Search

Abstract Agricultural social services (ASS) play an important role in improving the efficiency of agricultural operations, reducing agricultural production costs, and promoting sustainable agricultural development. Using data from the 2020 China Rural Revitalization Survey, this study analyzes the impact of ASS on reducing pesticide inputs. The results show: (1) ASS play a significantly positive role in reducing pesticide inputs. (2) Heterogeneity analyses show that ASS’ role in reducing pesticide inputs is stronger for farming households with small farms, which participate in cooperatives, and do not have members involved in non-farm employment than that for farming households with large farms, which do not participate in cooperatives, and have members involved in non-farm employment. (3) Mechanism analysis shows that ASS’ green perception and demonstration-led effects contribute to reducing pesticide inputs by 148.6% and 36.8%, respectively, at the 1% level. Finally, this study proposes relevant policy recommendations for promoting ASS, promoting the continuous operation of farmland, and encouraging farmers to participate in ASS.

Fertilizers and pesticides are important agricultural production materials. Excessive use of fertilizers and pesticides can cause negative impacts such as decline in the quality of farm products and damage to the ecological environment. Increasing the efficiency of fertilizers and pesticides is an important step to accelerate the green transformation of agriculture. Using data from the 2015 China Household Finance Survey, this paper analyzes the impacts of Internet use on fertilizers and pesticides by farmers, and the mechanism behind the action with the help of the propensity score matching method and the instrumental variable method. The findings are: (1) Farmers who use the Internet have greater inputs of fertilizers and pesticides compared to farmers who do not use the Internet. (2) Farmers who do not use the Internet would have their inputs of fertilizers and pesticides increased by 21.9% and 47.7%, respectively, if they had used the Internet under the counterfactual hypothesis. (3) In the heterogeneity analysis, Internet use plays a positive and significant role in the inputs of fertilizer and pesticide in the eastern part of China, and Internet use has greater impact on pesticide inputs than on fertilizer in central and western regions. (4) Internet use has a positive and significant effect on fertilizer and pesticide inputs in both lower scale and higher scale operations, and a positive but not significant effect in moderate-scale operations.

AIM:To evaluate the application effect of iris hooks combined with suture fixation of capsular tension ring(CTR)in the treatment of phacoemulsification with secondary glaucoma associated with lens subluxation.METHODS: Retrospective case series. A total of 18 eyes of patients with serious secondary glaucoma associated with lens subluxation were enrolled in Aier Eye Hospital(Changchun)from October 2017 to May 2020 as the observation group. All the patients had iris hooks inserted to support the capsule during the phacoemulsification combined with intraocular lens(IOL)implantation. Iris hooks were inserted through the incisions and placed in the capsulorhexis to support the capsule, then CTR was implanted and sutured onto the sclera of the dislocation side. A total of 11 eyes of patients had intracapsular cataract extraction combined with IOL suspension were enrolled in Aier Eye Hospital(Changchun)from October 2015 to October 2017 as the control group. The postoperative complications were recorded. The postoperative examinations included visual acuity, intraocular pressure(IOP)and positions of the capsule and IOL.RESULTS: The postoperative follow-up at least 12(12-40)mo, both the IOP of observation group and control group returned to normal, with statistically significant difference compared with pre-operation(t=9.994, 8.790, all P

The technological evolution has formed new challenges for organizations to safeguard their information as digital assets. Information Security Awareness (ISA) is the cognitive state where individuals comprehend information security, threats, and the capability to develop preventive strategies. Prior studies discovered that human mistakes or misbehavior is the most vulnerable link in information security due to insufficient security awareness. There were massive data breaches reported throughout the years globally. Literature shows that individuals will develop their evaluations of risks and sense of security awareness when receiving security risk information such as data breach incidents. These indications motivated us to examine the effect of an unexplored factor, that is, data breach publicity (DBR) on ISA. The purpose of this research is to discover if DBR significantly improves a model’s ability to predict ISA and its magnitude in influencing ISA. A 3-stage hierarchical linear regression approach was used to build up the model with prior known influential factors to predict ISA. To the extent of our knowledge, there is no study reported to date regarding the implication of DBR on ISA. Our main findings reveal that DBR significantly explains 6.7% of ISA and achieves the highest coefficient comparing with prior known factors. Our research contributes to a novel discovery of a new factor that significantly influences ISA and its magnitude in increasing ISA. This discovery implies the need to incorporate the knowledge of data breach incidents into ISA-related educative programs or strategies to increase ISA.

Background. Coronavirus disease 2019 (COVID-19) has been a global threat that pushes healthcare to its limits. Hypertension is one of the most common risk factors for cardiovascular complications in COVID-19 and is strongly associated with disease severity and mortality. To date, clinical mechanisms by which hypertension leads to increased risk in COVID-19 are still unclear. Furthermore, additional factors might increase these risks, such as the consideration of age and sex, which are of interest when in search of personalized treatments for hypertensive COVID-19 patients. Methods. We conducted a retrospective cohort study of 543 COVID-19 patients in seven provinces of China to examine the epidemiological and clinical characteristics of COVID-19 in this population and to determine risk factors of hypertensive COVID-19 patients. We also used univariable and multivariable logistic regression methods to explore the risk factors associated with hypertensive COVID-19 patients in different age and sex subgroups. Results. Among the enrolled COVID-19 patients, the median age was 47 years (interquartile range (IQR) 34.0–57.0), and 99 patients (18.23%) were over 60 years old. With regard to comorbidities, 91 patients (16.75%) were diagnosed with hypertension, followed by diabetes, coronary disease, and cerebrovascular disease. Of the hypertensive COVID-19 patients, 51 (56.04%) were male. Multivariable analysis showed that old age, comorbid diabetes or coronary heart disease on admission, increased D-dimer, increased glucose, and decreased lymphocyte count were independent risk factors associated with hypertensive COVID-19 patients. Elevated total bilirubin (odds ratio [OR]: 1.014, 95% confidence interval [CI]: 0.23–1.05; p=0.043) and triglycerides (OR: 1.173, 95% CI: 0.049–1.617; p=0.007) were found to be associated with elderly hypertensive COVID-19 patients. In addition, we found that decreased lymphocytes, basophil, high-density lipoprotein, and increased fibrinogen and creatinine were related to a higher risk of disease severity in male patients. The most common abnormal clinical findings pertaining to female hypertensive COVID-19 patients were hemoglobin, total bile acid, total protein, and low-density lipoprotein. Conclusions. Factors associated with increased risk of hypertensive COVID-19 patients were identified. Results to the different age and sex subgroups in our study will allow for better possible personalized care and also provide new insights into specific risk stratification, disease management, and treatment strategies for COVID-19 patients with hypertension in the future.

Medical science-related studies have reinforced that the prevalence of coronary heart disease which is associated with the heart and blood vessels has been the most significant cause of health loss and death globally. Recently, data mining and machine learning have been used to detect diseases based on the unique characteristics of a person. However, these techniques have often posed challenges due to the complexity in understanding the objective of the datasets, the existence of too many factors to analyze as well as lack of performance accuracy. This research work is of two-fold effort: firstly, feature extraction and selection. This entails extraction of the principal components, and consequently, the Correlation-based Feature Selection (CFS) method was applied to select the finest principal components of the combined (Cleveland and Statlog) heart dataset. Secondly, by applying datasets to three single and three ensemble classifiers, the best hyperparameters that reflect the pre-eminent predictive outcomes were investigated. The experimental result reveals that hyperparameter optimization has improved the accuracy of all the models. In the comparative studies, the proposed work outperformed related works with an accuracy of 97.91%, and an AUC of 0.996 by employing six optimal principal components selected from the CFS method and optimizing parameters of the Rotation Forest ensemble classifier.

Background and Purpose. Diabetes is common in COVID-19 patients and associated with unfavorable outcomes. We aimed to describe the characteristics and identify the risk factors for COVID-19 patients complicated with diabetes. Methods. In this multicenter retrospective study, patients with COVID-19 in China were included and classified into two groups according to whether they were complicated with diabetes or not. Demographic symptoms and laboratory data were extracted from medical records. Univariable and multivariable logistic regression methods were used to explore the risk factors. Results. 538 COVID-19 patients were finally included in this study, of whom 492 were nondiabetes and 46 were diabetes. The median age was 47 years (IQR 35.0-56.0). And the elderly patients with diabetes were more likely to have dry cough, and the alanine aminotransferase, lactate dehydrogenase, Ca, and mean hemoglobin recovery rate were higher than the other groups. Furthermore, we also found the liver and kidney function of male patients was worse than that of female patients, while female cases should be paid more attention to the occurrence of bleeding and electrolyte disorders. Moreover, advance age, blood glucose, gender, prothrombin time, and total cholesterol could be considered as risk factors for COVID-19 patients with diabetes through the multivariable logistic regression model in our study. Conclusion. The potential risk factors found in our study showed a major piece of the complex puzzle linking diabetes and COVID-19 infection. Meanwhile, focusing on gender and age factors in COVID-19 patients with or without diabetes, specific clinical characteristics, and risk factors should be paid more attention by clinicians to figure out a targeted intervention to improve clinical efficacy worldwide.

Cardiovascular diseases (CVDs) kill about 20.5 million people every year. Early prediction can help people to change their lifestyles and to ensure proper medical treatment if necessary. In this research, ten machine learning (ML) classifiers from different categories, such as Bayes, functions, lazy, meta, rules, and trees, were trained for efficient heart disease risk prediction using the full set of attributes of the Cleveland heart dataset and the optimal attribute sets obtained from three attribute evaluators. The performance of the algorithms was appraised using a 10-fold cross-validation testing option. Finally, we performed tuning of the hyperparameter number of nearest neighbors, namely, ‘k’ in the instance-based (IBk) classifier. The sequential minimal optimization (SMO) achieved an accuracy of 85.148% using the full set of attributes and 86.468% was the highest accuracy value using the optimal attribute set obtained from the chi-squared attribute evaluator. Meanwhile, the meta classifier bagging with logistic regression (LR) provided the highest ROC area of 0.91 using both the full and optimal attribute sets obtained from the ReliefF attribute evaluator. Overall, the SMO classifier stood as the best prediction method compared to other techniques, and IBk achieved an 8.25% accuracy improvement by tuning the hyperparameter ‘k’ to 9 with the chi-squared attribute set.

BackgroundA worldwide outbreak of coronavirus disease (COVID-19), since 2019, has brought a disaster to people all over the world. Many researchers carried out clinical epidemiological studies on patients with COVID-19 previously, but risk factors for patients with different levels of severity are still unclear.Methods562 patients with laboratory-confirmed COVID-19 from 12 hospitals in China were included in this retrospective study. Related clinical information, therapies, and imaging data were extracted from electronic medical records and compared between patients with severe and non-severe status. We explored the risk factors associated with different severity of COVID-19 patients by logistic regression methods.ResultsBased on the guideline we cited, 509 patients were classified as non-severe and 53 were severe. The age range of whom was 5-87 years, with a median age of 47 (IQR 35.0-57.0). And the elderly patients (older than 60 years old) in non-severe group were more likely to suffer from fever and asthma, accompanied by higher level of D-dimer, red blood cell distribution width and low-density lipoprotein. Furthermore, we found that the liver and kidney function of male patients was worse than that of female patients in both severe and non-severe groups with different age levels, while the severe females had faster ESR and lower inflammatory markers. Of major laboratory markers in non-severe cases, baseline albumin and the lymphocyte percentage were higher, while the white blood cell and the neutrophil count were lower. In addition, severe patients were more likely to be accompanied by an increase in cystatin C, mean hemoglobin level and a decrease in oxygen saturation. Besides that, advanced age and indicators such as count of white blood cell, glucose were proved to be the most common risk factors preventing COVID-19 patients from aggravating.ConclusionThe potential risk factors found in our study have shown great significance to prevent COVID-19 patients from aggravating and turning to critical cases during treatment. Meanwhile, focusing on gender and age factors in groups with different severity of COVID-19, and paying more attention to specific clinical symptoms and characteristics, could improve efficacy of personalized intervention to treat COVID-19 effectively.

AIM: To observe the clinical effect of posterior continuous curvilinear capsulorhexis(PCCC)in phacoemulsification with posterior capsular rupture. METHODS: Thrity-eight age-related cataract patients(38 eyes)from March 2013 to October 2013 were selected as experimental group and 50 age-related cataract patients(50 eyes)from March 2013 to October 2013 as control group. In experimental group, PCCC were used in the intraoperative posterior capsule tears in phacoemulsification. And in control group phacoemulsification was applied. The visual acuity and surgical complications were compared between two groups, the follow-up period was continued to 3mo after operation.RESULTS: The visual acuity and corneal edema in two groups had statistically significant(PP>0.05). At 1d after operation, the intraocular pressure in two groups was not statistically significant(P>0.05). Three months after operation, the pupil and cystoid macular edema, retinal detachment in two groups was not statistically significant(P>0.05).CONCLUSION: PCCC can improve the therapeutic effect and prevent surgical complications for phacoemulsification with posterior capsular rupture.

AIM: To evaluate the effect of all capsule polishing with polishing mode for decreasing the occurrence of posterior capsular opacification(PCO).METHODS: Totally 194 eyes(162 patients)were performed phacoemulsification, posterior chamber intraocular lens were implanted. One hundred eyes were performed with all capsule polishing with polishing mode; 94 eyes were performed with the polishing apparatus.RESULTS: The patients were followed up for 12mo.Six eyes(6.0%)of PCO were found in the 100 eyes with polishing mode group; 15 eyes(16.0%)of PCO were found in the 94 eyes with the polishing apparatus group. The difference was significant(PCONCLUSION: It can decrease the occurrence of PCO by performing all capsule polishing with polishing mode.

AIM:To explore safety and effectiveness of stratified emulsion splitting technology in highly hard nuclear cataract ultrasonic emulsification. METHODS: Totally 43 cases(47 eyes)of highly hard nuclear cataract was performed cataract extraction combined IOL implantation with stratified emulsion splitting cataract ultrasonic emulsification technology. RESULTS:Postoperative visual acuity 1 day, 1 week and 1 month best-corrected visual acuity ≥0.5 was accounted for 70.21%, 87.23% and 89.36%. Intraoperative capsule rupture was found in 2 eyes. Corneal mild edema was found in 7 eyes(14.89%)after the first l day post-operation. CONCLUSION:Stratified emulsion splitting technique has a practical application with little tissue damage, low capsular rupture rate and few complications for highly hard nuclear cataract patients and can bring a desirable outcome.