Search Results (640)

The Statistics Anxiety Rating Scale (STARS) is a 51-item scale commonly used to measure college students’ anxiety regarding statistics. To date, however, limited empirical research exists that examines statistics anxiety among ethnically diverse or first-generation graduate students. We examined the factor structure and reliability of STARS scores in a diverse sample of students enrolled in graduate courses at a Minority-Serving Institution (n = 194). To provide guidance on assessing dimensionality in small college samples, we compared the performance of best-practice factor analysis techniques: confirmatory factor analysis (CFA), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM). We found modest support for the original six-factor structure using CFA, but ESEM and BSEM analyses suggested that a four-factor model best captures the dimensions of the STARS instrument within the context of graduate-level statistics courses. To enhance scale efficiency and reduce respondent fatigue, we also tested and found support for a reduced 25-item version of the four-factor STARS scale. The four-factor STARS scale produced constructs representing task and process anxiety, social support avoidance, perceived lack of utility, and mathematical self-efficacy. These findings extend the validity and reliability evidence of the STARS inventory to include diverse graduate student populations. Accordingly, our findings contribute to the advancement of data science education and provide recommendations for measuring statistics anxiety at the graduate level and for assessing construct validity of psychometric instruments in small or hard-to-survey populations. Full article

We present a framework that aims to investigate the role of thermal fluctuations in matter composition and color superconductivity in the nucleation of three-flavor deconfined quark matter in the typical conditions of high-energy astrophysical systems related to compact stars. It is usually assumed that the flavor composition is locally fixed during the formation of the first seed of deconfined quark matter, since a weak interaction acts too slowly to re-equilibrate flavors. However, the matter composition fluctuates around its average equilibrium values at the typical temperatures of high-energy astrophysical processes. Here, we extend our previous two-flavor nucleation formalism to a three-flavor case. We develop a thermodynamic framework incorporating finite-size effects and thermal fluctuations in the local composition to compute the nucleation probability as the product of droplet formation and composition fluctuation rates. Moreover, we discuss the role of color superconductivity in nucleation, arguing that it can play a role only in systems larger than the typical coherence length of diquark pairs. We found that thermal fluctuations in the matter composition led to lowering the potential barrier between the metastable hadronic phase and the stable quark phase. Moreover, the formation of diquark pairs reduced the critical radius and thus the potential barrier in the low baryon density and temperature regime. Full article

We describe an attempt to derive the binarity rate of samples of 166 A-, F-, G-, and K-type stars from LAMOST DR5 and 1000 randomly selected presumably single stars from Gaia DR3 catalogs. To this end, we compared continua of the observed spectra with the continua of synthetic spectra from within 3700 $<\lambda <9097$ Å. The latter spectra were reduced to the LAMOST set of wavelengths, while the former ones were smoothed. Next, we searched for every observed star of the nearest synthetic spectrum using a four-parameter representation—$T_{\mathrm{eff}}$, $logg$, $\left[\mathrm{Fe}/\mathrm{H}\right]$, and a range of interstellar absorption values. However, rms deviations of observed spectra from synthetic ones appeared to be not sufficient to claim that any of the stars is a binary. We conclude that comparison of the intensity of pairs of spectral lines remains the best way to detect binarity. Full article

Globally, construction is among the leading sectors causing carbon emissions. Sustainable practices have become the focus, which aligns with the nation’s commitments to the Paris Agreement by targeting a 30% reduction in emissions from the 2005 levels by 2030. However, evaluation for sustainability is critical, and the Green Star certification provides assurance. Building information modelling has emerged as a transformative technology, integrating environmental sustainability into building design and construction. This study explores the use of BIM to enhance green building outcomes, focusing on optimising stakeholder engagement, energy efficiency, waste control, and environmentally sustainable design. This study employed a case study of an educational building, illustrating how BIM frameworks support Green Star certifications by streamlining design analysis, enhancing project value, and improving compliance with sustainability metrics. Findings highlight BIM’s role in advancing low-carbon, energy-efficient building designs while fostering collaboration across disciplines. This research investigates the foundational approach required to establish a framework for implementing the Green Star certification in non-residential, environmentally sustainable designs. Further, this study underscores the importance of integrating BIM in achieving Green Star benchmarks and provides a roadmap for leveraging digital modelling to meet global sustainability goals. Recommendations include expanding BIM capabilities to support broader environmental assessments and operational efficiencies. Full article

This paper investigated the issues of unstable data collection links and low efficiency in IoT data collection for smart cities by combining active STAR-RIS with UAVs to enhance channel quality, achieving efficient data collection in complex environments. To this end, we propose an active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted UAV-enabled NOMA data collection system that jointly optimizes active STAR-RIS beamforming, SN power allocation, and UAV trajectory to maximize the system energy efficiency (EE) and the data complete collection rate (DCCR). We apply block coordinate ascent (BCA) to decompose the non-convex problem into three alternating subproblems: combined beamforming optimization of phase shift and amplification gain matrices, power allocation, and trajectory optimization, which are iteratively processed through successive convex approximation (SCA) and fractional programming (FP) methods, respectively. Simulation results demonstrate the proposed algorithm’s rapid convergence and significant advantages over conventional NOMA and OMA schemes in both throughput rate and DCCR. Full article

Low regression rate is the most critical issue for the development and application of hybrid rocket motors (HRMs). Paraffin-based fuels are potential candidates for HRMs due to their high regression rates but adding polymers to improve strength results in insufficient regression rates for HRMs applications. In this work, Computational Fluid Dynamics (CFD) modeling and analysis were used to investigate the mixing and combustion of gaseous fuels and oxidizers in HRMs for various fuel grains and injector combinations. In addition, the regression rate characteristics and combustion efficiency were evaluated using a ground test. The results showed that the swirling flow with both high mixing intensity and high velocity could be formed by using the swirl injector. The highest mixing degree attained for the star-swirl grain and swirl injector was 86%. The reported combustion efficiency calculated by the CFD model attained a maximum of 93% at the nozzle throat. In addition, a spatially averaged regression rate of 1.40 mm·s⁻¹ was achieved for the star-swirl grain and swirl injector combination when the mass flux of N₂O was 89.94 kg·m⁻²·s⁻¹. This is around 191% higher than the case of non-swirling flow. However, there were obvious local regression rate differences between the root of the star and the slot. The regression rate increase was accompanied by a decrease in the combustion efficiency for the strong swirling flow condition due to the remarkable higher mass flow rate of gasified fuels. It was shown that the nano-sized aluminum was unfavorable for the combustion efficiency, especially under extreme fuel-rich conditions. Full article

Currently, the dismantling of aluminum stranded conductors remains predominantly manual due to their structural complexity. To enhance the efficiency and reduce the labor intensity for dismantling aluminum stranded conductors, this study presents an innovative torque-driven dismantling method validated through dynamic simulation analysis. To demonstrate the proposed method, a modular prototype machine that includes four main functional modules (transmission, untwisting, separation, and shearing) was developed. Experimental results from the prototype dismantling machine demonstrated that when processing JL/G3A-500/65 conductors (Sichuan Star Cable Co., Ltd., Leshan, China) under the following operational parameters—0.5 rad/s rotational speed, 10 cm extension length, 2400 N clamping force, and 40 N·m torque application—the system achieved a single-layer dismantling efficiency exceeding 98%. This represents a significant improvement in operational speed compared to traditional manual methods. The developed machine achieved collaborative control of axial feed, reverse untwisting, and automatic shearing, elevating the untwisting qualification rate to 95%. This solution provides an efficient and safe approach to conductor inspection, demonstrating substantial engineering application value. Full article

The flowering stage is a critical phase in the growth of rapeseed crops, and non-destructive, high-throughput quantitative analysis of rape flower clusters in field environments holds significant importance for rapeseed breeding. However, detecting and counting rape flower clusters remains challenging in complex field conditions due to their small size, severe overlapping and occlusion, and the large parameter sizes of existing models. To address these challenges, this study proposes a lightweight rape flower clusters detection model, SPL-YOLOv8. First, the model introduces StarNet as a lightweight backbone network for efficient feature extraction, significantly reducing computational complexity and parameter counts. Second, a feature fusion module (C2f-Star) is integrated into the backbone to enhance the feature representation capability of the neck through expanded spatial dimensions, mitigating the impact of occluded regions on detection performance. Additionally, a lightweight Partial Group Convolution Detection Head (PGCD) is proposed, which employs Partial Convolution combined with Group Normalization to enable multi-scale feature interaction. By incorporating additional learnable parameters, the PGCD enhances the detection and localization of small targets. Finally, channel pruning based on the Layer-Adaptive Magnitude-based Pruning (LAMP) score is applied to reduce model parameters and runtime memory. Experimental results on the Rapeseed Flower-Raceme Benchmark (RFRB) demonstrate that the SPL-YOLOv8n-prune model achieves a detection accuracy of 92.2% in Average Precision (AP₅₀), comparable to SOTA methods, while reducing the giga floating point operations per second (GFLOPs) and parameters by 86.4% and 95.4%, respectively. The model size is only 0.5 MB and the real-time frame rate is 171 fps. The proposed model effectively detects rape flower clusters with minimal computational overhead, offering technical support for yield prediction and elite cultivar selection in rapeseed breeding. Full article

Research shows mnemonics promote knowledge retention in different contexts; hence, they are increasingly being used in serious games aimed to support long-term learning while providing “edutainment.” However, there is limited research on their effectiveness. As such, we conducted a systematic review of 32 mnemonics mobile apps and evaluated them using two established frameworks from the literature. Our analysis revealed that most of the games teach language or medicine, take the form of puzzles or quizzes, and feature acronyms and/or images, with players rating them at least three out of five stars on average. All 32 apps supported feedback, interactivity, and challenge. A few supported agency, identity and self-presence, while many did not support key characteristics such as social and spatial presences. The overall finding indicates a need to create a mnemonics-based and tailored framework to guide the design of mnemonics games in the future to make them more effective. Full article

Multi-agent reinforcement learning has demonstrated excellent performance in complex decision-making tasks such as electronic games, power grid management, and autonomous driving. However, its vulnerability to adversarial attacks may impede its widespread application. Currently, research on adversarial attacks in reinforcement learning primarily focuses on single-agent scenarios, while studies in multi-agent settings are relatively limited, especially regarding how to achieve optimized attacks with fewer steps. This paper aims to bridge the gap by proposing a heuristic exploration-based attack method named the Search for Key steps and Key agents Attack (SKKA). Unlike previous studies that train a reinforcement learning model to explore attack strategies, our approach relies on a constructed predictive model and a T-value function to search for the optimal attack strategy. The predictive model predicts the environment and agent states after executing the current attack for a certain period, based on simulated environment feedback. The T-value function is then used to evaluate the effectiveness of the current attack. We select the strategy with the highest attack effectiveness from all possible attacks and execute it in the real environment. Experimental results demonstrate that our attack method ensures maximum attack effectiveness while greatly reducing the number of attack steps, thereby improving attack efficiency. In the StarCraft Multi-Agent Challenge (SMAC) scenario, by attacking 5–15% of the time steps, we can reduce the win rate from 99% to nearly 0%. By attacking approximately 20% of the agents and 24% of the time steps, we can reduce the win rate to around 3%. Full article

The galaxy NGC 7469 is a bright infrared source with an active galactic nucleus (AGN) and an intense star-forming region with a radius of approximately 500 parsecs, where the star formation rate is estimated to be 20–50 $M_{\odot }{\mathrm{yr}}^{-1}$. This study presents the results of spectral and photometric observations carried out during the period from 2020 to 2024 at the Fesenkov Astrophysical Institute (Almaty, Kazakhstan) and the Nasreddin Tusi Shamakhy Astrophysical Observatory (Shamakhy, Azerbaijan). Photometric data were obtained using B, V, and Rc filters, while spectroscopic observations covered the wavelength range of $\lambda$ 4000–7000 Å. Data reduction was performed using the IRAF and MaxIm DL Pro6 software packages. An analysis of the light curves revealed that after the 2019–2020 outburst, the luminosity level of NGC 7469 remained relatively stable until the end of 2024. In November–December 2024, an increase in brightness (∼0.3–0.5 magnitudes) was recorded. Spectral data show variations in the Ha fluxes and an enhancement of them at the end of 2024. On BPT diagrams, the emission line flux ratios [OIII]/H $\beta$ and [NII]/H $\alpha$ place NGC 7469 on the boundary between regions dominated by different ionization sources: AGN and star-forming regions. The electron density of the gas, estimated from the intensity ratios of the [SII] 6717, 6731 Ålines, is about $900–1000{\mathrm{cm}}^{-3}$. Continued observations will help to determine whether the trend of increasing brightness and emission line fluxes recorded at the end of 2024 will persist. Full article

Background and Objectives: Conventional methods for removing cemented fixed prosthetic restorations (FPRs) are unreliable and lead to unsatisfactory outcomes. At their best, they allow the tooth to be saved at the expense of a laborious process that also wears down rotating tools and handpieces and occasionally results in abutment fractures. Restorations are nearly never reusable in any of these situations. Erbium-doped yttrium-aluminum-garnet (Er:YAG) and erbium-chromium yttrium-scandium-gallium-garnet (Er,Cr:YSGG) lasers casafely and effectively remove FPRs, according to scientific studiesre. This study sets out to examine the impact of Er:YAG laser radiation on the debonding of different ceramic restorations, comparing the behavior of various ceramic prosthetic restoration types under laser radiation action and evaluating the integrity of prosthetic restorations and dental surfaces exposed to laser radiation. Materials and Methods: The study included a total of 16 removed teeth, each prepared on opposite surfaces as abutments.y. Based on the previously defined groups, four types of ceramic restorations were included in the study: feldspathic (F), lithium disilicates (LD), layered zirconia (LZ), and monolithic zirconia (MZ). The thickness of the prosthetic restorations was measured at three points, and two different materials were used for cementation. The Er:YAG Fotona StarWalker MaQX laser was used to debond the ceramic FPR at a distance of 10 mm using an R14 sapphire tip with 275 mJ, 20 Hz, 5.5 W, with air cooling (setting 1 of 9) and water. After debonding, the debonded surface was visualized under electron microscopy. Results: A total of 23 ceramic FPRs were debonded, of which 12 were intact and the others fractured into two or three pieces. The electron microscopy images showed that debonding took place without causing any harm to the tooth structure. The various restoration types had the following success rates: 100% for the LZ and F groups, 87% for the LD group, and 0% for the MZ group. In terms of cement type, debonding ceramic FPRs cemented with RELYX was successful 75% of the time, compared to Variolink DC’s 69% success rate. Conclusions: In summary, the majority of ceramic prosthetic restorations can be successfully and conservatively debonded with Er:YAG radiation. Full article

Wildfires are complex natural disasters that significantly impact ecosystems and human communities. The early detection and prediction of forest fire risk are necessary for effective forest management and resource protection. This paper proposes an innovative early detection system based on a wireless sensor network (WSN) composed of interconnected Arduino nodes arranged in a hybrid circular/star topology. This configuration reduces the number of required nodes by 53–55% compared to conventional Mesh 2D topologies while enhancing data collection efficiency. Each node integrates temperature/humidity sensors and uses ZigBee communication for the real-time monitoring of wildfire risk conditions. This optimized topology ensures 41–81% lower latency and 50–60% fewer hops than conventional Mesh 2D topologies. The system also integrates artificial intelligence (AI) algorithms (multiclass logistic regression) to process sensor data and predict fire risk levels with 99.97% accuracy, enabling proactive wildfire mitigation. Simulations for a 300 m radius area show the non-dense hybrid topology is the most energy-efficient, outperforming dense and Mesh 2D topologies. Additionally, the dense topology achieves the lowest packet loss rate (PLR), reducing losses by up to 80.4% compared to Mesh 2D. Adaptive routing, dynamic round-robin arbitration, vertical tier jumps, and GSM connectivity ensure reliable communication in remote areas, providing a cost-effective solution for wildfire mitigation and broader environmental monitoring. Full article

Studying the host galaxies of fast radio bursts (FRBs) is critical to understanding the formation processes of their sources and, hence, the mechanisms by which they radiate. Toward this end, we have extended the Blinkverse database version 1.0, which already included burst information about FRBs observed by various telescopes, by adding information about 92 published FRB host galaxies to make version 2.0. Each FRB host has 18 parameters describing it, including redshift, stellar mass, star-formation rate, emission line fluxes, etc. In particular, each FRB host includes images collated by FASTView, streamlining the process of looking for clues to understanding the origin of FRBs. FASTView is a tool and API for quickly exploring astronomical sources using archival imaging, photometric, and spectral data. This effort represents the first step in building Blinkverse into a comprehensive tool for facilitating source observation and analysis. Full article

In this paper, we investigate a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted non-orthogonal multiple access (NOMA) communication system where the STAR-RIS is mounted on an unmanned aerial vehicle (UAV) with adjustable altitude. Due to severe blockages in urban environments, direct links from the base station (BS) to users are assumed unavailable, and signal transmission is realized via the STAR-RIS. We formulate a joint optimization problem that maximizes the system sum rate by jointly optimizing the UAV’s altitude, BS beamforming vectors, and the STAR-RIS phase shifts, while considering Rician fading channels with altitude-dependent Rician factors. To tackle the maximum achievable rate problem, we adopt a block-wise optimization framework and employ semidefinite relaxation and gradient descent methods. Simulation results show that the proposed scheme achieves up to 22% improvement in achievable rate and significant reduction in bit error rate (BER) compared to benchmark schemes, demonstrating its effectiveness in integrating STAR-RIS and UAV in NOMA networks. Full article