Miracle-dz/UniFAR-FTUs huggingface dataset:
Earthquake Vulnerability Mapping Using Different Hybrid Models

The main purpose of the present study was to mathematically integrate different decision support systems to enhance the accuracy of seismic vulnerability mapping in Sanandaj City, Iran. An earthquake is considered to be a catastrophe that poses a serious threat to human infrastructures at different scales. Factors affecting seismic vulnerability were identified in three different dimensions; social, environmental, and physical. Our computer-based modeling approach was used to create hybrid training datasets via fuzzy-multiple criteria analysis (fuzzy-MCDA) and multiple criteria decision analysis-multi-criteria evaluation (MCDA-MCE) for training the multi-criteria evaluation–logistic regression (MCE–LR) and fuzzy-logistic regression (fuzzy-LR) hybrid model. The resulting dataset was validated using the seismic relative index (SRI) method and ten damaged spots from the study area, in which the MCDA-MCE model showed higher accuracy. The hybrid learning models of MCE-LR and fuzzy-LR were implemented using both resulting datasets for seismic vulnerability mapping. Finally, the resulting seismic vulnerability maps based on each model were validation using area under curve (AUC) and frequency ratio (FR). Based on the accuracy assessment results, the MCDA-MCE hybrid model (AUC = 0.85) showed higher accuracy than the fuzzy-MCDA model (AUC = 0.80), and the MCE-LR hybrid model (AUC = 0.90) resulted in more accurate vulnerability map than the fuzzy-LR hybrid model (AUC = 0.85). The results of the present study show that the accuracy of modeling and mapping seismic vulnerability in our case study area is directly related to the accuracy of the training dataset.
Use of Determination of the Importance of Criteria in Business-Friendly Certification of Cities as Sustainable Local Economic Development Planning Tool

One of the essential activities for sustainable local economic development is continuous improvement of business environment which can be carried out through the business-friendly certification as objective benchmarking process, which is influenced by many factors - criteria that could be analyzed using multi-criteria decision-making methods. Determining criteria weights is the most important task regarding these methods for which a number of methodologies based on different approaches were developed. These methodologies could be generally divided into two groups: subjective and objective. Shortly, these methodologies quantify given preferences using knowledge of experts if they are subjective or using calculations from available data if they are objective. Methodologies from these two groups give different results in a wide range of values. Therefore, it is useful to create composite indicators using aggregation of both approaches in order to reduce the influence of their bad individual characteristics and, therefore, achieve a balanced symmetrical approach. The purpose of this paper is constructing one efficient model that solves a problem of the planning of sustainable local economic development in the Republic of Serbia. Our approach uses the aggregation of the entropy method, as one objective approach, and the analytical hierarchy process, as a subjective approach, in executing business-friendly certification process. The implementation of the proposed approach has been demonstrated as a part of a business-to-government (B2G) platform called “Multi-Criteria Support System for Analysis of the Local Economic Environment” in the City of Niš.
Review of SADC’s market integration agenda: opportunities and challenges for regional industrial development

Industrial development concerns have now become a priority focus in Africa. This is, in some measure, prompted by developments in commodities sectors, where the need for beneficiation of minerals and value addition of the continent’s natural resources is enjoying priority focus. There is also recognition that a traditional trade and integration agenda that focuses predominantly on border issues to enhance market access, is not addressing Africa’s fundamental challenges of lack of industrial capacity and diversity. In short, the capacity to produce tradeables competitively and the policy mix that will enhance this capacity, are very much occupying the minds of policy makers. The adoption of the Action Plan for Accelerated Industrial Development of Africa (AIDA) by the 10 African Union Assembly of Heads of State and Government in January 2008, is a continental policy response to these challenges. This plan provides a framework for addressing industrial development challenges at national, regional and continental levels. Regional economic communities are also addressing specific industrial development concerns in their integration agendas. This paper focuses very much on the regional aspects of industrial development and specifically, the role of the Southern African Development Community’s (SADC) market integation agenda in supporting regional industrial development.
Use of Precise Area Fraction Model for Fine Grid DEM Simulation of ICFB with Large Particles

The heterogeneous structures in a gas–solid fluidized bed can be resolved in discrete element simulation so long as the grid is fine enough. In order to conveniently calculate mean porosity in fine grid simulations, a precise area fraction model is given for two-dimensional simulations. The proposed area fraction model is validated by the discrete element simulation test on a small-scale internal circulation fluidized system of large particles, using a fine grid size of two particle diameters. Simulations show that the discrete element method can perform well in modelling time-varying waveforms for the physical quantities in an internal circulating fluidized bed, employing the precise gas area fraction model. This thought of precise calculation can be generalized to construct a volume fraction porosity model for three-dimensional simulation by use of the similar symmetry of a rectangular grid. Moreover, to construct these area and volume fraction models is to enrich and perfect the underlying model of fine grid simulation.
Artificial Intelligence in the Cyber Domain: Offense and Defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.
EMBLR: A High-Performance Optimal Routing Approach for D2D Communications in Large-scale IoT 5G Network

Coping with the skyrocketing needs for massive amounts of data for the future Fifth Generation (5G) network, Device-to-Device (D2D) communications technology will provide seamless connectivity, high data rates, extended network coverage, and spectral efficiency. The D2D communications are a prevalent emerging technology to achieve the vision of symmetry in the Internet of Things (IoT) services. However, energy resource constraints, network stability, traffic congestion, and link failure of the devices are the crucial impediments to establish an optimal route in the D2D communications based IoT 5G network. These obstacles induced packet drop, rapid energy depletion, higher end-to-end delay, and unfairness across the network, leading to significant route and network performance degradation. Therefore, in this paper, an energy, mobility, queue length, and link quality-aware routing (EMBLR) approach is proposed to overcome the challenges and boost network performance. Moreover, a multicriteria decision making (MCDM) technique is utilized for the selection of the intermediate device in an optimal route. Extensive simulation has been conducted and proven that the proposed routing approach significantly enhances network performance. Overall, results have been carried out in Quality of Service (QoS) performance metrics and compared with other well-known routing approaches.
Numerical Picard Iteration Methods for Simulation of Non-Lipschitz Stochastic Differential Equations

In this paper, we present splitting approaches for stochastic/deterministic coupled differential equations, which play an important role in many applications for modelling stochastic phenomena, e.g., finance, dynamics in physical applications, population dynamics, biology and mechanics. We are motivated to deal with non-Lipschitz stochastic differential equations, which have functions of growth at infinity and satisfy the one-sided Lipschitz condition. Such problems studied for example in stochastic lubrication equations, while we deal with rational or polynomial functions. Numerically, we propose an approximation, which is based on Picard iterations and applies the Doléans-Dade exponential formula. Such a method allows us to approximate the non-Lipschitzian SDEs with iterative exponential methods. Further, we could apply symmetries with respect to decomposition of the related matrix-operators to reduce the computational time. We discuss the different operator splitting approaches for a nonlinear SDE with multiplicative noise and compare this to standard numerical methods.
A Smart, Sensible Agriculture System Using the Exponential Moving Average Model

Smart agriculture systems with combinations of advanced technologies are used in an attempt to increase the competence of certain farming activities and the standard of living for farm employees by reducing significant labor and tedious tasks. Internet-of-things-based sensors are capable of providing such information about smart agriculture and then acting upon predictions using data analysis. The proposed methodology works alongside a cloud-based server and a mobile-based device (ideally an Android/iOS device) to assist the user in regulating the standing of the plant as monitored by a mix of software packages and hardware devices. Our system detects changes in the moisture, temperature, and light intensity conditions in and around the plant and performs a learning-based call to supply necessary irrigation and illumination to plants. It permits the user to update, manage, and monitor using wireless sensing element networks. The sensors measure the aforementioned parameters and store the data within the cloud, which users can access at any time from anywhere. Farmers will have access to the most up-to-date knowledge so that they can act accordingly and make modifications as needed. This smart planting has become a core tool associated with cost-effective technology in agricultural modernization technologies. The proposed smart modern agriculture tool can be used to monitor climatic factors such as temperature, moisture, and virtually all environmental parameters relevant to the growth of plants.
Metrics

Numbers, enumeration, and the quantification of contemporary life seem to govern our existence more and more. Particularly since the dawn of the twenty-first century, the importance of quantification for governance has grown, and anthropologists have increasingly turned their attention to the ramifications of metrics, or numeric representation that translates assumed realities into numbers (Rottenburg & Merry 2015: 2). They study whether and how the production, synthesis, analysis, and use of metrics is tied to the rise and decentralization of audit and accountability in contemporary capitalism. This entry will first provide a theoretical framework for the anthropology of metrics, drawing on science and technology studies and the history of science. Then, it will discuss how anthropologists have analysed the social impact of enumerative practices. Looking at the practices and infrastructures that produce metrics and that metrics in turn produce, this entry will highlight the importance of colonial legacies for shaping what is ‘knowable’ in the realms of global governance, economics, and health. Finally, the entry will point to tensions at the heart of contemporary critiques of metrics: in our ‘post-truth’ world, these critiques cannot reject the usefulness of truthfully describing and estimating human phenomena. However, these critiques foreground the idea that metrics are always just one form of evidence among many.
Double Cross & Deep Network for News Recommendation

News recommendation algorithms are widely used in many Internet products that people use. With the increase of commercial value, the research on various recommendation algorithms has become more and more interesting. This paper proposes the Double Cross & Deep Network (DCDN) algorithm, which is used in news recommendation. On the basis of the DCN network, the features of "relevant articles" involved in the field of news recommendation are separately extracted, and high-level intersections are made with user information and seed information, respectively. The parameters of the two Cross Network and Deep Network of the DCDN network are independent, and users can change the parameters according to the predicted demand. When the number of layers of the Cross Network is increased, the relevance of the recommendation can be increased, while the equivalent number of layers of the Deep Network can increase the diversity of recommendations. Experiments show that compared with DCN networks, DCDN networks have better parameter performance and faster model operation.
GOES-R series image navigation and registration performance assessment tool set

Abstract. An image navigation (NAV) and registration (INR) performance assessment tool set (IPATS) was developed to assess the US Geostationary Operational Environmental Satellite R-series (GOES-R) Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance. IPATS produces five INR metrics for level 1B ABI images: navigation, channel-to-channel registration, frame-to-frame registration, swath-to-swath registration, and within-frame registration. IPATS also produces one INR metric for GLM: navigation of background images. The high-precision INR metrics produced by IPATS are critical to INR performance evaluation and long-term monitoring. IPATS INR metrics also provide feedback to INR engineers for tuning the navigation algorithms and parameters to further refine INR performance. IPATS utilizes a modular algorithm design to allow the user-selectable data processing sequence and configuration parameters. We first describe the algorithmic design and the implementation of IPATS. Next, it describes the investigation of the optimization of the configuration parameters to reduce measurement errors. Finally, sample INR performance is presented, including GOES-16 and GOES-17 ABI NAV performance from postlaunch test to November 2019 and the comparison of example 24-h INR performance against the mission performance requirements. The INR assessment results show that both GOES-R ABIs are in compliance with the mission INR requirements.
MOLECULAR BASE OF COAGULATION FACTOR XI DEFICIENCY IN KERRY BLUE TERRIER

Factor XI (FXI) deficiency has been reported in the Kerry Blue Terrier (KBT) breed and an isolated family of Great Pyrenees and English Springer Spaniel dogs (Knowler et al., 1994). Affected dogs have a mild bleeding tendency, but many remain asymptomatic. The FXI deficiency seems to be inherited as an autosomal trait with incomplete penetrance. Here we describe the molecular defect responsible for FXI deficiency in KBT dogs. Five FXI-deficient KBTs with either no or mild bleeding tendencies, were studied. Their partial thromboplastin times, but not their prothrombin times, were markedly prolonged and their plasma FXI activities were <10% of normal healthy controls. Genomic DNA and cDNA were prepared and sequenced utilizing primers for the canine FXI sequence derived from the published canine genome sequence. All 15 exons of the sequenced canine FXI gene were identical between the normal KBTs’ DNA and the published canine genome sequence. However, the 7th coding exon differs between normal and affected animals. It is normally 110 bp long, but in affected KBTs it contains a short interspersed nucleotide element (SINE) insertion. This exonic SINE is 90 bp long, consisting mostly of adenines coding for lysine which is presumed to affect the 3rd apple domain of the FXI gene. Further studies are in progress to characterize the effects of this SINE mutation on FXI function. Simple genomic DNA test has been developed to screen the KBT population to determine the mutant allele frequency within the breed and its association to any clinical bleeding tendencies.
18S RRNA GENE- AND COX1-GENE-BASED DIVERSITY OF MONIEZIA SPP. AND AVITELLINA CENTRIPUNCTATA IN RUMINANTS FROM AL-QADISIYAH PROVINCE, IRAQ

The present 18S rRNA gene and Cox1 gene-based work was proposed and performed to detect and study the history of evolution of tapeworms that could be recognized through the use of molecular techniques. For this investigation, 14 tapeworms were recovered from 50 intestinal-based content samples of 50 animals (sheep, goat, cattle, and buff alo). The collected adult tapeworms were fi rst identifi ed morphologically showing 6 Moniezia expansa and 7 Moniezia benedeni. Then, DNA from 14 worms (9 for the 18S rRNA gene and 5 for the Cox1 gene) was employed for polymerase chain reaction (PCR) and partial gene sequencing techniques. The results of the PCR recorded amplifi cations of all the sequences used for both genes. The sequencing indicated, via the use of 18S rRNA gene, 5 Moniezia benedeni, 3 Moniezia expansa, and 1 Avitellina centripunctata. Moreover, it resulted, via the utilization of Cox1 gene, 3 Moniezia expansa and 2 Moniezia benedeni. The isolates stood in close matching with global isolates. The current results provide important criteria for the reliable use of the techniques utilized in this study.
CEEP-BIT WORKING PAPER SERIES Estimating the ‘value at risk’ of EUA futures prices based on the extreme value theory

This paper employs the Extreme Value Theory (EVT) to measure the 'Value at Risk' (VaR) of EUA futures prices. The results show that during the sample period, first, the EVT approach can be used to reliably measure the extreme risk of carbon futures markets of the EU ETS, both for Phase I and Phase II. Second, the downside extreme risk of carbon futures market outweighs the upside risk, with evident asymmetric features. Moreover, the average VaR of carbon futures contract DEC10 proves much less than that of contract DEC07 during the sample period.
Salamander: a Holistic Scheduling of MapReduce Jobs on Ephemeral Cloud Resources

Most cloud data centers are over-provisioned and underutilized, primarily to handle peak loads and sudden failures. This has motivated many researchers to reclaim the unused resources, which are by nature ephemeral, to run data-intensive applications at a lower cost. Hadoop MapReduce is one of those applications. However, it was designed on the assumption that resources are available as long as users pay for the service. In order to make it possible for Hadoop to run on unused (ephemeral) resources, we have designed a heterogeneity and volatility-aware holistic scheduler consisting of three different components: (1) A MapReduce task and job scheduler that relies on a global vision of resource utilization predictions, (2) a scheduler-based data placement strategy that improves the data locality, and (3) a reactive QoS controller that ensures customers’ service-level agreement (SLA) and minimizes interference between co-located workloads. Our framework makes it possible to take advantage of ephemeral resources efficiently. Indeed, for a given set of jobs, it reduces the overall execution time by up to 47.6% and an average of 18.7% as compared to state-of-the-art strategies.
Enduring functional consequences of early-life insults: mechanisms, prediction and intervention

Author(s): Curran, Megan Margaret | Advisor(s): Baram, Tallie Z | Abstract: Early life experiences, whether positive or negative, have the potential to enduringly shape the brain. This work investigates two types of early experiences and the mechanisms by which they influence the developing brain: prenatal adversity and developmental prolonged febrile seizures. This work finds that maternal stress and placental corticotropin releasing hormone (CRH) are able to influence neuronal development by diminishing dendritic branching, a potential mechanism for the decreased cortical thickness found in children exposed to high levels of CRH in utero. The region-selective reduction in cortical thickness is associated with cognitive and emotional outcomes in childhood. Prolonged febrile seizures of childhood, also known as Febrile Status Epilepticus (FSE), can lead to the development of spontaneous epileptic seizures and cognitive problems that last into adulthood. The experiments described here examine the mechanisms of those effects on a molecular, cellular, and structural level. There are many perturbations that arise after experimental FSE (eFSE) within the brain that potentially crucial for the observed detrimental outcomes of these seizures in rodents and humans. This work focuses on two of them: the induction of inflammation in the brain and increased expression of the transcription factor Neuron Restrictive Silencer Factor (NRSF). This thesis describes experiments that acutely modulate these signaling cascades, resulting in the prevention of eFSE-induced structural and functional changes within the hippocampal formation. Importantly, this work also addresses the need for predictive markers for individuals experiencing eFSE who will go on to develop epilepsy and cognitive deficits, in order to target any intervention. Magnetic resonance imaging (MRI) allows for real time measurements of brain changes in vivo, enabling the measurement of structural alterations and prediction of epileptogenesis before the first spontaneous seizure occurs.
MR detection of local prostate cancer recurrence after transrectal high-intensity focused US treatment: preliminary results

igh intensity focused ultrasound (HIFU) is increasingly being used in the treatment of tumors (1). By focusing high intensity US waves, the energy is concentrated on a focal point. This causes the temperature to rise between 80 and 100 oC within seconds, resulting in coagulation necrosis of the target area. The area destroyed is usually small (cylinder about 5-20 mm high by 13 mm wide) (2, 3). In order to treat the entire tumor, the firing sequence is repeated multiple times at different focal points. Currently, the most developed medical application for HIFU is for the treatment of clinically localized prostate carcinoma in patients declining or having a contraindication to prostatectomy (4, 5). In these circumstances, the disease-free survival at 5 years is similar to radiation therapy (68). Classically, the PSA level rapidly declines following treatment to reach its nadir within 6 months. Most groups systematically obtain prostate biopsies in all patients 3-6 months after treatment irrespective of PSA level. If biopsies are negative, the PSA level is routinely monitored. If it increases, biopsies are repeated (6, 7). Résumé Abstract