Clivia biosensor: Soil moisture identification based on electrophysiology signals with deep learning.

Research has shown that plants have the ability to detect environmental changes and generate electrical signals in response. These electrical signals can regulate the physiological state of plants and produce corresponding feedback. This suggests that plants have the potential to be used as biosensors for monitoring environmental information. However, there are current challenges in linking environmental information with plant electrical signals, especially in collecting and classifying the corresponding electrical signals under soil moisture gradients. This study documented the electrical signals of clivia under different soil moisture gradients and created a dataset for classifying electrical signals. Subsequently, we proposed a lightweight convolutional neural network (CNN) model (PlantNet) for classifying the electrical signal dataset. Compared to traditional CNN models, our model achieved optimal classification performance with the lowest computational resource consumption. The model achieved an accuracy of 99.26%, precision of 99.31%, recall of 92.26%, F1-score of 99.21%, with 0.17M parameters, a size of 7.17MB, and 14.66M FLOPs. Therefore, this research provides scientific evidence for the future development of plants as biosensors for detecting soil moisture, and offers insight into developing plants as biosensors for detecting signals such as ozone, PM2.5, Volatile Organic Compounds(VOCs), and more. These studies are expected to drive the development of environmental monitoring technology and provide new pathways for better understanding the interaction between plants and the environment.

A new imperialist competitive algorithm with spiral rising mechanism for solving path optimization problems.

Intelligent optimization algorithms have now become important means for solving global optimization problems. The imperialist competitive algorithm (ICA) is a nature-inspired meta-heuristic algorithm that imitates social behavior. ICA has been widely used in optimization problems, however, ICA tends to fall into a local optimal solution because of its fast convergence speed, which may lead to premature convergence when solving optimization problems. To solve these problems, a new improved ICA algorithm is proposed. Based on the original ICA algorithm, the theory of spiral rising is introduced to enlarge the search space and enhance the global search ability of the algorithm based on ensuring the necessary speed of convergence. In this paper, the improved optimization algorithm is applied to 19 classical benchmark functions, and the improved ICA is applied to the robot path optimization problems to solve the optimal path. The improved ICA algorithm improves the optimization ability and algorithm stability.

The Effect of Nursing Management of Patients Undergoing Interventional Therapy for Liver Cancer Compared with Standard Care on Patient-Reported Outcomes.

BACKGROUND: To investigate the efficacy of individualized symptom management based on patients' self-reports during interventional therapy (IT) for liver cancer. METHODS: Patients with liver cancer who recieved IT from April to August 2019 were assigned to either the intervention (n=70) or control group (n=70). The control group received routine nursing care and the intervention group received a nursing management program. The severity of specific symptoms, as measured by the Karnofsky Performance Scale (KPS), and satisfaction with nursing care, were analyzed. RESULTS: Compared to the control group, patients given individualized management experienced significantly less severe pain, nausea, anxiety, and fatigue (p < .05). The scores for KPS and satisfaction with care were both significantly improved in the intervention group than in the control group (p < .05). CONCLUSION: This high-quality nursing management program predicated on patients' self-reports is worthy of clinical application and popular adoption.

Azithromycin inhibits the production of MUC5AC in the airway mucosa of patients with bronchiectasis induced by Pseudomonas aeruginosa.

To explore the clinical benefits of azithromycin in the treatment of Pseudomonas aeruginosa induced bronchiectasis and to evaluate its effect on MUC5AC. From April 2018 to June 2020, 160 patients with bronchiectasis due to Pseudomonas aeruginosa infection were selected. The patients were divided into a control groupand an azithromycin group. Statistics of patients' general clinical data, lung function indexes, sputum volume, oxidative stress level, Bhalla score before and after treatment; Western blot analysis of MUC5AC expression; RT-PCR analysis of TNF-α, IL-8, IL- 1ß mRNA expression. The mRNA expression of TNF-α, IL-8 and IL-1ß in the azithromycin group was lower than that in the control group (P<0.05). After treatment, the protein expression of MUC5AC in the azithromycin group was lower than that in the control group (P<0.05). The improvement rate in the azithromycin group was significantly higher than that in the control group (P<0.05). The azithromycin group had a lower lung infection rate than the control group (P<0.05). The azithromycin group had a lower dyspnea rate than the control group (P<0.05). Azithromycin treatment has certain clinical benefits for patients with bronchiectasis induced by Pseudomonas aeruginosa and inhibits the MUC5AC expression.

Adaptive fuzzy dynamic surface sliding mode control of large-scale power systems with prescribe output tracking performance.

This paper focuses on an adaptive fuzzy based dynamic surface sliding mode control (ADSSMC) strategy for multi-machine power systems with SVC. The main characteristics of the proposed ADSSMC strategy are: (1) System robustness and the convergency speed of all surface error are enhanced by combining dynamic surface control with sliding mode, leading to the structural complexity of the controller in backstepping control scheme is eliminated; (2) By introducing an error performance conversion function, the errors of desired trajectory and system output are always within a predetermined range; (3) In the adaptive regulation laws, the number of estimated parameters of the control system is significantly reduced due to the utilization of the estimations of weight vector norm instead of the weight vectors itself. The stability analysis proves that the proposed ADSSMC control strategy can guarantee the boundaries of all signals in the closed-loop control system. Experimental results verify the effectiveness of the proposed ADSSMC scheme.

Efficient degradation of tetrabromobisphenol A via electrochemical sequential reduction-oxidation: Degradation efficiency, intermediates, and pathway.

Tetrabromobisphenol A (TBBPA), a toxic persistent pollutant, should be effectively removed from the environment. In this study, an electrochemical sequential reduction-oxidation system was proposed by controlling reaction atmosphere with Pd-Fe nanoparticles modified Ni foam (Pd-Fe/Ni) electrode as cathode for TBBPA degradation. To obtain an efficient Pd-Fe/Ni electrode for TBBPA degradation, various factors, like Pd loading, Fe2+ adding amounts, were examined. The Pd-Fe/Ni electrode exhibited higher TBBPA conversion and debromination than the counterparts, due to the synergism of Fe0 and electrochemical reduction. Similar TBBPA conversions and debromination ratios were observed for the cases of sparging N2 only and sparging N2 followed by air, which were higher than those of aeration. Reductive debromination occurred while first bubbling N2, forming tri-BBPA, di-BBPA, mono-BBPA and BPA; and these intermediates were likely to be further oxidized by OH generated from H2O2 together with Pd-Fe/Ni electrode under aeration. Reductive and oxidative intermediates (including aromatic ring-opened product) were identified by HPLC and UPLC-QTOF-MS. Based on the intermediates, the possible TBBPA degradation mechanism and pathway were proposed. This study demonstrates that sequential reduction-oxidation process tuned by N2 and air bubbling was favored for TBBPA degradation, thus, it should be a promising process for HOCs degradation.