Search

The heading and flowering stages are crucial for wheat growth and should be used for fusarium head blight (FHB) and other plant prevention operations. Rapid and accurate monitoring of wheat growth in hilly areas is critical for determining plant protection operations and strategies. Currently, the operation time for FHB prevention and plant protection is primarily determined by manual tour inspection of plant growth, which has the disadvantages of low information gathering and subjectivity. In this study, an unmanned aerial vehicle (UAV) equipped with a multispectral camera was used to collect wheat canopy multispectral images and heading rate information during the heading and flowering stages in order to develop a method for detecting the appropriate time for preventive control of FHB. A 1D convolutional neural network + decision tree model (1D CNN + DT) was designed. All the multispectral information was input into the model for feature extraction and result regression. The regression revealed that the coefficient of determination (R2) between multispectral information in the wheat canopy and the heading rate was 0.95, and the root mean square error of prediction (RMSE) was 0.24. This result was superior to that obtained by directly inputting multispectral data into neural networks (NN) or by inputting multispectral data into NN via traditional VI calculation, support vector machines regression (SVR), or decision tree (DT). On the basis of FHB prevention and control production guidelines and field research results, a discrimination model for FHB prevention and plant protection operation time was developed. After the output values of the regression model were input into the discrimination model, a 97.50% precision was obtained. The method proposed in this study can efficiently monitor the growth status of wheat during the heading and flowering stages and provide crop growth information for determining the timing and strategy of FHB prevention and plant protection operations. [ABSTRACT FROM AUTHOR]

This paper takes the flying defense team as the operating unit and studies the route planning and tasks in the process of plant protection operations as system problems. In the route planning, the flight defense team's operating time is used as the optimization goal of route planning. Taking the UAV battery and drug load as constraints, the flight mission is divided into subtasks that can be completed by a single UAV in a single flight. On this basis, the particle swarm algorithm is used to optimize the task allocation, so as to achieve the goal of UAV plant protection route and task division with minimum time loss. In order to verify the effectiveness of the method in this paper, 3 and 4 UAVs flying defense teams were used as the experimental objects, and experiments were carried out in large and small field groups. In terms of time consumption, the method in this paper is compared with the unit operation area method and the single size method. The energy consumption advantage is weaker than time consumption. In terms of time consumption, the method in this paper can greatly improve the efficiency of large-area operation areas. Compared with the unit operation area method, 3 drones can increase the efficiency by 58.98%, which is increased by 10.22% compared with the unit size method. The efficiency of 4 units can be increased by 58.08% compared with the unit operation area method, and the efficiency is increased by 10.22% compared with the unit size method. [ABSTRACT FROM AUTHOR]

Topping is an important part in cotton field management, the spraying time has a great impact on cotton quality. In agricultural production, the strategy of timing the cotton topping mainly relies on manual inspections and experience, which is lack of efficiency and science. To solve the problem, this paper uses a drone equipped with a multispectral camera to collect the multispectral information of the cotton canopy of 12 days which includes before and after the topping operation in Shihezi. At the same time, the information of cotton plant height, the number of fruiting branches, and flower buds are collected. Compare multiple band combinations and vegetation index; the combined data of 550 + 730 + 790 nm band is selected as the model input. AdaBoost + decision tree method is proposed as a fitting model, the fitting results show that the coefficient of determination (R2) between multispectrum and cotton plant height is 0.96, and the average prediction error (RMSEP) is 0.40 cm, the coefficient of determination (R2) between multispectrum information and the fruiting branches is 0.97, the prediction mean error (RMSEP) is 0.54, and the correlation determination (R2) with the flower buds is 0.84, and the prediction mean error is (RMSEP) 0.49. The output data of the fitting model is used as the input of the topping time discriminant model, and the discriminant model can obtain an accuracy of 94.03%. The method in this paper can effectively monitor the growth status of cotton in the topping time and provide a technical path to scientifically determine the cotton topping time. [ABSTRACT FROM AUTHOR]

People need help to recover after crises. With the help of information and communication technologies (ICTs), people can engage in resilience collaboratively. Specifically, they can develop new routines to access various infrastructures and meet their societal needs via ICTs. Therefore, the role of ICTs in supporting collaborative resilience needs further examination and clarification. In this study, we use actor-network theory (ANT) to understand collaborative resilience during and after a war, which is a specific kind of crisis that may last a long time. We revisit a published case to clarify how ICTs help people develop new routines so that their societal needs still can be met. We find that a number of issues suggested by ANT, such as problematisation and translation, contribute significantly to the process and outcomes of the case. We provide practical implications to present the essence of our findings. Overall, our study suggests that an ANT-informed understanding can help practitioners better understand the role of ICTs in supporting collaborative resilience. [ABSTRACT FROM AUTHOR]

Aims Ketamine analgesia is limited by low intrinsic efficacy compounded by large interindividual variability in drug responses, possibly due to the heterogeneity in drug concentration. The CYP2B6* 6 allele is associated with substantially reduced ketamine metabolism in vitro and, therefore, may affect ketamine clearance. Our aims were to examine the impact of the CYP2B6* 6 allele on ketamine plasma clearance and on adverse effects in chronic pain patients. Methods CYP2B6 genotypes were identified in 49 chronic pain patients who received 24 h continuous subcutaneous infusions of ketamine. Steady-state plasma concentrations of ketamine ( Css,k) and norketamine ( Css,nk) were determined using HPLC. Results The median plasma clearance of ketamine after 100 mg 24 h-1 dose was significantly lower in patients with the CYP2B6* 6/* 6 (21.6 l h-1) and CYP2B6* 1/* 6 (40.6 l h-1) genotypes compared with patients with the CYP2B6* 1/* 1 genotype (68.1 l h-1, P < 0.001). The ketamine : norketamine plasma metabolic ratio was significantly higher in patients with the CYP2B6* 6/* 6 genotype than in those with the CYP2B6* 1/* 6 and the CYP2B6* 1/* 1 genotypes ( P < 0.001). Patients who experienced adverse effects had lower plasma clearance (45.6 l h-1) than those who did not (52.6 l h-1, P = 0.04). The CYP2B6* 6 genotype and age, and their combined impact explained 40%, 30% and 60% of the variation in Css,k, respectively. Similar results were observed after higher doses. Conclusions The CYP2B6* 6 allele is associated with a substantial decrease in steady-state ketamine plasma clearance in chronic pain patients. The decreased clearance and resultant higher plasma concentrations may be associated with a higher incidence of ketamine adverse effects. [ABSTRACT FROM AUTHOR]

Incorporating social justice (SJ) in business education is gaining attention amid criticism arguing that business school curricula remain too narrowly focused on maximizing profits and shareholder wealth. The authors examine the applicability of an established scale for measuring attitudes, values, and beliefs about SJ as an assessment tool in this context. They illustrate how the Social Justice Scale (SJS) can be used to understand business students' perceptions and intentions, evaluate its psychometric properties, and suggest changes for scale refinement. The study supports using the SJS to develop effective strategies for integrating SJ into business curricula. [ABSTRACT FROM AUTHOR]

Social networking sites (SNSs) allow users to connect with each other by overcoming geographical and temporal boundaries and thus empower people to search for social support from online. Social support has been considered a key social value that online users can obtain from SNSs. However, few studies have systematically investigated social support in such a context. Motivated to address this gap, we have developed an advanced and theoretical framework to delineate social support on SNSs by clearly revealing the dimensions of online social support on SNSs and examining their effects on users' commitment and SNS continuance. Further, we introduce gender as a key moderator and explain in theory how differently men and women perceive the importance of the dimensions in evaluating online social support over SNSs. Our research results indicate that the identified three dimensions (informational support, emotional support, and network management) are important components of the online social support on SNSs, which is positively associated with commitment and continuance. In addition, the weight of each dimension in the evaluation of online social support varies by gender. This study is among the very first to explore online social support in the context of SNS and its effects, and has rich theoretical and practical implications. [ABSTRACT FROM AUTHOR]