Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on the Causal Relationship between “the Flow of a Game” and its Outcome in Basketball : Focusing on the Interdependence Relations of Four Periods

The purpose of this study was to clarify the causal relationship between the “flow of a game” that can be defined “the situation on which 4 periods which consist of division time as 10 minutes are advancing gradually while having an influence each other” and its outcome, fo cusing on the interdependence relations of four periods in basketball. In order to accomplish this purpose in this study, a hypothesis was set in that, the “flow of a game,” in which “factors causing changes in conditions” cannot be overlooked in basketball, consists of four periods, with each first generating functions as opportunity, in the end affecting the outcome. In order to test this hypothesis, an analysis was performed over 1044 periods in 261 games in Japan’s strongest university league, Kanto Men’s First Division League, based on the following three analysis perspectives: (1) the importance of each period; (2) the mutual dependency between periods; and (3) the relationship between the difference in cumulative scoring and outcome. Results observed using logistic regression analysis and structural equation modeling of the ob tained data clarified the following three points: (1) periods that exerted influence on the out come were the first, third and fourth, with their importance ranked as “third > first > fourth > second”; (2) as for mutual dependency for each period, the point difference in the preceding period in the sequence, “first → second (cumulative),” “second (cumulative) → third (cumula -tive),” “third (cumulative) → fourth” creates an opportunity for the following period; and (3) if there is the cumulative score difference within eight points by the end of the third period, there is a high possibility (potential) for coming back to win. These findings will be able to apply to coaching in various games under the official rules of FIBA as new practical guide lines for closely analyzing the causal relationships between the unique “flow of a game” and outcomes in basketball that take place over four periods.
Transactive Energy Market Framework for Decentralized Coordination of Demand Side Management Within a Cluster of Buildings

Flexibility in electricity demand can be leveraged for demand side management (DSM) to enable aspects such as “demand following generation” and provide ancillary services to support grid integration of renewable and distributed energy resources. The upcoming connected devices in demand centers such as commercial buildings and households may be leveraged for this. However, the coordination among distributed demand centers in a scalable decentralized manner to achieve DSM objectives is still a challenge. In this article, a generalized hierarchical transactive energy based multiagent framework is proposed. This framework includes energy management demand agents (EMDAs) at the building level, which coordinate the operation of different appliances within the buildings. EMDAs also actively participate on day-ahead Walrasian market at the cluster of buildings level. In this market, the effect of wide-adoption of DSM on generation dispatch is also studied. A particular instance of this framework is also implemented in a cyber-test system consisting of standard industrial microcontroller platforms to emulate practical implementations via smart meters. Experimental results of the proposed system are included to demonstrate the possibility of avoiding disptach from expensive generation units by reduction in peak demand and providing demand response inherently.
ASTM F3269 - An Industry Standard on Run Time Assurance for Aircraft Systems

This paper discusses the philosophy and editorial considerations behind the ongoing second revision of the ASTM F38 Committee standard on run time assurance for aircraft systems – ASTM F3269, titled " Standard Practice for Methods to Safely Bound Flight Behavior of Unmanned Aircraft Systems Containing Complex Functions ". It describes the key aspects of the Run Time Assurance (RTA) architecture as depicted in the current revision of the standard and provides some insights on the design best practices suggested in the standard. RTA is a certification strategy for unmanned aircraft systems that contain complex functions, which may not be certifiable using traditional design assurance practices. This challenge may arise in part due to the inherent algorithmic complexity of these functions. It may also be due to the inability to produce design assurance artifacts according to industry standards such as RTCA DO-178C (software) or DO-254 (hardware) for commercial off-the-shelf components used on-board the aircraft. RTA adds value not only to unmanned applications, but also to manned aviation – particularly in General Aviation (GA) and Advanced Air Mobility (AAM). It has the potential to enable technologies for autonomous aircraft systems and simplified vehicle operations. The strategy will also play a role in the design assurance and certification of adaptive controllers and functions using artificial intelligence and machine learning algorithms.
Tropical Cyclone Intensity Estimation From Geostationary Satellite Imagery Using Deep Convolutional Neural Networks

In this study, a set of deep convolutional neural networks (CNNs) was designed for estimating the intensity of tropical cyclones (TCs) over the Northwest Pacific Ocean from the brightness temperature data observed by the Advanced Himawari Imager onboard the Himawari-8 geostationary satellite. We used 97 TC cases from 2015 to 2018 to train the CNN models. Several models with different inputs and parameters are designed. A comparative study showed that the selection of different infrared (IR) channels has a significant impact on the performance of the TC intensity estimate from the CNN models. Compared with the ground truth Best Track data of the maximum sustained wind speed, with a combination of four channels of data as input, the best multicategory CNN classification model has generated a fairly good accuracy (84.8%) and low root mean square error (RMSE, 5.24 m/s) and mean bias (−2.15 m/s) in TC intensity estimation. Adding attention layers after the input layer in the CNN helps to improve the model accuracy. The model is quite stable even with the influence of image noise. To reduce the side-effect of the very unbalanced distribution of TC category samples, we introduced a focal_loss function into the CNN model. After we transformed the multiclassification problem into a binary classification problem, the accuracy increased to 88.9%, and the RMSE and the mean bias are significantly reduced to 4.62 and −0.76 m/s, respectively. The results show that our CNN models are robust in estimating TC intensity from geostationary satellite images.
Key drivers of microbial abundance, activity, and diversity in karst spring waters across an altitudinal gradient in Slovenia

In aquatic ecosystems, the biomass, activity and composition of microorganisms are determined to a large extent by local and regional environmental conditions. While karst aquifers are an important source for drinking water, the ecology of microbial communities in karst waters has hardly been studied. This study examined the regional variations and seasonal dynamics of microbial communities in pristine karst spring waters of Slovenia (Central Europe). Fifteen springs distributed across 5 eco-regions exhibiting a strong altitudinal gradient were sampled 4 times a year. Evaluation of the microbial communities included quantification of prokaryotic biomass via total cell counts and microbial activity estimated via measurements of electron transport system activity. The taxonomic structure of the bacterial communities was analysed by terminal restriction fragment length polymorphism fingerprinting. Biological measures were complemented by a set of physico-chemical parameters, including dissolved organic carbon, nutrients, major ions, temperature, electrical conductivity, pH, and dissolved oxygen. Bacterial community structure differed significantly with seasons and eco-regions, with the latter causing greater variation. While the overall power of the environmental variable tested was a moderate factor (15%) in explaining the variability in microbial community structure, catchment altitude was a key determinant. Pro karyotic cell density and microbial activity in spring water decreased with an increase in catchment altitude and were significantly positively correlated. For a better understanding of karst ecosystems and the ecosystem service of water purification, future investigation should address karst microbial communities at a higher phylogenetic and functional resolution.
Essential qualities for elementary teachers of the 21st century: Voices of key stakeholders

This study aimed to explore the essential qualities for elementary teachers specifically in the 21st century based on the perspectives of teacher educators, pre-service and in-service elementary teachers, and the authorities of the Ministry of National Education and the Council of Higher Education in Turkey. Designed as phenomenological research, the participants included 58 teacher educators, 29 pre-service elementary teachers, 28 in-service elementary teachers, four authorities from the Ministry of National Education and one authority from the Council of Higher Education, who were selected through criterion sampling, maximum variation sampling, and snowball sampling strategies. The data were collected through semi-structured face-to-face interviews, and analyzed by content analysis method. The participants reported that elementary teachers needed to possess certain qualities that include the essential knowledge, skills, and attitudes in the following domains: (1) information and communication technologies, (2) higher-order thinking skills, (3) autonomy and collaboration, (4) flexibility, (5) adaptation, (6) culturally responsiveness, (7) local and universal issues, (8) personal and professional development, (9) students with special needs, (10) differentiation, and (11) effective communication. The study does not only reflect the evolving needs of elementary teachers along with the changing characteristics of the societies in the 21st century, but it also offers a ground for the future directions of elementary teacher admission, preparation, certification, evaluation, and appointment policies.
Application of translinguism in teaching foreign languages to students (specialty “Ecology”)

This article explores the concept of translinguism and its application in the educational environment. The authors have developed a learning model that includes flipped classroom, face-to-face classes, team work and project-based learning. The study involved 3rd year students (N=26) of Peter the Great St. Petersburg Polytechnic University (SPbPU), studying in the field of “Ecology”. During one semester (February 2020 - June 2020), students studied the discipline “Spanish” in English according to proposed methodology. The study examined the impact of the proposed model on the study of the Spanish language and the development of translational competence. For the analysis descriptive statistics and pairsamples Students’s t-test were conducted. Interviews were also conducted with students to analyze their perception of the new learning model. Generally, the overall quality of students’ Spanish knowledge improved. According to the descriptive results, students developed their translingual competence significantly. Thus, it can be assumed that the proposed learning model positively influenced students’ level of such competence. The results of the interview showed that students positively evaluate the proposed teaching model, and also consider it to be effective for learning Spanish and developing translingualism.
MataMorph 2: A new experimental UAV with twist-morphing wings and camber-morphing tail stabilizers

Morphing technology aims to improve both aerodynamic and power efficiency of aircrafts by eliminating traditional control surfaces and implementing uniform wings with seamless shape-changing ability. A lot of research has focused on proposing new designs for morphing wings, without implementation in a flying aircraft. Only few papers reported the design and flight-testing of unmanned aerial vehicles (UAVs) with morphing surfaces. Most of such designs focused only on wings, while the tail stabilizers are conventionally designed. This paper presents Matamorph-2 (XM-2), a fully morphing UAV with twisting wings and variable-camber tail stabilizers. XM-2 can perform all required maneuvers without any discrete control surfaces. The wings feature balsa wood structure, wing-root and wing-tip laminated composite skin sections and a twisting section made of polyurethane foam covered by a smooth flexible skin. With a ±15° range of twisting motion, XM-2 wings do not need flaps, slats or ailerons to control lift and roll generated by the UAV. Each tail stabilizer consists of a rigid leading-edge section connected to a camber-morphing corrugated trailing edge section. The tail rib design is a new version of the “FishBAC” rib with flexible carbon fiber composite ribbons running through the corrugated section to actuate the rib. The corrugated trailing edge section is 3D printed of flexible PCTPE plastic that balances between rigidity and flexibility. When compared to much-smaller traditional control surfaces, these large camber-morphing surfaces provide more power and control in a much smaller dimensional envelope. The paper presents the detailed design of all components, simulations, assembly
2-Amino-4-arylthiazole Derivatives as Anti-giardial Agents: Synthesis, Biological Evaluation and QSAR Studies

Abstract A series of seven 2-amino-4-arylthiazoles were prepared following Hantzsch’s modified method under microwave irradiation. A set of 50 derivatives was obtained and the in vitro activity against Giardia intestinalis was evaluated. The results on the biological activity revealed that, in general, the N-(5-bromo-4-aryl-thiazol-2-yl)-acetamide scaffold showed high bioactivity. In particular, compounds 6e (IC50 = 0.39 μM) and 6b (IC50 = 0.87 μM) were found to be more potent than the positive control metronidazole. Citoxicity and acute toxicity tests performed showed low toxicity and high selectivity of the most active compounds (6e SI = 139, 6b SI = 52.3). A QSAR analysis was applied to a data set of 37 obtained 2-amino-4-arylthiazoles derivatives and the best model described a strongly correlation between the anti-giardiasic activity and molecular descriptors as E2M, RDF115m, F10, MATS6v, and Hypnotic-80, with high statistical quality. This finding indicates that N-substituted aminothiazole scaffold should be investigated for the development of highly selective anti-giardial agent. Graphical Abstract
Simulation of Time-Lapse Resistivity Logging During Two-Phase Well Testing in Petroleum Reservoirs

Well testing methods are widely used in petroleum industry to identify reservoir parameters in-situ, at real reservoir flow conditions. Two-phase tests with water injection extend capabilities of well testing to evaluation of oil-water flow characteristics through the solution of an inverse problem based on well measurements during the test. Near-wellbore water saturation is used as one of the informative measured parameters. However, water saturation is actually not directly measured on wells. Instead, it is interpreted from time-lapse well logging data, with resistivity logging being one of the options. This means that forward and inverse problems are to be extended to incorporate direct simulations of resistivity logging. In this paper we address the forward problem. It is formulated and solved for different types of logging tools, both for open hole and through the casing. A numerical algorithm and software implementation have been developed for repeated solution of an axisymmetric forward problem of resistivity logging – a 2D elliptical partial differential equation for electrical potential. Distributions of formation resistivity at the moments of logging are calculated from distributions of water saturation and salinity. The latter are computed through numerical solution of the forward problem of well testing – a two-phase oil-water flow in the reservoir with changing salinity of the water phase, with full account for real fluid and reservoir properties. The software implementation of the resistivity logging problem has been tested in comparison with the known theoretical solutions. Examples are presented to show how time-lapse changes in resistivity logging data reflect the specifics of the two-phase flow with changing water salinity during the well test.
Synthesis and performances of polycarboxylate superplaticizer with clay-inerting and high slump retention capability

Macromolecules with pendant chlorine groups on their main chains were synthesized via free-radical copolymerization of 2-hydroxyethyl acrylate(HEA), TPEG and vinylbenzyl chloride(VBC) in the presence of intiator and chain transfer agent according to molecular structure design principle. The subsequent Arbuzov reaction between trimethoxyphosphine(TMP) and chlorine groups of macromolecules(PHVT) gave rise to new type of polycarboxylate superplaticizer with clay-inerting and high slump retention capability. The molecular structure of superplasticizer was determined by Fourier Transform Infrared Spectroscopy (FT-IR) and Gel Permeation Chromatography (GPC), respectively. The adaptability of clays, Zata potential, adsorption behavior and application performance in concrete were measured. The results shows that the polycarboxylate superplaticizer we prepared has good clay compatibility, excellent clay-inerting and high slump retention capability.
A Study of Eu(III)-Humate Complexation Using Eu(III) Luminescence Spectroscopy

Luminescence excitation spectra (F0—D0 transition) of Eu(III) bound to humic acid (HA) in aqueous solution have been studied. Analysis of these spectra and measurements of the luminescence lifetimes confirmed that Eu(III) was bound to HA in two different binding modes with different numbers of carboxylate groups of HA bound to the Eu(III). The data indicate one binding mode involved three carboxylate groups, and the second involved one or two carboxylate groups. The relative population by Eu(III) of the two types of binding modes was dependent on the Eu(III)-to-HA concentration ratio and on the contact time of Eu(III) with HA prior to the spectral analysis. The excitation spectra of Eu(III) complexed with humic acids of different molecular size groups is consistent with an increase with the molecular size of HA in the degree of binding of the Eu in the mode involving three carboxylate groups.
Quantified Assessment of Internal Short-Circuit State for 18 650 Batteries Using an Extreme Learning Machine-Based Pseudo-Distributed Model

To facilitate the diagnosis of battery internal short circuit (ISC) using thermal behaviors, this work integrates several thermal effects, including the commonly ignored heat conduction hysteresis and radiation, to elaborate a lumped thermal model. Then, a pseudo-distributed model structure is built up to approximate the characteristics of real batteries by synthesizing multiple isomorphic electrical/thermal submodels with the extreme learning machine network. Besides, three kinds of configurable destructions are conducted to incur ISC consequences. From thermal and electrical model residuals, four ISC features are extracted and the multiclass relevance vector machine is utilized to assess ISC intensity, in which not only qualitative judgments are given but also quantitative confidences can be derived according to the posterior probabilities. Finally, experiments on 18650 Li-ion cells verify the reliability of the synthesized models and suggest that the diagnosis scheme can recognize ISC faults effectively with low grade and state misjudgment rates (14.59% and 3.13%, respectively).
A Smeared Crack Modelling Approach for Aggregate Interlock and Mixed Mode Fracture of Concrete

The intention of this contribution is the numerical description of the rarely investigated phenomenon of mixed mode fracture in plain concrete. Since cracks in concrete are typically subjected to both normal and shear displacements, a new material model called fictitious rough crack model (FRCM) is proposed which combines mode I fictitious crack models with aggregate interlock models. For modelling the mixed mode behavior as the result of coexisting cohesive concrete behavior and aggregate interlock stresses along concrete cracks, mode I behavior is considered as the main influence on crack formation at the crack tip and mode II behavior (aggregate interlock) is assumed to occur when translations are induced along the crack surfaces (slip). The combination of these tension-softening and shear-transfer laws and the resulting shear and normal stresses of both mechanisms in the crack characterizes the main idea of the model. Well-known experimental benchmark problems are solved both for validation of the proposed model as well as for comparison with renowned concrete models of commercial FE software. The analysis shows that the FRCM can simulate the transition from mode I fracture to mixed mode fracture in the structural response while the comparison with commercial numerical approaches demonstrates the lack of appropriate consideration of aggregate interlock and mixed mode behavior in commercial FE software.
Research on Named Entity Recognition Technology for Chinese Titles

Aiming at the problem that the feature extraction of Chinese title data is insufficient in the process of named entity recognition and the marking data is difficult to obtain, this paper presents a CNN-BiLSTM-CRF model. The model trains large-scale corpus to generate word vectors with semantic information and trains the character vectors in words through CNN to generate character vectors containing character features, the word vector and the character vectors are merged as the input of BiLSTM network. Through the BiLSTM network, the front and back texts features are fully extracted, and CRF is used to restrict the relevance of tags. In addition, this paper proposes an algorithm based on active learning. This algorithm uses the proposed model construction committee to select the samples with large amount of information to mark, which effectively reduces the amount of data annotation.