Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Saving Impact of Air Curtains in Commercial Buildings

This study investigates the energy saving impact replacing vestibules with air curtain doors (single doors fitted with air curtain units) has on energy usage of commercial buildings. Laboratory experiments were used to investigate the infiltration characteristics of air curtains in a small size chamber and to validate numerical simulation methods. Experimental flow and pressure as well as particle image velocimetry data confirmed that air curtains can significantly reduce air infiltration when compared to the single and vestibule doors. In addition, the results indicated that the CFD modeling methods used are valid in capturing the characteristics of the airflow at the door with air curtains. CFD simulations were conducted for a full scale door with consideration for the door operation cycle and the existence of people in the doorway. The results of the simulations were used to conduct energy simulations for two reference building models. The end use site-energy performances of the whole buildings with air curtain, vestibule and single doors are compared using two infiltration calculation methods. The energy simulations conducted show that, on a national average level, air curtain doors save energy (in comparison to code requirements). Finally, considering the space saving benefit of air curtains and their lower initial costs, air curtains are concluded to be a valid and energy saving alternative to vestibule doors in climate zones 3 to 8 and a valuable energy saving addition in climate zones 1 & 2.
Achieving high parallel performance for an unstructured unsteady turbomachinery CFD code

This paper describes the work done to achieve high parallel performance for an unstructured, unsteady turbomachinery computational fluid dynamics (CFD) code. The aim of the work described here is to be able to scale problems to the thousands of processors that current and future machine architectures will provide. The CFD code is in design use in industry and is also used as a research tool at a number of universities. High parallel scalability has been achieved for a range of turbomachinery test cases, from steady-state hexahedral mesh cases to fully unsteady unstructured mesh cases. This has been achieved by a combination of code modification and consideration of the parallel partitioning strategy and resulting load balancing. A sliding plane option is necessary to run fully unsteady multistage turbomachinery test cases and this has been implemented within the CFD code. Sample CFD calculations of a full turbine including parts of the internal air system are presented.
Partial least Square Structural Equation Modeling (PLS-SEM) of Patient Satisfaction on Service Quality in Katsina Public Hospitals

This research examines the influence of Hospitals’ service quality on Patient satisfaction in Katsina metropolis, Nigeria. It is a survey research where by questionnaires were randomly administered on patients of four hospitals conveniently selected for the study (General Hosptial Katsina, Federal Medical Centre Katsina, Turai Yaradua Maternity and Children’s hospital and General Amadi Rimi Orthopedic Hospital Katsina). Structural equation modeling technique was employed in this research. Results show that the hospitals’ service quality positively influences patients’ satisfaction at hospitals. The study then concludes that hospitals’ service quality determines patient satisfaction. Patients’ level of satisfaction is increased by improving tangibility reliability, responsiveness, assurance and empathy dimensions of hospitals service quality.
New Chemotypes for the Inhibition of (p)ppGpp Synthesis in the Quest for New Antimicrobial Compounds

Antimicrobial resistance (AMR) poses a serious threat to our society from both the medical and economic point of view, while the antibiotic discovery pipeline has been dwindling over the last decades. Targeting non-essential bacterial pathways, such as those leading to antibiotic persistence, a bacterial bet-hedging strategy, will lead to new molecular entities displaying low selective pressure, thereby reducing the insurgence of AMR. Here, we describe a way to target (p)ppGpp (guanosine tetra- or penta-phosphate) signaling, a non-essential pathway involved in the formation of persisters, with a structure-based approach. A superfamily of enzymes called RSH (RelA/SpoT Homolog) regulates the intracellular levels of this alarmone. We virtually screened several fragment libraries against the (p)ppGpp synthetase domain of our RSH chosen model RelSeq, selected three main chemotypes, and measured their interaction with RelSeq by thermal shift assay and STD-NMR. Most of the tested fragments are selective for the synthetase domain, allowing us to select the aminobenzoic acid scaffold as a hit for lead development.
An action-oriented approach to didactic dubbing in foreign language education: Students as producers

This article discusses the action-oriented foundations of TRADILEX (Audiovisual Translation as a Didactic Resource in Foreign Language Education), a project funded by the Spanish Ministry of Science and Innovation, which involves researchers from twelve universities across Europe and the UK. This project focuses on the improvement in the linguistic skills perceived through audiovisual translation (AVT) practices such as the use of captioning (i.e., interlingual and intralingual subtitling) and revoicing (i.e., dubbing, voice-over, and audio description) through an action-oriented approach (AoA). The ultimate objective is the study of AVT as a means to enhance learners’ communicative competence and reception, production, and mediation skills in an integrated manner. Following the design of a methodological proposal for a didactic sequence of AVT tasks, proposals are currently being piloted with B1 and B2 adult learners of English as a foreign language utilising – and adapting – the recent illustrative descriptors (Council of Europe, 2018) for AVT instruction. The potential benefits of action-oriented AVT tasks in foreign language education (FLE), in which foreign-language learners become active producers of AVT work, are put to the test employing empirical inquiry and thereafter advocating for more comprehensive integration of AVT in the FLE curriculum overall.
Modeling of thin-film lithium niobate modulator for visible light

Abstract. Integrated modulators are essential components for optical sensing and communication applications. However, most efforts have been focused on infrared telecommunications wavelengths, with very little consideration for visible light applications. Here, we design an electro-optic (EO) modulator at 532 nm with the waveguides formed by SU8 polymer structures on the thin-film lithium niobate (LN). The simulation results show a low loss, high modulation efficiency, and large bandwidth simultaneously for the modulator. The predicted low loss is attributed to the LN etchless waveguide and high coupling efficiency, i.e., 93%, with the single-mode fiber theoretically using a SU8 edge coupler. The simulated low voltage-length product of 1.15 V · cm and a 3-dB-bandwidth of >120 GHz at a length of 5 mm are superior to any other modulator in the optical communication band. The modeling of the modulator proposed here shows great potential for visible light communications applications such as energy-efficient and large-capacity underwater wireless optical interconnects.
Measurement algorithm of notch length of vehicle accessories based on contour points

Accurate and rapid measurement of the notch of vehicle accessories produced in real time on the assembly line is an important way to reduce labor cost and improve production efficiency. The current dimension measurement algorithms pay attention to the extraction of edge high-precision contour which needs complex parameter adjustment and despise the extraction of contour corner information. It is difficult to be used in industrial real-time detection environment because of its large amount of calculation, easy to be disturbed by the environment and weak robustness. In order to realize the noncontact, high-precision and wide application range of notch length measurement based on machine vision, an algorithm is proposed to extract corner points with contour point features and contour edges with geometric restrictions, so as to finally obtain the length of notch. The algorithm uses moments to estimate the centroid coordinates and overall direction of the accessory, and calculates the distance to the centroid and the offset angle from the overall direction for each side of the accessory one by one. The results show that compared with the traditional corner extraction and line detection algorithms, the algorithm can extract the accessory corner and the notch length more accurately. The average processing time for the accessory image with a resolution of 200 pixels (vertical) × 600 pixels (horizontal) is 15 milliseconds, and the notch length can still be extracted for the slight shaking generated in the manufacturing process. The relative error of the extracted notch length is less than 1%, which is suitable for the industrial field environment with high requirements for real-time and robustness. It has the characteristics of strong adaptability and low cost.
Exploring Model Stability of Deep Neural Networks for Reliable RRAM-Based In-Memory Acceleration

RRAM-based in-memory computing (IMC) effectively accelerates deep neural networks (DNNs). Furthermore, model compression techniques, such as quantization and pruning, are necessary to improve algorithm mapping and hardware performance. However, in the presence of RRAM device variations, low-precision and sparse DNNs suffer from severe post-mapping accuracy loss. To address this, in this work, we investigate a new metric, model stability, from the loss landscape to help shed light on accuracy loss under variations and model compression, which guides an algorithmic solution to maximize model stability and mitigate accuracy loss. Based on statistical data from a CMOS/RRAM 1T1R test chip at 65nm, we characterize wafer-level RRAM variations and develop a cross-layer benchmark tool that incorporates quantization, pruning, device variations, model stability, and IMC architecture parameters to assess post-mapping accuracy and hardware performance. Leveraging this tool, we show that a loss-landscape-based DNN model selection for stability effectively tolerates device variations and achieves a post-mapping accuracy higher than that with 50% lower RRAM variations. Moreover, we quantitatively interpret why model pruning increases the sensitivity to variations, while a lower-precision model has better tolerance to variations. Finally, we propose a novel variation-aware training method to improve model stability, in which there exists the most stable model for the best post-mapping accuracy of compressed DNNs. Experimental evaluation of the method shows up to 19%, 21%, and 11% post-mapping accuracy improvement for our 65nm RRAM device, across various precision and sparsity, on CIFAR-10, CIFAR-100, and SVHN datasets, respectively.
Testing of Analog-Digital Converters with Sine Wave Fitting

Nowadays, the manufacturers of analog-digital converters (ADCs) use self-defined methods for testing, and self defined datasheets for highlighting the good attributes of their devices. Therefore engineers waste a lot of time finding out the meaning of different parameters of ADCs. The solution of this problem would be a new method, which would standardize the testing methods. The Institute of Electrical and Electronics Engineers (IEEE) created the IEEE-1241 standard [1] for ADC testing in 2001. This standard describes full testing methods. However, these descriptions are not totally complete. The testing procedure is simple. First we need a high precision sine wave generator. Its signal is connected to the ADC, which digitizes it. From the collected samples we try to estimate the original sine wave’s parameters. The standard suggests a 3 and a 4 parameters sine wave fitting method. Both of these methods are Least Squares fitting procedures. In 3 parameters fitting we have to know the frequency of the sine wave. In this case the fitting is linear. When we use 4 parameters fitting we do not know the frequency, therefore this is a nonlinear procedure. So it is solved by an iteration algorithm. (Matrix-based proof of these algorithms derived by me can be found in appendix (7.8)). After the fitting we compare the measured samples with the estimated sine wave’s samples. The differences of these samples (residuals) are the ADC’s error. With these residuals we can calculate every test parameter like effective number of bits (ENOB), signal-to-noise and distortion ratio (SINAD) and root mean square of the residuals (RMS). These calculations are explained in the standard. Among others starting values are needed for the above iteration procedure. The most important is to estimate the initial frequency because the remain parameters can be calculated from the 3 parameter method. Even convergence can change with proper setting of the initial frequency. That is the reason why this thesis is dealing with high accuracy frequency estimation methods with Interpolated Fast Fourier Transform.
miR-29a-5p Inhibits Prenatal Hair Placode Formation Through Targeting EDAR by ceRNA Regulatory Network

Hair placode formation is an important stage of hair follicle morphogenesis and it is a complex process facilitated by non-coding RNAs. In this study, we conducted whole transcriptome sequencing analysis of skin, heart, liver, lung, and kidney tissues of day 41 (E41) normal and hairless pig embryos, and respectively detected 15, 8, and 515 skin-specific differentially expressed (DE) lncRNAs, miRNAs, and mRNAs. Furthermore, 18 competing endogenous RNA (ceRNA) networks were constructed. Following weighted gene co-expression network analysis (WGCNA) of stages E39, E41, E45, E52, and E60, between normal and hairless pig embryos, only two ceRNAs (lncRNA2162.1/miR-29a-5p/BMPR1b and lncRNA627.1/miR-29a-5p/EDAR) that showed period-specific differential expression in E41 skin were retained. Dual-luciferase reporter assays further indicated that EDAR was a direct, functioning target of miR-29a-5p and that no binding site was found in BMPR1b. Moreover, miR-29a-5p overexpression inhibited the mRNA and protein expression of EDAR while no significant differential expression of BMPR1b was detected. In addition, over-expressed lncRNA627.1 reduces the expression of miR-29a-5p and increase EDAR expression while inhibits lncRNA627.1 resulted in a opposite expression trend. Cell proliferation result demonstrated that lower expression of EDAR and lncRNA627.1 inhibited hair placode precursor cells (HPPCs) proliferation in a manner similar to that shown by over-expressed miR-29a-5p. This study identified that miR-29a-5p inhibited HPPCs proliferation via the suppression of EDAR expression in the EDA/EDAR signaling pathway, while lncRNA627.1 rescues EDAR expression. Our study provides a basis for a better understanding of the mechanisms underlying the ceRNA complex, miR29a-5p/EDAR/lncRNA627.1, that could regulate hair placode formation, which may help decipher diseases affecting human hair.
Uniting model-based and model-free methods for robotic manipulator direction control

Model-based and model-free control methods each have associated strengths and weaknesses, This paper discusses the positive effect in performance obtained by uniting the two approaches within a hybrid controller. In the domain of robotic manipulator direction control, the authors describe and compare: (a) the performance of resolved motion rate control, a well-known model-based control method; with (b) the performance of machine nervous systems for motor control, a novel model-free control method; and (c) the performance of a hybrid control scheme which incorporates the functionality of the other two methods. Their results show that the hybrid control possesses all the strengths of its constituent methods and none of their weaknesses.
Fuzzy dynamic output-feedback control of non-linear networked discrete-time system with missing measurements

This article studies the problem of H ∞ dynamic output-feedback control of non-linear networked discrete-time systems with data packet dropouts. Specifically, the data missing happens randomly from the sensor to the controller and the controller to the actuator. A sequence of independent variables obeying the Bernoulli random binary distribution is introduced to account for the measurement missing. The Takagi–Sugeno fuzzy model is used to describe the non-linear plant. In addition, the strategy of parallel distributed compensation is adopted to design the fuzzy dynamic output-feedback controller. Here, attention is focused on obtaining the sufficient conditions to guarantee the closed-loop system to be stochastically stable as well as the prescribed H ∞ performance. An approach based on fuzzy Lyapunov function is developed to solve it. To tackle the non-convex problem, the cone-complementarity linearisation procedure is adopted. Finally, a numerical example is employed to show the usefulness of the proposed result.
Forecasting Preliminary Order Cost to Increase Order Management Performance

In this study, the cost estimation to be used in the optimization of proposed order price offer is made by artificial neural network (ANN) method. A case study is performed by the real data of a company, and the forecast results of the traditional arithmetic model used by the company and the proposed ANN based method are compared and it is seen that the proposed method results outperform the other. The biggest contribution of this study to companies is to increase the company’s order management performance by helping the company to make more accurate pricing due to more accurate cost estimation. Moreover, to the best of our knowledge, this is the first study on forecasting preliminary order cost in the apparel industry and fills an important gap in the literature.
The fuzzy front end of innovation : hearing the voice of customer on credit cards

The title “Behind the Fuzzy Front End” points to the traditionally messy getting-started period of product development, when the new product idea is still very “fuzzy”. Preceding a more formal product development process, it generally consists of three tasks: strategic planning, concept generation and pre-technical evaluation. The main aim with this thesis is to answer the research problem “What say the Voice Of Customer for a future credit card product in the Norwegian market? ”, to potentially be used as input for a concept generation phase for new credit card products. This is done with the use of an explorative research design, conducted through qualitative one-to-one interviews with customers and sales personnel. The empirical study gives a theoretical and practical guide to how Voice Of Customer could be extracted. This particular research results in 194 unique customer needs related to the buyers‟ experience cycle of credit cards. In addition to finding the Voice Of Customer, two accompanying important questions are explored theoretically: (1) How could innovation influence profit growth? , and (2) How could innovative opportunities be found? The theoretical part explores central innovation and strategy related literature. Firstly, the interrelationship between innovation and competitive advantage is discussed in order to see whether, why and potentially how innovation could spur profit growth. The second question uses theories in constructing a “how-to-guide” for reaping potential innovative opportunities. Two key conclusions are derived from the literature study. Firstly, a strong relationship between innovation and competitive advantage does exist, thus profit growth is influenced by innovation. On this basis, innovation should be added as an important parameter for business strategising. Secondly, several types of innovative opportunities could be reached through conscious and systematic focus on the use of innovation as a strategic parameter. By tuning the company‟s innovation strategy and innovative processes, in addition to building a strong innovative business culture, creating a competitive edge is within reach.
Uncertainty Updating in Oil Reservoir Simulation

It is generally accepted that in order to make reliable predictions a reservoir model must be able to reproduce the available measurement and production history of the real reservoir. The standard procedure to achieve this is to take an initial model, created from seismic data and the geological knowledge of an engineer, and condition it to the measurements. The conditioning is typically performed by minimising a quadratic mismatch function of the measurements over the most sensitive uncertain parameters of the model. This inverse problem, in the oil reservoir sector called history matching, is an ill-posed problem in the sense of Hadamard [1, p. 31]. Optimisation techniques are usual methods to solve it, for example gradient based ones or Evolutionary Algorithms like Evolution Strategies (ES) and Genetic Algorithms (GA). Also manual optimisation by a specialist is still a common approach. These methods are limited to handling a small number of parameters, typically on the order of 30-50, but industry scale reservoir models contain much higher numbers. Thus often a suitable parametrisation of the model has to be found, as a manual task by reservoir engineers. The history matching problem can also be formulated in a probabilistic setting [2], which is not an ill-posed problem in general: the knowledge about the problem is contained in an a priori probability density rM(m) over the full model parameter space. New knowledge enters through measurements, which are given by a probability density on the data space rD(d). These two sources of information can be combined into Bayesian-like updated or a posteriori knowledge sM(m) on the problem itself according to
The Relationship between Metaphorical Perceptions of Consumption and Mental Well-Being in the Period of COVID-19

This study aims to examine the relationship between the interpretation of the consumption concept of individuals and their mental well-being levels during the pandemic period. Metaphors are the essential communication tools used in defining and interpreting the consumption concept. The effort to reveal the interpretations loaded on a complex and multidimensional concept such as individuals' consumption expresses the important contribution of this research. The fact that these interpretations were made during the pandemic turned individuals' lives upside-down increases the degree of contribution. The study's originality has been tried to be revealed by establishing a relationship between the interpretations of the consumption phenomenon of individuals and their mental well-being. The Conceptual Metaphor Theory developed by Lakoff and Johnson was used in metaphorical interpretation. The metaphorical approach, which is one of the alternative approaches based on subjective experiences, enables the comprehension of concrete and well-known concepts, abstract concepts, symbols, or elements that are more difficult and complex to understand. The Warwick-Edinburgh Mental Well-being Scale (WEMWBS) was used to determine the mental well-being levels of individuals. All the metaphors developed and evaluated regarding the concept of consumption have been grouped into three conceptual categories: «pleasure, insatiability and vanity», «necessity and requirement», and «addictive and harmful». In this study, the mental well-being average of all participants was calculated as 53.46. The lowest average was calculated in the «addictive and harmful» category according to conceptual categories in terms of mental well-being averages. It was determined that the mental well-being averages of the participants who interpreted the concept of consumption as «negative» were also low.