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2022 ◽  
Vol 326 ◽  
pp. 107810
Author(s):  
Rémi Duflot ◽  
Magali San-Cristobal ◽  
Emilie Andrieu ◽  
Jean-Philippe Choisis ◽  
Diane Esquerré ◽  
...  

Author(s):  
Özerk Yavuz

Epidemic diseases can be extremely dangerous with its hazarding influences. They may have negative effects on economies, businesses, environment, humans, and workforce. In this paper, some of the factors that are interrelated with COVID-19 pandemic have been examined using data mining methodologies and approaches. As a result of the analysis some rules and insights have been discovered and performances of the data mining algorithms have been evaluated. According to the analysis results, JRip algorithmic technique had the most correct classification rate and the lowest root mean squared error (RMSE). Considering classification rate and RMSE measure, JRip can be considered as an effective method in understanding factors that are related with corona virus caused deaths.


Coronavirus has greatly impacted various aspects of human life, including human psychology & human disposition. In this paper, we attempted to analyze the impact of the COVID-19 pandemic on human health. We propose Human Disposition Analysis during COVID-19 using machine learning (HuDA_COVID), where factors such as age, employment, addiction, stress level are studied for human disposition analysis. A mass survey is conducted on individuals of various age groups, regions & professions, and the methodology achieved varied accuracy ranges of 87.5% to 98%. The study shows people are worried about lockdown, work & relationships. Furthermore, 23% of the respondents have not had any effect. 45% and 32% have had positive and negative effects, respectively. It is a novel study in human disposition analysis in COVID-19 where a novel weighted assignment indicating the health status is also proposed. HuDA_COVID clearly indicates a need for a methodical approach towards the human psychological needs to help the social organizations formulating holistic interventions for affected individuals.


2023 ◽  
Vol 83 ◽  
Author(s):  
P. S. Silva ◽  
V. A. Royo ◽  
H. M. Valerio ◽  
E. G. Fernandes ◽  
M. V. Queiroz ◽  
...  

Abstract Interactions between endophytic fungi (EFs) and their host plants range from positive to neutral to negative. The results of such interactions can vary depending on the organ of the infected host plant. EFs isolated from the leaves of some species of plants have potential for use as agents to inhibit seed germination and control invasive plants. The objectives of this study were to identify EFs present in the leaves of Copaifera oblongifolia and to evaluate the role of these fungi in seed germination and seedling development. A total of 11 species of EFs were isolated, which were identified using the internal transcribed spacers (ITS) sequence of the nuclear ribosomal DNA. The isolated species of EFs are generalists and probably are transmitted horizontally. Laboratory tests revealed that filtrates of these fungal isolates differently affect seed germination and seedling development of C. oblongifolia. The species Curvularia intermedia, Neofusicoccum parvum, Pseudofusicoccum stromaticum and Phomopsis sp. negatively affected seed germination, with N. parvum standing out for its negative effects, inhibiting seedling germination and survival in 89 and 222%, respectively. In addition, Cochliobolus intermedius negatively affected seedling development. Thus, the combined use of N. parvum and C. intermedius, or products from the metabolism of these microorganisms, in the control of invasive plants deserves attention from future studies.


Author(s):  
Charu Gupta ◽  
Dev Gaur ◽  
Prateek Agrawal ◽  
Deepali Virmani

Coronavirus has greatly impacted various aspects of human life, including human psychology & human disposition. In this paper, we attempted to analyze the impact of the COVID-19 pandemic on human health. We propose Human Disposition Analysis during COVID-19 using machine learning (HuDA_COVID), where factors such as age, employment, addiction, stress level are studied for human disposition analysis. A mass survey is conducted on individuals of various age groups, regions & professions, and the methodology achieved varied accuracy ranges of 87.5% to 98%. The study shows people are worried about lockdown, work & relationships. Furthermore, 23% of the respondents have not had any effect. 45% and 32% have had positive and negative effects, respectively. It is a novel study in human disposition analysis in COVID-19 where a novel weighted assignment indicating the health status is also proposed. HuDA_COVID clearly indicates a need for a methodical approach towards the human psychological needs to help the social organizations formulating holistic interventions for affected individuals.


Author(s):  
Chuyun Hu ◽  

Since its outbreak in late 2019, the COVID-19 (the new coronavirus pandemic disease) has spread throughout the globe at an unexpectedly rapid pace. It brought severe negative effects to all walks of life, and this paper analyzes especially its impacts on Chinese students studying or planning to study abroad by sending out a survey. As the United States has become the country with most confirmed cases as well as most related deaths since May 27th, 2020, the survey mainly focused on the Chinese students planning to study in the U.S. It asked the respondents about their decisions regarding their plan of studying abroad, and the reasons behind them by different scales of significance. Considering that the questions of the survey are relatively detailed and that the number of respondents (269) is limited, this paper applied qualitative analysis to the study. The hypothesis is that students making different decisions (generally either choosing in-person study or virtual/online study) are impacted by different considerations, which is generally tested as effective. However, the epidemic-related elements are the most influential among all options for those who decide not to go abroad for their studies. The result of the study is partially consistent with the hypothesis that the number of students staying home exceeds that of students going abroad. There are also unexpected outcomes, including that the deteriorating U.S.-China relationship plays an overwhelming part in the avoidance of going to the States.


2022 ◽  
Vol 185 ◽  
pp. 111246
Author(s):  
Jessica D. Ayers ◽  
Diego Guevara Beltrán ◽  
Andrew Van Horn ◽  
Lee Cronk ◽  
Peter M. Todd ◽  
...  

2022 ◽  
Vol 6 (1) ◽  
pp. 1-24
Author(s):  
Liuwang Kang ◽  
Haiying Shen

For a modern vehicle, if the sensor in a vehicle anti-lock braking system (ABS) or controller area network (CAN) bus is attacked during a brake process, the vehicle will lose driving direction control and the driver’s life will be highly threatened. However, current methods for detecting attacks are not sufficiently accurate, and no method can provide attack mitigation. To ensure vehicle ABS security, we propose an attack detection method to accurately detect both sensor attack (SA) and CAN bus attack in a vehicle ABS, and an attack mitigation strategy to mitigate their negative effects on the vehicle ABS. In our attack detection method, we build a vehicle state space equation that considers the real-time road friction coefficient to predict vehicle states (i.e., wheel speed and longitudinal brake force) with their previous values. Based on sets of historical measured vehicle states, we develop a search algorithm to find out attack changes (vehicle state changes because of attack) by minimizing errors between the predicted vehicle states and the measured vehicle states. In our attack mitigation strategy, attack changes are subtracted from the measured vehicle states to generate correct vehicle states for a vehicle ABS. We conducted the first real SA experiments to show how a magnet affects sensor readings. Our simulation results demonstrate that our attack detection method can detect SA and CAN bus attack more accurately compared with existing methods, and also that our attack mitigation strategy almost eliminates the attack’s effects on a vehicle ABS.


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