Tree spatial structure, host composition and resource availability influence mirid density or black pod prevalence in cacao agroforests in Cameroon.

Impact of removing diseased pods on cocoa black pod caused by Phytophthora megakarya and on cocoa production in Cameroon

Black pod rot, caused by Phytophthora megakarya, is the main cause of cocoa losses in Cameroon. A few studies have focused on describing black pod epidemics in cocoa yet numerous questions remain. Here, an epidemiological model describing the temporal evolution of cocoa black pod, taking into account the development stages of pods, is developed and studied. In particular, the relative importance of primary and secondary infection in disease dynamics is investigated. Our theoretical study shows the existence of a disease free equilibrium and at least one endemic equilibrium. We highlight two threshold parameters, related to direct and indirect infections that summarize all possible dynamics of the system. Then, based on the literature, we define a periodic pod recruitment function and provide several numerical simulations to study the impact of phytosanitary pod removal on disease dynamics. We show that intense and regular sanitary harvest could lead to complete disease eradication. Our results also highlight the importance of the environmental spores reservoir in disease dynamics, such that future field experiments and observations should focus on it.

A method for describing and analyzing selection decisions is applied to the problem of the acceptance and admission of patients referred to psychiatric hospitals. The model directs attention to the separate decision points that constitute the overall selection process and permits an examination of the relative importance of different variables at different points in this process. The total populations of referrals to two psychiatric hospitals over an elevenweek period were followed to determine those persons who were accepted, or not accepted; and those admitted or not admitted to the hospital. These sub-groups were compared with each other on a number of social characteristics. Differences and similarities among these groups reflect the selection criteria of the institutions and indicate the ways in which the populations of referred, accepted, and admitted patients are related to each other.

Many studies have examined how fuels, topography, climate, and fire weather influence fire severity. Less is known about how different forest management practices influence fire severity in multi-owner landscapes, despite costly and controversial suppression of wildfires that do not acknowledge ownership boundaries. In 2013, the Douglas Complex burned over 19,000ha of Oregon & California Railroad (O&C) lands in Southwestern Oregon, USA. O&C lands are composed of a checkerboard of private industrial and federal forestland (Bureau of Land Management, BLM) with contrasting management objectives, providing a unique experimental landscape to understand how different management practices influence wildfire severity. Leveraging Landsat based estimates of fire severity (Relative differenced Normalized Burn Ratio, RdNBR) and geospatial data on fire progression, weather, topography, pre-fire forest conditions, and land ownership, we asked (1) what is the relative importance of different variables driving fire severity, and (2) is intensive plantation forestry associated with higher fire severity? Using Random Forest ensemble machine learning, we found daily fire weather was the most important predictor of fire severity, followed by stand age and ownership, followed by topographic features. Estimates of pre-fire forest biomass were not an important predictor of fire severity. Adjusting for all other predictor variables in a general least squares model incorporating spatial autocorrelation, mean predicted RdNBR was higher on private industrial forests (RdNBR 521.85±18.67 [mean ± SE]) vs. BLM forests (398.87±18.23) with a much greater proportion of older forests. Our findings suggest intensive plantation forestry characterized by young forests and spatially homogenized fuels, rather than pre-fire biomass, were significant drivers of wildfire severity. This has implications for perceptions of wildfire risk, shared fire management responsibilities, and developing fire resilience for multiple objectives in multi-owner landscapes.

Evapotranspiration is the most important variable next to rainfall in the context of irrigation to crops and it is a multivariate phenomenon as it is influenced by many hydrological variables. In this paper, the relative importance of different variables involved in evapotranspiration has been investigated through a multivariate technique, namely factor analysis. Monthly data pertaining to eight meteorological stations located in two tropical states of India are used in this analysis. Factor analysis was applied to determine the relative effect of rainfall, temperature (maximum and minimum), wind speed, relative humidity, and sunshine duration on evapotraspiration. The analysis was performed in two stages: in the first stage, the principal components were obtained and in the second stage, a set of factors was derived using Varimax factor rotation. Three factors were extracted which explained more than 85% of the total variation in the original data set. The identified factors were related to physical processes influencing the evapotranspiration. The results of the study reveal that the variables, relative humidity, temperature, and wind speed, are those with the most influence in the evapotranspiration process, in that order. The variables rainfall and sunshine duration have been found to have less influence on the ET process.

Perfluorooctanoic acid (PFOA) is used in a variety of industries and is highly persistent in the environment, with potential human health risks. Photocatalysis has been extensively used for the decomposition of various organic pollutants, yet its simulation and modeling are challenging. This research aimed to establish different machine learning (ML) algorithms which can simulate and predict the photocatalytic degradation of PFOA. The published results were used to estimate and predict the photocatalytic degradation of PFOA. Statistical criteria including the coefficient of determination (R2 ), mean absolute error (MAE), and mean squared error (MSE) were considered in assessing the best method of modeling. Among the seven ML algorithms pre-screened, Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) showed the best performance and were chosen for deep modeling and analysis. Grid search was used to optimize the models developed by AdaBoost, GBM, and RF; and permutation variable importance (PVI) was used to analyze the relative importance of different variables. Based on the modeling results, GBM model (R2 = 0.878, MSE = 106.660, MAE = 6.009) and RF model (R2 = 0.867, MSE = 107.500, MAE = 6.796) showed superior performances compared with AdaBoost model (R2 = 0.574, MSE = 388.369, MAE = 16.480). Furthermore, the PVI results suggested that the GBM model provided the best outcome, with the light irradiation time, type of catalyst, dosage of catalyst, solution pH, irradiation intensity, initial PFOA concentration, oxidizing agents (peroxymonosulfate, ammonium persulfate, and sodium persulfate), irradiation wavelength, and solution temperature as the most important process variables in decreasing order.

Prognosis in malignant melanoma

대부분의 사회과학분야 연구에서는 다중선형회귀모형에 포함되는 설명변수들의 상대적 중요도에 대해 많은 관심을 가지고 있다. SPSS를 비롯한 여러 상용 통계패키지에서는 표준화 회귀계수의 추정값을 계산해 주고 있으며 표준화 회귀계수의 추정값의 절대값이 큰 설명변수가 중요하다고 해석하기도 하지만 이러한 주장에는 많은 논란이 있다. 여러 회귀분석 교과서에서도 표준화 회귀계수의 맹목적인 사용에 유의할 필요가 있다고 강조하고 있지만 대안은 제시되고 있지않다. 본 연구에서는 설명변수의 상대적 중요도에 대한 구체적인 의미를 생각해보고 몇 가지 잔차산점도에서의 상관계수와 표준화 회귀계수의 관계를 알아보고 설명변수들의 상대적 중요도를 나타내는 적절한 척도를 제안한다. 추가변수그림의 상관계수는 추가적인 설명력을 검정하는 부분 검정통계량의 크기를 나타내는 척도로 사용될 수 있고, 추가결정계수그림의 상관계수는 설명변수의 추가를 통해서 발생하는 결정계수의 증가분을 나타내는 척도로 사용될 수 있다. 또한 보정계수를 이용하여 이러한 척도들과 표준화 회귀계수와의 관계를 구해본다. 제시된 척도들을 실제 자료에 적용시켜본다.In most social science research work there are some interests concerning the rank of relative importance of different variables in the regression model. Statistical packages such as SPSS provide the printouts on the standardized coefficient. But many people make cautionary remarks that the rankings of the standardized coefficients in terms of absolute magnitude does not necessarily reflect the importance of variables in explaining the variability of response variable. Many textbooks give warnings against the misuse of this automatic computer generated output. We consider the relationship between this standardized coefficient and the correlation coefficients of various residual plots. And this suggests that the appropriate correlation coefficients may be used in ranking the relative importance of variables. The correlation coefficient derived from added variable plot is a good measure in determining the ranks of variables according to the size of partial -values for regression coefficient. The correlation coefficient from the additional   plot compares each variable s contribution in terms of additional increase of coefficient of determination over the variability explained by other variables in the model.

Prognostic personal credit risk model considering censored information

Many problems in mathematical finance can be formulated as high-dimensional integrals, where the large number of dimensions arises from small time steps in time discretization and/or a large number of state variables. Quasi--Monte Carlo (QMC) methods have been successfully used for approximating such integrals. To understand this success, this paper focuses on investigating the special features of some typical high-dimensional finance problems, namely, option pricing and bond valuation. We provide new insight into the connection between the effective dimension and the efficiency of QMC, and we present methods to analyze the dimension structure of a function. We confirm the observation of Caflisch, Morokoff, and Owen that functions from finance are often of low effective dimension, in the sense that they can be well approximated by their low-order ANOVA (analysis of variance) terms, usually just the order-1 and order-2 terms. We explore why the effective dimension is small for many integrals from finance. By deriving explicit forms of the ANOVA terms in simple cases, we find that the importance of each dimension is naturally weighted by certain hidden weights. These weights characterize the relative importance of different variables or groups of variables and limit the importance of the higher-order ANOVA terms. We study thevariance ratios captured by low-order ANOVA terms and their asymptotic properties as the dimension tends to infinity, and we show that with the increase of dimension the lower-order terms continue to play a significant role and the higher-order terms tend to be negligible. This provides some insight into high-dimensional problems from finance and explains why QMC algorithms are efficient for problems of this kind.

The Van Nuys Prognostic Index (VNPI), an algorithm based on tumour size, tumour grade, presence of necrosis and excision margin width, is claimed to predict local recurrence after breast-conserving surgery for ductal carcinoma in situ (DCIS). The aim of this study was to examine the validity of the VNPI in a UK population. Clinicopathological data, including VNPI subgroups, for 237 patients who had breast-conserving operations for DCIS were examined. Multivariate data analysis was performed using a Cox regression model to examine the independence and relative importance of different variables in predicting recurrence, and to compare the data with those used in derivation of the VNPI. The median follow-up was 47 months. There were 37 ipsilateral local recurrences. Excision margin width (P < 0.001) and tumour grade (by Van Nuys grading (P = 0.014) or simple nuclear grading (P = 0.004)) were the only independent risk factors for local recurrence. Excision margin width had three times more power than grade in predicting local recurrence. Subgrouping data by VNPI score predicted recurrence-free survival (P < 0.001), but stratified 78 per cent of patients into a group with a moderate risk of local recurrence. Excision margin width is the most important predictor of local recurrence after breast-conserving surgery for DCIS. The VNPI lacked discriminatory power for guiding further patient management.

Converging farmers' and scientists' perspectives on researchable constraints on organic cocoa production in Ghana: results of a diagnostic study

To address the growing global demand for cocoa, sustainable intensification of its production in West Africa is considered crucial. This paper analyzes the determinants of cocoa productivity and profitability by smallholder farmers in Ghana to provide insights into challenges for future cocoa farming, which will guide the formulation and prioritization of tailored policies to address them. A four-stage sampling technique was used to select a total of 731 cocoa farmers from various districts in all six cocoa growing regions in Ghana. Selected farmers were interviewed using a semi-structured questionnaire. The results show that cocoa productivity and profitability was very low with an average of 234 kg ha−1 and Gh¢ 568 (ca. US$ 150) per ha, respectively. Farm management practices, namely control of capsid and black pod disease, fertilizer application and pruning, significantly (p < 0.05) influenced cocoa productivity. Capsid control and fertilizer application showed the highest impact on productivity. Farm size, however, had a negative impact, which implies that increase in farm size results in decreased smallholder cocoa productivity. Farmers should be encouraged to sustainably intensify farm management through controlling black pod disease and capsids, regular pruning and efficient application of fertilizer rather than focusing on excessive land expansion, which eventually hampers productivity and biodiversity.

NEP1 orthologs encoding necrosis and ethylene inducing proteins exist as a multigene family in Phytophthora megakarya, causal agent of black pod disease on cacao

Black pod disease is a major constraint to cocoa production in Côte d'Ivoire. Varietal selection and fungicide treatments are the principal means of control. Fungicide Ridomil Gold 66 WP (6% Metalaxyl and 60% copper oxide) is commonly used. This study aimed at evaluating the effectiveness of three doses of Ridomil Gold 66 WP in the control of black pod disease. An experiment on the rates of this fungicide was conducted in two localities (Aboisso and Duékoué). The experimental design was a randomized complete block with four treatments, replicated four times. The treatments comprised untreated or controlled, single dose (3.3 g/l) or recommended dose, double dose (6.6 g/l) or 2×recommanded dose and triple dose (9.9 g/l) or 3×recommanded dose. All data collected were subjected to ANOVA with the test of Student and Newmann-Keuls at the 5% threshold. As result, no significant difference was observed between the treatments of the three doses of Ridomil. But, a significant difference was observed between the fungicide treatments and the control. In Duékoué, black pod rates ranged from 4.5 to 9.52% in fungicide treated plots against 33.8% in control plots. In Aboisso, black pod rates ranged from 7.11 to 13.83% for fungicide treated plots against 30.83% for the control plots. Fungicide treatments increased healthy cocoa pod yields in both localities. The yields were increased by 51.35 to 89.63% in treated plots of Aboisso and by 34.63 to 101.06% in treated plots of Duékoué. This study confirmed the efficacy of the recommended dose (3.3 g/l) of Ridomil Gold 66 WP in the management of black pod disease. Yet, even if high doses of Ridomil Gold 66 WP improved yield, it could also be led to the development of resistant strains of Phytophthora spp. and increase production costs.