Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of CO emissions in turbulent super lean premixed combustion under pressurized conditions using an LES/non-adiabatic FGM approach

A large eddy simulation (LES) employing a non-adiabatic flamelet generated manifolds (NA-FGM) approach, which can account for the effects of heat loss, is applied to CH 4 -air super lean premixed combustion fields generated by an axisymmetric jet burner with cooled walls under pressurized conditions. In addition, the validity in predicting the CO emissions is examined. The NA-FGM approach captures the trends of CO emissions well during the experiments, in which the CO emissions increase with a decreasing equivalence ratio. It is shown that the increase in CO emissions for low equivalence ratios is not due to the increase in the CO production, but to the slow rate of CO consumption, which keeps the CO concentration high downstream. The results suggest that capturing such a sensitive reduction of CO consumption rate by heat loss is important for accurately predicting the CO emissions in developing a low-emissions gas turbine combustor at low load.
The Impacts of Ongoing Health Crisis Caused by Novel Coronavirus “COVID-19” on the Economics of the Countries

This study examines the impact of COVID-19 cases on GDP growth and Unemployment in developed countries represented by Germany, France, and Spain and developing countries represented by Mexico, Uruguay, Brazil, Turkey, and China. Various statistical tests were employed; ADF-Fisher Chi-Square for stationarity, Pedroni Cointegration test, Hausemn test, and panel granger causality test. The empirical results suggest that data from developed and developing countries are stationary at I (1). The Pedroni cointegration test revealed no long-run relationship between the dependent variables (Covid cases) and independent variables represented by GDP growth and Unemployment. The Hausman test showed that the fixed effect model is suitable for this study. The fixed effect model’s estimation suggests that there is a positive and significant association between COVID-19 with unemployment rate and GDP growth in industrialized countries. However, in developing countries, the rise in COVID cases causes a decrease in GDP growth and Unemployment. The study contribution is critically significant for policymakers and scientists to set a plan for the comprehensive economic recovery for the world after the pandemic.
A Game-Theoretical Approach for Secure Crowdsourcing-Based Indoor Navigation System With Reputation Mechanism

At present, the crowdsourcing-based indoor navigation system (CINS) has attracted extensive attention from both industry and academia owing to its low-cost and high-accuracy performance. Unfortunately, the system that relies on crowdsourced data is vulnerable to the collusion attack, which leads to severe security issues. To address the security issues in the CINS, we propose to utilize a fully trusted fog server platform to advocate secure transactions between service requesters and responders. First, we propose a novel reputation incentive mechanism based on the behaviors of responders. Then, we employ the offensive and defensive game to model the interactions between the fog server platform and the responders, whereby a social welfare optimization problem is formulated to maximize the social welfare of the system. Next, the game equilibriums are found by using the replicator dynamic equation while the game stability is discussed. Finally, the simulation results show that the proposed mechanism can effectively encourage responders to provide positive navigation services and obtain more social welfare of the system compared with the conventional mechanisms.
Assessing the levels of intraspecific admixture and interspecific hybridization in Iberian wild goats (Capra pyrenaica)

Iberian wild goats (Capra pyrenaica, also known as Iberian ibex, Spanish ibex, and Spanish wild goat) underwent strong genetic bottlenecks during the 19th and 20th centuries due to overhunting and habitat destruction. From the 1970s to 1990s, augmentation translocations were frequently carried out to restock Iberian wild goat populations (very often with hunting purposes), but they were not systematically planned or recorded. On the other hand, recent data suggest the occurrence of hybridization events between Iberian wild goats and domestic goats (Capra hircus). Augmentation translocations and interspecific hybridization might have contributed to increase the diversity of Iberian wild goats. With the aim of investigating this issue, we have genotyped 118 Iberian wild goats from Tortosa‐Beceite, Sierra Nevada, Muela de Cortes, Gredos, Batuecas, and Ordesa and Monte Perdido by using the Goat SNP50 BeadChip (Illumina). The analysis of genotypic data indicated that Iberian wild goat populations are strongly differentiated and display low diversity. Only three Iberian wild goats out from 118 show genomic signatures of mixed ancestry, a result consistent with a scenario in which past augmentation translocations have had a limited impact on the diversity of Iberian wild goats. Besides, we have detected eight Iberian wild goats from Tortosa‐Beceite with signs of domestic goat introgression. Although rare, hybridization with domestic goats could become a potential threat to the genetic integrity of Iberian wild goats; hence, measures should be taken to avoid the presence of uncontrolled herds of domestic or feral goats in mountainous areas inhabited by this iconic wild ungulate.
Validation of Monaco Treatment Planning System for Intensity-Modulated Radiation Therapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT®) on ELEKTA Infinity™ Linear Accelerator

The objective of this study is to evaluate the dosimetric precision of the Monte Carlo (MC) algorithm to validate the Monaco® (Elekta) treatment planning system for the two radiotherapy techniques IMRT and VMAT® on the Infinity™ Elekta linear accelerator. Several irradiation plans were created on the Monaco® treatment planning system (TPS) and calculated by the integrated MC algorithm for its validation. The same plans were applied experimentally using the Matrixx Evolution 2D array with its appropriate phantom. All measurements were performed by superimposition with those calculated on the Infinity™ linear accelerator (ELEKTA). The calculated and measured dosimetric data were overlaid to make the comparison of what is realistic and what was simulated using the MyQA (IBA) software associated with the Matrixx. Good agreement was observed between calculated and measured data using 3%, 3mm distance to agreement (DTA) and low dose threshold 5% criteria. Global gamma analysis passing rates for all tests are greater than 95%. An agreement less than 2 mm is shown for open fields and homogenous dose test. However, there was increase in the agreement criteria above 3 mm for chair and pyramid test as a result of high gradient dose regions especially at the edge of target volumes. Results obtained from this study allowed, in one hand to confirm the accuracy of our MC model dose calculation with Monaco® TPS, and in the other hand, the use of the matrix detector as a standard tool for IMRT/VMAT® patient quality control.
Proximal and distal antecedents of problematic information technology use in organizations

PurposeExcessive use of work-related information technology (IT) devices can lead to major performance and well-being concerns for organizations. Extant research has provided evidence of the incidence of such problematic IT use in organizations. We extend the understanding of problematic IT use by examining its individual (proximal) and organizational (distal) antecedents.Design/methodology/approachDrawing from the self-worth theory and the concept of fear of being left behind, we address proximal antecedents that lead to problematic IT use. Drawing from the concept of autonomy paradox, we address distal antecedents of problematic IT use through a positive association with the two proximal antecedents. We report the results of a field study involving 846 individuals who use IT for work. Structural equation modeling was employed to analyze the data.FindingsThe results indicate that the proximal antecedents (IT insecurity and fear of missing out) are positively associated with problematic IT use. The distal antecedents (IT use autonomy and involvement facilitation) are positively associated with the proximal antecedents except for the relationship between IT use autonomy and IT insecurity, which was found statistically non-significant. Furthermore, fear of missing out fully mediates the effect of IT use autonomy on problematic IT use, whereas IT insecurity and fear of missing out fully mediate the effects of involvement facilitation on problematic IT use.Originality/valueThe paper theoretically extends the understanding of problematic IT use and identifies novel its proximal and distal antecedents.
Silver diamine fluoride as a minimally invasive and aerosol free approach in pediatric dentistry: An extensive case series

The current pandemic of COVID-19 warrants a repeal from conventional dentistry to an aerosol free, minimally invasive yet maximally effective clinical approach. Silver diamine fluoride (SDF) is an established modality for caries arrest in children fulfilling all the above. This extensive 25 case series highlights various clinical situations in which SDF was successfully used in children between 1-12 years of age with asymptomatic carious lesions of ICDAS score 2 or more in primary and permanent teeth. Primary outcome measure was caries arrest in the form of the hard and shiny lesion with no/minimal sensitivity. The secondary outcome measure was the zombie effect which was the residual staining on other tooth surfaces and indications of substantivity. SDF can be effectively and efficaciously used across different behaviour patterns and age groups of children in a various clinical scenario for both primary and permanent teeth routinely experienced in a paediatric dental office especially during these pandemic times with minimal aerosol generation.
Comparison of Decentralized ADMM Optimization Algorithms for Power Allocation in Modular Fuel Cell Vehicles

The advanced modular powertrains are envisioned as primary part of future hybrid fuel cell vehicles (FCVs). The existing papers in the literature solely cope with the hardware side of modularity, while the software side is also vital to capitalize on the total capacity of these powertrains. Driven by this motivation, this article puts forward a comparative study of two novel decentralized convex optimization frameworks based on alternating direction method of multipliers (ADMM) to solve a multi-objective power allocation strategy (PAS) problem in a modular FCV (MFCV). The MFCV in this article is composed of two fuel cell (FC) stacks and a battery pack. Despite the existing centralized strategies for such a modular system, this manuscript proposes two decentralized PASs (Dec-PASs) based on Consensus ADMM (C-ADMM) and Proximal Jacobian ADMM (PJ-ADMM) to bridge the gap regarding the appreciation of modularity in software terms. Herein, after formulating the central PAS optimization problem, the principle of utilizing such decentralized algorithms is presented in detail. Subsequently, the performance of the proposed Dec-PASs is examined through several numerical simulations as well as experiments on a developed small-scale test bench. The obtained results illustrate that decomposition into decentralized forms enables solving the complex PAS optimization problem faster and provides modularity and flexibility. Furthermore, the proposed Dec-PASs can cope with fault and malfunction and thus augment the durability and robustness of modular powertrain systems.
Using Autoencoders and Output Consolidation to Improve Machine Learning Models for Turbomachinery Applications

Machine learning models are becoming an increasingly popular way to exploit data from fluid dynamics simulations. This project investigates how autoencoders and output consolidation can be used to increase the accuracy of machine learning models, by injecting knowledge of the full flow field in the predictions of Quantities of Interest (QoI) used in the optimisation of highly loaded transonic compressor blades. Results show that the accuracy of the predicted QoI can indeed be increased, by using both an appropriate autoencoder and output consolidation. Most significantly, the prediction accuracy is increased in the range of QoI values which is involved in optimisation problems. As a result, a more accurate and faster computational design approach driven by machine learning methods has been demonstrated.
Time spatial spectrum correlation based multiband infrared target detection

Multiband infrared sensors based dim and small target detection is a core technique in reconnaissance and early warning applications. However, there exist two difficulties including noise disturbance in the single band image and low SNR in single frame image. We propose a novel and effective multiband infrared target detection algorithm by utilizing time spatial spectrum correlation technology. Specifically, a multiband image preprocessing is advocated to filter noise, together with the multiscale patch contrast measure to extract the potential target. Afterwards, a multiple bands image based target spectrum detection algorithm is utilized to extract candidate targets effectively. Besides, the long-shortterm target correlation method in sequential images is exploited to eliminate the false alarm in a single frame. Experimental results on six infrared image sequences demonstrate that our algorithm improves the detection rate and reduces the false alarm rate with multiband sequential images, which outperforms the other state-of-the-art algorithms.
Introduction to a creative philosophy of anticipation

There were many anticipations creating this book. We start with the Second International Conference on Anticipation, held at Senate House in London 2017, where we (Jamie Brassett and John O’Reilly) organized and delivered a curated discussion panel – though this is by no means the earliest encounter germane to this story. Anticipation took the form of an atmosphere in this 1930s Art Deco building. Senate House was initiated by William Beveridge when he was elected to the post of Vice-Chancellor of the University of London in 1926. Beveridge’s work anticipated and therefore created a new timeline of the future as the thinker of the welfare state that enabled heterogenous futures. Beveridge also had a different, more homogenous version of the future – like many of the pre-war left in Britain who were motivated by the idea of eugenics, the engineering out of undesired genetic qualities (Freedland, 2012; Renwick, 2019). For Beveridge, the anticipatory power of the welfare state gave way to the predictive power of eugenics. The 19-floor building of Senate House was designed by vegetarian Quaker Charles Holden, a specialist in the atmosphere of the subterranean, having designed over 40 underground stations in London. The slightly dystopic atmosphere generated by the severe beauty of the building’s Portland Stone materials haunted the future in the present in perfectly anticipatory fashion. George Orwell’s Ministry of Truth in Nineteen Eighty-Four was modelled on Senate House. The strategy of organizational threat as a mode of political control by Orwell’s Ministry of Truth, anticipates the operational logic behind the strategies of the US and UK governments nearly 20 years after the fictional year of the book – especially with the War on Terror. ‘Fear is the anticipatory reality in the present of a threatening future,’ writes Brian Massumi in his essay ‘The future birth of the affective fact’. He continues: ‘It is the felt reality of the nonexistent, loomingly present as the affective fact of the matter’ (Massumi, 2010, p. 54). In everyday experience such prophecies can become self-fulfilling, but this disguises the affective, material power of threat as an atmosphere. The affective fact, Massumi argues, opens the way to generate and legitimate many different kinds of actions, actors and their networks to pre-empt the Jamie Brassett and John O’Reilly Introduction
Delay-Aware Admission Control and Beam Allocation for 5G Functional Split Enhanced Millimeter Wave Wireless Fronthaul Networks

In this paper, we study a delay-aware admission control (AC) and beam allocation (BA) problem with the consideration of quality of experience (QoE) and dynamic variation of channel condition for centralized unit (CU) and distributed unit (DU) based functional split options (FSOs) in millimeter wave (mmWave) fronthaul downlink networks. The original optimization problem aims to maximize time-average QoE subject to delay and queue stability constraints. The intractability of the considered problem comes from the necessity to allocate resources across periods of time slots. After applying virtual queue transformation and Lyapunov optimization method, the targeting problem can be converted into two independent AC and BA sub-problems in each time slot. The converted problem involves actual and virtual queues, which causes conflicting tendency. With the design of a flexible Lyapunov function, the influences of actual and virtual queues are theoretically to be proved balanced. Moreover, the AC sub-problem can be solved by Karush-Kuhn-Tucker condition, whereas mmWave BA sub-problem is tackled by genetic algorithm and matching game for the CU-based and DU-based FSOs, respectively. Simulation results demonstrate the applicability of the proposed CU-/DU-based algorithms in terms of average data rate, admission control rate, queue length, and corresponding system delay under either uniform or dense deployment of DUs. Moreover, the proposed scheme achieves the lowest delay and highest QoE performances while sustaining the system stability compared with the state-of-the-art mechanisms in open literatures.
Iron Sulfides and Anomalous Electrical Resistivity in Cratonic Environments

The interpretation of low‐resistivity anomalies in the lithospheric mantle of several cratonic regions has invoked hydrogen, or connected networks of graphite with iron‐rich silicates, and/or metal sulfides. Electrical laboratory measurements are a powerful approach for exploring these alternatives. We report electrical measurements of two xenoliths (pyroxenite and dunite) from Tanzania; two metal sulfides (FeS and Fe‐S‐Ni); and several mixtures of metal sulfides (3.4–18.2 vol.%) with xenolith. A multi‐anvil press was employed to maintain a 2 GPa pressure and temperatures up to 1,627 K. The addition of 3.4 vol.% FeS to the pyroxenite or dunite matrix has little effect on bulk resistivity, particularly for T > 800 K. However, the resistivity drops dramatically–by factors of up to 1,000, depending on temperature–upon addition of 6.5 vol.% FeS in the dunite. Addition of 18.2% FeS causes a further decrease of the same magnitude relative to the 6.5% sample. Scanning electron microscope images do not reveal the formation of a connected FeS network as part of the decreased resistivity. Possible explanations for the apparently conflicting results include connections of the sulfide that are not imaged in the back‐scattered images, either because of limited resolution, or perhaps the inherent limitations of the 2‐D perspective. The complete data set (xenoliths, metal sulfides, and mixtures) was modeled with a modified version of Archie's law, and we find satisfactory agreement over a truncated temperature range. We conclude that the low‐resistivity anomalies in Tanzania, Kaapvaal, and Gawler cratons can be explained by the presence of a few vol.% of solid sulfide.
Production and Characterization of Charred Briquettes from Various Agricultural Waste

ABSTRACT This study seeks to char and briquette various agricultural wastes to be used as fuel in cookstove. To realize this, charred briquettes were produced from various agricultural wastes such as rice husk, maize cobs, palm kernel shell, and sawdust using the fast pyrolysis method at three different particle sizes (0.6 mm, 1.8 mm, and 2.36 mm) and compacting pressures (10 MPa, 20 MPa, and 30 MPa). Fuel tests such as physical test, proximate, and ultimate analysis, combustion test, and mechanical test were determined to assess the quality and suitability of the charred briquettes for use as fuel in cookstoves. The findings show that for all the three factors considered, palm kernel shell briquettes recorded the highest calorific value of 20,836.32 kJ/kg, volatile matter content of 17.3%, and fixed carbon of 76.5% while recording the least amounts of ash content of 2.7% and moisture content 3.95% compared to the other briquettes. The water boiling test results revealed that Palm kernel shell and sawdust briquette fuels reached their boiling point of 100 ℃ at the fastest time of 10 minutes. Furthermore, palm kernel shell briquette of particle size of 0.60 mm and compacting pressure of 30 MPa exhibited an outstanding performance than the other briquettes with regard to the values of specific fuel consumption of 2.38 kg/liters, burning rate of 0.00428 kg/min, shatter index of 96.93%, impact resistance of 500%, relax density of 753,291 kg/m3 and water resistance of 92.9%.
QoS-Driven Load Balancing in Hybrid LiFi and WiFi Networks

This work studies the quality of service (QoS) performance of wireless networks that integrate light fidelity (LiFi) and wireless fidelity (WiFi). While the hybrid network is potential for improving network capacity, load balancing becomes essential and challenging due to the nature of heterogeneous access points (APs). A number of studies have been conducted to address this issue, focusing on maximising the network capacity with user fairness constraints. However, in practice, QoS metrics including packet loss ratio and latency are important to network services. In this paper, QoS-driven load balancing is studied for hybrid LiFi and WiFi networks (HLWNets) in two scenarios: single-AP association (SA) and multi-AP association (MA). In each case, an optimisation problem is formulated to minimise the packet loss ratio and latency, and a low-complexity iterative algorithm is proposed to solve the problem. Results show that the novel methods, especially MA, can effectively balance the traffic loads among the APs and improve the QoS performance. In addition, the more subflows the better performance MA provides. Targeting the same level of QoS, MA can achieve a system throughput up to 160% higher than the signal strength strategy and 130% higher than the proportional fairness load balancing.
End-to-End Multi-Sensor Fusion for 3d Object Detection in Lidar Point Clouds

In this paper, we propose a new method to fuse camera and lidar point clouds at the same time that outperforms the state-of-the-art network at long range and challenge conditions like poor weather and highly reflective surfaces. Point clouds from LiDAR tend to be sparse and highly variable, especially at long range and poor weather, which may cause 3D object detectors to fail to detect small but important objects (pedestrians, traffic signs, cones, etc.). Our neural network model does not need labels of objects in the 2D images. The neural network model we proposed is evaluated both on the Waymo Open Dataset and on the KITTI dataset [1] and we demonstrate that our model achieves state-of-the-art accuracy on 3D object detection.