement of piezoelectric ceramics.

Designing a terahertz optical sensor based on helically twisted photonic crystal fiber for toxic gas sensing

A novel helically twisted photonic crystal fiber (PCF) is designed and proposed for sensing toxic gases with refractive indices ranging from 1.00 to 1.08. The PCF consists of twelve hollow pipes arranged circularly around the hollow core to support THz radiation propagation. Low-loss polymer Topas is used as the background material of cladding. The fiber is twisted 360° over 50 cm to enhance anti-resonance in the THz region. The fundamental LP01 mode is analyzed using the finite-difference eigenmode (FDE) method. The sensor operates across four frequency bands (0.2 to 3.0 THz) with minimal transmission loss (~ 10⁻⁴ 1/cm). Key parameters such as refractive index sensitivity, relative sensitivity, resolution, and figure of merit (FOM) are evaluated. The average refractive index sensitivities are 1450, 2250, 3000, and 2550 for Bands 1 to 4, respectively, with 100% relative sensitivity across all bands. The sensor detects refractive index changes as small as 10⁻⁴. The FOM, defined as the inverse of the full width at half maximum, exceeds 30 1/RIU, reaching up to 250 1/RIU due to sharp resonance peaks. Compared to other THz sensors, this design offers enhanced performance in sensing gases like SOx, NOx, and CO, while maintaining a simple structure.

Exploration of linear and interpretable models for quantification of cell parameters via contactless short-wave infrared hyperspectral sensing

The development of optical sensors for label-free quantification of cell parameters has numerous uses in the biomedical arena. However, using current optical probes requires the laborious collection of sufficiently large datasets that can be used to calibrate optical probe signals to true metabolite concentrations. Further, most practitioners find it difficult to confidently adapt black box chemometric models that are difficult to troubleshoot in high-stakes applications such as biopharmaceutical manufacturing. Replacing optical probes with contactless short-wave infrared (SWIR) hyperspectral cameras allows efficient collection of thousands of absorption signals in a handful of images. This high repetition allows for effective denoising of each spectrum, so interpretable linear models can quantify metabolites. To illustrate, an interpretable linear model called L-SLR is trained using small datasets obtained with a SWIR HSI camera to quantify fructose, viable cell density (VCD), glucose, and lactate. The performance of this model is also compared to other existing linear models, namely Partial Least Squares (PLS) and Non-negative Matrix Factorization (NMF). Using only 50% of the dataset for training, reasonable test performance of mean absolute error (MAE) and correlations (r2) are achieved for glucose (r2 = 0.88, MAE = 37 mg/dL), lactate (r2 = 0.93, MAE = 15.08 mg/dL), and VCD (r2 = 0.81, MAE = 8.6 × 105 cells/mL). Further, these models are also able to handle quantification of a metabolite like fructose in the presence of high background concentration of similar metabolite with almost identical chemical interactions in water like glucose. The model achieves reasonable quantification performance for large fructose level (100–1000 mg/dL) quantification (r2 = 0.92, MAE = 25.1 mg/dL) and small fructose level (< 60 mg/dL) concentrations (r2 = 0.85, MAE = 4.97 mg/dL) in complex media like Fetal Bovine Serum (FBS). Finally, the model provides sparse interpretable weight matrices that hint at the underlying solution changes that correlate to each cell parameter prediction.

Research on Ecological Environment Monitoring and Protection Applications Based on Remote Sensing Technology

As global ecological and environmental issues become increasingly severe, remote sensing technology, as an effective monitoring tool, plays a crucial role in ecological environment monitoring and protection. Remote sensing technology can provide large-scale, high-resolution data, supporting a wide range of applications such as ecosystem health assessment, land use/land cover change monitoring, biodiversity conservation, water resource management, pollution monitoring, and climate change monitoring. This paper reviews the application progress of remote sensing technology in ecological environment protection, analyzes the challenges and development trends it faces, and discusses potential directions for future research.

CdSe/ZnS quantum dots-doped polymer optical fiber microprobe for pH sensing

pH is an important physiological parameter within organisms, playing a crucial role in functional activities in cells and tissues. Among various pH sensing methods, optical fiber pH sensors have gained a wide attention due to their unique advantages. However, current silica optical fiber-based pH sensors face some challenges such as weak biocompatibility, low biological safety, complex or unstable surface modification. Herein, we develop what we believe to be a novel pH sensor based on a CdSe/ZnS quantum dots-doped polymer optical fiber microprobe (POF MP) grown at the end of the silica optical fiber using the free radical photopolymerization process, which has the advantages of significant compactness, high flexibility, good biocompatibility, easy functionalization, high structural stability and safety. Moreover, the size of the POF MP are controllable, which is highly significant for applications requiring specific probe sizes or those used in special terrains. The proposed sensor is demonstrated to have a sensitivity of 0.18097/pH in a wide pH range from 4.5 to 9.0, while it exhibits a highly linear correlation between fluorescence intensity and pH value (R2 = 0.99448) and good reversibility and reusability. This proposed pH sensor offers a promising solution for pH monitoring in biological environments, contributing to advancements in biosensing, microenvironment monitoring, and potential therapeutic applications.

Dual-Emissive Iridium(III) Complex with Aggregation-Induced Emission: Mechanistic Insights into Electron Transfer for Enhanced Hypoxia Detection in 3D Tumor Models.

Accurate oxygen detection and measurement of its concentration is vital in biological and industrial applications, necessitating highly sensitive and reliable sensors. Optical sensors, valued for their real-time monitoring, nondestructive analysis, and exceptional sensitivity, are particularly suited for precise oxygen measurements. Here, we report a dual-emissive iridium(III) complex, IrNPh2, featuring "aggregation-induced emission" (AIE) properties and used for sensitive oxygen sensing. IrNPh2 exhibits dual emissions at 450 and 515 nm, with 515 nm triplet-state emission demonstrating remarkable oxygen sensitivity due to its long-lived excited state (12.12 μs) and high quantum yield (68%). Stern-Volmer analysis reveals a notable quenching constant (Ksv = 12.44%-1) and an ultralow detection limit of 0.0397%, emphasizing its superior performance. The oxygen quenching mechanism is driven by electron transfer (ET), supported by computational studies showing the lowest-unoccupied molecular orbital (LUMO) alignment of IrNPh2 with the πg* orbitals of triplet oxygen, leading to superoxide radical (O2•-) formation. Electron paramagnetic resonance (EPR) studies further confirm this pathway. Biological evaluations using a three-dimensional (3D) U87-MG glioma spheroid model highlight the ability of IrNPh2 to detect hypoxic regions, with significant fluorescence enhancement under hypoxia and minimal cytotoxicity (>80% viability at 100 μM). With high sensitivity, low detection limits, and biocompatibility, IrNPh2 emerges as a promising candidate for oxygen sensing in environmental and biomedical applications, especially tumor hypoxia detection.

Screen-Printing vs Additive Manufacturing Approaches: Recent Aspects and Trends Involving the Fabrication of Electrochemical Sensors.

A few decades ago, the technological boom revolutionized access to information, ushering in a new era of research possibilities. Electrochemical devices have recently emerged as a key scientific advancement utilizing electrochemistry principles to detect various chemical species. These versatile electrodes find applications in diverse fields, such as healthcare diagnostics and environmental monitoring. Modern designs have given rise to innovative manufacturing protocols, including screen and additive printing methods, for creating sophisticated 2D and 3D electrochemical devices. This perspective provides a comprehensive overview of the screen-printing and additive-printing protocols for constructing electrochemical devices. It is also informed that screen-printed sensors offer cost-effectiveness and ease of fabrication, although they may pose challenges due to the use of toxic volatile inks and limited design flexibility. On the other hand, additive manufacturing, especially the fused filament fabrication (or fused deposition modeling) strategies, allows for intricate three-dimensional sensor designs and rapid prototyping of customized equipment. However, the post-treatment processes and material selection can affect production costs. Despite their unique advantages and limitations, both printing techniques show promise for various applications, driving innovation in the field toward more advanced sensor designs. Finally, these advancements pave the way for improved sensor performance and expand possibilities for academic, environmental, and industrial applications. The future is full of exciting opportunities for state-of-the-art sensor technologies that will further improve our ability to detect and determine various substances in a wide range of environments as researchers continue to explore the many possibilities of electrochemical devices.

Nucleic Acid Aptamer-Based Sensors for Bacteria Detection: A Review.

Bacteria have a significant impact on human production and life, endangering human life and health, so rapid detection of infectious agents is essential to improve human health. Aptamers, which are pieces of oligonucleotides (DNA or RNA) have been applied to biosensors for bacteria detection due to their high affinity, selectivity, robust chemical stability, and their compatibility with various signal amplification and signal transduction mechanisms. In this review, we summarize the different bacterial aptamers selected in recent years using SELEX technology and discuss the differences in optical and electrochemical bacterial aptamer sensors. In addition the technological developments and innovations in bacterial aptamer sensor technology are introduced. Combining new materials and methods, the efficiency and stability of the sensors have also been improved. This review summarizes the progress of current bacterial aptamer sensors based on their practical application status and provides an outlook on their future development.

Molecular Determinants of Optical Modulation in ssDNA-Carbon Nanotube Biosensors.

Most traditional optical biosensors operate through molecular recognition, where ligand binding causes conformational changes that lead to optical perturbations in the emitting motif. Optical sensors developed from single-stranded DNA-functionalized single-walled carbon nanotubes (ssDNA-SWCNTs) have started to make useful contributions to biological research. However, the mechanisms underlying their function have remained poorly understood. In this study, we combine experimental and computational approaches to show that ligand binding alone is not sufficient for optical modulation in this class of synthetic biosensors. Instead, the optical response that occurs after ligand binding is highly dependent on the chemical properties of the ligands, resembling mechanisms seen in activity-based biosensors. Specifically, we show that in ssDNA-SWCNT catecholamine sensors, the optical response correlates positively with the electron density on the aryl motif, even among ligands with similar ligand binding affinities. Importantly, despite the strong correlations with electrochemical properties, we find that catechol oxidation itself is not necessary to drive the sensor optical response. We discuss how these findings could serve as a framework for tuning the performance of existing sensors and guiding the development of new biosensors of this class.

A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis

Analysis of the human gait represents a fundamental area of investigation within the broader domains of biomechanics, clinical research, and numerous other interdisciplinary fields. The progression of visual sensor technology and machine learning algorithms has enabled substantial developments in the creation of human gait analysis systems. This paper presents a comprehensive review of the advancements and recent findings in the field of vision-based human gait analysis systems over the past five years, with a special emphasis on the role of vision sensors, machine learning algorithms, and technological innovations. The relevant papers were subjected to analysis using the PRISMA method, and 72 articles that met the criteria for this research project were identified. A detailing of the most commonly used visual sensor systems, machine learning algorithms, human gait analysis parameters, optimal camera placement, and gait parameter extraction methods is presented in the analysis. The findings of this research indicate that non-invasive depth cameras are gaining increasing popularity within this field. Furthermore, depth learning algorithms, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, are being employed with increasing frequency. This review seeks to establish the foundations for future innovations that will facilitate the development of more effective, versatile, and user-friendly gait analysis tools, with the potential to significantly enhance human mobility, health, and overall quality of life. This work was supported by [GOBIERNO DE ESPANA/PID2023-150967OB-I00].

A Magnetoelectric Distance Estimation System for Relative Human Motion Tracking

Clinical motion analysis plays an important role in the diagnosis and treatment of mobility-limiting diseases. Within this assessment, relative (point-to-point) tracking of extremities could benefit from increased accuracy. Given the limitations of current wearable sensor technology, supplementary spatial data such as distance estimates could provide added value. Therefore, we propose a distributed magnetic tracking system based on early-stage demonstrators of novel magnetoelectric (ME) sensors. The system consists of two body-worn magnetic actuators and four ME sensor arrays (body-worn and fixed). It is enabled by a comprehensive signal processing framework with sensor-specific signal enhancement and a gradient descent-based system calibration. As a pilot study, we evaluated the technical feasibility of the described system for motion tracking in general (Scenario A) and for operation during treadmill walking (Scenario B). At distances of up to 60 cm, we achieved a mean absolute distance error of 0.4 cm during gait experiments. Our results show that the modular system is capable of centimeter-level motion tracking of the lower extremities during treadmill walking and should therefore be investigated for clinical gait parameter assessment.

A Review of Advanced Reactive Materials in Fiber Sensor Technology: Challenges and Requirements of Precise Humidity Control in Mechanochemical Treatment Process

A Review of Advanced Reactive Materials in Fiber Sensor Technology: Challenges and Requirements of Precise Humidity Control in Mechanochemical Treatment Process

Technologies for enhancing tactile perception in prosthetic devices

Tactile perception is a vital sensory function, and its role has been widely researched in the development of sensor technologies. The role of tactile feedback in augmenting the functional attributes of prosthetic devices is well-documented, as it facilitates a more seamless and natural engagement between the user and the surrounding environment. This paper explores advancements in tactile sensor technologies used to enhance sensory feedback in prosthetics, focusing on piezoresistive, piezoelectric, and capacitive sensors. The integration of these sensors allows for precise control, improving object manipulation and overall user experience. By reviewing recent innovations such as multimodal sensors, artificial skin technologies, and nerve interfacing systems, this research highlights how these advancements have contributed to enhanced motor control and real-time feedback mechanisms in prosthetics. Despite significant progress, challenges such as high costs and limited accessibility remain. However, potentially leading to the widespread adoption of more effective and equitable prosthetic solutions.

Research and application of bionic robotic arms

The research on bionic robotic arms originates from the aspiration to imitate and replicate the structure and functions of the human arm. With advancements in technology, particularly in the rapid development of robotics, sensor technology, and materials science, robust technical support has been provided for the study of bionic robotic arms. Additionally, as people's demands for a higher quality of life increase, so does the need for mechanical devices that can undertake arduous, hazardous, or high-precision tasks in place of humans. Especially in fields such as industrial production, healthcare, and military security, the research and application of bionic robotic arms have become a focal point in today's technological landscape. This article presents a comprehensive overview of the research background, development trajectory, and future prospects of three specific areas: flexible robotic arms for confined space operations, bionic robotic arms for rotorcraft, and bionic jellyfish robotic arms. The aim is to provide theoretical support and technical references for research and engineering applications in related fields

Capillary-Assisted Confinement Assembly for Advanced Sensor Fabrication: From Superwetting Interfaces to Capillary Bridge Patterning.

Precise patterning of sensing materials, particularly the long-range-ordered assembly of micro/nanostructures, is pivotal for improving sensor performance, facilitating miniaturization, and enabling seamless integration. This paper examines the importance of interfacial confined assembly in sensor patterning, including gas-liquid and liquid-liquid confined assembly, wettability-assisted or microstructure-assisted solid-liquid interfacial confined assembly, and tip-induced confined assembly. The application of capillary bridge confined assembly technology in chemical sensors, flexible electronics, and optoelectronics is highlighted. The advantages of capillary bridge confined assembly technology include the ability to achieve high-resolution patterning, scalability, and material arrangement in long-range order. It is, therefore, an ideal processing platform for next-generation sensors. Finally, the broad prospects of this technology in the miniaturization and integration of high-performance multifunctional sensors are discussed.

Simultaneous vibration and soil moisture sensing using a single mode fiber for structural health monitoring applications

A dual-purpose single mode optical fiber sensor was developed for simultaneous soil moisture and structural health monitoring. Unmodified G655 SMF with polarization coupled phase components was used to detect environmental changes due to ground movement and moisture content variations. Spectral distribution profiles of the baseband frequencies below 10 Hz were analyzed for structural health monitoring, while resonant peak frequencies higher than 110 Hz had notable dependence on the damping effect of moisture on the spectral peak intensity of the detected acoustic waves. Using these peaks, moisture content was measured within a root mean square error value of +/−0.04. The sensor demonstrates a critical resource optimization tool for distributed sensing applications and monitoring in PON systems.