Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of different malolactic fermentation techniques on changes in chemical properties and volatile compounds of cv. Teran red wine (Vitis vinifera L.)

Malolactic fermentation (MLF) is a complex biochemical process playing an important role in the production of red wines. The main reasons for its implementation are the reduction of titratable acidity and the consequent increase of pH, microbiological stability of wine, and changes in aromatic and sensory properties of wine. The aim of this study was to determine the compatibility of yeast and bacteria used by different malolactic fermentation techniques and their influence on the fermentation duration, concentration of individual organic acids, aroma compounds, and on chemical and sensory properties of Teran wines. The experiment included control treatment (without MLF), spontaneous MLF, induced MLF at the beginning of alcoholic fermentation with simultaneous inoculation of yeast and bacteria (coinoculation) and induced MLF after alcoholic fermentation (sequential MLF). In the co-inoculation treatment MLF had no negative effect on the alcoholic fermentation kinetic. Alcoholic fermentation was complete in all treatments. Coinoculation resulted in a significantly shorter duration of the MLF process. In all MLF treatments, significant reduction of titratable acidity and the increase of pH values was noted. Wines of the spontaneous MLF treatment obtained the highest concentration of volatile acidity and ethyl acetate. In all MLF treatments a complete consumption of malic acid and a decrease in concentration of citric acid, total amount of higher alcohols, and acetaldehyde were observed. Furthermore, significantly higher concentrations of ethyl esters, diacetyl, acetoin, and 2,3-butanediol were present in wines from all MLF treatments.
Developing Universally Applicable Service Quality Assessment Model Based on the Theory of Consumption Values, and Using Fuzzy Linguistic Preference Relations to Empirically Test Three Industries

Service quality is an important factor for a successful company. The SERVQUAL model is widely used. However, it has been controversial over the past 30 years. This research aims to develop a service quality measurement model that can be supported in conceptualization and universal applicability, and uses this model to identify the most important key factors of service quality for three industries. First, based on the theory of consumption values, this study used conceptualization and the modified Delphi method to develop a service quality assessment model—the consumption values-based service quality model (CV-SQ). The CV-SQ model was then used in conjunction with the fuzzy linguistic preference relations (Fuzzy LinPreRa) method to address MCDM problems. The findings suggested that the most important key factors of service quality comprised safety in the aviation companies, innovativeness in the travel agencies, and comfort in the hotels. The CV-SQ model can be supported by theoretical and empirical tests in conceptualization and universal applicability, and has made theoretical contributions to service quality management. The research results have provided practical contributions to the improvement of service quality in the three industries. What is more noteworthy is the weight of epistemic value ranked first and second among the three industries, but it had not been included in any service quality aspect classification schemes during the past three or four decades.
Intelligent search for accurate solutions to the planning open-pit mining

A method of intelligent search for accurate solutions to the planning of open-pit mining has been developed. The method is implemented within the framework of the Constraint Programming Paradigm that allows us to process heterogeneous qualitative and quantitative constraints (in particular, economic, technological, etc.) simultaneously, as well as to maintain the model of subject domain being developed which is open to adding new constraints or deleting existing constraints. Various constraints can be added to the model, including those for which it is difficult to find a suitable analytical expression. In contrast to existing methods of local search the proposed method systematically explores the search space. The method allows us to find a global optimum in large dimensional spaces that describe practically significant problems arising in production. Currently, to solve this problem, the methods of integer linear programming are widely used. But its fundamental disadvantage is the need to represent all the constraints in the form of linear equalities and inequalities. However, in practice, some combinatorial optimization problems cannot be linearized and solved using traditional methods of mathematical programming. The developed method is illustrated by the example of a three-dimensional problem of finding the position of an intermediate pit wall by the processing periods taking into account the specified performance for mineral and overburden rocks and the objective profit function taking into account discounting. The types of constraints necessary for modeling the problem under consideration are identified. The possibility of applying the existing inference procedures on constraints is considered for these types. The method proposed makes it possible to obtain accurate solutions due to the intellectualization of the solution process by using highly efficient algorithms of reducing the search space for each type of constraints and specialized heuristics for pruning unpromising alternatives in the search tree.
Using Link-Based Consensus Clustering for Mixed-Type Data Analysis

: A mix between numerical and nominal data types commonly presents many modern-age data collections. Examples of these include banking data, sales history and healthcare records, where both continuous attributes like age and nominal ones like blood type are exploited to character-ize account details, business transactions or individuals. However, only a few standard clustering techniques and consensus clustering methods are provided to examine such a data thus far. Given this insight, the paper introduces novel extensions of link-based cluster ensemble, LCE WCT and LCE WTQ that are accurate for analyzing mixed-type data. They promote diversity within an ensemble through different initializations of the k-prototypes algorithm as base clusterings and then refine the summarized data using a link-based approach. Based on the evaluation metric of NMI (Normalized Mutual Information) that is averaged across different combinations of benchmark datasets and experimental settings, these new models reach the improved level of 0.34, while the best model found in the literature obtains only around the mark of 0.24. Besides, parameter analysis included herein helps to enhance their performance even further, given relations of clustering quality and algorithmic variables specific to the underlying link-based models. Moreover, another significant factor of ensemble size is examined in such a way to justify a tradeoff between complexity and accuracy.
Bathymetric Data Requirements for Operational Coastal Erosion Forecasting Using XBeach

There is an increasing interest in the broad-scale implementation of coastal erosion early warning systems (EWS) with the goal of enhancing community preparedness to extreme coastal storm wave events. These emerging systems typically rely on process-based models to predict the storm-induced morphological change. A key challenge with incorporating these models in EWSs is the need for up-to-date nearshore and surf zone bathymetry data, which is difficult to measure routinely, but potentially important for accurate erosion forecasting. This study evaluates the degree to which up-to-date bathymetry is required for accurate coastal erosion predictions using the morphodynamic model XBeach and, subsequently, whether a range of “representative” and/or “synthetic” bathymetries can be used for the bottom boundary, when a survey of the immediate pre-storm bathymetry is not available. Twelve storm events at two contrasting sites were modelled using six different bathymetry scenarios, including the expected “best case” bathymetry surveyed immediately pre-storm. These results indicate that alternative bathymetries can be used to obtain sub-aerial erosion predictions that are similar (and in some cases better) than those resulting from the use of an immediately pre-storm surveyed bathymetry, provided that rigorous model calibration is undertaken prior. This generalized finding is attributed to specific parametrizations in the XBeach model structure that are optimized during the calibration process to match the particular bottom boundary condition used. This study provides practical guidance for the selection of suitable nearshore bathymetry for use in operational coastal erosion EWSs.
Constructivism of Problem-Based Learning Failure in Increasing Students Critical Thinking and Strategy of Cognitive Conflict

: The aim of this research was described and analyzed the apply constructivism based on Problem-based learning model and cognitive conﬂict strategy for the increasing ability of MCT (mathematical critical thinking) and MCA (mathematical curiosity ability). This research adopted a quasi-experimental with pretest-posttest control group design. This research used the design of the mixed method with sequential explanatory strategy. The population consists of students teachers of Mathematics Education Program of Riau Islamic University. The instrument of collecting quantitative data is essay test and questionnaire and interview sheet, observation sheet to collect qualitative data. The ﬁndings show that (1) no difference MCT ability and MCA disposition with taught by Problem based learning model and cognitive conﬂict strategy with explicit direct instruction (EDI) approach, namely; high, medium, low; (2) but in thinking process development in teaching and learning, were founded that there were difference MCT ability and MCA dispositions with have taught by problem-based learning model and cognitive conﬂict strategy(high, medium, low); It known through observation, interview and result in teaching and learning.
The Long Arm of Early Exclusionary School Discipline? A Multi-Model Analysis

In this analysis, we consider how a potentially important triggering event in the life course—exclusionary school discipline—may affect students’ high school outcomes. We extend the literature to focus on the long-term effects of exclusionary discipline that occurs in the early grades, when students are relatively young and when a significant share of exclusionary discipline first occurs. We further evaluate the potential, long-term effects of exclusionary discipline on different high school outcomes (non-completion, GED certification, high school diploma) in statistical models that account for observed and unobserved heterogeneity. Overall, we find robust and consistent evidence that very young children are not somehow more resilient or more protected from negative, long-term effects of suspension or expulsion in early elementary school. Moreover, previous research might underestimate the effects of (early) exclusionary discipline more generally by ignoring the independent effects on GED certification.
Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings

In this paper, three main approaches (univariate, multivariate and multistep) for electricity consumption forecasting have been investigated. In fact, three major algorithms (XGBOOST, LSTM and SARIMA) have been evaluated in each approach with the main aim to figure out which one performs the best in forecasting electricity consumption. The motivation behind this work is to assess the forecasting accuracy and the computational time/complexity for an embedded forecasting and model training at the smart meter level. Moreover, we investigate the deployment of the most efficient model in our platform for an online electricity consumption forecasting. This solution will serve for deploying predictive control solutions for efficient energy management in buildings. As a proof of concept, an already existing public dataset has been used. These data were mainly collected thanks to the usage of already deployed sensors. These provide accurate data related to occupancy (e.g., presence) as well as contextual data (e.g., disaggregated electricity consumption of equipment). Experiments have been conducted and the results showed the effectiveness of these algorithms, used in each approach, for short-term electricity consumption forecasting. This has been proved by performance evaluation and error calculations. The obtained results mainly shed light on the challenging trade-off between embedded forecasting model training and processing for being deployed in smart meters for electricity consumption forecasting.
Evaluation of the Humphead Wrasse, Cheilinus undulatus, in Shallow Water Habitats in Saipan Lagoon, Commonwealth of the Northern Mariana Islands1

Abstract: Visual census surveys utilizing a distance sampling approach were undertaken in Saipan Lagoon to determine the density of humphead wrasse (HHW) in four shallow water habitats, and compared to observations in the Saipan nighttime commercial spear fishery. Fish observed from the survey ranged in total length from 6.5 to 35 cm total length, generally smaller than the range of fish sampled from the commercial fishery of 16 to 47 cm total length. The majority of survey observations occurred in the central and northern part of Saipan Lagoon, which correlated with capture locations documented in the commercial fishery landings. Analysis of contemporary and historical commercial fishery data showed that HHW could not be considered primary fishery target species. Distance sampling results indicated that coral/hard bottom and Enhalus acroroides seagrass were the habitats most important to HHW, as reflected by higher density estimates. The use of distance sampling was useful in estimating the spatial distribution and density of HHW, and should be considered when assessing other rare marine finfish species.
Understanding Rubredoxin Redox Potentials: Role of H-Bonds on Model Complexes.

The energetics of redox states in different models of rubredoxin-like iron-sulfur complexes (ISC) was computed using a combination of density functional and electrostatic continuum theories. In agreement with experiment, the calculated redox potential for the small ISC model [Fe(SCH2CH3)4](1-,2-) in acetonitrile was -813 mV [Galstyan, A. S. ; Knapp, E. W. J. Comput. Chem. 2009, 30, 203-211] as compared to the measured value of -838 mV. Surprisingly the experimental values for rubredoxin (Rd) are much higher ranging between -87 and +39 mV. These large variations in redox potentials of ISC models and ISC in Rd are due to specific conformational symmetries adopted by the ligands due to both the protein environment and type and the number of H-bonds, and the dielectric environment. In a dielectric environment corresponding to proteins (ε = 20), the computed ISC redox potentials shift positive by about 64 mV for Fe-S···H-N and 95 mV for Fe-S···H-O H-bonds, correlating well with data estimated from experiments on ISC proteins. In aqueous solutions (ε = 80), a positive shift of 58 mV was computed for Fe-S···H-O H-bonds (using a model with the same ISC conformation as in Rd) in agreement with a measured value for Rd with partially solvent exposed ISC. The latter demonstrates the dependence of the ISC redox potentials on the environment (solvent or protein). For a model whose chemical composition is analog to the relevant part of ISC in a specific Rd, the computed redox potential of the model agrees with the measured value in Rd. This study allows to understand redox potential shifts for small ISC models and ISC in proteins.
Seamless connectivity with 5G enabled unmanned aerial vehicles base stations using machine programming approach

Deployment of a small unmanned aerial vehicle (UAV) mounted 5G base station is a promising solution for providing seamless network connectivity to users in a modern, data‐centric thrust areas. The key challenge is to find the location, the height and the optimum number of mounts. A machine programming based approach is proposed here for optimal placement of UAV‐mounted base station. The location of the deployment is determined using three clustering algorithms such as K‐means, K‐medoids, and fuzzy cluster means. Different sets of UAV‐mounted base stations have been deployed with variable user density at different heights. The impact on the network performance has been quantified through measurements of received power, signal to interference plus noise ratio (SINR), and path loss per active user equipment (UEs). To gain further insights, a scenario where UEs are connected only to the terrestrial base station, that is, the network is devoid of any sort of UAV mounted base station is evaluated. Numerical computations reaffirm that the proposed technique reduces the average path loss of the active UEs. Moreover, the use of height‐mounted base station also alleviates the issues arising due to low SINR values. The said technique shows immense potential in terms of seamless connectivity to end users in events of emergency and remote deployment scenarios, where ground‐based base station is not possible. The big transition of on‐ demand connectivity for 5G networks shall be benefitted from purpose‐built UAV infrastructure with specific locations or areas in mind.
Relative importance of host‐dependent versus physical environmental characteristics affecting the distribution of an ectoparasitic copepod infecting the mouth cavity of stream salmonid

21 Understanding parasite distributional patterns is fundamental for elucidating 22 host-parasite relationships. The genus Salmincola is an ectoparasitic copepod group 23 specifically infecting freshwater salmonids. Considering their strong association with 24 their hosts, we can predict that the distribution and prevalence (analogues to abundance) 25 of Salmincola reflect host salmonids. An alternative hypothesis is that their distribution 26 will be strongly affected by environmental factors like stream drift because they have a 27 free-living stage with low swimming ability. If this is the case, we predict a longitudinal 28 gradient with higher occurrence or infection levels in downstream areas. To estimate the 29 relative strength among factors affecting infection levels, we investigated the 30 distribution pattern of Salmincola sp. on wild white-spotted charr Salvelinus 31 leucomaenis in a southern Hokkaido river system. Based on data from 19 sites across 32 three seasons, we found that host density and flow velocity affected the prevalence of 33 Salmincola . On the other hand, no longitudinal gradient was observed and the 34 prevalence was extremely low in some fragmented habitats (i.e., above dams and 35 waterfalls). This indicates some compensation mechanisms against unidirectional 36 downstream dispersal. We found that parasite prevalence and intensity were much 37 higher in large migratory (anadromous) fish and, therefore, hypothesize that 38 long-distance upstream migration helps the redistribution and population persistence of 39 parasites in upstream areas. 40
A self-determination theory perspective on the association between the childhood home reading environment and Canadian young adult leisure reading

ABSTRACT Despite the numerous lifelong benefits associated with continued leisure reading, adult reading habits are steadily declining (Statistics Canada, 2019). The purpose of this study was to understand how the recalled childhood home reading environment, and the associated psychological need-satisfaction, were related to young adults’ leisure reading beliefs and practices. Based on Self Determination Theory (Ryan & Deci, 2017), we used recalled basic psychological need-satisfaction during parent-child shared reading experiences as a reflection of the degree of intrinsic reading motivation support. We collected data from 214 Canadian young adults (age 18–29). Almost all participants reported some degree of leisure reading. Moreover, we found that home reading environment may significantly contribute to the development and reinforcement of positive reading attitudes, identities and habits. Basic psychological need-satisfaction only partially mediate the relationship between home reading environment and current reading identity. These results are discussed in terms of implications for both researchers and parents.
Human ovarian cryopreservation: vitrification versus slow freezing from histology to gene expression

Abstract Cryopreservation of ovarian tissue is one of the strategies offered to girls and women needing gonadotoxic treatment to preserve their fertility. The reference method to cryopreserve is slow freezing; vitrification is an alternative method. The aim was to evaluate which of the two is the best method for human ovarian tissue cryopreservation. Each ovary was divided into three groups: (i) fresh; (ii) slow freezing; and (iii) vitrification. An evaluation of the follicular density, quality and the expression six genes (CYP11A, STAR, GDF9, ZP3, CDK2, CDKN1A) were performed. We observed no significant difference in follicular density within these three groups. Slow freezing altered the primordial follicles compared to the fresh tissue (31.8% vs 55.9%, p = 0.046). The expression of genes involved in steroidogenesis varied after cryopreservation compared to the fresh group; CYP11A was under-expressed in slow freezing group (p = 0.01), STAR was under-expressed in the vitrification group (p = 0.01). Regarding the expression of genes involved in cell cycle regulation, CDKN1A was significantly under-expressed in both freezing groups (slow freezing: p = 0.0008; vitrification: p = 0.03). Vitrification had no effect on the histological quality of the follicles at any stage of development compared to fresh tissue. There was no significant difference in gene expression between the two techniques.