Miracle-dz/UniFAR-FTUs huggingface dataset:
Class Year Differences in Anthropometric and Fitness Measures in Division I Field Hockey Athletes Pre and Post Season

The purpose of this study was to investigate the way in which anthropometric and fitness measures vary by class year before and after a Division I female field hockey competitive season. Anthropometric (mass and percent body fat) and fitness measures (vertical jump and Yo-Yo Intermittent Recovery Test 1 (Yo-Yo IR1) distance) were collected on 16 athletes (mean ± SD: age = 19.5 ± 0.9 years) before and after a competitive season. Class year, based on the athlete’s year in college, was used to group athletes in the study. Body mass, vertical jump height, and Yo-Yo IR1 distance all increased pre to post season (p < 0.05). First-year athletes had a significantly lower body mass, vertical jump height and Yo-Yo IR1 distance compared to other class years across the testing period (p < 0.05). Post season levels for first-years were lower than both pre and post season levels for sophomore or junior athletes. Team mean vertical jump increased 8.9%, with all but two athletes improving. Team mean Yo-Yo IR1 distance increased 16.4%. Athlete training over the course of the study was designed to improve on field playing performance and training was not directly select to improve measures in the study; therefore, the potential success of integrating fitness, speed, and strength development into technical training sessions is indicated. Coaches should be aware that first-year athletes may not be at the same level of conditioning as sophomores and juniors. Coaches should work specifically with the first-year athletes during the season to monitor their fitness levels.
Bacterial abundance and community composition in green, brown and red water from intensive catfish (Clarias sp.) culture ponds in Yogyakarta, Indonesia

Abstract. Wijayanti KAN, Istiqomah I, Murwantoko. 2021. Bacterial abundance and community composition in green, brown and red water from intensive Catfish (Clarias sp.) culture ponds in Yogyakarta, Indonesia. Biodiversitas 22: 3677-3684. Catfish (Clarias sp.) is an important aquaculture commodity in Indonesia and cultured in an intensive system. Microorganisms play an important role in maintaining water quality of aquaculture system. The objective of this study was to determine the bacterial abundance and community composition of green, brown and red water collected from intensive catfish culture ponds in Yogyakarta using next-generation sequencing method. The water samples were collected from intensive catfish culture ponds with different colors, namely green, brown and red ponds located in Yogyakarta. The DNA from water samples was extracted using DNA extraction kit and used as template for 16S rRNA amplification. The V3-V4 hypervariable regions of the 16S rRNA gene were amplified apply for next-generation sequencing technology. This study could explore effectively the bacterial community in water samples. The bacterial communities in this catfish culture water showed higher bacterial richness compared to the other aquaculture system. The diversity of the green, brown and red catfish culture water ponds was similar with the number OTUs of the green, brown and red water samples, which were 1269; 1387 and 1323 OTUs respectively. The 694 OTUs (34.42%) were common core microbiomes in all catfish culture ponds, the 212 OTUs (10.51%) are present on green and brown water ponds, the 182 OTUs (9.02%) were on green and red water ponds, and the 183 OTUs (9.07%) were present on green and brown water ponds. However, the composition of the bacterial community was different. The most dominant phylum in green and brown water ponds was Proteobacteria with relative abundance in green water and brown water 71.6% and 47.0% respectively, whereas, the most dominant phylum in red water was Firmicutes (29.5%). The dominance of Firmicutes phylum in red water ponds may be caused by application of probiotic bacteria, the high organic content, and low oxygen concentration.
Three-step approach for prediction of limit cycle pressure oscillations in combustion chambers of gas turbines

Currently, gas turbine manufacturers frequently face the problem of strong acoustic combustion driven oscillations inside combustion chambers. These combustion instabilities can cause extensive wear and sometimes even catastrophic damages to combustion hardware. This requires prevention of combustion instabilities, which, in turn, requires reliable and fast predictive tools. This work presents a three-step method to find stability margins within which gas turbines can be operated without going into self-excited pressure oscillations. As a first step, a set of unsteady Reynolds-averaged Navier–Stokes simulations with the Flame Speed Closure (FSC) model implemented in the OpenFOAM® environment are performed to obtain the flame describing function of the combustor set-up. The standard FSC model is extended in this work to take into account the combined effect of strain and heat losses on the flame. As a second step, a linear three-time-lag-distributed model for a perfectly premixed swirl-stabilized flame is extended to the nonlinear regime. The factors causing changes in the model parameters when applying high-amplitude velocity perturbations are analysed. As a third step, time-domain simulations employing a low-order network model implemented in Simulink® are performed. In this work, the proposed method is applied to a laboratory test rig. The proposed method permits not only the unsteady frequencies of acoustic oscillations to be computed, but the amplitudes of such oscillations as well. Knowing the amplitudes of unstable pressure oscillations, it is possible to determine how these oscillations are harmful to the combustor equipment. The proposed method has a low cost because it does not require any license for computational fluid dynamics software.
Synthetic characterization and Structural Properties of Nanocellulose from Moringa oleifera seeds

In this research, nanocellulose is isolated from Moringa oleifera seed using acid hydrolysis and the structural properties were determined. X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy were used for the characterization of the isolated nanocellulose. The most noticeable peak is observed at and the value of the crystallinity index () from the XRD pattern is 63.1%. The calculated values of hydrogen bond intensity (HBI), lateral order index (LOI) and total crystalline index (TCI) are 0.93, 1.17and 0.94 respectively exhibited high degree of crystallinity and well arranged cellulose crystal structure. The isolated nanocellulose has an average length and diameter of 14.3 and 36.33 respectively. Furthermore, the FTIR peaks revealed the presence of C-H bending, C-O stretching and O-H stretching functional groups.
firm-level determinants of Business Process outsourcing decisions

Although there are numerous contributions why firms outsource IT activities, research on Business Process Outsourcing (BPO) decisions is scarce. As BPO might affect outsourcer’s core, decisions are likely to be taken on firm level. We therefore aim at answering the following research question: What is the impact of firm-level characteristics on the decision to outsource business processes? Using publicly available data we analyze BPO decisions in the German banking market. Our findings show that BPO is perceived to provide significant strategic value for outsourcers. We provide evidence that large and diversified banks with low cost efficiency are more likely to outsource. Contradictory to prior research findings we further demonstrate that more activities are sourced out, if outsourcer’s profit increases. Further research is suggested to explain BPO success using publicly available data.
RESEARCH ON THE DIGITAL RETENTION MECHANISM OF TIBETAN BUDDHISM ARCHITECTURE BASED ON UAV AND TLS: A CASE STUDY OF BAOGUANG HALL

Abstract. Tibetan Buddhist architecture embodies ancient Chinese architectural culture and religious culture. In the past, the information retention mechanisms for ancient buildings were implemented as photos, tracings, and rubbings, which cannot fundamentally document the authenticity of architectural heritage. To explore the digital retention method for the unique style of Han Tibetan architecture，this research that based on the idea of reverse documentation first collects point cloud data with the technical support of unmanned aerial vehicle (UAV) photogrammetry and terrestrial laser scanner (TLS) and then uses registration method to obtain the integral of the point cloud model of Baoguang Hall. This paper explores the possibility of extracting 2D and 3D information, such as architectural plans, facades, decorative components, and models of the temple architecture, by processing point cloud data. Finally, this study proves the feasibility of using digital technology for the preservation and protection of architectural heritage.
WA-GPSR: Weight-Aware GPSR-Based Routing Protocol for VANET

The extremely fast topology has created new requirements for the geographic routing protocol, which has been the most efficient solution for Vehicular Ad-hoc Networks (VANETs). The frequent disconnection of links makes the choice of the next routing node extremely difficult. Hence, an efficient routing algorithm needs to deliver the appropriate path to transfer the data packets with the most relevant quality of service (QoS). In this work, the weight-aware greedy perimeter stateless (WA-GPSR) routing protocol is presented. The enhanced GPSR protocol computes the reliable communication area and selects the next forwarding vehicle based on several routing criteria. The proposal has been evaluated and compared to Maxduration-Minangle GPSR (MM-GPSR) and traditional GPSR using strict metric analysis. Our experimental results using NS-2 and VanetMobiSim, have demonstrated that WA-GPSR has the ability to enhance network performance.
Learners’ perspectives on interaction in a language MOOC

This study examines how 136 language learners interacted with other learners in and out of a Language MOOC on English presentation. it also investigates the learner-reported reasons that encourage them to interact, and that prevent them from interacting with other learners. The results demonstrate that the level of learner–learner interaction was quite low in the LMOOC overall. More active learners cited a sense of belonging to the group and confidence in their English ability as the reasons for interacting with others, and less active learners reported a preference for F2F (face-to-face) interaction, lack of time and lack of English proficiency as factors preventing them from doing so. Learners also reported frequent use of personal communication tools to interact with other learners outside of the LMOOC. We conclude with a number of suggestions and implications for future LMOOC design, implementation and research.
Diversity and species composition of diatom communities of Ardley Island, South Shetland Islands

The Antarctic diatom flora has been at the centre of interest of many studies in past decades. The present paper brings new information on the species richness, biogeography and community composition of diatoms on the Ardley Island, South Shetland Islands. One fresh-water and ten soil samples had been collected from the Antarctic Special protected area (ASPA) in the spring of 2019. The following analysis revealed eighty-six diatom taxa in well-developed communities, dominated by Luticola muticopsis, L. truncata, Pinnularia australoschoenfelderi, P. austroshetlandica and P. borealis. According to the current biogeographical knowledge, the majority of species have restricted distribution among the Antarctic Realm; 46.5% of them are reported from various islands of the Maritime Antarctic Region. Based on the dominance of species as Luticola muticopsis and L. truncata and their ecological preferences, we concluded that the species composition of the diatom communities is driven by high nutrient input from breeding seabirds and the moisture availability during the austral summer.
A General Origin for Multiplanetary Systems With Significantly Misaligned USP Planets

Ultra-short-period (USP) planets are exoplanets that have orbital periods of less than one day and are unique because they orbit inside the nominal magnetic truncation gap of their host stars. In some cases, USP planets have also been observed to exhibit unique dynamical parameters such as significant misalignments in inclination angle with respect to nearby planets. In this paper, we explore how the geometry of a multiplanet system hosting a USP planet can be expected to evolve as a star ages. In particular, we explore the relationship between the mutual inclination of the USP planet and the quadrupole moment (J 2) of the host star. We use secular perturbation theory to predict the past evolution of the example TOI-125 system, and then confirm the validity of our results using long-term N-body simulations. Through investigating how the misalignment between the candidate USP planet and the three other short-period planets in the TOI-125 system arose, we intend to derive a better understanding of the population of systems with misaligned USP planets and how their observed parameters can be explained in the context of their dynamical histories.
A Generative Federated Learning Framework for Differential Privacy

—In machine learning, differential privacy and federated learning concepts are gaining more and more importance in an increasingly interconnected world. While the former refers to the sharing of private data characterized by strict security rules to protect individual privacy, the latter refers to distributed learning techniques in which a central server exchanges information with different clients for machine learning purposes. In recent years, many studies have shown the possibility of bypassing the privacy shields of these systems and exploiting the vulnerabilities of machine learning models, making them leak the information with which they have been trained. In this work, we present the 3DGL framework, an alternative to the current federated learning paradigms. Its goal is to share generative models with high levels of ε -differential privacy. In addition, we propose DDP-β VAE, a deep generative model capable of generating synthetic data with high levels of utility and safety for the individual. We evaluate the 3DGL framework based on DDP-β VAE, showing how the overall system is resilient to the principal attacks in federated learning and improves the performance of distributed learning algorithms.
Formal verification of a distributed dynamic reconfiguration protocol

We present a formal, machine checked TLA+ safety proof of MongoRaftReconfig, a distributed dynamic reconfiguration protocol. MongoRaftReconfig was designed for and implemented in MongoDB, a distributed database whose replication protocol is derived from the Raft consensus algorithm. We present an inductive invariant for MongoRaftReconfig that is formalized in TLA+ and formally proved using the TLA+ proof system (TLAPS). We also present a formal TLAPS proof of two key safety properties of MongoRaftReconfig, LeaderCompleteness and StateMachineSafety. To our knowledge, these are the first machine checked inductive invariant and safety proof of a dynamic reconfiguration protocol for a Raft based replication system.
Cohomology of semisimple local systems and the Decomposition theorem

In this paper, we study the cohomology of semisimple local systems in the spirit of classical Hodge theory. On the one hand, we establish a generalization of Hodge-Riemann bilinear relations. For a semisimple local system on a smooth projective variety, we define a canonical isomorphism from the complex conjugate of its cohomology to the cohomology of the dual local system, which is a generalization of the classical Weil operator for pure Hodge structures. This isomorphism establishes a relation between the twisted Poincar\'e pairing, a purely topological object, and a positive definite Hermitian pairing. On the other hand, we prove a global invariant cycle theorem for semisimple local systems. As an application, we give a new and geometric proof of Sabbah's Decomposition Theorem for the direct images of semisimple local systems under proper algebraic maps, without using the category of polarizable twistor D-modules.
Quantum K-theory Chevalley formulas in the parabolic case

We derive cancellation-free Chevalley-type multiplication formulas in the T-equivariant quantum K-theory of Grassmannians of type A and C, and also those of two-step flag manifolds of type A. They are obtained based on the uniform Chevalley formula in the T-equivariant quantum K-theory of arbitrary flag manifolds G/B, which was derived earlier in terms of the quantum alcove model, by the last three authors.
Solving Triad Reduced Interactions for Drift Kinetic Equations

We present the TRIDK code, which is written in Python, that solves Triad Reduced Interactions for a Drift Kinetic system of equations. The four-dimensional drift kinetic system of equations captures nonlinear electromagnetic (or electrostatic) interactions for a magnetized plasma at scales larger than the ion gyroradius. In the TRIDK code, the nonlinear interaction are modelled using a compound of nested icosahedra-dodecohedra shells in wave space, which allows to perform computationally efficient simulations over a large span of scales. This reduced approach captures essential nonlinear dynamics, while allowing parallel mixing in velocity space to take place. The intent is to offer a cost efficient way to probe the balance between linear and nonlinear dynamics, and analyse the interplay between linear Landau damping and turbulence.
A Bayesian-Based Approach to Human Operator Intent Recognition in Remote Mobile Robot Navigation

This paper addresses the problem of human operator intent recognition during teleoperated robot navigation. In this context, recognition of the operator’s intended navigational goal, could enable an artificial intelligence (AI) agent to assist the operator in an advanced human-robot interaction framework. We propose a Bayesian Operator Intent Recognition (BOIR) probabilistic method that utilizes: (i) an observation model that fuses information as a weighting combination of multiple observation sources providing geometric information; (ii) a transition model that indicates the evolution of the state; and (iii) an action model, the Active Intent Recognition Model (AIRM), that enables the operator to communicate their explicit intent asynchronously. The proposed method is evaluated in an experiment where operators controlling a remote mobile robot are tasked with navigation and exploration under various scenarios with different map and obstacle layouts. Results demonstrate that BOIR outperforms two related methods from literature in terms of accuracy and uncertainty of the intent recognition.
Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning

In this work, we present and study a training set-up that achieves fast policy generation for real-world robotic tasks by using massive parallelism on a single workstation GPU. We analyze and discuss the impact of different training algorithm components in the massively parallel regime on the final policy performance and training times. In addition, we present a novel game-inspired curriculum that is well suited for training with thousands of simulated robots in parallel. We evaluate the approach by training the quadrupedal robot ANYmal to walk on challenging terrain. The parallel approach allows training policies for flat terrain in under four minutes, and in twenty minutes for uneven terrain. This represents a speedup of multiple orders of magnitude compared to previous work. Finally, we transfer the policies to the real robot to validate the approach. We open-source our training code to help accelerate further research in the field of learned legged locomotion.
Cryptanalyzing Two Image Encryption Algorithms Based on a First-Order Time-Delay System

Security is a key problem for the transmission, interchange, and storage process of multimedia systems and applications. In 2018, Li et al. proposed in-depth security analysis on an image encryption algorithm based on a first-order time-delay system (IEATD) and gave a specific chosen-plaintext attack on it. Moreover, an enhanced version called as IEACD was designed to fix the reported security defects. This article analyzes the essential structures of the two algorithms and evaluates their real security performances: 1) no efficient nonlinear operations are adopted to assure the sensibility of keystream; 2) the equivalent secret key of IEATD can be efficiently recovered from one known plain-image and the corresponding cipher-image; and 3) IEACD can still be efficiently cracked with a chosen-plaintext attack. Both rigorous theoretical analyses and detailed experimental results are provided to demonstrate effectiveness of the advanced cryptanalytic methods.
Big categories, big spectra

We introduce a new topological invariant of a rigidly-compactly generated tensor-triangulated category and two new notions of support. The first is based on smashing subcategories: it is unknown whether the frame of smashing subcategories is spatial in general, but supposing it is as in the examples we understand, we show it is equipped with a morphism to the Balmer spectrum which detects the failure of the telescope conjecture and we develop the corresponding support theory. The new invariant, the big spectrum, results from taking the entire collection of localizing ideals seriously and considering prime localizing ideals. Although there are, in principle, a proper class of localizing ideals, we are able to prove the existence of at least one big prime lying over every Balmer prime. We conclude with a pair of examples illustrating our constructions.
Slicing functional aspects out of legacy applications

Aspect-oriented software development builds upon object-oriented development by offering additional abstraction boundaries that help us separate different types of requirements into so-called aspects. Given a legacy OO application, it pays to identify existing aspects to help understand the structure of the application and potentially to extract those aspects into separate software modules that can be maintained and reused individually. We present an approach to extract functional aspects using program slicing. We first characterize what we mean by functional aspect, and explore the extent to which program slicing techniques could improve over existing aspect mining and feature location methods for such aspects. We then describe the results of applying our approach to two medium-sized open source programs. Our preliminary results show that program slicing can yield welldefined functional aspects when the appropriate slicing criteria are used. Finally, we explore the effect of design quality on the candidate functional aspects.