Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation on Machine Learning Algorithms to Predict High School Students Performance at Risk of Failure

Successful learning is dependent on the success of the learners; ensuring students’ achievement is the responsibility of the educational system. Over the years, educational institutions have utilized Machine Learning (ML) technology to facilitate and support decision-making in a variety of learning areas. Predicting students’ performance in the early stages of their learning journey is one of ML’s most significant contributions to educational institutions. Using ML technology, the institutes will be able to improve teaching and learning practices for students who are at risk of failing. The objective of this study is to build a model that can predict students’ performance and identify students at risk of failure. The study examines the application of three ML algorithms in predicting: Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF). The study used a sample size of 647 with one dependent variable and 33 distinct variables. The study found that the two tree-based methods performed better than LR, although the precession measure out of four measures shows that LR is slightly better. Overall, in this study RF has showed to be the best among them with an accuracy of80.37%, recall of 87.98%, and F1 rate of 91.78%.
Momentum analysis of sitback failures in sit-to-stand trials

The authors characterize successful sit-to-stand maneuvers and compare these to sitback and step failures, using whole body (WE) and upper body (HAT) linear and angular momentum parameters. Controls consisted of 11 normal elders. Six failed sit-to-stand maneuvers in which sitbacks occurred were extracted from archived tests of patients; primarily patients with vestibular pathology. Whole body (WB) momentum and upper body or HAT momentum parameters were calculated. Sitback failures were associated with lower HAT AP linear momentum at the time of maximum ground reaction force (GRF); i.e., just after liftoff. The sitback failures were also associated with lower knee torques at the time of maximum GRF and lower values of the maximum GRF. Thus, sitback failures appear to be due lack of adequate momentum and torque generation. These findings improve one's understanding of the biomechanics of sit-to-stand and the mechanism of loss of balance during this demanding activity.
An Algorithm Framework for Traveling Salesman Problem Using Geometric Information

Some traditional approaches of solving traveling salesman problem (TSP), such as: genetic algorithm, large neighborhood search algorithm, etc., aim to obtain a satisfactory solution of TSP through continuous search, and do not use the geometric characteristics of TSP. In this paper, by studying the path graph of the optimal solution of TSP, the authors find a special geometric characteristic of the best connections—points with closer distance between each other in the optimal connection form a path connected with the outside. Based on the geometric characteristic, a cluster first-insert second algorithm framework to solve TSP is proposed. The comparison between example algorithm and other algorithms shows that the algorithm framework has advantage in solution quality.
Network Intrusion Detection Through Machine Learning With Efficient Feature Selection

Network Intrusion detection is an essential aspect of implementing secure networks. Network Intrusion detection systems help mitigate network attacks preemptively and let the system take preventive measures. Machine Learning techniques enable the system to build attack classification models based on previously available real-world data. Advanced security Network Metrics dataset(ASNM) is one such dataset that contains derived network features that help create a highly accurate network attack classifier. In this paper, we pre-process the ASNM dataset and use feature selection techniques to filter out unwanted features and retain features that are highly correlated to the classification in the ASNM dataset. We apply the Variance threshold and Chi-square Test feature selection techniques on the ASNM dataset. Finally, a neural network model based attack classifier is developed and shown to have a prediction accuracy of 99%.
SDMA: An Efficient and Flexible Sparse-Dense Matrix-Multiplication Architecture for GNNs

In recent years, graph neural networks (GNNs) as a deep learning model have emerged. Sparse-Dense Matrix Multiplication (SpMM) is the critical component of GNNs. However, SpMM involves many irregular calculations and random memory accesses, resulting in the inefficiency of general-purpose processors and dedicated accelerators. The highly sparse and uneven distribution of the graph further exacerbates the above problems. In this work, we propose SDMA, an efficient architecture to accelerate SpMM for GNNs. SDMA can collaboratively address the challenges of load imbalance and irregular memory accesses. We first present three hardware-oriented optimization methods: 1) The Equal-value partition method effectively divides the sparse matrix to achieve load balancing between tiles. 2) The vertex-clustering optimization method can explore more data locality. 3) An adaptive on-chip dataflow scheduling method is proposed to make full use of computing resources. Then, we combine and integrate the above optimization into SDMA to achieve a high-performance architecture. Finally, we prototype SDMA on the Xilinx Alveo U50 FPGA. The results demonstrate that SDMA achieves 2.19x-3.35x energy efficiency over the GPU implementation and 2.03x DSP efficiency over the FPGA implementation.
Examining The Effects of Playing Difficulty and Playing Duration in Unwinnable Persuasive Games

Unwinnable persuasive games' persuasion strategy is to force the players to lose and use the ensuing experience to persuade them. After two decades, currently, we only know the different effects of winning and losing a persuasive game. We argue that research should start investigating how to design game loss that can positively contribute to the persuasion process. In particular, this study focuses on the effects of playing difficulty and playing duration. To force players to lose, designers can increase the playing difficulty to create an unwinnable challenge and use it to portray the severity of the issue. While there is a rationale for it, whether the increase can lead to positive outcomes has not been established with empirical evidence, especially because frustration from repeated failure might have negative impacts. In addition, longer playtime can help players become more familiar with the game. However, it is unfair to let them invest a significant time only to realize that it is unwinnable. To address the research gap, we conducted a factorial between-subject design experiment with an additional no-treatment control group. The results suggest that playing a more difficult and longer game can lead to a significantly higher donation. Based on the result, there are indeed benefits for game designers to increase playing difficulty and enable extended playtime when designing unwinnable persuasive games.
Computing the homology of Koszul complexes

Let R be a commutative ring and I an ideal in R which is locally generated by a regular sequence of length d. Then, each projective R/I-module V has an R-projective resolution P. of length d. In this paper, we compute the homology of the n-th Koszul complex associated with the homomorphism P_1 --> P_0 for all n, if d = 1. This computation yields a new proof of the classical Adams- Riemann-Roch formula for regular closed immersions which does not use the deformation to the normal cone any longer. Furthermore, if d = 2, we compute the homology of the complex N Sym^2 K(P.) where K and N denote the functors occurring in the Dold-Kan correspondence.
The stability of two superposed viscous fluids in a channel

The Couette/Poiseuille flow of two superposed viscous fluids in a channel is studied. There are two mechanisms for instability of this flow: one mechanism is associated with the presence of the interface, the other with the presence of the boundary walls. Analytical results are presented based on the short wave perturbation scheme of Hooper and Boyd [J. Fluid Mech. 179, 201 (1987)] and the long wave perturbation scheme of Yih [J. Fluid Mech. 27, 337 (1967)], together with numerical results. Similarities between the stability of plane Poiseuille flow and one fluid and two‐layer Couette/Poiseuille flow are discussed.
Ionization processes in the atmosphere of Titan: I. Ionization in the whole atmosphere

Context. The Cassini probe regularly passes in the vicinity of Titan, revealing new insights into particle precipitation thanks to the electron and proton spectrometer. Moreover, the Huygens probe has revealed an ionized layer at 65 km induced by cosmic rays. The impact of these different particles on the chemistry of Titan is probably very strong. Aims. In this article, we compute the whole ionization in the atmosphere of Titan: from the cosmic rays near the ground to the EUV in the upper atmosphere. The meteoritic layer is not taken into account. Methods. We used the transTitan model to compute the electron and EUV impact, and the planetocosmics code to compute the influence of protons and oxygen ions. We coupled the two models to study the influence of the secondary electrons obtained by planetocosmics through the transTitan code. The resulting model improves the accuracy of the calculation through the transport of electrons in the atmosphere. Results. The whole ionization is computed and studied in details. During the day, the cosmic ray ionization peak is as strong as the UV-EUV one. Electrons and protons are very important depending the precipitation conditions. Protons can create a layer at 500 km, while electrons tend to ionize near 800 km. The oxygen ion impact is near 900 km. The results shows few differences to precedent models for the nightside T5 fly-by of Cassini, and can highlight the sources of the different ion layers detected by radio measurements. Conclusions. The new model successfully computes the ion production in the atmosphere of Titan. For the first time, a full electron and ion profile has been computed from 0 to 1600 km, which compares qualitatively with measurements. This result can be used by chemical models.
Symbiont composition and coral genotype determines massive coral species performance under end-of-century climate scenarios

The recent decline of coral health and substantial loss of coral cover along Florida’s Coral Reef (FCR) results from local stressors such as degraded water quality and disease outbreaks in addition to anthropogenically driven global stressors including ocean warming and acidification. Intervention strategies intended for the restoration of degraded reef habitats need a better understanding of the influence of ocean warming and acidification on coral health to target coral species and individual genotypes that may be more resistant or resilient to such stressors. Here, we examined a suite of physiological traits (coral host and algal symbiont) in response to experimentally elevated water temperatures and pCO2 levels, both separately and in concert, using threatened reef-building corals Pseudodiploria clivosa and Orbicella faveolata reared within a land-based coral nursery. After two months of exposure, responses differed by coral species, where P. clivosa showed declined physiology in response to combined ocean warming and acidification stress and ocean warming alone, whereas O. faveolata showed a positive response under ocean acidification. Responses to temperature could be associated with the algal symbionts harbored, as P. clivosa was dominated by the thermally sensitive Breviolum, and O. faveolata was dominated by the thermally tolerant Durusdinium. Additionally, corals were raised in well-sourced seawater that was naturally high in pCO2, which could have led to corals acclimating to acidified conditions. Of the three P. clivosa genets tested, we determined a top-performing genotype under the combined warming and acidification treatment. O. faveolata, however, displayed high genet variation by treatment and phenotypic trait, making genotype performance rankings challenging to discern. The evidence provided in this study demonstrates that high phenotypic variation in nursery-reared corals contributes to variable warming-acidification responses, suggesting that high-standing genetic variation in nursery-reared corals could support diverse coral restoration population outcomes along FCR.
Photosensitization of standard fibers with deep UV laser radiation

A strong and permanent ultraviolet photosensitivity enhancement was locked into standard optical fiber pre-treated with above-bandgap 157-nm F/sub 2/-laser radiation. Below-bandgap 248-nm irradiation of the pre-treated fiber yielded fiber Bragg gratings (FBGs) with an index contrast of 6.5 /spl times/ 10/sup -4/, a value four-fold larger than that available in 16% Ge-doped photosensitive fiber. The F/sub 2/-laser photosensitivity locking was 300 times more effective than with KrF-laser pre-treatment in terms of the accumulated fluence required to lock in a similar refractive index change. Accelerated FBG aging tests equivalent to 25 years at 70/spl deg/C revealed a less than 2% reflectivity decay in both 157- and 248-nm pre-treated fibers, which is sufficient to eliminate the postannealing step typically required in hydrogen-loaded fibers. The large photosensitization and the superior thermal stability of both 248- and 157-nm pre-treated fibers are associated with a new Si-H bond absorption feature at 1387 nm, which appears only during the FBG formation step.
The effects of vortex finder on the pressure drop in cyclone separators

In this study, three cylinder-shaped vortex finders with diameters of 80, 120 and 160 mm were designed and manufactured to find out the pressure drop of the cyclones by experimentally investigating the effects of gas inlet velocity, the vortex finder diameter and length on the cyclone performance at different gas concentration. As a result of this experimental analysis, a critical diameter of vortex finder is obtained as 120 mm. Furthermore, analyzing the experimental findings with a statistical regression method indicated that there was a linear relationship between length of vortex finder and pressure loss. Then, according to the analysis results, relevance values were obtained as 98.87, 98.37 and 97.59% for these vortex finders (with diameters of 80, 120 and 160 mm), respectively. Key words: Cyclone, vortex finder, pressure drop.
Testing Composite Hypotheses for Locally Stationary Processes

For a class of locally stationary processes introduced by Dahlhaus, this paper discusses the problem of testing composite hypotheses. First, for the Gaussian likelihood ratio test (GLR), Wald test (W) and Lagrange multiplier test (LM), we derive the limiting distribution under a composite hypothesis in parametric form. It is shown that the distribution of GLR, W and LM tends to a χ2 distribution under the hypothesis. We also evaluate their local powers under a sequence of local alternatives, and discuss their asymptotic optimality. The results can be applied to testing for stationarity. Some examples are given. They illuminate the local power property via simulation. On the other hand, we provide a nonparametric LAN theorem. Based on this result, we obtain the limiting distribution of the GLR under both null and alternative hypotheses described in nonparametric form. Finally, the numerical studies are given.
Fairer Shootouts in Soccer: The m - n Rule

Winning the coin toss at the end of a tied soccer game gives a team the right to choose whether to kick either first or second on all five rounds of penalty kicks, when each team is allowed one kick per round. There is considerable evidence that the right to make this choice, which is usually to kick first, gives a team a significant advantage. To make the outcome of a tied game fairer, we suggest a rule that handicaps the team that kicks first (A), requiring it to succeed on one more penalty kick than the team that kicks second (B). We call this the $m - n$ rule and, more specifically, propose $(m, n)$ = (5, 4): For A to win, it must successfully kick 5 goals before the end of the round in which B kicks its 4th; for B to win, it must succeed on 4 penalty kicks before A succeeds on 5. If both teams reach (5, 4) on the same round -- when they both kick successfully at (4, 3) -- then the game is decided by round-by-round"sudden death,"whereby the winner is the first team to score in a subsequent round when the other team does not. We show that this rule is fair in tending to equalize the ability of each team to win a tied game in a penalty shootout. We also discuss a related rule that precludes the teams from reaching (5, 4) at the same time, obviating the need for sudden death and extra rounds.
A new criterion of delay-dependent stability for uncertain time-delay systems

A new stability criterion of uncertain time-delay systems is proposed. The uncertainty is of linear fractional form, which includes norm-bounded uncertainty as a special case. By introducing a free weighting parameter, free distribution parameter and free weighting matrices simultaneously, a new delay-dependent sufficient stability condition is obtained in terms of a linear matrix inequality. It is shown that this new criterion can provide less conservative results than some existing ones. Numerical examples are given to demonstrate the applicability of the proposed approach.
Turbulent energy at accelerating and shocked interfaces

The turbulent energy generated at accelerating or shocked interfaces between two fluids is calculated. Assuming a linear density profile across the mix region it was found that Eturb/Edir=2.3A2% for a constant acceleration and 9.3A2% for a shock, where A is the Atwood number of the two fluids. Somewhat less turbulent energy is generated if density profiles based on self‐similar solutions to nonlinear diffusion equations were used. These equations also predict eddy sizes: λ/h=26%–29% and λ/h=16%–18% were found for a constant acceleration and a shock, respectively, where λ is the eddy size controlling the diffusion coefficient and h is the mixing depth into the heavier fluid. The present results were compared with other models and with experiments conducted at AWE.
Transport equation for the mean turbulent energy dissipation rate in low- $R_{\lambda }$ grid turbulence

Abstract A direct numerical simulation (DNS) based on the lattice Boltzmann method (LBM) is carried out in low-Reynolds-number grid turbulence to analyse the mean turbulent kinetic energy dissipation rate, $\overline{\epsilon }$ , and its transport equation during decay. All the components of $\overline{\epsilon }$ and its transport equation terms are computed, providing for the first time the opportunity to assess the contribution of each term to the decay. The results indicate that although small departures from isotropy are observed in the components of $\overline{\epsilon }$ and its destruction term, there is sufficient compensation among the components for these two quantities to satisfy isotropy to a close approximation. A short distance downstream of the grid, the transport equation of $\overline{\epsilon }$ simplifies to its high-Reynolds-number homogeneous and isotropic form. The decay rate of $\overline{\epsilon }$ is governed by the imbalance between the production due to vortex stretching and the destruction caused by the action of viscosity, the latter becoming larger than the former as the distance from the grid increases. This imbalance, which is not constant during the decay as argued by Batchelor & Townsend (Proc. R. Soc. Lond. A, vol. 190, 1947, pp. 534–550), varies according to a power law of $x$ , the distance downstream of the grid. The non-constancy implies a lack of dynamical similarity in the mechanisms controlling the transport of $\overline{\epsilon }$ . This is consistent with the fact that the power-law-decay ( $\overline{q^2} \sim x^n$ ) exponent $n$ is not equal to $-$ 1. It is actually close to $-$ 1.6, a value in keeping with the relatively low Reynolds number of the simulation. These results highlight the importance of the imbalance in establishing the value of $n$ . The $\overline{\epsilon }$ -transport equation is also analysed in relation to the power-law decay. The results show that the power-law exponent $n$ is controlled by the imbalance between production and destruction. Further, a relatively straightforward analysis provides information on the behaviour of $n$ during the entire decay process and an interesting theoretical result, which is yet to be confirmed, when $R_{\lambda } \rightarrow 0 $ , namely, the destruction coefficient $G$ is constant and its value must lie between $15/7$ and $30/7$ . These two limits encompass the predictions for the final period of decay by Batchelor & Townsend (1947) and Saffman (J. Fluid Mech., vol. 27, 1967, pp. 581–593).
Statistical Analyses of Scatterplots to Identify Important Factors in Large-Scale Simulations

The robustness of procedures for identifying patterns in scatterplots generated in Monte Carlo sensitivity analyses is investigated. These procedures are based on attempts to detect increasingly complex patterns in the scatterplots under consideration and involve the identification of (1) linear relationships with correlation coefficients, (2) monotonic relationships with rank correlation coefficients, (3) trends in central tendency as defined by means, medians and the Kruskal-Wallis statistic, (4) trends in variability as defined by variances and interquartile ranges, and (5) deviations from randomness as defined by the chi-square statistic. The following two topics related to the robustness of these procedures are considered for a sequence of example analyses with a large model for two-phase fluid flow: the presence of Type I and Type II errors, and the stability of results obtained with independent Latin hypercube samples. Observations from analysis include: (1) Type I errors are unavoidable, (2) Type II errors can occur when inappropriate analysis procedures are used, (3) physical explanations should always be sought for why statistical procedures identify variables as being important, and (4) the identification of important variables tends to be stable for independent Latin hypercube samples.
An Optimal Distributed RES Sizing Strategy in Hybrid Low Voltage Networks Focused on EVs’ Integration

The new era of the energy sector has already begun, and therefore, new challenges need to be tackled. A major challenge that residential distribution grids are going to encounter with the integration of photovoltaic (PV) panels and electric vehicles (EVs) is the unsynchronized new demand and the time-limited production of distributed generation, in combination with space limitations. Therefore, the necessity of energy storage systems (ESSs) is more than evident. ESSs have excessive manufacturing costs, implying that the purchase cost for residential users can be prohibitive. In the present work, a distributed optimal small-scale PV energy system sizing strategy is proposed, by considering the individual energy needs of each residence and their EVs. The strategy is formulated based on the demand of the households and EVs’ charging. By enabling the fuzzy cognitive maps theory, a graph is designed, aiming to establish the correlation among the individual energy parameters and the characteristics of the renewable energy sources (RES). The optimization results reveal that the adopted hybrid approach can reduce the energy cost significantly, up to almost 40%, while enabling distribution system operators (DSOs) to incorporate additional loads, without the need for network expansion. Finally, based on the extracted results, a short discussion about the concept of EVs’ charging by residential RES is presented.