Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-phase (gas/liquid) flow metering of viscous oil using a Coriolis mass flow meter: a case study

Coriolis mass flow metering has been established as the most accurate widely-used industrial flow measurement technology since its introduction in the mid 1980s. Coriolis meters operate (Fig. 1) by oscillating a flow-tube (typically 1-300 mm in diameter), at the natural frequency of a selected mode of vibration, the so-called drive mode. Two sensors monitor the flow-tube vibration as the process fluid passes through. The frequency of oscillation (in the range 50Hz - 1kHz depending on flow-tube geometry) is determined by the overall mass of the vibrating system, and hence for a given flow-tube, this varies with the density of the process fluid. Accurate determination of the frequency of vibration thus enables the process fluid density to be calculated. The geometry of the flow-tube is arranged so that Coriolis forces act to give a phase difference between the two sensor signals, roughly proportional to the mass flow of the process fluid (which in the largest meters may approach 1 tonne/s). While the flow-tube is essentially a mechanical device with a few electrical transducers (sensors and drivers), the transmitter is an electronic and computational device which drives and monitors the flow-tube, and which generates the measurement data. A long-term research programme at the University of Oxford has been developing all-digital transmitter technology [3, 4, 5] with various improvements including a very fast response time [6] and an ability to operate in two-phase flow [1-5]. The transmitter architecture in Fig. 1 includes audio quality analog-to-digital converters (ADCs) and digital-to-analog convertors (DACs), with 24-bit samples delivered at 48kHz. Field Programmable Gate Arrays (FPGAs) are chips consisting of configurable logic blocks, capable of carrying out complex digital algorithms in real time and in parallel. FPGA tasks include interfacing to the ADCs and DACs, generating the drive waveform and pre-filtering of the measurement data. This architecture is used in Invensys Foxboro’s commercial product, the CFT-50 Coriolis transmitter, which was used in the trials. Reizner [7] provides a good background to the problems associated with metering two-phase flow using Coriolis meters. In brief, it is difficult to maintain flow-tube oscillation in two-phase, as the condition induces very high and rapidly fluctuating damping (up to 3 orders of magnitude higher than for single phase conditions). When the transmitter is unable to maintain oscillation, the meter is described as “stalled”, and no (valid) measurement can be provided. Even where stalling is averted, large measurement errors may be induced into the mass flow and density measurements. Previous papers have described the use of a digital drive and an agile control system to maintain flow-tube oscillation through two-phase flow or batching to or from an empty flow-tube [2,8]. In this paper, it is assumed that the transmitter has this capability. The focus is restricted to
Prediction of Wind Power Generation with Modern Artificial Intelli-gence Technology

In view of the continuous growth of energy demand and interest in environmental protection, the use of clean energy to replace fossil fuels is a global trend. Wind energy is the fastest growing renewable energy in the world in recent years. However, in the case of Mexico, there are still some difficulties in promoting its use in some areas of the national territory. One difficulty is knowing in advance how much energy can be injected into the grid. This paper introduces the development of artificial intelligence technology for wind power generation prediction based on multi-year meteorological information. In particular, the potential application of Bayesian network in these prediction applications is studied in detail. A weather forecasting method based on Dynamic Bayesian network (RBD) is proposed. The forecasting system was tested using meteorological data from the regional wind energy technology center (CERT) of the National Institute of Electricity and Clean Energy (INEEL) in Oaxaca, Mexico. The results are compared with the time series prediction results. The results show that dynamic Bayesian network is a promising wind power generation prediction tool.
RUNOFF ESTIMATION BASED ON HYBRID-PHYSICS-DATA MODEL

Abstract. Runoff estimations play an important role in water resource planning and management. Existing hydrological models can be divided into physical models and data-driven models. Although the physical model contains certain physical knowledge and can be well generalized to new scenarios, the application of physical models is limited by the high professional knowledge requirements, difficulty in obtaining data and high computational costs. The data-driven model can fit the observed data well, but the estimation may not be physically consistent. In this letter, we propose a hybrid physical data (HPD) model combining physical model and deep learning model for runoff estimation. The model uses the output of a physical hydrological model together with the driving factors as another input of the neural network to estimate the monthly runoff of the upper Heihe River Basin in China. We show that the use of the HPD model improves the quality of runoff estimation, and results in high R2, NSE values of 0.969, and a low RMSE value of 9.645. It is indicated that the new model had an excellent learning capability to simulate runoff and flexible ability to extract complex relevant information; At the same time, the estimation capacity of peak runoff is optimized.
Consumer Response to Brand Placement in Movies: Investigating the Brand-Event Fit

Executive Summary Movies offer the perfect media site for placement of brands as part of the emerging marketing strategy. Although attempts to analyse brand placements have been made in the past, the same needs more attention in the Indian context. Given the exposure of Indian audiences to both national and international entertainment industry, it is only reasonable to expect the entertainment event context to have an impact on consumers’ evaluation of the brands placed in each context. The present research attempts to extend the applicability of the idea of fit, which was till now largely confined to sponsorship and subjects it to the exploration of finding a fit between brands and specific events, in particular, movies. Because the link between country of origin of the entertainment event (national/international) and brand, placement is a relevant area of speculation, the present research aims to study this relationship within the national/international context. Results of an experimental study among 120 respondents are as follows: Brands placed in a national event will create more positive brand evaluations in terms of positive attitude towards the placed brand and intention to purchase than brands that are placed in an international event. When the presence of a brand is consistent with the context in which it is placed, it would evoke more positive attitudes and behaviour than an incongruent placement. Evaluation of results further reveals that although a brand that fits well with the context in which it is placed generates a positive evaluation of the placed brand, the condition of a brand-event misfit in a Hollywood context will create more negative evaluations among the Indian audiences than if such a disconnect appeared in a Hindi film. In other words, a brand may have more to lose in case of a misfit with the international entertainment event than with a national entertainment event. Based on the findings of the present study, it is suggested that multinational brands must look at the Indian movies as a suitable medium for reaching out to the prospective buyers as Indians have become consumers of global brands and thus pose to be a huge market for global brands.
Green Trade Credit and Sustainable Firm Performances During COVID-19: A Conceptual Review

The modernization of the economic world creates environmental issues around the globe, to improve global problems such as global warming, inequality, climate change, hunger and poverty; it is necessary to improve sustainable performance. Mostly, the product manufacturers have a greater influence on the supply chain and the environment. Before COVID-19, the economy of Pakistan was belligerent but not collapsed; however, COVID-19 created a situation where most of the companies will go bankrupt. Pakistan is considered less developed financially, where extensive growth is predictable in the use of green trade credit demand by firms listed at the Pakistan Stock Exchange. Thus, the study analyzes the conceptual linkages between trade credit demand and sustainable firm performances through green credit by adopting the theory of RBV and also assesses the role and impact of COVID-19; further, suggest some policies to cope with COVID-19 challenges. Secondary literature is being reviewed after searching electronic databases like Scopus, Directory of Open Access Journals, Web of Science, ProQuest, ScienceDirect, Google Scholar and JSTOR. This study will help policymakers whenever they need to consider green trade credit decisions. Thus, buyers need to be proactive while adopting green trade credit to compete in this sustainable competitive marketplace.
Analyzing the long run linkage between Population, Economic Development and Energy Consumption on Carbon emissions of ASEAN Nations

This study examined the effect of population, energy consumption, economic development on environment degradation in the context of ASEAN countries. For this purpose, the study used the panel data of nine ASEAN countries (Indonesia, Singapore, Malaysia, Brunei, Philippines, Cambodia, Thailand, Myanmar, and Vietnam) from 1995 to 2018 with the help of the World Development Indicators (WDI) dataset. Panel FMOLS and Panel ARDL methodology are used to examine the econometric model. ARDL results show that economic growth increases the emission of carbon dioxide which means economic development makes the environment unhealthy, moreover it confirmed the validity of the EKC hypothesis for ASEAN countries. There is a long-term positive impact of GDP and the consumption of energy on CO2 emission. In contrast, the impact of population growth is significant on the per capita emission of carbon dioxide. However, the emission of carbon dioxide hurts economic growth in the long run. Consequently, emission of carbon dioxide can be reduced, and economic growth can be sustainable by using low carbon emission technologies such as renewable energy.
Who Does What to Whom? Making Text Parsers Work for Sociological Inquiry

Over the past decade, sociologists have become increasingly interested in the formal study of semantic relations within text. Most contemporary studies focus either on mapping concept co-occurrences or on measuring semantic associations via word embeddings. Although conducive to many research goals, these approaches share an important limitation: they abstract away what one can call the event structure of texts, that is, the narrative action that takes place in them. I aim to overcome this limitation by introducing a new framework for extracting semantically rich relations from text that involves three components. First, a semantic grammar structured around textual entities that distinguishes six motif classes: actions of an entity, treatments of an entity, agents acting upon an entity, patients acted upon by an entity, characterizations of an entity, and possessions of an entity; second, a comprehensive set of mapping rules, which make it possible to recover motifs from predictions of dependency parsers; third, an R package that allows researchers to extract motifs from their own texts. The framework is demonstrated in empirical analyses on gendered interaction in novels and constructions of collective identity by U.S. presidential candidates.
Between Europeanism and Nativism: Exploring a Cleavage Model of European Public Sphere in Social Media

The European Union’s common public sphere project dates back to the 1960s and relies on Europeanisation through the gradual eradication of communication boundaries between its member countries. However, it is evident by now that Europeanisation of national public spheres is hard to achieve by increasing overlaps between national public spheres, synchronisation of news reporting across national boundaries, or diffusion of Europeanist norms into national politics. The European Union’s common public sphere project may hence be in danger. This calls for explorations of other imaginable models of the public sphere for Europe. Are there traces of other modes of transnational public sphere emerging in Europe? In this article, we explore a models of the transnational public sphere which is based on an alternative concept of Europeanisation derived from the cleavage theory. By drawing on social media data and employing tools of social network analysis, we demonstrate the empirical possibility of a cleavage model of the European public sphere.
Consumer shopping emotion and interest database: a unique database with a multimodal emotion recognition method for retail service robots to infer consumer shopping intentions better than humans

Abstract. Empowering retail service robots with empathy is one of the current research hotspots in the field of artificial intelligence. Identifying consumer emotions, understanding the changes in shopping interests, and developing appropriate sales strategies is a challenging task for retail service robots. We investigate the feasibility of using computer vision methods for empowering robots with empathy by examining the correlation between consumer emotion and levels of shopping interest. To this end, we construct the first video database of consumer sentiment changes in a business context and propose a deep learning method that uses multimodal information to infer consumers’ shopping intentions, and conduct preliminary experimental validation on this database. The experimental results show that the proposed method is 7% and 10% more accurate than manual assessment (n = 40) in identifying consumer emotions and predicting consumer shopping interest levels, respectively. Thus, the proposed method is valid and effective. We anticipate that the results of this study will have considerable implications for human–computer interaction research in service robots.
MITformer: A Multiinstance Vision Transformer for Remote Sensing Scene Classification

The latest vision transformer (ViT) has stronger contextual feature representation capability than the existing convolutional neural networks and thus has the potential to depict the remote sensing scenes, which usually have more complicated object distribution and spatial arrangement than ground image scenes. However, recent researches reflect that while ViT learns global features, it also ignores the key local features, which poses a bottleneck for understanding remote sensing scenes. In this letter, we tackle this challenge by proposing a novel multiinstance vision transformer (MITformer). Its originality mainly lies in the classic multiple instance learning (MIL) formulation, where each image patch embedded in ViT is regarded as an instance and each image is regarded as a bag. The benefit of designing ViT under MIL formulation is straightforward, as it helps highlight the feature response of key local regions of remote sensing scenes. Moreover, to enhance the feature propagation of local features, attention-based multilayer perceptron (AMLP) head is embedded at the end of each encoder unit. Finally, to minimize the potential semantic prediction differences between the classic ViT and our MIL head, a semantic consistency loss is designed. Experiments on three remote sensing scene classification benchmarks show that our proposed MITformer outperforms the existing state-of-the-art methods and validate the effectiveness of each component in our MITformer.
Early development of a novel scale to measure adaptation in people diagnosed with inflammatory bowel disease - the A-inflammatory bowel disease

Objective To undergo the preliminary development of a new measure of patient adaptation to Inflammatory Bowel Disease (IBD): A-IBD. Design Based on a prior conceptualisation of adaptation, a 40-item scale was generated and completed by 304 people diagnosed with IBD. Main outcome measures Psychometric analysis of the measure. Association with the Brief Illness Perception Questionnaire (Brief IPQ) and the Inflammatory Bowel Disease Questionnaire (IBDQ). Results The 18-item scale consisted of four subscales (patient identity, person identity, acceptance, expectations). Weak to moderate correlations were found between subscales of the A-IBD and the Brief IPQ and IBDQ. Conclusion The A-IBD shows potential for assessing adaptation. Further analysis could confirm its usefulness.
Deteksi Ruang Kosong pada Jalan Menggunakan Semantic Segmentation pada Mobil Otonom

Abstrak—Perkembangan mobil otonom tentunya tidak terlepas dari masalah navigasi, dimana navigasi untuk menentukan apakah jalan di depanya aman dan bebas dari halangan. Beberapa masalah pada mobil otonom yang diteliti belakangan ini seperti deteksi halangan dan deteksi garis jalan hanya melakukan pendeteksian dan pengenalan objek namun tidak memberikan informasi mengenai jalan yang dapat dilewati. Oleh karena itu, diperlukan deteksi ruang kosong pada jalan yang aman dari halangan agar tidak terjadi tabrakan. Pada umumnya deteksi ruang kosong jalan yang ada masih menggunakan sensor kamera saja, sehingga hasil yang didapat kurang baik. Pada penelitian ini, metode Semantic Segmentation dengan menggunakan data fusion antara sensor LiDAR dan kamera diterapkan untuk menghasilkan suatu sistem yang dapat mendeteksi ruang kosong jalan. Algoritma yang digunakan pada sistem ini adalah SNE-RoadSeg. Deteksi ruang kosong yang dihasilkan adalah berupa segmentasi pada jalan untuk memberikan infomasi pengenalan dan klasifikasi jalan di tingkat piksel. Performa sistem kemudian diuji menggunakan dataset jalan yang berasal dari KITTI pada kelima jenis backbone yaitu, ResNet-18, ResNet-34, ResNet-50, ResNet-101, dan ResNet-152. Algoritma sistem dapat menghasilkan skor F1 score terkecil dengan nilai 0.9548 pada ResNet-18 dan skor F1 score terbesar dengan nilai 0.9675 untuk ResNet-101. Skor precision ResNet-152 dengan nilai 0.9686 lebih tinggi 0.0024 dibandingkan dengan ResNet-101. Sistem yang diajukan memiliki rata-rata kecepatan deteksi pada tiap jenis ResNet sekitar 0.343 detik.
Improved Iterative Prediction Reconstruction for Compressive Whiskbroom Imaging

Compressive Sampling is suitable for remote hyperspectral imaging, as it can simplify the architecture of the onboard sensors. The reconstruction process is an indispensable component of the hyperspectral imaging as it decodes the compressive measurements to render a three-dimensional spatio-spectral estimate of the scene. The existed reconstruction methods mainly concentrated in reducing the algorithm complexity and increasing the reconstruction accuracy, not taking into account the sensing paradigm of the onboard sensors, such as compressive whiskbroom imaging. For this reason, an improved iterative prediction reconstruction algorithm employing Multi-Task Compressive Sensing for compressive whiskbroom imaging is proposed. Experimental results run on raw data from AVIRIS confirm the validity of the proposed method.
Participatory governance in China: ‘Informal public participation’ through neighbourhood mobilisation

Public participation is an emergent feature of urban governance in Nanjing, China. Though introduced to address social conflicts around urban redevelopment, the fact is that government-led public participation did not successfully soothe tensions, but resistance instead played a role like ‘public participation’ which influenced the governing processes. This Chinese case thus requires us to rethink the limitations of Western governance theories in explaining this Chinese story. With the purpose to research urban political studies beyond the West to have more plural and heterodox voices to depict a diversity of urban knowledge, this paper focuses on this heterodox phenomenon in China to understand how Chinese insurgent urbanism reconfigured and made a new form of urban governance. Through critical investigation of public participation in Nanjing, this paper argues that public participation is the buffer that mediates and ‘repairs’ the inconsistencies between different levels of governmental agencies, while also because of this disjointed and stratified administrative feature, resisting by appropriating government policies thus can influence the governing processes and become a way of ‘informal public participation’. By introducing the concept of ‘informal public participation’, this paper’s research findings shed light on governance studies about how participatory governance is made through both governmental approaches and bottom-up insurgent urbanism, and also illustrates how resistance can reshuffle power dominance in urban politics to make a new form of urban governance.
Research Article NEW LIGHT ON NJANJA IRON WORKING: TOWARDS A SYSTEMATIC ENCOUNTER BETWEEN ETHNOHISTORY AND ARCHAEOMETALLURGY

Victorian ideas on evolution had strong adherents amongst the European men and women who colonized Africa. Such individuals perceived themselves as superior and viewed African societies and cultures as primitive. Yet, some missionaries who encountered Njanja iron-workers in what is now Zimbabwe were astonished by the sophis tication of their industry and even labelled it 'the Wolverhampton of Mashonaland'. This is unexpected given the stereotypical and derogatory perception of African cultures and technologies that was deeply entrenched at the time. Throughout the 20th century, historical and archaeological research revealed that Njanja iron production was specialized and that it conferred economic power on master smelters, hence promoting their politicalfortunes. Despite this consistent story of successful economic specialization, the technical parameters, such as the conditions of operation in thefurnaces, the quality of the ores and the skills of the smiths in manipulating furnace conditions, remain largely unknown. The results of preliminary metallurgical analyses are presented in this paper. Comparisons with the physico-chemical characteristics of slagfrom historical sites revealed that even though Njanja smelting was constrained by the underlying principles of the bloomery process, there were some subtle links between specialized production, efficiency in reduction and product quality.
Identifying correlations between depression and urban morphology through generative deep learning

Mental health disorders, such as depression, have been estimated to account for the largest proportion of global disease burden. Existing research has established significant correlations between the built environment and mental health. This research, however, has relied on traditional statistical methods that are not amenable to working with large remote sensing image-based datasets. This research addresses this challenge and contributes new knowledge and a novel method for using generative deep learning for urban analysis and synthesis tasks involving mental health. The research specifically investigates three mental state measures: depression, anxiety, and the perception of safety. The experimental results demonstrate the efficacy of the process—providing a new method to find correlational signals, while providing insights on the correlation between specific urban design features and the incidence of depression.