Research collaboration between China and Denmark for development of systemic approaches to agro-ecological pest management without pesticides with focus on vegetable, fruit and berry crops. Proceedings and recommendations from two network workshops

This report is the result of a network project which was established to discuss the potential for collaboration on development of systemic approaches to pest management without pesticides between Chinese and Danish researchers. The focus is on systemic approaches rather than input substitution of synthetic chemicals with agents of natural origin, however, the latter is considered as an integrated tool for the development and design of systemic approaches. The discussions were, furthermore, limited to management of invertebrate pests as well as diseases, while other pests such as weeds have not been included in the discussions. The discussions took place at two workshops and were based on presentations of research from the two countries and field visits in China and Denmark. After the first workshop that took place in China, it was agreed that Chinese and Danish researchers in this particular field had mutual interests and priorities and that there was a potential for creating collaboration that could yield results beneficial for the agricultural/horticultural sectors in both countries. It was also agreed that in spite of the many differences between variation in climate and ecosystems, as well as in farming systems and their organization in China and Denmark, there were many similarities in the production of high-value crops in the two countries, such as vegetables, fruit and berries and, therefore, an obvious focus for joint research efforts. It was also agreed that joint research efforts could aim at specific crops as well as aiming at the development of specific research approaches. Based on the observations and the agreements of the first workshop, the second workshop, which took place in Denmark, focused more specifically on the development of a research framework with specified research questions/topics. Two groups were formed – one working with vegetables and one with fruit and berries working in parallel – both looking into what kind of research is needed for development of systemic approaches to pesticide-free pest management should include both well-known practices and new practices. Although the discussions in the two groups took separate routes and unfolded and described the research topics in each their way, there was a clear consistency between the outputs of the work of the two groups. Each had identified three main research themes that more or less followed the same line and has been merged into three specific recommendations on themes for collaboration, namely: 1) ‘Research to provide the biological foundation and understanding of mechanisms and interactions for development of non-chemical solutions and to improve efficiency of new and existing control methods for severe pest problems’. 2) Research in ‘How best to integrate multifunctional plants (and crops) and use diversification to create a more healthy and productive farming system which is resilient to pests?’ 3) Research in ‘How to design and integrate pest management in eco-functional cropping systems at field and farm/landscape level?

Machine learning-assisted prediction of pneumonia based on non-invasive measures

BackgroundPneumonia is an infection of the lungs that is characterized by high morbidity and mortality. The use of machine learning systems to detect respiratory diseases via non-invasive measures such as physical and laboratory parameters is gaining momentum and has been proposed to decrease diagnostic uncertainty associated with bacterial pneumonia. Herein, this study conducted several experiments using eight machine learning models to predict pneumonia based on biomarkers, laboratory parameters, and physical features.MethodsWe perform machine-learning analysis on 535 different patients, each with 45 features. Data normalization to rescale all real-valued features was performed. Since it is a binary problem, we categorized each patient into one class at a time. We designed three experiments to evaluate the models: (1) feature selection techniques to select appropriate features for the models, (2) experiments on the imbalanced original dataset, and (3) experiments on the SMOTE data. We then compared eight machine learning models to evaluate their effectiveness in predicting pneumoniaResultsBiomarkers such as C-reactive protein and procalcitonin demonstrated the most significant discriminating power. Ensemble machine learning models such as RF (accuracy = 92.0%, precision = 91.3%, recall = 96.0%, f1-Score = 93.6%) and XGBoost (accuracy = 90.8%, precision = 92.6%, recall = 92.3%, f1-score = 92.4%) achieved the highest performance accuracy on the original dataset with AUCs of 0.96 and 0.97, respectively. On the SMOTE dataset, RF and XGBoost achieved the highest prediction results with f1-scores of 92.0 and 91.2%, respectively. Also, AUC of 0.97 was achieved for both RF and XGBoost models.ConclusionsOur models showed that in the diagnosis of pneumonia, individual clinical history, laboratory indicators, and symptoms do not have adequate discriminatory power. We can also conclude that the ensemble ML models performed better in this study

The role of green tea intake in thromboprophylaxis of venous thromboembolism in patients with cancer

BackgroundGreen tea intake has been reported to improve the clinical outcomes of patients with cardiovascular diseases or cancer. It may have a certain role in the development of venous thromboembolism (VTE) among cancer patients. The current study aimed to address this issue, which has been understudied.MethodsWe carried out a retrospective study to explore the role of green tea intake in cancer patients. Patients with and without green tea intake were enrolled in a 1:1 ratio by using propensity scoring matching. The primary and secondary outcomes were VTE development and mortality 1 year after cancer diagnosis, respectively.ResultsThe cancer patients with green tea intake (n = 425) had less VTE development (10 [2.4%] vs. 23 [5.4%], p = 0.021), VTE-related death (7 [1.6%] vs. 18 [4.2%], p = 0.026), and fatal pulmonary embolism (PE) (3 [0.7%] vs. 12 [2.8%], p = 0.019), compared with those without green tea intake (n = 425). No intake of green tea was correlated with an increase in VTE development (multivariate hazard ratio (HR) 1.758 [1.476–2.040], p < 0.001) and VTE-related mortality (HR 1.618 [1.242–1.994], p = 0.001), compared with green tea intake. Patients with green tea intake less than 525 mL per day had increased VTE development (area under the curve (AUC) 0.888 [0.829–0.947], p < 0.001; HR1.737 [1.286–2.188], p = 0.001) and VTE-related mortality (AUC 0.887 [0.819–0.954], p < 0.001; HR 1.561 [1.232–1.890], p = 0.016) than those with green tea intake more than 525 mL per day. Green tea intake caused a decrease in platelet (p < 0.001) instead of D-dimer (p = 0.297). The all-cause mortality rates were similar between green tea (39 [9.2%]) and non-green tea (48 [11.3%]) intake groups (p = 0.308), whereas the VTE-related mortality rate in the green tea intake group (7 [1.6%]) was lower than that of the non-green tea intake group (18 [4.2%]) (p = 0.026). The incidences of adverse events were similar between the green tea and non-green tea intake groups.ConclusionIn conclusion, the current study suggests that green tea intake reduces VTE development and VTE-related mortality in cancer patients, most likely through antiplatelet mechanisms. Drinking green tea provides the efficacy of thromboprophylaxis for cancer patients

Photocatalytic abstraction of hydrogen atoms from water using hydroxylated graphitic carbon nitride for hydrogenative coupling reactions

Employing pure water, the ultimate green source of hydrogen donor to initiate chemical reactions that involve a hydrogen atom transfer (HAT) step is fascinating but challenging due to its large H−O bond dissociation energy (BDEH-O=5.1 eV). Many approaches have been explored to stimulate water for hydrogenative reactions, but the efficiency and productivity still require significant enhancement. Here, we show that the surface hydroxylated graphitic carbon nitride (gCN−OH) only requires 2.25 eV to activate H−O bonds in water, enabling abstraction of hydrogen atoms via dehydrogenation of pure water into hydrogen peroxide under visible light irradiation. The gCN−OH presents a stable catalytic performance for hydrogenative N−N coupling, pinacol-type coupling and dehalogenative C−C coupling, all with high yield and efficiency, even under solar radiation, featuring extensive impacts in using renewable energy for a cleaner process in dye, electronic, and pharmaceutical industries

A Novel Fault Diagnosis Method for Diesel Engine Based on MVMD and Band Energy

Vibration signal, as an important means for diesel engine condition detection and fault diagnosis, has attracted attention for many years. In traditional vibration signal analysis, most processing methods are for single-channel data. However, single-channel vibration signal cannot reflect the operating information of the diesel engine comprehensively because diesel engine vibration is coupled by multiple source signals. This paper proposes the MVMD band energy method for fault diagnosis by four channels of vibration signals. First, the original multivariate signals are decomposed adaptively by MVMD, which obtains a series of components with modal alignment. Then, the band energy values of each measuring point are calculated as the fault characteristics. Finally, SVM is used to realize the diagnosis and identification of diesel engine misfire. The working conditions have a great influence on the vibration signal of the cylinder. In order to obtain the best diagnostic working conditions, six working conditions are set for testing. The result shows that the fault identification rate is highest under the 1500 rpm and 50% load working condition. The fault recognition rate of this method reaches more than 99%, which is superior to the other four common methods

Electrical Phase Control Based on Graphene Surface Plasmon Polaritons in Mid-infrared

Phase modulation of light is the core of many optoelectronic applications, such as electro-optic switch, sensors and modulators. Graphene Surface plasmon polaritons (SPPs) exhibit unique properties in phase modulation including dynamic tunability, a small driving voltage and small device size. In this paper, the novel phase modulation capability of graphene SPPs in mid-infrared are confirmed through theory and simulation. The results show that graphene SPPs can realize continuous tuning of the phase shift at multiple wavelengths in mid-infrared, covering the phase range from 0° to 360°. Based on these results, a sandwich waveguide structure of dielectric–graphene–dielectric with a device length of 800 nm is proposed, which shows up to 381° phase modulation range at an operating wavelength of 6.55 µm, given a 1 V driving voltage. In addition, the structure size is much shorter than the wavelength in mid-infrared and can realize sub-wavelength operation. This work paves the way to develop graphene-based tunable devices for mid-infrared wave-front control

Optimization of the Winding Layer Structure of High-Pressure Composite Overwrapped Pressure Vessels

The large thickness COPV is designed by netting theory and the finite element simulation method, but the actual performance is low and the cylinder performance still cannot be improved after increasing the thickness of the composite winding layer. This paper analyzes the reasons for this and puts forward a feasible solution: without changing the thickness of the winding layer, the performance of COPV can be effectively increased by increasing the proportion of annular winding fiber. This method has been verified by tests and is supported by theory

Thermal characteristic of GaAs-based laser diodes

In order to analyze the thermal characteristic of GaAs-based laser diodes during degradation, aging tests were carried out under the conditions of the constant current stress for808 nm GaAs-based laser diodes. The temperature of active layer and the thermal resistance were investigated by using electrical method. It was found that the temperature of active layer raise with the increase of aging time, while thermal resistance had not changed during aging tests. At the same time, the electrical and optical properties were measured, which indicated that the main reason for degradation was the increase of nonradiative recombination in the active layer. The results show that the degradation of the laser diodes can be observed effectively through thermal property measuring by using electrical method. The experimental results establish the foundation of improving the thermal management technology and thermal properties of laser diodes