Research on collaborative edge network service migration strategy based on crowd clustering

The innovative application of Crowd Intelligent Devices (CIDS) in edge networks has garnered attention due to the rapid development of artificial intelligence and computer technology. This application offers users more reliable and low-latency computing services through computation offloading technology. However, the dynamic nature of network terminals and the limited coverage of edge servers pose challenges, such as data loss and service interruption. Furthermore, the high-speed mobility of intelligent terminals in the dynamic edge network environment further complicates the design of computation offloading and service migration strategies. To address these challenges, this paper explores the computation offloading model of cluster intelligence collaboration in a heterogeneous network environment. This model involves multiple intelligences collaborating to provide computation offloading services for terminals. To accommodate various roles, a switching strategy of split-cluster group collaboration is introduced, assigning the cluster head, the alternate cluster head, and the ordinary user are assigned to a group with different functions. Additionally, the paper formulates the optimal offloading strategy for group smart terminals as a Markov decision process, taking into account factors such as user mobility, service delay, service accuracy, and migration cost. To implement this strategy, the paper utilizes the deep reinforcement learning-based CCSMS algorithm. Simulation results demonstrate that the proposed edge network service migration strategy, rooted in groupwise cluster collaboration, effectively mitigates interruption delay and enhances service migration efficiency.

Characteristics of surrounding rock damage and control technology of a facing-mining excavating roadway in north Shaanxi mining area

In a coal mine in the northern region of Shaanxi Province, China, a facing-mining excavating roadway exists, which is intended to be retained for subsequent working face safety services. This paper investigates the deformation and damage characteristics of the surrounding rock in different stages using FLAC 3D numerical simulation, taking the facing-mining excavating roadway of this coal mine as the research context. At 20 m ahead of the working face, a discontinuous plastic zone appears in the surrounding rock of the roadway, a phenomenon attributed to the varying hardness of the lithologyand termed 'plastic zone jumping.' The numerical simulation results have been were verified using drill hole peeping. Real-time monitoring of the roadway's stability is conducted on-site, showing that the roadway is significantly affected by mining at the 50 m point ahead of the working face. Based on the numerical simulation and on-site monitoring results, the support strength was increased at 50 m from the working face along the roadway, and a new support scheme was adopted. In the lagging section of the roadway, where mining pressure is strongly evident, differentiated reinforcement using anchor rods, anchor ropes, and W steel belts has been employed, resulting in a satisfactory on-site effect.

Weather-Domain Transfer-Based Attention YOLO for Multi-Domain Insulator Defect Detection and Classification in UAV Images

Insulator defect detection of transmission line insulators is an important task for unmanned aerial vehicle (UAV) inspection, which is of immense importance in ensuring the stable operation of transmission lines. Transmission line insulators exist in complex weather scenarios, with small and inconsistent shapes. These insulators under various weather conditions could result in low-quality images captured, limited data numbers, and imbalanced sample problems. Traditional detection methods often struggle to accurately identify defect information, resulting in missed or false detections in real-world scenarios. In this paper, we propose a weather domain synthesis network for extracting cross-modality discriminative information on multi-domain insulator defect detection and classification tasks. Firstly, we design a novel weather domain synthesis (WDSt) module to convert various weather-conditioned insulator images to the uniform weather domain to decrease the existing domain gap. To further improve the detection performance, we leverage the attention mechanism to construct the Cross-modality Information Attention YOLO (CIA-YOLO) model to improve the detection capability for insulator defects. Here, we fuse both shallow and deep feature maps by adding the extra object detection layer, increasing the accuracy for detecting small targets. The experimental results prove the proposed Cross-modality Information Attention YOLO with the weather domain synthesis algorithm can achieve superior performance in multi-domain insulator datasets (MD-Insulator). Moreover, the proposed algorithm also gives a new perspective for decreasing the multi-domain insulator modality gap with weather-domain transfer, which can inspire more researchers to focus on the field.

Electric field effects on neuronal input-output relationship by regulating NMDA spikes

Evidence shows that the dendritic polarization induced by weak electrical field (EF) can affect the neuronal input-output function via modulating dendritic integration of AMPA synapses, indicating that the supralinear dendritic integration of NMDA synapses can also be influenced by dendritic polarization. However, it remains unknown how dendritic polarization affects NMDA-type dendritic integration, and then contributes to neuronal input-output relationship. Here, we used a computational model of pyramidal neuron with inhomogeneous extracellular potentials to characterize the relationship among EF, dendritic integration, and somatic output. Basing on singular perturbation we analyzed the subthreshold dynamics of membrane potentials in response to NMDA synapses, and found that the equilibrium mapping of a fast subsystem can characterize the asymptotic subthreshold input-output (sI/O) relationship for EF-regulated supralinear dendritic integration, allowing us to predict the tendency of EF-regulated dendritic integration by showing the variation of equilibrium mapping under EF stimulation. EF-induced depolarization at distal dendrites receiving synapses plays a crucial role in shifting the steep change of sI/O left by facilitating dendritic NMDA spike generation and in decreasing the plateau of sI/O via reducing driving force. And more effective EF modulation appears at sparsely activated NMDA receptors compared with clustered synaptic inputs. During the action potential (AP) generation, the respective contribution of EF-regulated dendritic integration and EF-induced somatic polarization was identified to show their synergetic or antagonistic effect on AP generation, depending on neuronal excitability. These results provided insight in understanding the modulation effect of EF on neuronal computation, which is important for optimizing noninvasive brain stimulation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09922-y.

SEA-NET: medical image segmentation network based on spiral squeeze-and-excitation and attention modules

BACKGROUND

Medical image segmentation is an important processing step in most of medical image analysis. Thus, high accuracy and robustness are required for them. The current deep neural network based medical segmentation methods have good effect on image with balanced foreground and background, but it will loss the characteristics of small targets on image with imbalanced foreground and background after multiple convolutions.

METHODS

In order to retain the features of small targets in the deep network, we proposed a new medical image segmentation model based on the U-Net with squeeze-and-excitation and attention modules which form a spiral closed path,callled as Spiral Squeeze-and-Excitation and Attention NET (SEA-NET) in this paper. The segmentation model used squeeze-and-extraction modules to adjust the channel information to enhance the useful information and used attention modules to adjust the spatial information of the feature map to highlight the target area for small target segmentation when up-sampling. The deep semantic information is integrated into the shallow feature map by the attention model. Therefore, the deep semantic information cannot be scattered by continuous up-sampling. We used cross entropy loss + Tversky loss function for fast convergence and well processing the imbalanced data sets. Our proposed SEA-NET was tested on the brain MRI dataset LPBA40 and peripheral blood smear images.

CONCLUSIONS

On brain MRI data, the average value of the Dice coefficient we obtained reached 98.1[Formula: see text]. On the peripheral blood smear dataset, our proposed model has a good segmentation effect on adhesion cells.

RESULTS

The experimental results proved that the proposed SEA-Net performed better than U-Net, U-Net++, etc. in medical image segmentation.

A Nanoporous Polymer Modified with Hexafluoroisopropanol to Detect Dimethyl Methylphosphonate

The increasing threat of nerve agents has prompted the need for gas sensors with fast response, high sensitivity, and good stability. In this work, the hexafluoroisopropanol functional group was modified on a porous aromatic framework material, which served as a sensitive material for detecting dimethyl methylphosphonate. A nerve agent sensor was made by coating sensitive materials on a surface acoustic wave device. Lots of pores in sensitive materials effectively increase the specific surface area and provide channels for diffusion of gas molecules. The introduction of hexafluoroisopropanols enables the sensor to specifically adsorb dimethyl methylphosphonate and improves the selectivity of the sensor. As a result, the developed gas sensor was able to detect dimethyl methylphosphonate at 0.8 ppm with response/recovery times of 29.8/43.8 s, and the detection limit of the gas sensor is about 0.11 ppm. The effects of temperature and humidity on the sensor were studied. The results show that the baseline of the sensor has a linear relationship with temperature and humidity, and the temperature and humidity have a significant effect on the response of the sensor. Furthermore, a device for real-time detection of nerve agent is reported. This work provides a new strategy for developing a gas sensor for detecting nerve agents.

Direction-of-Arrival Estimation for a Random Sparse Linear Array Based on a Graph Neural Network

This article proposes a direction-of-arrival (DOA) estimation algorithm for a random sparse linear array based on a novel graph neural network (GNN). Unlike convolutional layers and fully connected layers, which do not interact well with information between different antennas, the GNN model can adapt to the goniometry problem of non-uniform random sparse linear arrays without any prior information by applying neighbor nodes' aggregation and update operations. This helps the model in learning signal features under complex environmental conditions. We train the model in an end-to-end way to reduce the complexity of the network. Experiments are conducted on the uniform and sparse linear arrays for various signal-to-noise ratio (SNR) and numbers of snapshots for comparison. We prove that the GNN model has superior angle estimation performance on arrays with large sparsity that cannot be used by traditional algorithms and surpasses existing deep learning models based on convolutional or fully connected structures. The proposed algorithm shows excellent DOA estimation performance under the complex conditions of limited snapshots, low signal-to-noise ratio, and large array sparsity as well. In addition, the algorithm has a low time calculation cost and is suitable for scenarios that require low latency.

Metabolic diversity of tumor-infiltrating T cells as target for anti-immune therapeutics

Tumor-infiltrating T cells are promising drug targets to modulate the tumor microenvironment. However, tumor-infiltrating T lymphocytes, as central targets of cancer immunotherapy, show considerable heterogeneity and dynamics across tumor microenvironments and cancer types that may fundamentally influence cancer growth, metastasis, relapse, and response to clinical drugs. The T cell heterogeneity not only refers to the composition of subpopulations but also divergent metabolic states of T cells. Comparing to the diversity of tumor-infiltrating T cell compositions that have been well recognized, the metabolic diversity of T cells deserves more attention for precision immunotherapy. Single-cell sequencing technology enables panoramic stitching of the tumor bulk, partly by showing the metabolic-related gene expression profiles of tumor-infiltrating T cells at a single-cell resolution. Therefore, we here discuss T cell metabolism reprogramming triggered by tumor microenvironment as well as the potential application of metabolic targeting drugs. The tumor-infiltrating T cells metabolic pathway addictions among different cancer types are also addressed in this brief review.

Detection of Dimethyl Methyl Phosphonate by Silica Molecularly Imprinted Materials

In recent years, the increasing severity of chemical warfare agent threats to public safety has led to a growing demand for gas sensors capable of detecting these compounds. However, gas sensors used for the detection of chemical warfare agents must overcome limitations in sensitivity, selectivity, and reaction speed. This paper presents a sensitive material and a surface acoustic gas sensor for detecting dimethyl methyl phosphonate. The results demonstrate that the sensor exhibits good selectivity and could detect 80 ppb of dimethyl methyl phosphonate within 1 min. As an integral component of the sensor, the microstructure and adsorption mechanism of silica molecular imprinting material were studied in detail. The results show that the template molecule could significantly affect the pore volume, specific surface area, and hydroxyl density of mesoporous materials. These properties further affect the performance of the sensor. This study provides a valuable case study for the design of sensitive materials.

A dynamic multi-objective optimization method based on classification strategies

The dynamic multi-objective optimization problem is a common problem in real life, which is characterized by conflicting objectives, the Pareto frontier (PF) and Pareto solution set (PS) will follow the changing environment. There are various dynamic multi-objective algorithms have been suggested to solve such problems, but most of the methods suffer from the inability to balance the diversity of populations with convergence. Prediction based method is a common approach to solve dynamic multi-objective optimization problems, but such methods only search for probabilistic models of optimal values of decision variables and do not consider whether the decision variables are related to diversity and convergence. Consequently, we present a prediction method based on the classification of decision variables for dynamic multi-objective optimization (DVC), where the decision variables are first pre-classified in the static phase, and then new variables are adjusted and predicted to adapt to the environmental changes. Compared with other advanced prediction strategies, dynamic multi-objective prediction methods based on classification of decision variables are more capable of balancing population diversity and convergence. The experimental results show that the proposed algorithm DVC can effectively handle DMOPs.

Suppression of Secondary Electron Emission from Nickel Surface by Graphene Composites Based on First-Principles Method

Secondary electron emission (SEE) is a fundamental phenomenon of particle/surface interaction, and the multipactor effect induced by SEE can result in disastrous impacts on the performance of microwave devices. To suppress the SEE-induced multipactor, an Ni (111) surface covered with a monolayer of graphene was proposed and studied theoretically via the density functional theory (DFT) method. The calculation results indicated that redistribution of the electron density at the graphene/Ni (111) interface led to variations in the work function and the probability of SEE. To validate the theoretical results, experiments were performed to analyze secondary electron yield (SEY). The measurements showed a significant decrease in the SEY on an Ni (111) surface covered with a monolayer of graphene, accompanied by a decrease in the work function, which is consistent with the statistical evidence of a strong correlation between the work function and SEY of metals. A discussion was given on explaining the experimental phenomenon using theoretical calculation results, where the empty orbitals lead to an electron trapping effect, thereby reducing SEY.

Network Evolution Model-based prediction of tumor mutation burden from radiomic-clinical features in endometrial cancers

BACKGROUND

Endometrial Cancer (EC) is one of the most prevalent malignancies that affect the female population globally. In the context of immunotherapy, Tumor Mutation Burden (TMB) in the DNA polymerase epsilon (POLE) subtype of this cancer holds promise as a viable therapeutic target.

METHODS

We devised a method known as NEM-TIE to forecast the TMB status of patients with endometrial cancer. This approach utilized a combination of the Network Evolution Model, Transfer Information Entropy, Clique Percolation (CP) methodology, and Support Vector Machine (SVM) classification. To construct the Network Evolution Model, we employed an adjacency matrix that utilized transfer information entropy to assess the information gain between nodes of radiomic-clinical features. Subsequently, using the CP algorithm, we unearthed potentially pivotal modules in the Network Evolution Model. Finally, the SVM classifier extracted essential features from the module set.

RESULTS

Upon analyzing the importance of modules, we discovered that the dependence count energy in tumor volumes-of-interest holds immense significance in distinguishing TMB statuses among patients with endometrial cancer. Using the 13 radiomic-clinical features extracted via NEM-TIE, we demonstrated that the area under the receiver operating characteristic curve (AUROC) in the test set is 0.98 (95% confidence interval: 0.95-1.00), surpassing the performance of existing techniques such as the mRMR and Laplacian methods.

CONCLUSIONS

Our study proposed the NEM-TIE method as a means to identify the TMB status of patients with endometrial cancer. The integration of radiomic-clinical data utilizing the NEM-TIE method may offer a novel technology for supplementary diagnosis.

A Two-Phase Feature Selection Method for Identifying Influential Spreaders of Disease Epidemics in Complex Networks

Network epidemiology plays a fundamental role in understanding the relationship between network structure and epidemic dynamics, among which identifying influential spreaders is especially important. Most previous studies aim to propose a centrality measure based on network topology to reflect the influence of spreaders, which manifest limited universality. Machine learning enhances the identification of influential spreaders by combining multiple centralities. However, several centrality measures utilized in machine learning methods, such as closeness centrality, exhibit high computational complexity when confronted with large network sizes. Here, we propose a two-phase feature selection method for identifying influential spreaders with a reduced feature dimension. Depending on the definition of influential spreaders, we obtain the optimal feature combination for different synthetic networks. Our results demonstrate that when the datasets are mildly or moderately imbalanced, for Barabasi-Albert (BA) scale-free networks, the centralities' combination with the two-hop neighborhood is fundamental, and for Erdős-Rényi (ER) random graphs, the centralities' combination with the degree centrality is essential. Meanwhile, for Watts-Strogatz (WS) small world networks, feature selection is unnecessary. We also conduct experiments on real-world networks, and the features selected display a high similarity with synthetic networks. Our method provides a new path for identifying superspreaders for the control of epidemics.

Differential Confocal Optical Probes with Optimized Detection Efficiency and Pearson Correlation Coefficient Strategy Based on the Peak-Clustering Algorithm

Quantifying free-form surfaces using differential confocal microscopy can be challenging, as it requires balancing accuracy and efficiency. When the axial scanning mechanism involves sloshing and the measured surface has a finite slope, traditional linear fitting can introduce significant errors. This study introduces a compensation strategy based on Pearson's correlation coefficient to effectively reduce measurement errors. Additionally, a fast-matching algorithm based on peak clustering was proposed to meet real-time requirements for non-contact probes. To validate the effectiveness of the compensation strategy and matching algorithm, detailed simulations and physical experiments were conducted. The results showed that for a numerical aperture of 0.4 and a depth of slope < 12°, the measurement error was <10 nm, improving the speed of the traditional algorithm system by 83.37%. Furthermore, repeatability and anti-disturbance experiments demonstrated that the proposed compensation strategy is simple, efficient, and robust. Overall, the proposed method has significant potential for application in the realization of high-speed measurements of free-form surfaces.

A TEC Cooling Soft Robot Driven by Twisted String Actuators

Similar to biological muscles in nature, artificial muscles have unique advantages for driving bionic robots. However, there is still a large gap between the performance of existing artificial muscles and biological muscles. Twisted polymer actuators (TPAs) convert rotary motion from torsional to linear motion. TPAs are known for their high energy efficiency and large linear strain and stress outputs. A simple, lightweight, low-cost, self-sensing robot powered using a TPA and cooled using a thermoelectric cooler (TEC) was proposed in this study. Because TPA burns easily at high temperatures, traditional soft robots driven by TPAs have low movement frequencies. In this study, a temperature sensor and TEC were combined to develop a closed-loop temperature control system to ensure that the internal temperature of the robot was 5 °C to cool the TPAs quickly. The robot could move at a frequency of 1 Hz. Moreover, a self-sensing soft robot was proposed based on the TPA contraction length and resistance. When the motion frequency was 0.01 Hz, the TPA had good self-sensing ability and the root-mean-square error of the angle of the soft robot was less than 3.89% of the measurement amplitude. This study not only proposed a new cooling method for improving the motion frequency of soft robots but also verified the autokinetic performance of the TPAs.

The Key Metabolic Network and Genes Regulating the Fresh Fruit Texture of Jujube (Ziziphus jujuba Mill.) Revealed via Metabolomic and Transcriptomic Analysis

The texture of fresh jujube fruit is related to its popularity and commercial value. The metabolic networks and essential genes that regulate the texture of jujube (Ziziphus jujuba) fruit are still unknown. In this study, two jujube cultivars with significantly different textures were selected by a texture analyzer. The four developmental stages of the exocarp and mesocarp of jujube fruit were studied separately using metabolomic and transcriptomic analyses. Differentially accumulated metabolites were enriched in several critical pathways related to cell wall substance synthesis and metabolism. Transcriptome analysis confirmed this by finding enriched differential expression genes in these pathways. Combined analysis showed that 'Galactose metabolism' was the most overlapping pathway in two omics. Genes such as β-Gal, MYB and DOF may affect fruit texture by regulating cell wall substances. Overall, this study provides an essential reference for the establishment of texture-related metabolic and gene networks of jujube fruit.

The prediction of postoperative delirium with the preoperative bispectral index in older aged patients: a cohort study

BACKGROUND

Postoperative delirium (POD) is the most common postoperative complication in elderly patients, especially in older aged patients (aged 75 years or over). The development of electroencephalography analysis could provide indicators for early detection, intervention, and evaluation. If there are pathophysiological changes in the brain, the BIS value will also change accordingly. In this study, we investigated the predictive value of the preoperative bispectral (BIS) index in POD for patients aged over 75 years.

METHODS

In this prospective study, patients (≥ 75 years) undergoing elective non-neurosurgery and non-cardiac surgery under general anesthesia were included (n = 308). Informed consent was obtained from all involved patients. Before the operation and during the first 5 postoperative days, delirium was assessed with the confusion assessment method by trained researchers twice every day. Thereafter, the preoperative bedside BIS of each patient was dynamically acquired by the BIS VISTA monitoring system and the BIS monitoring of electrodes. A series of evaluation scales were assessed before and after surgery. A preoperative predictive score was generated according to the results of multivariable logistic regression. The receiver operating characteristic curves were drawn and the area under the curves was estimated to evaluate the perioperative diagnostic values of BIS and preoperative predictive score for POD. The specificity, sensitivity, positive predictive value (PPV), and negative predictive (NPV) value were calculated.

RESULTS

Delirium occurred in 50 of 308 (16.2%) patients. The median BIS of delirious patients was 86.7 (interquartile range [IQR] 80.0-94.0), lower than that of the non-delirious 91.9 (IQR 89.7-95.4, P < 0.001). According to the ROC curve of the BIS index, the optimal cut-off value was 84, with a sensitivity of 48%, specificity of 87%, PPV 43%, NPV 89% for forecasting POD and the area under curves was 0.67. While integrating BIS, mini-mental state examination, anemia, activities of daily living, and blood urea nitrogen, the model had a sensitivity of 78%, specificity of 74%, PPV of 0.37%, and NPV of 95% for forecasting POD, and the area under curves was 0.83.

CONCLUSIONS

Preoperative bedside BIS in delirium patients was lower than that in non-delirium patients when undergoing non-neurosurgery and non-cardiac surgery in patients aged over 75. The model of integrating BIS, mini-mental state examination, anemia, activities of daily living, and blood urea nitrogen is a promising tool for predicting postoperative delirium in patients aged over 75.

Identification of two key UDP-glycosyltransferases responsible for the ocotillol-type ginsenoside majonside-R2 biosynthesis in Panax vietnamensis var. fuscidiscus

MAIN CONCLUSION

Two UDP-glycosyltransferases from Panax vienamensis var. fuscidiscus involved in ocotillol-type ginsenoside MR2 (majonside-R2) biosynthesis were identified. PvfUGT1 and PvfUGT2 sequentially catalyzes 20S,24S-Protopanxatriol Oxide II and 20S,24R-Protopanxatriol Oxide I to pseudoginsenoside RT4/RT5 and RT4/RT5 to 20S, 24S-MR2/20S, 24S-MR2. Ocotilol type saponin MR2 (majonside-R2) is the main active component of Panax vietnamensis var. fuscidiscus (commonly known as 'jinping ginseng') and is well known for its diverse pharmacological activities. The use of MR2 in the pharmaceutical industry currently depends on its extraction from Panax species. Metabolic engineering provides an opportunity to produce high-value MR2 by expressing it in heterologous hosts. However, the metabolic pathways of MR2 remain enigmatic, and the two-step glycosylation involved in MR2 biosynthesis has not been reported. In this study, we used quantitative real-time PCR to investigate the regulation of the entire ginsenoside pathway by MeJA (methyl jasmonate), which facilitated our pathway elucidation. We found six candidate glycosyltransferases by comparing transcriptome analysis and network co-expression analysis. In addition, we identified two UGTs (PvfUGT1 and PvfUGT2) through in vitro enzymatic reactions involved in the biosynthesis of MR2 which were not reported in previous studies. Our results show that PvfUGT1 can transfer UDP-glucose to the C6-OH of 20S, 24S-protopanaxatriol oxide II and 20S, 24R-protopanaxatriol oxide I to form pseudoginsenoside RT4 and pseudoginsenoside RT5, respectively. PvfUGT2 can transfer UDP-xylose to pseudoginsenoside RT4 and pseudoginsenoside RT5 to form 20S, 24S-MR2 and 20S, 24S-MR2. Our study paves the way for elucidating the biosynthesis of MR2 and producing MR2 by synthetic biological methods.

Membrane Protein Bcest Is Involved in Hyphal Growth, Virulence and Stress Tolerance of Botrytis cinerea

Botrytis cinerea is a necrotrophic model fungal plant pathogen that causes grey mould, a devastating disease responsible for large losses in the agriculture sector. As important targets of fungicides, membrane proteins are hot spots in the research and development of fungicide products. We previously found that membrane protein Bcest may be closely related to the pathogenicity of Botrytis cinerea. Herein, we further explored its function. We generated and characterised ΔBcest deletion mutants of B. cinerea and constructed complemented strains. The ΔBcest deletion mutants exhibited reduced conidia germination and germ tube elongation. The functional activity of ΔBcest deletion mutants was investigated by reduced necrotic colonisation of B. cinerea on grapevine fruits and leaves. Targeted deletion of Bcest also blocked several phenotypic defects in aspects of mycelial growth, conidiation and virulence. All phenotypic defects were restored by targeted-gene complementation. The role of Bcest in pathogenicity was also supported by reverse-transcriptase real-time quantitative PCR results indicating that melanin synthesis gene Bcpks13 and virulence factor Bccdc14 were significantly downregulated in the early infection stage of the ΔBcest strain. Taken together, these results suggest that Bcest plays important roles in the regulation of various cellular processes in B. cinerea.

A novel method for Pu-erh tea face traceability identification based on improved MobileNetV3 and triplet loss

Ensuring the traceability of Pu-erh tea products is crucial in the production and sale of tea, as it is a key means to ensure their quality and safety. The common approach used in traceability systems is the utilization of bound Quick Response (QR) codes or Near Field Communication (NFC) chips to track every link in the supply chain. However, counterfeiting risks still persist, as QR codes or NFC chips can be copied and inexpensive products can be fitted into the original packaging. To address this issue, this paper proposes a tea face verification model called TeaFaceNet for traceability verification. The aim of this model is to improve the traceability of Pu-erh tea products by quickly identifying counterfeit products and enhancing the credibility of Pu-erh tea. The proposed method utilizes an improved MobileNetV3 combined with Triplet Loss to verify the similarity between two input tea face images with different texture features. The recognition accuracy of the raw tea face dataset, ripe tea face dataset and mixed tea face dataset of the TeaFaceNet network were 97.58%, 98.08% and 98.20%, respectively. Accurate verification of tea face was achieved using the optimal threshold. In conclusion, the proposed TeaFaceNet model presents a promising approach to enhance the traceability of Pu-erh tea products and combat counterfeit products. The robustness and generalization ability of the model, as evidenced by the experimental results, highlight its potential for improving the accuracy of Pu-erh tea face recognition and enhancing the credibility of Pu-erh tea in the market. Further research in this area is warranted to advance the traceability of Pu-erh tea products and ensure their quality and safety.

Extensive prevalence and significant genetic differentiation of Blastocystis in high- and low-altitude populations of wild rhesus macaques in China

BACKGROUND

Blastocystis is a common intestinal protist with a wide range of hosts. Thus far, 38 subtypes have been identified. In recent years, wild animals have been confronted with habitat fragmentation as well as an increasing risk of zoonotic disease transmission due to human disturbance. Only limited data are available on Blastocystis infection and subtype distribution in wild rhesus macaques in China. The aim of the present study was to investigate the prevalence and genetic diversity of Blastocystis in nine wild rhesus macaque populations in China.

METHODS

A total of 276 faecal samples were collected from five high-altitude populations (high-altitude group [HAG]; 2800-4100 m a.s.l.) and four low-altitude populations (low-altitude group [LAG]; 5-1000 m a.s.l) of rhesus macaques. PCR-based analysis, using a new primer pair for the amplification of a 1690-bp sequence of the small subunit ribosomal RNA (SSU rRNA) gene, was used for prevalence and genetic diversity analysis.

RESULT

Analysis of faecal samples revealed that Blastocystis infection was common in rhesus macaques, with an infection positivity rate of 80.1% (n = 221/276 samples). There was no significant difference (P = 0.121) in positivity rate between the LAG (84.3%) and HAG (76.8%). Overall, 33 haplotypes were obtained and classified into four subtypes (STs), of which three were potentially zoonotic subtypes (ST1, 29.7%; ST2, 16.7%; ST3, 31.9%) and one that was first identified in this study and named ST39 (12.0%). The STs were distributed differently among the rhesus macaque populations, except for ST3, which was found in all populations. Phylogenetic analyses revealed two major divergent clades of ST3 for the HAG and LAG. Genetic diversity analysis showed a high genetic diversity of ST3 (haplotype diversity: 0.846; nucleotide diversity: 0.014) in the rhesus macaques, but a high genetic differentiation (F_ST > 0.25) and a low gene flow (Nm = 0.09) between the HAG and LAG.

CONCLUSION

Our study, which is the first investigation on Blastocystis infection in multiple wild rhesus macaque populations in China, indicates a potential risk of zoonotic transmission of Blastocystis in the study areas. Blastocystis ST3 showed high genetic diversity in wild rhesus macaques and significant genetic differentiation between the HAG and LAG. Our results provide fundamental information on the genetic diversity and prevalence of Blastocystis in wild rhesus macaque populations.

Correction to "Experimental Research on the Disruptive Evaporation and the Motion Characteristics of Secondary Droplets for Emulsified Biodiesel with Suspended-Droplet Configuration"

[This corrects the article DOI: 10.1021/acsomega.1c01091.].

Optimization of annealing conditions for Ag/p-GaN ohmic contacts

The electrical and optical properties of Ag/p-GaN contacts have been investigated as a function of the annealing temperature, oxygen concentration, and annealing time. Specific contact resistance (ρ _c) values as low as 1.2 × 10^-4 Ω·cm² were obtained from the Ag/p-GaN contact annealed at 400 °C for 60 s in ambient O₂/N₂ (1:10). We found that the participation of oxygen improves the formation of ohmic contacts. Oxygen might remove the H in Mg-H complexes to activate the Mg acceptors and enhance Ga out-diffusion to form an Ag-Ga solid solution. We also found that the reflectivity of the Ag layer decreases with increasing annealing temperature in the O₂-containing ambient environment. Thus, an optimal annealing condition of Ag/p-GaN for blue and green LEDs is suggested based on these results. We also used the suggested annealing conditions to form ohmic contacts on DUV LEDs and achieved good electrical performance. The forward voltages of UVC LEDs fabricated with annealed Ag contacts were 6.60 V (7.66 V) at a 40 mA (100 mA) injection current.

A Robust Dual-Microphone Generalized Sidelobe Canceller Using a Bone-Conduction Sensor for Speech Enhancement

The use of multiple spatially distributed microphones allows performing spatial filtering along with conventional temporal filtering, which can better reject the interference signals, leading to an overall improvement of the speech quality. In this paper, we propose a novel dual-microphone generalized sidelobe canceller (GSC) algorithm assisted by a bone-conduction (BC) sensor for speech enhancement, which is named BC-assisted GSC (BCA-GSC) algorithm. The BC sensor is relatively insensitive to the ambient noise compared to the conventional air-conduction (AC) microphone. Hence, BC speech can be analyzed to generate very accurate voice activity detection (VAD), even in a high noise environment. The proposed algorithm incorporates the VAD information obtained by the BC speech into the adaptive blocking matrix (ABM) and adaptive noise canceller (ANC) in GSC. By using VAD to control ABM and combining VAD with signal-to-interference ratio (SIR) to control ANC, the proposed method could suppress interferences and improve the overall performance of GSC significantly. It is verified by experiments that the proposed GSC system not only improves speech quality remarkably but also boosts speech intelligibility.

Block Diagonal Hybrid Precoding and Power Allocation for QoS-Aware BDMA Downlink Transmissions

Beam Division Multiple Access (BDMA) with hybrid precoding has recently been proposed for multi-user multiple-input multiple-output (MU-MIMO) systems by simultaneously transmitting multiple digitally precoded users' data-streams via different beams. In contrast to most existing works that assume the number of radio frequency (RF) chains must be greater than or equal to that of data-streams, this work proposes a novel BDMA downlink system by first grouping transmitting data-streams before digitally precoding data group by group. To fully harvest the benefits of this new architecture, a greedy user grouping algorithm is devised to minimize the inter-group interference while two digital precoding approaches are developed to suppress the intra-group interference by maximizing the signal-to-interference-and-noise ratio (SINR) and the signal-to-leakage-and-noise ratio (SLNR), respectively. As a result, the proposed BDMA system requires less RF chains than the total number of transmit data-streams. Furthermore, we optimize the power allocation to satisfy each user's quality of service (QoS) requirement using the D.C. (difference of convex functions) programming technique. Simulation results confirm the effectiveness of the proposed scheme.

Analysis on the stability and evolutionary trend of the symbiosis system in the supply chain of fresh agricultural products

In order to increase the stability of fresh agricultural product supply chain, farmers and enterprises need to evolve into a symbiotic system of supply chain. At the present stage, symbiotic relations and evolutionary trends in a symbiotic system for fresh agricultural product supply chains lack quantitative methods for determining symbiotic criteria. In the sense of quantification -oriented criteria, symbiotic systems for fresh agricultural product supply chains are defined, and an improved stationary state analysis method is proposed. Three key steps in this method are quantifying a symbiotic energy model with an evaluation model of ecological carrying capacity, setting up a system evolution model based on the logistic growth function, and verifying the symbiotic system's singularity and phase transition boundary by Lyapunov indirect method. MATLAB numerical simulation shows that types of singularity and the phase transition boundary of symbiotic system are divided effectively. And in both conditions, infinite exponential growth and convergence to steady state, the mutualism mode is the optimal choice for the symbiotic system we defined, symbiotic relations between farmers and cooperative companies are stable and long-term at this time. Those conclusions provide a reference approach to enhance the overall prospective benefits to the fresh agricultural products supply chain.

Leisure activities and leisure motivations of Chinese residents

453 residents of Hangzhou China (56.1% women, average age 34.68 with range of 18-65) were used to investigate leisure activities and leisure motivations. Demographic data such as gender, age, marital status, education level, and income were collected and analyzed. The results indicated the following: (a) Residents' top three favorite activities were internet surfing, drinking tea and chatting, and traveling; (b) The Leisure Motivation Scale had a high reliability, with a Cronbach's alpha of 0.93; (c) In general, residents' intellectual, social, competence mastery, and stimulus avoidance scores all reached high levels; and (d) Significant differences among gender, marital status, and education level were found in leisure motivation. The findings are discussed in regard to the status quo of leisure behaviors and leisure motivations of Chinese residents.

Testing the utility of DNA barcodes and a preliminary phylogenetic framework for Chinese freshwater mussels (Bivalvia: Unionidae) from the middle and lower Yangtze River

The middle and lower portions of the Yangtze River basin is the most species-rich region for freshwater mussels in Asia. The management and conservation of the taxa in this region has been greatly hampered by the lack of a well-developed phylogeny and species-level taxonomic framework. In this study, we tested the utility of two mitochondrial genes commonly used as DNA barcodes: the first subunit of the cytochrome oxidase c gene (COI) and the first subunit of the NADH dehydrogenase gene (ND1) for 34 putative species representing 15 genera, and also generated phylogenetic hypotheses for Chinese unionids based on the combined dataset of the two mitochondrial genes. The results showed that both loci performed well as barcodes for species identification, but the ND1 sequences provided better resolution when compared to COI. Based on the two-locus dataset, Bayesian Inference (BI) and Maximum Likelihood (ML) phylogenetic analyses indicated 3 of the 15 genera of Chinese freshwater mussels examined were polyphyletic. Additionally, the analyses placed the 15 genera into 3 subfamilies: Unioninae (Aculamprotula, Cuneopsis, Nodularia and Schistodesmus), Gonideninae (Lamprotula, Solenaia and Ptychorhychus) and Anodontinae (Cristaria, Arconaia, Acuticosta, Lanceolaria, Anemina and Sinoanodonta). Our results contradict previous taxonomic classification that placed the genera Arconaia, Acuticosta and Lanceolaria in the Unioninae. This study represents one of the first attempts to develop a molecular phylogenetic framework for the Chinese members of the Unionidae and will provide a basis for future research on the evolution, ecology, and conservation of Chinese freshwater mussels.

Fairness in optimizing bus-crew scheduling process

This work proposes a model considering fairness in the problem of crew scheduling for bus drivers (CSP-BD) using a hybrid ant-colony optimization (HACO) algorithm to solve it. The main contributions of this work are the following: (a) a valid approach for cases with a special cost structure and constraints considering the fairness of working time and idle time; (b) an improved algorithm incorporating Gamma heuristic function and selecting rules. The relationships of each cost are examined with ten bus lines collected from the Beijing Public Transport Holdings (Group) Co., Ltd., one of the largest bus transit companies in the world. It shows that unfair cost is indirectly related to common cost, fixed cost and extra cost and also the unfair cost approaches to common and fixed cost when its coefficient is twice of common cost coefficient. Furthermore, the longest time for the tested bus line with 1108 pieces, 74 blocks is less than 30 minutes. The results indicate that the HACO-based algorithm can be a feasible and efficient optimization technique for CSP-BD, especially with large scale problems.