Alnus glutinosa is important woody plant in Lithuanian forest ecosystems. Knowledge of fluorescence properties of black alder pollen is necessary for scientific and practical purposes. By the results of the study we aimed to evaluate possibilities of identifying Alnus glutinosa pollen fluorescence properties by modeling ozone effect and applying two different fluorescence-based devices. To implement experiments, black alder pollen was collected in a typical habitat during the annual flowering period in 2018-2019. There were three groups of experimental variants, which differed in the duration of exposure to ozone, conditions of pollen storage before the start of the experiment, and the experiment start time. Data for pollen fluorescence analysis were collected using two methods. The microscopy method was used in order to evaluate the possibility of employing image analysis systems for investigation of pollen fluorescence. The second data collection method is related to the automatic device identifying pollen in real-time, which uses the fluorescence method in the pollen recognition process. Data were assessed employing image analysis and principal component analysis (PCA) methods. Digital images of ozone-exposed pollen observed under the fluorescence microscope showed the change of the dominant green colour towards the blue spectrum. Meanwhile, the automatic detector detects more pollen whose fluorescence is at the blue light spectrum. It must be noted that assessing pollen fluorescence several months after exposure to ozone, no effect of ozone on fluorescence remains.

Tissue-like phantoms are widely used as a model for mimicking the optical properties of live tissue. This paper presents the results of a diffusion reflection method as well as fluorescence lifetime imaging microscopy measurements of fluorescein-conjugated gold nanorods in solution as well as inserted in solid tissue-imitating phantoms. A lack of consistency between the fluorescence lifetime results of the solutions and the phantoms raises a question about the ability of tissue-like phantoms to maintain the optical properties of inserted contrast agents.

Single-molecule fluorescence detection (SMFD) experiments are useful in distinguishing sub-populations of molecular species when measuring heterogeneous samples. One experimental platform for SMFD is based on a confocal microscope, where molecules randomly traverse an effective detection volume. The non-uniformity of the excitation profile and the random nature of Brownian motion, produce fluctuating fluorescence signals. For these signals to be distinguished from the background, burst analysis is frequently used. Yet, the relation between the results of burst analyses and the underlying information of the diffusing molecules is still obscure and requires systematic assessment. In this work we performed three-dimensional Brownian motion simulations of SMFD, and tested the positions at which molecules emitted photons that passed the burst analysis criteria for different values of burst analysis parameters. The results of this work verify which of the burst analysis parameters and experimental conditions influence both the position of molecules in space when fluorescence is detected and taken into account, and whether these bursts of photons arise purely from single molecules, or not entirely. Finally, we show, as an example, the effect of bursts that are not purely from a single molecule on the accuracy in single-molecule Förster resonance energy transfer measurements.

Herein, we developed a highly sensitive and specific bimolecular fluorescence complementation (BiFC)-based influenza A virus (IAV) sensing system created by combining a galactose/glucose-binding protein (GGBP) with an N-terminal large domain (YN1-172) and a C-terminal small domain (YC173-239) of enhanced yellow fluorescence protein (eYFP). The GGBP-based BiFC reporter exhibits the fluorescence reconstitution as a result of conformational changes in GGBP when lactose, derived from 6’-silalyllactose used as a substrate for neuraminidase (NA), binds to GGBP in the presence of IAV. The system showed a linear dynamic range from 1 x 100 TCID50/mL to 1 x 107 TCID50/mL, with a detection limit of 4 x 101 TCID50/mL for IAV (H1N1), demonstrating ultra-high sensitivity. Our system exhibited fluorescence intensity enhancements in the presence of IAV, while displaying weak fluorescence signals when exposed to NA-deficient viruses such as RSV A, RSV B, adenovirus and rhinovirus, thereby indicating selective responses for IAV detection. Taken together, our system provides a simple, highly sensitive and specific IAV detection platform based on BiFC capable of detecting the ligand-induced protein conformational changes, obviating the need for virus culture or RNA extraction processes.

Berberine (BBR), as a natural isoquinoline alkaloid, has been demonstrated various pharmacological activities, and widely applied in the treatment of diseases. The quantitative analysis of BBR is important for its pharmacological researches and clinical applications. In the work, utilizing the specific interaction between BBR and triplex DNA, a sensitive and selective fluorescent detecting method was established with DNA-templated silver nanoclusters (DNA-AgNCs). After binding with the triplex structure in the template of DNA-AgNCs, BBR quenched the fluorescence of DNA-AgNCs and formed BBR-triplex complex with yellow-green fluorescence. The ratiometric fluorescence signal showed a linear relationship with BBR concentration in a range between 10 nM to 1000 nM, with a detect limit of 10 nM. Our method exhibited excellent sensitivity and selectivity, and it was further applied in BBR detection in real samples.

Dibutyl phthalate (DBP) is widely used as a plasticizer in the production of polymeric materials to give them flexibility, strength and extensibility. However, due to its negative impact on human health, in particular, reproductive function and fetal development, the content of DBP must be controlled in food and environmental objects. The present study aims to develop a sensitive, fast and simple polarization fluorescent immunoassay (FPIA) using monoclonal antibodies derived against DBP (MAb-DBP) for its detection in open waters. New conjugates of DBP with various fluorescein derivatives were obtained and characterized: 5-aminomethylfluorescein (AMF) and dichlorotriazinylaminofluorescein (DTAF). The advantages of using the DBP-AMF conjugate in the FPIA method are shown, the kinetics of binding of this drug with antibodies is studied, the analysis is optimized, and the concentration of monoclonal antibodies is selected for sensitive analysis - 16 nM. The calibration dependence of the fluorescence polarization signal for the detection of DBP was obtained. The observed IC50 (40 ng/mL) and LOD (10 ng/mL) values were improved by a factor of 35 for the previously described FPIA using polyclonal antibodies. This technique was tested by the added/found method, the percentage of DBP discovery in water ranged from 85 to 110%. Using the developed method, real water samples from Lake Onega were tested, and a good correlation was shown between the results of the determination of DBP by the FPIA method and GC-MS/MS. Thus, the FPIA method developed in this work can be used to determine DBP in open water reservoirs.

Drinking water contamination of lead from various environmental sources, leaching consumer products and intrinsic water-pipe infrastructure is still today a matter of great concern. Therefore, new highly sensitive and convenient Pb²⁺ measurement schemes are necessary, especially for in-situ measurements at a low-cost. Within this work dye/ionophore/Pb²⁺ co-extraction and effective water phase de-colorization was utilized for highly sensitive lead measurements and sub-ppb naked-eye detection. A low-cost ionophore Benzo-18-Crown-6-ether was used, and a simple test-tube mix and separate procedure was developed. Instrumental detection limits were in the low ppt region (LOD=3, LOQ=10), and naked-eye detection was 500 ppt. Note, however, that this sensing scheme still has improvement potential as concentrations of fluorophore and ionophore were not optimized. Artificial tap-water samples, leached by a standardized method, demonstrated drinking water application. Implications for this method are convenient in-situ lead ion measurements.

A new type of gold nanoparticles was synthesized by using benzoyl pyrazolone as a capping agent. The synthesized AuNPs exhibited a spherical shape and a monodisperse and fluorescence emission characteristic peak at 650 nm. The AuNPs –BMPBP was demonstrated as sensitive and selective fluorescent chemosensor for detection of Al ³⁺ ion. In the presence of Al ³⁺ ion, the fluorescent emission of BMPBP-AuNPs at 650 nm, increased with an increasing concentration of Al ³⁺ ion with a low detection limit (2 µM), the proposed method provided to apply determination of Al ³⁺ ion of tap water with satisfactory results.

Objective: Surgical eradication of malignant glioma cells is theoretically impossible. Therefore, reducing the number of remaining tumor cells around the brain-tumor interface (BTI) is crucial for achieving satisfactory clinical results. The usefulness of fluorescence-guided resection for the treatment of malignant glioma was recently reported, but the detection of infiltrating tumor cells in the BTI using a surgical microscope is not realistic. Therefore, we developed an intraoperative rapid fluorescence cytology system, and evaluated its clinical feasibility for the management of malignant glioma. Materials and methods: Twenty-five selected patients with malignant glioma (newly diagnosed: 17; recurrent: 8) underwent surgical resection under photodiagnosis using photosensitizer Talaporfin sodium and a semiconductor laser. Intraoperatively, a crush smear preparation was made from a tiny amount of tumor tissue, and the fluorescence emitted upon 620/660 nm excitation was evaluated rapidly using a compact fluorescence microscope in the operating theater. Results: Fluorescence intensities of tumor tissues measured using a surgical microscope correlated with the tumor cell densities of tissues evaluated by measuring the red fluorescence emitted from the cytoplasm of tumor cells using a fluorescence microscope. A “weak fluorescence” indicated a reduction in the tumor cell density, whereas “no fluorescence” did not indicate the complete eradication of the tumor cells, but indicated that few tumor cells were emitting fluorescence.Conclusion: The rapid intraoperative detection of fluorescence from glioma cells using a compact fluorescence microscope was a useful to evaluate the presence of tumor cells in the resection cavity walls, and provides surgical implications for the more complete resection of malignant gliomas.

Vitamin B2, also known as riboflavin (RF) is an essential micronutrient for human health and must be obtained from dietary sources. Plants biosynthesize riboflavin and are important dietary sources of vitamin B2 for humans. Our present study reports sensitive detection of Vitamin B2 in widely consumed for tea infusions, namely black, green, sage and rosemary tea infusions, by a capillary electrophoresis method combined with laser induced fluorescence detection. Moreover, the correlation between Vitamin B2 contents of tea plants with their total phenolics (TPs) and antioxidant capacity are evaluated in this study. Whereas green teas have the highest TPs and antioxidant capacity, the highest RF contents are in sage infusions. The RF contents range between 0.34 and 10.36 µg/g for all tea samples studied. Comparing RF contents of tea samples found in this study to the RF contents of known RF sources, tea infusions are proposed as important dietary sources of Vitamin B2.

Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with various sensors are expensive and not suitable for mountainous roads due to poor conditions. To address these challenges, this study proposes a customized Unmanned Aerial Vehicles (UAV) inspection system designed for automatic crack detection. The system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method is proposed to enhance the autonomous navigation of UAV flights by generating path planning in mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the YOLOv8 algorithm is introduced to conduct autonomous crack detection from UAV imagery in the offboard module. To validate our UAV inspection system's performance, we conducted several experiments to assess its accuracy, robustness, and efficiency. The experimental results demonstrate that our UAS inspection system accurately estimates the flight trajectory along the mountainous road for autonomous navigation. Furthermore, the experimental detection results also demonstrate that the YOLOv8 algorithm achieves higher recognition accuracy compared to the YOLOv5, and YOLOv7-tiny algorithms, exceeding 3.1%,and 6.8% of mAP, respectively. In summary, the UAV inspection system proposed in this study can provide a useful tool and technological guidance for the regular inspection of mountainous roads.

Perilla frutescens leaves are expected to exhibit antioxidant activity and amyloid-β (Aβ) aggregation inhibitory activity due to the presence of polyphenol-type compounds, etc. These activities fluctuate daily; however, measuring the contents of constituents and functionalities is cumbersome and unsuitable for instantaneous measurements in the field. In this study, we developed a facile method to analyze the activity and content of the components in leaves on-site based on the fluorescence spectra obtained by irradiating the perilla leaves with excitation light. The constituent contents (Chlorophyl (Chl), Total polyphenol content (TPC), Total flavonoid content (TFC), rosmarinic acid (RA) and functional properties DPPH radical scavenging activity, ferric reducing antioxidant power (FRAP), oxygen radical absorbance capacity (ORAC), Aβ aggregation inhibitory activity) were evaluated using established analytical techniques. The correlation between the fluorescence spectrum and the contents and activities was calculated using normalized difference spectral index (NDSI), ratio spectral index (RSI), and difference spectral index (DSI) analyses. We identified a predictive model with a high coefficient of determination R² ≥ 0.57 for content of the constituents, and R² ≥ 0.49 for functional properties. This allows convenient, simultaneous, and non-destructive monitoring of the constituent contents and the functionalities of perilla leave and a simple harvest-time diagnosis. The model established using this method is simple and suggesting that it can simultaneously infer the constituents and functionalities of perilla leaves.

All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller quad skate variant of hockey team sports, it is of great interest to automatically track player’s movements and positions, player’s sticks and, also, making other judgments, such as being able to locate the ball. In this work, we introduce a real-time pipeline composed by an object detection model, created specifically for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and quick motions, our deep learning object detection model effectively identifies and tracks, in real-time, important visual elements such as: ball; players; sticks; referees; crowd; goalkeeper; and goal. Using a curated dataset composed by a collection of videos of rink hockey, comprising 2525 annotated frames, we trained and evaluated the algorithm performance and compare it to state of the art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80%, and presents a good performance in terms of accuracy and speed, according to our results, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected one important event type in rink hockey games, the occurrence of penalties.

The Doppler band compensated by the keystone transform (KT) is limited. Therefore, it needs to be used in conjunction with the Doppler ambiguity compensation function to correct the range migration (RM) caused by maneuvering targets with Doppler ambiguity. However, the KT implemented by sinc interpolation suffers from significant performance loss at boundaries of compensation Doppler bands. Additionally, in a multi-target scenario, KT implementation methods occupy high complexity when the Doppler range of targets spans over two compensation Doppler bands. To address aforementioned issues, this study presents a variable Doppler starting point keystone transform (VDSPKT) method, where a new form of ambiguity compensation function is constructed, turning the Doppler starting point of the compensation band in KT variable. Firstly, the position of the compensation Doppler band is changed from fixed to adjustable as needed, enhancing the flexibility of KT. Crucially, the connection points of the compensation Doppler bands in sinc interpolation are reset, avoiding the performance loss at their boundaries. Also, the compensation band is adjusted to cover the narrow Doppler frequency range caused by targets, significantly improving the computational efficiency. Finally, the simulation results demonstrate that the proposed approach effectively addresses the performance degradation and high computational complexity of KT in the aforementioned scenarios, resulting in a computational load reduced by approximately 50% compared to traditional methods.

In the present protocol, we determined the presence and concentrations of bisphenol A (BPA) spiked in surface water samples using EEM fluorescence spectroscopy in conjunction with modelling using partial least squares (PLS) and parallel factor (PARAFAC). PARAFAC modelling of the EEM fluorescence data obtained from surface water samples contaminated with BPA unraveled four fluorophores including BPA. The best outcomes for BPA concentration (R2 = 0:996; Standard deviation to prediction error's root mean square ratio (RPD) = 3.41; and a Pearson's r value of 0.998). With these values of R2 and Pearson's r, the PLS model showed a strong correlation between the predicted and measured BPA concentrations. The detection and quantification limits of the methods were 3.512 and 11.708 micro molar (µM), respectively. In conclusion, BPA can be precisely detected and its concentration in surface water predicted using the PARAFAC and PLS models developed in this study and fluorescence EEM data collected from BPA-contaminated water. It is necessary to spatially relate surface water contamination data with other datasets in order to connect drinking water quality issues with health, environmental restoration, and environmental justice concerns.

Fluorescein anisotropy, which is a widely used technique to study the folding state of proteins or affinity of ligands, is used in the present work to study the temperature sensing of fluid in a microchannel, by adding fluorophore in the fluid. Fluorescein was used as a temperature probe, while glycerol-aq. ammonia was used as a working fluid. Fluorescence anisotropy of fluorescein were measured by varying various parameters. Apart from this, a comparison of fluorescence anisotropy and fluorescence intensity is also performed.

Within the spectra of lanthanide atoms quite often overlapping spectral lines (blends) are observed. On the example of complicated blend situations within Pr I and La I it is shown that in such cases a combination of optogalvanic and laser-induced fluorescence detection is necessary to find all transitions contributing to the observed spectral signature.

ABSTRACT Purpose: Tongue image analysis for disease diagnosis is an ancient traditional non-invasive diagnosis technique widely used by traditional medicine practitioners. Deep Learning based multi-label disease detection models offer tremendous potential to clinical decision support systems, by facilitating preliminary diagnosis. Methods: In this work, we propose a multi-label disease detection pipeline, where in observation and analysis of tongue images captured and received via smartphones assist in predicting the health status of an individual. All images are voluntarily given by subjects consulting collaborating physicians. Images thus acquired are first and foremost classified either into a diseased or a normal category by a 5-fold cross-validation algorithm using a convolution neural network (MobileNetV2) model for binary classification. Once the diseased label is predicted, the image is used to diagnose multiple diseases using the prediction algorithm based on DenseNet121. Results: Average accuracy of 93 % was achieved in classifying diseased from normal healthy tongue by detection model with MobileNetV2 architecture. Multilabel disease classification produced more than 90% accurate results for the seven class labels considered. Conclusion: AI based image analysis shows promising results and an extensive dataset could provide further improvements to this approach. Rather than employing high-cost sophisticated image capturing setup, experimenting with smartphone images opens opportunity to provide preliminary health status to individuals on smartphone prior to further line of treatment and diagnosis.

Proteomics research has become one of the most important topics in the fields of life science and natural science. At present, research on protein–protein interaction networks (PPINs) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate function module detection mechanisms in PPIN, including open databases, existing detection algorithms and recent solutions. After that, we describe the proposed solution based on simplified swarm optimization (SSO) algorithm and gene ontology knowledge. The proposed solution implements SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species dataset for experiment: Fruitfly, Mouse, Scere, and Human. The testing and analysis result show that the proposed solution is feasible, efficient and could achieve a higher accuracy of prediction than existing approaches.

Members of the genus Armillaria are widespread forest pathogens against which effective protection has not yet been developed. Due to their longevity and the creation of large-scale cloning of Armillaria individuals, the use of mycoviruses as biocontrol agents (BCAs) against these pathogens could be an effective alternative. This work describes the screening of double-stranded RNA banding patterns as a first approach for potential virus discovery and the use of stranded total RNA-seq for viral detection and characterization in Armillaria spp. collected in the Czech Republic. A total of five single-stranded RNA viruses were detected in Armillaria ostoyae and A. cepistipes, including the first virus of the family Tymoviridae found infecting a member the genus Armillaria and four viruses belonging to the recently described virus group "ambiviruses" with circular ambisense genome organization. Both hammerhead (HHRz) and hairpin (HpRz) ribozymes were detected in all the ambiviricot sequences. Armillaria viruses were compared by phylogenetic analysis and confirmed their specific host by direct RT-PCR, which showed the occurrence of interspecies transmission.

Rapid and accurate detection of road cracks is of great significance for road health monitoring, but currently this work is mainly completed through manual site surveys. Low-altitude UAV remote sensing can obtain images with centimeter or even subcentimeter ground resolution, which provides a new efficient and economical approach for rapid crack detection. Nevertheless, crack detection networks face challenges such as edge blurring and misidentification due to the heterogeneity of road cracks and the complexity of the background. To address these issues, we propose a real-time edge reconstruction crack detection network (ERNet) which adopts multi-level information aggregation to reconstruct crack edges and improve the accuracy of segmentation between the target and background. To capture global dependencies across spatial and channel levels, we propose an efficient bilateral decomposed convolutional attention module (BDAM) that combines depth separable convolution and dilated convolution to capture global dependencies across spatial and channel levels. To enhance the accuracy of crack detection, we use a coordinate-based fusion module that integrates spatial, semantic, and edge reconstruction information. In addition, we propose an automatic measurement of crack information for extracting the crack trunk and its corresponding length and width. Experimental results demonstrate that our network achieves the best balance between accuracy and inference speed.

Abstract In order to solve the problem that it is difficult to obtain the accurate trigger signal moment when using the time difference of UAV flight acoustic signals collected by acoustic sensors arranged in advance to locate the position of UAVs when they cannot be tracked by radar and are difficult to be observed by the human eye, an acoustic source localisation method with improved empirical modal decomposition (REMD) under an adaptive frequency window is proposed. Firstly, the collected UAV flight signals are smoothed and filtered, then the robust empirical modal decomposition (REMD) is used for IMF modal decomposition and spectral analysis of the collected signals, and a panning frequency window with variable bandwidth is introduced for corresponding frequency locking and extracting the IMFs; the GWO optimisation is used and the sliding metrics are set to determine the position of the panning frequency window automatically; finally, the extracted Finally, the extracted IMF components are reconstructed according to the criterion, and the trigger signal moments in the reconstructed IMF components are resolved and recorded, and the algorithm is used to calculate the difference between the reception moments of different sensors; for the traditional sound source localisation algorithms with a small detection range, which are prone to fall into the no-solution or false-solution, i.e., unable to achieve the localisation, a weighted least-squares-based Chan-Taylor algorithm is proposed for the localisation computation, and a weighted least-squares algorithm is proposed for the localisation computation, through the Inputting the sensor delay parameter and solving the linear equation system to get the target position information; finally, this paper verifies the robustness and performance of the design method with simulated and actual signals. The results show that the maximum positioning error is no more than 5% in an area with a side length of 15m in the measurement area. With the improved accuracy of time delay estimation, the positioning simulation results meet the positioning requirements even though the positioning error is further expanded when the measurement area is further expanded.

Statistical denoising techniques have been well studied, however many challenges still remain. This 9 article focuses on determining the least necessary number of significant samples in a statistical pop-10 ulation sampling, while minimizing the impact on the statistical detection of the fiber Bragg grating 11 (FBG) power spectra. This is achieved by using Bayesian decision making, small population statis-12 tics while excluding false detection. First, we investigate the impact of two-sided sliding window 13 technique applied near the cell under the test. The window size varies from 8 to 60 samples. The 14 considered maximum size of population sampling is connected with the number of wavelength 15 samples within the bandwidth of the symmetrical FBG power spectra peaks. Regarding the sym-16 metry of FBG power spectra peaks and the possible reduction of computational demands, we inves-17 tigate the impact of a one-sided sliding window. This enables to achieve less processing latency for 18 real time applications thus brings benefits to FBG-based fiber optical sensing. Both, two- and one-19 sided small population sampling techniques are then experimentally verified. Detection results are 20 analyzed in depth via calculating detection thresholds by using the small size window sliding along-21 side the waveband. From results achieved, it was found that the normality three-sigma rule does 22 not need to be followed while using the small population sampling. Experimental demonstrations 23 and analyses showed that the proposed concept of denoising and statistical threshold detection 24 based on the sliding window does not depend on the prior knowledge of probability distribution 25 functions of the FBG power spectra peaks level and background noise. We have also found that the 26 thresholds’ adaptability mainly depends on the statistical numerical characteristics of the above ad-27 ditive mixture of the FBG power spectra peaks signals and background noise.

Background: MRI-guided prostate biopsies from visible tumor-specific lesions (TBx) can be used to diagnose clinically significant carcinomas (csPCa) requiring treatment more selectively than conventional systematic biopsies (SBx). Ex vivo fluorescence confocal microscopy (FCM) is a novel technique that can be used to examine TBx prior to conventional histologic workup. Methods: TBx from 150 patients were examined with FCM on the day of collection. Preliminary findings were reported within 2 hours of collection. The results were statistically compared with the final histology. Results: 27/40 (68%) of the csPCa were already recognized in the intraday FCM in accordance with the results of conventional histology. Even non-significant carcinomas (cisPCa) of the intermediate and high-risk groups (PSA > 10 or 20 ng/ml) according to conventional risk stratifications were reliably detectable. In contrast, small foci of cisPCa were often not detected or were difficult to distinguish from reactive changes. Conclusion: The rapid reporting of preliminary FCM findings helps reduce the psychological stress on patients an can improve the clinical management of csPCa. Additional SBx can be avoided in individual cases, leading to lower rates of complications and scaring in the future surgical area. Additional staging examinations can be arranged without losing time. FCM represents a promising basis for future AI-based diagnostic algorithms.

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

Pavement crack detection is one of the key links in highway pavement maintenance management. In the current road crack detection, the network model easily ignores the shallow geometric features of cracks and cannot extract the key feature information of cracks. Aiming at the above problems, an EC-YOLOX network model is proposed. The CFPN is constructed to cross-fuse different scale features to solve the feature scale invariance. In the strengthen feature extraction layer, ECANet is fused to enhance the ability to pay attention to key information. The WEIoU loss function is proposed, which assigns different penalty terms to the target box in the vertical and horizontal directions. In terms of data, geometric distortion constraint correction is performed on the single-point perspective pavement image. The experimental data show that the accuracy of EC-YOLOX on the self-built data set reaches 76.34% [email protected], which is 4.45% higher than that of YOLOX. The loss curve of EC-YOLOX is smoother than that of YOLOX, and the minimum training loss value is 2.136, which is 0.648 lower than the minimum loss value 2.784 of YOLOX. Many experiments have verified the effectiveness of EC-YOLOX in improving the detection effect in pavement crack detection.

Meat serves as a vital component of the human diet, offering a rich source of protein and essential trace elements. China stands as both a significant producer and consumer of meat on a global scale, leading the world in terms of total meat production and consumption. As the standard of living within the country continues to rise, there is a growing emphasis on the nutritional value and safety of meat consumption. The tenderness of meat is a critical factor influencing its overall quality. Traditionally, tenderness has been assessed through shear force testing, a method that is inherently destructive, inefficient, and results in considerable sample wastage. In light of these drawbacks, non-destructive testing techniques have emerged as a preferred alternative, promising greater accuracy, efficiency, and convenience without compromising the integrity of the samples. This paper delves into the application of five advanced non-destructive testing technologies in the realm of meat tenderness assessment. These include near-infrared spectroscopy, hyperspectral imaging, Raman spectroscopy, airflow optical fusion detection, and nuclear magnetic resonance detection. Each technology is scrutinized for its respective strengths and limitations, providing a comprehensive overview of their current utility and potential for future development. Moreover, the integration of these techniques with the latest advancements in artificial intelligence (AI) technology is explored. The fusion of AI with non-destructive testing offers a promising avenue for the development of more sophisticated, rapid, and intelligent systems for meat tenderness evaluation. This integration is anticipated to significantly enhance the efficiency and accuracy of quality assessment in the meat industry, ensuring a higher standard of safety and nutritional value for consumers. The paper concludes with a set of technical recommendations aimed at guiding the future direction of non-destructive, AI-enhanced meat tenderness detection, thereby contributing to the ongoing improvement of the meat industry in China and beyond.

In review, the operating principles of the most common bio sensing devices, detection methods and the identification sensitivity of analyzed molecules were shown. The central focus was done on hybridization analysis of nucleic acids (NA), which are considered to be one of the most important analytes in terms of diagnostic point of view. Constructions enabling to transfer the fact of formation of nucleotide probe/target complex in to detectable signal by optical, electrochemical or micromechanical (piezoelectric) sensors were presented in this review.

The existence of low-cost and accurate tools that can equip the First Responders (FRs) to detect trapped victims and provide insights about their health condition in the aftermath of a disaster, is imperative. To address the problem of detecting trapped victims behind walls or debris, we have developed a tool that exploits an Ultra-Wide Band (UWB) radar sensor for data collection and Machine Learning (ML) algorithms for data analysis. To evaluate the efficacy of our approach, we collected data from nine humans, both in standing and lying down positions. Next, we applied various ML algorithms to the collected dataset for two discrete sub-tasks that are of interest from an FR’s perspective. The first task is the detection of the victim’s presence, where the algorithms attained more than 95% accuracy and F1-Score. The second task is the estimation of the distance between the radar sensor and the victim, where the tool showed an average error of less than 40 cm.

The effects of simulated acid rain (SAR) on the photosynthetic performance of subtropical coniferous species have not been thoroughly investigated. In this study, we treated two coniferous species, Pinus massoniana (PM) and Cunninghamia lanceolata (CL), with SAR and then analyzed photosynthetic activities via the simultaneous measurement of prompt fluorescence(PF), delayed fluorescence(DF), and modulated reflection at 820 nm(MR820). Following the low-pH SAR treatment, the I–P phase of the OJIP curves of PM and CL decreased significantly and then the K-band and L-band were detectable. The delayed fluorescence for both PM and CL decreased as the pH of the SAR solution decreased, with a more significant decrease in the I1 peak than in the I2 peak. According to the MR820 curve, the exposure to SAR stress inhibited the rapid decrease and slow increase in the MR820 transients of PM and CL. The gradients of the decreasing and increasing phases were lower for CL than for PM at pH 2.5. In addition, SAR decreased the light energy captured per unit area (ABS/CSm) and the quantum yield of electron transfer per unit area (ETO/CSm), but it increased the accumulation of QA−, thereby decreasing primary photochemical efficiency (TRO/CSm, φPO). These changes in photosystem II (PSII) resulted in a decrease in the maximum quantum yield of PSII (Fv/Fm) and the performance index (PIABS). Moreover, PSII was protected from thylakoid membrane photodamages through the increase in heat dissipation per unit area (DIO/CSm) in response to SAR. The increase in δRO as the pH of the SAR solution decreased was observed for PM, but not for CL. Considered together, the study findings suggest that SAR treatments can decrease chlorophyll contents and damage the OEC in PM and CL, which obstructs the transfer of electrons on the donor and acceptor sides of PSII. However, PM is better able to withstand SAR stress than CL, likely because of the activation of a protective mechanism (reflected by the change in δRO). Therefore, in coniferous forest areas severely affected by acid rain, CL may need to be protected more than PM.

Experimental systems to simulate future elevated CO2 conditions in the field often have large, rapid fluctuations in CO2. To examine possible impacts of such fluctuations on photosynthesis, intact leaves of field grown plants of five species were exposed to two-minute cycles of CO2 between 400 and 800 mol mol-1 lasting a total of 10 minutes, with photosynthesis, stomatal conductance and PSII fluorescence measured at the end of each half-cycle, and also 10 minutes after the end of the cycling. Prior to the cyclic CO2 treatments, steady-state responses of leaf gas exchange and fluorescence to CO2 were determined. In four of the five species, in which stomatal conductance decreased with increasing CO2, the cyclic CO2 treatments reduced stomatal conductance. In those species, both photosynthesis and the photochemical efficiency of PSII were reduced at limiting internal CO2 levels, but not at saturating CO2. In the fifth species, there was no change in stomatal conductance with CO2, and no change in either photosynthesis or PSII efficiency at any CO2 level with CO2 cycling. It is concluded that in many, but not all, species fluctuations in CO2 may reduce photosynthesis at low CO2 partly by decreasing the photochemical efficiency of photosystem II, as well as by decreasing stomatal conductance.

Deep Learning is a recent and important addition to the computational toolbox available for image reconstruction in fluorescence microscopy. We review state-of-the-art applications such as image restoration, super-resolution, and light-field imaging, and discuss how the latest Deep Learning research can be applied to other image reconstruction tasks such as structured illumination, spectral deconvolution, and sample stabilisation. Despite its successes, Deep Learning also poses significant challenges, has often misunderstood capabilities, and overlooked limits. We will address key questions, such as: What are the challenges in obtaining training data? Can we discover structures not present in the training data? And, what is the danger of inferring unsubstantiated image details?

Apurinic/apyrimidinic endonuclease 1 (APE1), also known as redox factor-1 (Ref-1), is a multifunctional protein which is widely existing in living organisms. It can specifically recognize and cleave the DNA in apurinic/apyrimidinic (AP) site in the base excision repair (BER) pathway, as well as regulate the expression of genes to activate some transcription factors. The abnormal ex-pression and disruptions in the biological functions of APE1 are linked to a number of diseases, including inflammation, immunodeficiency, and cancer. Hence, it is extremely desired to monitor the activity of APE1, acquiring a thorough understanding of the healing process of damaged DNA and making clinical diagnoses. Thanks to the advent of DNA nanotechnology, some nanodevices are used to image the activity of APE1 with great sensitivity and simplicity. In this review, we will summarize DNA nanotechnology-empowered fluorescence imaging in the past years for APE1 activity according to the types of DNA probe, which are classified into linear DNA probes, composite DNA nanomaterials and three-dimensional (3D) DNA nanostructures. We also highlight the future research directions in the field of APE1 activity imaging.

The state detection of power cable is very important to ensure the reliability of power supply. Traditional sensors are mostly based on electric field detection, the operation is complex, and its efficiency needs to be improved. This paper optimizes the design and development of the magnetic field detection sensor for AC power cables. Firstly, through the establishment of the magnetic field sensor model, it is determined that permalloy is the material of the magnetic core, the optimal aspect ratio of the magnetic core is 20, and the ratio of coil length to core length is 0.3. Secondly, the coil simulation model is established, and it is determined that the optimal number of turns of the coil is 11000 turns, the diameter of the enamelled copper wire is 0.08mm, and the equivalent magnetic field noise of the sensor is 0.06pT. Finally, the amplifying circuit based on negative magnetic flux feedback is designed and the sensor is assembled, and the experimental circuit is built for sensitivity test. the results show that the sensitivity of the magnetic field sensor is 327.6mV/μT. The sensor designed in this paper has the advantages of small size, high sensitivity, easy to carry and high reliability.

Background: Microscopic examination of acid-fast stained sputum smears for detecting mycobacterial acid-fast bacilli (AFB) remains the most economical and readily available method for laboratory diagnosis of tuberculosis (TB). However, this conventional approach has limitations, including low sensitivity and labor-intensive procedures. Methods: An automated microscopy system incorporating artificial intelligence (AI) and machine learning for AFB identification was evaluated. The study was conducted at an Infectious Disease Hospital in Jiangsu Province, China, utilizing an intelligent microscope system (TB-Scan, Wellgen Medical, Kaohsiung). A total of 1,000 sputum smears were included in the analysis, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFB. Referee technicians provided the gold standard for discrepant results. Results: The automated system demonstrated an overall accuracy of 95.00% (950/1,000), sensitivity of 91.24% (177/194), and specificity of 95.91% (773/806). Notably, the system identified 21 smears as positive that were previously reported as negative, with referee technicians confirming 17 of these as true positive and retracting the test results accordingly. Recalculating the performance, the accuracy increased to 96.70% (967/1,000), sensitivity to 91.94% (194/211), and specificity to 97.97% (773/789), with a false negative rate of 8.06% (17/211) and a false positive rate of 2.03% (16/789). Conclusions: The incorporation of AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.

Epilepsy is a common neurological condition, typically diagnosed using Electroencephalogram (EEG). Large scale EEG datasets have recently been made publicly available, allowing the use of advanced Machine Learning (ML) algorithms to analyze EEG patterns associated with epilepsy. While most existing studies focus on identifying seizures in the EEG, few have tried to identify preictal EEG segments. Identifying preictal EEG segments are not only useful in developing early warning systems but also helps inform the course of treatment for the patient. In this study, we propose to represent EEG segments as images, instead of time-series data, and identify preictal EEG segments using a preexisting ML algorithm (YOLOv8) designed for image processing. Multiplexed images (containing the original EEG signal represented on a 2D grid, kurtosis, and spectral entropy) achieve the best accuracy of 95.75% on the dataset while images just containing the EEG signal result in an accuracy of 91.25%. Using only spectrograms, generated from the original EEG signal, results in an accuracy of 90.15%.

Few-shot object detection (FSOD) aims to address the challenge of requiring a substantial amount of annotations for training in conventional object detection, which is very labor-intensive. However, existing few-shot methods achieve high precision with the sacrifice of time-consuming for exhaustive fine-tuning, or take poor performance in novel-class adaptation. We presume the major reason is that the valuable correlation feature among different categories is insufficiently exploited, hindering the generalization of knowledge from base to novel categories for object detection. In this paper, we propose Few-Shot object detection via Correlation-RPN and Transformer Encoder-Decoder (CRTED), a novel training network to learn object-relevant features of inter-class correlation and intra-class compactness while suppressing object-agnostic features in the background with limited annotated samples. And we also introduce a 4-way tuple-contrast training strategy to positively activate the training progress of our object detector. Experiments over two few-shot benchmarks (Pascal VOC, MS-COCO) demonstrate that, our proposed CRTED without further fine-tuning can achieve comparable performance with current state-of-the-art fine-tuned works. The codes and pre-trained models will be released.

Large datasets play a crucial role in the progression of surgical robotics, facilitating advancements in the fields of surgical task recognition and automation. Moreover, public datasets enable the comparative analysis of various algorithms and methodologies, thereby assessing their effectiveness and performance. The ROSMA (Robotics Surgical Maneuvers) dataset provides 206 trials of common surgical training tasks performed with the da Vinci Research Kit (dVRK). In this work, we extend ROSMA dataset with two annotated subsets: ROSMAT24, which contains bounding box annotations for instrument detection, and ROSMAG40, which contains high and low level gesture annotations. We propose an annotation method that provides independent labels for the right-handed tool and the left-handed tool. For instrument identification, we validate our proposal with a YOLOv4 model in two experimental scenarios. We demonstrate the generalization capabilities of the network to detect instruments in unseen scenarios. For gesture segmentation, we propose two label categories: high-level annotations that describe gestures at a maneuvers level, and low-level annotations that describe gestures at a fine-grain level. To validate this proposal, we have designed a recurrent neural network based on a bidirectional long-short term memory layer. We present results for four cross-validation experimental setups, reaching up to a 77.35% mAP.

Exposure to malachite green (MG) may pose great health risks to humans, thus it is of prime importance to develop fast and robust methods to quantitatively screen the presence malachite green in environment. Herein application of extractive electrospray ionization mass spectrometry (EESI-MS) has been extended to the trace detection of MG within lake water and aquiculture water, due to the intensive use of MG as biocide in fisheries. This method has the advantage of obviating offline liquid-liquid extraction or tedious matrix separation prior to the measurement of malachite green in native aqueous medium. The experimental results indicate that the extrapolated detection limit for MG was ~3.8 ug L-1 (S/N=3) in lake water samples and ~ 0.5 ug L-1 in ultrapure water under optimized experimental conditions. The signal intensity of MG showed good linearity over the concentration range of 10–5000 ug L-1. Measurement of practical water samples fortified with MG at 0.01 and 0.1 mg L-1 gave a good validation of the established calibration curve. The average recoveries of malachite green in lake water and Carassius carassius fish farm effluent water were 114.9% (6.6% RSD) and 85.4% (9.2% RSD), respectively. Overall, the established EESI-MS/MS method has been demonstrated suitable for sensitive and rapid (<2 min per sample) quantitative detection of malachite green in various aqueous media, indicating its potential for online real-time monitoring of real life samples.

Polyanilines are a class of conductive polymers that have attracted significant interest in the field of organic electronics due to their unique properties. In this article, the fluorescence spectroscopy and photovoltaic properties of polyaniline-based thin films and copolymers are investigated. The study focuses on understanding the relationship between the composition and structure of polyanilines and their optical and electrical properties. The article discusses the results of experiments on the photoluminescent properties of S1-S9 copolymers in a DMSO solution and of thin-film photoresistors based on polyanilines. The findings reveal that the photovoltaic properties of the thin films depend on the conditions of their fabrication and their surface morphology. The solubility of polyanilines is excellent, making the production of electronic components compatible with modern printing technologies in organic electronics. The work also points out that the photoconductivity of polyanilines can be improved by incorporation of additional molecular groups into the polymer matrix. Overall, this study sheds light on the potential of polyanilines as promising materials for organic electronics and provides insights into how their properties can be optimized for specific applications.

Fire detection from satellite sensors relies on an accurate estimation of the unperturbed state of a target pixel, from which an anomaly can be isolated. Methods for estimating the radiation budget of a pixel without fire depend upon training data derived from the location's recent history of brightness temperature variation over the diurnal cycle, which can be vulnerable to cloud contamination and the effects of weather. This study proposes a new method that utilises the common solar budget found at a given latitude in conjunction with an area's local solar time to aggregate a broad-area training dataset, which can be used to model the expected diurnal temperature cycle of a location. This training data is then used in a temperature fitting process with the measured brightness temperatures in a pixel, and compared to pixel-derived training data and contextual methods of background temperature determination. Results of this study show similar accuracy between clear-sky medium wave infrared upwelling radiation and the diurnal temperature cycle estimation compared to previous methods, with demonstrable improvements in processing time and training data availability. This method can be used in conjunction with brightness temperature thresholds to provide a baseline for upwelling radiation, from which positive thermal anomalies such as fire can be isolated.

Detection of subsurface archaeological remains using a range of remote sensing methods, poses several challenges until today. Recent studies regarding the detection of archaeological proxies like those of cropmarks highlight the complexity of the phenomenon. In this work we present three different methods, and associated indices, for identifying stressed reflectance signatures indicating buried archaeological remains, based on a dataset of measured ground spectroradiometric reflectance. Several spectral profiles between the visible and near infrared part of the spectrum, were taken over a controlled environment in Cyprus during the 2011-2012 and are re-used in this study. The first two (spectral) methods are based on a suitable analysis of the spectral signatures in (1) the visible part of the spectrum, and in particular in the neighborhood of 570 nm, and (2) the red edge-near infrared part of the spectrum, in the neighborhood of 730 nm. Machine learning (decision trees) allows for the deduction of suitable wavelengths to focus on, in order to formulate the proposed indices and the associated classification criteria (decision boundaries) that can enhance the detection probability of stressed vegetation. Noise in the signal is taken into account by simulating reflectance signatures perturbed by white noise. Applying decision tree classification on the ensemble of simulations and basic statistical analysis we refine the formulation of the indices and criteria for the noisy signatures. The success rate of the proposed methods is over 90%. The third method rests on the estimation of vegetation/canopy reflectance parameters through inversion of the physical-based PROSAIL reflectance model and the associated classification through machine learning methods. The obtained results provide further insights into the formation of stress vegetation that occurred due to the presence of shallow buried archaeological remains, which are well aligned with physical-based models and existing empirical knowledge. To the best of the authors' knowledge, this is the first study demonstrating the usefulness of radiative transfer models such as PROSAIL for understanding the formation of cropmarks. Similar studies can support future research directions towards the development of regional remote sensing methods and algorithms if systematic observations are adequately dispersed in space and time.

Over the past couple of decades, Global positioning system (GPS) technology has been utilized to collect large-scale data from travel surveys. As the precise spatiotemporal characteristics of travel could be provided by GPS devices, the issues of traditional travel survey, such as misreporting and non-response, could be addressed. Considering the defects of dedicated GPS devices (e.g., need much money to buy devices, forget to take devices to collect data, limit the simple size because of the number of devices, etc.), and the phenomenon that the smartphone is becoming one of necessities of life, there is a great chance for the smartphone to replace dedicated GPS devices. Although, several general reviews have been done about smartphone-based GPS travel survey in the literature review section in some articles, a systematic review from smartphone-based GPS data collection to travel mode detection has none. The included studies were searched from six databases. The purpose of this review is to critically assess the current literature on the existing methodologies of travel mode detection based on GPS raw data collected by smartphones. Meanwhile, according to the systematic comparison among different methods from data-preprocessing to travel mode detection, this paper could carefully provide the Strengths and Weaknesses of existing methods. Furthermore, it is the crucial step to develop the methodologies and applications of GPS raw data collected by smartphones.

A reliable and efficient rail track defect detection system is essential for maintaining rail track integrity and avoiding safety hazards and financial losses. Eddy current (EC) testing is a non-destructive technique that can be employed for this purpose. The trade-off between spatial resolution and lift-off should be carefully considered in practical applications to distinguish closely spaced cracks such as those caused by rolling contact fatigue (RCF). A multi-channel eddy current sensor array has been developed to detect defects on rails. Based on the sensor scanning data, defect reconstruction along the rails is achieved using an inverse algorithm that includes both direct and iterative approaches. In experimental evaluations, the EC system with the developed sensor is used to measure defects on a standard test piece of rail with a probe lift-off of 4-6 mm. The reconstruction results clearly reveal cracks at various depths and spacings on the test piece.

Vertical Indoor Farming (VIF) with hydroponics offers a promising perspective for sustainable food production. Intelligent control of VIF system components plays a key role in reducing operating costs and increasing crop yields. Modern machine vision (MV) systems use Deep Learning (DL) in combination with camera systems for various tasks in agriculture, such as disease and nutrient deficiency detection, and flower and fruit identification and classification for pollination and harvesting. This study presents the applicability of MV technology with DL modelling to detect the growth stages of chilli plants using YOLOv8 networks. The influence of different bird’s eye and side view datasets and different YOLOv8 architectures was analysed. To generate the image data for training and testing the YOLO models, chilli plants were grown in a hydroponic environment and imaged throughout their life cycle using four camera systems. The growth stages were divided into growing, flowering and fruiting classes. All trained YOLOv8 models showed reliable identification of growth stages with high accuracy. The results indicate that models trained with data from both views show better generalisation. YOLO’s middle architecture achieved the best performance.

Congenital heart disease (CHD) is a prevalent congenital disorder diagnosed with high frequency, affecting approximately 0.8% to 1.2% of all live births. Research has demonstrated that the use of pulse oximetry and clinical examination as a screening tool for CHD diagnosis has proven extremely valuable. We conducted a retrospective longitudinal study over a 3-year period that involved screening 1188 newborns admitted to the NICU during the first 72 hours of life within this timeframe. 201 newborns were diagnosed with CHD. Almost all (n=198) had a positive screening test during the 72-hour period, while 3 had a negative screening test, but were later diagnosed using echocardiography. The prevalence rate was 16.67%. A confusion matrix was constructed with 198 true positives (TP), 3 false negatives (FN), no false positives (FP), and 987 true negatives (TN). The sensitivity of the screening test was 98.51%, while the specificity 100%, with no false positives. In our study, a total of 9 deaths occurred. Using the Kaplan-Meier method, the survival probability appeared to be slightly above 0.9 (95.5%), indicating that almost all patients survived past 72 hours after being diagnosed with CHD. Pulse oximetry is an effective, low cost and easy-to-use method and a useful tool to assess and lead to the diagnosis of CHD.

Deadlock is a real problem that can cause expensive, time-dependent hang-ups or failures in distributed computing systems. Some studies have been conducted with deadlock detection and resolution in generalized request computing models, most of these studies are based on the diffusing computation scheme, where forward and backward propagated probe and reply messages respectively are diffused between dependent processes. The replies carry the dependency information between processes for the initiator. Using this dependency information initiator can detect and resolve the deadlock cycle. It has been proven in the literature that a deadlock cycle length distribution is skewed with a large majority of cycles (90%) having length two. Based on this fact an improved algorithm is proposed for handling deadlock detection in distributed computing systems under the distributed request model where each process has several requests at a time. The improved algorithm depends mainly on message propagation and timeout policy. Probe and reply messages are propagated between the initiator and its successors only to detect deadlock cycles between them. Global timeout policy is used to detect potential deadlock cycles with large length distribution. The proposed algorithm can handle concurrent executions. Simulation experiments are performed to see the effectiveness of the improved algorithm. It is found that the improved algorithm compares favorably with other existing algorithms and it shows better results for several performance metrics especially in reducing deadlock latency and execution time.

A quantitative analysis of X-ray emission from an electron cyclotron resonance (ECR) plasma was performed to probe the spatial properties of electrons having energy for effective ionisation. A series of measurements were taken by INFN-LNS and ATOMKI, capturing spatially and spectrally resolved X-ray maps as well as volumetric emissions from argon plasma. Comparing the former with model generated maps (involving space-resolved phenomenological electron energy distribution function and geometrical efficiency calculated using ray-tracing MC routine) furnished information on structural aspects of the plasma. Similarly, fitting a model composed of bremsstrahlung and fluorescence to the volumetric X-ray spectrum provided valuable insight into the density and temperature of confined and lost electrons. The latter can be fed back to existing electron kinetics models for simulating more relevant energies, consequently improving theoretical X-ray maps and establishing the method as an excellent indirect diagnostic tool for warm electrons, required for both fundamental and applied research in ECR plasmas.

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional manual inspection methods are time-consuming, labor-intensive, and often lack the spatial resolution required for detailed analysis. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary objective was to develop a methodology for precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This was achieved by integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. UAV imagery was ac-quired bi-weekly at altitudes of 40m and 70m, capturing the entire growth cycle of a corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. Local spatial analysis and image processing techniques were em-ployed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. For enhanced accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area esti-mates by eliminating interference from soil and shadows. The proposed methodology enables precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time, providing valuable insights for precision agriculture practices. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop mon-itoring at the individual plant level.

Fabric phase sorptive extraction (FPSE) combines the advanced material properties of sol–gel derived microextraction sorbents and the flexible and permeable fabric support to create a robust, simple and green sample preparation device. It simultaneously improves the extraction sensitivity, and the speed of the extraction by incorporating high volume of sponge-like porous sol–gel hybrid inorganic–organic sorbents into permeable fabric substrates that is capable of extracting target analytes directly from simple to complex aqueous sample matrices. For the first time, this technique was applied to the trace level determination of selected polycyclic aromatic hydrocarbons (PAHs) in environmental water samples using a non-polar sol–gel C18 coated FPSE media. Several extraction parameters were optimized to improve extraction efficiency and to achieve high detection sensitivity. Validation tests of spiked samples showed good linearity for four selected PAHs (R² = 0.9983–0.9997) over a wide range of concentrations (0.010-10 ng/mL). Limits of detection (LODs) and quantification (LOQs) were measured at pg/mL levels, 0.1–1 pg/mL and 0.3–3 pg/mL, respectively. Inter- and intra-day precision tests showed variations of 1.1–4.1% for four selected PAHs. Average absolute recovery values were in the range of 88.1–90.5% surpassed the recovery prediction model, with relative standard deviations below 5%. The developed FPSE-HPLC-FLD protocol was finally applied to analyze 8 environmental water samples. Out of four selected PAHs, fluoranthene (Flu) and phenanthrene (Phen) were the most frequently detected in four samples, at concentration levels of 5.6–7.7 ng/mL and 4.1-11 ng/mL, respectively followed by anthracene (Anth) and pyrene (Pyr) in two samples. The newly developed FPSE-HPLC-FLD protocol is simple, green, fast and economical, with adequate sensitivity for trace levels of four selected PAHs and seems to be promising in routine monitoring of water quality and safety.

The interaction between single or a fixed number of photons with a single absorber is of fundamental interest in quantum technology. The harnessing of light matter interactions at the single particle limit has several potential applications ranging from quantum communication and quantum metrology to quantum imaging. In this letter, a setup for heralded two-photon absorption at the single absorber level is proposed. The setup is based on a heralded two-photon source utilizing spontaneous parametric down-conversion, entanglement swapping and sum frequency generation for joint detection. The feasibility of the scheme is discussed by reviewing recent achievements in utilizing entangled and correlated photons for two-photon absorption as well as single photon absorption experiments at the limit of single absorbers in the context of applications in imaging (here mainly microscopy) and spectroscopy.

The intentional damages to local Cultural Heritage sites carried out in recent months by the Islamic State have received wide coverage from the media worldwide. Earth Observation data provide important information to assess these damages in such non-accessible areas, and automated image processing techniques would be needed to speed up the analysis if a fast response is desired. This paper shows the first results of applying fast and robust change detection techniques to sensitive areas, based on the extraction of textural information and robust differences of brightness values related to pre- and post-disaster satellite images. A map highlighting potentially damaged buildings is derived, which could help experts at timely assessing the damages to the Cultural Heritage sites of interest. Encouraging results are obtained for two archaeological sites in Syria and Iraq.

Single-phase-to-ground fault in non-effectively grounded systems poses serious public safety concern. Along with increasing deployment of low-voltage (LV) sensors, new opportunities and ways are created for solving this traditional problem in distribution systems. A new approach, identifying primary single-phase-to-ground line fault and locating it through measurement data from smart sensors deployed in the secondary side of distribution transformers, is proposed in this paper. When a fault occurs in primary distribution feeder, the transient aerial mode voltage (AMV) of each LV smart sensor is calculated. The fault line can be identified by an abrupt change value of AMV at LV side. Next, by finding the position of the maximum value of this AMV from LV sensors on the fault line, the fault location can be pinpointed to two neighboring distribution transformers. The proposed method has been proven to be efficient and reliable by simulation studies.

Lettuce is a salt-sensitive crop and has a threshold electrical conductivity of 1.3–2.0 mS cm-1 and above that is considered detrimental. As there has been very little information on the physio-logical response of different critical stages of lettuce under different salt stress (SS), the current study is focused on investigating the effects of SS on the critical physiological traits influencing the carbon assimilation in different growth stages of lettuce. The experiment was conducted in deep-water culture hydroponic system in a greenhouse condition. Four levels of sodium chloride salt treatments (EC: 20, 16, 8, and 1.8 mS cm-1) were applied. During both growth stages (day 11 (GS1) and day 19 (GS2) after salt treatment), the leaf transpiration rate, stomatal conductance, and intercellular CO2 were severely decreased. However, the carbon assimilation rate remained unchanged under SS. Similarly, the water use efficiency increased under the SS. It is concluded that the increasing SS increased stomatal and non-stomatal limiting factors during GS1 suggesting the enhanced limitation in photosynthetic activity while no such trend was observed during GS2. The decreased gm with increased SS at GS1 and GS2 suggested that SS induced the irreversible decrease of gm, which in turn can be responsible for the transient reduction in the Vcmax and Jmax during GS2. Taken together, the evidence from this research recommends that varying the SS levels can significantly affect the physiological performance of lettuce at both growth stages.

The appearance of bubbles and foam can influence the likeability of a wine even before its consumption. Since foams are essential to visual and taste attributes of sparkling wines, it is of great importance to understand which compounds affect bubbles and foam characteristics. The aim of this work was to investigate the effect of interactions among proteins, amino acids, and phenols on the characteristics of foam in sparkling wines by using synchronous fluorescence spectroscopy techniques. Results has shown that several compounds present in sparkling wines influence foam quality differently, and importantly, highlighted how the interaction of those compounds might result in different effects on foam parameters. Amongst the results, mannoproteins were found to be most likely to promote foam and collar stability, while phenols were likely to increase the ratio of small bubbles and collar height in the foam matrix. In summary, this work contributes to a better understanding of the effect of wine compounds on foam quality as well as the effect of the interactions between those compounds.

The COVID-19 pandemic over the past years has shown a great need for rapid, low-cost and on-site detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this paper, an aptamer-based colloidal gold nanoparticle lateral flow test strip was well developed to realize the visual detection of wild type SARS-CoV-2 spike protein (SP) and multiple variants. Under the optimal reaction conditions, a low detection limit of SARS-CoV-2 S protein of 0.68 nM was acquired, and the actual detection recovery was 83.3% to 108.8% for real world samples. This suggests a potential tool for the prompt detection of SARS-CoV-2 with good sensitivity and accuracy, and a new method for the development of alternative antibody test strips for the detection of other viral targets.

The presence of antibiotic residues in honey has recently become a growing public health concern due to the overuse of these products in beekeeping and their transfer to the food industry. The aim of this study was to develop and validate a simple, selective and sensitive method for the simultaneous identification and quantification of residues of twenty-two sulfonamides, trimethoprim and dapsone in honey using a rapid and reliable liquid chromatography–tandem mass spectrometry (LC–MS/MS) method. The developed method showed good linearity, specificity, precision (repeatability and intra-laboratory reproducibility) and recovery. The decision limit (CCalpha) ranged between 2.16-22.12 µg/kg for CCα, while the detection capability (CCbeta) was between 2.16- 2.67-27.36 µg/kg. The method proved to be rapid, reliable and effective for the determination of sulfonamides, trimethoprim and dapsone from honey samples. The average recoveries were estimated to be between 70 and 106%. The relative standard deviations (RSDs) were between 6-18%. In practice, the method is essential to check the honey sold on the market and ensure its safety before it reaches the consumer.

Ag+ pollution is great of harm to the human body and the biology. Therefore, it is an urgent need to develop inexpensive and accurate detection methods. Herein, lignin-derived structural memory carbon nanodots (CSM-dots) with outstanding fluorescence property were fabricated via a green method, which reserve functional and structural units of the precursor molecules. The CSM-dots could specifically bind Ag+, accompanied with a remarkable fluorescence quenching response. This “turn-off” fluorescence behavior was used for Ag+ determination in a linear range of 5-290 μM with the detection limit as low as 500 nM. Furthermore, the finding showed that this sensing nano-platform was successfully used for Ag+ determination in real samples and intracellular imaging, showing great potential in biological and environmental monitoring applications.

In the context of improving aircraft safety, this study focuses on the development and evaluation of a graphene-based ice detection system using an environmental chamber. The research is driven by the need for more accurate and efficient ice detection methods, crucial in mitigating in-flight icing hazards. The methodology employed involves testing flat graphene-based sensors in a controlled environment, simulating a variety of climatic conditions that could be experienced in an aircraft during its entire flight. The environmental chamber enabled precise manipulation of temperature and humidity levels, thereby providing a realistic and comprehensive test bed for sensor performance evaluation. The results were significant, revealing the graphene sensors’ heightened sensitivity and rapid response to the subtle changes in environmental conditions, especially the critical phase transition from water to ice. This sensitivity is the key to detecting ice formation at its onset, a critical requirement for aviation safety. The study concludes that graphene-based sensors, tested under varied and controlled atmospheric conditions, exhibit a remarkable potential in enhancing ice detection systems for aircraft. Their lightweight, efficient and highly responsive nature makes them a superior alternative to traditional ice detection technologies, paving the way for more advanced and reliable aircraft safety solutions.

Contaminants of Emerging Concern (CECs), a new category of contaminants in limelight, is a major issue of global concern. The pervasive nature of CECs and its harmful effects, such as cancer, reproductive disorders, neurotoxicity etc., make the situation alarming. The perilous nature of CECs lies in the fact that even very small concentration of CECs can cause great impacts on living beings. It also has a nature of bioaccumulation. Thus, it is the ultimate need to have efficient sensors for detection of CECs to ensure safe living environment. Electrochemical sensors are an efficient platform for CEC detection as they are highly selective, sensitive, stable, reproducible, and prompt, and can detect very low concentrations of analyte. Major classes of CECs are pharmaceuticals, illicit drugs, personal care products, endocrine disruptors, newly registered pesticides, and disinfection by-products. This review focusses on CECs, including their sources and pathways, health effects caused by them, and electrochemical sensors as reported in literatures under each category for the detection of major CECs.

Based on the Flashing Ratchet Potential (FRP) and Electro-Spectroscopy detection techniques, a new detection method was experimentally examined. This method allows the detection of sub-micro particles in the human exhaled air such as viral particles and other biological agents. The detection signal created by this method is carried out by electron current buildup forming an Electric Current-Spectrum (ECS) distinguishing the contents of the exhaled air samples. The detection method was developed based on FRP and a beam of free charges passing through the electrodes of the FRP. The ECS is characterized by low-energy free charges interacting with the medium through which they pass, where numerous free charges are scattered (delayed) as a result of these interactions. The resulting ECS is characterized by FRP's capacity to drift back the delayed free charges to their original starting positions. By comparing the ECS of two exhale air samples, this new technology can be utilized to identify the presence of viral and microorganisms’ particles. The time-escalating ECS difference quickly indicates the presence of such particles. Moreover, the FRP's optimal operating settings was experimentally defined, in addition to the experimental evaluation of its functionality to confirm the FRP's promising sensing capabilities, which were statistically examined several times to ensure the reproducibility of the results.

High-content fluorescence microscopy combines the efficiency of high-throughput techniques with the ability to extract quantitative information from biological systems. The planarian community has developed sensitive and robust assays for whole animals, yet cell based assays, despite their practical aspects, have not been explored to the same extent. Here we describe a modular collection of detailed protocols adapted for fixed planarian cells that enable multiplexed measurements of biomarkers in microwell plates. Methods include the detection of RNA transcripts by RNA fluorescent in situ hybridization combined with tyramide signal amplification using hapten-labeled riboprobes. In addition, immunocytochemical protocols for quantifying proliferating cells by the detection of phosphorylated histone H3 as well as 5-bromo-2'-deoxyuridine incorporation into the nuclear genome are described. The assays are compatible with planarians of virtually any size, as the tissue is disaggregated into a single cell suspension before fixation and staining. By sharing many reagents with established planarian whole mount staining protocols, preparation of samples for high-content microscopy adoption requires little additional investment. Recommendations for successful experimental workflows and common sources of errors are discussed.

Silk fibroin is a well-known biopolymer used in several applications in which the interaction with biological tissue is required. In fact, fibroin is extremely versatile and can be shaped to form several constructs useful in tissue engineering applications. Confocal imaging is usually per-formed to test the cells behaviour on the construct and in this context the fibroin autofluorescence is regarded as a problem. In addition, the autofluorescence is not intense enough to provide useful morphological images. In fact, to control study the constructs morphology other techniques are used (i.e. SEM, Micro-CT). In this work we propose a method based on the fluorescence energy transfer (FRET) to suppress the fibroin autofluorescence moving it to higher wavelength accessible to the confocal microscopy for a direct imaging.

The European heatwave of 2018 led to record-breaking temperatures and extremely dry conditions in many parts of the continent resulting in widespread decrease in agricultural yield, early tree-leaf senescence, and increase in forest fires in Northern Europe. Our study aims to capture the impact of the 2018 European heatwave on terrestrial ecosystem through the lens of a high-resolution solar-induced fluorescence (SIF) data acquired from the Orbiting Carbon Observatory (OCO-2) satellite. SIF is proposed to be a direct proxy for gross primary productivity (GPP) and thus can be used to draw inferences about changes in photosynthetic activity in vegetation due to extreme events. We explore spatial and temporal SIF variation and anomaly during spring and summer months across different vegetation types (agriculture, broadleaved forest, coniferous forest, and mixed forest) during the European heatwave of 2018 and compare it to non-drought conditions (most of Southern Europe). About one-third of Europe’s land area experienced a consecutive spring and summer drought in 2018. Comparing 2018 to mean (2015-2017) conditions, we found a change in intra-spring season SIF dynamics for all vegetation types, with lower SIF during the start of spring followed by an increase in fluorescence from mid-April. Summer, however, showed a significant decrease in SIF. Our results show that particularly agricultural areas were severely affected by the hotter drought of 2018. Furthermore, the intense heat wave in Central Europe showed about 31% decrease in SIF values during July and August as compared to the mean over three previous years. Furthermore, our MODIS and OCO-2 comparative results indicate that especially for forests, OCO-2 SIF has a quicker response and possible higher sensitivity to drought in comparison to MODIS’s fPAR and NDVI when considering shorter reference periods, which highlights the added value of remotely sensed solar-induced fluorescence for studying the impact of drought on vegetation.

Monitoring glycosylation changes within cells upon response to stimuli remains challenging because of the complexity of this large family of post-translational modifications (PTMs). We have developed an original tool enabling labeling and visualization of the cell cycle key-regulator b-catenin in its O-GlcNAcylated form based on intramolecular Förster resonance energy transfer (FRET) technology in cells. We opted for a bioorthogonal chemical reporter strategy based on the dual-labeling of b-catenin with a green fluorescent protein (GFP) for protein sequence combined with a chemically-clicked imaging probe for PTM resulting in a fast and easy to monitor qualitative FRET assay. We validated this technology by imaging the O-GlcNAcylation status of b-catenin in HeLa cells. Moreover, the changes in O-GlcNAcylation of b-catenin were varied by perturbing global cellular O-GlcNAc levels with inhibitors of O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA). Finally, we provided a flowchart demonstrating how this technology is transposable to any kind of glycosylation.

A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral feature and color feature at the same time, and has higher spatial resolution than a single spectral camera. This system was applied to detect impurities in Pu ’er tea to verify its high efficiency. The spectral and color features of each pixel in the images of Pu ’er tea were obtained by this system and used for pixel classification. The experimental results show that the accuracy of Support Vector Machine (SVM) model based on combined features is 93%, which is 7% higher than that based on only spectral features. By applying median filtering algorithm and contour detection algorithm to the label matrix extracted from pixel-classified images, 8 impurities except hair were detected successfully. Moreover, taking advantage of the high resolution of visible light camera, small impurities can be clearly imaged. By comparing the segmented color image with the pixel-classified image, small impurities such as hair could be detected successfully. Finally, it is proved that the system can obtain multiple images to allow a more detailed and comprehensive understanding of the detected items, and has excellent ability to detect small impurities.

Robotic systems have been widely used in various industries, so the security of communication between robots and their components has become an issue that needs to be focused on. As a framework for developing robotic systems, the security of ROS2 can directly affect the security of the upper-level robotic systems. Therefore, it is a worthwhile research topic to detect and analyze the security of ROS2. In this paper, we adopt a formal approach to analyze the security of the communication mechanism of ROS2. First, we use a state transition system to model the potential vulnerabilities of ROS2 based on the ROS2 communication mechanism and the basic process of penetration testing. Secondly, we introduce the CIA model on the basis of the established vulnerability model and use LTL to define its security properties. Then, we design and implement a vulnerability detection tool for ROS2 applications based on the vulnerability model and security properties. Finally, we experimentally test some ROS2-based applications, and the results show that ROS2 has vulnerabilities without additional protection safeguards.

This study introduces SkinLiTE, a lightweight supervised contrastive learning model, tailored to enhance the detection and typification of skin lesions in dermoscopic images. The core of SkinLiTE lies in its unique integration of supervised and contrastive learning approaches, which leverages labeled data to learn generalizable representations. This approach is particularly adept at handling the complexities and imbalances inherent in skin lesions datasets. The methodology encompasses a two-phase learning process. In the first phase, SkinLiTE utilizes an encoder network and a projection head to transform and project dermoscopic images into a feature space where contrastive loss is applied, focusing on minimizing intra-class variations while maximizing inter-class differences. The second phase freezes the encoder's weights, leveraging the learned representations for classification through a series of dense and dropout layers. The model was evaluated using three datasets from Skin Cancer ISIC 2019-2020, covering a wide range of skin conditions. SkinLiTE demonstrated superior performance across various metrics—accuracy, AUC , and F1 score—particularly when compared with traditional supervised learning models. Notably, SkinLiTE achieved an accuracy of 0.9087 using AugMix augmentation for binary classification of skin lesions. It also showed comparable results with the state-of-the-art approaches of ISIC challenge without relying on external data, underscoring its efficacy and efficiency. The results highlight the potential of SkinLiTE as a significant step forward in the field of dermatological AI, offering a robust, efficient, and accurate tool for skin lesion detection and classification. Its lightweight architecture and ability to handle imbalanced datasets make it particularly suited for integration into Internet of Medical Things environments, paving the way for enhanced remote patient monitoring and diagnostic capabilities. This research contributes to the evolving landscape of AI in healthcare, demonstrating the impact of innovative learning methodologies in medical image analysis.

Indocyanine Green (ICG) has significantly advanced minimally invasive surgery. It is widely recognized for its ability to visualize blood vessel patency in real-time across various surgical specialties. While its primary use in colorectal surgery is to evaluate anastomoses for leaks, nu-merous other applications have been documented in the literature. In this review, we aim to explore both established and emerging applications of ICG fluorescence in colorectal surgery, with the goal of improving patient outcomes. This includes preoperative tumor marking and the detection of metastatic disease. Some applications, such as lymphatic mapping, require further research to determine their impact on clinical practices. Conversely, oth-ers, like the intraoperative localizations of ureters, necessitate additional procedures and are not yet widely accepted by the surgical community. However, the development of alternative com-pounds could offer better solutions. Future research should focus on areas like quantitative ICG and protocol standardization in prospective multicenter studies.

Gold nanohole arrays are periodic metastructures gathering huge interest for biosensing applica-tions. The grating-like structure defines their plasmonic mode features and their angular disper-sion can be used to highlight efficiently their interaction with the fluorescence emission of a properly tuned fluorophore. Resorting to a properly tuned/engineered/optimised plasmonic na-nohole array and a commercial organic dye, we performed an accurate resolved angle-resolved optical characterization. The plasmonic fingerprints was consequently identified as a modification of the dye photoluminescence signal in terms of both spectral redistribution and enhancement. Interestingly, by analyzing the results, an advantageous measurement configuration can be iden-tified to better engineer a suitable platform for efficiency maximization in plasmon-enhanced flu-orescence-based applications. Therefore, the coupling mechanism with the plasmonic modes, and nanostructure properties can be carefully co-optimized.

Histidine is an essential amino acid with significant implications in human growth and neuromodulation. Its concentration within cells, when either elevated or reduced, can indicate different diseases. While various methods exist to measure increased histidine levels, there remains a significant lack of sensors capable of actively responding to histidine deficiency within cells and releasing strong signals. In this study, we exploited the high induction levels of the his operon in S. Typhimurium SL1344, a histidine auxotroph, within a histidine-deficient environment, to develop a specific bacterial sensor with sensitivity towards low histidine concentrations. The SL1344-pGEX sensor, with a high copy number, exhibited remarkable sensitivity and selectivity to histidine in the range of 0 to 50 μM. Notably, even a minute addition of histidine (approximately 2.5 μM) to the M9 medium led to observable fluorescence reduction, rendering it highly suitable for monitoring histidine-deficient cellular environments. This bacterial fluorescence sensor holds great potential for detecting changes in intracellular histidine concentrations during pathogenic bacterial infections and for assessing the impact of drugs on cellular metabolism.

Fluorescence microscopy is an important tool for disease diagnosis, often requiring costly optical components, such as fluorescence filter cubes and high-power light sources. Due to its high cost, conventional fluorescence microscopy cannot be fully exploited in low-income settings. Smartphone-based fluorescence microscopy becomes an interesting low-cost alternative, but raises challenges in the optical system. We present the development of a low-cost inverted laser fluorescence microscope, that uses a smartphone to visualize the fluorescence image of biological samples. Our fluorescence microscope uses a laser-based simplified optical filter system, that provides analog optical filtering capabilities of a fluorescence filter cube. Firstly, we validated our inverted optical filtering by visualizing microbeads labeled with three different fluorescent compounds or fluorophores, commonly used for disease diagnosis. Secondly, we validated the disease diagnosis capabilities, by comparing the results of our device with those of a commercial fluorescence microscope. We successfully detected and visualized Trypanosoma cruzi parasites, responsible of the Chagas infectious disease, and the presence of Antineutrophil cytoplasmic antibodies of the ANCA non-communicable autoimmune disease. The samples were labeled with the fluorescein isothiocyanate (FITC) fluorophore, one of the most commonly used for disease diagnosis. Our device provides a 400 X magnification and is at least two orders magnitude cheaper than conventional commercial fluorescence microscopes.

In this study, we evaluated chlorophyll fluorescence (CF) under two extreme illumination conditions at plant scale with a passive spectroradiometer. Fluorescence (F) was estimated by reading directly from radiance spectra of a variety of plants illuminated with light-emitting diode (LED) grow lights in the laboratory. Solar-induced fluorescence (SIF) was estimated from spectral measurements of the same plants under sunlight using the Fraunhofer Line Depth (FLD) method. Chlorophyll fluorescence yield (F_yield) and solar-induced fluorescence yield (SIF_yield) were calculated by normalizing F and SIF with absorbed photosynthetically active radiation (APAR). Two approaches to estimating APAR were compared: utilizing white reference spectra and reflected spectra versus white reference spectra combined with the fraction of absorbed photosynthetically active radiation (fPAR) derived from literature. Average F and SIF were different by a factor of approximately twenty-four (F = 0.110 ± 0.038 Wm⁻²μm⁻¹sr⁻¹ versus SIF = 2.60 ± 1.87 Wm⁻²μm⁻¹sr⁻¹). In contrast, the average normalized values F_yield and SIF_yield were within the margin of error of one another (F_yield = 0.022 ± 0.008 μm⁻¹sr⁻¹ and SIF_yield = 0.030 ± 0.020 μm⁻¹sr⁻¹). This study highlights the influence of APAR on CF and the importance of properly accounting for it when estimating yield and demonstrates the ability of two simple and portable experimental setups with a passive instrument to obtain fluorescence metrics.

.With the increasing use of carbon fiber reinforced plastics in various area, carbon fiber composites based on prepregs have attracted attention in industries and academia research. However, prepreg manufacturing processes are costly, and the strength of structures varies depending on the orientation and defects (pores and delamination). For non-contact evaluation of internal defects, we proposed lock-in infrared thermography to investigate orientation angles after a compression test. We also conducted a drop-weight impact test to study the behaviour of the composites after impact according the fibers orientation for composite fabricated using unidirectional carbon fiber prepregs. Using CAI tests, we determined the residual compressive strength and confirmed the damage modes using a thermal camera. The results of the drop weight impact tests show that the specimen laminated at 0° suffered the largest damage because of susceptibility of the resin to impact. In contrast, the specimens oriented in of 0°/90° and +45°/–45° directions transferred more than 90% of the impact energy back to the impactor because of the lamination of fibers in the orthogonal directions. Furthermore, the specimens that underwent complete damage in the impact tests were subjected to the lock-in method and showed internal delamination and cut fibers. With the finite elements analysis, the damage of each ply could be observed. Moreover, the temperature differences in the residual compression tests were not significant.

Effective agricultural management in maize production operations starts with the early quantification of seedlings. Accurately determining plant presence allows growers to optimize planting density, allocate resources, and detect potential growth issues early on. This study presents an in-depth analysis of multiple object detection models used by drones to count maize seedlings, while concurrently investigating the influence of planting density, flight altitude, and plant growth stage. The findings of this study demonstrate that one-stage models are able to more accurately detect maize seedlings than two-stage models. In particular, YOLOv8n exhibits outstanding performance, achieving an F1-score of over 0.92 under various density conditions and maintaining a stable performance, especially under densities below 105,000 plants/ha. Additionally, planting density and growth stage were observed to significantly affect detection accuracy, with performance declining as density and growth stage increased. Image resolution and detection were impacted by flight altitude, with lower flights producing higher-quality results. Ultimately, YOLOv8n displays optimal performance under various conditions, providing crucial data to support intelligent decision-making. This research offers valuable insights into the application of object detection technology and provides a basis for the future development of precision agriculture.

Reactive NO_x is one of the major air pollutants, which also plays a key role as greenhouse gas. Many research efforts have been devoted to not only detection of NO_x in air but also abatement of NO_x emission. The aim of this mini review is to provide a panoramic snapshot of the electrochemical analysis methods for the emission and detection of NO_x in atmosphere, with special emphasis on NO_x sensor. The electrochemical detecting mechanism and materials for fabricating electrochemical gas sensors are discussed and the prospects and challenges in this area are also evaluated. This work will serve as a useful source to inform the interested audience of the latest developments and applications in the field of NO_x emission and electrochemical detection.

The adulteration of goat milk powder occurs frequently, cattle-derived and soybean-derived ingredients are common adulterants in goat milk powder. However, simultaneously and rapidly detecting cattle-derived and soybean-derived components is still a challenge. high throughput and efficient screening method for adulteration detection is need. In this study, a rapid method was developed to detect the adulteration of common cattle-derived and soybean-derived components simultaneously in goat milk powder by combining the CRISPR/ Cas12a system with recombinant polymerase amplification (RPA). A dual DNA extraction method was employed, primers and crRNA for dual detection were designed and screened, and a series of condition optimizations were carried out in this experiment. The optimized assay can rapidly detect cattle-derived and soybean-derived components in 40 min. The detection limits of both cattle-derived and soybean-derived components were 1% (w/w) for the mixed adulteration models. The established method was applied into a blind survey of 55 commercially available goat milk powder products. The results showed that 36.36% of the samples contained cattle-derived or soybean-derived ingredients, which revealed the noticeable adulteration situation in the goat milk powder market. This study realizes a fast flow of dual extraction, dual amplification and dual detection of cattle-derived and soybean-derived components in goat milk powder for the first time. It can be used for high-throughput and high-efficiency on-site primary screening of goat milk powder adulterants, and provide a technical reference for combating with food adulteration.

Microwave radiation diagnosis technology can detect thermal changes in skin tissue earlier than anatomical changes, but its detection accuracy is limited by non-target radiation interference in the measurement environment, additional measurement errors of system units, and energy scattering and transmission between skin tissues. This paper aims to address the scientific challenges of analyzing the forward and inversion modelling detection mechanism of layered accurate temperature measurement of human skin tissue based on multi-band. The study focuses on the construction of a pencil beam antenna optimization system, the optimization strategy of high-sensitivity correlated radiometer architecture, and the high-precision multi-band forward and inversion modelling detection algorithm. The key technologies include: (1) A new method of integrated modeling and multi-index optimization of antenna with high directivity and small aperture is proposed, and a priori knowledge-guided neural network of pencil beam distribution is constructed to realize the inversion model of antenna structural parameters; (2) The influence mechanism of high sensitivity correlated radiometer architecture error is analyzed, and a periodic phase shift error correction algorithm based on uniform polar circle is designed; (3) Combining deep learning theory and hyperparameter optimization framework, an iterative model is established, and the objective function modified by the penalty factor is defined to realize a new detection method combining forward and inversion. This paper presents a theoretical foundation for the industrialization of microwave radiation diagnostic technology.

Considering the spectrum shortage problem of IoT devices, we introduce a collaborative spectrum sensing (CSS) framework in this paper to identify available spectrum resources, so that IoT devices can access it and meanwhile avoid causing harmful interference to the normal communication of the primary user (PU). However, in the process of the PU’s signal detection in IoT devices, the issue about the stopping time and decision cost arises. To this end, we propose a distributed cognitive IoT model, which includes two IoT devices independently using sequential decision rules to detect the PU. On this basis, we define the stopping time and cost function for IoT devices, and formulate an average cost optimization problem in CSS. To solve this problem, we further regard the optimal stopping time problem as a finite horizon problem, and solve the threshold of the optimal decision rule by dynamic programming. At last, numerical simulation results demonstrate the correctness of our proposal in terms of the global false alarm and miss detection probability, and it always achieves minimal average cost under various cost of each observation taken and thresholds.

This paper presents a study to accurately evaluate defects in reinforced concrete decks using ultrasonic pulse-echo signals. Two validation specimens were designed and tested where reinforced concrete deck slab specimens included planned voids and defects. A commercial Ultrasonic Pulse-Echo (UPE) device obtains 2-D images of the void/defect locations of the reinforced concrete deck. The UPE is based on the ultrasonic shear-wave test method using dry-point-contact transmitting and receiving transducers and employing a synthetic aperture focusing technique. The authors analyzed the recorded UPE A-scan data to enhance the accuracy of estimating the defects' locations using the synthetic aperture focusing technique imaging method. The analysis benefitted from an advanced denoising approach and defect echo peak extraction based on empirical modal decomposition, Hurst exponent characterization, and Hilbert envelope estimation. While output from conventional UPC devices can only provide qualitative results, the new method provides quantitative information on the anomalies inside the reinforced concrete deck. The developed approach accurately assists with assessing the location and depth of the voids/defects in the reinforced concrete deck slabs.

(1) Background: There is a risk of Aflatoxin B1(AFB1) pollution in yellow rice wine[1],threatening human life and health, the risk is a major focus of government attention and social concern.The current detection technology cannot meet the needs of large-scale testing due to the disadvantages of high false positive, cumbersome operating procedures or high cost.Therefore, it is urgent to study and establish a high-sensitivity and high-fast detection method for AFB1 to provide technical support for yellow rice wine quality supervision and risk assessment.; (2) Methods: In this study, three-factor and three-level orthogonal experiments were carried out for oscillation time, reaction temperature and tomography time to determine the best reaction conditions for pretreatment.AFB1 detection was carried out by aflatoxin time-resolved fluorescence immunochromatography technology，Through the ratio of the T signal value of the detection line to the C signal value of the quality control line and the natural logarithmic value of the standard solution concentration[2], a rapid detection technology of time-resolution fluorescence immunochromatography is established.The technology was methodically assessed, the actual yellow rice wine samples were tested, and the results were compared with the HPLC method; (3) Results: The results show that the optimal reaction conditions for pretreatment are: the sample oscillation time is 20min, the sample reaction temperature is 37°C, and the sample chromatography time is 6min. In the actual sample detection of yellow rice wine, The time-resolved fluorescence immunochromatography detection technology established in this study detects AFB1. The detection limit is 0.3 μg·kg-1,The linear range is 0.8-12.0μg·kg-1,The linear equation of the corresponding standard curve y=-0.1913x+0.5206 (R2=0.9948),The standard deviation is 0.029.The results of the in-batch accuracy and precision of this method show that the addition and recovery rate is 77.9% - 105.7%,The coefficient of variation is in 4.5% - 10.3%; Inter-batch accuracy and precision test results show that the addition recovery rate is75.9% - 85.8%,the coefficient of variation is in 8.1% - 13.9%,explain that the detection technology has good accuracy and precision.Compared with HPLC, the relative error is less than 10%,It shows that aflatoxin time-resolution fluorescence immunochromatography technology is in good agreement with the test results of national standard methods; (4) Conclusions:he rapid detection technology of time-resolution fluorescence immunochromatography of AFB1 in yellow rice wine is easy to operate with fast detection speed, high sensitivity, good repeatability and stability, and has broad application prospects.

Rikenella microfusus is an essential intestinal probiotic with great potential. The latest research shows that its imbalance in the intestinal flora is related to the occurrence of various diseases, such as intestinal diseases, immune diseases, and metabolic diseases. Rikenella may be a target or biomarker for some diseases, providing a new possibility for preventing and treating these diseas-es by monitoring and changing the abundance of Rikenella in the intestine. However, the current detection methods have disadvantages such as long detection time, complicated operation, and high cost, which seriously limit the possibility of clinical application of this new treatment method. Therefore, developing rapid and low-cost detection methods has become an urgent problem to be solved. In this study, we used Rikenella as the target bacterium, meanwhile including five other prominent gut bacteria Akkermansia muciniphila, Allobaculum stercoricanis, Blautia producta, Rose-buria intestinalis and Parabacteroides distasonis as control organisms. The aptamer library R.m-R13 was evolved with high specificity and strong affinity (Kd = 9.597 nM) in an iterative Cell-SELEX process after 13 rounds of selection. R. microfusus can efficiently be discriminated from other major gut bacteria in complex mixtures in different analysis techniques including fluorescence micros-copy or fluorometric suspension assays and even in human stool samples. These preliminary re-sults open new avenues towards the development of aptamer-based microbiome bio-sensing ap-plications for fast and reliable R. microfusus monitoring.

Unlike plants which have special gravity-sensing cells, such special cells in animals are yet to be discovered. However, microgravity, the condition of apparent weightlessness, causes bone, muscular and immune system dysfunctions in astronauts following spaceflights. Decades of investigations show correlations between these organ and system-level dysfunctions with changes induced at the cellular level both by simulated microgravity as well as microgravity conditions in outer space. Changes in single bone, muscle and immune cells include morphological abnormalities, altered gene expression, protein expression, metabolic pathways and signaling pathways. These suggest that human cells mount some response to microgravity. However, the implications of such adjustments on many cellular functions and responses are not clear. Here, we addressed the question whether microgravity induces alterations to drug response in cancer cells. We used both adherent cancer cells (T98G) and cancer cells in suspension (K562) to confirm known effects of microgravity and then treated the K562 cells with common cancer drugs (hydroxyurea and paclitaxel) following 48 hours of exposure to microgravity via a NASA-developed rotary cell culture system. Fluorescence-guided morphometry revealed microgravity-induced loss of the significant reduction (p &lt; 0.0l) to the nuclear to cytoplasm ratio of cancer cells treated with hydroxyurea. Our results call for more studies on impact of microgravity on cellular drug-response, in view of the growing need for space medicine, as space exploration grows.

Keywords: flow mediated skin fluorescence; FMSF technique; NADH fluorescence, microcirculation, flowmotion, reactive hyperemia response, RHR, hypoxia sensitivity, HS, normoxia oscillatory index, NOI

Background: In 2014 the World Health Organization (WHO) warned of an emerging world-wide crisis of antibiotic resistant microorganisms. In response, government and professional organizations recommended that health care systems adopt antimicrobial stewardship programs (ASPs). In the United States, the Centers for Medicare Services (CMS) mandated antimicrobial stewardship in the hospital inpatient setting. Effective January 1, 2020, the Joint Commission required ambulatory centers that prescribe antibiotics, such as wound centers, to institute an ASP. Chronic wounds often remain open for months, during which time patients may receive multiple courses of antibiotics and numerous antimicrobial topical treatments. The wound clinician plays an integral role in reducing antimicrobial resistance in the outpatient setting: antibiotics prescribed for skin and soft tissue infections are among the most common in an outpatient setting. One of the most challenging aspects of antimicrobial stewardship in treating chronic wounds is the inaccuracy of bacterial and infection diagnosis. Methods: Joint Commission lists five elements of performance (EP): (1) Identifying an antimicrobial stewardship leader, (2) establishing an annual antimicrobial stewardship goal, (3) implementing evidence-based practice guidelines related to the antimicrobial stewardship goal, (4) providing clinical staff with educational resources related to the antimicrobial stewardship goal, and (5) collecting, analyzing, and reporting data related to the antimicrobial stewardship goal. This article focuses on choosing and implementing an evidence-based ASP goal for 2020. Discussion: Clinical trials have demonstrated the ability of fluorescence imaging (MLiX) to detect clinically significant levels of bacteria in chronic wounds. Combined with clinical examination of signs and symptoms of infection, the MLiX procedure improves the clinician’s ability to diagnose infection and can guide antimicrobial use. In order to satisfy the elements of performance, the MLiX procedure was incorporated into the annual ASP goal for several wound care centers. Clinicians were educated on the fluorescence imaging device and guidelines were instituted. Collection of antimicrobial utilization data is underway.

This report covers the analysis of Intrusion Detection Systems nowadays. Therefore, it focuses on the IDS classification, which includes not only a well-known explanation of Network/Host-Based IDS type but also cloud-based solutions. The paper explores ways of intrusion detection, providing examples of the best detecting tools (OSSEC, Snort or Bro (ZEEK)). Another aspect covered in this report is the juxtaposition of three systems: IDS, IPS and IRS and their response. Finally, there will be presented Intrusion Detection Systems' evasion techniques and challenges, followed by critical conclusions.

Scedosporium species are human pathogenic fungi, responsible for chronic, localised, and life-threatening disseminated infections in both immunocompetent and immunocompromised individuals. Diagnosis of Scedosporium infections currently relies on non-specific CT, lengthy and insensitive culture from invasive biopsy, and time-consuming histopathology of tissue samples. At present, there are no rapid antigen tests that detect Scedosporium-specific biomarkers. Here we report the development of a rapid (30 min) and sensitive (pmol/L sensitivity) lateral-flow device (LFD) test, incorporating a Scedosporium-specific IgG1 monoclonal antibody (mAb), HG12, which binds to extracellular polysaccharide (EPS) antigens of between ~15 kDa and 250 kDa secreted during hyphal growth of the pathogens. The test is compatible with human serum, and allows the detection of the Scedosporium species most-frequently reported as agents of human disease (Scedosporium apiospermum, Scedosporium aurantiacum, and Scedosporium boydii), with limits of detection (LOD) of the EPS biomarkers in human serum of ~0.81 ng/mL (S. apiospermum), ~0.94 ng/mL (S. aurantiacum), and ~1.95 ng/mL (S. boydii). The Scedosporium-specific LFD (ScedLFD) test therefore provides a potential novel opportunity for the detection of infections caused by different Scedosporium species.

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a simplified RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

Extra virgin olive oil (EVOO) is the highest quality of olive oil and is characterized by highly beneficial nutritional properties. The large increase in both consumption and fraud, for example through adulteration, creates new challenges and an increasing demand for developing new quality assessment methodologies that are easier and cheaper to perform. As of today, the determination of olive oil quality is performed by producers through chemical analysis and organoleptic evaluation. The chemical analysis requires the advanced equipment and chemical knowledge of certified laboratories, and has therefore a limited accessibility. In this work a minimalist, portable and low-cost sensor is presented, which can perform olive oil quality assessment using fluorescence spectroscopy. The potential of the proposed technology is explored by analyzing several olive oils of different quality levels, EVOO, virgin olive oil (VOO), and lampante olive oil (LOO). The spectral data were analyzed using a large number of machine learning methods, including artificial neural networks. The analysis performed in this work demonstrates the possibility of performing classification of olive oil in the three mentioned classes with an accuracy of 100%. These results confirm that this minimalist low-cost sensor has the potential of substituting expensive and complex chemical analysis.

Kaposi’s sarcoma-associated herpesvirus (KSHV) is the etiologic agent of Kaposi’s sarcoma, primary effusion lymphoma (PEL) and multicentric Castleman’s disease. During KSHV lytic infection, lytic-related genes, categorized as immediate-early, early, and late genes, are expressed in a temporal manner. The transcription of late genes requires the virus-specific pre-initiation complex (vPIC), which consists of viral transcription factors. However, the characterization of the protein-protein interactions of the vPIC factors has not been completely elucidated. KSHV ORF18 is one of the vPIC factors, and its interaction with other viral factors has not been sufficiently revealed. Here, we analyzed the interaction between ORF18 and another vPIC factor, ORF30, in living cells using the bimolecular fluorescence complementation (BiFC) assay. We identified four amino-acid residues (Leu29, Gln36, His41 and Trp170) on ORF18 that were responsible for its interaction with ORF30. The artificial intelligence (AI) system AlphaFold2 predicted that identified four residues are exposed to the surface of ORF18 and located in proximity to each other in the surface of ORF18. Thus, AI-predicted model supports the importance of four residues for binding of ORF18 to ORF30. Our results indicated that wet experiments in combination with AI may enhance the structural characterization of vPIC protein-protein interactions.

This study aimed to investigate the appearance and frequencies of the booroola polymorphism of the bone morphogenetic protein receptor 1b (BMPR1B) gene (FecB) and the embrapa polymorphism of the growth differentiation factor 9 (GDF9) gene (FecGE) in sheep in Thailand. A total of 454 cross-bred sheep blood samples were collected from four provinces in Thailand during August 2022 to July 2023. The polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method was used to identify the FecB and FecGE genotype. The history of ewe birth types was collected from the owners to analyze the association between Fecundity (Fec) genotypes and the history of birth types. The genotypic frequencies of FecB for homozygous genotype (B/B), heterozygous genotype (+/B) and wild-type (+/+) were 0.22%, 1.54%, and 98.24%, respectively. Meanwhile, the genotypic frequencies of FecGE for homozygous genotype (E/E), heterozygous genotype (+/E), and wildtype (+/+) were 0.00%, 2.42%, and 97.58%, respectively. Furthermore, three ewes exhibited both FecB and FecGE genotypes. Fisher’s exact test revealed that the possessor of FecB genotype was associated with multiple births (p&lt;0.01). Both FecB and FecGE mutations were identified in crossbred sheep in Thailand. Sheep containing FecB allele is a better candidate to be selected to improve the prolificacy of crossbred sheep in Thailand.

In the field of 2-D image processing and computer vision, accurately detecting and segmenting objects in scenarios where they overlap or are obscured remains a challenge. This difficulty is worse in the analysis of shoeprints used in forensic investigations because they are embedded in noisy environments such as the ground and can be indistinct. Traditional Convolutional Neural Networks (CNNs), despite their success in various image analysis tasks, struggle with accurately delineating overlapping objects due to the complexity of segmenting intertwined textures and boundaries against a background of noise. This study introduces employs the YOLO (You Only Look Once) model enhanced by edge detection and image segmentation techniques to improve the detection of overlapping shoeprints. By focusing on the critical boundary information between shoeprint textures and the ground, our method demonstrates improvements in sensitivity and precision, achieving confidence levels above 85% for minimally overlapped images and maintaining above 70% for extensively overlapped instances. Heatmaps of convolution layers were generated to show how the network converges towards successful detection using these enhancements. This research may provide a potential methodology for addressing the broader challenge of detecting multiple overlapping objects against noisy backgrounds.

Fluorescence microscopic images of cells contain a large number of morphological features that are used as an unbiased source of quantitative information about cell status, through which researchers can extract quantitative information about cells and study the biological phenomena of cells through statistical and analytical analysis. As an important research object of phenotypic analysis, images have a great influence on the research results. Saturation artifacts present in the image result in a loss of grayscale information that does not reveal the true value of fluorescence intensity. From the perspective of data post-processing, we propose a two-stage cell image recovery model based on generative adversarial network to solve the problem of phenotypic feature loss caused by saturation artifacts. The model is capable of restoring large areas of missing phenotypic features. In the experiment, we adopt the strategy of progressive restore to improve the robustness of the training effect, and add the contextual attention structure to enhance the stability of the restore effect. We hope to use deep learning methods to mitigate the effects of saturation artifacts to reveal how chemical, genetic, and environmental factors affect cell state, providing an effective tool for studying the field of biological variability and improving image quality in analysis.

Treatment with tumor-selective photosensitive substances, such as photodynamic therapy and photoimmunotherapy, has been reported to enable the visualization of tumor drug accumulation in real time and identify the required light irradiation dose by fluorescence imaging. However, fluorescence observation is difficult during treatments performed under gastrointestinal endoscopy because of the challenges associated with including the fluorescence filter in the camera part. To address this issue, this study developed a device that integrates a narrow camera and a laser diffuser to enable fluorescence imaging through a forceps port. This device was employed to demonstrate that a laser diffuser with an NIR fluorescence imaging sensor could be delivered through a 3.2-mm-diameter port. In addition, fluorescence images of Cetuximab-IR700 were successfully observed in two mice, and the fluorescence intensity confirmed that the fluorescence decayed within 330 s. This device is expected to have practical application as a tool to identify the optimal irradiation dose for tumor-selective photosensitive substances under endoscopy.

High-throughput research on bimetallic nanoparticles brought a vast overview of their characteristics and catalytic activities.¹ However, traditional bimetallic nano-allloy quantification methods serving catalyst preparations are not suitable for the fast and robust data output of high-throughput experiments. Hence, this work designed and validated a more fast, more neat, and reliable quantitative analysis using X-ray fluorescence spectroscopy for bimetallic nanoparticles synthesized by high-throughput setups. The results demonstrated this method to be accurate with high precision. Furthermore, the agreement to qualitative results from an energy-dispersive X-ray spectrometer equiped on a scanning electron microscope provided an option to quantify a bimetallic nano-alloy by just analyzing one of the two composing elements. Thus, this method is proved to be highly compatible with high-throughput experiments.

Photosynthesis is one the most important biological processes on earth, producing life-giving oxygen and is the basis for a large variety of plant products. Measurable properties of photosynthesis provide information about its biophysical state and, in turn, the physiological conditions of a photoautotrophic organism. For instance, chlorophyll fluorescence of an intact photosystem is not linear as in the case of a single fluorescent dye in solution, but shows temporal changes related to the quantum yield of the photosystem. Commercial photosystem analyzers already use the fluorescence kinetics characteristics of photosystems to infer the viability of organisms under investigation. Here, we provide a novel approach based on an optical Fabry-Pérot microcavity or that enables the readout of photosynthetic properties and activity for an individual cyanobacterium. This approach offers a completely new dimension of information, which would normally be lost due to averaging in ensemble measurements obtained from a large population of bacteria.

The surface enhanced fluorescence（SEF）detection bases by plasmonic nanopillars array with nanoparticles has opened up a new gate in the application of biological imaging and sensing. The fluorescence enhancement of the probe molecule depends on its position in equilibrium, which is close to the hot spot leading to the electromagnetic field enhancement, but not too close to the metal surface resulting in quenching. Here, a large scale SiO₂-Ag-cicada wing SEF substrate was fabricated by magnetron sputtering with correction enhancement factor of 797.6. Thereinto the cicada wing provides the skeleton of the nanopillars array structure, the deposited Ag constructs two kinds of hot spots, and SiO₂ forms a separation layer to prevent quenching. Moreover, the substrate exhibited good reproducibility, high sensitivity with low limits of detection (LOD) and high stability for oxidation resistance. We propose that SEF substrate with modification of SiO₂ can not only improve the enhancement performance, but also expanding its application in the biological investigations.

Brushless Synchronous Machines (BSM) are replacing conventional synchronous machines with static excitation in generation facilities due to the absence of sparking and lower maintenance. However, this excitation system makes challenging measuring electric parameters in the rotor. This fact highly difficult the detection of ground faults, which are the most common type of electrical fault in electric machines. In this paper, a ground fault detection method for BSM is proposed. It is based on an inductive AC/DC rotating current sensor installed in the shaft. In case of a ground fault in the rotating parts of the BSM, a fault current will flow through the rotor’s sensor, inducing voltage in its stator. Analyzing the frequency components of the induced voltage the detection of a ground fault in the rotating elements is possible. The ground faults detection method proposed covers the whole rotor and discern between DC and AC sides. To corroborate the detection method, experimental tests have been performed using a prototype of this sensor connected to laboratory synchronous machines achieving satisfactory results.

The importance of oxygen in the winemaking process is widely known, as it affects the chemical aspects and therefore the organoleptic characteristics of the final product. Hence, it is evident the usefulness of a continuous and real-time measurements of the levels of oxygen in the various stages of the winemaking process, both for monitoring and for control. The WOW project has focused on the design and the development an innovative device for monitoring the oxygen levels in wine. This system is based on the use of an optical fiber to measure the luminescent lifetime variation of a reference metal/porphyrin complex, which decays in presence of oxygen. The developed technology results in a high sensitivity and low cost sensor head that can be employed for measuring the dissolved oxygen levels at several points inside a wine fermentation or aging tank. This system can be complemented with dynamic modeling techniques to provide predictive behavior of the nutrient evolution in space and time given few sampled measuring points for both process monitoring and control purposes. The experimental validation of the technology has been first performed in a controlled laboratory setup to attain calibration and study sensitivity with respect to different photo-luminescent compounds and alcoholic or non-alcoholic solutions, and then in an actual case study during a measurement campaign at a renown Italian winery.

Laser-induced fluorescence spectroscopy (LIFS) is actively used for remote sensing of atmospheric aerosols and currently is one of the most sensitive and selective techniques for determining small concentrations of substances inside particles. The use of high-power femtosecond laser sources for LIFS-based remote sensing of aerosol contributes to the development of new-generation fluorescence atmospheric lidars since it makes it possible overcoming the energy threshold for the nonlinear-optical effects of multiphoton absorption in particles and receiving the emission signal at long distances in the atmosphere. Our study is aimed to the development and experimental demonstration the technique of nonlinear laser-induced fluorescence spectroscopy (NLIFS) based on the remote excitation of aerosol fluorescent emission stimulated by a spatially-structured high-power femtosecond laser pulse. Importantly, for the first time to our knowledge, we demonstrate the advances of using stochastically-structured plasma-free intense light channels (postfilaments) specially formed by propagation of femtosecond laser radiation through a turbulent air layer for improving NLIFS efficiency. A multiple increase in the received signal of two-photon-excited fluorescence of polydisperse dyed aqueous aerosol by the structured postfilaments is reported.