Search Results (26,054)

Acute stress impairs executive functions, and these higher-order cognitive processes are often positively associated with intelligence. Even though intelligence is generally stable over time, performance in an intelligence test can be influenced by a variety of factors, including psychological processes like motivation or attention. For instance, test anxiety has been shown to correlate with individual differences in intelligence test performance, and theoretical accounts exist for causality in both directions. However, the potential impact of acute stress before or during an intelligence test remains elusive. Here, in a research context, we investigated the effects of test anxiety and acute stress as well as their interaction on performance in the short version of the Intelligence Structure Test 2000 in its German version (I-S-T 2000 R). Forty male participants completed two sessions scheduled 28 days apart, with the order counterbalanced across participants. In both sessions, participants underwent either the socially evaluated cold-pressor test (SECPT) or a non-stressful control procedure, followed by administration of I-S-T 2000 R (parallelized versions on both days). The SECPT is a widely used laboratory paradigm that elicits a stress response through the combination of psychosocial and physical components. Trait test anxiety scores were obtained via the German Test Anxiety Inventory (TAI-G). Stress induction was successful as indicated by physiological and subjective markers, including salivary cortisol concentrations. We applied linear mixed models to investigate the effects of acute stress (elicited by our stress manipulation) and test anxiety on the intelligence quotient (IQ). The analysis revealed that neither factor had a significant effect, nor was there a significant interaction between them. Consistent with these findings, Bayesian analyses provided evidence supporting the absence of these effects. Notably, IQ scores increased significantly from the first to the second testing day. These results suggest that neither test anxiety nor stress is significantly impacting intelligence test performance. However, improvements due to repeated testing call for caution, both in scientific and clinical settings. Full article

In this study, a guar gum-based adsorbent was developed and evaluated for the removal of Ni(II) ions from aqueous solutions through a combined experimental and machine learning (ML) approach. The adsorbent was characterized using FTIR, SEM, XRD, TGA, and BET analyses to confirm surface functionality and porous morphology suitable for metal binding. Batch adsorption experiments were conducted to optimize the effects of pH, adsorbent dosage, contact time, temperature, and initial metal concentration. The adsorption efficiency increased with higher pH and adsorbent dosage, achieving a maximum Ni(II) removal of 97% (qₘ = 86.0 mg g⁻¹) under optimal conditions (pH 6.0, dosage 1.0 g L⁻¹, contact time 60 min, and initial concentration 50 mg L⁻¹). The process followed the pseudo-second-order kinetic and Langmuir isotherm models. Thermodynamic results revealed the spontaneous, endothermic, and physical nature of the adsorption process. To complement the experimental findings, artificial neural network (ANN) and k-nearest neighbor (KNN) models were developed to predict Ni(II) removal efficiency based on process parameters. The ANN model yielded a higher prediction accuracy (R² = 0.97) compared to KNN (R² = 0.95), validating the strong correlation between experimental and predicted outcomes. The convergence of experimental optimization and ML prediction demonstrates a robust framework for designing eco-friendly, biopolymer-based adsorbents for heavy metal remediation. Full article

We developed a self-consistent double-null description of an evaporating white-hole spacetime by embedding the outgoing Vaidya solution in a coordinate system that remains regular across the future horizon. Starting from the radiation-coordinate form, we specialize in retarded time so that a monotonically decreasing mass function $M\left(u\right)$ encodes outgoing positive-energy flux. Expressing the metric in null coordinates $(u,v)$, Einstein’s equations for a single-directional null-dust stress–energy tensor, $T_{uu}=\rho \left(u\right)$, then reduce to one first-order PDE for the areal radius: ${\mathsf{\partial }}_{v}r=B\left(u\right)\left(1-2M\left(u\right)/r\right)$. Its integral, $r+2M\left(u\right)ln|r-2M(u\left)\right|=v-C\left(u\right)$, defines an implicit foliation of outgoing null cones. The metric coefficient follows algebraically as $f(u,v)=1-2M\left(u\right)/r$. Residual gauge freedom in $B\left(u\right)$ and $C\left(u\right)$ is fixed so that u matches the Bondi retarded time at null infinity, while v remains analytic at the apparent horizon, generalizing the Kruskal prescription to dynamical mass loss. In the limit $M\left(u\right)\to M$, the construction reduces to the familiar Eddington–Finkelstein and Kruskal forms. Our solution, therefore, provides a compact analytic framework for studying white-hole evaporation, Hawking-like energy fluxes, and back-reaction in spherically symmetric settings without encountering coordinate singularities. Full article

Background: We investigated the effect of mental fatigue (MF) on artistic swimmers’ (AS) physiological and cognitive responses and physical and technical AS performance. Methods: Twelve young female ASs completed a free team routine (FT) involving 4 × 4 min trials separated by a 2 min rest in two sessions a week apart. Pre- and post-FT, athletes performed three “boosts” for vertical displacement and a 50 m maximum effort front crawl swim. Before each session, a 30 min MF test (Stroop condition; SC) or an emotionally neutral video (control condition; CC) were implemented in counterbalanced order. Choice reaction time and central executive function tests were applied before and after the completion of both conditions. Technical performance was evaluated by five official judges. Heart rate was continuously recorded, whilst blood lactate was measured before the start and after the second and fourth FT. Rating of perceived exertion (RPE) was recorded after each FT. Results: Technical performance scores during FT were lower in SC than CC (6.82 ± 0.92 vs. 7.17 ± 0.69, p < 0.001, and d = 0.43). The choice reaction time was decreased by 3.4 ± 9.3% in SC but increased 4.4 ± 8.1% in CC (p < 0.05). Central executive function was no different between conditions despite a medium effect size in SC (d = 0.58). The “boost” height was lower in SC compared to CC (70 ± 5 vs. 72 ± 5 cm, p < 0.05, and d = 0.45). Heart rate, RPE, and 50 m time did not differ between conditions (p > 0.05), but blood lactate was higher in the CC compared to SC (5.3 ± 2.6 vs. 4.6 ± 2.9 mmol/l, p < 0.05, and d = 0.25). Conclusion: Mental fatigue may impair technical performance during FT, primarily via cognitive dysfunction, with reduced glycolytic activation as a potential additional factor. Full article

Microwave devices based on microstrip resonators are widely used today in communication, radar, and navigation systems. The requirements to these devices may include specific frequency-selective properties, as well as size and production costs. The design of resonators and filters are mostly performed manually, as the process requires expert knowledge and computationally expensive modeling, so practitioners are usually limited to tuning a chosen example from a set of known, typical topologies. However, the set of possible topologies remains unexplored and may contain specific constructions, which have not been discovered yet. In this study we propose an approach to automatically search the space multimode resonator topologies using a zero-order optimization algorithm and numerous computational experiments. In particular, a family of symmetrical resonators constructed out of four rectangles is considered, and the parameters are tuned by the recently proposed L-SRTDE algorithm. We state the problem of building the topology of a microwave device conductor with specified frequency-selective characteristics as an optimization problem, and the minimized function (target function) in this problem is based on the evaluation of the deviation between the specified frequency-selective characteristics and their values obtained via electrodynamic modeling. The experiments with two target function formulations have shown that the proposed approach allows finding novel topologies and automatically tune them according to the required frequency-selective properties. It is shown that some of the topologies are different from the known ones but still demonstrate high-quality properties. Full article

University course scheduling is a kind of timetable problem and can be mathematically formulated as an integer linear programming problem. Essentially, a university course scheduling problem is an optimization problem that aims at most efficiently minimizing a cost function according to a set of constraints. The huge searching space for the course scheduling problem means a long time will be needed to find the optimal solution. Therefore, some studies have used soft computing approaches to solve course scheduling problems in order to reduce the searching space. However, in order to use soft computing approaches to solve university course scheduling problems, we may need to design algorithms and conduct numerous experiments to achieve maximum efficiency. Thus, in this study, instead of employing soft computing methods, we propose a SWI-PROLOG-based expert system to solve the course scheduling problem. An experiment was conducted using real-world data from a department at a national university in southern Taiwan. During the experiment, each teacher in the department chose five preferential time slots. The experimental results have shown that about 99% of courses were scheduled in teachers’ five preferential time slots with an acceptable computational time of executing SWI-PROLOG (127 milliseconds on a regular personal computer). This study has thus provided a framework for solving course scheduling problems using an expert system. This would be the main contribution of this study. Full article

In the domain of mining and mineral processing, LIDAR sensors are employed to obtain precise three-dimensional measurements of the surrounding environment. However, the functionality of these sensors is hindered by the dust produced by mining operations. In order to address this problem, a neural network-based method is proposed. This method is capable of filtering dust measurements in real time from point clouds obtained using LIDARs. The proposed method is trained and validated using real data, yielding results that are at the forefront of the field. Furthermore, a public database is constructed using LIDAR sensor data from diverse dusty environments. The database is made public for use in the training and benchmarking of dust filtering methods. Full article

This paper addresses the critical multiple-uncertainty challenge in laser link acquisition for space gravitational wave detection missions—a key bottleneck where spacecraft attitude alignment for laser link establishment is perturbed by inherent random disturbances in such missions, while also needing to balance ultra-high attitude precision, fuel efficiency, and compliance with engineering constraints. To tackle this, a convex optimization-based attitude control strategy integrating covariance control and free terminal time optimization is proposed. Specifically, a stochastic attitude dynamics model is first established to explicitly incorporate the aforementioned random disturbances. Subsequently, an objective function is designed to simultaneously minimize terminal state error and fuel consumption, with three key constraints (covariance constraints, pointing constraints, and torque saturation constraints) integrated into the convex optimization framework. Furthermore, to resolve non-convex terms in chance constraints, this study employs a hierarchical convexification method that combines Schur’s complementary theorem, second-order cone relaxation, and Taylor expansion techniques. This approach ensures lossless relaxation, renders the optimization problem computationally tractable without sacrificing solution accuracy, and overcomes the shortcomings of traditional convexification methods in handling chance constraints. Finally, numerical simulations demonstrate that the proposed method adheres to engineering constraints while maintaining spacecraft attitude errors below 1 μrad under environmental uncertainties. This study provides a convex optimization solution for laser link acquisition in space gravitational wave detection missions considering uncertainty conditions, and its framework can be extended to the optimal design of other stochastically uncertain systems. Full article

Background: G-quadruplexes (G4s) are non-canonical higher-order nucleic acid structures that form at guanine-rich motifs, with features spanning both secondary and tertiary structural levels. These dynamic structures play pivotal roles in diverse cellular processes. Endogenous G4s (eG4s) function through their dynamically formed structures, prompting the hypothesis that their thermostability, as a key structural property, may critically influence their functionality. This study investigates the relationship between G4 stability and other functional genomic signals within eG4 regions and examines its broader impact on chromatin organization and transcriptional regulation. Methods: We developed a mapping strategy to associate in vitro-derived thermostability metrics and multi-omics functional signals with eG4 regions. A stability-centric analytical framework combining correlation analysis and causal inference using the Bayesian networks was applied to decipher causal relationships between G4 stability and the other related signals. We further analyzed the association between the stability of transcription start site (TSS)-proximal eG4s and the biological functions of their downstream genes. Results: Our analyses demonstrate that G4 thermostability exerts causal effects on epigenetic states and transcription factor binding, thereby influencing chromatin and transcription regulation. We further show distinct network architectures for G4-binding versus non-binding transcription factors. Additionally, we find that TSS-proximal eG4s are enriched in genes involved in core proliferation and stress-response pathways, suggesting that eG4s may serve as regulatory elements facilitating rapid stress responses through genome-wide coordination. Conclusions: These findings establish thermostability—though measured in vitro—as an intrinsic property that shapes eG4 functionality. Our study not only provides novel insights into the functional relevance of G4 thermostability but also introduces a generalizable framework for high-throughput G4 data interpretation, significantly advancing the functional decoding of eG4s across biological contexts. Full article

Activated carbons were synthesized from coffee grounds using phosphoric acid as a chemical activator and microwave-assisted carbonization as a rapid and energy-efficient method. Then the prepared carbons were surface-treated with cold plasma to improve their chemical properties and adsorption efficiency. The structural properties and chemical structure of the carbons were determined using nitrogen adsorption–desorption analysis, X-ray photoelectron spectroscopy, as well as X-ray microanalysis by means of scanning electron microscopy. The effect of cold plasma treatment on surface functionality and porosity was investigated. The resulting activated carbons were tested for their potential use as sorbents for the removal of ciprofloxacin, a commonly used antibiotic, from aqueous solutions. The effects of solution pH, sorption kinetics, and initial concentration were investigated. Adsorption kinetics followed a pseudo-second-order model, and the equilibrium data were well described by both the Langmuir and Freundlich isotherms, indicating a combination of monolayer adsorption on homogeneous sites and multilayer adsorption on heterogeneous surfaces. Plasma-treated carbon demonstrated significantly increased adsorption capacity (42.6–120.6 mg g⁻¹) compared to the unactivated samples (20.2–92.4 mg g⁻¹). Desorption experiments revealed that the plasma-treated carbon retained over 90% efficiency after seven cycles, confirming its excellent reusability and regeneration potential for practical water treatment applications. Full article

By imposing finite order constraints on Fibonacci anyon braid relations, we construct the finite quotient $G={\mathbb{Z}}_5⋊2I$, where $2I$ is the binary icosahedral group. The Gröbner basis decomposition of its $SL(2,\mathbb{C})$ character variety yields elliptic curves whose L-function derivatives $L^{\prime }(E,1)$ remarkably match fundamental biological structural ratios. Specifically, we demonstrate that the Birch–Swinnerton-Dyer conjecture’s central quantity: the derivative $L^{\prime }(E,1)$ of the L-function at 1 encodes critical cellular geometries: the crystalline B-DNA pitch-to-diameter ratio ($L^{\prime }(E,1)=1.730$ matching $34\AA /20\AA =1.70$), the B-DNA pitch to major groove width ($L^{\prime }=1.58$) and, additionally, the fundamental cytoskeletal scaling relationship where $L^{\prime }(E,1)=3.570\approx 25/7$, precisely matching the microtubule-to-actin diameter ratio. This pattern extends across the hierarchy ${\mathbb{Z}}_5⋊2P$ with $2P\in \{2O,2T,2I\}$ (binary octahedral, tetrahedral, icosahedral groups), where character tables of $2O$ explain genetic code degeneracies while $2T$ yields microtubule ratios. The convergence of multiple independent mathematical pathways on identical biological values suggests that evolutionary optimization operates under deep arithmetic-geometric constraints encoded in elliptic curve L-functions. Our results position the BSD conjecture not merely as abstract number theory, but as encoding fundamental organizational principles governing cellular architecture. The correspondence reveals arithmetic geometry as the mathematical blueprint underlying major biological structural systems, with Gross–Zagier theory providing the theoretical framework connecting quantum topology to the helical geometries that are essential for life. Full article

Increased seawater temperatures and nutrient loading are stressors that affect coral reefs and their microbiomes. In this study, filamentous algae were collected and exposed to different temperatures and nutrient concentrations through a laboratory experiment. Microbial DNA was extracted and analyzed using amplicon sequencing of the V3-V4 hypervariable region of the 16S rRNA gene. In total, 1 domain, 51 phyla, 131 classes, 335 orders, 549 families, and 1905 species were identified. Proteobacteria and Bacteroidota were the dominant taxa reported. Elevated seawater temperatures and nutrient enrichment impacted microbial communities associated with turf algae under laboratory culture. Bacterial species diversity and abundance differed under different temperature and nutrient conditions. Proteobacteria and Actinobacteria were abundant in lower-temperature conditions, while Desulfobacterota, Spirochaetota, and Firmicutes were abundant in higher-temperature conditions. Ruegeria was abundant in low-temperature conditions, whereas Vibrio abundance was low. Regarding nutrient conditions, Proteobacteria and Cyanobacteria were abundant under high-nutrient conditions, while Firmicutes and Desulfobacterota were abundant under ambient-nutrient conditions. The higher nutrient concentration increased the abundance of pathogenic bacteria, such as Vibrio and Photobacterium, while Pseudoalteromonas, which is beneficial for reefs, was present under ambient nutrient conditions. This study demonstrates that temperature and nutrient enrichment can shape microbial communities under laboratory conditions, providing an experimental setting for further studies of bacterial functions and metabolic processes in natural conditions under thermal and nutrient stresses. Full article

Physics-Informed Neural Networks (PINNs) have emerged as a promising paradigm for solving partial differential equations (PDEs) by embedding physical laws into the learning process. However, standard PINNs often suffer from training instabilities and unbalanced optimization when handling multi-term loss functions, especially in problems involving singular perturbations, fractional operators, or multi-scale behaviors. To address these limitations, we propose a novel gradient variance weighting physics-informed neural network (GVW-PINN), which adaptively adjusts the loss weights based on the variance of gradient magnitudes during training. This mechanism balances the optimization dynamics across different loss terms, thereby enhancing both convergence stability and solution accuracy. We evaluate GVW-PINN on three representative PDE models and numerical experiments demonstrate that GVW-PINN consistently outperforms the conventional PINN in terms of training efficiency, loss convergence, and predictive accuracy. In particular, GVW-PINN achieves smoother and faster loss reduction, reduces relative errors by one to two orders of magnitude, and exhibits superior generalization to unseen domains. The proposed framework provides a robust and flexible strategy for applying PINNs to a wide range of integer- and fractional-order PDEs, highlighting its potential for advancing data-driven scientific computing in complex physical systems. Full article

In DC microgrids, the combination of pulsed loads and renewable energy sources significantly impairs system stability, especially in highly dynamic operating environments. The resilience and reaction time of conventional proportional–integral (PI) controllers are often inadequate when managing the nonlinear dynamics of hybrid energy storage systems. This research suggests a frequency-decomposed fractional-order control strategy for stabilizing DC microgrids with solar, batteries, and supercapacitors. The control architecture divides system disturbances into low- and high-frequency components, assigning high-frequency compensation to the ultracapacitor (UC) and low-frequency regulation to the battery, while a fractional-order controller (FOC) enhances dynamic responsiveness and stability margins. The proposed approach is implemented and assessed in MATLAB/Simulink (version R2023a) using comparison simulations against a conventional PI-based control scheme under scenarios like pulsed load disturbances and fluctuations in renewable generation. Grey Wolf Optimizer (GWO), a metaheuristic optimization procedure, has been used to tune the parameters of the FOPI controller. The obtained results using the same conditions were compared using an optimal fractional-order PI controller (FOPI) and a conventional PI controller. The microgrid with the best FOPI controller was found to perform better than the one with the PI controller. Consequently, the objective function is reduced by 80% with the proposed optimal FOPI controller. The findings demonstrate that the proposed method significantly enhances DC bus voltage management, reduces overshoot and settling time, and lessens battery stress by effectively coordinating power sharing with the supercapacitor. Also, the robustness of the proposed controller against parameters variations has been proven. Full article

The localization and remodeling of mRNPs is inextricably linked to translational control. In recent years there has been great progress in the field of mRNA translational control due to the characterization of the proteins and small RNAs that compose mRNPs. But our initial assumptions about the physical nature and participation of germ cell granules/condensates in mRNA regulation may have been misguided. These “granules” were found to be non-membrane-bound liquid–liquid phase-separated (LLPS) condensates that form around proteins with intrinsically disordered regions (IDRs) and RNA. Their macrostructures are dynamic as germ cells differentiate into gametes and subsequently join to form embryos. In addition, they segregate translation-repressing RNA-binding proteins (RBPs), selected eIF4 initiation factors, Vasa/GLH-1 and other helicases, several Argonautes and their associated small RNAs, and frequently components of P bodies and stress granules (SGs). Condensate movement, separation, fusion, and dissolution were long conjectured to mediate the translational control of mRNAs residing in contained mRNPs. New high-resolution microscopy and tagging techniques identified order in their organization, showing the segregation of similar mRNAs and the stratification of proteins into distinct mRNPs. Functional transitions from repression to activation seem to corelate with the overt granule dynamics. Yet increasing evidence suggests that the resident mRNPs, and not the macroscopic condensates, exert the bulk of the regulation. Full article