Qualitative and Quantitative Analysis of Green Supply Chain Management (GSCM) Literature From 2000 to 2015

In the past several decades, Green Supply Chain Management (GSCM) has been rapidly evolving in academia and industry and investigation in all countries and nation growing dramatically. The significant impact of GSCM is visible by the rate of academic publications in recent years. With the increased attention paid to environmental aspects of green supply chain management, finding new directions by critically evaluating the research and identifying future directions becomes important in advancing knowledge for the field. Both Qualitative and quantitative are applied to examine global scientific production of green supply chain management in Science Citation Index (SCI-Index) and Social Sciences Citation Index (SSCI-Index) documents to develop a viable green supply chain study. The analytical results of this study illustrate the study evolution over time and eventually provide a systematic mapping of the field that helps to identify of research interests and potential directions for future research

Analysis and Extraction of Tempo-Spatial Events in an Efficient Archival CDN with Emphasis on Telegram

This paper presents an efficient archival framework for exploring and tracking cyberspace large-scale data called Tempo-Spatial Content Delivery Network (TS-CDN). Social media data streams are renewing in time and spatial dimensions. Various types of websites and social networks (i.e., channels, groups, pages, etc.) are considered spatial in cyberspace. Accurate analysis entails encompassing the bulk of data. In TS-CDN by applying the hash function on big data an efficient content delivery network is created. Using hash function rebuffs data redundancy and leads to conclude unique data archive in large-scale. This framework based on entered query allows for apparent monitoring and exploring data in tempo-spatial dimension based on TF-IDF score. Also by conformance from i18n standard, the Unicode problem has been dissolved. For evaluation of TS-CDN framework, a dataset from Telegram news channels from March 23, 2020 (1399-01-01), to September 21, 2020 (1399-06-31) on topics including Coronavirus (COVID-19), vaccine, school reopening, flood, earthquake, justice shares, petroleum, and quarantine exploited. By applying hash on Telegram dataset in the mentioned time interval, a significant reduction in media files such as 39.8% for videos (from 79.5 GB to 47.8 GB), and 10% for images (from 4 GB to 3.6 GB) occurred. TS-CDN infrastructure in a web-based approach has been presented as a service-oriented system. Experiments conducted on enormous time series data, including different spatial dimensions (i.e., Khabare Fouri, Khabarhaye Fouri, Akhbare Rouze Iran, and Akhbare Rasmi Telegram news channels), demonstrate the efficiency and applicability of the implemented TS-CDN framework

A New Recommendation Approach Based on Implicit Attributes of Learning Material

AbstractA personalized recommendation is an enabling mechanism to overcome information overload occurred in the new learning environments and deliver suitable learner materials to learners. But recommender system technology suffers from some problems such as cold-start and sparsity. Since users express their opinions based on some specific attributes of materials, this paper proposes a new recommender system for learning materials based on their attributes to address these problems. Weight of implicit or latent attributes for learners is considered as chromosomes in genetic algorithm then this algorithm optimizes the weight of implicit attributes for each learner according to historical rating. Then, recommendation is generated using Nearest Neighborhood Algorithm (NNA). The experimental results show that our proposed method outperforms current algorithms and can perform superiorly and alleviates problems such as cold-start and sparsity

Effects of Chemical and Biological Fertilizers on Growth, Yield and Essential Oil of Salvia officinalis

This experiment was conducted in 2012 at the research field of Alborz Research Station, Research Institute of Forests and Rangelands, Karaj, Iran, to study the effect of chemical and biological fertilizers on Sage (Salvia officinalis L.) and replacing biofertilizers instead of high doses of chemical fertilizers. The experiment was conducted in factorial in the form of a randomized complete block design with three replications and two factors: chemical nitrogen and phosphorus fertilizers in four levels (N0P0, N0P150, N300P0 and N300P150) and biological fertilizers in four levels (non inoculated control, mycorrhizal inoculation with Glomus mosseae (T.H. Nicolson & Gerd.) Gerd. & Trappe + Glomus intraradices N.C. Schenck & G.S. Sm., bacterial inoculation with Pseudomonas fluorescens, and dual inoculation with G. mosseae +G. intraradices + P. fluorescens). The measured traits included: plant height, the number of tillers, leaf area, leaf yield, shoot yield, root weight, essential oil percentage and essential oil yield. Results indicated the significant effect of chemical fertilizer on all measured traits except for the number of tillers. Biofertilizer application had also significant effect on all measured traits except for essential oil percentage. The interaction of the two factors had only a significant effect on leaf area and leaf yield. Mean comparison showed that the highest essential oil yield (37.02 kg/ha) was achieved in N0P150×Pseudomonas which was significantly the same as N0P150×mycorrhizal inoculation and N0P150×dual inoculation with mycorrhizae + Pseudomonas. Generally, results of this experiment indicated that it is possible to replace biofertilizers instead of high doses of chemical fertilizers in order to reduce the need for chemical fertilizers and prevent the associated problems

Hexaconazole foliar application alleviates water deficit effects in common bean

Currently, the world is facing many problems of crop production. Among them, water deficit is the most dangerous one. This study aimed at evaluating the possibility of enhancing the water deficit tolerance of common bean plants, during two growth stages, by the exogenous application of hexaconazole. The experimental design was completely randomized, in a factorial arrangement, with three replicates. Hexaconazole (0 mg L-1, 10 mg L-1 and 20 mg L-1) was sprayed at flowering (R3), at 60 days after sowing, and at the grain-filling stage (R8), at 90 days after sowing. After the application, the plants were subjected to water deficit by withholding irrigation for seven days. Although all hexaconazole concentrations improved the water deficit tolerance in bean plants, in terms of plant growth and yield, the application of 20 mg L-1 provided a better protection, when compared to the other concentrations (p < 0.01). The exogenous application of hexaconazole improved the water deficit tolerance, if compared to non-treated plants, affecting the morphological characteristics, yield components, total chlorophyll, proline, relative water content and enzymatic antioxidants (p < 0.01). The results showed that the hexaconazole-induced tolerance to water deficit in common bean is related to changes in the growth variables and antioxidants. In conclusion, the hexaconazole application could improve the bean growth and yield under water deficit conditions

Logistic autonomous vehicles assessment using decision support model under spherical fuzzy set integrated Choquet Integral approach

Autonomous vehicles (AVs) are the newest products in the intelligent transportation system that can move around with minimal human intervention. These products continue their path with all kinds of sensors with different parts. Effective use of these technologies in the logistics industry can create a competitive advantage. Nowadays, there are many AVs, some of which are superior to others in terms of build quality, variety of features, and design. Choosing an efficient, optimal, and reliable vehicle is one of the most important challenges in logistics planning. Therefore, choosing an AV based on a series of criteria can be considered a multi-criteria decision-making (MCDM) problem. Due to the complication of decision-making issues, criteria are usually not independent of each other and there are relationships between them. Therefore, this study develop an extended MCDM framework based on Choquet integral (CI) under group decision-making with a Spherical fuzzy set (SFS) for assessing logistics AVs. The CI technique is expanded with SFS to increase the power of CI. Furthermore, the combination of CI with SFS leads to greater freedom for decision makers to express opinions and use three independent membership functions. Accordingly, the interactions between the criteria are considered and the skepticism and uncertainty present during the decision are controlled. The proposed approach is implemented in selecting the best AVs in the logistics industry, and the results are compared with Pythagorean fuzzy CI and Intuitionistic fuzzy CI. Moreover, sensitivity analysis is done by changing the weights and creating different scenarios to confirm and check the robustness of the proposed approach results. The results indicate the suggested approach's efficiency and the ranking's stability in different scenarios

Heat transfer enhancement of a heat exchanger using novel multiple perforated magnetic turbulators (MPMT): an experimental study

The magnetic turbulator and electromagnetic vibration (EMV) methods have recently been employed to enhance heat transfer in heat exchangers. This method involves placing a magnetic oscillator inside the tube and attaching a magnet with specific dimensions to this oscillator. Creating an AC magnetic field near the tube causes the magnet and the oscillator to vibrate, acting as a magnetic turbulator. In this study, multiple perforated magnetic turbulators were used inside the tube of a heat exchanger for the first time, and their impact on hydrothermal parameters was assessed. Various factors were examined, including perforation diameter, pitch, and fluid flow rate. The thermal enhancement factor (TEF) was used to identify the optimal configuration. The results showed that simple and perforated turbulators increased heat transfer up to 156% and 150%, respectively. However, the pressure drop in the presence of these turbulators was up to 1.97 and 1.86 times higher than that of a simple heat exchanger. In addition, the maximum value of TEF was observed in the presence of a perforated magnetic turbulators with a hole diameter of 2 mm and a hole pitch of 12 mm. This turbulator was the optimal choice, providing a TEF equivalent to 2.06

Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images

Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems in a Cascade Deep Learning model. In the second step, as we are dealing with a smaller part of brain images in each slice, a simple and efficient Cascade Convolutional Neural Network (C-ConvNet/C-CNN) is proposed. This C-CNN model mines both local and global features in two different routes. Also, to improve the brain tumor segmentation accuracy compared with the state-of-the-art models, a novel Distance-Wise Attention (DWA) mechanism is introduced. The DWA mechanism considers the effect of the center location of the tumor and the brain inside the model. Comprehensive experiments are conducted on the BRATS 2018 dataset and show that the proposed model obtains competitive results: the proposed method achieves a mean whole tumor, enhancing tumor, and tumor core dice scores of 0.9203, 0.9113 and 0.8726 respectively. Other quantitative and qualitative assessments are presented and discussed