Orthogonality of non-coaxial Laguerre-Gaussian beams.

The conventional orthogonality of Laguerre-Gaussian (LG) beams is widely recognized and serves as the fundamental basis for mode division multiplexing utilizing LG beams as channels. However, this conventional orthogonality holds true only when two LG beams share the same axis. Whether non-coaxial LG beams, with parallel axes separated by a distance, are orthogonal to each other remains an open question. To address this issue and reveal the orthogonality of non-coaxial LG beams, we analytically derive the projection of one LG beam onto another. This projection exhibits a helical phase and a rotationally symmetric amplitude with dark rings present. These dark rings indicate that two non-coaxial LG beams are indeed orthogonal to each other when their axes are appropriately separated. Furthermore, any pair of non-coaxial LG beams within a set also exhibit orthogonality when their axes are fixed at a certain separation distance. This novel form of orthogonality predicts an innovative compound multiplexing technique that simultaneously combines mode and space, enabling more efficient packing of multiple LG beams within a single transmission aperture while maintaining parallel and closely spaced beam axes.

Vegetation and land classification method based on the background noise rate of a photon-counting LiDAR.

The changing of vegetation is a sensitive signature of global warming, and satellite photon-counting laser altimeters provide an effective way to monitor the changing of vegetation. Based on the background noise difference between vegetation-covered areas and bare lands, we proposed a classification method to distinguish vegetation-covered areas from the raw photons measured by photon-counting laser altimeters in relatively flat areas. First, a theoretical noise model was established considering the influence of the sunlight incident direction and reflection characteristics of different surfaces. Second, the thresholds from the proposed theoretical model were calculated and tested to classify the along-track land-cover types for the Ice, Cloud, and Elevation Satellite-2 (ICESat-2) photon-counting laser altimeter. Then, the study areas near Seattle and Romania in summer were selected and the classification method was verified to achieve an overall accuracy of over 77% (the strong beam) and over 76% (the weak beam) for both thresholds and areas. Our method utilized the noise photons with vegetation canopy reflection information, which are enormous in quantity and easy to extract compared to the signal photons. More importantly, this method reduces the requirements of the optical images (that are used as prior knowledge). The results show that using the noise photons of the weak beam may be more potential for the classification of vegetation and land than using the signal photons of the weak beam. We extended the research on the mechanism and application of ICESat-2 in forestry.

MiR-24 Protects Cardiomyocytes Against Hypoxia/Reoxygenation-Induced Injury Through Regulating Mitogen-Activated Protein Kinase 14.

This study aimed to explore the function of miR-24 in hypoxia/reoxygenation (H/R) -induced cardiomyocyte injury.We constructed a cardiomyocyte model of H/R using the primary cardiomyocytes isolated from Sprague-Dawley rats. To explore the role of miR-24, cells were transfected with a miR-24 mimic or miR-24 inhibitor. The RNA expression levels of miR-24 and Mapk14 were determined using qRT-PCR. The proliferation and apoptosis of cells were determined using a CCK8 assay and a flow cytometer. The TargetScan website was used to predict the targets of miR-24. A dual-luciferase reporter gene assay was conducted to verify whether Mapk14 is indeed a target of miR-24. A Western blot was applied for protein detection.H/R exposure decreased the expression of miR-24 in rat cardiomyocytes. Transfection of the miR-24 mimic into cardiomyocytes reduced H/R-induced injury as evidenced by an increase in proliferation and a decrease in the apoptotic rate. By contrast, transfection of the miR-24 inhibitor aggravated H/R-induced injury. The expression of Bcl-2 was increased while the levels of Bax and Active-caspase 3 were reduced in the H/R+miR-24 mimic group compared to those in the H/R group. H/R+miR-24 inhibitor group showed the opposite results. Mapk14 was identified as a target of miR-24. The mRNA level of Mapk14 and its protein (p38 MAPK) level were negatively affected by miR-24. Furthermore, we discovered that depletion of Mapk14 reduced the promoting effect of the miR-24 inhibitor on cell apoptosis.Overall, our results illustrated that miR-24 could attenuate H/R-induced injury partly by regulating Mapk14.

Practical Finite-Time Command-Filtered Adaptive Backstepping With Its Applications to Quadrotor Hovers.

In this article, a practical finite-time command-filtered adaptive backstepping (PFTCFAB) control method is presented for a class of uncertain nonlinear systems with nonparametric unknown nonlinearities and external disturbances. Unlike PFTCFAB control techniques that use neural networks (NNs) or fuzzy-logic systems (FLSs) to deal with system uncertainties, the proposed method is capable of handling such uncertainties without the need for NNs or FLSs, thus reducing complexity and increasing reliability. In the proposed approach, novel function adaptive laws are designed to directly estimate unknown nonparametric nonlinearities and external disturbances by means of command filter techniques, and a type of practical finite-time command filters is proposed to obtain such laws. Moreover, the PFTCFAB controllers and finite-time command filters are designed with practical finite-time Lyapunov stability, which ensures finite-time stability of system tracking and filter estimation errors. Experimental results with a quadrotor hover system are presented and discussed to demonstrate the advantages and effectiveness of the proposed control strategy.

Characterization of the effect of different cooking methods on volatile compounds in fish cakes using a combination of GC-MS and GC-IMS.

In this study, gas chromatography-mass spectrometry (GC-MS) and gas chromatography-ion mobility spectrometry (GC-IMS) were used to analyze the volatiles of fish cakes obtained using five cooking methods, namely, steaming, baking, air frying, pan frying and deep frying. The odor activity value (OAV) and relative odor activity value (ROAV) were used to screen for the major aroma compounds. Orthogonal partial least squares discriminant analysis (OPLS-DA) and the variable influence on projection (VIP) were used to determine the characteristic flavor compounds in the fish cakes. A total of 72 volatile compounds were identified by GC-MS, and 41 volatile compounds were detected by GC-IMS. 3-ethyl-2,5-dimethylpyrazine and 2,5-dimethylpyrazine were not detected in either CK or SS. The OPLS-DA models for GC-MS and GC-IMS analyses were constructed based on VIP values, and 8 and 7 compounds, respectively, were screened as characteristic aroma compounds. The results of this study provide new insights into the dynamics of flavor formation in reheated fish cakes and provide a theoretical basis for the optimization of the production process of this food product.

GC-MS, GC-IMS, and E-Nose Analysis of Volatile Aroma Compounds in Wet-Marinated Fermented Golden Pomfret Prepared Using Different Cooking Methods.

The cooking method is extremely important for the production of low-salt, wet-marinated, fermented golden pomfret because it strongly influences its flavor components and organoleptic quality. There are also significant differences in flavor preferences in different populations. The present study analyzed differences in the aroma characteristics of wet-marinated fermented golden pomfret after boiling, steaming, microwaving, air-frying, and baking using a combination of an electronic nose, GC-IMS, and SPME-GC-MS. Electronic nose PCA showed that the flavors of the boiled (A), steamed (B), and microwaved (C) treatment groups were similar, and the flavors of the baking (D) and air-frying (E) groups were similar. A total of 72 flavor compounds were detected in the GC-IMS analysis, and the comparative analysis of the cooked wet-marinated and fermented golden pomfret yielded a greater abundance of flavor compounds. SPME-GC-MS analysis detected 108 flavor compounds, and the results were similar for baking and air-frying. Twelve key flavor substances, including hexanal, isovaleraldehyde, and (E)-2-dodecenal, were identified by orthogonal partial least-squares discriminant analysis (OPLS-DA) and VIP analysis. These results showed that the cooking method could be a key factor in the flavor distribution of wet-marinated fermented golden pomfret, and consumers can choose the appropriate cooking method accordingly. The results can provide theoretical guidance for the more effective processing of fish products and the development of subsequent food products.

Changes in protein and volatile flavor compounds of low-salt wet-marinated fermented Golden Pomfret during processing.

In this experiment, the changes in protein hydrolysis, protein oxidation, and flavor of low-salt wet-marinated fermented golden pomfret were studied during processing. During processing, a decrease in sulfhydryl content (P < 0.05), a significant increase in protein surface hydrophobicity (P < 0.05), a significant increase in carbonyl content and TCA-soluble peptide (P < 0.05), an increase in TVB-N and amino acid nitrogen (P < 0.05), and a significant increase in the content of free amino acids (P < 0.05), indicating that proteins were gradually oxidized and degraded to small molecules and flavor precursors under the action of bacterial reduction pretreatment, deodorization, marination and fermentation processes, small molecules and aroma precursors was generated by gradual oxidative decomposition. In the course of processing, a total of 113 volatile flavor compounds were identified using GC-MS analysis, while OPLS-DA analysis and VIP value determination led to the identification of 10 characteristic flavor compounds. The results showed that an abundance of flavor compounds was generated during the processing, thereby imparting a more pronounced taste profile to the low-salt wet-marinated fermented golden carp. The results showed that a large number of flavor substances were generated during the processing to give a richer flavor to low-salt wet-marinated fermented golden pomfret that could provide data and theoretical support for the subsequent processing industry of golden pomfret.

The Cryoprotective Effect of an Antifreeze Collagen Peptide Complex Obtained by Enzymatic Glycosylation on Tilapia.

Antifreeze peptides have become effective antifreeze agents for frozen products, but their low quantity of active ingredients and high cost limit large-scale application. This study used the glycosylation of fish collagen peptides with glucosamine hydrochloride catalyzed by transglutaminase to obtain a transglutaminase-catalyzed glycosylation product (TGP) and investigate its antifreeze effect on tilapia. Compared with the blank group, the freshness (pH value of 6.31, TVB-N value of 21.7 mg/100 g, whiteness of 46.28), textural properties (especially hardness and elasticity), and rheological properties of the TGP groups were significantly improved. In addition, the protein structures of the samples were investigated using UV absorption and fluorescence spectroscopy. The results showed that the tertiary structure of the TGP groups changed to form a dense polymer. Therefore, this approach can reduce the denaturation and decomposition of muscle fibers and proteins in fish meat more effectively and has a better protective effect on muscle structure and protein aggregation, improving the stability of fish meat. This study reveals an innovative method for generating antifreeze peptides by enzymatic glycosylation, and glycosylated fish collagen peptide products can be used as new and effective green antifreeze agents in frozen foods.

Further Results on Optimal Tracking Control for Nonlinear Systems With Nonzero Equilibrium via Adaptive Dynamic Programming.

This article develops a novel cost function (performance index function) to overcome the obstacles in solving the optimal tracking control problem for a class of nonlinear systems with known system dynamics via adaptive dynamic programming (ADP) technique. For the traditional optimal control problems, the assumption that the controlled system has zero equilibrium is generally required to guarantee the finiteness of an infinite horizon cost function and a unique solution. In order to solve the optimal tracking control problem of nonlinear systems with nonzero equilibrium, a specific cost function related to tracking errors and their derivatives is designed in this article, in which the aforementioned assumption and related obstacles are removed and the controller design process is simplified. Finally, comparative simulations are conducted on an inverted pendulum system to illustrate the effectiveness and advantages of the proposed optimal tracking control strategy.

Gradient Descent-Barzilai Borwein-Based Neural Network Tracking Control for Nonlinear Systems With Unknown Dynamics.

In this article, a combined gradient descent-Barzilai Borwein (GD-BB) algorithm and radial basis function neural network (RBFNN) output tracking control strategy was proposed for a family of nonlinear systems with unknown drift function and control input gain function. In such a method, a neural network (NN) is used to approximate the controller directly. The main merits of the proposed strategy are given as follows: first, not only the NN parameters, such as weights, centers, and widths but also the learning rates of NN parameter updating laws are updated online via the proposed learning algorithm based on Barzilai-Borwein technique; and second, the controller design process can be further simplified, the controller parameters that should be tuned can be greatly reduced. Theoretical analysis about the stability of the closed-loop system is manifested. In addition, simulations were conducted on a numerical discrete time system and an inverted pendulum system to validate the presented control strategy.

A Potential Nine-lncRNAs Signature Identification and Nomogram Diagnostic Model Establishment for Papillary Thyroid Cancer.

The purpose of our current study was to establish a long non-coding RNA(lncRNA) signature and assess its prognostic and diagnostic power in papillary thyroid cancer (PTC). LncRNA expression profiles were obtained from the Cancer Genome Atlas (TCGA). The key module and hub lncRNAs related to PTC were determined by weighted gene co-expression network analysis (WGCNA) and LASSO Cox regression analyses, respectively. Functional enrichment analyses, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis were implemented to analyze the possible biological processes and signaling pathways of hub lncRNAs. Associations between key lncRNA expressions and tumor-infiltrating immune cells were identified using the TIMER website, and proportions of immune cells in high/low risk score groups were compared. Kaplan-Meier Plotter was used to evaluate the prognostic significance of hub genes in PTC. A diagnostic model was conducted with logistic regression analysis, and its diagnostic performance was assessed by calibration/receiver operating characteristic curves and principal component analysis. A nine-lncRNAs signature (SLC12A5-AS1, LINC02028, KIZ-AS1, LINC02019, LINC01877, LINC01444, LINC01176, LINC01290, and LINC00581) was established in PTC, which has significant diagnostic and prognostic power. Functional enrichment analyses elucidated the regulatory mechanism of the nine-lncRNAs signature in the development of PTC. This signature and expressions of nine hub lncRNAs were correlated with the distributions of tumor infiltrating immune cells. A diagnostic nomogram was also established for PTC. By comparing with the published models with less than or equal to nine lncRNAs, our signature showed a preferable performace for prognosis prediction. In conclusion, our present research established an innovative nine-lncRNAs signature and a six-lncRNAs nomogram that might act as a potential indicator for PTC prognosis and diagnosis, which could be conducive to the PTC treatment.

SGD-Based Adaptive NN Control Design for Uncertain Nonlinear Systems.

In this paper, a stochastic gradient descent (SGD)-based adaptive neural network (NN) control scheme is presented for a class of uncertain nonlinear systems. The introduction of the SGD algorithm results in a better tracking performance compared with some other adaptive NN methods without using SGD. This is because the proposed SGD-based adaptive NN control strategy provides optimization algorithms for the weights, the widths, and the centers of the NNs, which can achieve a good function approximation performance. In order to implement the proposed method, extended differentiators are introduced to get the differential estimations of error signals, such that the loss function of the optimization algorithm can be constructed approximatively. Moreover, adaptive laws are designed to reduce the overall approximation errors, such that the tracking performance is further improved. By using the Lyapunov stability theory, it can be proved that the target signal is tracked by the system output within a small error. Finally, simulation and comparison results are given to show the effectiveness and advantages of the proposed method.

A Locally Weighted Project Regression Approach-Aided Nonlinear Constrained Tracking Control.

An intelligent data-driven predictive control strategy is proposed in this paper. The predictive controller is designed by combining predictive control and local weighted projection regression. The presented control strategy needs less prior knowledge and has fewer parameters that are hard to determine compared to other data-driven predictive controller, e.g., the one in dynamic partial least square (PLS) framework. Furthermore, the proposed predictive controller performs better in the control of nonlinear processes and is able to update its parameters based on the online data. The predictive model validity and intelligence of the control strategy are guaranteed by the online updating strategy to a certain degree. The control performance of the proposed predictive controller against the model predictive control (MPC) in dynamic PLS framework is illustrated through the simulation of a typical numerical example and the benchmark of a continuous stirred tank heater system. It can be observed from the simulation that the proposed MPC strategy has higher prediction precision and stronger ability in coping with nonlinear dynamic processes which are quite common in practical applications, for instance, the industrial process.

Benzo[a]phenoxazinium-based red-emitting chemosensor for zinc ions in biological media.

A benzo[a]phenoxazinium-based chemosensor bearing an N,N-di(2-picolyl)ethylenediamine unit was successfully synthesized. It is a long-wave emission and fully water-soluble fluorescent sensor with good membrane permeability for the selective detection of Zn(2+).