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A novel demodulation scheme for a point-type fiber sensor is designed for salinity concentration monitoring based on a Sagnac interferometer (SI) composed of a tapered polarization-maintaining fiber (TPMF) and optical time stretching technology. The SI, constructed using a PMF with a taper region of 5.92 μm and an overall length of 30 cm, demonstrated a notable enhancement in the evanescent field, which intensifies the interaction between the light field and external salinity. This enhancement allows for a direct assessment of salinity concentration changes by analyzing the variations in the SI reflection spectra and the experimental results indicate that the sensitivity of the sensor is 0.151 nm/‰. In contrast to traditional fiber optic sensors that depend on spectral demodulation with slower response rates, this work introduces a new approach where the spectral shift is translated to the time domain, utilizing a dispersion compensation fiber (DCF) with the demodulation rate reaching up to 50 MHz. The experimental outcomes reveal that the sensor exhibits a sensitivity of −0.15 ns/‰ in the time domain. The designed sensor is anticipated to play a pivotal role in remote, real-time monitoring of ocean salinity.

To meet the requirement of autonomous driving development, high-quality point cloud generation of the environment has become the focus of 4D mmWave radar development. On the basis of mass producibility and physical verifiability, a design method for improving the quality and density of point cloud imagery is proposed in this paper, including antenna design, array design, and the dynamic detection method. The utilization of apertures is promoted through antenna design and sparse MIMO array optimization using the genetic algorithm (GA). The hybrid strategy for complex point clouds is adopted using the proposed dynamic CFAR algorithm, which enables dynamic adjustment of the threshold by discriminating and calculating different scanning regions. The effectiveness of the proposed method is verified by simulations and practical experiments. Aiming at system manufacture, analysis methods for the ambiguity function (AF) and shooting and bouncing rays (SBR) tracing are introduced, and an mmWave radar system is realized based on the proposed method, with its performance proven by practical experiments.

In the past decade, Spodoptera frugiperda has emerged as a significant invasive pest globally, posing a serious threat to agriculture due to its broad diet, migratory behavior, and ability to cause extensive plant damage. While extensive research has focused on the olfactory capabilities of adult S. frugiperda, understanding of the olfactory process in larvae remains limited, despite larvae playing a crucial role in crop damage. To address this gap, we identified an odorant receptor (OR), SfruOR40, expressed in the first-instar larvae through phylogenetic analysis. Using quantitative real-time PCR, we compared SfruOR40 expression levels in larvae and adults. We then characterized the function of SfruOR40 against 67 compounds using the Xenopus oocyte expression system and found that SfruOR40 responded to three plant volatiles. Further, behavioral experiments revealed a larval attraction to (−)-trans-Caryophyllene oxide. This study elucidates SfruOR40’s role in the olfactory recognition of newly hatched S. frugiperda larvae, expanding our knowledge of such mechanisms in Noctuid moths. Furthermore, it highlights the potential of plant-derived natural products for biological pest control from a behavioral ecology perspective.

System architecture design is crucial for forward design in aerostat system engineering, yet a comprehensive research framework has been lacking. This paper presents a new method for stratospheric airship architecture preliminary design and optimization trade-off, grounded in Model-Based Systems Engineering (MBSE) theory. Firstly, a requirement analysis for a stratospheric airship is conducted using SysML, leading to the analysis and acquisition of the airship’s mission architecture design. Additionally, a multidisciplinary coupling simulation platform is developed with MATLAB, and the architecture preliminary design’s Pareto front is derived using the NSGA-II algorithm. Finally, based on the Pareto optimization set, the TOPSIS algorithm is applied to derive the optimal architecture preliminary design scheme for the airship. The optimization results validate the accuracy of the architecture preliminary design obtained from the requirement analysis, the reliability of the multidisciplinary coupling simulation platform, and the feasibility of the optimization algorithms. This comprehensive study spans the requirement analysis to the optimal architecture scheme, providing theoretical reference and design guidance for the forward design of airship systems engineering.

In microfluidic systems, it is important to maintain flow stability to execute various functions, such as chemical reactions, cell transportation, and liquid injection. However, traditional flow sources, often bulky and prone to unpredictable fluctuations, limit the portability and broader application of these systems. Existing fluidic stabilizers, typically designed for specific flow sources, lack reconfigurability and adaptability in terms of the stabilization ratios. To address these limitations, a modular and standardized stabilizer system with tunable stabilization ratios is required. In this work, we present a Lego-like modular microfluidic stabilizer system, which is fabricated using 3D printing and offers multi-level stabilization combinations and customizable stabilization ratios through the control of fluidic RC constants, making it adaptable to various microfluidic systems. A simplified three-element circuit model is used to characterize the system by straightforwardly extracting the RC constant without intricate calculations of the fluidic resistance and capacitance. By utilizing a simplified three-element model, the stabilizer yields two well-fitted operational curves, demonstrating an R-square of 0.95, and provides an optimal stabilization ratio below 1%. To evaluate the system’s effectiveness, unstable input flow at different working frequencies is stabilized, and droplet generation experiments are conducted and discussed. The results show that the microfluidic stabilizer system significantly reduces flow fluctuations and enhances droplet uniformity. This system provides a new avenue for microfluidic stabilization with a tunable stabilization ratio, and its plug-and-play design can be effectively applied across diverse applications to finely tune fluid flow behaviors in microfluidic devices.

Olfaction is critical for guiding the physiological activities of insects, with antennae being the primary olfactory organs. However, recent evidence suggests that other tissues may also participate in olfactory recognition. Among these, the genitalia of moths have received attention due to their roles in mating and oviposition. Sensilla and odorant receptors (ORs) in moth genitalia highlight the potential olfactory function of these structures. In this study, we examined the olfactory sensing capacity of the genitalia in Helicoverpa armigera by analyzing their structure in males and females and characterizing the expressed ORs. Scanning electron microscopy uncovered many sensilla distributed throughout the male and female genitalia. Transcriptome sequencing identified 20 ORs in the genitalia, with HarmOR68 exhibiting significant responses to methyl esters: methyl benzoate and salicylate. Our findings provide theoretical evidence that H. armigera genitalia may have significant olfactory perception functions.

In the medical field, microwave imaging technology has experienced rapid development due to its non-invasive and non-radioactive nature. The confocal algorithm is a method commonly used for microwave breast cancer imaging, with the key objective of removing clutter in images to achieve high-quality results. However, the current methods are facing challenges in removing clutter. In order to reduce the clutter in images, a multiplicative improved coherence factor delay multiply and sum algorithm based on the maximum interclass differencing method is proposed. The algorithm compares the starting and ending moments of tumor signals in different channels to determine whether the tumor-scattered signals in different channels overlap in time. An improved coherence coefficient is obtained by summing the non-overlapping signals and multiplying the time window. The multiplicative improved coherence factor, which is obtained by multiplying the coherence coefficients of the improved multi-pair signals, is then multiplied by the focal point intensity obtained using the delay multiply and sum algorithm to reduce clutter in an image. To evaluate the performance of the proposed algorithm, several low-cost uniform and non-uniform models of human breast and tumor tissue with dielectric properties were prepared for testing. The experimental results show that, compared to the existing algorithm, the proposed algorithm can greatly reduce the clutter in images, with a signal-to-clutter ratio of at least 4 dB higher as well as contrast at least six-fold higher.

Graph neural networks (GNNs) have shown significant success in graph representation learning. However, the performance of existing GNNs degrades seriously when their layers deepen due to the over-smoothing issue. The node embedding incline converges to a certain value when GNNs repeat, aggregating the representations of the receptive field. The main reason for over-smoothing is that the receptive field of each node tends to be similar as the layers increase, which leads to different nodes aggregating similar information. To solve this problem, we propose an adaptive multi-channel deep graph neural network (AMD-GNN) to adaptively and symmetrically aggregate information from the deep receptive field. The proposed model ensures that the receptive field of each node in the deep layer is different so that the node representations are distinguishable. The experimental results demonstrate that AMD-GNN achieves state-of-the-art performance on node classification tasks with deep models.

Random lasers have attracted much attention in recent years owing to their advantages of a simple fabrication process, low processing cost, and material flexibility for any lasing wavelengths. They provide a roadmap for the design of ultra-bright lighting, displays, etc. However, the threshold reduction in random nanolasers remains a challenge in practical applications. In this work, lower-threshold random laser action from monolayer molybdenum disulfide film-encapsulated Au nanoparticles (MoS2/Au NPs) is demonstrated. The observed laser action of the MoS2/Au NPs shows a lower threshold of about 0.564 µJ/mm2, which is about 46.2% lower than the threshold of random lasers based on Au NPs. We proposed that the charge transfer between MoS2 and the gain material is the main reason for the reduction in the random laser threshold. The finite-difference time-domain (FDTD) method was used to calculate the lasing action of these two nanostructures. When charge transfer is taken into account, the theoretically calculated threshold of the MoS2/Au NPs is reduced by 46.8% compared to Au NP samples, which is consistent with the experimental results. This study provides a new mechanism to achieve low-threshold and high-quality random lasers, which has the potential to facilitate the application of random lasers and the development of high-performance optoelectronic devices.

Sarcomas are heterogeneous connective tissue malignancies that have been historically categorized into soft tissue and bone cancers. Although multimodal therapies are implemented, many sarcoma subtypes are still difficult to treat. Lipids play vital roles in cellular activities; however, ectopic levels of lipid metabolites have an impact on tumor recurrence, metastasis, and drug resistance. Thus, precision therapies targeting lipid metabolism in sarcoma need to be explored. In this study, we performed a comprehensive analysis of molecular stratification based on lipid metabolism-associated genes (LMAGs) using both public datasets and the data of patients in our cohort and constructed a novel prognostic model consisting of squalene epoxidase (SQLE) and tumor necrosis factor (TNF). We first integrated information on gene expression profile and survival outcomes to divide TCGA sarcoma patients into high- and low-risk subgroups and further revealed the prognosis value of the metabolic signature and immune infiltration of patients in both groups, thus proposing various therapeutic recommendations for sarcoma. We observed that the low-risk sarcoma patients in the TCGA-SARC cohort were characterized by high proportions of immune cells and increased expression of immune checkpoint genes. Subsequently, this lipid metabolic signature was validated in four external independent sarcoma datasets including the CHCAMS cohort. Notably, SQLE, a rate-limiting enzyme in cholesterol biosynthesis, was identified as a potential therapeutic target for sarcoma. Knockdown of SQLE substantially inhibited cell proliferation and colony formation while promoting the apoptosis of sarcoma cells. Terbinafine, an inhibitor of SQLE, displayed similar tumor suppression capacity in vitro. The prognostic predictive model and the potential drug target SQLE might serve as valuable hints for further in-depth biological, diagnostic, and therapeutic exploration of sarcoma.

Ionic liquids (ILs) are molten organic salts consisting of organic cations and weakly coordinating organic/inorganic anions at room temperature. ILs have excellent physical and chemical properties such as high thermal stability, high combustible temperature, high miscibility with organic compounds and so on, making them good candidates for high performance lubricants and lubricant additives. The functional designability of ILs makes them novel lubrication materials that can break through the bottleneck of the active control of friction and lubrication. This paper firstly briefly introduces how to design the physical and chemical properties of the ILs required for different friction conditions by bonding specific cations with anions. Then, the lubrication mechanisms of ILs as base lubricants and additives for oils and water are focused on. The correlation between the structure of ILs and the lubrication results are established, which can guide the structural design of ILs in different applications. The response behaviors of friction characteristics under external electric fields are analyzed, which can provide a theoretical basis for the intelligent control of friction based on ILs.

This paper proposes a non-contact current measurement method for three-phase rectangular busbars based on TMR (tunneling magneto-resistance) sensors, due to their advantages of large dynamic range, wide bandwidth, light weight, and easy installation. A non-contact current sensor composed of only three TMR sensors is developed and the TMR sensors are respectively placed at a location with a certain distance from the surface of each rectangular busbar to measure the magnetic fields generated by the busbar currents. To calibrate the developed current sensor, i.e., to establish the relationship between the magnetic fields measured by the TMR sensors and the currents flowing in the three-phase rectangular busbars, we designed a thyristor-controlled resistive load as a calibrator, which is connected to a downstream branch of the distribution cabinet. By switching the resistive load, a calibration current, which can be identified from the background current, is generated in one rectangular busbar and its value is measured at the location of the calibrator, and transmitted wirelessly to the location of the TMR sensors. A new and robust method is proposed to extract the voltage components, corresponding to the calibration current, from the voltage waveforms of the TMR sensors. By calculating the proportional coefficients between the calibration currents and the extracted voltage components, online calibration of the current sensor is achieved. We designed and implemented a current measurement system consisting of a current sensor using TMR sensors, a thyristor-controlled resistive load for current sensor calibration, and a data acquisition circuit based on a multi-channel analog-to-digital converter (ADC). Current measurement experiments were performed in a practical distribution cabinet installed in our laboratory. Compared to the measurement results using a commercial current probe with an accuracy of 1%, the relative error of the measured currents in RMS is less than 2.5% and the phase error is less than 1°, while the nonlinearity error of the current sensor is better than 0.8%.

Carcinoembryonic antigen (CEACAM5), as a broad-spectrum tumor biomarker, plays a crucial role in analyzing the therapeutic efficacy and progression of cancer. Herein, we propose a novel biosensor based on specklegrams of tapered multimode fiber (MMF) and two-dimensional convolutional neural networks (2D-CNNs) for the detection of CEACAM5. The microfiber is modified with CEA antibodies to specifically recognize antigens. The biosensor utilizes the interference effect of tapered MMF to generate highly sensitive specklegrams in response to different CEACAM5 concentrations. A zero mean normalized cross-correlation (ZNCC) function is explored to calculate the image matching degree of the specklegrams. Profiting from the extremely high detection limit of the speckle sensor, variations in the specklegrams of antibody concentrations from 1 to 1000 ng/mL are measured in the experiment. The surface sensitivity of the biosensor is 0.0012 (ng/mL)−1 within a range of 1 to 50 ng/mL. Moreover, a 2D-CNN was introduced to solve the problem of nonlinear detection surface sensitivity variation in a large dynamic range, and in the search for image features to improve evaluation accuracy, achieving more accurate CEACAM5 monitoring, with a maximum detection error of 0.358%. The proposed fiber specklegram biosensing scheme is easy to implement and has great potential in analyzing the postoperative condition of patients.

Retinal ischemia, after cerebral ischemia, is an easily overlooked pathophysiological problem in which inflammation is considered to play an important role. Pyroptosis is a kind of cell death pattern accompanied by inflammation. Homer scaffold protein 1 (Homer1) has anti-inflammation properties and protects against ischemic injury. However, little is known about pyroptosis following middle cerebral artery occlusion (MCAO)-induced retinal ischemia and the regulatory mechanisms involved by Homer1 for the development of pyroptosis. In the present study, retinal ischemic injury was induced in mice by permanent MCAO in vivo, and retinal ganglion cells (RGCs) were subjected to Oxygen and Glucose Deprivation (OGD) to establish an in vitro model. It was shown that TXNIP/NLRP3-mediated pyroptosis was located predominantly in RGCs, which gradually increased after retinal ischemia and peaked at 24 h after retinal ischemia. Interestingly, the RGCs pyroptosis occurred not only in the cell body but also in the axon. Notably, the occurrence of pyroptosis coincided with the change of Homer1 expression in the retina after retinal ischemia and Homer1 also co-localized with RGCs. It was demonstrated that overexpression of Homer1 not only alleviated RGCs pyroptosis and inhibited the release of pro-inflammatory factors but also led to the increase in phosphorylation of AMPK, inhibition of ER stress, and preservation of visual function after retinal ischemia. In conclusion, it was suggested that Homer1 may protect against MCAO-induced retinal ischemia and RGCs pyroptosis by inhibiting endoplasmic reticulum stress-associated TXNIP/NLRP3 inflammasome activation after MCAO-induced retinal ischemia.

Learning effective graph representations in an unsupervised manner is a popular research topic in graph data analysis. Recently, contrastive learning has shown its success in unsupervised graph representation learning. However, how to avoid collapsing solutions for contrastive learning methods remains a critical challenge. In this paper, a simple method is proposed to solve this problem for graph representation learning, which is different from existing commonly used techniques (such as negative samples or predictor network). The proposed model mainly relies on an asymmetric design that consists of two graph neural networks (GNNs) with unequal depth layers to learn node representations from two augmented views and defines contrastive loss only based on positive sample pairs. The simple method has lower computational and memory complexity than existing methods. Furthermore, a theoretical analysis proves that the asymmetric design avoids collapsing solutions when training together with a stop-gradient operation. Our method is compared to nine state-of-the-art methods on six real-world datasets to demonstrate its validity and superiority. The ablation experiments further validated the essential role of the asymmetric architecture.

Aims: The present study aimed to investigate whether and how the subcomponents of language control during spoken and written productions were modulated by cognitive control. Method: In the current study, unbalanced Chinese–English bilinguals were recruited from a convenience sample at a university to complete the cued language naming task in spoken production and written production, which measured the local language control (as indexed by language switch costs) and the global language control (as indexed by language mixing costs and reversed language dominance effect). In addition, all the participants performed the Simon task, which measured their general inhibitory control ability by calculating the Simon effect, and performed the AX-CPT task to measure their reactive/proactive control preference by calculating their BSI score. All the data were collected using E-prime 2.0 and analyzed in R. Linear mixed-effect model analyses were conducted to reveal the similarities and differences between spoken production and written production for the first-step analysis. Then, the Simon effect and BSI scores were inserted into the mixed-effect models of the switch costs and mixing costs in spoken production and written production, respectively, to explore whether cognitive control can predict the subcomponents of bilingual control. Results: The results showed similar symmetrical switch costs in spoken and written modalities. In contrast, there was a reversed language dominance effect (in the mixed language context) and asymmetrical mixing costs in spoken production but neither in written production. Furthermore, we found that the Simon effect significantly negatively predicted the L2 mixing costs in spoken production, whereas the BSI score significantly negatively predicted both the L1 and L2 mixing costs in written production. Conclusion: The findings indicated that, for unbalanced bilinguals, local language control is shared between two modalities, while global language control is modality-independent between spoken production and written production. More importantly, the findings also suggested that global language control in spoken production relies more on the individuals’ general inhibitory control, while in written production, it relies more on their cognitive control strategy. Global language control in spoken and written productions separately engages specific aspects of cognitive control, which may account for different forms of processing in global language control between speaking and writing.

The Vernier effect created using an incorporated Lyot–Sagnac loop is used to create an ultra-high sensitivity temperature sensor based on a ring laser cavity. Unlike standard double Sagnac loop systems, the proposed sensor is fused into a single Sagnac loop by adjusting the welding angle between two polarization-maintaining fibers (PMFs) to achieve effective temperature sensitivity amplification. The PMFs are separated into two arms of 0.8 m and 1 m in length, with a 45° angle difference between the fast axes. The sensor’s performance is examined both theoretically and experimentally. The experimental results reveal that the Vernier amplification effect can be achieved via PMF rotating shaft welding. The temperature sensitivity in the laser cavity can reach 2.391 nm/°C, which is increased by a factor of more than eight times compared with a single Sagnac loop structure (0.298 nm/°C) with a length of 0.8 m without the Vernier effect at temperatures ranging from 20 °C to 30 °C. Furthermore, unlike traditional optical fiber sensing that uses a broadband light source (BBS) for detection, which causes issues such as low signal-to-noise ratio and broad bandwidth, the Sagnac loop can be employed as a filter by inserting itself into the fiber ring laser (FRL) cavity. When the external parameters change, the laser is offset by the interference general modulation, allowing the external temperature to be monitored. The superior performance of signal-to-noise ratios of up to 50 dB and bandwidths of less than 0.2 nm is achieved. The proposed sensor has a simple structure and high sensitivity and is expected to play a role in biological cell activity monitoring.

Hydrogen peroxide (H2O2) is a versatile and effective disinfectant against common pathogenic bacteria such as Escherichia coli (E. coli). Electrochemical H2O2 generation has been studied in the past, but a lack of studies exists on miniaturized electrochemical platforms for the on-demand synthesis of H2O2 for antibacterial applications. In this article, a chemically modified cotton textile platform capable of in situ H2O2 production is demonstrated for E. coli deactivation. The cotton textile was modified by layer-by-layer coating with conductive carbon nanotubes/cellulose nanocrystals (CNT/CNC) and a polymer of polyaniline (PANI) decorated with anthraquinone (AQ), designated as the AQ@PANI@CNT/CNC@textile antibacterial patch. The AQ@PANI@CNT/CNC@textile antibacterial textile patch H2O2 production capabilities were evaluated using both electrochemical and colorimetric methods. The AQ@PANI@CNT/CNC@textile antibacterial patch electrochemically produced H2O2 concentrations up to 209 ± 25 µM over a 40 min period and displayed a log reduction of 3.32 for E. coli over a period of 2 h. The AQ@PANI@CNT/CNC@textile antibacterial patch offers promise for use as a self-disinfecting pathogen control platform.

An optical fiber ring laser (FRL) cavity-based sensitive temperature and salinity sensor is proposed and experimentally demonstrated. The sensor consists of a Sagnac loop with a waist of 15 µm and a total length of 30 cm made of tapered polarization-maintaining fiber (PMF). Sagnac loop dual parameter sensing was theoretically modeled and presented. The salinity sensitivity of 0.173 nm/‰ was made possible by the efficient interaction between the tapered PMF cladding mode and the external refractive index. In addition, temperature sensitivity of 0.306 nm/°C was achieved through ultrahigh birefringence of PMF. Apart from that, the previous sensing system used a broadband light source (BBS) as the input light, resulting in a wide bandwidth and a poor signal-to-noise ratio (SNR). The Sagnac loop integrated into the FRL system can achieve a high SNR of approximately 50 dB and a narrow bandwidth of 0.15 nm while serving as the filter and sensor head. Additionally, the developed sensor has the advantages of simple design, low cost, and easy fabrication. It can also extend sensing distance indefinitely within a given range, which is anticipated to have positive effects on the testing of marine environments in laboratories.

Although the fabrication of controllable three-dimensional (3D) microstructures on substrates has been proposed as an effective solution for SERS, there remains a gap in the detection and manufacturability of 3D substrates with high performance. In this study, photolithography is adopted to obtain a pyramid-like array on a patterned sapphire substrate (PSS), with Al2O3 as the dielectric layer. In addition, silver nanoparticles (AgNPs) are used to decorate Au films to obtain mass-producible 3D SRES substrates. In the case of low fluorescence, the substrate realizes the coupling of localized surface plasmon polaritons (LSPs) and surface plasmon polaritons (SPPs), which is consistent with the simulation results obtained using the finite element method. The performance of the SERS substrate is evaluated using rhodamine 6G (R6G) and toluidine blue (TB) as probe molecules with detection limits of 10−11 M and 10−9 M, respectively. The substrate exhibits high hydrophobicity and excellent light-capturing capability. Moreover, it shows self-cleaning ability and long-term stability in practical applications. Allowing for the consistency of the composite substrate in the preparation process and the high reproducibility of the test results, it is considered to be promising for mass production.

High salinity is a major stress factor affecting the quality and productivity of rice (Oryza sativa L.). Although numerous salt tolerance-related genes have been identified in rice, their molecular mechanisms remain unknown. Here, we report that OsJRL40, a jacalin-related lectin gene, confers remarkable salt tolerance in rice. The loss of function of OsJRL40 increased sensitivity to salt stress in rice, whereas its overexpression enhanced salt tolerance at the seedling stage and during reproductive growth. β-glucuronidase (GUS) reporter assays indicated that OsJRL40 is expressed to higher levels in roots and internodes than in other tissues, and subcellular localization analysis revealed that the OsJRL40 protein localizes to the cytoplasm. Further molecular analyses showed that OsJRL40 enhances antioxidant enzyme activities and regulates Na+-K+ homeostasis under salt stress. RNA-seq analysis revealed that OsJRL40 regulates salt tolerance in rice by controlling the expression of genes encoding Na+/K+ transporters, salt-responsive transcription factors, and other salt response-related proteins. Overall, this study provides a scientific basis for an in-depth investigation of the salt tolerance mechanism in rice and could guide the breeding of salt-tolerant rice cultivars.

Privacy-preserving has been crucial technique of multi-UAV systems, including cooperative detection, cooperative penetration and strike. Unprocessed interactive information poses a serious privacy threat to UAV swarm collaborative tasks. Considering not only privacy-preserving but also bandwidth constraints and the convergence performance of multi-UAV systems, this paper comprehensively proposes an original event-triggered-based finite-time privacy-preserving formation control scheme to resolve these three factors. Firstly, this paper adopted a local, deterministic, time-varying output mapping function for a privacy mask, which encodes the internal states of the UAV prior to its public transmission, and the initial true value of each UAV’s states is kept indecipherable for honest-but-curious UAVs and other malicious eavesdropping attackers. Then, considering the limited communication bandwidth and channels, we employed a distributed event-triggered strategy and deduced the triggering condition for consensus-based formation control, which effectively reduces the excessive consumption of communication and computational resources in contrast to time-triggered strategy. In terms of the convergence performance of the UAVs, finite-time stability theory was introduced to make the system reach the desired formation in finite time and obtain a settling time related to the initial state. Compared with the existing literature, this paper systematically took into account the above three factors for multi-UAV systems and provides a convergence analysis and a privacy analysis in detail. Finally, the effectiveness of the finite-time privacy-preserving protocol based on an event-triggered strategy was demonstrated by numerical simulation examples and comparative experiments. The proposed method achieves the formation control under privacy-preserving, improves the convergence rate and reduces the frequency of controller updates and information transmission.

This paper studies the consensus tracking control for a class of uncertain high-order nonlinear multi-agent systems under an undirected leader-following architecture. A novel distributed finite-time adaptive control framework is proposed based on the barrier function. The distributed cascaded high-gain observers are introduced to solve the problem of robust consensus tracking with unmeasured intermediate states in multi-agent systems based on the proposed control framework. The proposed control schemes guarantee the finite-time consensus of multi-agent systems, which is proven by the finite-time Lyapunov stability and singular perturbation theory. In conclusion, numerical simulations verify the proposed control protocols’ effectiveness, and their performance advantages are shown by comparing them with another existing method.

Glass products are important evidence of early East–West cultural exchanges. Ancient glass in China mostly consisted of lead glass, and potassium glass is widely believed to be imported abroad. In order to figure out the origin of glass artefacts, it is crucial to define the type of glass products accurately. In contemporary research on the chemical composition of ancient glass products, potassium glass is separated from lead glass primarily by the weight ratio of oxides or the proportion of lead-containing compounds. This approach can be excessively subjective and prone to mistakes while calculating the mass fraction of compounds containing potassium. So, it is better to find out the link between the proportion of glass’s chemical composition and its classifications during the weathering process of the glass products, to develop an effective classification model using machine learning techniques. In this research, we suggest employing the slime mould approach to optimise the parameters of a support vector machine and examine a 69-group glass chemical composition dataset. In addition, the results of the proposed algorithm are compared to those of commonly used classification models: decision trees (DT), random forests (RF), support vector machines (SVM), and support vector machines optimised by genetic algorithms (GA-SVM). The results of this research indicated that the support vector machine method with the sticky slime mould algorithm strategy is the most effective. On the training set, 100% accuracy was attained, while on the test set, 97.50% accuracy was attained in this research. The research results demonstrate that the support vector machine algorithm combining the slime mould algorithm strategy is capable of providing a trustworthy classification reference for future glass artefacts.

A novel method for ultra-sensitive and ultra-fast temperature sensing has been successfully implemented by cascading Saganc rings to generate the Vernier effect and doing the same dispersive fibers to achieve the optical time-stretching effect. This is different from the traditional point fiber sensor demodulated by optical spectrum analyzer (OSA) whose demodulation speed is usually at the second level. The designed system maps the wavelength domain to the time domain through the dispersive fiber, which can realize the ultra-fast temperature monitoring at the nanosecond level. The cascaded Sagnac ring is composed of polarization maintaining fiber (PMF) which is significantly affected by the thermal-optical coefficient. When the temperature changes, the variation is as high as −6.228 nm/°C, which is 8.5 times higher than the sensitivity based on the single Sagnac ring system. Furthermore, through the optical time stretching scheme, the corresponding response sensitivity is increased from 0.997 ns/°C to 7.333 ns/°C, and the magnification is increased 7.4 times with a response speed of 50 MHz.

We report an ytterbium (Yb) doped fiber Mach Zehnder interferometer (MZI) based on the up-taper fiber structure in a fiber ring laser (FRL) cavity. Different from the traditional FRL sensing system, in which additional filters are required, the designed structure simultaneously acts as a filter, sensor and gain medium. Furthermore, thanks to the high thermal–optical coefficient of Yb doped fiber, the temperature sensitivity of 0.261 nm/°C can be achieved in the range of 10–50 °C. In addition, benefiting from the unique characteristics of the laser system itself, the designed structure has a narrower linewidth (−0.2 nm) and a higher signal-to-noise ratio (SNR) (−40 dB) than the sensor system based on a broadband light source (BBS). Meanwhile, the refractive index (RI) response and stability of the system are measured. The RI sensitivity is up to 151 nm/RIU, and the wavelength fluctuation range within two hours is less than 0.2 nm. Therefore, the designed structure is expected to play a significant role in human life safety monitoring, aircraft engine temperature monitoring, etc.

Traditional bathymetry LiDAR (light detection and ranging) onboard manned and/or unmanned airborne systems cannot operate in the context of narrow rivers in urban areas with high buildings and in mountainous areas with high peaks. Therefore, this study presents a prototype of a lightweight bathymetry LiDAR onboard an unmanned shipborne vehicle (called “GQ-Cor 19”). The GQ-Cor 19 system primarily includes an emitting optical module, a receiving optical module, control module, detection module, high-speed A/D sampling module, and data processing system. Considering that the “GQ-Cor 19” is extremely close to the water surface, various new technical challenges are encountered, such as significant laser scattering energy from the surface of the water, which saturates signals received by the photomultiplier tube detector. Therefore, this study presents various new technical solutions, including (1) an improved Bresenham algorithm, (2) a small and lightweight receiving optical system with a split-field method, and (3) a data acquisition module with a high-speech A/D collector. Following a series of different experimental verifications, the results demonstrate that the new generation of single-band LiDAR onboard an unmanned shipborne vehicle can swiftly measure the underwater depth, and the maximum measurement depth is more than 25 m. The measurement accuracy is better than 30 cm and the weight is less than 12 kg.

This work presents a multipurpose and multilayered stainless steel microneedle sensor for the in situ redox potential monitoring in food and drink samples, termed MN redox sensor. The MN redox sensor was fabricated by layer-by-layer (LbL) approach. The in-tube multilayer coating comprised carbon nanotubes (CNTs)/cellulose nanocrystals (CNCs) as the first layer, polyaniline (PANI) as the second layer, and the ferrocyanide redox couple as the third layer. Using cyclic voltammetry (CV) as a transduction method, the MN redox sensor showed facile electron transfer for probing both electrical capacitance and redox potential, useful for both analyte specific and bulk quantification of redox species in various food and drink samples. The bulk redox species were quantified based on the anodic/cathodic redox peak shifts (Ea/Ec) on the voltammograms resulting from the presence of redox-active species. The MN redox sensor was applied to detect selected redox species including ascorbic acid, H2O2, and putrescine, with capacitive limits of detection (LOD) of 49.9, 17.8, and 263 ng/mL for each species, respectively. For the bulk determination of redox species, the MN redox sensor displayed LOD of 5.27 × 103, 55.4, and 25.8 ng/mL in ascorbic acid, H2O2, and putrescine equivalents, respectively. The sensor exhibited reproducibility of ~1.8% relative standard deviation (%RSD). The MN redox sensor was successfully employed for the detection of fish spoilage and antioxidant quantification in king mushroom and brewed coffee samples, thereby justifying its potential for food quality and food safety applications. Lastly, the portability, reusability, rapid sampling time, and capability of in situ analysis of food and drink samples makes it amenable for real-time sensing applications.

This paper investigates the problem of rotating formation control for multi-spacecraft systems with prescribed performance in the presence of model uncertainties. Firstly, The spacecraft dynamics containing unmodelled parts is described in a polar coordinate system, which is to solve the problem of the controllable angular velocity of rotating formation. Then, the prescribed performance control method is improved by developing new prescribed performance functions. Based on the improved prescribed performance control method, the distributed controller is designed for multi-spacecraft systems to achieve rotating formations with prescribed performance, i.e., the formations error converges to a predefined arbitrarily small residual set, with convergence time no less than a prespecified value. And an RBF neural network is used to fit the unmodelled components of the spacecraft dynamics. Compared with the existing works of literature, this paper not only solves the robust prescribed performance rotating formation control of multi-spacecraft system, but also acheives rotating formation with adjustable angular velocity. Finally, the Lyapunov approach is employed for convergence analysis, and simulation results are provided to illustrate the effectiveness of the theoretical results.

A portable, molecularly imprinted polymer (MIP)-based microneedle (MN) sensor for the electrochemical detection of imidacloprid (IDP) has been demonstrated. The MN sensor was fabricated via layer-by-layer (LbL) in-tube coating using a carbon nanotube (CNT)/cellulose nanocrystal (CNC) composite, and an IDP-imprinted polyaniline layer co-polymerized with imidazole-functionalized CNCs (PANI-co-CNC-Im) as the biomimetic receptor film. The sensor, termed MIP@CNT/CNC MN, was analyzed using both cyclic voltammetry (CV) and differential pulse voltammetry (DPV) and showed excellent electrochemical performance for the detection of IDP. The CV detection range for IDP was 2.0–99 µM, with limits of detection (LOD) of 0.35 µM, while the DPV detection range was 0.20–92 µM with an LOD of 0.06 µM. Additionally, the MIP@CNT/CNC MN sensor showed excellent reusability and could be used up to nine times with a 1.4 % relative standard deviation (% RSD) between uses. Lastly, the MIP@CNT/CNC MN sensor successfully demonstrated the quantification of IDP in a honey sample.

This paper presented a new kind of salinity and temperature dual-parameter sensor based on a fiber ring laser (FRL) with tapered side-hole fiber (SHF) embedded in a Sagnac interferometer. The sensing structure is majorly composed of tapered SHF located in the middle of SHF inside the Sagnac interferometer loop structure. The influences of the SHF’s diameters of different tapered in the Sagnac interferometer loop on the FRL sensing system are studied. The presence of air holes in the SHF makes the cladding mode easier to excite, and the interaction between the cladding mode with its surroundings is enhanced, thus having higher salinity sensitivity. Besides, the unique advantages of high resolution, narrower linewidth, and high signal-to-noise ratio (SNR) of fiber laser make the measurement results more accurate. In this experiment, the SHF with different taper diameters was made, and it was found that reducing the diameter of the taper waist diameter could further improve the salinity sensitivity. When the waist diameter was 9.70 μm, the maximum salinity sensitivity of 0.2867 nm/‰ was achieved. Temperature sensing experiments were also carried out. The maximum temperature sensitivity of the FRL sensing system was −0.3041 nm/°C at the temperature range from 20 to 30 °C. The sensor has the characteristics of easy manufacture, good selectivity, and high sensitivity, proving the feasibility of simultaneous measurement of seawater salinity and temperature.

Though AlphaFold2 has attained considerably high precision on protein structure prediction, it is reported that directly inputting coordinates into deep learning networks cannot achieve desirable results on downstream tasks. Thus, how to process and encode the predicted results into effective forms that deep learning models can understand to improve the performance of downstream tasks is worth exploring. In this study, we tested the effects of five processing strategies of coordinates on two single-sequence protein binding site prediction tasks. These five strategies are spatial filtering, the singular value decomposition of a distance map, calculating the secondary structure feature, and the relative accessible surface area feature of proteins. The computational experiment results showed that all strategies were suitable and effective methods to encode structural information for deep learning models. In addition, by performing a case study of a mutated protein, we showed that the spatial filtering strategy could introduce structural changes into HHblits profiles and deep learning networks when protein mutation happens. In sum, this work provides new insight into the downstream tasks of protein-molecule interaction prediction, such as predicting the binding residues of proteins and estimating the effects of mutations.

A wearable, textile-based molecularly imprinted polymer (MIP) electrochemical sensor for cortisol detection in human sweat has been demonstrated. The wearable cortisol sensor was fabricated via layer-by-layer assembly (LbL) on a flexible cotton textile substrate coated with a conductive nanoporous carbon nanotube/cellulose nanocrystal (CNT/CNC) composite suspension, conductive polyaniline (PANI), and a selective cortisol-imprinted poly(glycidylmethacrylate-co-ethylene glycol dimethacrylate) (poly(GMA-co-EGDMA)) decorated with gold nanoparticles (AuNPs), or plated with gold. The cortisol sensor rapidly (

We demonstrated a new method for temperature measurement inside a fiber ring laser (FRL) cavity. Different from traditional FRL temperature sensing system which need additional filter working as a sensor, a micro-fiber coupler (MFC) was designed as a beam splitter, filter, and temperature sensor. In addition, isopropanol, a liquid with very high photothermal coefficient, is selectively filled in the MFC in order to improve the sensitivity of the system on temperature. In the dynamic range of 20–40 °C, we obtained a good temperature sensitivity of −1.29 nm/°C, with linear fitting up to 0.998. Benefiting from the advantages of laser sensing, the acquired laser has a 3 – dB bandwidth of less than 0.2 nm and a signal-to-noise ratio (SNR) of up to 40 dB. The proposed sensor has a low cost and high sensitivity, which is expected to be used in biomedical health detection, real-time monitoring of ocean temperature, and other application scenarios.

The abuse of AGPs in animal husbandry has led to severe problems such as drug resistance and ecological, and environmental destruction, which seriously threaten human health and public health security. In recent years, extracts of oregano oil and macleaya cordata have become a hot spot in the research and application of AGP substitutes for their safety and high efficiency. This study is the first to report the effect of oregano oil combined with macleaya cordata oral solution on broiler growth performance. A total of 960 one-day-old broiler chickens were randomly divided into four treatment groups (240 chickens per group). Each treatment group was divided into six replicate groups (40 birds per replicate group). There were four groups in this study: the solvent control group, the oregano essential oil combined with macleaya cordata extract oral solution group (OS group), the oregano essential oil oral solution group (OEO group), and the macleaya cordata extract oral solution group (MCE group). Two chickens from each replicate group were collected and mixed into a composite sample. Six composite samples were obtained for each treatment group. The results showed that the oregano oil combined with macleaya cordata oral solution significantly improved the growth performance of broiler chickens. At the same time, serum biochemical indices, serum antioxidant indices, serum immune indices, serum cytokines, and intestinal morphology were significantly improved by the OS group. This study shows that oregano oil combined with macleaya cordata oral solution has substantial potential to be an alternative to AGPs for broilers.

Perfluoroisobutyronitrile (C4F7N), an environment-friendly insulating gas, has excellent insulating properties and has the potential to be used in gas-insulated equipment when mixed with CO2. Selecting suitable adsorption materials to adsorb the decomposition products of the C4F7N mixture can ensure the safe and stable operation of the gas-insulated equipment and the personal safety of the operators in the electric power industry. The adsorption characteristics of the ZSM-5 molecular sieve on C4F7N and its five fluorocarbon decomposition products were investigated by adsorption experiments. The results show that the ZSM-5 molecular sieve has a certain adsorption effect on six fluorocarbon gases; the adsorption performance of C3F6 and C3F8 are the best, with an adsorption efficiency over 85%, while the concentration of CO2 and C4F7N is affected by the ZSM-5 molecular sieve. At the same time, the paper based on the Metropolis Monte Carlo simulation of Materials Studio software found that the ZSM-5 molecular sieve has the strongest adsorption effect on C4F7N molecules and the weakest adsorption effect on CO2 molecules. The stronger the polarity of the gas molecule, the more obvious the adsorption effect of molecular sieve structure on it. As a result, the ZSM-5 molecular sieve could be used in tail gas purification of insulated equipment, as well as to provide solutions for the development and production of protective equipment.

This paper proposes a real-time multi-class disturbance detection algorithm based on YOLO for distributed fiber vibration sensing. The algorithm achieves real-time detection of event location and classification on external intrusions sensed by distributed optical fiber sensing system (DOFS) based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). We conducted data collection under perimeter security scenarios and acquired five types of events with a total of 5787 samples. The data is used as a spatial–temporal sensing image in the training of our proposed YOLO-based model (You Only Look Once-based method). Our scheme uses the Darknet53 network to simplify the traditional two-step object detection into a one-step process, using one network structure for both event localization and classification, thus improving the detection speed to achieve real-time operation. Compared with the traditional Fast-RCNN (Fast Region-CNN) and Faster-RCNN (Faster Region-CNN) algorithms, our scheme can achieve 22.83 frames per second (FPS) while maintaining high accuracy (96.14%), which is 44.90 times faster than Fast-RCNN and 3.79 times faster than Faster-RCNN. It achieves real-time operation for locating and classifying intrusion events with continuously recorded sensing data. Experimental results have demonstrated that this scheme provides a solution to real-time, multi-class external intrusion events detection and classification for the Φ-OTDR-based DOFS in practical applications.

The research on driver fatigue detection is of great significance to improve driving safety. This paper proposes a real-time comprehensive driver fatigue detection algorithm based on facial landmarks to improve the detection accuracy, which detects the driver’s fatigue status by using facial video sequences without equipping their bodies with other intelligent devices. A tasks-constrained deep convolutional network is constructed to detect the face region based on 68 key points, which can solve the optimization problem caused by the different convergence speeds of each task. According to the real-time facial video images, the eye feature of the eye aspect ratio (EAR), mouth aspect ratio (MAR) and percentage of eye closure time (PERCLOS) are calculated based on facial landmarks. A comprehensive driver fatigue assessment model is established to assess the fatigue status of drivers through eye/mouth feature selection. After a series of comparative experiments, the results show that this proposed algorithm achieves good performance in both accuracy and speed for driver fatigue detection.

An optical fiber temperature sensor based on Mach–Zehnder interferometer and thermo-optic effect of the liquid crystal (LC) in fiber ring laser (FRL) system is proposed and experimentally demonstrated. The LC is infiltrated into the core of hollow core fiber, and the resonant wavelength is more sensitive to temperature variation due to the interaction between the incident light and the cavity infiltrating liquid crystal with high thermal light coefficient. Meanwhile, the FRL system was further used to make the sensor have good performance in the case of high signal-to-noise ratio (∼35 dB), narrow half-height width (FWHM = 0.15 nm), and high sensitivity in the temperature range from 20 °C to 50 °C, with the maximum sensitivity of 1.318 nm/°C. As far as we know, in the FRL system, the liquid crystal material has a better temperature sensing performance than the previous fiber. Nevertheless, the system has the advantages of good repeatability, low cost, simple production, small volume, high sensitivity. In marine microbial culture and detection, it is necessary to carry out high sensitivity measurement within a small temperature variation range. This reliable and excellent temperature performance has a potential application prospect.

With the rapid development of integrated circuit technology, the heat flux of electronic chips has been sharply improved. Therefore, heat dissipation becomes the key technology for the safety and reliability of the electronic equipment. In addition, the electronic chips are distributed discretely and used periodically in most applications. Based these problems, the characteristics of the heat transfer performance of flow boiling in parallel channels with discrete heat source distribution are investigated by a VOF model. Meanwhile, the two-phase flow instability in parallel channels with discrete heat source distribution is analyzed based on a one-dimensional homogeneous model. The results indicate that the two-phase flow pattern in discrete heat source distribution is more complicated than that in continuous heat source distribution. It is necessary to optimize the relative position of the discrete heat sources, which will affect the heat transfer performance. In addition, compared with the continuous heat source, the flow stability of discrete heat sources is better with higher and lower inlet subcooling. With a constant sum of heating power, the greater the heating power near the outlet, the better the flow stability.

We demonstrate a highly sensitive and practical fiber-based temperature sensor system. The sensor is constructed based on a fiber ring laser (FRL) as well as a side-polished fiber filled with isopropanol. The laser cavity of the sensing part fiber is polished by the wheel fiber polishing system with residual thickness (RT) is selected to detect the temperature in the FRL. Thanks to the high thermo-optic coefficient of isopropanol, the sensitivity of the proposed temperature sensor could be effectively improved by filling isopropanol in the cost-less side polished single mode fiber. Refractive index (RI) of isopropanol changes with the surrounding temperature variation allowing high-sensitivity temperature sensing. Experimental results demonstrate that the side polished fiber can efficiently excite high-order cladding modes which enhance the modular interference increase the interaction between the evanescent wave and the isopropanol. Besides, the results show that the sensitivity can be as high as 2 nm/°C in the temperature range of 25–35 °C.

We demonstrate a new concept for an all-fiber inclinometer based on a tapered fiber Bragg grating (tFBG) in a fiber ring laser (FRL) with the capability of measuring the tilt angle and temperature simultaneously. The sensor performance is analyzed theoretically and investigated experimentally. The dependence of tilt angle on the spectral response in variable temperature conditions was measured. Two inclinometers with different lengths have been fabricated and characterized in FRL. The sensitivity is 0.583 dB/° and 0.849 dB/°, respectively, in the range of 0° to 90°. Thanks to the FRL system, narrow 3-dB bandwidth (