Recommendation Systems Based on Association Rule Mining for a Target Object by Evolutionary Algorithms

Recommender systems are designed for offering products to the potential customers. Collaborative Filtering is known as a common way in Recommender systems which offers recommendations made by similar users in the case of entering time and previous transactions. Low accuracy of suggestions due to a database is one of the main concerns about collaborative filtering recommender systems. In this field, numerous researches have been done using associative rules for recommendation systems to improve accuracy but runtime of rule-based recommendation systems is high and cannot be used in the real world. So, many researchers suggest using evolutionary algorithms for finding relative best rules at runtime very fast. The present study investigated the works done for producing associative rules with higher speed and quality. In the first step Apriori-based algorithm will be introduced which is used for recommendation systems and then the Particle Swarm Optimization algorithm will be described and the issues of these 2 work will be discussed. Studying this research could help to know the issues in this research field and produce suggestions which have higher speed and quality

A review of methods for resource allocation and operational framework in cloud computing

The issue of management and allocation of resources in cloud computing environments, according to the breadth of scale and modern technology implementation, is a complicated issue. Issues such as: the heterogeneity of resources, resource dependencies to each other, the dynamics of the environment, virtualization, workload diversity as well as a wide range of management objectives of cloud service providers to provide services in this environment. In this paper, first, the description of cloud computing environment and related issues have been reported. According to the performed studies, challenges such as: the absence of a comprehensive management for resources in the cloud environment, the method of predicting the resource allocation process, optimum resource allocation methods to reduce energy consumption and reducing the time to access resources and also implementation of dynamic resources allocation methods in the mobile cloud environments, have been addressed. Finally, with regard to the challenges, some recommendations to improve the process of allocation of resources in a cloud computing environment is has been proposed

The COVID-19 pandemic and healthcare utilization in Iran: evidence from an interrupted time series analysis

Objectives This study aimed to examine the effect of the coronavirus disease 2019 (COVID-19) outbreak on the hospitalization rate, emergency department (ED) visits, and outpatient clinic visits in western Iran. Methods We collected data on the monthly hospitalization rate, rate of patients referred to the ED, and rate of patients referred to outpatient clinics for a period of 40 months (23 months before and 17 months after the COVID-19 outbreak in Iran) from all 7 public hospitals in the city of Kermanshah. An interrupted time series analysis was conducted to examine the impact of COVID-19 on the outcome variables in this study. Results A statistically significant decrease of 38.11 hospitalizations per 10,000 population (95% confidence interval [CI], 24.93–51.29) was observed in the first month of the COVID-19 outbreak. The corresponding reductions in ED visits and outpatient visits per 10,000 population were 191.65 (95% CI, 166.63–216.66) and 168.57 (95% CI, 126.41–210.73), respectively. After the initial reduction, significant monthly increases in the hospitalization rate (an increase of 1.81 per 10,000 population), ED visits (an increase of 2.16 per 10,000 population), and outpatient clinic visits (an increase of 5.77 per 10,000 population) were observed during the COVID-19 pandemic. Conclusion Our study showed that the utilization of outpatient and inpatient services in hospitals and clinics significantly declined after the COVID-19 outbreak, and use of these services did not return to pre-outbreak levels as of June 2021

Sustainable supply chain management: framework and further research directions

This paper argues for the use of Total Interpretive Structural Modeling (TISM) in sustainable supply chain management (SSCM). The literature has identified antecedents and drivers for the adoption of SSCM. However, there is relatively little research on methodological approaches and techniques that take into account the dynamic nature of SSCM and bridge the existing quantitative/qualitative divide. To address this gap, this paper firstly systematically reviews the literature on SSCM drivers; secondly, it argues for the use of alternative methods research to address questions related to SSCM drivers; and thirdly, it proposes and illustrates the use of TISM and Cross Impact Matrix-multiplication applied to classification (MICMAC) analysis to test a framework that extrapolates SSCM drivers and their relationships. The framework depicts how drivers are distributed in various levels and how a particular driver influences the other through transitive links. The paper concludes with limitations and further research directions

The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols

Climate change is expected to alter a multitude of factors important to agricultural systems, including pests, diseases, weeds, extreme climate events, water resources, soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration ([CO2]), temperature, andwater (CTW) will be the primary drivers of change in crop growth and agricultural systems. Therefore, establishing the CTW-change sensitivity of crop yields is an urgent research need and warrants diverse methods of investigation. Crop models provide a biophysical, process-based tool to investigate crop responses across varying environmental conditions and farm management techniques, and have been applied in climate impact assessment by using a variety of methods (White et al., 2011, and references therein). However, there is a significant amount of divergence between various crop models’ responses to CTW changes (R¨otter et al., 2011). While the application of a site-based crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple crops, crop models, and sites is needed to aid model development and enhance the assessment of climate impacts (Deser et al., 2012)..

PRONTO: Preamble Overhead Reduction with Neural Networks for Coarse Synchronization

In IEEE 802.11 WiFi-based waveforms, the receiver performs coarse time and frequency synchronization using the first field of the preamble known as the legacy short training field (L-STF). The L-STF occupies upto 40% of the preamble length and takes upto 32 us of airtime. With the goal of reducing communication overhead, we propose a modified waveform, where the preamble length is reduced by eliminating the L-STF. To decode this modified waveform, we propose a machine learning (ML)-based scheme called PRONTO that performs coarse time and frequency estimations using other preamble fields, specifically the legacy long training field (L-LTF). Our contributions are threefold: (i) We present PRONTO featuring customized convolutional neural networks (CNNs) for packet detection and coarse CFO estimation, along with data augmentation steps for robust training. (ii) We propose a generalized decision flow that makes PRONTO compatible with legacy waveforms that include the standard L-STF. (iii) We validate the outcomes on an over-the-air WiFi dataset from a testbed of software defined radios (SDRs). Our evaluations show that PRONTO can perform packet detection with 100% accuracy, and coarse CFO estimation with errors as small as 3%. We demonstrate that PRONTO provides upto 40% preamble length reduction with no bit error rate (BER) degradation. Finally, we experimentally show the speedup achieved by PRONTO through GPU parallelization over the corresponding CPU-only implementations

QUALITY OF LIFE IN PATIENTS WITH THALASSEMIA MAJOR AND INTERMEDIA IN KERMAN-IRAN (I.R.)

Thalassemia is the most common hemoglobin disorder in the world and thalassemia major and intermedia stand among the most severe forms. Due to recent improvements in treatment, patients with thalassemia have longer life expectancies; hence it is of utmost importance to pay careful attention to their quality of life together with life expectancy. This study was conducted to assess the quality of life in patients with thalassemia and also to compare it between thalassemia major and intermedia. In this cross-sectional study, patients who referred for blood transfusion or follow-up visits were evaluated for their quality of life (QOL). Short Form-36 questionnaire was applied to evaluate QOL. In this study, 308 patients with a mean age of 22.95±4.82 years were evaluated. The scores of QOL were regarded as moderate in eight domains under evaluation; the least score was given to General Health (53.05±16.96) whereas the highest score was given to Physical Functioning (67.95±22.68). The QOL in the patients with thalassemia major was better than those with thalassemia intermedia regarding Physical Functioning and Role Limitation Emotional domains. Compared to injecting chelators, patients who received oral chelators showed to have a better QOL considering Social Functioning and Mental Health domain. The patients under study didn’t have a satisfying QOL ;  the QOL of patients with thalassemia major was better than that of patients with  thalassemia intermedia in only 2 domains of sf-36(Physical Functioning & Role limitation-Emotional). It is then essential that experts pay proper attention to improve QOL among patients

An integrated decision-making approach for green supplier selection in an agri-food supply chain: Threshold of robustness worthiness

Along with the increased competition in production and service areas, many organizations attempt to provide their products at a lower price and higher quality. On the other hand, consideration of environmental criteria in the conventional supplier selection methodologies is required for companies trying to promote green supply chain management (GSCM). In this regard, a multi-criteria decision-making (MCDM) technique based on analytic hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) is used to evaluate and rate the suppliers. Then, considering the resource constraint, weight of criteria and a rank of suppliers are taken into account in a multi-objective mixed-integer linear programming (MOMILP) to determine the optimum order quantity of each supplier under uncertain conditions. To deal with the uncertain multi-objectiveness of the proposed model, a robust goal programming (RGP) approach based on Shannon entropy is applied. The offered methodology is applied to a real case study from a green service food manufacturing company in Iran in order to verify its applicability with a sensitivity analysis performed on different uncertainty levels. Furthermore, the threshold of robustness worthiness (TRW) is studied by applying different budgets of uncertainty for the green service food manufacturing company. Finally, a discussion and conclusion on the applicability of the methodology is provided, and an outlook to future research projects is given

Paediatric pre-B acute lymphoblastic leukaemia-derived exosomes regulate immune function in human T cells

Exosomes derived from solid tumour cells are involved in immune suppression, angiogenesis and metastasis; however, the role of leukaemia-derived exosomes has less been investigated. Hence, changes in immune response-related genes and human T cells apoptosis co-incubated with exosomes isolated from patients' pre-B cell acute lymphoblastic leukaemia were evaluated in this in vitro study. Vein blood sample was obtained from each newly diagnosed acute lymphoblastic leukaemia (ALL) patient prior any therapy. ALL serum exosomes were isolated by ultrafiltration and characterized using Western blotting and transmission electron microscopy. Exosomes were then co-incubated with T lymphocytes and the gene expressions, as well as functions of human T cells were quantified by qRT-PCR. Apoptosis and caspase-3 and caspase-9 protein expression were also evaluated by flowcytometry and Western blotting analysis, respectively. Exosomes isolated from ALL patients affected T lymphocytes and elevated the apoptosis. Moreover, these exosomes altered the T cells profile into regulatory type by increasing the expression of FOXP3 and Tregs-related cytokines, including TGF-B and IL-10. The expression level of Th17-related transcription factors (RoRγt) and interleukins (IL-17 and IL-23) decreased after this treatment. According to our findings, exosomes derived from ALL patients' sera carry immunosuppressive molecules, indicating the possible effect of exosomes as liquid biomarkers for cancer staging