Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling the Effect of External Flue Gas Recirculation on NOx and CO Emissions in a Premixed Gas Turbine Combustor With Chemical Reactor Networks

The increasing amount of renewable energy and emission norms challenge gas turbine power plants to operate at part-load with high efficiency, while reducing NOx and CO emissions. A novel solution to this dilemma is external Flue Gas Recirculation (FGR), in which flue gases are recirculated to the gas turbine inlet, increasing compressor inlet temperature and enabling higher part load efficiencies. FGR also alters the oxidizer composition, potentially leading to reduced NOx levels. This paper presents a kinetic model using chemical reactor networks in a lean premixed combustor to study the impact of FGR on emissions. The flame zone is split in two perfectly stirred reactors modelling the flame front and the recirculation zone. The flame reactor is determined based on a chemical time scale approach, accounting for different reaction kinetics due to FGR oxidizers. The recirculation zone is determined through empirical correlations. It is followed by a plug flow reactor. This method requires less details of the flow field, has been validated with literature data and is generally applicable for modelling premixed flames. Results show that due to less O2 concentration, NOx formation is inhibited down to 10–40% and CO levels are escalated up to 50%, for identical flame temperatures. Increasing combustor pressure leads to a rise in NOx due to thermal effects beyond 1800 K, and a drop in CO levels, due to the reduced chemical dissociation of CO2. Wet FGR reduces NOx by 5–10% and increases CO by 10–20%.
U-net-based multiscale feature preserving method for low light image enhancement

Abstract. Low light image enhancement is a challenging task, and it has been a hot research topic. Inspired by retinex theory and U-Net network, we propose a U-Net-based multiscale feature preserving method for low light image enhancement, which can realize the extraction of low-level features and high-level semantic features. Before feature extraction, we carry out multiscale pre-extraction processing on the image to improve the feature extraction ability of the network. Considering the discontinuity between low-level features and high-level semantic features, we propose a spatial consistency method to maintain the global feature correlation. Finally, we propose a new multiscale structure calculation method, which greatly alleviates the phenomenon of uneven illumination and color deviation after enhancement and makes the enhancement results more consistent with human visual perception. Extensive experiments demonstrate that compared with other advanced enhancement methods, our method has better enhancement effect and can retain more details. The enhanced image not only has good visual perception but also is better than other methods in objective evaluation.
SENTIGRADE: A SENTIMENT BASED USER PROFILING STRATEGY FOR PERSONALISATION

Nowadays, the availability of folksonomy data is increased to make importance for user profiling approaches to provide results of the retrieval data or personalized recommendation. The approach is used for detecting the preferences for users and can be able to understand the interest of the user in a better way. In this approach, the incorporation of information with numerous data which depends upon sentiment is implemented using a framework SentiGrade by User Profiles (UP) and Resource Profiles (RP) for user Personalized Search (PS). From the folksonomy data, the discovery of User Preference (UsP) is presented by a rigorous probabilistic framework and relevance method are proposed for obtaining Sentiment-Based Personalized (SBP) ranking. According to the evaluation of the approach, the proposed SBP search is compared with the existing method and uses the two datasets namely, Movielens and FMRS databases. The experimental outcome of the research proved the effectiveness of the framework and works well when compared to the existing method. Through user study, the evaluation of approaches and developed systems are made which shows that considering information such as relevance and probabilistic data in Web Personalization (WP) systems can able to offer better recommendations and provide much effective personalization services to users.
Performance Analysis of NOMA Downlink for Next- Generation 5G Network with Statistical Channel State Information

Non-orthogonal multiple access (NOMA) is a compelling strategy that helps wireless networks of the fifth-generation (5G) to fulfill the diverse demands of increased fairness, high reliability, extensive connectivity, low delay and superior performance. Traditionally, NOMA network presumes perfect transmitter-side channel state information (CSI), which is almost impossible for a number of communication scenarios. Furthermore, this paper contemplates the realistic NOMA downlink method in the Rayleigh fading networks, where the statistical CSI linked with each user is known to the transmitter. We evaluate the outage probability of the proposed model and obtain a closed-form framework of the probability of outage for each user. Besides, the source can optimize the network’s achievable sum-rate for different users based on statistical CSI. The precision of our outage probability analysis and the optimum power allocation algorithm proposed are both verified by the analytical and simulation results.
CNMF: A Community-Based Fake News Mitigation Framework

Fake news propagation in online social networks (OSN) is one of the critical societal threats nowadays directing attention to fake news mitigation and intervention techniques. One of the typical mitigation techniques focus on initiating news mitigation campaigns targeting a specific set of users when the infected set of users is known or targeting the entire network when the infected set of users is unknown. The contemporary mitigation techniques assume the campaign users’ acceptance to share a mitigation news (MN); however, in reality, user behavior is different. This paper focuses on devising a generic mitigation framework, where the social crowd can be employed to combat the influence of fake news in OSNs when the infected set of users is undefined. The framework is composed of three major phases: facts discovery, facts searching and, community recommendation. Mitigation news circulation is accomplished by recruiting a set of social crowd users (news propagators) who are likely to accept posting the mitigation news article. We propose a set of features that identify prospect OSN audiences and news propagators. Moreover, we inspect the variant properties of the news circulation process, such as incentivizing news propagators, determining the required number of news propagators, and the adaptivity of the MN circulation process. The paper pinpoints the significance of facts searching and news propagator’s behavior features introduced in the experimental results.
A Data Mining Technique to Improve Configuration Prioritization Framework for Component-Based Systems: An Empirical Study

Department of Software Engineering, In the current application development strategies, families of productsare developed with personalized configurations to increase stakeholders’ satisfaction. Product lines have theability to address several requirements due to their reusability and configuration properties. The structuringand prioritizing of configuration requirements facilitate the development processes, whereas it increases theconflicts and inadequacies. This increases human effort, reducing user satisfaction, and failing to accommodatea continuous evolution in configuration requirements. To address these challenges, we propose a framework formanaging the prioritization process considering heterogeneous stakeholders priority semantically. Featuresare analyzed, and mined configuration priority using the data mining method based on frequently accessed andchanged configurations. Firstly, priority is identified based on heterogeneous stakeholder’s perspectives usingthree factors functional, experiential, and expressive values. Secondly, the mined configuration is based on frequentlyaccessed or changed configuration frequency to identify the new priority for reducing failures or errorsamong configuration interaction. We evaluated the performance of the proposed framework with the help ofan experimental study and by comparing it with analytical hierarchical prioritization (AHP) and Clustering.The results indicate a significant increase (more than 90 percent) in the precision and the recall value of theproposed framework, for all selected cases.
The Leading Edge of Disruptive Innovations in Health Care Data, Communications, and Information Technologies: Artificial Intelligence

This issue brief explores the topic of artificial intelligence (AI) and the basis for its current surge in broad interest across the health care sector. Here, we explore AI from a historical perspective, terms and definitions, the major science and technology components, and, relevant to the Global Digital Health Partnership (GDHP), the newfound integration of artificial intelligence with health care and government. Our intent is to provide a general view of the pressing issues that are emerging at a rapid pace with this fascinating technology, and our work is not intended to represent a thorough, in-depth analysis from a policy perspective on science and technology implications. In addition, examples of the application of AI in consumer health, medical practice, and health care management are provided to lay the groundwork for conversations about the wide-ranging potential for these technologies to have a powerful impact and societal benefit. As a result of the basic understanding presented here, coupled with unique perspectives and experiences of each of the individuals in the audience, we encourage thoughtful introspection and commentary to be followed by considerations for collaborative activities on the topic among the GDHP participants.
UNIVERSITY OF EASTERN FINLAND Faculty of Social Sciences and Business Studies Business School IS CIRCULAR ECONOMY THE NEW BLACK? BUSINESS OPPORTUNITY RECOGNITION WITHIN COMPANIES WITH WOOD APARTMENT BUILDING EXPERIENCE OPERATING IN THE FINNISH MARKET

UNIVERSITY OF EASTERN FINLAND Faculty Faculty of Social Sciences and Business Studies Department Business School Author Sini-Tuulia Suokas Supervisor Kaisa Henttonen Title Is circular economy the new black? Business opportunity recognition within companies with wood apartment building experience operating in the Finnish market Main subject Innovation Management Level Master’s degree Date 31 October 2019 Number of pages 89 and 1 Appendix (2 pages) Abstract The goal for this study is to find out the main factors that are connected with SME’s business opportunity recognition within the circular economy field, more precisely in wood construction and apartment house building using timber, and understand how SME’s within the circular economy, specifically in wood construction and apartment house building with timber, recognize business opportunities. The context is set within these limits and located in Finland. In this study wood construction is seen as a part of the circular economy by its core element. By this it is meant that it belongs within the biological cycles with its renewable form, but also its features as a carbon sink. The theoretical framework of this study is built with the theories concentrating on business opportunity recognition and opportunity construct, lifting up the issues how the opportunities are recognized and what are the influencing factors behind opportunity recognition. The framework continued beyond the discoverer level and concentrated also on opportunity with its many-sided form and their nexus. This qualitative multiple-case study collected the data with semi-structured expert and SME interviews from the parties that had experience in timber apartment building projects in Finland. The data was collected during the autumn in 2019 and six interviews were conducted. The data was analysed with inductive content analysis and the literature research had been done largely beforehand. The findings propose ten factors that had a connection with opportunity recognition and three of them were different than in the linear economy. Also, the opportunities were discovered, created or both depending on the context. The nexus was also playing a part with the effect of the characteristics that were connected with the discoverer but also with the characteristics that the opportunity had.
Design and Fabrication of Micromachined Absolute Micro Pressure Sensor

Absolute micro pressure sensors have been designed and fabricated in batches, which will be capable to give output responses in form of voltage signal as a function of pressure. The piezoresistive effect has been chosen for this device because it provides an observable resistance changes that is a linear function of pressure and is observable at low stress levels. A membrane is used as a stressmagnifying device. The pressure-induced stresses in the membrane are sensed by properly oriented piezoresistors interconnected to form a bridge. For the purpose of pressure sensing, boron doped polysilicon piezoresistors have been fabricated in half Wheatstone bridge configuration. A 100 μm sized square membrane, over which polysilicon resistors are deposited, has been realized having thickness of 0.8 μm with the help of a sacrificial layer of LPCVD polysilicon thin film. Highly selective anisotropic etching of silicon has been performed in 45% wt KOH at 75 C for conical cavity formation under the membrane. The developed fabrication process is competent to provide nearly 1089 chips of 1mm x 1mm size on a two-inch diameter silicon (100) wafer.
Embracing Smart Industry

Due to the developments in the field of Smart Industry, manufacturers are facing new technologies that take over the industry. This research focuses on the issue of how technology adoption and perceived usefulness, as part of technology acceptance, affect each other. This matter is investigated within SME manufacturing firms in the metal industry in the Netherlands, which resulted in the following research question: “How do technology adoption and the perceived usefulness of new Smart Industry technologies affect each other within SME manufacturing firms in the Netherlands? ” For technology adoption, a model by Langley & Truax (1994) is used, in which this concept is subdivided into three sub-processes: the strategic commitment process, technology choice process, and financial justification process. Perceived usefulness is part of the technology acceptance model by Davis et al. (1989) and is further operationalized by the indicators of Segars and Grover (1993): makes the job easier, makes the job more useful, and increases productivity. This is investigated qualitatively by conducting nine interviews within three SME manufacturing firms in the metal industry and by interviewing two industry experts. The results show that the relationship between technology adoption and perceived usefulness can be seen as being bidirectional. The perception and support of employees are necessary for successful technology adoption, but how technology adoption is performed, in turn, can highly influence the perceived usefulness. Management perception and employee perception turn out not to differ substantially. By aligning management vision and employee vision, the best out of Smart Industry technologies can be achieved, since every user then perceives it as useful. The feedback loop between technology adoption and perceived usefulness can become reinforcing by paying attention to four variables: creating understanding for change, involving employees in technology adoption process, educating people to work with technologies, and employee development possibilities. This research contributes to science by demonstrating the bidirectional relationship between technology adoption and perceived usefulness, and by making old and static models around this topic more dynamic. Additionally, it contributes to practice by giving insights to SME manufacturing firms in the way they can deal with the Smart Industry revolution. By paying attention to the insights of this research, future technology adoptions become more successful and organizations can improve and develop their business.
Construction and Prediction of Antimicrobial Peptide Predicition Model Based on BERT

In daily life, the abuse of antibiotics causes bacteria to develop resistance to antibiotics, which is not conducive to public health. Antimicrobial peptide is a small molecule polypeptide that forms a key component of the biological innate immune system. It kills target bacteria by destroying cell membranes and interfering with DNA. Therefore, antimicrobial peptides are promising alternatives to antibiotics. However, antimicrobial peptides have different lengths and various structures. In order to apply them to various ﬁelds, they need to be predicted and identiﬁed. The existing antimicrobial peptide identiﬁcation and prediction methods mainly adopt biological experiments, sequence alignment or machine learning methods. Through deep learning, the accuracy and speed of antimicrobial peptide identiﬁcation and prediction can be further improved. At present, there are few deep learning network models that specialize in amino acid sequences, and there is no special database to provide samples of non-antimicrobial peptides. Therefore, how to apply the relevant cutting-edge methods of deep learning to the identiﬁcation and prediction of antimicrobial peptides is a problem worthy of study. At the same time, the accuracy and speed of antimicrobial peptide identiﬁcation still have room for further improvement.The Bidirectional Encoder Representations from Transformers (BERT) model is a model applied in the ﬁeld of natural language processing. It builds a network structure with Transformer as the core, and its core mechanism is the self-attention mechanism. We used the BERT model based on the existing protein database, used a large number of protein sequences for pre-training, and combined with the comprehensive data set for ﬁne-tuning, and constructed an antimicrobial peptide prediction model.
Alternation

Alternation is a generalization of nondeterminism in which existential and universal quantitiers can alternate during the course of a computation, whereas in a nondeterministic computation there are only existential quantifiers. Alternating Turing machines are defined and shown to accept precisely the recursively enumerable sets. Complexity classes of languages accepted by time(space-) bounded alternating Turing machines are characterized in terms of complexity classes of languages accepted by space(time-) bounded deterministic Turing machines. In particular, alternating polynomial time is equivalent to deterministic polynomial space and alternating polynomial space is equivalent to deterministic 'exponential time. Subrecursive quantifier hierarchies are defined in terms of timeor space-bounded alternating Tufing machines by bounding the number of alternations allowed during computations. Alternating finite-state automata are defined and shown to accept only regular languages, although, in general, 2 2 states are necessary and sufficient to simulate a k-state alternating finite automaton deterministically. Finally, it is shown that alternating pushdown automata are strictly more powerful than nondeterministic pushdown automata.
IMPLEMENTATION OF THE ANN MODEL TO A COMPLICATED DRAINAGE SYSTEM

Automatic and adaptive pumping operations on drainage management in a lowland have been required to reduce run costs on efficient, regular pumping and promote effective water-supply for paddy fields and proper controls for flood events. To satisfy these requirements in controlling the entire water volume in the lowland, an effective and efficient, real-time prediction model is required. The model is usually run using observed data from the fields. For a sustainable operation with reduction of run cost and manpower, this study focused on investigating minimum observed instruments based on locations and data items to maintain accurate model predictions. We employed the long short-term memory (LSTM) model as a data-driven model, which can predict long-term, time-series data accurately. The LSTM model that predicts water level and discharge was implemented to a mid-size agricultural lowland with a complicated drainage system. In the area, several stations for intake (four irrigation pumping stations) and for drainage (five drainage pumping stations), numerous canals and a regulating pond exist. Continuous, long-term observed data are available. Water level predictions were conducted with a variety of cases with different inputs based on the number of stations and the combinations of data items (e.g., water level & rainfall, and water level & drain discharge) during April to August (irrigation season). The error evaluation was conducted by K-fold cross-validation. The results showed that the information of water level as input data was greatly crucial for accurate outputs and that four irrigation pumping stations and a main drainage pumping station were proper to minimize observed locations during irrigation season.
The Importance of Engagement on Social Media Platforms: A Case Study

The tourism sector has long been associated with new technologies, and more specifically with social media. The objective of this study is to analyse the interaction between tourist satisfaction, tourist engagement, and user generated content on social media, especially on Twitter. We propose a model based on these three related variables. The context of the model is tourist related events and locations, and consists of three hypotheses which are supported by the literature. To test our model we conducted our studies in two independent tourist related settings using both surveys and an analysis of the conversations relating to the event on the Twitter platform. We determined that the strongest influence on User Generated Content comes from tourist engagement as a mediator variable. Additionally, we found that achieving engagement among tourists visiting the destination is an effective way to generate positive content in social media. The study makes a new contribution to literature ‐ the proposed model is innovative, in particular with the introduction of engagement as a mediator variable, and the incorporation of an analysis of social media content into the model validation. This information supports decisions at all levels that in turn improve educational process performance further.
Flower Image Classifications Using Cloud Machine Learning With Tensorflow

: The main purpose of this study is to recognize the apt algorithm for flower image classification. The study of flower classification is an important aspect in computer science due to its aggregate impact in the human life from botany, science, medical etc., but due to it complex environment for extracting features, similarity of features in many species, changing features in the life of a flower having etc. An accurate classification is considered as a novel work since many classification algorithms are still newly presented. This paper presents a comparative study of different t flower image segmentation and classification algorithms containing their experiment dataset and the result accuracy rate to assist future researchers in this domain.