Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiphase Flow and Boiling Heat Transfer Modelling of Nanofluids in a Metal Foam Embedded in Horizontal Tubes

The aim of this numerical study is to evaluate the boiling process of nanofluid in horizontal tubes in the presence of a metal foam as a porous medium and represent the experimental work of Zhao et al. in a numerical aspect with a different range of dependent variables. High conductive metal foams are employed to increase the rate of heat transfer and enhance the boiling performance in the domain. The two-phase mixture model is used to simulate the characteristics of nanofluid and solve the governing equations in the two-phase flow and boiling heat transfer problem. R134a and ZnO are considered as the base-fluid and nanoparticles, respectively. The characteristics of the metal foam including the porosity and pore density as well as operating conditions including the fluid flow including the velocity, induced heat flux and concentration of nanoparticles on the pressure drop, vapour quality and heat transfer coefficient are examined. The results show the positive effect of the metal foam on the vapour production and overall heat transfer coefficient of the nanofluid in the pipe outlet; however, due to the flow resistance as a result of porous medium addition, a higher pressure drop is achieved. For the heat flux of 19 kW/m 2 and inlet velocity of 0.05m/s, by using a metal foam with the porosity of 70 % and pore density of 20 PPI, the vapour quality, heat transfer coefficient and pressure drop enhances by 7.1%, 9.4% and 82.7%, respectively, compared with the case of without metal foam. However, by using the porosity of 90 %, the vapour quality, heat transfer coefficient and pressure drop enhances by 1.6%, 3.5%, and 7.0%, respectively. Consequently, according to the developed results in this paper, a system with a moderate porosity and high pore density is recommended which is finally determined based on the required vapour production and allowed pressure drop.
Document on Transcatheter Aortic Valve Replacement

Writing Committee Members: David R. Holmes, Jr., MD, Chair*, Michael J. Mack, MD, Vice Chair, Sanjay Kaul, MBBS, Vice Chair*, Arvind Agnihotri, MD, Karen P. Alexander, MD*, Steven R. Bailey, MD, John H. Calhoon, MD, Blase A. Carabello, MD*, Milind Y. Desai, MBBS , Fred H. Edwards, MD, Gary S. Francis, MD, Timothy J. Gardner, MD, A. Pieter Kappetein, MD, PhD**, Jane A. Linderbaum, MS, CNP*, Chirojit Mukherjee, MD, Debabrata Mukherjee, MD*, Catherine M. Otto, MD*, Carlos E. Ruiz, MD, PhD, Ralph L. Sacco, MD, Donnette Smith, James D. Thomas, MD
Classification of Human Interactions with Tools Using a Tool-Mounted Wireless Sensor Node to Support Sustainable Manufacturing

—In this contribution, a customized wireless sensor node with onboard acceleration sensor is applied to support optical motion analysis systems. An integrated signal processing algorithm to classify interactions between user and tool is developed for the specific use case of a cordless screwdriver. Results of the first demonstrator evaluation are discussed with respect to further development of the sensor architecture for smart tools.
In-depth Analysis of the Effects of Smart Services on Value Creation

: The manufacturing industry is currently undergoing a tremendous change in value creation caused by two megatrends: digitalization and servitization. In response to these two trends, new hybrid market offerings are emerging, so-called Smart Services. Traditional manufacturing companies planning to provide these data-based Services are confronted with new requirements of value creation that are no longer fulfilled by their historically grown value networks. Since the change from a pure manufacturer to a Smart Service provider is a challenging task, companies may follow a strategy-driven transformation process. Planning and realizing such a transformation process require anticipated effects of future value creation as Smart Service Provider on a company’s current value network. This is the basis for planning strategic transformation initiatives with corresponding goals. The paper at hand addresses these issues with a methodology for an in-depth analysis of the effects of Smart Services on value creation. It consists of four consecutive phases: initial analysis, effect analysis, in-depth analysis and implementation planning. The methodology is validated by four case studies from tooling machine industry.
Learning Search Guidance from Failures with Eliminable Edge Sets

What can be learned from previous planning experience when none of it was successful in ﬁnding any plans? We study this question in the planning-as-graph-search setting. Our main insight is that certain eliminable edge sets can be identiﬁed from failed graph searches. These edge sets can then be used to train a generalized predictor of eliminable edges, which in turn can be used to guide search on new planning problems from the same domain. Our preliminary experimental ﬁndings across four visual navigation domains suggest that this technique of learning from failed search attempts can result in substantially improved planning in terms of the number of nodes expanded before ﬁnding a plan. I have not failed. I’ve just found 10,000 ways that won’t work.
AI Planning Annotation in Reinforcement Learning: Options and Beyond

AI planning and reinforcement learning (RL) both solve sequential decision-making problems, taking fundamentally different approaches. In this work, we aim to bring AI plan- ning and RL closer by investigating the relationship between abstractions in AI planning and the options framework in RL. To this end, we propose annotating RL tasks with AI planning models, allowing us to deﬁne options based purely on the planning model. Our experimental investigation shows that these options can be quickly trained ofﬂine and can improve the sample efﬁciency of a reinforcement learning algorithm.
Augmented Reality In Smart Manufacturing: A User Experience Evaluation

Abstract In recent years, technological advancements including computational capabilities have rapidly increased. This advancement has led to the feasibility of developing advanced technological applications through the integration of Augmented Reality. This article presents an optimal model and application utilising Augmented Reality, Simultaneous Location, and Mapping algorithm for device identification within a smart manufacturing environment. The devices are identified based on their positions by detecting their features utilising the ORB-SLAM algorithm. The device identification is based on the use of markerless identifiers for the purpose of reducing the manual process of pasting markers on every device within the manufacturing plant. This model offers an alternative solution, with the properties of detecting devices based on their position and features. Thus, validation of this approach involved a control group to provide a broad-base evaluation. The results are positive, with the majority of the individuals recommending the adoption of the application and its utilisation within a wider-scale deployment. Specifically, the findings demonstrate that 70% of the users approve of the use of the application in an industrial setup, 65% are satisfied with the accuracy and reliability of the app and 80% recommend the adoption and utilisation of this app in an industrial setup.
Systemic risk and policy interventions: monetary and macroprudential policy

Throughout the world, the global financial crisis fostered the design and adoption of macroprudential policies to safeguard the financial system. This raises important questions for monetary policy, which, by contrast, primarily focuses on maintaining price stability. What, if any, is the relationship between (conventional) monetary policy and macroprudential policy? In particular, how does the effectiveness of macroprudential policies influence the conduct of monetary policy? This article reviews recent theoretical and empirical research addressing these questions. The main conclusion is that monetary policy can also perform macroprudential functions, but it does so by deviating from its focus on price stability. The quantification of this trade-off remains an exciting question.
High conversion ratio DC-DC converter with isolated transformer and switched-clamp capacitor for Taiwan photon source

A new high step-up voltage converter that combines a switch capacitor and isolated transformer, together with a passive clamp circuit, is employed to reduce voltage stress on the main power switch. The voltage stress of the power switch should be clamped to 1/4 Vo, and the proposed converter can achieve high step-up voltage gain with appropriate duty ratio. The energy of the leakage inductor can be recycled by the clamp capacitor because of the passive clamp circuit, and low On-state resistance RDS(on) of the power switch can be adopted to reduce the conduction loss. In this paper, several mathematical derivations are presented, CCM and DCM operating principle are discussed, and experimental results are provided to verify the effectiveness of converter topology. Finally, a 24-V-input voltage to 200-V-output voltage and a 150 W output power prototype converter are fabricated in the laboratory.
The Effect of Corporate Governance on Firm Value in Non-Financial Companies Listed on the Indonesia Stock Exchange for the 2017-2019 Period

This study aims to analyze the effect of the implementation of corporate governance on the firm value of non-financial companies listed on the Indonesia Stock Exchange (IDX) during 2017-2019. The research data was obtained from the company's annual reports through the Indonesia Stock Exchange website, and the Diponegoro University Bloomberg Laboratory for the period 2017 to 2019. The final sample used in the study came from 85 companies with 126 data observations. The research sample was analyzed using multiple linear regression with SPSS version 25 and the method used was Ordinary Least Square (OLS). The results show that board size, board meetings, institutional ownership have a positive effect on firm value, audit committee meetings and managerial ownership have a negative effect on firm value, while public ownership and ownership concentration have no effect to value of a company.
Agent-based modeling for a complex world

The main goal of this paper has been to summarize selected developments in the field of artificial societies and agent-based modeling and to suggest how this fundamentally new toolkit can contribute to solving some of the most complex scientific and practical problems of our time. This paper consists of several parts. The first one considers the history of agent-based models. The second part is devoted to models of this class related to epidemiology, and the third considers agent-based modeling of pedestrian traffic and evacuation of the population. The fourth part is devoted to issues of demographic process modeling, and the fifth to the simulation of transport systems. Ecological forecasting is considered in part six, while the seventh is devoted to issues of land use, and the eighth to urban dynamics. The ninth part considers models used for the reconstruction of historical episodes, and in the tenth part, we will briefly touch on the issues of conflict simulation. In the eleventh part of the paper, we consider issues related to studying social networks using an agent-based approach. Agent-based models of economic systems are considered in the twelfth part. Undoubtedly, the scope of applications of agent-based models is wider, but we elected to focus on these indisputably significant areas.
Health effects of PM 2.5 constituents and source contributions in major metropolitan cities, South Korea

Ambient PM 2.5 is one of the major risk factors for human health, and is not fully explained solely by mass concentration. We examined the short-term associations of cause-specific mortality (i.e., all-cause, cardiovascular, and respiratory mortality) with the 15 chemical constituents and sources of PM 2.5 in four metropolitan cities of South Korea during 2014–2018. We found transition metals consistently showed significant associations with all-cause mortality, while the effects of other constituents varied across the cities and for cause of death. Carbonaceous components strongly affected the all-cause, cardiovascular, and respiratory mortality in Daejeon. Secondary inorganic aerosols, SO 42− and NH 4+ , showed significant associations with respiratory mortality in Gwangju. We also found the sources from which species closely linked to mortality generally increased the relative mortality risks. Heavy metal markers from soil or industrial sources were significantly associated with mortality in all cities. However, several sources influenced mortality despite their marker species not being significantly associated with it. Secondary nitrate and secondary sulfate sources were linked to mortality in DJ. This could be attributed to the deep inland location, which might have facilitated formation of secondary inorganic aerosols. In addition, primary sources including mobile and coal combustion seemed to have acute impacts on respiratory mortality in Gwangju. Our findings suggest the necessity of positive matrix factorization (PMF)-based approaches for evaluating health effects of PM 2.5 while considering the spatial heterogeneity in the compositions and source contributions of PM 2.5 .
Based on the Role of Intelligent Multimedia Man-Machine Exchange in Product Design

Designing human-computer interaction, as the name suggests, is a branch of the study of how humans and machines (or systems) communicate or operate to better adapt machines to humans. We must first have a complete understanding of the human body. Only by fully understanding your own body’s ability to stretch and range of activities can you be confident in your own abilities, so that people can better use us in the design and construction of machinery or systems. This article aims to study the role of intelligent multimedia man-machine exchange in product design, so as to realize the application and development of intelligent multimedia man-machine exchange technology in various fields. This paper proposes to focus on human-computer interaction design and also proposes the algorithm of PNSR index and the method of remote control of equipment and human-machine monitoring. Through the performance, development prospects, and development trends of human-computer interaction design in product design, human-computer interaction is introduced. The importance is of interaction design in today’s product design. The experimental results of this paper show that the intelligence coefficient of my country’s main intelligent network equipment has reached 245, which has exceeded the world average coefficient level and its utilization rate has exceeded 39%. Therefore, the conclusion of this paper is that human-computer interaction design plays a role in promoting the development of mobile products. It plays an important role.
Using feature selection to improve intrusion detection

Recent high profile cyber-attacks have further showed the importance of cyber security in society, with many smaller organizations now starting to employ external security assistance. The basis of any successful cyber security team is the Security Operations Centre (SOC). The SOC team are responsible for monitoring and mitigating potential threats. The key to a successful SOC team is their Security Information and Event Management system (SIEM) which can be used to monitor Intrusion Detection traffic. The more reliable that a SIEM is at detecting intrusion traffic, the more successful the SOC team can be. Although many researchers have proposed a variety of ways to improve intrusion detection, this paper proposes a model which makes use of multi-layer feature selection. The Random Forest and K-nearest neighbours classifiers were applied after the multi-layer feature selection and evaluated. The Random Forest outperformed K-nearest neighbours overall and achieved 98% for its accuracy, while also achieving 99% for precision, recall and F1-score. Therefore, the multi-layer feature selection model can be considered an appropriate solution at improving intrusion detection.
Drag Reduction Characteristics of Bionic Mucous Membrane Acting on the Turbulent Boundary Layer

The mucous membrane on the fish surface has excellent drag reduction performance. The mucous membrane can be regarded as the viscoelastic fluid, and a bionic friction drag reduction model is proposed with the consideration of a Carreau viscoelastic model-based mucus secretion process. Then, the drag reduction effect of the mucous membrane on the classical wall turbulence boundary layer is investigated by large-eddy simulations. Results show that the bionic mucous membrane is conducive to reducing the turbulence, and can achieve a drag reduction rate of about 14%. This study provides a hydrodynamics understanding of the drag reduction characteristics of the bionic mucous membrane.
Observation-constrained estimates of the global ocean carbon sink from Earth system models

Abstract. The ocean slows global warming by currently taking up around one-quarter of all human-made CO2 emissions. However, estimates of the ocean anthropogenic carbon uptake vary across various observation-based and model-based approaches. Here, we show that the global ocean anthropogenic carbon sink simulated by Earth system models can be constrained by two physical parameters, the present-day sea surface salinity in the subtropical–polar frontal zone in the Southern Ocean and the strength of the Atlantic Meridional Overturning Circulation, and one biogeochemical parameter, the Revelle factor of the global surface ocean. The Revelle factor quantifies the chemical capacity of seawater to take up carbon for a given increase in atmospheric CO2. By exploiting this three-dimensional emergent constraint with observations, we provide a new model- and observation-based estimate of the past, present, and future global ocean anthropogenic carbon sink and show that the ocean carbon sink is 9 %–11 % larger than previously estimated. Furthermore, the constraint reduces uncertainties of the past and present global ocean anthropogenic carbon sink by 42 %–59 % and the future sink by 32 %–62 % depending on the scenario, allowing for a better understanding of the global carbon cycle and better-targeted climate and ocean policies. Our constrained results are in good agreement with the anthropogenic carbon air–sea flux estimates over the last three decades based on observations of the CO2 partial pressure at the ocean surface in the Global Carbon Budget 2021, and they suggest that existing hindcast ocean-only model simulations underestimate the global ocean anthropogenic carbon sink. The key parameters identified here for the ocean anthropogenic carbon sink should be quantified when presenting simulated ocean anthropogenic carbon uptake as in the Global Carbon Budget and be used to adjust these simulated estimates if necessary. The larger ocean carbon sink results in enhanced ocean acidification over the 21st century, which further threatens marine ecosystems by reducing the water volume that is projected to be undersaturated towards aragonite by around 3.7×106–7.4×106 km3 more than originally projected.
A Cost-Efficient Method for Big Geospatial Data on Public Cloud Providers

—The rise of big geospatial data creates the need for an environment with powerful computational resources to process this large amount of geographical information. Spatial Cloud Computing is a solution to this problem as it offers facilities to overcome the challenges of a big data environment, providing significant computer power and vast storage. However, the software to process this data requires great performance capacity. These requirements are met by SpatialHadoop, a fully-fledged MapReduce framework with native support for spatial data. This paper presents a cost-efficient method for processing geospatial data on public cloud providers, optimizing the number of data nodes in a Hadoop cluster according to dataset size. Tests have proven that it can optimize the use of computational resources for available SpatialHadoop datasets.
PHYSIOLOGICAL AND BIOCHEMICAL RESPONSES OF CHICKPEA ( Cicer arietinum L.) GENOTYPES TO DIFFERENT MOISTURE STRESSES

Moisture stress influence seed germination, growth including physiological, biochemical attributes and yield of chickpea ( Cicer arietinum L.). Genotypes may vary in their capacity to tolerate moisture stress. Therefore, the study was undertaken to evaluate physiological and biochemical responses of selected chickpea genotypes in the drought prone ecosystems. Relative water content and carotenoids content significantly decreased when stress imposed until pod formation stage. Moisture stress imposed during pre-flowering stage significantly decreased chlorophyll a and chlorophyll b content. Proline accumulation was higher in BD-6048 compared to other genotypes under all moisture stress conditions. Phosphorus, potassium and protein content were lower under moisture stress until pod formation stage. Under moisture stress conditions the genotypes BD-6048 had the highest yield compared to other genotypes. Moisture stress until pre-flowering and pod formation stage reduced seed yield more severe than that on flowering stage.
Engineering synthetic pathways for adipic acid biosynthesis

Utilization of petroleum in consumer product manufacturing is causing irreversible environmental damage. Its impact on land, sea, and air calls for the development of more sustainable technologies based on the use of renewable materials such as lignocellulosic biomass and its conversion into platform chemicals. Engineering microorganisms to produce chemicals is an important undertaking to address such issues and bio-based production of adipic acid especially has gained recent attention. In the present thesis I assess the in vivo and in silico action of enzymes involved in microbial production of adipic acid from simple sugar molecules. The aim of this work was to comprehensively map out the metabolic pathways leading to adipic acid biosynthesis and to investigate the enzymatic components of the L-lysine pathway, the reverse βoxidation pathway, and cis,cis-muconic acid reduction. Investigation of theoretical and in silico aspects in the deamination step in the L-lysine pathway revealed deamination of L-lysine to be chemically difficult to occur. Removal of the βamino group from β-D-lysine was deemed more feasible than the α-amino group from L-lysine, and an alternative route via β-D-lysine deamination was suggested. Homology modeling and molecular docking studies shed light on the substrate binding mechanisms of enzymes responsible for the reduction of the intermediates in the L-lysine pathway. Potential mechanism and feasibility of α,β-reduction were explained in terms of substrate interaction in the enzymebinding pockets. Corynebacterium glutamicum was chosen as the host chassis for achieving adipic acid synthesis via reverse β-oxidation. Stepwise construction of a five-step synthetic pathway demonstrated functionality of each step in C. glutamicum. Biosynthesized and secreted 3-hydroxyadipate was detected in the cultivation broth using GC/MS. Weak trans-2-hexenedioic acid and adipic acid signals were observed using LC/MS after concentrating the cultivation broth. Dehydration of 3-hydroxyadipyl-CoA was identified as a potential bottleneck in the pathway. While implementing the reverse β-oxidation pathway, a new pathway involving cis,cismuconic acid and 3-oxoadipic acid was identified and experimented on. The modified strategy for bio-conversion of benzoic acid to cis,cis-muconic acid was successful and molecular docking studies were carried out to better understand how oxidoreductases might reduce cis,cis-muconic acid. Taking multiple approaches to generate adipic acid revealed different challenges in each pathway. One approach led to biosynthesis of adipic acid.