Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of the payback period of a modernized pump unit with induction electric motors of advanced energy efficiency classes

Aim. The comparative analysis of energy consumption, electricity costs during lifetime cycle and payback period of a pump unit with 90 kW 2-pole induction motors, belonging to various energy efficiency classes, feeding directly from power grid. Methods. The examined operating modes aligned with a typical operating cycle of a pump unit with approximately constant flow rate of 75-110 % of the rated flow. The calculations were based on the pump and induction motors nameplate data, which, in their turn, were based on the manufacturers’ experimental data. Results. The calculations of energy consumption, electricity costs and payback periods of a pump unit with 90 kW 2-pole induction motors, feeding directly from power grid have been performed in the article. The application of induction motors belonging to IE2, IE3 and IE4 energy efficiency classes has been discussed. Practical value. It has been demonstrated, than in case of replacement of an induction motor of energy efficiency class IE2 due to planned retrofit, payback period for an IE4 induction motor is 2.18 years, energy savings within a calculated 20-year operating period are 268MW·h, which makes €41110 in money terms. Under the same conditions, the replacement of an induction motor of energy efficiency class IE2 with an induction motor of energy efficiency class IE3 will allow to save 88 MW·h within a calculated operating period, which, expressed in monetary terms, is €13500 and the payback period is 5.11 years. Thus, the article proves that despite a higher initial price, the choice of an induction motor of energy efficiency class IE4 tends to be more economically advantageous.
Effects of vermicomposts obtained from rose oil processing wastes, dairy manure, municipal open market wastes and straw on plant growth, mineral nutrition, and nutrient uptake of corn

ABSTRACT Study aims to investigate the effects of vermicomposts containing oil processing wastes, dairy manure, municipal open market wastes and straw on the growth, nutrient concentrations and nutrient uptakes of corn plant. For this, there different mixtures were prepared. Vermicomposts were applied with the rates of 0, 5000, 10000, and 20000 kg ha−1 to 2 kg soil containing pots. Study was conducted in growth chamber for 2 months. Vermicompost applications increased plant growth, some plant nutrient concentrations and uptake. Also, vermicomposts showed the variation on parameters depending on their mixtures. Results showed that nutrients taken by the plant increased with the vermicompost until 10000 kg ha−1 dose. Most of the nutrient concentrations such as phosphorus, potassium, calcium, magnesium, iron, and manganese (P, K, Ca, Mg, Fe, and Mn) were not increased in plant tissues, whereas uptake of them by the plant showed a significant increase. In addition, residual soil nutrients increased with the increase in vermicompost levels.
An indexing algorithm independent of peak position extraction for X-ray powder diffraction patterns

Lattice parameter determination from X-ray powder diffraction patterns, called indexing, invariably requires the extraction of peak positions which are then used by indexing algorithms that are peak position dependent. The success of these algorithms depends on the accuracy of the extracted peak positions. Peak positions that do not overlap significantly with nearby peaks can be readily determined with great accuracy. However, in heavily overlapped regions it is difficult to determine the number of peaks and even more difficult to determine the peak positions accurately. This paper describes a new indexing algorithm, Lp-Search, that is implemented in the computer program TOPAS Version 7 (Bruker AXS, Karlsruhe, Germany). Lp-Search does not require peak position extraction nor does it require knowledge of the number of peaks present. LpSearch combines Monte Carlo searches of lattice parameter space with a Pawley refinement used at the end of each search. Critical to the success of the Monte Carlo search is a new figure of merit function which allows the parameter space to be searched efficiently. Lp-Search has proved to be effective for patterns with heavily overlapped peaks; monoclinic to cubic lattices are successfully indexed in a matter of seconds and triclinic lattices within a minute or two. Diffraction patterns spanning a limited range, such that 30–40 peaks of the highest d spacing peaks are omitted, can be successfully indexed; this demonstrates the robust nature of Lp-Search.
Comparison of the inhibitory capacity of vegetable oils, and their nonionic surfactants on Iran crude oil asphaltene precipitation using Quartz crystal microbalance

ABSTRACT Asphaltene deposition is one of the main problems in some oil field production. Using the cheap chemical compounds to control asphaltene precipitation is one of the important tools in crude oil field development. Vegetable oil are natural chemical compounds which can be used as asphaltene stabilizer. In this work, the inhibitory capacity of the vegetable oils, their binary mixtures, and their binary mixtures with nonionic surfactants were studied by Quartz Crystal Microbalances. The results show that the turnip oil, binary mixtures of rosemary oil/sesame oil, sweet almond oil/camomile oil, and olive oil/tritoonX-100 are more efficient than other additives and the percentages of the inhibitory capacity are 87%, 89%, 90, and 90% respectively.
Application of bayesian regularized artificial neural networks to predict pour point of crude oil treated by pour point depressant

ABSTRACT The pour point of the crude oil treated with the pour point depressant (PPD) is easily affected by the shear history effect. Models for pour point of PPD-treatment crude oil affected by the shear history effect based on Bayesian regularized artificial neural network (BRANN) were established. The results showed that BRANN models not only had a good ability of fitting to the training data, but also had a good ability of predicting the testing data. By evaluating network performance with several statistical indicators, the three models have excellent performance, high accuracy, and strong generalization ability. The influence of each parameter on the pour point were also investigated through a sensitivity analysis, which shows that the entropy generation due to viscous flow is the most important parameter in predicting the pour point.
Green Synthesis of Iron Nanoparticles Using Green Tea Leaves Extract

The interest in synthesizing Nanoparticles in an easy and environmental friendly way has been increasing in the recent years. Physical and Chemical methods are conventionally used for synthesis of Nanoparticles, however due to limitations of these methods, the focus of research has been recently shifted towards the development of clean and eco-friendly synthesis protocols. The green synthesis of Iron Nanoparticles has been achieved using environmental acceptable plant extract. It was observed that Camellia sinensis leaf extract can reduce Iron ions into Iron Nanoparticles at room temperature. The aim of this study is to synthesize Iron Nanoparticles using Camellia sinensis extract in an environmental and sustainable way. The synthesized Iron Nanoparticles were characterized using Scanning Electron Microscope (SEM) and Fourier Transform Infrared (FTIR) analysis. This study shows that the Iron Nanoparticles can be synthesized using Camellia sinensis leaf extract as a reducing agent.
Impact of Specialty Malts on Wort and Beer Characteristics

Specialty malts are commonly used in brewing to provide flavor, aroma, and color to wort and beer. The use of specialty malts contributes to the variety of beer products; therefore, it is important to understand their effect on the characteristics of wort and beer. This study investigates the impact of various specialty malts on wort and beer properties. A control beer was prepared with 100% base malt, and four beer treatments were prepared with the addition of kilned, roasted, and caramel specialty malts. For each treatment, 20% of the base malt was substituted with the various specialty malts when preparing the wort. The fermentable sugars and free amino nitrogen (FAN) content for each wort were analyzed. Alcohol by volume (ABV), international bitterness units (IBU), diacetyl, and polyphenol content of each prepared beers were subsequently analyzed. Results showed that wort prepared with the addition of roasted and caramel malts contained a lower concentration of fermentable sugars and FAN than wort prepared with the base and kilned malts. Beers prepared with the addition of roasted and caramel malts exhibited the lowest levels of ABV, as well as the lowest levels of diacetyl. These beers also exhibited higher levels of total phenolic compounds compared to the other beer samples. No change was observed in IBU levels as a result of brewing with the different specialty malts. This study illustrates how the use of specialty malts impacts wort and beer properties, providing useful information to aid in the production of quality beer products.
Spatial Variability and Radiative Impact of Aerosol along the Brahmaputra River Valley in India: Results from a Campaign

The first ever land campaign to study the spatial variability of the aerosol characteristics along the Brahmaputra river valley (BRV) in Assam, North-Eastern India, was conducted during 2011. Measurements were made over 13 locations for Aerosol Optical Depth (AOD), scattering coefficient, particulate matter, black carbon (BC) concentration and meteorological parameters. The BRV is divided into three sectors longitudinally viz western sector (WS), central sector (CS), and eastern sector (ES). Significant Spatial heterogeneity in AOD and BC concentration was observed (p < 0.05) with the highest values over WS and a continual decrease from WS to ES with aerosol dominance in PM2.5 category along the entire valley. The Angstrom coefficient measured using different wavelength pairs showed spatial variability indicating dominance of fine particles over WS and coarse particles in ES with a probable bimodal distribution. The scattering and absorption coefficient shows dominance of both types of aerosol over WS than other areas. The shortwave radiative forcing was higher over the WS than CS and ES of the valley. The campaign revealed that under favorable wind conditions, the BRV is loaded with significant amount of natural and anthropogenic aerosol during local winter and is influenced by the long-range transport of aerosols from the Indo-Gangetic plain.
TPRM: A Topic-based Personalized Ranking Model for Web Search

Ranking models have achieved promising results, but it remains challenging to design personalized ranking systems to leverage user profiles and semantic representations between queries and documents. In this paper, we propose a topic-based personalized ranking model (TPRM) that integrates user topical profile with pretrained contextualized term representations to tailor the general document ranking list. Experiments on the real-world dataset demonstrate that TPRM outperforms state-of-the-art ad-hoc ranking models and personalized ranking models significantly.
Towards Structured Dynamic Sparse Pre-Training of BERT

Identifying algorithms for computational efficient unsupervised training of large language models is an important and active area of research. In this work, we develop and study a straightforward, dynamic always-sparse pre-training approach for BERT language modeling task, which leverages periodic compression steps based on magnitude pruning followed by random parameter re-allocation. This approach enables us to achieve Pareto improvements in terms of the number of floating-point operations (FLOPs) over statically sparse and dense models across a broad spectrum of network sizes. Furthermore, we demonstrate that training remains FLOP-efficient when using coarse-grained block sparsity, making it particularly promising for efficient execution on modern hardware accelerators.
Diachronic Analysis of German Parliamentary Proceedings: Ideological Shifts through the Lens of Political Biases

We analyze bias in historical corpora as encoded in diachronic distributional semantic models by focusing on two specific forms of bias, namely a political (i.e., anti-communism) and racist (i.e., antisemitism) one. For this, we use a new corpus of German parliamentary proceedings, Deuparl, spanning the period 1867–2020. We complement this analysis of historical biases in diachronic word embeddings with a novel measure of bias on the basis of term co-occurrences and graph-based label propagation. The results of our bias measurements align with commonly perceived historical trends of antisemitic and anticommunist biases in German politics in different time periods, thus indicating the viability of analyzing historical bias trends using semantic spaces induced from historical corpora.
Charts and atlases for nonlinear data-driven models of dynamics on manifolds

We introduce a method for learning minimal-dimensional dynamical models from high-dimensional time series data that lie on a low-dimensional manifold, as arises for many processes. For an arbitrary manifold, there is no smooth global coordinate representation, so following the formalism of diﬀerential topology we represent the manifold as an atlas of charts. We ﬁrst partition the data into overlapping regions. Then undercomplete autoencoders are used to ﬁnd low-dimensional coordinate representations for each region. We then use the data to learn dynamical models in each region, which together yield a global low-dimensional dynamical model. We apply this method to examples ranging from simple periodic dynamics to complex, nominally high-dimensional non-periodic bursting dynamics of the Kuramoto-Sivashinsky equation. We demonstrate that it: (1) can yield dynamical models of the lowest possible dimension, where previous methods generally cannot; (2) exhibits computational beneﬁts including scalability, parallelizability, and adaptivity; and (3) separates state space into regions of distinct behaviours.
CNN-Based Two-Stage Parking Slot Detection Using Region-Specific Multi-Scale Feature Extraction

Although it is well-known that the two-stage approach outperforms the one-stage approach in general object detection, they have similarly performed in parking slot detection so far. We consider this is because the two-stage approach has not yet been adequately specialized for parking slot detection. Thus, this paper proposes a highly specialized two-stage parking slot detector that uses region-specific multi-scale feature extraction. In the first stage, the proposed method finds the entrance of the parking slot as a region proposal by estimating its center, length, and orientation. The second stage of this method designates specific regions that most contain the desired information and extracts features from them. That is, features for the location and orientation are separately extracted from only the specific regions that most contain the locational and orientational information. In addition, multi-resolution feature maps are utilized to increase both positioning and classification accuracies. A high-resolution feature map is used to extract detailed information (location and orientation), while another low-resolution feature map is used to extract semantic information (type and occupancy). In experiments, the proposed method was quantitatively evaluated with two large-scale public parking slot detection datasets: SNU and PS2.0 datasets. In SNU dataset, the proposed method achieved state-of-the-art performance with 95.75% recall and 95.78% precision.
Investigation of the Physical Origin of Overionized Recombining Plasma in the Supernova Remnant IC 443 with XMM-Newton

The physical origin of the overionized recombining plasmas (RPs) in supernova remnants (SNRs) has been attracting attention because its understanding provides new insight into SNR evolution. However, the process of the overionization, although it has been discussed in some RP-SNRs, is not yet fully understood. Here, we report on spatially resolved spectroscopy of X-ray emission from IC 443 with XMM-Newton. We find that RPs in regions interacting with dense molecular clouds tend to have lower electron temperature and lower recombination timescale. These tendencies indicate that RPs in these regions are cooler and more strongly overionized, which is naturally interpreted as a result of rapid cooling by the molecular clouds via thermal conduction. Our result on IC 443 is similar to that on W44 showing evidence for thermal conduction as the origin of RPs at least in older remnants. We suggest that evaporation of clumpy gas embedded in a hot plasma rapidly cools the plasma as was also found in the W44 case. We also discuss if ionization by protons accelerated in IC 443 is responsible for RPs. Based on the energetics of particle acceleration, we conclude that the proton bombardment is unlikely to explain the observed properties of RPs.
A Comparison of Latent Semantic Analysis and Correspondence Analysis for Text Mining

: Both latent semantic analysis (LSA) and correspondence analysis (CA) use a singular value decomposition (SVD) for dimensionality reduction. In this article, LSA and CA are compared from a theoretical point of view and applied in both a toy example and an authorship attribution example. In text mining interest goes out to the relationships among documents and terms: for example, what terms are more often used in what documents. However, the LSA solution displays a mix of marginal effects and these relationships. It appears that CA has more attractive properties than LSA. One such property is that, in CA, the effect of the margins is effectively eliminated, so that the CA solution is optimally suited to focus on the relationships among documents and terms. Three mechanisms are distinguished to weight documents and terms, and a unifying framework is proposed that includes these three mechanisms and includes both CA and LSA as special cases. In the authorship attribution example, the national anthem of the Netherlands, the application of the discussed methods is illustrated.
Thermally induced rotons in two-dimensional dilute Bose gases and the Kosterlitz-Thouless transition to an anomalous normal state

We show that rotonlike excitations are thermally induced in a two-dimensional dilute Bose gas as a consequence of the strong phase fluctuations in two dimensions. At low momentum, the rotonlike excitations lead for small enough temperatures to an anomalous phonon spectrum with a temperature-dependent exponent reminiscent of the Kosterlitz-Thouless transition. Despite the anomalous form of the energy spectrum, it is shown that the corresponding effective theory of vortices describes the usual Kosterlitz-Thouless transition. The possible existence of an anomalous normal state in a small temperature interval is also discussed.
Enhanced Accuracy Simulator for a Future Korean Nationwide eLoran System

The Global Positioning System (GPS) has become the most widely used positioning, navigation, and timing system. However, the vulnerability of GPS to radio frequency interference has attracted significant attention. After experiencing several incidents of intentional high-power GPS jamming trials by North Korea, South Korea decided to deploy the enhanced long-range navigation (eLoran) system, which is a high-power terrestrial radio-navigation system that can complement GPS. As the first phase of the South Korean eLoran program, an eLoran testbed system was recently developed and declared operational on June 1, 2021. Once its operational performance is determined to be satisfactory, South Korea plans to move to the second phase of the program, which is a nationwide eLoran system. For the optimal deployment of additional eLoran transmitters in a nationwide system, it is necessary to properly simulate the expected positioning accuracy of the said future system. In this study, we propose enhanced eLoran accuracy simulation methods based on a land cover map and transmitter jitter estimation. Using actual measurements over the country, the simulation accuracy of the proposed methods was confirmed to be approximately 10%–91% better than that of the existing Loran (i.e., Loran-C and eLoran) positioning accuracy simulators depending on the test locations.
Reinforcement Learning for Robot Navigation with Adaptive ExecutionDuration (AED) in a Semi-Markov Model

—Deep reinforcement learning (DRL) algorithms have proven effective in robot navigation, especially in unknown environments, through directly mapping perception inputs into robot control commands. Most existing methods adopt uniform execution duration with robots taking commands at ﬁxed intervals. As such, the length of execution duration becomes a crucial parameter to the navigation algorithm. In particular, if the duration is too short, then the navigation policy would be executed at a high frequency, with increased training difﬁculty and high computational cost. Meanwhile, if the duration is too long, then the policy becomes unable to handle complex situations, like those with crowded obstacles. It is thus tricky to ﬁnd the “sweet” duration range; some duration values may render a DRL model to fail to ﬁnd a navigation path. In this paper, we propose to employ adaptive execution duration to overcome this problem. Speciﬁcally, we formulate the navigation task as a Semi-Markov Decision Process (SMDP) problem to handle adaptive execution duration. We also improve the distributed proximal policy optimization (DPPO) algorithm and provide its theoretical guarantee for the speciﬁed SMDP problem. We evaluate our approach both in the simulator and on an actual robot. The results show that our approach outperforms the other DRL-based method (with ﬁxed execution duration) by 10 . 3% in terms of the navigation success rate.
Tight inclusions of C*-dynamical systems

We study a notion of tight inclusions of Cand W -dynamical systems which is meant to capture a tension between topological and measurable rigidity of boundary actions. An important case of such inclusions are C(X) ⊂ L∞(X, ν) for measurable boundaries with unique stationary compact models. We discuss the implications of this phenomenon in the description of Zimmer amenable intermediate factors. Furthermore, we prove applications in the problem of maximal injectivity of von Neumann algebras.
A Driven Disordered Systems Approach to Biological Evolution in Changing Environments

Biological evolution of a population is governed by the fitness landscape, which is a map from genotype to fitness. However, a fitness landscape depends on the organisms environment, and evolution in changing environments is still poorly understood. We study a particular model of antibiotic resistance evolution in bacteria where the antibiotic concentration is an environmental parameter and the fitness landscapes incorporate tradeoffs between adaptation to low and high antibiotic concentration. With evolutionary dynamics that follow fitness gradients, the evolution of the system under slowly changing antibiotic concentration resembles the athermal dynamics of disordered physical systems under quasistatic external drives. Specifically, our model can be described as a system with interacting hysteretic elements, and it exhibits effects such as hysteresis loops and memory formation under antibiotic concentration cycling. Using methods familiar from studies in this field, we derive a number of analytical and numerical results. Our approach provides a general framework for studying motifs of evolutionary dynamics in biological systems in a changing environment.