Spatio-temporal data prediction of multiple air pollutants in multi-cities based on 4D digraph convolutional neural network.

In response to the problem that current multi-city multi-pollutant prediction methods based on one-dimensional undirected graph neural network models cannot accurately reflect the two-dimensional spatial correlations and directedness, this study proposes a four-dimensional directed graph model that can capture the two-dimensional spatial directed information and node correlation information related to multiple factors, as well as extract temporal correlation information at different times. Firstly, A four-dimensional directed GCN model with directed information graph in two-dimensional space was established based on the geographical location of the city. Secondly, Spectral decomposition and tensor operations were then applied to the two-dimensional directed information graph to obtain the graph Fourier coefficients and graph Fourier basis. Thirdly, the graph filter of the four-dimensional directed GCN model was further improved and optimized. Finally, an LSTM network architecture was introduced to construct the four-dimensional directed GCN-LSTM model for synchronous extraction of spatio-temporal information and prediction of atmospheric pollutant concentrations. The study uses the 2020 atmospheric six-parameter data of the Taihu Lake city cluster and applies canonical correlation analysis to confirm the data's temporal, spatial, and multi-factor correlations. Through experimentation, it is verified that the proposed 4D-DGCN-LSTM model achieves a MAE reduction of 1.12%, 4.91%, 5.62%, and 11.67% compared with the 4D-DGCN, GCN-LSTM, GCN, and LSTM models, respectively, indicating the good performance of the 4D-DGCN-LSTM model in predicting multiple types of atmospheric pollutants in various cities.

Lake eutrophication prediction based on improved MIMO-DD-3Q Learning.

As for the problem that the traditional single depth prediction model has poor strain capacity to the prediction results of time series data when predicting lake eutrophication, this study takes the multi-factor water quality data affecting lake eutrophication as the main research object. A deep reinforcement learning model is proposed, which can realize the mutual conversion of water quality data prediction models at different times, select the optimal prediction strategy of lake eutrophication at the current time according to its own continuous learning, and improve the reinforcement learning algorithm. Firstly, the greedy factor, the fixed parameter of Agent learning training in reinforcement learning, is introduced into an arctangent function and the mean value reward factor is defined. On this basis, three Q estimates are introduced, and the weight parameters are obtained by calculating the realistic value of Q, taking the average value and the minimum value to update the final Q table, so as to get an Improved MIMO-DD-3Q Learning model. The preliminary prediction results of lake eutrophication are obtained, and the errors obtained are used as the secondary input to continue updating the Q table to build the final Improved MIMO-DD-3Q Learning model, so as to achieve the final prediction of water eutrophication. In this study, multi-factor water quality data of Yongding River in Beijing were selected from 0:00 on July 26, 2021 to 0:00 on September 5, 2021. Firstly, data smoothing and principal component analysis were carried out to confirm that there was a certain correlation between all factors in the occurrence of lake eutrophication. Then, the Improved MIMO-DD-3Q Learning prediction model was used for experimental verification. The results show that the Improved MIMO-DD-3Q Learning model has a good effect in the field of lake eutrophication prediction.

A Cooperative Hunting Method for Multi-USV Based on the A* Algorithm in an Environment with Obstacles.

A single unmanned surface combatant (USV) has poor mission execution capability, so the cooperation of multiple unmanned surface ships is widely used. Cooperative hunting is an important aspect of multi USV collaborative research. Therefore, this paper proposed a cooperative hunting method for multi-USV based on the A* algorithm in an environment with obstacles. First, based on the traditional A* algorithm, a path smoothing method based on USV minimum turning radius is proposed. At the same time, the post order traversal recursive algorithm in the binary tree method is used to replace the enumeration algorithm to obtain the optimal path, which improves the efficiency of the A* algorithm. Second, a biomimetic multi USV swarm collaborative hunting method is proposed. Multiple USV clusters simulate the hunting strategy of lions to pre-form on the target's path, so multiple USV clusters do not require manual formation. During the hunting process, the formation of multiple USV groups is adjusted to limit the movement and turning of the target, thereby reducing the range of activity of the target and improving the effectiveness of the algorithm. To verify the effectiveness of the algorithm, two sets of simulation experiments were conducted. The results show that the algorithm has good performance in path planning and target search.

Remote sensing image analysis and prediction based on improved Pix2Pix model for water environment protection of smart cities.

Background: As an important part of smart cities, smart water environmental protection has become an important way to solve water environmental pollution problems. It is proposed in this article to develop a water quality remote sensing image analysis and prediction method based on the improved Pix2Pix (3D-GAN) model to overcome the problems associated with water environment prediction of smart cities based on remote sensing image data having low accuracy in predicting image information, as well as being difficult to train. Methods: Firstly, due to inversion differences and weather conditions, water quality remote sensing images are not perfect, which leads to the creation of time series data that cannot be used directly in prediction modeling. Therefore, a method for preprocessing time series of remote sensing images has been proposed in this article. The original remote sensing image was unified by pixel substitution, the image was repaired by spatial weight matrix, and the time series data was supplemented by linear interpolation. Secondly, in order to enhance the ability of the prediction model to process spatial-temporal data and improve the prediction accuracy of remote sensing images, the convolutional gated recurrent unit network is concatenated with the U-net network as the generator of the improved Pix2Pix model. At the same time, the channel attention mechanism is introduced into the convolutional gated recurrent unit network to enhance the ability of extracting image time series information, and the residual structure is introduced into the downsampling of the U-net network to avoid gradient explosion or disappearance. After that, the remote sensing images of historical moments are superimposed on the channels as labels and sent to the discriminator for adversarial training. The improved Pix2Pix model no longer translates images, but can predict two dimensions of space and one dimension of time, so it is actually a 3D-GAN model. Third, remote sensing image inversion data of chlorophyll-a concentrations in the Taihu Lake basin are used to verify and predict the water environment at future moments. Results: The results show that the mean value of structural similarity, peak signal-to-noise ratio, cosine similarity, and mutual information between the predicted value of the proposed method and the real remote sensing image is higher than that of existing methods, which indicates that the proposed method is effective in predicting water environment of smart cities.

Stress load and volunteer motivation of patient volunteers in Fangcang shelter hospitals in Shanghai, China during the COVID-19 pandemic: a cross-sectional survey.

OBJECTIVES: This study aims to assess the volunteer motivation and stress load of patient volunteers in the Fangcang shelter hospitals (FSHs), examine their associations, and explore the potential influence factors of volunteer motivation. DESIGN: Cross-sectional online survey conducted from 21 April to 20 May 2022. SETTING: Questionnaires were collected from patient volunteers selected by random cluster sampling in the FSHs in Shanghai, China. PARTICIPANTS: 197 participants who met the inclusion criteria as patients who were asymptomatic or presenting with mild symptoms in the FSHs and who volunteered to assist with routine work under quarantined settings. OUTCOME MEASURES: We investigated sociodemographic information, stress load and volunteer motivation through an online survey using the Volunteer Function Inventory and the Stress Overload Scale. Comparisons between groups were conducted by applying t-tests or analysis of variance. The correlation between volunteer motivation and stress was analysed by Pearson correlation. Influencing factors of volunteer motivation were determined by multivariable linear regression models. A value of p<0.05 was used to declare statistical significance. RESULTS: The mean score of volunteer motivation of patient volunteers was 73.24 (SD 12.00), while that of stress load was 46.08 (SD 21.28). The mean scores of the personal vulnerability (PV) and event load (EL), two dimensions of stress load, were 26.99 (SD 12.46) and 19.09 (SD 9.63), respectively. The majority of the participants (136, 69.04%) were grouped in the low (PV)-low (EL) stress category. Participants' volunteer motivation was negatively correlated with stress load (r=-0.238, p<0.001), as well as PV (r=-0.188, p<0.01) and EL (r=-0.283, p<0.001). Multivariable linear regression analysis identified that the potential influencing factors of volunteer motivation were occupation (B=1.100, 95% CI 0.037 to 2.164, p=0.043), health condition (B=-3.302, 95% CI -5.287 to -1.317, p<0.001) and EL (B=-0.434, 95% CI -0.756 to -0.111, p=0.009). Participants who worked in the public sector, had better health conditions and had lower EL were more likely to have higher volunteer motivation. CONCLUSIONS: Our study suggested that reducing stress load might be a possible pathway to encourage and maintain volunteerism in the FSH context. Implications and suggestions for future research on patient volunteer recruitment and management could be drawn from our findings.

Research on the Construction of Grain Food Multi-Chain Blockchain Based on Zero-Knowledge Proof.

As the main food source of the world's population, grain quality safety is of great significance to the healthy development of human beings. The grain food supply chain is characterized by its long life cycle, numerous and complex business data, difficulty defining private information, and difficult managing and sharing. In order to strengthen the ability of information application processing and coordination of the grain food supply chain under many risk factors, an information management model suitable for the grain food supply chain is studied based on the blockchain multi-chain technology. First, the information on key links in the grain food supply chain is analyzed to obtain privacy data classifications. Second, a multi-chain network model of the grain food supply chain is constructed, and based on this model, the hierarchical encryption and storage mode of private data as well as the relay cross-chain communication mode, are designed. In addition, a complete consensus process, including CPBFT, ZKP, and KZKP algorithms, is designed for the global information collaborative consensus under the multi-chain architecture. Finally, the model is verified through performance simulation, theory analysis, and prototype system verification in terms of its correctness, security, scalability, and consensus efficiency. The results show that this research model effectively reduces the storage redundancy and deals with problems of data differential sharing in traditional single-chain research, as well as provides a secure data protection mechanism, a credible data interaction mechanism, and an efficient multi-chain collaborative consensus mechanism. By attempting to apply blockchain multi-chain technology to the grain food supply chain, this study provides new research ideas for the trusted protection of data and information collaborative consensus in this field.

Ginkgolide C attenuates cerebral ischemia/reperfusion-induced inflammatory impairments by suppressing CD40/NF-κB pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Ginkgo biloba L. (Ginkgoaceae), a traditional Chinese medicine, has been applied for thousands of years for the treatment of cardio-cerebral vascular diseases in China. It is written in Compendium of Materia Medica that Ginkgo has the property of "dispersing poison", which is now referred to as anti-inflammatory and antioxidant. Ginkgolides are important active ingredients in Ginkgo biloba leaves and ginkgolide injection has been frequently applied in clinical practice for the treatment of ischemic stroke. However, few studies have explored the effect and mechanism of ginkgolide C (GC) with anti-inflammatory activity in cerebral ischemia/reperfusion injury (CI/RI). AIM OF THE STUDY: The present study aimed to demonstrate whether GC was capable of attenuating CI/RI. Furthermore, the anti-inflammatory effect of GC in CI/RI was explored around the CD40/NF-κB pathway. MATERIALS AND METHODS: In vivo, middle cerebral artery occlusion/reperfusion (MCAO/R) model was established in rats. The neuroprotective effect of GC was assessed by neurological scores, cerebral infarct rate, microvessel ultrastructure, blood-brain barrier (BBB) integrity, brain edema, neutrophil infiltration, and levels of TNF-α, IL-1ß, IL-6, ICAM-1, VCAM-1, and iNOS. In vitro, rat brain microvessel endothelial cells (rBMECs) were preincubated in GC before hypoxia/reoxygenation (H/R) culture. The cell viability, levels of CD40, ICAM-1, MMP-9, TNF-α, IL-1ß, and IL-6, and activation of NF-κB pathway were examined. In addition, the anti-inflammatory effect of GC was also investigated by silencing CD40 gene in rBMECs. RESULTS: GC attenuated CI/RI as demonstrated by decreasing neurological scores, reducing cerebral infarct rate, improving microvessel ultrastructural features, ameliorating BBB disruption, attenuating brain edema, inhibiting MPO activity, and downregulating levels of TNF-α, IL-1ß, IL-6, ICAM-1, VCAM-1, and iNOS. Coherently, in rBMECs exposed to H/R GC enhanced cell viability and downregulated levels of ICAM-1, MMP-9, TNF-α, IL-1ß, and IL-6. Furthermore, GC suppressed CD40 overexpression and hindered translocation of NF-κB p65 from the cytosol to the nucleus, phosphorylation of IκB-α, and activation of IKK-ß in H/R rBMECs. However, GC failed to protect rBMECs from H/R-induced inflammatory impairments and suppress activation of NF-κB pathway when CD40 gene was silenced. CONCLUSIONS: GC attenuates cerebral ischemia/reperfusion-induced inflammatory impairments by suppressing CD40/NF-κB pathway, which may provide an available therapeutic drug for CI/RI.

Effect of acupuncture at Shuitong and Shuijin points on sufentanil-induced cough in general anesthesia induction: a randomized controlled trial. / é’ˆåˆºæ°´é€šã€æ°´é‡‘ç©´å¯¹å ¨èº«éº»é†‰è¯±å¯¼æœŸèˆ’èŠ¬å¤ªå°¼è¯±å‘å‘›å’³çš„å½±å“ï¼šéšæœºå¯¹ç §è¯•éªŒ.

OBJECTIVES: To observe the therapeutic effect of acupuncture at Shuitong and Shuijin points on preventing sufentanil-induced cough and its influence on hemodynamics in general anesthesia induction. METHODS: A total of 80 patients scheduled for elective surgery undergoing general anesthesia were randomly divided into an observation group (40 cases) and a control group (39 cases,1 case eliminated). In the control group, the routine anesthesia was performed,with intravenous injection of 1% sufentanil citrate 0.5 µg/kg, 1% propofol (total amount was calculated according to 2 mg/kg) and cisatracurium besilate 0.2 mg/kg. In the observation group, before routine anesthesia induction, acupuncture was applied to Shuitong and Shuijin points on the right and the needles were retained for 30 min. During anesthesia induction, the complications i.e. cough and chest wall stiffness were observed, and the systolic blood pressure (SBP), heart rate (HR) and pulse oxygen saturation (SpO2) were monitored 5 min after the patients entered the operation room (T0),at the moment of intravenous injection of sufentanil (T1) and 2 min after sufentanil injection (T2) , 1 min before and after endotracheal intubation (T3,T4) of the two groups, respectively. RESULTS: During anesthesia induction,the condition of mild, moderate and severe cough in the observation group was superior to that of the control group (P<0.05), the total cases of cough and its total incidence were lower than those of the control group (P<0.05). Two cases of chest wall stiffness were present in each group, but without statistical difference between the two groups (P>0.05). In comparison of SBP, HR and SpO2 at T0, T1, T2, T3 and T4, the differences were not significant statistically between the two groups (P>0.05). SBP and HR increased at T2 when compared with those at T1 in the control group (P<0.05), but there was no statistical difference in SpO2 (P>0.05); while, the differences in SBP, HR and SpO2 were not significant at T2 when compared with those at T1 in the observation group (P>0.05). CONCLUSIONS: Acupuncture at Shuitong and Shuijin points can effectively prevent from sufentanil-induced cough, reduce the severity of cough and stabilize the hemodynamic indicators.

Construction of rice supply chain supervision model driven by blockchain smart contract.

The outbreak of the COVID-19 and the Russia Ukraine war has had a great impact on the rice supply chain. Compared with other grain supply chains, rice supply chain has more complex structure and data. Using digital means to realize the dynamic supervision of rice supply chain is helpful to ensure the quality and safety of rice. This study aimed to build a dynamic supervision model suited to the circulation characteristics of the rice supply chain and implement contractualization, analysis, and verification. First, based on an analysis of key information in the supervision of the rice supply chain, we built a dynamic supervision model framework based on blockchain and smart contracts. Second, under the logical framework of a regulatory model, we custom designed three types of smart contracts: initialization smart contract, model-verification smart contract, and credit-evaluation smart contract. To implement the model, we combined an asymmetric encryption algorithm, virtual regret minimization algorithm, and multisource heterogeneous fusion algorithm. We then analyzed the feasibility of the algorithm and the model operation process. Finally, based on the dynamic supervision model and smart contract, a prototype system is designed for example verification. The results showed that the dynamic supervision model and prototype system could achieve the real-time management of the rice supply chain in terms of business information, hazard information, and personnel information. It could also achieve dynamic and credible supervision of the rice supply chain's entire life cycle at the information level. This new research is to apply information technology to the digital management of grain supply chain. It can strengthen the digital supervision of the agricultural product industry.

A Refined Supervision Model of Rice Supply Chain Based on Multi-Blockchain.

With the development of Agriculture 4.0, the requirements for sustainable agriculture and precision agriculture continue to grow. As one of the three major staple foods globally, the quality and safety of rice affect human health as well as social development. To ensure the quality and safety of rice and reduce the flow of problematic rice, a multi-layer blockchain-based rice supply chain refinement supervision model MBRRSM (Multi-blockchain Rice Refined Supervision Model) is proposed from the information level. First, the characteristics of information flow in the rice supply chain are analyzed, and a classification table of key information is constructed. Second, the MBRRSM framework is designed. Based on a multi-party hybrid encryption algorithm, secure multi-party computing algorithm, multi-mode storage mechanism, and SPOP (Supervision Proof of Peers) consensus algorithm, a set of mechanisms is designed for the transmission, use, storage, and consensus of rice data in MBRRSM. Subsequently, the security and performance capabilities of MBRRSM are analyzed. Meanwhile, the SPOP consensus algorithm is analyzed. Finally, a prototype system is built based on MBRRSM and verified through exemplary scenarios in different usage situations. The results show that research on the refined supervision of the rice supply chain based on multi-blockchain can finely supervise all types of data in the rice supply chain, and provide a guarantee for enterprise users to safely transmit and use data with different privacy levels. This study presents a unique research paradigm that introduces the theories and methods of the new information field generation into the field of agricultural research, and thus assists in the strategy implementation of "holding grain in the land and storing grain in technology".

Effect Observation of Electro-Acupuncture Anesthesia Combined with General Anesthesia in Elderly Patients Undergoing Gastrointestinal Tumor Resection.

Objective: To investigate the anesthetic effect of electro-acupuncture (EA) anesthesia combined with general anesthesia in elderly patients undergoing gastrointestinal tumor resection, and to analyze the effects of EA anesthesia on inflammatory factors, stress state and T lymphocyte subsets in elderly patients. Methods: Total of 118 elderly patients who underwent gastrointestinal tumor resection in our hospital from June 2018 to March 2021 were selected and divided into the control group (59 cases) and the observation group (59 cases) according to the random number method. General anesthesia was adopted in the control group and EA anesthesia combined with general anesthesia was adopted in the observation group. The anesthesia effect, stress state, levels of inflammatory factors, T-lymphocyte subsets and adverse reactions were compared. Results: The VAS score, agitation score and respiratory normalization time in the observation group were lower than those in the control group (p < 0.05). After surgery, the levels of serum Cor, ET, NE and DA in the observation group were lower than those in the control group (p < 0.05). At 24 h after surgery, the levels of serum TNF-α, IL-6 and IL-1ß in the observation group were lower than those in the control group (p < 0.05). At 24 h after surgery, the levels of C D 3 + , C D 4 + , and C D 4 + / / C D 8 + in the two groups were lower than those before surgery, and the levels of C D 3 + , C D 4 + , and C D 4 + / / C D 8 + in the observation group were higher than those in the control group (p < 0.05). During the hospitalization, the total incidence rate of adverse reactions after anesthesia in the observation group was lower than that in the control group (p < 0.05). Conclusion: EA anesthesia combined with general anesthesia has good anesthesia effect when used for gastrointestinal tumor resection in the elderly. It can stabilize the internal environment of patients, alleviate postoperative stress response and inflammatory response, and regulate the body immune function. Moreover, it has high safety and can significantly reduce the occurrence of postoperative adverse reactions.

Information Traceability Model for the Grain and Oil Food Supply Chain Based on Trusted Identification and Trusted Blockchain.

The grain and oil food supply chain has a complex structure, long turnover cycles, and many stakeholders, so it is challenging to maintain the security of this supply chain. A reliable traceability system for the whole grain and oil food supply chain will help to improve the quality and safety of these products, thus enhancing people's living standards. Driven by the trusted blockchain and trusted identity concepts, this paper constructs an information traceability model for the whole grain and oil food supply chain, and it describes how contract implementation and example verification are performed. First, an information traceability model framework of the whole grain and oil food supply chain is established based on the survey and analysis of the grain and oil food supply chain. Second, trusted identification, blockchain master-slave multi-chain storage, and trusted traceability mechanisms are designed. The trusted identification mechanism is used to track the data information of the whole grain and oil food supply chain. The blockchain master-slave multi-chain storage solves the problem of miscellaneous information caused by many links in the whole grain and oil supply chain, while the credible traceability mechanism ensures the credibility of information collection, storage, and transmission. Finally, based on the data flow, the model operation process is analyzed. Using the information traceability model, the grain and oil food trusted traceability system is designed and developed with the Hyperledger Fabric open-source framework, and a case study is conducted to verify the system. The results show that the model and system constructed in this study solve the problems of low data security and poor sharing, which exist widely in the traditional traceability mechanism, and enable the trusted uplink, storage, processing, and traceability of multi-source heterogeneous information in the lifecycle of the whole grain and oil food supply chain. The proposed system improves the granularity and accuracy of grain and oil food traceability, and provides support for the strategic security of grain stock.

Research on the Cross-Chain Model of Rice Supply Chain Supervision Based on Parallel Blockchain and Smart Contracts.

Rice is one of the three major staple foods in the world, and the quality and safety of rice are related to the development of human beings. The new crown epidemic, pesticide residues, insect pests, and heavy metal pollution have a certain security impact on the food supply chain. The rice supply chain is characterized by a long life cycle; complex roles in the main links; many types of hazards; and multidimensional, multisource, and heterogeneous information. To strengthen the rice supply chain's supervision ability under the epidemic situation, a supervision cross-chain model suitable for the complicated data of the rice supply chain based on parallel blockchain theory and smart contract technology was built. Firstly, the data collected in the rice supply chain and different types of data stored in different parallel blockchains were analyzed. Secondly, based on data analysis, a collection/supervision cross-chain mechanism based on "hash lock + smart contract + relay chain", a concurrency mechanism based on the K-means algorithm and a Bloom filter, and a consensus mechanism suitable for multichain consensus named the Supervision Practical Byzantine Fault Tolerance (SPBFT) were proposed. Furthermore, a cross-chain model of rice supply chain supervision was constructed. Finally, theoretical verification and simulation experiments were used to analyze the operation process, safety, cross-chain efficiency, and scalability of the model. The results showed that the application of parallel blockchains and smart contracts to supervision of rice supply chain information improved the convenience and security of information interaction between various links in the rice supply chain, the storage cost of supply chain data and the high latency of interaction was reduced, and the refined management of the rice supply chain data and personnel was realized. This research applied new information technology to the coordination and resource sharing of the food supply chain, and provides ideas for the digital transformation of the food industry.

A Hybrid Multi-Target Path Planning Algorithm for Unmanned Cruise Ship in an Unknown Obstacle Environment.

To solve the problem of traversal multi-target path planning for an unmanned cruise ship in an unknown obstacle environment of lakes, this study proposed a hybrid multi-target path planning algorithm. The proposed algorithm can be divided into two parts. First, the multi-target path planning problem was transformed into a traveling salesman problem, and an improved Grey Wolf Optimization (GWO) algorithm was used to calculate the multi-target cruise sequence. The improved GWO algorithm optimized the convergence factor by introducing the Beta function, which can improve the convergence speed of the traditional GWO algorithm. Second, based on the planned target sequence, an improved D* Lite algorithm was used to implement the path planning between every two target points in an unknown obstacle environment. The heuristic function in the D* Lite algorithm was improved to reduce the number of expanded nodes, so the search speed was improved, and the planning path was smoothed. The proposed algorithm was verified by experiments and compared with the other four algorithms in both ordinary and complex environments. The experimental results demonstrated the strong applicability and high effectiveness of the proposed method.

Blockchain-Based Information Supervision Model for Rice Supply Chains.

Rice is a major food crop around the world, and its various quality and safety problems are closely related to human health. As an important area of food safety research, the rice supply chain has attracted increasing attention. Based on blockchain technology, this study investigated problems of data privacy and circulation efficiency caused by complex rice supply networks, long circulation cycles, and various risk factors in each link. First, we deconstructed the quality and safety of each link of the rice supply chain at the information level and established a key information classification table for each link. On that basis, we built a rice supply chain information supervision model based on blockchain. Various encryption algorithms are used to secure the sensitive data of enterprises in the supply chain to meet regulators' needs for efficient supervision. Moreover, we propose a practical Byzantine fault-tolerant consensus algorithm that scores the credit of enterprise nodes, optimizes the selection strategy of master nodes, and ensures high efficiency and low cost. Then, we built a prototype system based on the open-source framework of hyperledger fabric, analyzed the model's viability, and implemented the system using cases. The results indicated that the proposed system can optimize the information supervision process of rice supply chain regulators and provide a feasible solution for the quality and safety supervision of grain and oil.

Research on Grain Food Blockchain Traceability Information Management Model Based on Master-Slave Multichain.

Aiming at the problems such as slow traceability efficiency, poor sharing, and the difficulty of matching the throughput of a blockchain single chain structure due to the complexity of the grain food supply chain links, the large number of participants, and the large amount of data information, this paper proposes a grain food blockchain traceability information management model based on the master-slave multichain structure by analyzing the processes and data characteristics of each link in the grain food supply chain; on this basis, the PLEW consensus algorithm based on Raft + improved PoW algorithm is designed for the master chain, and the CI-PBFT consensus algorithm based on trusted information degree is designed for the slave chain. The master chain and slave chain are anchored to each other through hash locking, and the data is uploaded and queried through smart contracts. In order to verify the effectiveness of the model, the blockchain traceability system is designed and implemented based on Hyperledger Fabric2.2. At the same time, it is compared with the transaction throughput and traceability efficiency of the blockchain single chain structure. Through the safety analysis of the data information of a company in Hubei, the results show that the grain traceability system designed and implemented in this study has certain advantages over the blockchain single chain structure in all aspects. It can also solve the grain food security problems that consumers worry about, and provide reference for the research of grain blockchain traceability information management.

Efficacy and safety of CO2 laser in the treatment of chronic wounds: A Retrospective Matched Cohort Trial.

OBJECTIVES: Treating chronic cutaneous wounds is challenging, and debridement is a central concept in treating them. Studies have shown that CO2 laser debridement can control local infection and promote the wound healing process. The present study aimed to investigate the efficacy and safety of fully ablative CO2 laser debridement compared to routine surgical debridement in the treatment of chronic wounds. METHODS: The retrospective cohort study was conducted on patients with chronic (>1 month) cutaneous wounds (≥1 cm2 ) between December 1, 2017, and December 1, 2020, in the Wound Healing Center at Shanghai Ruijin Hospital, China. Patients treated with CO2 laser debridement with a DEKA SmartXide2 C80 (DEKA) (the CO2 laser group) were compared with matched control patients with similar baseline characteristics who had undergone routine surgical debridement (the routine group). The primary outcome was time-to-heal (days) for chronic wounds in two groups, and secondary outcomes included the wound area and BWAT (Bates-Jensen wound assessment tool) score before treatment, and at 1, 2, 3, and 4 weeks after treatment. RESULTS: The study included 164 patients (82 in the CO2 laser group and 82 matched in the routine group). The time-to-heal for patients in the CO2 laser group (41.30 ± 17.11) was significantly shorter than that of the patients in the routine group (48.51 ± 24.32) (p = 0.015). At 3 and 4 weeks after treatment, the absolute wound area of the CO2 laser group was significantly smaller than that of the routine group. Also, the CO2 laser group exhibited a significantly lower relative area at 2, 3, and 4 weeks after treatment. The CO2 laser group yielded significantly lower BWAT scores at 2, 3, and 4 weeks after treatment. Additionally, the relative BWAT score was significantly lower in the CO2 laser group than the relative scores in the routine group at 2, 3, and 4 weeks after treatment. No adverse events related to the treatments were observed in either group during the study period. CONCLUSIONS: The present study has shown that fully ablative CO2 laser debridement has several advantages over routine sharp surgical debridement. It is superior at ameliorating wound status and reducing wound area, and it also significantly reduces the time-to-heal for chronic wounds, without causing any adverse events.

Screening Candidates for Refractive Surgery With Corneal Tomographic-Based Deep Learning.

Importance: Evaluating corneal morphologic characteristics with corneal tomographic scans before refractive surgery is necessary to exclude patients with at-risk corneas and keratoconus. In previous studies, researchers performed screening with machine learning methods based on specific corneal parameters. To date, a deep learning algorithm has not been used in combination with corneal tomographic scans. Objective: To examine the use of a deep learning model in the screening of candidates for refractive surgery. Design, Setting, and Participants: A diagnostic, cross-sectional study was conducted at the Zhongshan Ophthalmic Center, Guangzhou, China, with examination dates extending from July 18, 2016, to March 29, 2019. The investigation was performed from July 2, 2018, to June 28, 2019. Participants included 1385 patients; 6465 corneal tomographic images were used to generate the artificial intelligence (AI) model. The Pentacam HR system was used for data collection. Interventions: The deidentified images were analyzed by ophthalmologists and the AI model. Main Outcomes and Measures: The performance of the AI classification system. Results: A classification system centered on the AI model Pentacam InceptionResNetV2 Screening System (PIRSS) was developed for screening potential candidates for refractive surgery. The model achieved an overall detection accuracy of 94.7% (95% CI, 93.3%-95.8%) on the validation data set. Moreover, on the independent test data set, the PIRSS model achieved an overall detection accuracy of 95% (95% CI, 88.8%-97.8%), which was comparable with that of senior ophthalmologists who are refractive surgeons (92.8%; 95% CI, 91.2%-94.4%) (P = .72). In distinguishing corneas with contraindications for refractive surgery, the PIRSS model performed better than the classifiers (95% vs 81%; P < .001) in the Pentacam HR system on an Asian patient database. Conclusions and Relevance: PIRSS appears to be useful in classifying images to provide corneal information and preliminarily identify at-risk corneas. PIRSS may provide guidance to refractive surgeons in screening candidates for refractive surgery as well as for generalized clinical application for Asian patients, but its use needs to be confirmed in other populations.

Development of low laser energy levels in small-incision lenticule extraction: clinical results, black area, and ultrastructural evaluation.

PURPOSE: To evaluate the outcomes of 4 low laser energy levels after small-incision lenticule extraction (SMILE) surgery. SETTING: Zhongshan Ophthalmic Center, Guangzhou, China. DESIGN: Prospective randomized clinical trial. METHODS: This study evaluated consecutive patients who had SMILE to correct myopia or myopia with astigmatism. Eyes were placed into groups based on the laser energy used during surgery (ie, 105 nJ, 110 nJ, 115 nJ, or 120 nJ). All patients had a thorough ophthalmic examination preoperative and at 4 timepoints over 3 months postoperatively. Black areas and surface regularity of the extracted lenticules were observed and evaluated qualitatively and quantitatively. RESULTS: The study comprised 124 eyes of 62 patients (40 women, 22 men), with 31 eyes in each laser energy group. The incidence of black areas was 45.16% (14 of 31 eyes), 12.90% (4 of 31 eyes), 16.13% (5 of 31 eyes), and 12.90% (4 of 31 eyes) for 105 nJ, 110 nJ, 115 nJ, and 120 nJ, respectively. The mean time for lenticule creation was the longest in the 105 nJ group (P = .015). The greatest increase in corneal thickness postoperatively occurred with 105 nJ (P < .05). Regression was highest in the 105 nJ group at 3 months (P < .01). However, corneal horizontal coma (C8) was lowest in the 105 nJ group at 1 week (P = .032). The lenticular surface in the 110 nJ group was the smoothest (P = .011). All contrast sensitivity values varied with time and recovered to preoperative levels by 1 week or 1 month. In all eyes, the uncorrected distance visual acuity and corrected distance visual acuity were good, with no statistically significant differences between the 4 energy groups. CONCLUSIONS: The 105 nJ group, in which the lowest energy was used, had the highest risk for black areas, serious postoperative corneal edema, and a significant healing response.

Comparison of outcome between small incision lenticule extraction and FS-LASIK in eyes having refractive error greater than negative 10 diopters.

PURPOSE: To compare small incision lenticule extraction (SMILE) and femtosecond laser-assisted laser in situ keratomileusis (FS-LASIK) in terms of safety, refractive outcomes, visual quality, and biomechanical responses in correcting myopia with maximum myopic meridian exceeding 10 diopters (D). SETTING: Zhongshan Ophthalmic Center, Sun Yat-sen University. DESIGN: Prospective, randomized, comparative study. METHODS: The study comprised 60 eyes (60 patients) with a maximum myopic meridian exceeding 10 D; 30 eyes were corrected using SMILE and 30 eyes were corrected using FS-LASIK. Patients received preoperative and 6-month postoperative examinations, including refractive outcomes, corneal curve, contrast sensitivity, ocular aberrometry, and corneal biomechanical responses. RESULTS: At 6 months postoperatively, the uncorrected distance visual acuity was -0.01 ± 0.06 and -0.05 ± 0.10 in the SMILE and LASIK eyes, respectively (P = .08). The corrected distance visual acuity was -0.07 ± 0.07 and -0.08 ± 0.08 (P = .624), respectively. The postoperative spherical equivalent (SE) was -0.20 ± 0.25 D and -0.03 ± 0.20 D, respectively (P = .008). The posterior corneal curvature was unchanged after SMILE and FS-LASIK (P > .05). The measured corneal thickness was reduced by 137.40 ± 15.01 µm and 155.06 ± 17.43 µm (P < .001). The change in the SE was -0.01 ± 0.26 and -0.13 ± 0.30 from 1 week (P = .103). Only the peak distance (the distance between the highest points of the nondeformed corneal parts) differed between the groups (1.06 ± 1.44 mm vs -0.26 ± 1.16 mm, P = .007). In the SMILE patients, changes in higher-order aberration (P = .018) and spherical aberration (P = .011) were smaller than in LASIK patients. CONCLUSIONS: Compared with LASIK, SMILE might offer superior safety and objective visual quality, comparable stability and efficacy, and a little inferior predictability in correcting maximum myopic meridian exceeding 10 D.