Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of financial market development on the CO2 Emissions in GCC countries

Article history: Received May 15 2020 Received in revised format May 16 2020 Accepted June 29 2020 Available online June 29 2020 Financial development market (FMD) may have positive or negative environmental consequences. This research investigated the effects of FMD and income on CO2 emissions in Gulf Cooperation Council (GCC) countries during 1980-2018. We found that income had positive effect but FMD had insignificant impact on emissions in GCC panel. Then, we tested these effects in the individual country time series and found that income had positive impact in Saudi Arabia, Kuwait and Oman and had insignificant effect in other GCC countries in long run. Effect of FMD was positive in Oman, was negative in UAE and was insignificant in rest of GCC countries. Effect of income was positive in Saudi Arabia and Kuwait and was insignificant for other countries in short run. The effect of FMD was positive in Kuwait and was negative in UAE. We recommend UAE to expand the financial market and suggest Oman and Kuwait to have a check on the financially supported pollution-oriented activities. by the authors; licensee Growing Science, Canada 20 © 20
Profit-Maximized Collaborative Computation Offloading and Resource Allocation in Distributed Cloud and Edge Computing Systems

Edge computing is a new architecture to provide computing, storage, and networking resources for achieving the Internet of Things. It brings computation to the network edge in close proximity to users. However, nodes in the edge have limited energy and resources. Completely running tasks in the edge may cause poor performance. Cloud data centers (CDCs) have rich resources for executing tasks, but they are located in places far away from users. CDCs lead to long transmission delays and large financial costs for utilizing resources. Therefore, it is essential to smartly offload users’ tasks between a CDC layer and an edge computing layer. This work proposes a cloud and edge computing system, which has a terminal layer, edge computing layer, and CDC layer. Based on it, this work designs a profit-maximized collaborative computation offloading and resource allocation algorithm to maximize the profit of systems and guarantee that response time limits of tasks are strictly met. In each time slot, this work jointly considers CPU, memory, and bandwidth resources, load balance of all heterogeneous nodes in the edge layer, maximum amount of energy, maximum number of servers, and task queue stability in the CDC layer. Considering the abovementioned factors, a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based migrating birds optimization procedure to obtain a close-to-optimal solution. The proposed method achieves joint optimization of computation offloading between CDC and edge, and resource allocation in CDC. Realistic data-based simulation results demonstrate that it realizes higher profit than its peers. Note to Practitioners—This work considers the joint optimization of computation offloading between Cloud data center (CDC) and edge computing layers, and resource allocation in CDC. It is important to maximize the profit of distributed cloud and edge computing systems by optimally scheduling all tasks between them given user-specific response time limits of tasks. It is challenging to execute them in nodes in the edge computing layer because their computation resources and battery capacities are often constrained and heterogeneous. Current offloading methods fail to jointly optimize computation offloading and resource allocation for nodes in the edge and servers in CDC. They are insufficient and coarse-grained to schedule arriving tasks. In this work, a novel algorithm is proposed to maximize the profit of distributed cloud and edge computing systems while meeting response time limits of tasks. It explicitly specifies the task service rate and the selected node for each task in each time slot by considering resource limits, load balance requirement, and processing capacities of nodes in the edge, and server and energy constraints in CDC. Real-life data-driven simulations show that the proposed method realizes a larger profit than several typical offloading strategies. It can be readily implemented and incorporated into large-scale industrial computing systems.
DQN-Based Optimization Framework for Secure Sharded Blockchain Systems

High levels of scalability and reliability are needed to support the massive Internet-of-Things (IoT) services. In particular, blockchains can be effectively used to safely manage data from large-scale IoT networks. However, current blockchain systems have low transactions per second (TPS) rates and scalability limitations that make them unsuitable. To solve the above issues, this article proposes a deep $Q$ network shard-based blockchain (DQNSB) scheme that dynamically finds the optimal throughput configuration. In this article, a novel analysis of sharded blockchain latency and security-level characterization is provided. Using the analysis equations, the DQNSB scheme estimates the level of maliciousness and adapts the blockchain parameters to enhance the security level considering the amount of malicious attacks on the consensus process. To achieve this purpose, deep reinforcement learning (DRL) agents are trained to find the optimal system parameters in response to the network status, and adaptively optimizes the system throughput and security level. The simulation results show that the proposed DQNSB scheme provides a much higher TPS than the existing DRL-enabled blockchain technology while maintaining a high security level.
Signature based Network Intrusion Detection System using Feature Selection on Android

This paper Smart Intrusion Detection System (IDS), is a contribution to efforts towards detecting intrusion and malicious activities on Android phone. The goal of this paper is to raise user’s awareness of the high rate of intrusions or malicious activities on Android phones and to provide counter measure system for more secured operations. The proposed system (SIDS) detects any intrusion or illegal activities on android and also takes a selfie of the intruder unknown to him/her and keep in the log for the view of the user. The object oriented analysis and design method (OOADM), was adopted in the development. This approach was used to model and develop the system using real intrusion features and processes to detect intrusions more flexibly and efficiently. Signature detection was also used to detect attacks by looking for specific patterns. The system detects intrusions and immediately sends an alert to the user to notify of an illegal or malicious attempt and the location of the intruder.
Fusing Algorithm for Dense Point Cloud in Multi-view Stereo

As technologies using digital camera have been developed, 3D images can be constructed from the pictures captured by using multiple cameras. The 3D image data is represented in a form of point cloud which consists of 3D coordinate of the data and the related attributes. Various techniques have been proposed to construct the point cloud data. Among them, Structure-from-Motion (SfM) and Multi-view Stereo (MVS) are examples of the image-based technologies in this field. Based on the conventional research, the point cloud data generated from SfM and MVS may be sparse because the depth information may be incorrect and some data have been removed. In this paper, we propose an efficient algorithm to enhance the point cloud so that the density of the generated point cloud increases. Simulation results show that the proposed algorithm outperforms the conventional algorithms objectively and subjectively.
Optimal Operation of Integrated Electrical and Natural Gas Networks with a Focus on Distributed Energy Hub Systems

Coordinated multi-carrier energy systems with natural gas and electricity energies provide specific opportunities to improve energy efficiency and flexibility of the energy supply. The interdependency of electricity and natural gas networks faces multiple challenges from power and gas flow in corresponding feeders and pipes and connection points between two infrastructures’ points of view. However, the energy hub concepts as the fundamental concept of multi-carrier energy systems with multiple conversion, storage, and generation facilities can be considered as a connection point between electricity and gas grids. Hence, this paper proposes an optimal operation of coordinated gas and electricity distribution networks by considering interconnected energy hubs. The proposed energy hub is equipped with combined heat and power units, a boiler, battery energy storage, a heat pump, and a gas-fired unit to meet the heating and electrical load demands. The proposed model is formulated as a two-stage scenario-based stochastic model aiming to minimize total operational cost considering wind energy, electrical load, and real-time power price uncertainties. The proposed integrated energy system can participate in real-time and day-ahead power markets, as well as the gas market, to purchase its required energy. The AC-power flow and Weymouth equation are extended to describe power and gas flow in feeders and gas pipelines, respectively. Therefore, a realistic model for the integrated electricity and gas grids considering coupling constraints is satisfied. The proposed model is tested on the integrated energy system and consists of a 33-bus electrical network and a 6-node gas grid with multiple interconnected energy hubs, where the numerical results reveal the effectiveness of the proposed model.
Finite/fixed-time synchronization for complex networks via quantized adaptive control

In this paper, a unified theoretical method is presented to implement the finite/fixed-time synchronization control for complex networks with uncertain inner coupling. The quantized controller and the quantized adaptive controller are designed to reduce the control cost and save the channel resources, respectively. By means of the linear matrix inequalities technique, two sufficient conditions are proposed to guarantee that the synchronization error system of the complex networks is finite/fixed-time stable in virtue of the Lyapunov stability theory. Moreover, two types of setting time, which are dependent and independent on the initial values, are given respectively. Finally, the effectiveness of the control strategy is verified by a simulation example.
Regulating the Direction That Power Flows in Microwave Transmission Line Systems With Huygens Sources

An innovative method based on Huygens sources to regulate the direction that power flows in microwave transmission line systems is developed and confirmed with measurements. The phase difference between the electric and magnetic currents in the Huygens source is utilized to finely control the ratio of the wave propagation amplitudes and, hence, power flows along the transmission line in opposite directions. The operating principles are elucidated with both the field distributions in a rectangular waveguide as the transmission line system and the voltages and currents in a transmission line circuit model driven by Huygens sources. The transmission line circuit models excited with electric and magnetic current sources separately and with their balanced combination provide a precise means to quantitatively demonstrate the tunability of the power flow with the Huygens source. A proof-of-concept experiment was implemented in a microwave rectangular waveguide to validate the theoretical analysis. The measured results, in good agreement with their simulated values, demonstrate that the reported approach leads to a wideband operation and large dynamic directional power ratios that are advantageous in the design of multifunctional electromagnetic devices and systems.
Hybrid Duty Modulation for Dual Active Bridge Converter to Minimize RMS Current and Extend Soft-Switching Range Using the Frequency Domain Analysis

In this article, an efficiency-oriented hybrid duty modulation (HDM) scheme is proposed for the dual active bridge (DAB) converter that can reduce the inductor root-mean-square (rms) current and extend the switch soft-switching range. First, a complete description of the combination forms of different modulation schemes is presented considering the allocation of the zero-voltage portion. A unified description for these combination forms is obtained in the frequency domain. Second, the symmetric–symmetric duty modulation (SSDM) scheme and asymmetric–symmetric duty modulation (A2SDM) scheme are established to minimize the inductor rms current by considering its fundamental component. Third, the characteristics of SSDM, A2SDM, and the traditional phase shift modulation schemes are compared from several aspects, mainly including the inductor rms current and the soft-switching performance. Furthermore, the loss breakdown analysis results for the three modulation schemes are presented and compared. According to these results, the HDM scheme is proposed to improve the overall efficiency of the DAB converter. Finally, an experimental prototype was built with a peak efficiency of 97%, which validates the effectiveness of the proposed modulation scheme.
Nutrient Digestibility in Labeo rohita Fingerlings Fed Citric Acid Acidified Phytase Sprayed Diet

Syed Zakir Hussain Shah1*, Muhammad Afzal2, Mahroze Fatima3, Syed Makhdoom Hussain4 and Tanveer Ahmed5 1Department of Zoology, University of Gujrat, Gujrat, Pakistan 2Fish Nutrition Laboratory, Department of Zoology, Wildlife and Fisheries, University of Agriculture, Faisalabad 3Department of Fisheries and Aquaculture, University of Veterinary and Animal Sciences, Lahore 4Department of Zoology, Government College University, Faisalabad 5Aquaculture Biotechnology Laboratory, Department of Zoology, Wildlife and Fisheries, University of Agriculture, Faisalabad Article Information Received 23 September 2015 Revised 02 May 2019 Accepted 12 June 2019 Available online 22 June 2020
New Record of the Non-Native Ophiothela mirabilis (Verill 1867) in St. Kitts, West Indies

AOMB . MS.ID. 000526. Abstract We describe the presence of the non-native Pacific ophiuroid species Ophiothela mirabilis on the island of St. Kitts, federation of St. Kitts and Nevis. Individuals were found in various states of asexual reproduction on common Caribbean sea fans ( Gorgonia ventalina ) at two locations. Definitive identification was based on morphological features and sequencing of the 18s ribosomal RNA gene. The mitochondrial CO1 gene was also sequenced to assist in future Atlantic population studies of this invasive species. The coloration of the individuals as well as their current distribution suggest they are of a single lineage connected to the initial Brazilian invasion site, possibly vectored by shipping. Histological of G. ventalina tissue colonized by O. mirabilis found no changes to indicate direct parasite-induced damage to the host. Further study is needed to understand rate and route of invasion and the impact on its
Determination of NP Fertilizer Rates and Plant Population Density on Yield and Yield Related Parameters of Quality Protein Maize (Zea mays L.) in Southwestern Ethiopia

Field studies were conducted in 2015 and 2016 under rain fed conditions in south western Ethiopia, Jimma zone to determine optimum plant population density and NP fertilizer on Maize. The experiment had four NP fertilizer rates; 69/52, 92/69, 115/86, and 138/104 N/P2O5 kg ha-1 and four plant population density; 44,444(75cm*30cm), 53,333(75cm*25cm), 66,666(75cm*20cm) and 88,888(75cm*15cm) plants per hectare. Each treatment was assigned to each plot in split plot design. Grain yield and above ground biomass significantly increased as plant populations and NP fertilizer rates increased. The number of ears per plant was significantly decreased as plant population density increased and increased with the increased rate of NP fertilizer. The greater lodging was recorded in 2015 cropping season when compared with that of 2016 cropping season. The highest grain yield 8752 kg ha-1 and above ground biomass 18.33 ton ha-1 were recorded from 138/104 N/P2O5 fertilizer. Regarding plant population density the highest grain yield 9401 kg ha-1 and above ground biomass 19.90 ton ha-1 were recorded from 88,888 plants ha-1 (75cm*15cm). The highest net benefit 30,669ETB ha-1 with highest MRR 164% was recorded from 115/86 N/P2O5 kg ha-1. Concerning plant population density the highest net benefit 36,705 ETB ha-1 with acceptable MRR 585% was obtained from 88,888 plants ha-1 (75*15cm). Therefore, plant population density 88,888 plants ha-1 (75*15cm a plant hill-1 or 75 x 30 cm two plants hill-1) with N/P2O5 fertilizer rate of 115/86 kg ha-1 can be advisable for farmers in the study area and adjacent woredas’ with similar agro-ecologies.
Nitrogen fertilization in pecan and its effect on leaf nutrient concentration, yield and nut quality

Several studies indicate that nitrogen fertilization is one of the main constraints in obtaining higher yields and better nut quality in pecan (Carya illinoensis [Wangenh] K. Koch). Therefore, the aim of this research was to evaluate a single and split application of nitrogen in ‘Western Schley’ variety pecan, as well as its effect on leaf nutrient concentration, yield and nut quality. A randomized complete block design with six replicates was established. N doses were 100, 150 and 200 kg∙ha-1; the single application was made in March and the split one in March and June. The leaf concentration of total N, P, K+ , Ca2+, Mg2+, Fe2+, Mn2+, Zn2+ and Cu2+, yield (kg∙tree-1), quality (nuts per kilogram and kernel percentage) and nitrogen use efficiency (NUE) were determined. Significant interaction between factors was detected for the Zn2+ concentration and the single applied dose of 200 kg∙ha-1. With the single application of 100 kg∙ha-1, the highest yield (44.60 kg∙tree-1) and nuts per kilogram (194.83) values were recorded, where the kernel percentage remained constant. The maximum NUE was obtained with 100 kg∙ha-1 applied in a single and split application (31.10 and 30.10 kg of nuts produced per kg of N applied, respectively). Increased yield and nut quality is the main objective in applying optimal N doses; however, ways of reducing the economic and environmental impact caused by its excessive application should also be sought.
Social-Aware Proactive Content Caching and Sharing in Multi-Access Edge Networks

The ever-increasing data explosion, particular mobile video traffic, increases the backhaul load and makes it difficult for the centralized cloud to meet the requirements of various services. Accordingly, proactive caching at mobile devices gains more attentions, which is envisioned as a promising technology to relieve the backhaul traffic and cater to diverse quality of service in multi-access edge networks. In this article, we study the problem of proactive caching and content sharing for mobile devices in multi-access edge networks. Since users’ mobility behaviors and social impacts on content preference distort the performance of content requests, a long-term strategy of proactive caching is proposed to minimize the sum of communication costs to get the requested contents. By taking the mutual interaction of devices into account, we design a local cooperative utility function that is used to derive a distributed proactive content caching algorithm using game theory. Theoretical analysis is provided to confirm the stability and optimality of the proposed algorithm. Furthermore, simulation results show that the proposed algorithm converges efficiently and, most importantly, it outperforms compared to no local cooperation caching scheme, non-social aware caching scheme and randomly caching scheme for various kinds of user behaviors and device caching capacity.
Deep Learning-Enhanced Internet of Medical Things to Analyze Brain CT Scans of Hemorrhagic Stroke Patients: A New Approach

Stroke is among the first pathologies that kill the most in the world, ranking second in deaths from illness. Each year, around 16 million people worldwide are victims of this disease, with approximately 38 % of cases brought to death. Computed Tomography (CT) is a super effective method to aid the medical diagnosis of stroke. But the analysis is subject to variations in the perception of specialists about its characteristics. Faced with the challenge of diagnosing stroke on CT images, this study proposes a fully automatic system based on Health of Things capable of classifying CT images of the skull through deep learning networks, classifying them into (Uninjured or Hemorrhagic stroke). After the image classification, Mask R-CNN segments the stroke through a learning transfer process, combined with machine learning methods. Our innovative method selected excellent results, both for classification with 100 % accuracy, and for segmentation in our best model (Mask + kNN) that reached 99.93% specificity and 99.73 accuracy, with segmentation time of 4.00 seconds, surpassing literature methods based on automatic models.
Multiphase flow in wells and pipelines

This conference focuses primarily on multi-phase flow modeling and calculation methods for oil and gas although two papers focus more on the fluid mechanics of fluidized beds. Papers include theoretical, numerical modeling, experimental investigation, and state-of-the-art review aspects of multiphase flow. The theme of the symposium being general, the papers reflect generality of gas-liquid, liquid-solid, and gas solid flows. One paper deals with nuclear reactor safety as it relates to fluid flow through the reactor.
Caenorhabditis elegans, a Host to Investigate the Probiotic Properties of Beneficial Microorganisms

Caenorhabditis elegans, a non-parasitic nematode emerges as a relevant and powerful candidate as an in vivo model for microorganisms-microorganisms and microorganisms-host interactions studies. Experiments have demonstrated the probiotic potential of bacteria since they can provide to the worm a longer lifespan, an increased resistance to pathogens and to oxidative or heat stresses. Probiotics are used to prevent or treat microbiota dysbiosis and associated pathologies but the molecular mechanisms underlying their capacities are still unknown. Beyond safety and healthy aspects of probiotics, C. elegans represents a powerful way to design large-scale studies to explore transkingdom interactions and to solve questioning about the molecular aspect of these interactions. Future challenges and opportunities would be to validate C. elegans as an in vivo tool for high-throughput screening of microorganisms for their potential probiotic use on human health and to enlarge the panels of microorganisms studied as well as the human diseases investigated.
JRC Statistical Audit of the Individual Deprivation Measure

The Individual Deprivation Measure (IDM) is a gender-sensitive, multidimensional measure of poverty. The measure assesses deprivation at the individual level, in relation to 15 key dimensions of life, namely Food, Water, Shelter, Health, Education, Energy/fuel, Sanitation, Relationships, Clothing, Violence, Family planning, Environment, Voice, Time-Use and Work. It offers information additional to other national surveys, providing a high level summary of deprivation through an index while enabling users to gain further understanding through the decomposition and disaggregation of the scalar, gender-sensitive, individual-level data on which it is based. European Commission’s Competence Centre on Composite Indicators and Scoreboards (COIN) at the Joint Research Centre (JRC) was invited by the International Women’s Development Agency (IWDA) to audit the IDM study concerning the Fiji (2014-17) dataset. In this dataset, composed by almost three thousands subjects, only 13 out of 15 key dimensions of the IDM are considered. The statistical audit presented herein aims to contribute to ensuring the transparency of the IDM methodology and the reliability of the results. The report touches upon data quality issues, the conceptual and statistical coherence of the framework and the impact of modelling assumptions on the results.