The Highly Sensitive Refractive Index Sensing of Seawater Based on a Large Lateral Offset Mach-Zehnder Interferometer.

A novel fiber sensor for the refractive index sensing of seawater based on a Mach-Zehnder interferometer has been demonstrated. The sensor consisted of a single-mode fiber (SMF)-no-core fiber (NCF)-single-mode fiber structure (shortened to an SNS structure) with a large lateral offset spliced between the two sections of a multimode fiber (MMF). Optimization studies of the multimode fiber length, offset SNS length, and vertical axial offset distance were performed to improve the coupling efficiency of interference light and achieve the best extinction ratio. In the experiment, a large lateral offset sensor was prepared to detect the refractive index of various ratios of saltwater, which were used to simulate seawater environments. The sensor's sensitivity was up to -13,703.63 nm/RIU and -13,160 nm/RIU in the refractive index range of 1.3370 to 1.3410 based on the shift of the interference spectrum. Moreover, the sensor showed a good linear response and high stability, with an RSD of only 0.0089% for the trough of the interference in air over 1 h.

High-sensitivity THz-ATR imaging of cerebral ischemia in a rat model.

The fast label-free detection of the extent and degree of cerebral ischemia has been the difficulty and hotspot for precise and accurate neurosurgery. We experimentally demonstrated that the fresh cerebral tissues at different ischemic stages within 24 hours can be well distinguished from the normal tissues using terahertz (THz) attenuated total reflection (ATR) imaging system. It was indicated that the total reflectivity of THz wave for ischemic cerebral tissues was lower than that for normal tissues. Especially, compared to the images stained with 2,3,5-triphenyl tetrazolium chloride (TTC), the ischemic tissues can be detected using THz wave with high sensitivity as early as the ischemic time of 2.5 hours, where THz images showed the ischemic areas became larger and diffused as the ischemic time increasing. Furthermore, the THz spectroscopy of cerebral ischemic tissues at different ischemic times was obtained in the range of 0.5-2.0 THz. The absorption coefficient of ischemic tissue increased with the increase of ischemic time, whereas the refractive index decreased with prolonging the ischemic time. Additionally, it was found from hematoxylin and eosin (H&E) staining microscopic images that, with the ischemic time increasing, the cell size and cell density of the ischemic tissues decreased, whereas the intercellular substance of the ischemic tissues increased. The result showed that THz recognition mechanism of the ischemia is mainly based on the increase of intercellular substance, especially water content, which has a stronger impact on absorption of THz wave than that of cell density. Thus, THz imaging has great potential for recognition of cerebral ischemia and it may become a new method for intraoperative real-time guidance, recognition in situ, and precise excision.

Development of new materials for electrothermal metals using data driven and machine learning.

After adopting a combined approach of data-driven methods and machine learning, the prediction of material performance and the optimization of composition design can significantly reduce the development time of materials at a lower cost. In this research, we employed four machine learning algorithms, including linear regression, ridge regression, support vector regression, and backpropagation neural networks, to develop predictive models for the electrical performance data of titanium alloys. Our focus was on two key objectives: resistivity and the temperature coefficient of resistance (TCR). Subsequently, leveraging the results of feature selection, we conducted an analysis to discern the impact of alloying elements on these two electrical properties.The prediction results indicate that for the resistivity data prediction task, the radial basis function kernel-based support vector machine model performs the best, with a correlation coefficient above 0.995 and a percentage error within 2%, demonstrating high predictive capability. For the TCR data prediction task, the best-performing model is a backpropagation neural network with two hidden layers, also with a correlation coefficient above 0.995 and a percentage error within 3%, demonstrating good generalization ability. The feature selection results using random forest and Xgboost indicate that Al and Zr have a significant positive effect on resistivity, while Al, Zr, and V have a significant negative effect on TCR. The conclusion of the composition optimization design suggests that to achieve both high resistivity and TCR, it is recommended to set the Al content in the range of 1.5% to 2% and the Zr content in the range of 2.5% to 3%.

Machine Learning Accelerates De Novo Design of Antimicrobial Peptides.

Efficient and precise design of antimicrobial peptides (AMPs) is of great importance in the field of AMP development. Computing provides opportunities for peptide de novo design. In the present investigation, a new machine learning-based AMP prediction model, AP_Sin, was trained using 1160 AMP sequences and 1160 non-AMP sequences. The results showed that AP_Sin correctly classified 94.61% of AMPs on a comprehensive dataset, outperforming the mainstream and open-source models (Antimicrobial Peptide Scanner vr.2, iAMPpred and AMPlify) and being effective in identifying AMPs. In addition, a peptide sequence generator, AP_Gen, was devised based on the concept of recombining dominant amino acids and dipeptide compositions. After inputting the parameters of the 71 tridecapeptides from antimicrobial peptides database (APD3) into AP_Gen, a tridecapeptide bank consisting of de novo designed 17,496 tridecapeptide sequences were randomly generated, from which 2675 candidate AMP sequences were identified by AP_Sin. Chemical synthesis was performed on 180 randomly selected candidate AMP sequences, of which 18 showed high antimicrobial activities against a wide range of the tested pathogenic microorganisms, and 16 of which had a minimal inhibitory concentration of less than 10 µg/mL against at least one of the tested pathogenic microorganisms. The method established in this research accelerates the discovery of valuable candidate AMPs and provides a novel approach for de novo design of antimicrobial peptides.

Alleviating sample imbalance in water quality assessment using the VAE-WGAN-GP model.

Water resources are essential for sustaining human life and promoting sustainable development. However, rapid urbanization and industrialization have resulted in a decline in freshwater availability. Effective prevention and control of water pollution are essential for ecological balance and human well-being. Water quality assessment is crucial for monitoring and managing water resources. Existing machine learning-based assessment methods tend to classify the results into the majority class, leading to inaccuracies in the outcomes due to the prevalent issue of imbalanced class sample distribution in practical scenarios. To tackle the issue, we propose a novel approach that utilizes the VAE-WGAN-GP model. The VAE-WGAN-GP model combines the encoding and decoding mechanisms of VAE with the adversarial learning of GAN. It generates synthetic samples that closely resemble real samples, effectively compensating data of the scarcity category in water quality evaluation. Our contributions include (1) introducing a deep generative model to alleviate the issue of imbalanced category samples in water quality assessment, (2) demonstrating the faster convergence speed and improved potential distribution learning ability of the proposed VAE-WGAN-GP model, (3) introducing the compensation degree concept and conducting comprehensive compensation experiments, resulting in a 9.7% increase in the accuracy of water quality assessment for multi-classification imbalance samples.

Narrow linewidth and wideband tunable continuous-wave terahertz generator based on difference frequency generation with DAST crystal.

A narrow linewidth and wideband tunable continuous-wave terahertz generator with DAST crystal has been demonstrated in this paper. Two narrow-linewidth CW fiber lasers were used as the pump sources for difference frequency generation. The terahertz wave can be continuously tunable in the range of 1.1-3 THz. The maximum output power of 2.79nW was obtained at 2.568 THz. The linewidth of the output THz wave was estimated to be 56.5 MHz by fitting transmission spectrum of CO gas at 450 Pa pressure around 80.52 cm-1 with the Vogit gas model. Furthermore, the output spectra at room temperature and pressure was in good agreement with the air absorption lines in Hitran database. Moreover, the narrower absorption characteristic spectrum of 2-Deoxy-D-Glucose sample has been obtained through the spectrum measurements. Therefore, it could promote the practical prospect of tunable CW-THz source, which will have good potential in THz high-precision spectroscopic detection and multispectral imaging.

Terahertz beam characterization by temporal-spatial mapping with a reflecting echelon.

A terahertz beam imaging method was proposed that involves scanning a reflecting echelon with temporal-spatial mapping inversion based on self-developed translation-scan and rotation-scan temporal-spatial mapping (TTSM and RTSM) algorithms. The beam characteristics of a terahertz time-domain spectroscopy (TDS) system, such as its size, shape, and energy distribution, were obtained. Besides the weak terahertz beam emitted from a TDS system, this scheme is also suitable for imaging large-size terahertz or laser beams in time-domain systems where existing beam imaging is impractical.

Serum-based Raman spectroscopic diagnosis of blast-induced brain injury in a rat model.

The diagnosis of blast-induced traumatic brain injury (bTBI) is of paramount importance for early care and clinical therapy. Therefore, the rapid diagnosis of bTBI is vital to the treatment and prognosis in clinic. In this paper, we reported a new strategy for label-free bTBI diagnosis through serum-based Raman spectroscopy. The Raman spectral characteristics of serum in rat were investigated at 3 h, 24 h, 48 h and 72 h after mild and moderate bTBIs. It has been demonstrated that both the position and intensity of Raman characteristic peaks exhibited apparent differences in the range of 800-3000cm-1 compared with control group. It could be inferred that the content, structure and interaction of biomolecules in the serum were changed after blast exposure, which might help to understand the neurological syndromes caused by bTBI. Furthermore, the control group, mild and moderate bTBIs at different times (a total of 9 groups) were automatically classified by combining principal component analysis and four machine learning algorithms (quadratic discriminant analysis, support vector machine, k-nearest neighbor, neural network). The highest classification accuracy, sensitivity and precision were up to 95.4%, 95.9% and 95.7%. It is suggested that this method has great potential for high-sensitive, rapid, and label-free diagnosis of bTBI.

Sensing Method for Wet Spraying Process of Tunnel Wall Based on the Laser LiDAR in Complex Environment.

In tunnel lining construction, the traditional manual wet spraying operation is labor-intensive and can be challenging to ensure consistent quality. To address this, this study proposes a LiDAR-based method for sensing the thickness of tunnel wet spray, which aims to improve efficiency and quality. The proposed method utilizes an adaptive point cloud standardization processing algorithm to address differing point cloud postures and missing data, and the segmented Lamé curve is employed to fit the tunnel design axis using the Gauss-Newton iteration method. This establishes a mathematical model of the tunnel section and enables the analysis and perception of the thickness of the tunnel to be wet sprayed through comparison with the actual inner contour line and the design line of the tunnel. Experimental results show that the proposed method is effective in sensing the thickness of tunnel wet spray, with important implications for promoting intelligent wet spraying operations, improving wet spraying quality, and reducing labor costs in tunnel lining construction.

Graphene-enhanced hybrid terahertz metasurface sensor for ultrasensitive nortriptyline sensing and detection.

Graphene is a two-dimensional material with unique physical and chemical properties, whose excellent biocompatibility has also attracted widespread attention in the field of biosensing and medical detection. Graphene provides a novel solution for dramatically improving the sensitivity of terahertz metasurface sensors, since the electrical conductivity can be modified by contact with biomolecules. In this paper, a metal-graphene hybrid metasurface is proposed and demonstrated for high-sensitive nortriptyline sensing based on the plasmon-induced transparency (PIT) resonances. The π-π stacks between nortriptyline and graphene lead to an increase in the Fermi level of graphene and a decrease in the conductivity, thus enhancing the PIT resonance. Experimental results show that the peak-to-peak amplitude magnitude of the PIT window is enhanced up to 3.4-fold with 1 ng nortriptyline analyte, and the minimum detection limit is extended down to 0.1 ng. But no significant change is observed from the samples without graphene as a comparative experiment, which demonstrates that the presence of graphene greatly enhances the bonding to the drug molecules and improves the sensing sensitivity. This metasurface sensor has the advantages of high sensitivity, fast detection speed, label-free and steady properties, which has potential applications in the fields of trace molecular sensing and disease diagnosis.

Tunable narrow-linewidth high-peak-power sub-nanosecond optical parametric generator by injection seeding.

We present an efficient tunable optical parametric generator (OPG) with its linewidth close to the Fourier transform limit by injection seeding a tunable diode laser. Benefitting from high-peak-power sub-nanosecond (426 ps) laser pumping and a high-gain MgO:PPLN (PPMgLN) crystal, the OPG produced signal peak power up to 0.343 MW at 1638 nm and the total conversion efficiency reached 47.9% at 1-kHz pulse repetition rate. Considering the linewidth limit of short signal pulses (∼ 350 ps), a tunable seeder with the linewidth at hundred-MHz level was applicable. The achieved OPG signal tuning range was 1510-1638 nm with linewidth at GHz level, which is two orders of magnitude narrower than the unseeded OPG. Injection seeding a non-resonant OPG device does not introduce extra cavity feedback electronics that are essential for an optical parametric oscillator (OPO), greatly improving robustness and reducing cost. It is believed such a compact, tunable and costless PPMgLN OPG with high peak power, high repetition rate and relatively narrow linewidth has great significance in lidar, spectroscopy, etc.

Low-Frequency Vibrational Spectroscopy Characteristic of Pharmaceutical Carbamazepine Co-Crystals with Nicotinamide and Saccharin.

The pharmaceutical co-crystal has attracted increasing interest due to the improvement of physicochemical properties of active pharmaceutical ingredients. The characterization of pharmaceutical co-crystal is an integral part of the pharmaceutical field. In this paper, the low-frequency vibrational properties for carbamazepine co-crystals with nicotinamide and saccharin (CBZ-NIC and CBZ-SAC) have been characterized by combining the THz spectroscopy with low-wavenumber Raman spectroscopy. The experiment results show that, compared with the individual constituents, CBZ-NIC and CBZ-SAC co-crystals not only have different characteristic absorption peaks in the 0.3-2.5 THz region, but also have significant low-wavenumber Raman characteristic peaks in 0-100 cm-1. Density functional theory was performed to simulate the terahertz and low-wavenumber Raman spectra of the two co-crystals, where the calculation agreed well with the measured vibrational peak positions. The vibrational modes of CBZ-NIC and CBZ-SAC co-crystals were assigned through comparing theoretical results with the experimental spectra. Meanwhile, the low-frequency infrared and/or Raman active of characteristic peaks for such co-crystals were discussed. The results indicate the combination of THz spectroscopy and low-wavenumber Raman spectroscopy can provide more comprehensive low-frequency vibrational information for pharmaceutical co-crystals, such as collective vibration and skeleton vibration, which could play an important role in pharmaceutical science.

Temperature dependent terahertz spectroscopy and imaging of orthotopic brain gliomas in mouse models.

Terahertz (THz) spectroscopy and imaging were used to differentiate brain gliomas in a mouse model at different temperatures. The THz spectral difference between brain glioma and normal brain tissues at -10°C and 20°C was obtained in the 0.4-2.53 THz range. The absorption coefficient and refractive index values varied with both temperature and frequency. The fresh ex vivo brain glioma tissues were mapped by THz attenuated total reflection (ATR) imaging at 2.52 THz in the temperature range from -20°C to 35°C. Compared with histological examination, THz-ATR imaging could better display the tumor areas at a higher temperature. And the averaged reflectivity of normal tissue was increased with the increase of temperature, whereas the tumor region showed a decreasing trend. Thus, the larger THz imaging difference between glioma and normal tissues could be obtained. Moreover, in vivo brain gliomas in mouse models could also be differentiated clearly from normal brain tissues using THz-ATR imaging at 2.52 THz under room temperature. The THz-ATR images corresponded well with those of visual and hematoxylin and eosin (H&E) stained images. Therefore, this pilot study demonstrated that temperature dependence THz spectroscopy and imaging are helpful to the brain gliomas in mouse model detection.

Molecular pathological recognition of freshly excised human glioma using terahertz ATR spectroscopy.

The diagnosis and treatment of glioma depends greatly on the rapid extraction of molecular pathological features. In this study, human brain tumor tissues of different grades were analyzed using terahertz (THz) attenuated total reflectance (ATR) time-domain spectroscopy. Substantial differences in THz parameters were observed between paracarcinoma tissue and grade I-IV gliomas, Furthermore, the difference of THz absorption coefficient increases with the increase of THz frequency. It was also demonstrated that the isocitrate dehydrogenase (IDH) mutant and wild-type glioma tissues can be well distinguished using THz spectroscopy. Therefore, THz ATR spectroscopy can realize molecular typing recognition based on molecular pathology. This will provide a theoretical basis for developing intraoperative real-time glioma recognition and diagnosis technology.

Highly sensitive multi-stage terahertz parametric upconversion detection based on a KTiOPO4 crystal.

In this Letter, we demonstrate a highly sensitive multi-stage terahertz (THz) wave parametric upconversion detector based on a KTiOPO4 (KTP) crystal pumped by a 1064-nm pulsed-laser (10 ns, 10 Hz). The THz wave was upconverted to near-infrared light in a trapezoidal KTP crystal based on stimulated polariton scattering. The upconversion signal was amplified in two KTP crystals based on non-collinear and collinear phase matching, respectively, to improve detection sensitivity. A rapid-response detection in the THz frequency ranges of 4.26-4.50 THz and 4.80-4.92 THz was achieved. Moreover, a dual-color THz wave generated from THz parametric oscillator using KTP crystal was detected simultaneously based on dual-wavelength upconversion. The minimum detectable energy of 2.35 fJ was realized with a dynamic range of 84 dB at 4.85 THz, which gives a noise equivalent power (NEP) of the order of 21.3 pW/Hz1/2. By changing the phase-matching angle or the wavelength of the pump laser, it is suggested that the detection of the THz frequency band of interest in a wide range from approximately 1 to 14 THz is possible.

Consensus for Second-Order Discrete-Time Agents With Position Constraints and Delays.

This article addresses a consensus problem of second-order discrete-time agents in general directed networks with nonuniform position constraints, switching topologies, and communication delays. A projection operation is performed to ensure the agents stay in some given convex sets, and a distributed algorithm is employed for the consensus achievement of all agents. The analysis approach is to use a linear transformation to convert the original system into an equivalent system and then merge the nonlinear error term into the convex null of the agents' states so as to prove the consensus convergence of the system based on the properties of the non-negative matrices. It is shown that all agents finally converge to a consensus point while their positions stay in the corresponding constraint sets as long as the union of the communication graphs among each certain time interval is strongly connected even when the communication delays are considered. Finally, numerical simulation examples are given to show the theoretical results.

Optimization for continuous-wave terahertz reflection imaging for biological tissues.

Continuous-wave terahertz reflection imaging is a potential tool for biological tissues. Based on our home-made continuous-wave terahertz reflection imaging system, the effect of both polarization mode and reflection window on the imaging performance is studied theoretically and experimentally, showing good agreement. By taking obtaining sample information and image contrast into consideration, p-polarized terahertz waves are recommended. Moreover, considering the sample boundary identification and the image contrast, selection criteria for reflection window are proposed. This work will help to improve the performance of continuous-wave terahertz reflection imaging and accelerate the THz imaging in biological application.

Rapid identification of ore minerals using multi-scale dilated convolutional attention network associated with portable Raman spectroscopy.

Electron portable Raman spectroscopy tools for ore mineral identification are widely used in raw ore analysis and mineral process engineering. This paper demonstrates an extremely fast and accurate method for identifying unknown ore mineral samples by portable Raman spectroscopy from the RRUFF database. Resampling and background subtraction procedures are used to eliminate the influence of the Raman spectrometer and fluorescence scattering. For the complex mineral spectral classification task, a multi-scale dilated convolutional attention network is designed. In addition, to investigate the identification performance of our method, several machine learning and two basic deep learning models, including k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), cosine similarity, extreme gradient boosting machine (XGBoost), Alexnet and ResNet 18, are also developed on the mineral spectra database and applied for mineral identification. Comparative studies show that our CNN network outperforms other models with state-of-the-art results, achieving a top-1 accuracy of 89.51% and a top-3 accuracy of 96.54%. The function of each module and the explanations of the feature extraction in our CNN network were analyzed by ablation experiments and the Grad-CAM algorithm. The identification of ore mineral samples also proves the outstanding performance of our method. In conclusion, the proposed novel approach that exploits the advantages of portable Raman spectroscopy and a deep learning method is promising for rapidly identifying ore mineral samples.

Continuous-wave long-distributed-cavity laser using cat-eye retroreflectors.

Here we demonstrate an efficient, long-distributed-cavity laser which uses a cat-eye retroreflector configuration to facilitate cavity alignment. The cavity parameters were optimized to meet the small stability region of the cavity, given the long working distance. We also found that intracavity spherical aberration seriously impacts laser efficiency, and an aspheric lens is used to correct the aberration when long working distance and receivers with compact dimensions are desired. The end-pumped Nd:YVO4 laser delivered 5.91 W continuous-wave output power at a long working distance of 5 m, under 16.6 W incident diode pump. Significantly, the fluctuation in output power over the whole working distance range of 1-5 m was less than 10%, this being achieved without any other cavity parameters being adjusted.