In this paper, a novel classification algorithm that is based on Data Importance (DI) reformatting and Genetic Algorithms (GA) named GADIC is proposed to overcome the issues related to the nature of data which may hinder the performance of the Machine Learning (ML) classifiers. GADIC comprises three phases which are data reformatting phase which depends on DI concept, training phase where GA is applied on the reformatted training dataset, and testing phase where the instances of the reformatted testing dataset are being averaged based on similar instances in the training dataset. GADIC is an approach that utilizes the exiting ML classifiers with involvement of data reformatting, using GA to tune the inputs, and averaging the similar instances to the unknown instance. The averaging of the instances becomes the unknown instance to be classified in the stage of testing. GADIC has been tested on five existing ML classifiers which are Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Logistic Regression (LR), Decision Tree (DT), and Na�ve Bayes (NB). All were evaluated using seven open-source UCI ML repository and Kaggle datasets which are Cleveland heart disease, Indian liver patient, Pima Indian diabetes, employee future prediction, telecom churn prediction, bank customer churn, and tech students. In terms of accuracy, the results showed that, with the exception of approximately 1% decrease in the accuracy of NB classifier in Cleveland heart disease dataset, GADIC significantly enhanced the performance of most ML classifiers using various datasets. In addition, KNN with GADIC showed the greatest performance gain when compared with other ML classifiers with GADIC followed by SVM while LR had the lowest improvement. The lowest average improvement that GADIC could achieve is 5.96%, whereas the maximum average improvement reached 16.79%.

Machining technologies are being adapted worldwide to the applications of new materials, but there are qualitative changes here, which are caused by further developments in the field of new cutting tools.\nThe article deals with the economic efficiency of the production of mechanical components for machine tools and CNC machines. The economic aspects of optimizing the production of components are currently a very topical topic. Reducing production costs is currently a much-discussed topic. The term \"machinability of materials\" is a set of properties of the material being machined, which we monitor from the point of view of its suitability for production with a certain method of machining. When optimizing cutting conditions and tool life, it is necessary to use a certain optimization criterion within certain limiting conditions. The limitations are given by the technical parameters of the machine, the tool, the machined material and the required quality of the machined surface. The main economic criterion is the amount of production costs.

In the present study, numerical study of bearing capacity and spudcan punch through failure in two-layer sand-on-clay soil system was performed using ABAQUS software with Coupled Lagrangian-Eulerian (CEL) method. In the present study, soil parameters including sand layer thickness, clay shear strength gradient, relative density of sand layer, roughness between spudcan foundation and soil, as well as spudcan geometrical parameters including impact of spudcan diameter, tip spudcan angle, thickness of largest spudcan cross section were assessed. According to the results of the present study, the parameters related to geotechnical characteristics such as shear strength gradient of clay layer, relative density of sand layer and sand layer thickness had a direct relation with the bearing capacity of spudcan foundation; increase in each of which increased the bearing resistance. On the other hand, the roughness between clay and foundation and the thickness of the largest spudcan cross section had no significant effect on the bearing capacity and spudcan punch through failure of the soil.

The present field study investigated hydraulic and sediment in tidal conditions. Measurements were made for two stations at neap tide and spring tide for 13-hour periods at no-dimensional depths of 0.2, 0.6 and 0.8 of the water level each time. In these measurements, the parameters of velocity, direction of flow, electrical conductivity, temperature and depth were measured directly, and to determine the amount of suspended sediments per hour, 3 samples of one liter from the mentioned depths were measured by the instantaneous vertical sampler and taken to a laboratory. An examination of the depth charts of the suspended sediments concentration for all stations, and in both the neap tide and the spring tide, showed that, in general the suspended sediments concentration increased with increasing depth. Moreover, the deep distribution of sediments concentration showed that the values obtained in the laboratory corresponded well with the values calculated from the Rouse equation, and as the depth increases, the sediments concentration usually increases.

Biofertilizer application has been proposed as a strategy for the management of soil quality or plant disease. In this study, a two-season pot experiment was conducted to evaluate the effects of sustainable biofertilizer application on luffa powdery mildew suppression, soil bio-chemical properties and microbial communities. The results show that sustainable biofertilizer application effectively controlled disease. Moreover, biofertilizer significantly improved luffa biomass and nutrient uptake. Soil bacterial and fungal communities were significantly different in soil amended with biofertilizer (BF) compared to soil treated with either high level (CF) or low level (CK) chemical fertilizer. Soil bio-chemical properties changed because of acidification by organic acids (oxalic acid, oxoglutaric acid and citric acid), as well as phosphatase, urease, and catalase activity. In conclusion, sustainable biofertilizer application suppressed powdery mildew and promoted luffa growth by manipulating the composition of the soil microbial community and improving soil chemical conditions including available nutrient elements (via enzyme activity and root exudate).

Agricultural products have an important place in human life and are basic foodstuffs for nutrition. Thus, these products must be grown, collected, preserved and delivered to consumers under suitable conditions for a healthy diet. In Turkey; products are sold from wholesale markets to marketers, greengrocers, food / beverage businesses and eventually reach consumers through these businesses. In this context, all combined solution proposals were required for the agri-food supply chain.\n\nIn this study, a methodology that deals with the agri-food supply chain in an integrated manner has been developed. The proposed methodology has five steps. In the first stage, Analytical Hierarchy Process (AHP) method was used for the selection of stakeholders in the agri-food supply chain. In the second phase, supply and demand planning was made with the cooperation agreement made within the framework of the Collaborative Planning, Forecasting and Supply (CPFR) method. In the third stage, the most suitable supply chains were determined to create a chain-long demand and supply balance with the linear programming method, and the demands were assigned to the chains according to the proximity and AHP score criteria. In the fourth stage, the simulation method was used in order to assign the randomly created orders to the most appropriate supply chains and the validity of the simulation results was shown statistically. Finally, an application on tomato product was analyzed with the 4.59x64 version of the AIMMS modeling package to verify the methods used, and the findings and results were given.

In this study, with the emphasis on the concept of dynamics in definition of sustainability, intergenerational consumption of water resources was introduced as a limitation in the mathematical programming model. In the first step, several seasonal time series techniques used to predict rainfall and sustainable balance of groundwater resources in Tuyserkan plain located south of Hamedan province. In the next step, the optimal cropping pattern is extracted based on available water (stability criterion). The results of this study indicate that water input is the most limiting factor in the agricultural sector of the region. Also, we find that, the use of new irrigation methods in different target models, in addition to increasing the water add value (meaning intergenerational sustainability), also increases the current value of the gross margin of the farmers (the main purpose of farmers). We our finding show that water consumption at a sustainable level, if accompanied by supplementary policies such as increased efficiency, will have no contradiction with the objectives of farmers. With the correct implementation of the policy of stabilization, there will be no worry of conflict between the government and the farmers.

In this framework, investigation deals with the propagation characteristics of horizontally polarized shear (SH) wave transmission in a piezo-composite layered structure. The rotating initially stressed structure consists of a layer and substrate, the considered materials are terfenol-D, and Barium titanate respectively, and both are bonded perfectly. In order to secure the relation between wave number and phase velocity, separation of variables method is adopted to bring out the displacement components for the mediums. Furthermore, dispersion relations are obtained by employing suitable boundary conditions for both electrically and magnetically open/short cases. The phase velocity and electromechanical coupling coefficient are calculated. Substantial effect of parameters (material width, initial stress and rotation parameter, magnetic permeability, piezoelectric coefficient and piezomagnetic coefficient) on phase velocity for fundamental mode illustrated graphically; moreover, comparative observations are presented throughout the graphical delineation for both electrically and magnetically open/short cases.

: Water quality is the measure of chemical, physical, biological and radiological characteristics of water. The quality of water decides whether the water is suitable for aquatic organisms, hu-man consumption and other water based activities. Predicting the quality of water is a very important issue in an ecosystem and it can be used to control the increase of water contamination. Also, water quality prediction is a complex non-linear and multi-target learning process. Furthermore, extracting a relevant subset of features from a large number of features with multiple target is a challenging task. In this paper, two techniques namely Classification and Regression Trees (CART) and non-linear Multivariate Adaptive Regression Splines (MARS) are proposed to predict the water quality. Also, an efficient feature selection technique is proposed to select the significant factor that influence the water quality. in this research work Experimental results show that the proposed methods CART and MARS is able to predict the target variables more precisely when compared with the existing techniques with the reduced number significant features. Also this study will be helpful for the authorities, to take necessary precautionary measures to control the pollution level in the water resources with the reduced number of sig-nificant features.