The main objective was to develop an artificial diet for two flat-headed borers, Capnodis tenebrionis L. and C. carbonaria Klug. (Coleoptera: Buprestidae), which are severe pests of stonefruit plantations in the Mediterranean basin. The effect of proteins from various sources, percentage of cortex tissue in the diet and diet structure on larval growth and survival were investigated. The most successful diet contained 2.8% casein and 4.6% dry brewer's yeast as the protein source. For complete larval development and successful pupation it is essential to include cortex tissue from the host plant in the diet. Mean larval development time was shortened by 10-12 days when reared on a diet containing 20% cortex tissue compared with rearing on diet containing 10% cortex tissue. Two different diet structures were required, a viscous matrix for the first and second instar larvae and drier crumbly diet, which allows the larvae to move within the diet, for older larvae. At 28°C on the artificial diet C. tenebrionis and C. carbonaria completed their development in 2-2.5 months compared to the 6-11 months recorded in Israeli orchards. C. tenebrionis successfully completed two generations on the artificial diet.
The present work proposes the architecture Clonart (Clonal Adaptive Resonance Theory), a Hybrid Model that employs techniques like intelligent operators, clonal selection principle, local search, memory antibodies and ART clusterization, in order to increase the performance of the algorithm. The approach uses a mechanism similar to the ART 1 network for storing a population of memory antibodies that will be responsible for the acquired knowledge of the algorithm. This characteristic allows the algorithm a self-organization of the antibodies in accordance with the complexity of the database.
In this paper some remarks on predictive modeling of traction power consumption and their use in intelligent control systems are stated. Special emphasis is put on discussing neural networks and genetic algorithms for such models described in the second Chapter. In the third Chapter, significant applications of neural networks and genetic algorithms in area of power consumption and train diagram are stated. A methodology of model development and assessment is presented in Chapter 4. In Chapter 5 there are up to now results of the author's traction power consumption prediction coming out from artificial neural network predictive models developed in Mathematica SW environment. Finally, summary and further work are stated in the last Chapter.
A literature survey was conducted to appraise the recent applications of artifical intelligence (AI)-based modeling studies in the environmental engineering field. A number of studies on artificial neural networks (ANN), fuzzy logic and adaptive neuro-fuzzy systems (ANFIS) were reviewed and important aspects of these models were highlighted. The results of the extensive literature survey showed that most AI-based prediction models were implemented for the solution of water/wastewater (55.7%) and air pollution (30.8%) related environmental problems compared to solid waste (13.5%) management studies. The present literature review indicated that among the many types of ANNs, the three-layer feed-forward and back-propagation (FFBP) networks were considered as one of the simplest and the most widely used network type. In general, the Levenberg-Marquardt algorithm (LMA) was found as the best-suited training algorithm for several complex and nonlinear real-life problems of environmental engineering. The literature survey showed that for water and wastewater treatment processes, most of AI-based prediction models were introduced to estimate the performance of various biological and chemical treatment processes, and to control effluent pollutant loads and flowrates from a specific system. In air polution related environmental problems, forecasting of ozone (O3) and nitrogen dioxide (NO2) levels, daily and/or hourly particulate matter (PM2.5) and PM10) emissions, and sulfur dioxide (SO2) and carbon monoxide (CO) concentrations were found to be widely modeled. For solid waste management applications, reseachers conducted studies to model weight of waste generation, solid waste composition, and total rate of waste generation.
In the present study, an alternative promising evaluation method was recommended for dead leaves of Posidonia oceanica (L.) Delile as an adsorbent for biosorption of Methylene Blue (MB). The data from batch experiments were modeled by using Artificial Neural Network (ANN). The optimal operation conditions for biosorption of MB by P. oceanica dead leaves were found for pH, adsorbent dosage, temperature and initial dye concentration as 6, 0.3 g, 303 K and 50 mg/L, respectively. The adsorption reached equilibrium after 30 minutes. According to the results of sensitivity analysis, relative importance of temperature, dye concentration, pH, adsorbent dosage and process time on the biosorption of MB were 33%, 27%, 21%, 10% and 8%, respectively. Minimum mean square error (MSE) was found as 0.0169 by ANN modeling. The present study reveals a novel strategy for adsorption studies to utilize the highly accumulated biomass of dead leaves of P. oceanica in Turkish coastlines instead of burning these dead leaves.
Objective: The anxiety of Alzheimer's disease (AD) contributes significantly to decreased quality of life, increased morbidity, higher levels of caregiver distress, and the decision to institutionalize a patient. However, the incidence of anxiety in AD patients hasn't been discussed. In this study, artificial neural networks were used to predict the incidence of anxiety inAD patients.
Methods: A large randomized controlled clinical trial was analyzed in this study, which involved AD patients and caregivers from 6 different sites in the United States. The incidence of anxiety in AD patients was predicted by backpropagation artificial neural networks with one and hidden layers. After cross validation, the Predictive Accuracy (PA) of the models was measured to select the best structure of artificial neural networks.
Results: Among all models for predicting the incidence of anxiety in AD patients, the artificial neural network with respectively 6 and 3 neurons in the first and second hidden layers achieved the highest predictive accuracy of 85.56%.
Conclusions: The incidence of anxiety in AD patients can be predicted by an accuracy of over 80%. When used for anxiety prediction, neural networks with two hidden layers perform better than those with one hidden layer. These findings will benefit the prevention and early intervention of anxiety in Alzheimer's patients.
Let $(R,\mathfrak {m})$ be a complete Noetherian local ring, $I$ an ideal of $R$ and $M$ a nonzero Artinian $R$-module. In this paper it is shown that if $\mathfrak p$ is a prime ideal of $R$ such that $\dim R/\mathfrak p=1$ and $(0:_M\mathfrak p)$ is not finitely generated and for each $i\geq 2$ the $R$-module ${\rm Ext}^i_R(M,R/\mathfrak p)$ is of finite length, then the $R$-module ${\rm Ext}^1_R(M,R/\mathfrak p)$ is not of finite length. Using this result, it is shown that for all finitely generated $R$-modules $N$ with $\operatorname {Supp}(N)\subseteq V(I)$ and for all integers $i\geq 0$, the $R$-modules ${\rm Ext}^i_R(N,M)$ are of finite length, if and only if, for all finitely generated $R$-modules $N$ with $\operatorname {Supp}(N)\subseteq V(I)$ and for all integers $i\geq 0$, the $R$-modules ${\rm Ext}^i_R(M,N)$ are of finite length.