Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of the Influence of Micellar Casein and Spelt Flour on Yoghurt Quality Indicators

The aim of the research is to study the effect of protein-containing ingredients of animal and plant origin on the quality indicators of yoghurt for the scientific substantiation of its recipe composition. Micellar casein and spelled flour are characterized by high nutritional value, exhibit functional and technological properties and can significantly affect the quality indicators of yogurt. To confirm this, the possibility of complete replacement of the structure stabilizer in the composition of yoghurt with micellar casein and spelled flour, both separately and in various ratios, was studied. As a single criterion for optimizing the recipe composition of yoghurt at various ratios between casein and spelled flour, product quality indicators were used: the degree of syneresis, effective viscosity, organoleptic indicators. The optimal values of the single criteria were obtained at different ranges of ratios between natural ingredients, which don’t allow developing uniform recommendations for the formulation of a new type of yogurt. Therefore, to study the combined effect of protein and spelled flour on the characteristics of yoghurt and the coefficients of their significance, a complex quality indicator was used. This indicator was determined as a function of estimates of single quality indicators, converted to scaled values, taking into account the coefficients of significance of individual indicators. Using a complex quality indicator, the ranges of optimal values of the content of micellar casein and spelled flour in yoghurt were established. So, when adding casein in an amount of 1.25 to 3.0 % and spelled flour - from 0.75 to 1.50 %, the quality of yoghurt reached its maximum value. The use of these ingredients alone showed a significantly lower technological effect in comparison with their compositions. Therefore, a conclusion was made about the synergistic interaction of casein and spelled flour, as well as the advisability of using the compositional composition of these ingredients in the yogurt technology
Trade openness, financial depth and economic development: Causality evidence from Asia-Pacific countries

This study investigates the causality patterns between financial deepening, trade openness and economic development for 14 countries in Asia and the Pacific. The Gregory Hansen cointegration tests which account for one endogenous structural break and Toda-Yamamoto non-Granger causality tests are used to add to the existing empirical evidence. In general, the evidence indicates (1) a strong link between financial depth and economic development, (2) a somewhat weaker linkage between financial depth and trade openness and (3) a sceptical linkage between trade openness and economic development, for most of the sample.
Influence of the magnetic nanoparticle coating on the magnetic relaxation time

Colloidal systems consisting of monodomain superparamagnetic nanoparticles have been used in biomedical applications, such as the hyperthermia treatment for cancer. In this type of colloid, called a nanofluid, the nanoparticles tend to agglomeration. It has been shown experimentally that the nanoparticle coating plays an important role in the nanoparticle dispersion stability and biocompatibility. However, theoretical studies in this field are lacking. In addition, the ways in which the nanoparticle coating influences the magnetic properties of the nanoparticles are not yet understood. In order to fill in this gap, this study presents a numerical simulation model that elucidates how the nanoparticle coating affects the nanoparticle agglomeration tendency as well as the effective magnetic relaxation time of the system. To simulate the self-organization of the colloidal nanoparticles, a stochastic Langevin dynamics method was applied based on the effective Verlet-type algorithm. The Néel magnetic relaxation time was obtained via the Coffey method in an oblique magnetic field, adapted to the local magnetic field on a nanoparticle.
SUPER-CONTINUOUS AND TOPOLOGIES ON FUNCTION SPACES

In this paper we study the notions of weakly continuity, weakly # continuity and super-continuity. We also de ne topologies on the sets of all forms of continuous functions mentioned above. These results generalize some basic results of R. Arens, D. Dugundji and A. Di Concilio (see [1], [5], [2], [3] and [8]).
PANEL DATA MODELS

In this paper, we have built several panel data models at industry level for the period 2000-2011, by considering the impact of various macroeconomic variables, such as labour cost and average gross earnings, upon employment in Romania. A forecasting scenario was then built to forecast the employment in Romania. The results of the econometric analysis were consistent with the empirical evidence, while the analysis offered relevant inside information about the performances of the economic activities of the Romanian industry.
Characterization of Conductor-Backed Absorbing Materials Using a Bottom-Filled Rectangular Waveguide

A technique is introduced to extract the permittivity and permeability of absorbing materials that are permanently adhered to a conductor backing. A sample is placed into the bottom of a rectangular waveguide, and the transmission and reflection coefficients are measured. Through comparison to a theoretical model for the bottom-filled waveguide, values of l and e can be extracted from the measurements. Experimental results match well with values obtained using the standard transmission/reflection method of Nicolson, Ross, and Weir.
On The Development of Robot Robot Fish Swarms in Virtual Reality with Digital Twins

— This paper puts forward the use of virtual reality in the development of robotics systems. The case examined is of robot fish designed to follow and monitor real schooling fish. Virtual reality allowed simulations to be setup and modified on the fly which were impossible in the real world. The paper demonstrates the benefits of virtual reality in engineering design and in swarm robotics.
SOME MODELS OF CAUSALITY AND STOCHASTIC DIFFERENTIAL EQUATIONS

We consider some concepts of causality between σ-algebras and between stochastic processes. Then, we give a generalization of a causality relationship "G is a cause of E within H" which was first given by Mykland [4] and which is based on Granger's definition of causality [1]. In the second part we apply the results to systems of stochastic differential equations. More precisely, we give conditions for weakly uniqueness of the solution of some stochastic differential equations.
A United Family Prospers*: Do Spouse-regarding Preferences Shape Household Production and Income?

Household production builds on the basis of cooperation among family members. However, field evidence on the extent to which variation in intrahousehold relationships accounts for household economic outcomes like income is altogether missing. Here, combining experimental measures of spouse-regarding preferences and household survey data for a sample of rural households in Burkina Faso, I find a great variation in spouses’ experimental behaviors, which explains substantial differences in real-world household income: (i) controlling for household-level covariates, households in which at least one member is approximately indifferent between payments to her spouse and to herself, have substantially higher income than those whose members either prefer payments to themselves or strictly prefer payments to their spouses; (ii) women’s experimental types provide majority of the explanatory power. Providing suggestive evidence of causality, I argue that the causal relationship is inconsistent with a unitary model, a collective model, or some existing noncooperative models. Instead I develop a noncooperative household production model with commitment failure to reconcile the empirical findings, which predicts intrahousehold productive inefficiency when individual spouses lack willingness to act collectively. ——————————————————— *A Chinese Proverb. **I thank Travis Lybbert, Michael Carter and Steve Vosti for their guidance throughout all stages of the project. The the iLiNS-Zinc project that provided household survey data I use in this paper, is funded by the Bill & Melinda Gates Foundation. For the intrahousehold experimental project, I appreciate the generous support of the BASIS Innovation Lab and the William and Flora Hewlett Foundation. I thank the Institut de Recherche en Sciences de la Santé (IRSS) in Burkina Faso, and its Director, Jean Bosco Ouedraogo for his leadership and support. Special thanks go to the large and efficient data collection team in Bama, Burkina Faso, especially Rosemonde Guissou, Shea Antrim, Djeneba Ouédraogo, and Mando Mohamed. I also thank Pierre Mérel, Thomas Barre, Quentin Stoeffler, Isabel Call, Emilia Tjernström, Jon Einar Flatnes, Asare TwumBarima, and Abbie Turiansky and participants of UC Davis ARE Development Workshop, PacDev 2015, MWIEDC 2015, for helpful discussions; Katherine Adams, Eliana Zeballos, Yingying Xu for collaboration in data collection. All errors are my own. Contact: Wenbo Zou, Department of Agricultural and Resource Economics, University of California, Davis; wbzou@ucdavis.edu.
Experimental Investigation into the Influence of Turbulence Intensity on Aerodynamic Performance of a Small-Scale Vertical Axis Wind Turbine

Wind in urban areas is extremely complex due to their location in the lower planetary boundary layer, irregular arrangement of buildings and enhanced surface roughness. Vertical axis wind turbines (VAWTs) are used in extracting energy within this environment. However, there is a huge knowledge gap as far as the effect of turbulence on the aerodynamic performance of VAWTs operating in non-uniform wind regimes are concerned. This study used an experimental method to investigate the effect of free stream turbulence intensity on the aerodynamic performance of a VAWT under non-uniform flow operating conditions in a wind tunnel environment. A small-scale Darrieus type VAWT was developed and tested. A wedge mechanism to generate non-uniform conditions was created and the effect of the generated turbulence intensity on the aerodynamic performance of the turbine investigated. The VAWT was subjected to freestream wind speeds ranging from 5 m/s to 10 m/s at different tip speed ratios. Torque and power coefficients were systematically analysed and an evaluation was made of how they varied at uniform and non-uniform flow. Results reveal that non-uniform turbulent wind impacted the aerodynamic performance of the turbine with higher power coefficients being observed at lower wind speeds and improved self-starting ability in the presence of turbulence.
Posterior Cram´er-Rao Lower Bound for RSS/TOA-based Indoor Localization Systems

—The great success of location-based applications that operate with GPS, has emphasized the necessity of technologies that make up for the lack of position information in harsh environments, as dense urban or indoor areas. Many solutions have been proposed, however, none of them has any widespread acceptance. In this case, it is mandatory to have a mechanism to compare the different proposed systems in terms of performance. In this paper, we derive the Cram´er-Rao lower bound (CRLB) on the minimum mean square error for distance estimation in a nonlinear/non-Gaussian localization system. For this purpose, we obtain the density function of the received signal strength (RSS) and time-of-arrival (TOA) measurements of a signal that reaches the MU coming from an access point. The proposed model considers both the random error and the bias caused by multipath and non-line-of-sight (NLOS) propagation conditions present in these harsh environments. Since the relationship among distances in time is likewise considered, the problem is addressed within a Bayesian context. The ﬁnal CRLB is compared to the mean square error obtained by conventional Kalman ﬁltering techniques.
Future Generation Computer Systems

Approximate Computing has been proven successful in reducing the energy consumption of Deep Neural Networks (DNNs) implemented in embedded systems. For efficient DNN approximation at software and hardware levels, a specialized simulation environment and optimization methodology are required, to reduce execution and optimization times, as well as to maximize energy savings. Traditional frameworks for cross-layer approximate computation of DNNs are generally built only for simulation of convolutional and fully-connected layers, limiting the DNN types to be optimized through approximations. In this work, we present a specialized simulation environment for approximate DNNs, which allows for optimization of several DNN architectures built with more complex DNN layers such as depthwise convolutions and Recurrent Neural Units (RNNs) for time series processing. Low execution time overhead is achieved hereby through efficient GPU acceleration. Additionally, we deliver an analysis of approximate DNN and RNN robustness against quantization noise and different approximation levels. Finally, through specialized approximate retraining, we achieve promising energy savings and negligible accuracy losses with highly complex DNNs for image classification with ImageNet, such as MobileNet, and RNNs for keyword spotting with the Speech Commands Dataset.
Security and Privacy in IoT Using Machine Learning and Blockchain

Security and privacy of users have become significant concerns due to the involvement of the Internet of Things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace making the existing security and privacy measures inadequate. Hence, everyone on the Internet is a product for hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based systems. Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT applications to solve security and privacy issues. Several studies have been conducted on either ML algorithms or BC techniques. However, these studies target either security or privacy issues using ML algorithms or BC techniques, thus posing a need for a combined survey on efforts made in recent years addressing both security and privacy issues using ML algorithms and BC techniques. In this article, we provide a summary of research efforts made in the past few years, from 2008 to 2019, addressing security and privacy issues using ML algorithms and BC techniques in the IoT domain. First, we discuss and categorize various security and privacy threats reported in the past 12 years in the IoT domain. We then classify the literature on security and privacy efforts based on ML algorithms and BC techniques in the IoT domain. Finally, we identify and illuminate several challenges and future research directions using ML algorithms and BC techniques to address security and privacy issues in the IoT domain.
Dynamic Multichannel Access for URLLC in Industrial Wireless Networks

—In industrial wireless networks, real-time transmission increases probability of collision and congestion thus degrading their performance in terms of reliability and latency. In this paper, dynamic multichannel access in industrial wireless networks for ultra reliable low latency communication (URLLC) is considered. For which, partially observable Markov decision process (POMDP) based formulation is carried out for unknown system dynamics of the industrial wireless networks. For the challenges of unknown system dynamics, double deep Q-learning (DDQL) is proposed which deals with two key requirements: reliability and latency Performance evaluation of DDQL is compared with Myopic and Whittle Index based heuristic policy. Simulation results clearly indicate that proposed DDQL ﬁnds better and optimal policy that maximizes the rewards in terms of reliability and latency.
Profiling direct mRNA-microRNA interactions using synthetic biotinylated microRNA-duplexes

MicroRNAs (miRNAs) are predominantly negative regulators of gene expression that act through the RNA-induced Silencing Complex (RISC) to suppress the translation of protein coding mRNAs. Despite intense study of these regulatory molecules, the specific molecular functions of most miRNAs remain unknown, largely due to the challenge of accurately identifying miRNA targets. Reporter gene assays can determine direct interactions, but are laborious and do not scale to genome-wide screens. Genomic scale methods such as HITS-CLIP do not preserve the direct interactions, and rely on computationally derived predictions of interactions that are plagued by high false positive rates. Here we describe a protocol for the isolation of direct targets of a mature miRNA, using synthetic biotinylated miRNA duplexes. This approach allows sensitive and specific detection of miRNA-mRNA interactions, isolating high quality mRNA suitable for analysis by microarray or RNAseq.
Determining the emotional experience evoked by films from online reviews

It is well established that reviews contain expressions of sentiment towards the reviewed items, e.g., "I liked it. " Here, we hypothesize that in the case of experience goods and specifically films, the reviews also contain a signal of the emotions evoked by watching the movie, emotions that were experienced when watching it. That is, online reviews for experience goods also reflect the reviewer’s emotions while experiencing the item, in the form of social sharing and can be reliably extracted from it. We postulate that the aggregated extracted emotional experiences for a movie form an emotional signature that reflects the emotions evoked by the movie. To establish that, we systematically conduct a set of analyses, each designed to offer evidence that supports our hypothesis. The ability to reliably, efficiently, and unobtrusively obtain the emotions evoked by films or other experience goods has numerous practical applications for both consumers and producers. For example, affective recommender systems can incorporate the film’s evoked
The Subjective Loss Function for Conceptual-based Customer Reviewer Analytics

—Deep neural networks (DNNs) are currently among the most commonly used machine learning methods in content understanding such as computer vision and natural language understanding (NLU). One of the best characteristics of these methods is their modular design – the ability to change the connectivity patterns of layers, try different activation functions, inject different statistical approaches such as normalization and dropout in the network, and many other actions – in every aspect of deep learning networks. While the majority of deep learning applications simply use cross-entropy, L 1 , and L 2 losses, subjective loss function can actually result in impressive performance improvement. In addition, architecting the last layer of DNNs – referred to as the prediction layer – according to the needs of the application increases the discriminative power of the DNNs. This paper aims to investigate how particular choices of loss functions and prediction layer architecture affect deep neural networks and their learning dynamics, as well as the robustness of various effects. Furthermore a real-life application to measure customer loyalty called Deep Net Promoter Score (DeepNPS) from online product reviews is also proposed. The results are promising for learning more latent features and matching the customer feedback with the NPS score.
Kaldor-Verdoorn’s Law and Institutions: A Study of the Brazilian Economy

The recent literature on economic growth stresses the importance of institutions as the main determinant of productivity growth without consider the demand as source of long run performance. The Kaldor-Verdoorn law stands out that industrial activity has increasing returns of scale, which suggests a positive relation between demand growth and productivity. The goal of this work is introduce institutions, controlling human capital and physical capital, considering demand growth to estimate the determinants of industrial productivity at level of Brazilian municipalities. Besides that, it was created interactions between demand growth and institutions to capture the relation between institutions, demand growth and productivity. We used the econometric methodology ordinary last square and instrumental variable to handle with endogeneity between economic performance and institutions. The results of estimations suggested that both elements of supply side and demand growth are important to productivity growth.
Board Independence and Firm Performance: Evidence from Jordan

Purpose: This study aims to investigate the influence of board independence in the form of representation of ‘outside independent directors’ on firm performance amongst the listed firms in the context of a developing country by considering Jordan as a case study. Design/methodology/approach: By using data from 880 firm-year observations of non-financial firms listed on Amman Stock Exchange for the period of 2006-2016, and by using accounting performance measure (i.e., Return on Asset), this study uses regression model to test the hypotheses. Findings: The empirical results show that the board independence in general has a positive impact on firm performance. The findings of this study supports agency theory. Research limitations/implications: This study has some limitations. The current study employed ROA as an accounting-based measure of firm performance. However, it can be argued that firm performance is a noisy measure and can be manipulated. Practical implications: The implication of this study is that, board independence in the form of representation of ‘outside independent directors’ becoming an important control mechanism that can add value to the firm. Originality/value: This contributes to the literature on the practices of corporate governance mechanisms, such as board independence in the context of developing countries.
Scaling up embodied sensemaking in strategic decision-making episodes

Within micro-foundations of strategic management, sensemaking remains constrained by a focus on cognition as thinking and talking. Little is known about how embodied storytelling in agents’ social interaction allows people make sense. This study sheds light on how individual sensemaking scales up to collective sensemaking by analysing video data. This study distils significant moments from observing three international groups of executives of a Fortune listed firm working on the same decision-making problem. Analysis yields insights, which were organized in relation to process, individuals and artefacts, and groups as ‘collective bodies’ and different drivers of scaling up. This study contributes to the literature on collective sensemaking by showing how participants’ bodies and (sub)groups of participants play an important role through three motions; dynamic configuration, selfmanagement and inclusion of materiality. Second contribution is to the literature on tools-in-use in strategy practice; the study reveals the role of participant bodies as tools-in-use in strategic episodes. The paper concludes with opportunities for further research and applications of embodied engagement in collective sensemaking.