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Improvement of the sense of gain as an internal driving force is the key factor to improve the training quality of graduate students in food science. Utilizing Jiangsu University graduate students majoring in food science as research samples, this study analyzed the present situation of the “sense of gain” demand. We analyzed the reasonable appeals of graduate students during their study based on fully respecting and advocating students’ right of speech, listened to their opinions and suggestions on higher education, analyzed the main contradictions, and further put forward a series of countermeasures. For improving the graduate students’ sense of gain during the period of study, it is necessary to improve the training quality from the following five aspects: constructing high-quality courses, cultivating people’s responsibilities, implementing “soft elimination” of training links, carrying out diversified extracurricular activities, and developing comprehensive quality. This research is significant in improving the training quality of food science graduate students.

Honeybees are the most prevalent insect pollinator species; they pollinate a wide range of crops. Colony collapse disorder (CCD), which is caused by a variety of biotic and abiotic factors, incurs high economic/ecological loss. Despite extensive research to identify and study the various ecological stressors such as microbial infections, exposure to pesticides, loss of habitat, and improper beekeeping practices that are claimed to cause these declines, the deep understanding of the observed losses of these important insects is still missing. Honeybees have an innate immune system, which includes physical barriers and cellular and humeral responses to defend against pathogens and parasites. Exposure to various stressors may affect this system and the health of individual bees and colonies. This review summarizes and discusses the composition of the honeybee immune system and the consequences of exposure to stressors, individually or in combinations, on honeybee immune competence. In addition, we discuss the relationship between bee nutrition and immunity. Nutrition and phytochemicals were highlighted as the factors with a high impact on honeybee immunity.

Stress detection of the conical frustum window is a very important issue to ensure the safety of deep manned submersibles. In this paper, we propose a method based on polarization imaging to evaluate the stress accumulation and recovery in the conical frustum window. An experimental setup of Mueller matrix polarimetry is built, and the samples are made by referring to the typical conical frustum windows in submersibles. By pressurizing different pressures on the samples, we can find the changes of their Mueller matrix images and further derived polarization parameters. The results show that the polarization parameters can characterize the stress transfer process and the elastic–plastic transformation process of the window under different pressurization pressures. We also use a two-layered wave plate model to simulate the stress distribution in the window, which reveals different performances of the former and latter layers of the window under pressurization. Finally, we use a finite element model to simulate and understand some of the above experimental results. This proposed method is expected to provide new possibilities for monitoring the window stress and further ensure the safety of deep manned submersibles.

Dengue fever is caused by dengue virus and transmitted by Aedes mosquitoes. A promising avenue to control this disease is to infect the wild Aedes population with the bacterium Wolbachia driven by cytoplasmic incompatibility (CI). To study the invasion of Wolbachia into wild mosquito population, we formulate a discrete competition model and analyze the competition between released mosquitoes and wild mosquitoes. We show the global asymptotic properties of the trivial equilibrium, boundary equilibrium, and positive equilibrium and give the conditions for the successful invasion of Wolbachia. Finally, we verify our findings by numerical simulations.

CoCrFeNiMn high entropy alloy (HEA) matrix composites with Cr _3 C _2 reinforcement (10 wt%) and different mass friction of Ag lubricant (10, 15 and 20 wt%) were produced by spark plasma sintering. The composites consist of HEA’s FCC phase, Cr _7 C _3 phase and Ag phase, and the microstructures of composites are dense and uniform. The ultimate plasticity strain and fracture toughness improve with the addition of Ag increasing at room temperature. The composites exhibit excellent self-lubricating and wear resistance below 400 °C, and the composite with 20 wt% Ag content has the lower friction coefficient and wear rate. The Ag addition exhibits positive role in improving the wear behavior at medium-low temperatures. The main wear mechanisms of the composites are micro-cutting and abrasive wear at low temperatures. At high temperatures, the oxide tribo-layer generates on the wear surface and deeply torn. Oxide wear and surface deformation are the main wear mechanisms.

Based on previous research results, this paper investigated the influence of fracture morphology on mechanical properties and failure modes of rock mass with two diagonal intersected fractures. This study carried out a series of triaxial compression tests on rock-like specimens with two crossed fractures under negative temperature, concluded the following conclusions. The strength and failure modes of rock mass are significantly influenced by the dips of two crossed fractures. The strength of rock mass with two fractures cannot simply be estimated using the method that was developed for the rock mass with a single fracture. When the intersecting angle is less than 30°, the failure plane initiates at the tip of “artificial ruptures” and extends to the upper and lower ends of the specimen. In case of a higher dip and intersecting angle ranging from 30° to 60°, the failure plane propagates along one of these two fractures. The mechanical parameters of rock mass are not only related to the trace length, but also depend on the trace length ratio. One could roughly calculate the strength parameters using the approximation proposed in this paper. For the rock mass with a trace length ratio

Due to the complexity of landing environments, precision guidance and high-precision control technology have become key to the rope-hook recovery of shipborne unmanned aerial vehicles (UAVs). The recovery process was divided into three stages and a reasonable guidance strategy had been designed for them, respectively. This study separated the guidance and control issues into an outer guidance loop and an inner control loop. The inner loop (attitude control loop) controled the UAV to follow the acceleration commands generated by the outer loop (trajectory tracking loop). The inner loop of the longitudinal controller and the lateral controller were designed based on active disturbance rejection control (ADRC), which has strong anti-interference ability. In the last phase, the outer loop of the longitudinal controller switched from a total energy control system (TECS), which greatly decoupled the altitude channel and speed channel, to the proportional navigation (PN) guidance law, while the outer loop of lateral controller switches from the proportional control law based on the L1 guidance law, which can reduce the tracking error and deviation, to the PN guidance law, which considerably enhances the tracking precision. Finally, the simulation data and flight test data show that the controller has strong robustness and good tracking precision, which ensures safe rope-hook recovery.

Image super-resolution (SR) technology aims to recover high-resolution images from low-resolution originals, and it is of great significance for the high-quality interpretation of remote sensing images. However, most present SR-reconstruction approaches suffer from network training difficulties and the challenge of increasing computational complexity with increasing numbers of network layers. This indicates that these approaches are not suitable for application scenarios with limited computing resources. Furthermore, the complex spatial distributions and rich details of remote sensing images increase the difficulty of their reconstruction. In this paper, we propose the pyramid information distillation attention network (PIDAN) to solve these issues. Specifically, we propose the pyramid information distillation attention block (PIDAB), which has been developed as a building block in the PIDAN. The key components of the PIDAB are the pyramid information distillation (PID) module and the hybrid attention mechanism (HAM) module. Firstly, the PID module uses feature distillation with parallel multi-receptive field convolutions to extract short- and long-path feature information, which allows the network to obtain more non-redundant image features. Then, the HAM module enhances the sensitivity of the network to high-frequency image information. Extensive validation experiments show that when compared with other advanced CNN-based approaches, the PIDAN achieves a better balance between image SR performance and model size.

Suspended particles affect the state and vitality of the marine ecosystem. In situ probing and accurately classifying the suspended particles in seawater have an important impact on ecological research and environmental monitoring. Individual measurement of the optical polarization parameters scattered by the suspended particles has been proven to be a powerful tool to classify the particulate compositions in seawater. In previous works, the temporal polarized light pulses are sampled and averaged to evaluate the polarization parameters. In this paper, a method based on dense sampling of polarized light pulses is proposed and the experimental setup is built. The experimental results show that the dense sampling method optimizes the classification and increases the average accuracy by at least 16% than the average method. We demonstrate the feasibility of dense sampling method by classifying the multiple types of particles in mixed suspensions and show its excellent generalization ability by multi-classification of the particles. Additional analysis indicates that the dense sampling method basically takes advantage of the high-quality polarization parameters to optimize the classification performance. The above results suggest that the proposed dense sampling method has the potential to probe the suspended particles in seawater in red-tide early warning, as well as sediment and microplastics monitoring.

Mycotoxin contamination of food crops is a global challenge due to their unpredictable occurrence and severe adverse health effects on humans. Therefore, it is of great importance to develop effective tools to prevent the accumulation of mycotoxins through the food chain. The use of magnetic nanoparticle (MNP)-assisted biosensors for detecting mycotoxin in complex foodstuffs has garnered great interest due to the significantly enhanced sensitivity and accuracy. Within such a context, this review includes the fundamentals and recent advances (2020-2023) in the area of mycotoxin monitoring in food matrices using MNP-based aptasensors and immunosensors. In this review, we start by providing a comprehensive introduction to the design of immunosensors (natural antibody or nanobody, random or site-oriented immobilization) and aptasensors (techniques for aptamer selection, characterization, and truncation). Meanwhile, special attention is paid to the multifunctionalities of MNPs (recoverable adsorbent, versatile carrier, and signal indicator) in preparingmycotoxin-specific biosensors. Further, the contribution of MNPs to the multiplexing determination of variousmycotoxins is summarized. Finally, challenges and future perspectives for the practical applications of MNP-assisted biosensors are also discussed. The progress and updates of MNP-based biosensors shown in this review are expected to offer readers valuable insights about the design of MNP-based tools for the effective detection of mycotoxins in practical applications. [ABSTRACT FROM AUTHOR]

Suspended particles play a significant role in aquatic systems. However, existing methods to probe suspended particles have several limitations. In this paper, we present a portable prototype to in situ probe individual particles in aquatic suspensions by simultaneously measuring polarized light scattering and fluorescence, aiming to obtain an effective classification of microplastics and microalgae. Results show that the obtained classification accuracy is significantly higher than that for either of these two methods. The setup also successfully measures submicron particles and discriminates two species of Synechococcus. Our study demonstrates the feasibility of simultaneously measuring polarized light scattering and fluorescence, and the promising capability of our method for further aquatic environmental monitoring.

Cyanobacterial bloom is one of the most urgent global environmental issues, which eventually could threaten human health and safety. Sonication treatment (ST) is a potential effective method to control cyanobacteria blooms in the field. Currently, the bottleneck of extensive application of ST is the difficulty to estimate the ST effect on the cyanobacterial cells and then determine suitable ST times in the field. In this study, cyanobacterial Microcystis samples sonicated at different times were first measured by a spectrophotometer to calculate the removal efficiency of Microcystis cells. Additionally, they were observed by TEM to reveal the intracellular structure changes of the cells. Then the samples were measured by an experimental setup based on polarized light scattering to measure the polarization parameters. Experimental results indicated that the polarization parameters can effectively characterize the intracellular structural changes of Microcystis cells with different ST times, which is quite consistent with the results for removal efficiency and TEM images. Further, the optimal ST time can be inferred by the polarization parameters. These results demonstrate that polarized light scattering can be a potentially powerful tool to explore suitable times for sonication treatment of cyanobacteria blooms.

This paper deals with the existence of traveling wave solutions to a delayed temporally discrete non-local reaction diffusion equation model, which has been derived recently for a single species with age structure. When the birth function satisfies monotonic condition, we obtained the traveling wavefront by using upper and lower solution methods together with monotonic iteration techniques. Otherwise, without the monotonicity assumption for birth function, we constructed two auxiliary equations. By means of the traveling wavefronts of the auxiliary equations, using the Schauder’ fixed point theorem, we proved the existence of a traveling wave solution to the equation under consideration with speed c>c*, where c*>0 is some constant. We found that the delayed temporally discrete non-local reaction diffusion equation possesses the dynamical consistency with its time continuous counterpart at least in the sense of the existence of traveling wave solutions.

Magnetically controlled capsule robots are predominantly used in the diagnosis and treatment of the human gastrointestinal tract. In this study, based on the permanent magnet method, magnetic driving and fluid measurement systems for in-pipe capsule robots were established. Using computational fluid dynamics (CFD) and particle image velocimetry (PIV), the fluid velocity and vorticity in the pipe of the capsule robot were calculated and measured. The running characteristics of the capsule robot were numerically analyzed in the curved pipe and the peristaltic flow. Furthermore, the range and variance method of orthogonal design was used to analyze the influence of four typical parameters (namely, pipe diameter, robotic translational speed, robotic rotational speed, and fluid viscosity) on the three operating performance indicators of the capsule robot (namely, the forward resistance of the robot, fluid turbulent intensity near the robot, and maximum fluid pressure to the pipe wall). In this paper, the relative magnitude and significance of the influence of each typical parameter on different performance indicators of the robot are presented. According to the different performance requirements of the robot, the different four parameter combinations are optimized. It is hoped that this work provides a reference for the selection of the appropriate mucus, translational speed, and rotational speed of the robot when it is working in pipes with different diameters.

Propolis, a resin produced by honeybees, has long been used as a dietary supplement and folk remedy, and more recent preclinical investigations have demonstrated a large spectrum of potential therapeutic bioactivities, including antioxidant, antibacterial, anti-inflammatory, neuroprotective, immunomodulatory, anticancer, and antiviral properties. As an antiviral agent, propolis and various constituents have shown promising preclinical efficacy against adenoviruses, influenza viruses, respiratory tract viruses, herpes simplex virus type 1 (HSV-1) and type 2 (HSV-2), human immunodeficiency virus (HIV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Over 300 chemical components have been identified in propolis, including terpenes, flavonoids, and phenolic acids, with the specific constituent profile varying widely according to geographic origin and regional flora. Propolis and its constituents have demonstrated potential efficacy against SARS-CoV-2 by modulating multiple pathogenic and antiviral pathways. Molecular docking studies have demonstrated high binding affinities of propolis derivatives to multiple SARS-CoV-2 proteins, including 3C-like protease (3CLpro), papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp), the receptor-binding domain (RBD) of the spike protein (S-protein), and helicase (NSP13), as well as to the viral target angiotensin-converting enzyme 2 (ACE2). Among these compounds, retusapurpurin A has shown high affinity to 3CLpro (ΔG = −9.4 kcal/mol), RdRp (−7.5), RBD (−7.2), NSP13 (−9.4), and ACE2 (−10.4) and potent inhibition of viral entry by forming hydrogen bonds with amino acid residues within viral and human target proteins. In addition, propolis-derived baccharin demonstrated even higher binding affinity towards PLpro (−8.2 kcal/mol). Measures of drug-likeness parameters, including metabolism, distribution, absorption, excretion, and toxicity (ADMET) characteristics, also support the potential of propolis as an effective agent to combat COVID-19.

A super-resolution (SR) reconstruction of remote sensing images is becoming a highly active area of research. With increasing upscaling factors, richer and more abundant details can progressively be obtained. However, in comparison with natural images, the complex spatial distribution of remote sensing data increases the difficulty in its reconstruction. Furthermore, most SR reconstruction methods suffer from low feature information utilization and equal processing of all spatial regions of an image. To improve the performance of SR reconstruction of remote sensing images, this paper proposes a deep convolutional neural network (DCNN)-based approach, named the deep residual dual-attention network (DRDAN), which achieves the fusion of global and local information. Specifically, we have developed a residual dual-attention block (RDAB) as a building block in DRDAN. In the RDAB, we firstly use the local multi-level fusion module to fully extract and deeply fuse the features of the different convolution layers. This module can facilitate the flow of information in the network. After this, a dual-attention mechanism (DAM), which includes both a channel attention mechanism and a spatial attention mechanism, enables the network to adaptively allocate more attention to regions carrying high-frequency information. Extensive experiments indicate that the DRDAN outperforms other comparable DCNN-based approaches in both objective evaluation indexes and subjective visual quality.

Bee pollen is a combination of plant pollen and honeybee secretions and nectar. The Bible and ancient Egyptian texts are documented proof of its use in public health. It is considered a gold mine of nutrition due to its active components that have significant health and medicinal properties. Bee pollen contains bioactive compounds including proteins, amino acids, lipids, carbohydrates, minerals, vitamins, and polyphenols. The vital components of bee pollen enhance different bodily functions and offer protection against many diseases. It is generally marketed as a functional food with affordable and inexpensive prices with promising future industrial potentials. This review highlights the dietary properties of bee pollen and its influence on human health, and its applications in the food industry.

Cyanobacteria are photosynthetic prokaryotic organisms which represent a significant source of novel, bioactive, secondary metabolites, and they are also considered an abundant source of bioactive compounds/drugs, such as dolastatin, cryptophycin 1, curacin toyocamycin, phytoalexin, cyanovirin-N and phycocyanin. Some of these compounds have displayed promising results in successful Phase I, II, III and IV clinical trials. Additionally, the cyanobacterial compounds applied to medical research have demonstrated an exciting future with great potential to be developed into new medicines. Most of these compounds have exhibited strong pharmacological activities, including neurotoxicity, cytotoxicity and antiviral activity against HCMV, HSV-1, HHV-6 and HIV-1, so these metabolites could be promising candidates for COVID-19 treatment. Therefore, the effective large-scale production of natural marine products through synthesis is important for resolving the existing issues associated with chemical isolation, including small yields, and may be necessary to better investigate their biological activities. Herein, we highlight the total synthesized and stereochemical determinations of the cyanobacterial bioactive compounds. Furthermore, this review primarily focuses on the biotechnological applications of cyanobacteria, including applications as cosmetics, food supplements, and the nanobiotechnological applications of cyanobacterial bioactive compounds in potential medicinal applications for various human diseases are discussed.

In this article, we study the existence of traveling wavefronts for integrodifference equation with a bilateral exponential kernel, namely, the Laplacian kernel. The existence of traveling wavefronts is proved by combining the monotone iteration technique with the upper and lower solution method. The minimal spreading speed c* is given, which can be figured out exactly when all parameters are given explicitly

Bee venom (BV) is a rich source of secondary metabolites from honeybees (Apis mellifera L.). It contains a variety of bioactive ingredients including peptides, proteins, enzymes, and volatile metabolites. The compounds contribute to the venom’s observed biological functions as per its anti-inflammatory and anticancer effects. The antimicrobial action of BV has been shown in vitro and in vivo experiments against bacteria, viruses, and fungi. The synergistic therapeutic interactions of BV with antibiotics has been reported. The synergistic effect contributes to a decrease in the loading and maintenance dosage, a decrease in the side effects of chemotherapy, and a decrease in drug resistance. To our knowledge, there have been no reviews on the impact of BV and its antimicrobial constituents thus far. The purpose of this review is to address the antimicrobial properties of BV and its compounds.

It is crucial for the efficacy of the apple storage to apply methods like electronic nose systems for detection and prediction of spoilage or infection by Penicillium expansum. Based on the acquisition of electronic nose signals, selected sensitive feature sensors of spoilage apple and all sensors were analyzed and compared by the recognition effect. Principal component analysis (PCA), principle component analysis-discriminant analysis (PCA-DA), linear discriminant analysis (LDA), partial least squares discriminate analysis (PLS-DA) and K-nearest neighbor (KNN) were used to establish the classification model of apple with different degrees of corruption. PCA-DA has the best prediction, the accuracy of training set and prediction set was 100% and 97.22%, respectively. synergy interval (SI), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) are three selection methods used to accurately and quickly extract appropriate feature variables, while constructing a PLS model to predict plaque area. Among them, the PLS model with unique variables was optimized by CARS method, and the best prediction result of the area of the rotten apple was obtained. The best results are as follows: Rc = 0.953, root mean square error of calibration (RMSEC) = 1.28, Rp = 0.972, root mean square error of prediction (RMSEP) = 1.01. The results demonstrated that the electronic nose has a potential application in the classification of rotten apples and the quantitative detection of spoilage area.

In this article, we study periodic solutions of a class of delay differential equations. By restricting our discussion on generalized Nehari Manifold, some sufficient conditions are obtained to guarantee the existence of infinitely many pairs of periodic solutions. Also, there exists at least one periodic solution with prescribed minimal period.

Plant attributes have direct and indirect effects on soil microbes via plant inputs and plant-mediated soil changes. However, whether plant taxonomic and functional diversities can explain the soil microbial diversity of restored forest ecosystems remains elusive. Here, we tested the linkage between plant attributes and soil microbial communities in four restored forests (Acacia species, Eucalyptus species, mixed coniferous species, mixed native species). The trait-based approaches were applied for plant properties and high-throughput Illumina sequencing was applied for fungal and bacterial diversity. The total number of soil microbial operational taxonomic units (OTUs) varied among the four forests. The highest richness of fungal OTUs was found in the Acacia forest. However, bacterial OTUs were highest in the Eucalyptus forest. Species richness was positively and significantly related to fungal and bacterial richness. Plant taxonomic diversity (species richness and species diversity) explained more of the soil microbial diversity than the functional diversity and soil properties. Prediction of fungal richness was better than that of bacterial richness. In addition, root traits explained more variation than the leaf traits. Overall, plant taxonomic diversity played a more important role than plant functional diversity and soil properties in shaping the soil microbial diversity of the four forests.