ENPP: Extended Non-preemptive PP-aware Scheduling for Real-time Cloud Services

By increasing the use of cloud services and the number of requests to processing tasks with minimum time and costs, the resource allocation and scheduling, especially in real-time applications become more challenging. The problem of resource scheduling, is one of the most important scheduling problems in the area of NP-hard problems. In this paper, we propose an efficient algorithm is proposed to schedule real-time cloud services by considering the resource constraints. The simulation results show that the proposed algorithm shorten the processing time of tasks and decrease the number of canceled tasks

Comparison of data mining techniques to predict and map the Atterberg limits in central plateau of Iran

The Atterberg limits display soil mechanical behavior and, therefore, can be so important for topics related to soil management. The aim of the research was to investigate the spatial variability of the Atterberg limits using three most common digital soil-mapping techniques, the pool of easy-to-obtain environmental variables and 85 soil samples in central Iran. The results showed that the maximum amount of liquid limit (LL) and plastic limit (PL) were obtained in the central, eastern and southeastern parts of the study area where the soil textural classes were loam and clay loam. The minimum amount of LL and PL were related to the northwestern parts of the study area, adjacent to the mountain regions, where the samples had high levels of sand content (>80%). The ranges of plasticity index (PI) in the study area were obtained between 0.01 to 4%. According to the leave-in-out cross-validation method, it should be highlighted the combination of artifiial bee colony algorithm (ABC) and artifiial neural network (ANN) techniques were the best model to predict the Atterberg limits in the study area, compared to the support vector machine and regression tree model. For instance, ABC-ANN could predict PI with RMSE, R2 and ME of 0.23, 0.91 and -0.03, respectively. Our fiding generally indicated that the proposed method can explain the most of variations of the Atterberg limits in the study area, and it could berecommended, therefore, as an indirect approach to assess soil mechanical properties in the arid regions, where the soil survey/sampling is difficult to undertake

Investigating the relationships among the knowledge sharing system indices in the educational organizations

The purpose of the present study is to investigate the inter-relationship among the indices that influence on knowledge sharing systems in lessons learned systems. To do this, while reviewing the literature, the researchers first identified the indices affecting KSS; then, they collected the related data through the use of a researcher-devised questionnaire. The results of surveying the indices of knowledge sharing system based on DEMATEL system analysis indicated that there were systemic relationships with the predominant characteristic of impressibility among the indices of the system. Investigating the internal relationship among the indices of knowledge sharing in learned lessons systems showed that in order to create a positive as well as optimum effect on knowledge sharing processes, the first priority should be updating and reinforcing "communication channels"; also, "reward systems and processes" should be reinforced as the second priority in line with the strengthening of the purposeful process of knowledge sharing system. All rights reserved. Growing Science Ltd.

Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

Estimation of the soil organic carbon content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines, artificial neural networks, regression tree, random forest, extreme gradient boosting, and conventional deep neural network for advancing prediction models of SOC. Models are trained with 1879 composite surface soil samples, and 105 auxiliary data as predictors. The genetic algorithm is used as a feature selection approach to identify effective variables. The results indicate that precipitation is the most important predictor driving 15 percent of SOC spatial variability followed by the normalized difference vegetation index, day temperature index of moderate resolution imaging spectroradiometer, multiresolution valley bottom flatness and land use, respectively. Based on 10 fold cross validation, the DNN model reported as a superior algorithm with the lowest prediction error and uncertainty. In terms of accuracy, DNN yielded a mean absolute error of 59 percent, a root mean squared error of 75 percent, a coefficient of determination of 0.65, and Lins concordance correlation coefficient of 0.83. The SOC content was the highest in udic soil moisture regime class with mean values of 4 percent, followed by the aquic and xeric classes, respectively. Soils in dense forestlands had the highest SOC contents, whereas soils of younger geological age and alluvial fans had lower SOC. The proposed DNN is a promising algorithm for handling large numbers of auxiliary data at a province scale, and due to its flexible structure and the ability to extract more information from the auxiliary data surrounding the sampled observations, it had high accuracy for the prediction of the SOC baseline map and minimal uncertainty.Comment: 30pages, 9 figure

The functionality of entrepreneurial passion and entrepreneurial bricolage on micro-entrepreneur’s wellbeing

This study investigates the relationships between entrepreneurial passion, entrepreneurial bricolage, and subjective wellbeing. A total of 253 usable data were collected from the micro-entrepreneurs in Bangladesh and data were analyzed by SEMPLS3.0 employing structure equation modelling. The results indicate that subjective wellbeing is significantly predicted by entrepreneurial passion and bricolage. Bricolage also found to play a mediating role between passion and wellbeing. The results of the study validate that passionate entrepreneurs who embrace bricolage will achieve wellbeing through their ventures. The paper makes contribution to the knowledge domain by bridging the concept of subjective wellbeing with entrepreneurial passion and bricolage

Designing Structure Evaluation Model at the Upstream of Automotive Supply Chains by an Adapted Spectral Clustering

The purpose of this study is to design supply chains' upstream structure evaluation model in the automotive industry with spectral clustering based on the theory of complex adaptive systems. In this research, a method for evaluating the intersectionalities related to the structural complexity (horizontal, vertical, and spatial) of supply chains by considering the functional characteristics of its components based on the resilience paradigm is presented. In this regard, a set of algebraic calculations and computational algorithms have been adapted to evaluate the structural design from the perspective of complex components. In the structural design evaluation model through spectral clustering, it is possible to enter information about supply chains in terms of interactions between components in the form of a network as a comprehensive model called similarity graph. According to the field findings, supply chain characteristics in terms of complexity can have interaction with component processing performance. This means that according to the concept of entanglement, the lack of a favorable environmental structure in supply chains can also negatively affect the resilience performance of its components. Findings from the perspective of achieving a supply chain evaluation model as an integrated whole have provided a suitable practical tool for evaluation and pathology of supply chains from the perspective of risk management

Hard dimensions evaluation in sustainable supply chain management for environmentally adaptive and mitigated adverse eco‐effect environmental policies

In the oil and gas industry, adopting policies that can reduce the negative environmental effect is vital. Environmentally Sustainable Supply Chain Management (ESSCM) is an approach to carrying out Supply Chain Management (SCM) in an eco‐friendly manner and according to environmental requirements. There are different environmental policies that companies can apply based on their resource availability. Therefore, this study aims to evaluate the impact of hard dimensions on Environmentally Adaptive (EA) and Mitigated Adverse Eco‐Effect (MAE) policies in the oil and gas industry. To rank the data, Bayesian Best‐Worst Method (BWM) and Ordinal Priority Approach (OPA) have been applied. Cause‐and‐effect relationships are then calculated by employing the Decision‐Making Trial and Evaluation Laboratory (DEMATEL) technique. The results indicate that the ranking of the hard dimensions varies based on the companies' business policies and their new product/technology development projects. In other words, the findings of this research demonstrate that ‘innovation’ is the crucial dimension in companies that are focussed on developing eco‐friendly products while ‘technologies for cleaner production’ is the most important dimension in the companies attempting to reduce destructive consequences on the environment. In both types of the company policies, ‘lean manufacturing’, ‘total quality management’, and ‘institutional pressures’ are the key dimensions for a successful implementation of ESSCM while the least important dimensions include ‘supplier relationship management’, ‘green purchasing’, and ‘green logistics’. The findings of this research can assist the decision‐makers in the oil and gas sector in prioritising and identifying the interrelationship of the dimensions that significantly impact the ESSCM

Dispersion properties of single-mode optical fibers in telecommunication region: Poly (methyl methacrylate) (PMMA) versus silica

In this paper, the dispersion characteristics of two standard single-mode optical fibers (SMFs), fabricated with silica and poly (methyl methacrylate) (PMMA) are studied in telecommunication spectral regions. The effect of structural parameters, such as the radius of the fiber core and the relative core-cladding index difference, is numerically investigated. It is found that over whole spectral range, the PMMA-based SMF shows lower dispersion than the silica SMF. Also, the zero-dispersion wavelength (ZDW) of PMMA-based SMF is longer than that of silica fiber. The results may be of practical importance for the telecommunication applications

Adaptive variable impedance control for a modular soft robot manipulator in configuration space

Compliance is a strong requirement for human-robot interactions. Soft-robots provide an opportunity to cover the lack of compliance in conventional actuation mechanisms, however, the control of them is very challenging given their intrinsic complex motions. Therefore, soft-robots require new approaches to e.g., modeling, control, dynamics, and planning. One of the control strategies that ensures compliance is the impedance control. During the task execution in the presence of coupling force and position constraints, a dynamic behavior increases the flexibility of the impedance control. This imposes some additional constraints on the stability of the control system. To tackle them, we propose a variable impedance control in configuration space for a modular soft robot manipulator (MSRM) in the presence of model uncertainties and external forces. The external loads are estimated in configuration space using a momentum-based approach in order to reduce the calculation complexity, and the adaptive back-stepping sliding mode (ABSM) controller is designed to guard against uncertainties. Stability analysis is performed using Lyapunov theory which guarantees not only the exponential stability of each state under the designed control law, but also the global stability of the closed-loop system. The system performance is benchmarked against other conventional control methods, such as the sliding mode (SM) and inverse dynamics PD controllers. The results show the effectiveness of the proposed variable impedance control in stabilizing the position error and diminishing the impact of the external load compared to SM and PD controllers