Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of the Corrosion Current Density in Reinforced Concrete Using a Self-Organizing Feature Map

A disadvantage of using linear polarization resistance (LPR) in the measurement of corrosion current density is the need to partially destroy a concrete cover. In this article, a new technique of predicting the corrosion current density in reinforced concrete using a self-organizing feature map (SOFM) is presented. For this purpose, air temperature, and also the parameters determined by the resistivity four-probe method and galvanostatic resistivity measurements, were employed as input variables. The corrosion current density, predicted by the destructive LPR method, was employed as the output variable. The weights of the SOFM were optimized using the genetic algorithm (GA). To evaluate the accuracy of the SOFM, a comparison with the radial basis function (RBF) and linear regression (LR) was performed. The results indicate that the SOFM–GA model has a higher ability, flexibility, and accuracy than the RBF and LR.
Estimation of spatial distributions from point sources with application to air pollution measurement. Technical report No. 9

A class of weighted estimators of a stationary distribution function is introduced which exploits correlation structure in the data as frequently found in air quality measurements. The nature of these weighted estimates is examined for several simple cases, in particular for spatially clustered measurements where the advantage over the usual empirical distribution function seems most pronounced. An illustration is presented using total suspended particulate concentrations from a 14-station monitoring network. The class of weighted estimators is also proposed for the related problem of estimating the actual realization of a spatial distribution function separately at each fixed time period.
Development of software for the calculation of kinetic parameters from solid phase reaction data, application to synthesized N’-(4-Chlorobenzoyl)-N,N-di-N-butylthiourea ligand and its nickel (II) complex

In gas and liquid phase reactions, the conversion of starting materials to products is directly controlled by the rate of the reaction, whereas in solid state reactions, it is determined by the rate of diffusion. When working with thermal analysis methods, tens of thousands of data to be obtained from measured differential thermal analysis (DTA), thermal gravimetry (TG), and differential thermal gravimetry (DTG) curves must first be digitized, and then these digitized data must be converted to concentrations and thermal values using appropriate software programs. Companies that produce devices using thermal analysis techniques generally adapt the encrypted software programs they use according to the needs of the user companies. Since the devices work like a black box, it is impossible for users to make any changes to the packaged programs. In addition, since the program cannot be seen, its logic cannot be understood most of the time. The program developed in this study will be put at the service of every researcher, and the system of the program can be used on any Windows Software compatible computer. While preparing the program, an easy-to-understand and flexible MATLAB environment was used. The thermal analysis data of synthesized N’-(4-chlorobenzoyl)-N,N-di-n-butylthiourea (HL) and its Ni(II) complex have been digitalized by measuring with thermal analyses such as DTA, TG with hardware technical equipment. By entering the raw experimental data taken into the prepared software program, thermodynamic data such as enthalpy and entropy can be calculated as well as kinetic parameters such as activation energy, reaction order, and rate constant. In this study, a software program was developed that can be used in the calculation of the desired thermodynamic parameters by using the experimentally measured ΔT, Δm, and dΔm values. The accuracy of the results was compared with the thermal results of the NaN3 reference material.
PRF : A Process-RAM-Feedback Performance Model to Reveal Bottlenecks and Propose Optimizations ①

Performance models provide insightful perspectives to allow us to predict performance and to propose optimization guidance. Although there has been much researches, pinpointing bottlenecks of various memory access patterns and reaching high accurate prediction of both regular and irregular programs on various hardware configurations are still not trivial. In this work, we propose a novel model called Process-RAM-Feedback (PRF) to quantify the overhead of computation and data transmission time on general-purpose multi-core processors. The PRF model predicts the cost of instruction for single-core by a DAG (Directed Acyclic Graph) and the transmission time of memory access between each memory hierarchy through a newly designed cache simulator. By using performance modeling and feedback optimization method, we use PRF model to analyze and optimize convolution, sparse matrix-vector multiplication and sn-sweep as case study for covering with typical regular kernel to irregular and data dependence. Through the PRF model, we obtain optimization guidances with various sparsity structures, algorithm designs, instruction sets support on different data sizes.
root transcriptional changes by rhizobia molecules

Summary: Transcriptional profiling of common bean roots in response to rhizobial 19 molecules uncovers novel aspects of root nodule symbiosis such as early modulation of 20 genes encoding circadian clock components Abstract 31 Establishment of nitrogen fixing symbiosis requires the recognition of rhizobial 32 molecules to initiate the development of nodules. Using transcriptional profiling of roots 33 inoculated with mutant strains defective in the synthesis of Nod Factor (NF), 34 exopolysaccharide (EPS) or lipopolysaccharide (LPS), we identified 2606 genes from 35 common bean ( Phaseolus vulgaris ) that are differentially regulated at early stages of its 36 interaction with Rhizobium etli . Many transcription factors from different families are 37 modulated by NF, EPS and LPS in different combinations, suggesting that the plant 38 response depends on the integration of multiple signals. Some receptors identified as 39 differentially expressed constitute excellent candidates to participate in signal perception 40 of molecules derived from the bacteria. Several components of the ethylene signal 41 response, a hormone that plays a negative role during early stages of the process, were 42 down-regulated by NF and LPS. In addition, genes encoding proteins involved in small 43 RNA-mediated gene regulation were regulated by these signal molecules, such as 44 Argonaute 7, a specific component of the TAS3 derived tasiRNAs biosynthetic pathway, 45 an RNA dependent RNA polymerase and a XH/XP domain-containing protein, which is 46 part of the RNA directed-DNA methylation. Interestingly, a number of genes encoding 47 components of the circadian central oscillator were down-regulated by NF and LPS, 48 suggesting that a root circadian clock is adjusted at early stages of symbiosis. Our results 49 reveal a complex interaction of the responses triggered by NF, LPS and EPS that 50 integrates information of the signals present in the surface or secreted by rhizobia. 51 availability R. etli mutant strains deficient synthesis of NF, or in order to characterize the transcriptome of the susceptible zone of common bean roots in response to wild type or mutant strains of rhizobia. Direct RNA sequencing revealed a set of genes differentially regulated specifically by one signal molecule, as well as genes that showed a transcriptional response that is common to either two or the three signals, suggesting that the plant can distinguish and integrate information from different determinants of rhizobia identity. Our results highlight novel aspects of plant responses to trigger genetic programs associated to symbiosis, such as circadian rhythms and gene regulation mediated by transcription factors and small RNAs (sRNAs). interesting early to and gene regulation mediated by small RNAs at both and post-transcriptional genes overlapping and distinct functions open frame-containing cytoplasmic localization
Splitting supersonic nozzle flow into separate jets by overexpansion into a multilobed divergent nozzle

Air flowing from a convergent nozzle at pressure ratios greater than 2.5 has been split into eight separate jets by overexpansion of the flow into a divergent, eight-lobed passage. The splitting of the flow is accompanied by a decrease in the nozzle axial centerline Mach number. This in part is due to the radial inflow of secondary air between the lobes toward the nozzle centerline. Each of the smaller jets is partially split after it leaves the end of the divergent lobed section of the nozzle, thus creating a velocity profile having 16 peaks. At a pressure ratio of 3.5 the flow decelerates to Mach 1 in three convergent nozzle throat diameters. Convergent nozzle flow normally requires 12 diameters to reach Mach 1. The nozzle has a sound attenuation of 12 decibels with a thrust loss of 9 percent for the best configuration tested.
Mediation Effect of Human Resources Practices Between Corporate Governance Principles and Business Performance: Evidence from Turkey Firms

:- The aim of this research, which is an exploratory study, was to determine whether human resources practices have a mediation effect between corporate governance principles and business performance. In this context, 304 managers were reached through survey management. Data were analyzed by the survey method. In the analysis performed, it was concluded that human resources practices had a mediation effect on the impact of corporate governance principles on business performance. The research results partially support studies in the literature. The results of the study were interpreted and the recommendations for managerial practices were presented in detail. The research can constitute a step for scientific studies to be carried out in this field in the literature.
Equipping SBMs with RBMs: An Explainable Approach for Analysis of Networks with Covariates

—Networks with node covariates offer two advantages to community detection methods, namely, (i) exploit covariates to improve the quality of communities, and more importantly, (ii) explain the discovered communities by identifying the relative importance of different covariates in them. Recent methods have almost exclusively focused on the ﬁrst point above. However, the quantitative improvements offered by them are often due to complex black-box models like deep neural networks at the expense of explainability. Approaches that focus on the second point are either domain-speciﬁc or have poor performance in practice. This paper proposes explainable, domain-independent statistical models for networks with node covariates that additionally offer good quantitative performance. Our models combine the strengths of Stochastic Block Models and Restricted Boltzmann Machines to provide interpretable insights about the communities. They support both pure and mixed community memberships. Besides providing explainability, our approach’s main strength is that it does not explicitly assume a causal direction between community memberships and node covariates, making it applicable in diverse domains. We derive efﬁcient inference procedures for our models, which can, in some cases, run in linear time in the number of nodes and edges. Experiments on several synthetic and real-world networks demonstrate that our models achieve close to state-of-the-art performance on community detection and link prediction tasks while also providing explanations for the discovered communities.
A Study on the Impacts of Risk Taking on Commercial Bank Profit Efficiency in China

: Using an unbalanced panel dataset of 38 commercial banks in China, stochastic frontier models and a one-stage analysis approach, we investigate the impacts of bank risk taking, market structure and governance structure on profit efficiency. Our empirical results show that large commercial banks’ profit efficiency is higher and stable, while the small and medium banks’ profit efficiency shows an increasing and convergence trend, but decline in the late two years. During the estimation process, ignoring risk factors will lead to significant underestimation of the efficiency score. Risk’s inefficiency effects are significantly positive, indicating that the higher the risk, the more inefficiency effects.
#2022-021 Automation exposure and implications in advanced and developing countries across gender, age and skills

This paper addresses three main objectives. First, the analysis estimates and compares the average share of workers at risk of automation in advanced and developing regions. Second, the study investigates the possible structural implications of automation across the Gender, Age, and Skill labour market structures at the sectoral, country, and regional levels. Third, the paper extends the analysis of the Gender structure from possible job implications to potential wage consequences; in particular, the potential effect of automation on the gender wage gap at the regional level is studied and the sources of the differentials are identified. This study uses data from the PIAAC dataset, which comprises detailed task data for individual workers including novel data for developing countries. The results indicate that, from a purely technological feasibility viewpoint, advanced countries are more vulnerable than developing countries on average. Male and middle-aged workers are also likely to be more affected by automation, whereas high-skilled workers are likely to be the least affected by automation. The results also indicate that automation could reduce gender inequality not only through jobs but also through wages.
Causes of Missing Snowmelt Following Drought

Water management in snowy mountainous regions hinges on forecasting snowmelt runoff. However, droughts are altering snowpack‐runoff relationships with ongoing debate about the driving mechanisms. For example, in 2021 in California, less than half of predicted streamflow arrived. Mechanisms proposed for this “missing” streamflow included changes in evapotranspiration (ET), rainfall, and subsurface moisture conditions. Here, we demonstrate that ET in drought years generates dry subsurface conditions that reduce runoff in subsequent years. A model including this legacy of depleted moisture storage reduced median error in 2021 forecasts from 60% to 20% at 15 minimally disturbed basins and from 18% to 2% at 6 water supply basins in the Sierra Nevada (basins range in area from 5 to 23,051 km2 and mean annual precipitation from 814 to 1,549 mm). Our findings indicate that the relationship between snowpack and runoff will evolve as plant ecosystems respond to climate change and alter subsurface water storage dynamics.
i t A r t i c l e Seasonal Dynamics of Feeding Habits of a Cyprinid Fish, Saurogobio dabryi in the Downstream of the Jialing River, China

This research studied the food composition and feeding rhythm of Saurogobio dabryi in the lower reaches of the Jialing River, aimed to explore the seasonal difference of its feeding habits. Gut contents of 268 S . dabryi in the downstream (Hechuan section) of the Jialing River were collected seasonally from September 2015 to August 2016. The feeding habits of S. dabryi were studied by microscopic examination, analysis of similarity, and the Amundsen graphical method. A total of 26 food categories were identified, among which algae and organic detritus were the dominant food source for S. dabryi . Animal prey contributed relatively low proportions to the food of S. dabryi . Seasonal variations of food composition of S. dabryi were significant ( P <0.05). Hierarchical cluster analysis shows that the food composition in autumn was significantly different from other seasons at a similarity level of 60%. The results indicated that the feeding rhythm of the S. dairyi in the lower reaches of the Jialing River showed significant seasonal changes ( P <0.05); The feeding intensity and food amount gradually increased from spring to winter, which may be related to the morphological structure, breeding period, special habitat, and seasonal changes of biological prey of S. dabryi .
Multi-Robot Assembly Scheduling for the Lunar Crater Radio Telescope on the Far-Side of the Moon

The Lunar Crater Radio Telescope (LCRT) is a proposed ultra-long-wavelength radio telescope to be constructed on the far side of the moon. The proposed telescope will be constructed by deploying a 1km wire mesh in a 3-5km crater using a team of wall-climbing DuAxel robots. In this work, we consider the problem of generating minimum-time assembly sequences for LCRT, using realistic models of travel speed and lighting. We pose the assembly sequencing problem as a mixed-integer linear program (MILP), which we solve to global optimality using commercial solvers. We present methods for modeling time-varying travel and assembly times, based on variable lighting conditions (including crater shadowing), and show how such time-varying parameters can be incorporated into the MILP. Finally, we present numerical studies of our method, showing how makespan varies with the number of assembly robots.
Operational water forecast ability of the iSnobal-HRRR coupling; an evaluation to adapt into production environments

10 Operational water-resource forecasters, such as the Colorado Basin River Forecast Center (CBRFC) in the Western United States rely on historical records to calibrate the temperature-index models currently used for snowmelt runoff predictions. This data dependence is increasingly challenged, with global and regional climatological factors changing the seasonal snowpack in mountain watersheds. To evaluate and improve the CBRFC modeling options, this work ran the physically based snow energy balance iSnobal model, forced with outputs from the High-Resolution Rapid Refresh (HRRR) numerical weather model across 15 four years in a subset region. Compared to in-situ, remotely sensed, and the current operational CBRFC model, the iSnobal-HRRR coupling showed well-reconstructed snow depths until peak accumulation (Mean differences between -0.20 and +0.28 m). Once snowmelt set in, iSnobal-HRRR showed that simulated snowmelt was slower relative to observations, depleting snow on average up to 34 days later. The melting period is a critical component for water forecasting. Based on the results, there is a need for revised energy balance calculations in iSnobal, which is a recommended future improvement to the model. Nevertheless, the presented 20 performance and architecture make iSnobal-HRRR a promising combination for the CBRFC production needs, where there is a demonstrated change to the seasonal snow in the mountain ranges around the Colorado River Basin. Long term goal is to introduce the iSnobal-HRRR coupling in day-to-day CBRFC operations, and this work created the foundation to expand and evaluate larger domains. For the overall iSnobal model assessment, the simulated snow depth was compared against measured observations at discrete in-situ snow measurement stations and spatially at discrete points in time against aerial snow depth maps. The iSnobal simulated runoff was compared to the basin hydrograph for basin-averaged
NUMERICAL METHOD OF ABEL’S TYPE INTEGRAL EQUATIONS WITH EULER’S OPERATIONAL MATRIX OF INTEGRATION

: In this paper, we describe a new numerical approach for the solution of integral equations with Abel type singularity based on normalized Euler polynomials. Euler’s operational matrix of integration is introduced and the Euler polynomials have been used to obtain it. This polynomial is orthonormalized ﬁrst, and then their operational matrix of integration is obtained. The orthogonality approach is used to convert integral equations into a set of algebraic equations in which it can be calculated fast. We demonstrated the accuracy and utility of the sug-gested operational matrix of integration using numerical examples. A comparative study with other methods reﬂects that the proposed algorithm has good degree of accuracy and also easy to compute.
RENEWABLE HYDROGEN PRODUCTION FROM BIOMASS

Hydrogen is highlighted as a key energy carrier in a future net zero green-house gas (GHG) emission society. It is a suitable energy carrier for many applications, e.g., heavy transportation and for industrial use. Furthermore, combusting hydrogen or using it in a fuel cell does not cause GHG emissions at site. This implies that, if produced without emitting GHG emissions, hydrogen can be a completely renewable energy carrier. However, currently over 90% of the world’s hydrogen supply is produced from fossil natural gas and coal. If hydrogen is to be used as a sustainable alternative to fossil fuels, it needs to be produced without net emissions of GHG; one such alternative is to use forest biomass. This work presents a literature review of technologies for hydrogen production from sustainably harvested forest biomass. An overview of available technologies is included, but thermochemical production methods are emphasized. Results show that biomass gasification coupled with steam methane reforming and water gas shift reactors reaches the highest biomass to hydrogen conversion yields. Compared to other biomass based, technological pathways, gasification and pyrolysis also have the highest technological readiness levels. The literature review suggests hydrogen production costs from gasification or pyrolysis reaching from 0.078 to 0.21 EUR/kWh. This compares to a production cost of 0.019 EUR/kWh for natural gas SMR and 0.023 EUR/kWh for coal gasification and production costs of 0.045-0.12 EUR/MWh for water electrolysis.
Knowledge Representation to Enable High-level Planning in Cloth Manipulation Tasks

Cloth manipulation is very relevant for domestic robotic tasks, but it presents many challenges due to the complexity of representing, recognizing and predicting the behaviour of cloth under manipulation. In this work, we propose a generic, compact and simplified representation of the states of cloth manipulation that allows for representing tasks as sequences of states and transitions semantically. We also define a Cloth Manipulation Graph that encodes all the strategies to accomplish a task. Our novel representation is used to encode two different cloth manipulation tasks, learned from an experiment with human subjects manipulating clothes with video data. We show how our simplified representation allows to obtain a map of meaningful steps that can serve to describe cloth manipulation tasks as domain models in PDDL, enabling high-level planning. Finally, we discuss on the existing skills that could enable the sensory motor grounding and the low-level execution of the plan.
Chemical Stability and Catalytic Activity of Redox Enzymes in NADES

: NADES represent a new generation of biocompatible solvents, formed by eutectic mixtures of two or more hydrogen bond donor and hydrogen bond acceptor compounds of natural origin, with a lower melting point compared to that of pure components. The ease of preparation, sustainability, low cost and low toxicity of NADES have allowed these solvents to be investigated in biocatalysis. In this communication we describe the stability and the enzymatic activity of two oxidoreductases, the HLADH and the Ts ER, in buffer solution and in mixtures choline-based NADES/buffer. In particular, we report the enantioselective lactonization of 3-methyl-1,5-pentanediol into 4-methyl-δ-valerolactone and the ketoisophorone bioreduction into levodione.