Search results for: network data mining

Authors: Lokendra Kumar Devangan, Ajay Mishra

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This paper describes the implementation of a large-scale SAS/OR model with significant pre-processing, scenario analysis, and post-processing work done using SAS. A large cement manufacturer with ten geographically distributed manufacturing plants for two variants of cement, around 400 warehouses serving as transshipment points, and several thousand distributor locations generating demand needed to optimize this multi-echelon, multi-modal transport supply chain separately for planning and allocation purposes. For monthly planning as well as daily allocation, the demand is deterministic. Rail and road networks connect any two points in this supply chain, creating tens of thousands of such connections. Constraints include the plant’s production capacity, transportation capacity, and rail wagon batch size constraints. Each demand point has a minimum and maximum for shipments received. Price varies at demand locations due to local factors. A large mixed integer programming model built using proc OPTMODEL decides production at plants, demand fulfilled at each location, and the shipment route to demand locations to maximize the profit contribution. Using base SAS, we did significant pre-processing of data and created inputs for the optimization. Using outputs generated by OPTMODEL and other processing completed using base SAS, we generated several reports that went into their enterprise system and created tables for easy consumption of the optimization results by operations.

Keywords: production planning, mixed integer optimization, network model, network optimization

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Authors: Dachuan Shi, M. Hecht, Y. Ye

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With the trend of digitalization in railway freight transport, a large number of freight wagons in Germany have been equipped with telematics devices, commonly placed on the wagon body. A telematics device contains a GPS module for tracking and a 3-axis accelerometer for shock detection. Besides these basic functions, it is desired to use the integrated accelerometer for condition monitoring without any additional sensors. Wheel flats as a common type of failure on wheel tread cause large impacts on wagons and infrastructure as well as impulsive noise. A large wheel flat may even cause safety issues such as derailments. In this sense, this paper proposes a machine learning approach for wheel flat detection by using car body accelerations. Due to suspension systems, impulsive signals caused by wheel flats are damped significantly and thus could be buried in signal noise and disturbances. Therefore, it is very challenging to detect wheel flats using car body accelerations. The proposed algorithm considers the envelope spectrum of car body accelerations to eliminate the effect of noise and disturbances. Subsequently, a 1-D convolutional neural network (CNN), which is well known as a deep learning method, is constructed to automatically extract features in the envelope-frequency domain and conduct classification. The constructed CNN is trained and tested on field test data, which are measured on the underframe of a tank wagon with a wheel flat of 20 mm length in the operational condition. The test results demonstrate the good performance of the proposed algorithm for real-time fault detection.

Keywords: fault detection, wheel flat, convolutional neural network, machine learning

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Authors: A. Prajapati, K. L. Koo, F. Ghassemi, M. Mulimakwenda

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In the UK, as the volume of non-linear loads connected to transmission grid continues to rise steeply, the harmonic distortion levels on transmission network are becoming a serious concern for the network owners and system operators. This paper outlines the findings of the study conducted to verify the proposal that the harmonic mitigation could be optimized and can be managed economically and effectively at the transmission network level by the Transmission Owner (TO) instead of the individual polluter connected to the grid. Harmonic mitigation studies were conducted on selected regions of the transmission network in England for recently connected offshore wind power plants to strategize and optimize selected harmonic filter options. The results – filter volume and capacity – were then compared against the mitigation measures adopted by the individual connections. Estimation ratios were developed based on the actual installed and optimal proposed filters. These estimation ratios were then used to derive harmonic filter requirements for future contracted connections. The study has concluded that a saving of 37% in the filter volume/capacity could be achieved if the TO is to centrally manage the harmonic mitigation instead of individual polluter installing their own mitigation solution.

Keywords: C-type filter, harmonics, optimization, offshore wind farms, interconnectors, HVDC, renewable energy, transmission owner

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Authors: Bhuyain Mobarok Hossain

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Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).

Keywords: image classification, object detection, localization, particle filter

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Authors: Tingyu Zhang

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This paper utilizes the Super-SBM model to assess water resources green efficiency (WRGE) among provinces in China and investigate its spatial and temporal features, based on the characteristic framework of “economy-environment-society.” The social network analysis is employed to examine the network pattern and spatial interaction of WRGE. Further, the quadratic assignment procedure method is utilized for examining the influencing factors of the spatial association of WRGE regarding “relationship.” The study reveals that: (1) the spatial distribution of WRGE demonstrates a distribution pattern of Eastern>Western>Central; (2) a remarkable spatial association exists among provinces; however, no strict hierarchical structure is observed. The internal structure of the WRGE network is characterized by the feature of "Eastern strong and Western weak". The block model analysis discovers that the members of the “net spillover” and “two-way spillover” blocks are mostly in the eastern and central provinces; “broker” block, which plays an intermediary role, is mostly in the central provinces; and members of the “net beneficiary” block are mostly in the western region. (3) Differences in economic development, degree of urbanization, water use environment, and water management have significant impacts on the spatial connection of WRGE. This study is dedicated to the realization of regional linkages and synergistic enhancement of WRGE, which provides a meaningful basis for building a harmonious society of human and water coexistence.

Keywords: water resources green efficiency, super-SBM model, social network analysis, quadratic assignment procedure

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Authors: Hang Yang, Jiahao Liu, Jichao Li, Kewei Yang, Minghao Li, Bingfeng Ge

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The system-of-systems (SoS) are utilized to characterize networks formed by integrating individual complex systems that demonstrate interdependence and interconnectedness. Research on the disintegration issue in SoS is significant in improving network survivability, maintaining network security, and optimizing SoS architecture. Accordingly, this study proposes an integrated framework called resilience-oriented disintegration in SoS (SoSRD), for modeling and solving the issue of SoS disintegration. Firstly, a SoS disintegration index (SoSDI) is presented to evaluate the disintegration effect of SoS. This index provides a practical description of the disintegration process and is the first integration of the network disintegration model and resilience models. Subsequently, we propose a resilience-oriented disintegration method based on reinforcement learning (RDRL) to enhance the efficiency of SoS disintegration. This method is not restricted by the problem scenario as well as considering the coexistence of disintegration (node/link removal) and recovery (node/link addition) during the process of SoS disintegration. Finally, the effectiveness and superiority of the proposed SoSRD are demonstrated through a case study. We demonstrate that our proposed framework outperforms existing indexes and methods in both node and link disintegration scenarios, providing a fresh perspective on network disintegration. The findings provide crucial insights into dismantling harmful SoS and designing a more resilient SoS.

Keywords: system-of-systems, disintegration index, resilience, reinforcement learning

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Authors: M. Ibrahim Ali, M. El Dawi, M. A. Mohamed Ali

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In this study, the use of magnetic gradient survey, and the geoelectrical ground methods used together to explore archaeological features in Nuri’s pyramids area. Research methods used and the procedures and methodologies have taken full right during the study. The magnetic survey method was used to search for archaeological features using (Geoscan Fluxgate Gradiometer (FM36)). The study area was divided into a number of squares (networks) exactly equal (20 * 20 meters). These squares were collected at the end of the study to give a major network for each region. Networks also divided to take the sample using nets typically equal to (0.25 * 0.50 meter), in order to give a more specific archaeological features with some small bipolar anomalies that caused by buildings built from fired bricks. This definition is important to monitor many of the archaeological features such as rooms and others. This main network gives us an integrated map displayed for easy presentation, and it also allows for all the operations required using (Geoscan Geoplot software). The parallel traverse is the main way to take readings of the magnetic survey, to get out the high-quality data. The study area is very rich in old buildings that vary from small to very large. According to the proportion of the sand dunes and the loose soil, most of these buildings are not visible from the surface. Because of the proportion of the sandy dry soil, there is no connection between the ground surface and the electrodes. We tried to get electrical readings by adding salty water to the soil, but, unfortunately, we failed to confirm the magnetic readings with electrical readings as previously planned.

Keywords: archaeological features, independent grids, magnetic gradient, Nuri pyramid

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Authors: Eric Asare, Jamie Evans, Mark Newton, Emiliano Bilotti

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Two types of filler shapes (sphere and flakes) and three different sizes are employed to study the size effect on PTC. The composite is prepared using a mini-extruder with high-density polyethylene (HDPE) as the matrix. A computer modelling is used to fit the experimental results. The percolation threshold decreases with decreasing filler size and this was observed for both the spherical particles as well as the flakes. This was caused by the decrease in interparticle distance with decreasing filler size. The 100 µm particles showed a larger PTC intensity compared to the 5 µm particles for the metal coated glass sphere and flake. The small particles have a large surface area and agglomeration and this makes it difficult for the conductive network to e disturbed. Increasing the filler content decreased the PTC intensity and this is due to an increase in the conductive network within the polymer matrix hence more energy is needed to disrupt the network.

Keywords: positive temperature coefficient (PTC) effect, conductive polymer composite (CPC), electrical conductivity

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Authors: M. K. Wijayarathna, S. M. Buddika Harshanath

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Monitoring student engagement has become a crucial part of the educational process and a reliable indicator of the capacity to retain information. As online learning classrooms are now more common these days, students' attention levels have become increasingly important, making it more difficult to check each student's concentration level in an online classroom setting. To profile student attention to various gradients of engagement, a study is a plan to conduct using machine learning models. Using a convolutional neural network, the findings and confidence score of the high accuracy model are obtained. In this research, convolutional neural networks are using to help discover essential emotions that are critical in defining various levels of participation. Students' attention levels were shown to be influenced by emotions such as calm, enjoyment, surprise, and fear. An improved virtual learning system was created as a result of these data, which allowed teachers to focus their support and advise on those students who needed it. Student participation has formed as a crucial component of the learning technique and a consistent predictor of a student's capacity to retain material in the classroom. Convolutional neural networks have a plan to implement the platform. As a preliminary step, a video of the pupil would be taken. In the end, researchers used a convolutional neural network utilizing the Keras toolkit to take pictures of the recordings. Two convolutional neural network methods are planned to use to determine the pupils' attention level. Finally, those predicted student attention level results plan to display on the graphical user interface of the System.

Keywords: HTML5, JavaScript, Python flask framework, AI, graphical user

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Authors: Jang kyun Cho, Jeong-dong Lee

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The advent of the Internet, mobile communications, and social network services has stimulated social interactions among consumers, allowing people to affect one another’s innovation adoptions by exchanging information more frequently and more quickly. Previous diffusion models, such as the Bass model, however, face limitations in reflecting such recent phenomena in society. These models are weak in their ability to model interactions between agents; they model aggregated-level behaviors only. The agent based model, which is an alternative to the aggregate model, is good for individual modeling, but it is still not based on an economic perspective of social interactions so far. This study assumes the presence of social utility from other consumers in the adoption of innovation and investigates the effect of individual interactions on innovation diffusion by developing a new model called the interaction-based diffusion model. By comparing this model with previous diffusion models, the study also examines how the proposed model explains innovation diffusion from the perspective of economics. In addition, the study recommends the use of a small-world network topology instead of cellular automata to describe innovation diffusion. This study develops a model based on individual preference and heterogeneous social interactions using utility specification, which is expandable and, thus, able to encompass various issues in diffusion research, such as reservation price. Furthermore, the study proposes a new framework to forecast aggregated-level market demand from individual level modeling. The model also exhibits a good fit to real market data. It is expected that the study will contribute to our understanding of the innovation diffusion process through its microeconomic theoretical approach.

Keywords: innovation diffusion, agent based model, small-world network, demand forecasting

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Authors: Nurunnahar Akter, Elena Kulinskaya, Nicholas Steel, Ilyas Bakbergenuly

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Although Hormone Replacement Therapy (HRT) is an effective treatment in ameliorating menopausal symptoms, it has mixed effects on different health outcomes, increasing, for instance, the risk of breast cancer. Because of this, many symptomatic women are left untreated. Untreated menopausal symptoms may result in other health issues, which eventually put an extra burden and costs to the health care system. All-cause mortality analysis may explain the net benefits and risks of the HRT therapy. However, it received far less attention in HRT studies. This study investigated the impact of HRT on all-cause mortality using electronically recorded primary care data from The Health Improvement Network (THIN) that broadly represents the female population in the United Kingdom (UK). The study entry date for this study was the record of the first HRT prescription from 1984, and patients were followed up until death or transfer to another GP practice or study end date, which was January 2017. 112,354 HRT users (cases) were matched with 245,320 non-users by age at HRT initiation and general practice (GP). The hazards of all-cause mortality associated with HRT were estimated by a parametric Weibull-Cox model adjusting for a wide range of important medical, lifestyle, and socio-demographic factors. The multilevel multiple imputation techniques were used to deal with missing data. This study found that during 32 years of follow-up, combined HRT reduced the hazard ratio (HR) of all-cause mortality by 9% (HR: 0.91; 95% Confidence Interval, 0.88-0.94) in women of age between 46 to 65 at first treatment compared to the non-users of the same age. Age-specific mortality analyses found that combined HRT decreased mortality by 13% (HR: 0.87; 95% CI, 0.82-0.92), 12% (HR: 0.88; 95% CI, 0.82-0.93), and 8% (HR: 0.92; 95% CI, 0.85-0.98), in 51 to 55, 56 to 60, and 61 to 65 age group at first treatment, respectively. There was no association between estrogen-only HRT and women’s all-cause mortality. The findings from this study may help to inform the choices of women at menopause and to further educate the clinicians and resource planners.

Keywords: hormone replacement therapy, multiple imputations, primary care data, the health improvement network (THIN)

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Authors: Umesh Kumar Singh, Chanchala Joshi

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With increasing dependency on IT infrastructure, the main objective of a system administrator is to maintain a stable and secure network, with ensuring that the network is robust enough against malicious network users like attackers and intruders. Security risk management provides a way to manage the growing threats to infrastructures or system. This paper proposes a framework for risk level estimation which uses vulnerability database National Institute of Standards and Technology (NIST) National Vulnerability Database (NVD) and the Common Vulnerability Scoring System (CVSS). The proposed framework measures the frequency of vulnerability exploitation; converges this measured frequency with standard CVSS score and estimates the security risk level which helps in automated and reasonable security management. In this paper equation for the Temporal score calculation with respect to availability of remediation plan is derived and further, frequency of exploitation is calculated with determined temporal score. The frequency of exploitation along with CVSS score is used to calculate the security risk level of the system. The proposed framework uses the CVSS vectors for risk level estimation and measures the security level of specific network environment, which assists system administrator for assessment of security risks and making decision related to mitigation of security risks.

Keywords: CVSS score, risk level, security measurement, vulnerability category

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Authors: Qais M. Yousef, Yasmeen A. Alshaer

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Over the last few years, the amount of data available on the globe has been increased rapidly. This came up with the emergence of recent concepts, such as the big data and the Internet of Things, which have furnished a suitable solution for the availability of data all over the world. However, managing this massive amount of data remains a challenge due to their large verity of types and distribution. Therefore, locating the required file particularly from the first trial turned to be a not easy task, due to the large similarities of names for different files distributed on the web. Consequently, the accuracy and speed of search have been negatively affected. This work presents a method using Electroencephalography signals to locate the files based on their contents. Giving the concept of natural mind waves processing, this work analyses the mind wave signals of different people, analyzing them and extracting their most appropriate features using multi-objective metaheuristic algorithm, and then classifying them using artificial neural network to distinguish among files with similar names. The aim of this work is to provide the ability to find the files based on their contents using human thoughts only. Implementing this approach and testing it on real people proved its ability to find the desired files accurately within noticeably shorter time and retrieve them as a first choice for the user.

Keywords: artificial intelligence, data contents search, human active memory, mind wave, multi-objective optimization

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Authors: (Ted) Edward Holmberg, Mahdi Abdelguerfi, Elias Ioup

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This research details a coverage measure for evaluating the effectiveness of observer node placements in a spatial bipartite network. This coverage measure can be used to optimize the configuration of stationary or mobile spatially oriented observer nodes, or a hybrid of the two, over time in order to fully utilize their capabilities. To demonstrate the practical application of this approach, we construct a SpatioTemporal Bipartite Network (STBN) using real-time crime center (RTCC) camera nodes and NOPD calls for service (CFS) event nodes from New Orleans, La (NOLA). We use the coverage measure to identify optimal placements for moving mobile RTCC camera vans to improve coverage of vulnerable areas based on temporal patterns.

Keywords: coverage measure, mobile node dynamics, Monte Carlo simulation, observer nodes, observable nodes, spatiotemporal bipartite knowledge graph, temporal spatial analysis

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Authors: D. Y. Fong, C. Y. Kuo, Y. T. Chiang, W. C. Lin

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Objectives: Qigong is an ancient Chinese practice in pursuit of a healthier body and a more peaceful mindset. It emphasizes on the restoration of vital energy (Qi) in body, mind, and spirit. The practice is the combination of gentle movements and mild breathing which help the doers reach the condition of tranquility. On account of the features of Qigong, first, we use cross-sectional methodology to compare the differences among the varied levels of Qigong practitioners on cognitive function with event-related potential (ERP) and electroencephalography (EEG). Second, we use the longitudinal methodology to explore the effects on the Qigong trainees for pretest and posttest on ERP and EEG. Current study adopts Attentional Network Test (ANT) task to examine the participants’ cognitive function, and aging-related researches demonstrated a declined tread on the cognition in older adults and exercise might ameliorate the deterioration. Qigong exercise integrates physical posture (muscle strength), breathing technique (aerobic ability) and focused intention (attention) that researchers hypothesize it might improve the cognitive function in aging adults. Method: Sixty participants were involved in this study, including 20 young adults (21.65±2.41 y) with normal physical activity (YA), 20 Qigong experts (60.69 ± 12.42 y) with over 7 years Qigong practice experience (QE), and 20 normal and healthy adults (52.90±12.37 y) with no Qigong practice experience as experimental group (EG). The EG participants took Qigong classes 2 times a week and 2 hours per time for 24 weeks with the purpose of examining the effect of Qigong intervention on cognitive function. ANT tasks (alert network, orient network, and executive control) were adopted to evaluate participants’ cognitive function via ERP’s P300 components and P300 amplitude topography. Results: Behavioral data: 1.The reaction time (RT) of YA is faster than the other two groups, and EG was faster than QE in the cue and flanker conditions of ANT task. 2. The RT of posttest was faster than pretest in EG in the cue and flanker conditions. 3. No difference among the three groups on orient, alert, and execute control networks. ERP data: 1. P300 amplitude detection in QE was larger than EG at Fz electrode in orient, alert, and execute control networks. 2. P300 amplitude in EG was larger at pretest than posttest on the orient network. 3. P300 Latency revealed no difference among the three groups in the three networks. Conclusion: Taken together these findings, they provide neuro-electrical evidence that older adults involved in Qigong practice may develop a more overall compensatory mechanism and also benefit the performance of behavior.

Keywords: Qigong, cognitive function, aging, event-related potential (ERP)

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Authors: Joanna Grzybowska, Magdalena Igras, Mariusz Ziółko

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Association rule learning is an approach for discovering interesting relationships in large databases. The analysis of relations, invisible at first glance, is a source of new knowledge which can be subsequently used for prediction. We used this data mining technique (which is an automatic and objective method) to learn about interesting affects associations in a corpus of emergency phone calls. We also made an attempt to match revealed rules with their possible situational context. The corpus was collected and subjectively annotated by two researchers. Each of 3306 recordings contains information on emotion: (1) type (sadness, weariness, anxiety, surprise, stress, anger, frustration, calm, relief, compassion, contentment, amusement, joy) (2) valence (negative, neutral, or positive) (3) intensity (low, typical, alternating, high). Also, additional information, that is a clue to speaker’s emotional state, was annotated: speech rate (slow, normal, fast), characteristic vocabulary (filled pauses, repeated words) and conversation style (normal, chaotic). Exponentially many rules can be extracted from a set of items (an item is a previously annotated single information). To generate the rules in the form of an implication X → Y (where X and Y are frequent k-itemsets) the Apriori algorithm was used - it avoids performing needless computations. Then, two basic measures (Support and Confidence) and several additional symmetric and asymmetric objective measures (e.g. Laplace, Conviction, Interest Factor, Cosine, correlation coefficient) were calculated for each rule. Each applied interestingness measure revealed different rules - we selected some top rules for each measure. Owing to the specificity of the corpus (emergency situations), most of the strong rules contain only negative emotions. There are though strong rules including neutral or even positive emotions. Three examples of the strongest rules are: {sadness} → {anxiety}; {sadness, weariness, stress, frustration} → {anger}; {compassion} → {sadness}. Association rule learning revealed the strongest configurations of affects (as well as configurations of affects with affect-related information) in our emergency phone calls corpus. The acquired knowledge can be used for prediction to fulfill the emotional profile of a new caller. Furthermore, a rule-related possible context analysis may be a clue to the situation a caller is in.

Keywords: data mining, emergency phone calls, emotional profiles, rules

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Authors: Sergio V Davalos, Alison L. Watkins

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User generated content (UGC) such as website post has data associated with it: time of the post, gender, location, type of device, and number of words. The text entered in user generated content (UGC) can provide a valuable dimension for analysis. In this research, each user post is treated as a collection of terms (words). In addition to the number of words per post, the frequency of each term is determined by post and by the sum of occurrences in all posts. This research focuses on one specific aspect of UGC: sentiment. Sentiment analysis (SA) was applied to the content (user posts) of two sets of political forums related to the US presidential elections for 2012 and 2016. Sentiment analysis results in deriving data from the text. This enables the subsequent application of data analytic methods. The SASA (SAIL/SAI Sentiment Analyzer) model was used for sentiment analysis. The application of SASA resulted with a sentiment score for each post. Based on the sentiment scores for the posts there are significant differences between the content and sentiment of the two sets for the 2012 and 2016 presidential election forums. In the 2012 forums, 38% of the forums started with positive sentiment and 16% with negative sentiment. In the 2016 forums, 29% started with positive sentiment and 15% with negative sentiment. There also were changes in sentiment over time. For both elections as the election got closer, the cumulative sentiment score became negative. The candidate who won each election was in the more posts than the losing candidates. In the case of Trump, there were more negative posts than Clinton’s highest number of posts which were positive. KNIME topic modeling was used to derive topics from the posts. There were also changes in topics and keyword emphasis over time. Initially, the political parties were the most referenced and as the election got closer the emphasis changed to the candidates. The performance of the SASA method proved to predict sentiment better than four other methods in Sentibench. The research resulted in deriving sentiment data from text. In combination with other data, the sentiment data provided insight and discovery about user sentiment in the US presidential elections for 2012 and 2016.

Keywords: sentiment analysis, text mining, user generated content, US presidential elections

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Authors: Krishna Pal Singh, Shailendra Kumar Gupta, Olaf Wolkenhauer

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Background: Our study aimed to identify common genes and potential targets across the four diseases, which include rheumatoid arthritis, melanoma metastasis, ulcerative colitis, and Crohn’s disease. We used a network and systems biology approach to identify the hub gene, which can act as a potential target for all four disease conditions. The regulatory network was extracted from the PPI using the MCODE module present in Cytoscape. Our objective was to investigate the significance of hub genes in these diseases using gene ontology and KEGG pathway enrichment analysis. Methods: Our methodology involved collecting disease gene-related information from DisGeNET databases and performing protein-protein interaction (PPI) network and core genes screening. We then conducted gene ontology and KEGG pathway enrichment analysis. Results: We found that IL6 plays a critical role in all disease conditions and in different pathways that can be associated with the development of all four diseases. Conclusions: The theoretical importance of our research is that we employed various systems and structural biology techniques to identify a crucial protein that could serve as a promising target for treating multiple diseases. Our data collection and analysis procedures involved rigorous scrutiny, ensuring high-quality results. Our conclusion is that IL6 plays a significant role in all four diseases, and it can act as a potential target for treating them. Our findings may have important implications for the development of novel therapeutic interventions for these diseases.

Keywords: melanoma metastasis, rheumatoid arthritis, inflammatory bowel diseases, integrated bioinformatics analysis

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Authors: Mohammad Mahdi Paydar, Armin Cheraghalipour, Mostafa Hajiaghaei-Keshteli

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Nowadays, in advanced countries, agriculture as one of the most significant sectors of the economy, plays an important role in its political and economic independence. Due to farmers' lack of information about products' demand and lack of proper planning for harvest time, annually the considerable amount of products is corrupted. Besides, in this paper, we attempt to improve these unfavorable conditions via designing an effective supply chain network that tries to minimize total costs of agricultural products along with minimizing shortage in demand points. To validate the proposed model, a stochastic optimization approach by using a branch and bound solver of the LINGO software is utilized. Furthermore, to accumulate the data of parameters, a case study in Mazandaran province placed in the north of Iran has been applied. Finally, using ɛ-constraint approach, a Pareto front is obtained and one of its Pareto solutions as best solution is selected. Then, related results of this solution are explained. Finally, conclusions and suggestions for the future research are presented.

Keywords: perishable products, stochastic optimization, agricultural supply chain, ɛ-constraint

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Authors: Marc Romero-Estonllo, Judith Ramos-Castro, Yaiza San Miguel, Beatriz Rodríguez-Garrido, Carmela Monterroso

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Mining activity is among the main sources of trace and heavy metal(loid) pollution worldwide, which is a hazard to human and environmental health. That is why several projects have been emerging for the remediation of such polluted places. Phytomanagement strategies draw good performances besides big side benefits. In this work, a glasshouse assay with trace element polluted soils from an old Cu mine ore (NW of Spain) which forms part of the PhytoSUDOE network of phytomanaged contaminated field sites (PhytoSUDOE Project (SOE1/P5/E0189)) was set. The objective was to evaluate improvements induced by the following phytoremediation-related treatments. Three increasingly complex amendments alone or together with plant growth (Populus nigra L. alone and together with Tripholium repens L.) were tested. And three different rhizosphere bioinocula were applied (Plant Growth Promoting Bacteria (PGP), mycorrhiza (MYC), or mixed (PGP+MYC)). After 110 days of growth, plants were collected, biomass was weighed, and tree length was measured. Physical-chemical analyses were carried out to determine pH, effective Cation Exchange Capacity, carbon and nitrogen contents, bioavailable phosphorous (Olsen bicarbonate method), pseudo total element content (microwave acid digested fraction), EDTA extractable metals (complexed fraction), and NH4NO3 extractable metals (easily bioavailable fraction). On plant material, nitrogen content and acid digestion elements were determined. Amendment usage, plant growth, and bioinoculation were demonstrated to improve soil fertility and/or plant health within the time span of this study. Particularly, pH levels increased from 3 (highly acidic) to 5 (acidic) in the worst-case scenario, even reaching 7 (neutrality) in the best plots. Organic matter and pH increments were related to polluting metals’ bioavailability decrements. Plants grew better both with the most complex amendment and the middle one, with few differences due to bioinoculation. Using the less complex amendment (just compost) beneficial effects of bioinoculants were more observable, although plants didn’t thrive very well. On unamended soils, plants neither sprouted nor bloomed. The scheme assayed in this study is suitable for phytomanagement of these kinds of soils affected by mining activity. These findings should be tested now on a larger scale.

Keywords: aided phytoremediation, mine pollution, phytostabilization, soil pollution, trace elements

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Authors: Heri Suryoatmojo, Adi Kurniawan, Feby A. Pamuji, Nursalim, Syaffaruddin, Herbert Innah

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Photovoltaic (PV) system hybrid with diesel system is utilized widely for electrification in remote area. PV output power fluctuates due to uncertainty condition of temperature and sun irradiance. When the penetration of PV power is large, the reliability of the power utility will be disturbed and seriously impact the unstable frequency of system. Therefore, designing a robust frequency controller in PV-diesel hybrid power system is very important. This paper proposes new method of frequency control application in hybrid PV-diesel system based on artificial neural network (ANN). This method can minimize the frequency deviation without smoothing PV output power that controlled by maximum power point tracking (MPPT) method. The neural network algorithm controller considers average irradiance, change of irradiance and frequency deviation. In order the show the effectiveness of proposed algorithm, the addition of battery as energy storage system is also presented. To validate the proposed method, the results of proposed system are compared with the results of similar system using MPPT only. The simulation results show that the proposed method able to suppress frequency deviation smaller compared to the results of system using MPPT only.

Keywords: energy storage system, frequency deviation, hybrid power generation, neural network algorithm

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Authors: Raya Sulaiman

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Critical discourse analysis investigates how discourse is used to abuse power relationships. Political debates constitute discourses which mirror aspects of ideologies. The Arab world has been one of the most unsettled zones in the world and has dominated global politics due to the Arab revolutions which started in 2010. This study aimed at uncovering the ideological intentions in the formulation and circulation of hegemonic political ideology in the TV political debates of the 2011 to 2012 Yemen revolution, how ideology was used as a tool of hegemony. The study specifically examined the ideologies associated with the use of protesters as a social actor. Data of the study consisted of four debates (17350 words) from four live debate programs: The Opposite Direction, In Depth, Behind the News and the Revolution Talk that were staged at Al Jazeera TV channel between 2011 and 2012. Data was readily transcribed by Al Jazeera online. Al Jazeera was selected for the study because it is the most popular TV network in the Arab world and has a strong presence, especially during the Arab revolutions. Al Jazeera has also been accused of inciting protests across the Arab region. Two debate sites were identified in the data: government and anti-government. The government side represented the president Ali Abdullah Saleh and his regime while the anti-government side represented the gathering squares who demanded the president to ‘step down’. The study analysed verbal discourse aspects of the debates using critical discourse analysis: aspects from the Social Actor Network model of van Leeuwen. This framework provides a step-by-step analysis model, and analyses discourse from specific grammatical processes into broader semantic issues. It also provides representative findings since it considers discourse as representative and reconstructed in social practice. Study findings indicated that Al Jazeera and the anti-government had similarities in terms of the ideological intentions related to the protesters. Al Jazeera victimized and incited the protesters which were similar to the anti-government. Al Jazeera used assimilation, nominalization, and active role allocation as the linguistic aspects in order to reach its ideological intentions related to the protesters. Government speakers did not share the same ideological intentions with Al Jazeera. Study findings indicated that Al Jazeera had excluded the government from its debates causing a violation to its slogan, the opinion, and the other opinion. This study implies the powerful role of discourse in shaping ideological media intentions and influencing the media audience.

Keywords: Al Jazeera network, critical discourse analysis, ideology, Yemeni revolution

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Authors: Lu Zhao, Nadir Farhi, Yeltsin Valero, Zoi Christoforou, Nadia Haddadou

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In this paper, a long short-term memory (LSTM) neural network model is proposed to replicate simultaneously car-following and lane-changing behaviors in road networks. By combining two kinds of LSTM layers and three input designs of the neural network, six variants of the LSTM model have been created. These models were trained and tested on the NGSIM 101 dataset, and the results were evaluated in terms of longitudinal speed and lateral position, respectively. Then, we compared the LSTM model with a classical car-following model (the intelligent driving model (IDM)) in the part of speed decision. In addition, the LSTM model is compared with a model using classical neural networks. After the comparison, the LSTM model demonstrates higher accuracy than the physical model IDM in terms of car-following behavior and displays better performance with regard to both car-following and lane-changing behavior compared to the classical neural network model.

Keywords: traffic modeling, neural networks, LSTM, car-following, lane-change

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Authors: Fuyuan Ma, Ying Wang, Xin Wang

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Social networks have become breeding grounds for the rapid spread of rumors and malicious information, posing threats to societal stability and causing significant public harm. Existing research focuses on simulating the spread of information and its impact on users through propagation dynamics and applies methods such as greedy approximation strategies to approximate the optimal control solution at the global scale. However, the greedy strategy at the global scale may fall into locally optimal solutions, and the approximate simulation of information spread may accumulate more errors. Therefore, we propose a multi-scale control model for network group behavior, introducing individual and group scales on top of the greedy strategy’s global scale. At the individual scale, we calculate the propagation influence of nodes based on their structural attributes to alleviate the issue of local optimality. At the group scale, we conduct precise propagation simulations to avoid introducing cumulative errors from approximate calculations without increasing computational costs. Experimental results on three real-world datasets demonstrate the effectiveness of our proposed multi-scale model in controlling network group behavior.

Keywords: influence blocking maximization, competitive linear threshold model, social networks, network group behavior

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Authors: Sophia Shi

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Acute kidney injury (AKI) is the sudden onset of kidney damage in which the kidneys cannot filter waste from the blood, requiring emergency hospitalization. AKI patient mortality rate is high in the ICU and is virtually impossible for doctors to predict because it is so unexpected. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. De-identified patient data from the MIMIC-III database and de-identified kidney images and corresponding patient records from the Beijing Hospital of the Ministry of Health were collected. Using data features including serum creatinine among others, two numeric models using MIMIC and Beijing Hospital data were built, and with the hospital ultrasounds, an image-only model was built. Convolutional neural networks (CNN) were used, VGG and Resnet for numeric data and Resnet for image data, and they were combined into a hybrid model by concatenating feature maps of both types of models to create a new input. This input enters another CNN block and then two fully connected layers, ending in a binary output after running through Softmax and additional code. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, achieving an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU, so that doctors can take preventative measures and diminish mortality risks and severe kidney damage.

Keywords: Acute kidney injury, Convolutional neural network, Hybrid deep learning, Patient record data, ResNet, Ultrasound kidney images, VGG

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Authors: Thomas F. Ducey, Kristin M. Trippe, James A. Ippolito, Jeffrey M. Novak, Mark G. Johnson, Gilbert C. Sigua

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The Oronogo-Duenweg mining belt –approximately 20 square miles around the Joplin, Missouri area– is a designated United States Environmental Protection Agency Superfund site due to lead-contaminated soil and groundwater by former mining and smelting operations. Over almost a century of mining (from 1848 to the late 1960’s), an estimated ten million tons of cadmium, lead, and zinc containing material have been deposited on approximately 9,000 acres. Sites that have undergone remediation, in which the O, A, and B horizons have been removed along with the lead contamination, the exposed C horizon remains incalcitrant to revegetation efforts. These sites also suffer from poor soil microbial activity, as measured by soil extracellular enzymatic assays, though 16S ribosomal ribonucleic acid (rRNA) indicates that microbial diversity is equal to sites that have avoided mine-related contamination. Soil analysis reveals low soil organic carbon, along with high levels of bio-available zinc, that reflect the poor soil fertility conditions and low microbial activity. Our study looked at the use of several materials to restore and remediate these sites, with the goal of improving soil health. The following materials, and their purposes for incorporation into the study, were as follows: manure-based biochar for the binding of zinc and other heavy metals responsible for phytotoxicity, locally sourced biosolids and compost to incorporate organic carbon into the depleted soils, effective microorganisms harvested from nearby pristine sites to provide a stable community for nutrient cycling in the newly composited 'soil material'. Our results indicate that all four materials used in conjunction result in the greatest benefit to these mine-impacted soils, based on above ground biomass, microbial biomass, and soil enzymatic activities.

Keywords: locally effective microorganisms, biochar, remediation, reclamation

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Authors: Andrew T. Hendrickson, Liven De Marez, Marijn Martens, Gytha Muller, Tudor Paisa, Koen Ponnet, Catherine Schweizer, Megan Van Meer, Mariek Vanden Abeele

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Quantifying and understanding the myriad ways people use their phones and how that impacts their relationships, cognitive abilities, mental health, and well-being is increasingly important in our phone-centric society. However, most studies on the patterns of phone use have focused on theory-driven tests of specific usage hypotheses using self-report questionnaires or analyses of smaller datasets. In this work we present a series of analyses from a large corpus of over 3000 users that combine data-driven and theory-driven analyses to identify reliable smartphone usage patterns and clusters of similar users. Furthermore, we compare the stability of user clusters across user- and system-initiated sessions, as well as during the hypothesized ritualized behavior times directly before and after sleeping. Our results indicate support for some hypothesized usage patterns but present a more complete and nuanced view of how people use smartphones.

Keywords: data mining, experience sampling, smartphone usage, health and well being

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Authors: O. A. Ajigini, E. N. Mwim

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Cybersecurity is the protection of computers, programs, networks, and data from attack, damage, unauthorised, unintended access, change, or destruction. Organisations collect, process and store their confidential and sensitive information on computers and transmit this data across networks to other computers. Moreover, the advent of internet technologies has led to various cyberattacks resulting in dangerous consequences for organisations. Therefore, with the increase in the volume and sophistication of cyberattacks, there is a need to develop models and make recommendations for the management of cybersecurity threats in organisations. This paper reports on various threats that cause malicious damage to organisations in cyberspace and provides measures on how these threats can be eliminated or reduced. The paper explores various aspects of protection measures against cybersecurity threats such as handling of sensitive data, network security, protection of information assets and cybersecurity awareness. The paper posits a model and recommendations on how to manage cybersecurity threats in organisations effectively. The model and the recommendations can then be utilised by organisations to manage the threats affecting their cyberspace. The paper provides valuable information to assist organisations in managing their cybersecurity threats and hence protect their computers, programs, networks and data in cyberspace. The paper aims to assist organisations to protect their information assets and data from cyberthreats as part of the contributions toward community engagement.

Keywords: confidential information, cyberattacks, cybersecurity, cyberspace, sensitive information

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Authors: M. Heydari Vini

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There is a complicated relation between effective input parameters of cold rolling and output rolling force and exit thickness of strips.in many mathematical models, the effect of some rolling parameters have been ignored and the outputs have not a desirable accuracy. In the other hand, there is a special relation among input thickness of strips,the width of the strips,rolling speeds,mandrill tensions and the required exit thickness of strips with rolling force and the real exit thickness of the rolled strip. First of all, in this paper the effective parameters of cold rolling process modeled using an artificial neural network according to the optimum network achieved by using a written program in MATLAB,it has been shown that the prediction of rolling stand parameters with different properties and new dimensions attained from prior rolled strips by an artificial neural network is applicable.

Keywords: cold rolling, artificial neural networks, rolling force, real rolled thickness of strips

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Authors: Abdulaziz Alakeel, Roaa Alamri, Abdulrahman Alomair, Mohammed Fouda

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Introduction: Ibrutinib is a selective, potent, and irreversible small-molecule inhibitor of Bruton's tyrosine kinase (BTK). It forms a covalent bond with a cysteine residue (CYS-481) at the active site of Btk, leading to inhibition of Btk enzymatic activity. The drug is indicated to treat certain type of cancers such as mantle cell lymphoma (MCL), chronic lymphocytic leukaemia and Waldenström's macroglobulinaemia (WM). Cardiac failure is a condition referred to inability of heart muscle to pump adequate blood to human body organs. There are multiple types of cardiac failure including left and right-sided heart failure, systolic and diastolic heart failures. The aim of this review is to evaluate the risk of cardiac failure associated with the use of ibrutinib and to suggest regulatory recommendations if required. Methodology: Signal Detection team at the National Pharmacovigilance Center (NPC) of Saudi Food and Drug Authority (SFDA) performed a comprehensive signal review using its national database as well as the World Health Organization (WHO) database (VigiBase), to retrieve related information for assessing the causality between cardiac failure and ibrutinib. We used the WHO- Uppsala Monitoring Centre (UMC) criteria as standard for assessing the causality of the reported cases. Results: Case Review: The number of resulted cases for the combined drug/adverse drug reaction are 212 global ICSRs as of July 2020. The reviewers have selected and assessed the causality for the well-documented ICSRs with completeness scores of 0.9 and above (35 ICSRs); the value 1.0 presents the highest score for best-written ICSRs. Among the reviewed cases, more than half of them provides supportive association (four probable and 15 possible cases). Data Mining: The disproportionality of the observed and the expected reporting rate for drug/adverse drug reaction pair is estimated using information component (IC), a tool developed by WHO-UMC to measure the reporting ratio. Positive IC reflects higher statistical association while negative values indicates less statistical association, considering the null value equal to zero. The results of (IC=1.5) revealed a positive statistical association for the drug/ADR combination, which means “Ibrutinib” with “Cardiac Failure” have been observed more than expected when compared to other medications available in WHO database. Conclusion: Health regulators and health care professionals must be aware for the potential risk of cardiac failure associated with ibrutinib and the monitoring of any signs or symptoms in treated patients is essential. The weighted cumulative evidences identified from causality assessment of the reported cases and data mining are sufficient to support a causal association between ibrutinib and cardiac failure.

Keywords: cardiac failure, drug safety, ibrutinib, pharmacovigilance, signal detection

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