Attentional Bias of Individuals with Social Anxiety towards Facial and Somatic Emotional Cues in a Holistic Manner.

Attentional bias towards threatening information is a crucial factor contributing to the development and persistence of social anxiety. However, the attentional bias towards threat information and the preferential processing pattern of emotional cues in individuals with social anxiety disorder during integrated facial and physical stimuli processing remain unclear. In this study, we employed a dot-probe paradigm to investigate the attentional bias towards integrated emotions (facial-body) among students with high and low levels of social anxiety (Experiment 1). Experiments 2 and 3 examined the attentional bias of socially anxious individuals when faced with conflicting emotional cues from faces or bodies in relation to integrated emotions. The data revealed that participants both high and low levels of social anxiety participants exhibited accelerated orienting and biased attention towards facial-body emotional processing. When there was inconsistency between emotional cues from faces or bodies and integrated emotions, higher levels of social anxiety were associated with increased vigilance towards threatening faces or bodies. These findings underscore that individuals with social anxiety possess an ability to rapidly capture threatening cues during the processing of facial-body emotional stimuli while also demonstrating a tendency to avoid relying solely on facial cues by compensating through bodily cues for emotion perception.

Quality of life, sleep and anxiety status among patients with rosacea in the Yunnan plateau region: A 2-year retrospective study.

OBJECTIVE: To investigate the life, sleep quality and anxiety of rosacea patients in Yunnan and the improvement of these aspects after treatment. METHODS: A total of 141 patients with rosacea and 123 healthy controls were included in our study. The quality of life, sleep quality and anxiety of patients with rosacea and healthy controls were investigated by the Rosacea Severity Scores (RSSs), the Medical Outcomes Study 36-item short-form health survey (SF-36), the Pittsburgh Sleep Quality Index (PSQI) and Self-rating Anxiety Scale (SAS). The quality of life, sleep quality and anxiety of patients with rosacea were assessed again after treatment. RESULTS: Compared with healthy controls, patients with rosacea had significantly lower physical component scores (PCS) and mental component scores (MCS) but higher PSQI and SAS scores. After treatment, rosacea patients showed significantly higher MCS but lower PSQI and SAS scores. Correlation analysis showed a significant correlation between PCS, MCS, PSQI, SAS and RSSs. CONCLUSIONS: Patients with rosacea have a lower quality of life and sleep quality and tend to be more anxious than healthy controls. In addition, the mental quality of life, sleep quality and anxiety of rosacea patients can be significantly improved after treatment. Therefore, it is important to pay attention to the psychological status of rosacea patients. Psychological counseling and intervention are necessary to better prevent and treat rosacea.

Field trial of concurrent co-cable and co-trench optical fiber online identification based on ensemble learning.

The co-route optical fibers, comprising both co-cable and co-trench fibers, pose a significant potential risk to network service quality assurance by operators. They are incapable of achieving high-precision recognition and visual state management. In this study, we gathered both static and dynamic optical fiber data using a linewidth tunable light source (LTLS) and introduced a multimodal detection architecture that applies ensemble learning to the collected data. This constitutes what we believe to be the first field trial of concurrent recognition of optical fibers found both in co-cables and co-trenches. To identify co-cable fibers, we employed a double-layer cascaded Random Forest (DLC-RF) model based on the static features of fibers. For co-trench fiber, the dynamic characteristics of fiber vibrations are utilized in combination with multiple independent curve similarity contrast learners for classifying tasks. The proposed architecture is capable of automatically detecting the condition of the optical fiber and actively identifying the same routing segment within the network, eliminating the need for human intervention and enabling the visualization of passive optical fiber resources. Finally, after rigorous testing and validation across 11 sites in a typical urban area, including aggregation and backbone scenarios within the operator's live network environments, we have confirmed that the solution's ability to identify co-routes is accurate, exceeding 95%. This provides strong empirical evidence of its effectiveness.

Multi-dimensional resources allocation based on reconfigurable radio-wavelength selective switch in cloud radio over fiber networks.

To support the bandwidth-hungry applications and ubiquitous services, cloud radio access network (C-RAN) provides the large bandwidth connecting hundreds of terminals for different demands. Applying the technology of radio over fiber (RoF), cloud radio over fiber network (C-RoFN) migrates the processing and calculation functions of the base station to processing unit (PU) which simplifies the system complexity and reduces the data flood caused by digitization in 5G and beyond. The flexible allocation of multi-dimensional resources including radio and optical resources is necessary for C-RoFN to enhance the resource utilization and the quality of services. However, there is a lack of effective method to adjust spectral resources occupied by the RoF signal in C-RoFN. In our previous work, we realized multi-stratum resources optimization between processing and optical resources for C-RoFN from the perspective of network layer. In view of this, this work extends to consider the elastic switching architecture in the physical layer of C-RoFN and the scheduling of radio and optical spectral resources based on the proposed architecture. We propose a novel flexible switching and scheduling mechanism for multi-dimensional resources allocation in software defined C-RoFN. The main contributions on this work are twofold. 1) According to the flexible control demand of multi-dimensional resources, a reconfigurable radio-wavelength selective switch (RWSS) architecture is designed in a C-RoFN scenario to realize the tunability of spectral resources occupied by the RoF signal in optical network layer, which aims to break the restrictions of wavelength consistency and wavelength conflict. 2) Based on the proposed RWSS architecture, a routing, radio and wavelength assignment (RRWA) algorithm considering wavelength-frequency unsaturation is provided to implement the flexibility and allocation of radio and optical spectral resources. The feasibility and efficiency of the architecture are demonstrated on the testbed including date plane using software defined optical and wireless nodes experimentally. The performances of RRWA algorithm under heavy traffic load scenario are also quantitatively evaluated compared with the other algorithm in terms of resource utilization rate, blocking probability, and provisioning latency.

Removal of lead ions from aqueous solution by the dried aquatic plant, Lemna perpusilla Torr.

The aquatic plant, Lemna perpusilla Torr. strain, was dried, pulverized and used for Pb(II) removal from aqueous solution. Batch experiments were conducted to investigate the effects of pH, contact time, initial metal concentration and temperature on Pb(II) adsorption. A dose of 4 g L(-1) of dried L. perpusilla in a solution with an initial pH of 4.6, an initial Pb(II) concentration of 50 mg L(-1) and a contact time of 210 min resulted in the maximum Pb(II) removal efficiency (above 95%). The equilibrium adsorption capacities increased with increasing initial Pb(II) concentration. The adsorption isotherm was better described by a Langmuir model rather than a Freundlich model. Further, the adsorption kinetics followed a pseudo-second-order model. An FTIR examination revealed changes between the natural and Pb(II)-loaded plant material. Scanning electron micrograph (SEM) also revealed changes in the surface morphology of the biomass as a result of lead adsorption. Based on these results, it can be concluded that the dried L. perpusilla is effective in removing lead from aqueous solution and merits consideration for scaled-up trials.

[Prediction of blood tacrolimus concentration in liver transplantation recipients by artificial neural network].

This study is to establish an artificial neural network (ANN) for predicting blood tacrolimus concentration in liver transplantation recipients. Tacrolimus concentration samples (176 samples) from 37 Chinese liver transplantation recipients were collected. ANN established after network parameters were optimized by using momentum method combined with genetic algorithm. Furthermore, the performance of ANN was compared with that of multiple linear regression (MLR). When using accumulated dose of 4 days before therapeutic drug monitoring (TDM) of tacrolimus concentration as input factor, mean prediction error and mean absolute prediction error of ANN were 0.02 +/- 2.40 ng x mL(-1) and 1.93 +/- 1.37 ng x mL(-1), respectively. The absolute prediction error of 84.6% of testing data sets was less than 3.0 ng x mL(-1). Accuracy and precision of ANN are superior to those of MLR. The correlation, accuracy and precision of ANN are good enough to predict blood tacrolimus concentration.