Browse Articles
Discover research articles across all indexed journals
An analytic hierarchy process–based prioritization of psychological factors influencing academic performance among university students in China
Depression and long-term mortality among 5-year breast cancer survivors in Korea: a retrospective population-based cohort study
T-Cell “Rejuvenation” Nanovaccine: Enhancing Immunological Memory and Antitumor Responses through Telomere Extension
Significant ocular residual astigmatism reduces the effect of orthokeratology lenses in controlling myopia
Development and validation of a green analytical method to determine bromhexine hydrochloride in pharmaceuticals
Generating borderline test samples for randomness testers via intelligent optimization and evolutionary algorithms
Abstract Ensuring information security heavily relies on high-quality random sequences for encryption keys. Physical entropy sources, despite their use in generating true random sequences, are susceptible to environmental disturbances, necessitating real-time randomness testing to maintain high entropy. However, existing methods for generating test data for real-time randomness testers face significant challenges, including producing sequences that fail to meet specific randomness criteria, constructing borderline sequences with slight non-randomness, and addressing the difficulty of simultaneously violating multiple randomness criteria. This paper introduces a dynamic test data generation framework designed to address these challenges. The framework leverages evolutionary algorithm (EA) to transform the generation of borderline sequences into a multi-constrained optimization problem, where a large language model (LLM) acts as a dynamic parameter adjuster. By analyzing evolutionary trends in population statistics and interacting with evolutionary dynamics through a game-theoretic mechanism, the LLM adaptively adjusts scaling factors and weight coefficients, mitigating the curse of dimensionality in multi-objective optimization and enabling real-time parameter tuning. The experimental results also highlight the high quality of the generated sequences: our approach can generate borderline test data that slightly fail to satisfy the target randomness criteria, yet exhibit statistical properties very similar to those of high-entropy sources under standard test suites. These borderline sequences are fault-detectable and provide challenging, realistic test inputs for classical statistical-test-based real-time randomness testers.
Light-Induced Rotation of a Molecular Motor in the Confined Space of a Metal–Organic Nanocage
Elevation uncertainties in the Mekong Delta quantified using a transferable approach
Abstract The elevation of coastal lowlands relative to local sea level is a crucial determinant for their exposure and is key input for coastal hazard and relative sea-level rise impact assessments. For many data-sparse coastal lowlands worldwide, global digital elevation models are often the only source of information. While these provide an adequate spatial (i.e. horizontal) resolution for regional, delta-wide coastal assessments, their vertical errors in the range of several metres impede investigations of (relative) sea-level rise impact where changes occur on millimetre- to centimetre-scale. Assessing the quality of available elevation datasets is required to identify the best performing model(s) to use for generating reliable coastal impact and exposure assessments. While data-intrinsic inaccuracy has been extensively addressed both in dataset documentation and literature, the relevance and proper vertical datum conversion from global geoid and ellipsoid to local sea level is often still omitted in many applied studies from coastal research. Similarly, the impact of the actuality of elevation data (i.e. time since data acquisition) on assessments in coastal lowlands is so far understudied although elevation models may become quickly outdated, especially where coastal lowlands are facing high rates of elevation change resulting from the interplay of vertical land motion, (vertical) sediment accretion and sea-level change. Particularly for flat, low-lying subsiding coastal landscapes like the Mekong Delta, being in parts only a few decimetres elevated above sea level and experiencing land subsidence of up to several centimetres per year, the reliability of elevation data and adequate representation of elevation relative to local sea level as well as the consideration of factors impacting elevation over time is of utmost importance. We present a transferable and ultimately globally applicable approach to quantify and attribute uncertainties in elevation assessment for data-sparse coastal lowlands using global elevation models to sources such as inaccuracy, vertical datum offset and actuality. The approach combines openly available land elevation and sea-level datasets, integrated with non-linear time series and projections of sea-level change and vertical land motion while also pointing out the need of information on sediment accretion/erosion. We showcase this approach by revisiting land elevation in the Vietnamese Mekong Delta (i) by vertically referencing 11 commonly used global elevation models and an updated local elevation model to a common actual, local sea-level datum, and (ii) by conducting a thorough assessment of elevation model performance that not only allows for the quantification of errors and elevation assessment uncertainties but also their attribution to data-intrinsic inaccuracy, vertical datum offset and, tentatively, non-linear impact of elevation change due to vertical land motion (e.g. extraction-induced land subsidence) and sea-level change affecting the actuality of the elevation model. Our approach not only allows to improve the understanding of coastal elevation to further improve relative sea-level rise and flood impact assessments and to substantiate projections of future elevation in the Mekong Delta, but in its design, applying solely open data and commonly used GIS software, facilitates similar assessments of elevation model performance and elevation assessment uncertainties in other (data-sparse) coastal regions in the world.
Uncertainty propagation in financial models of photovoltaic systems
Abstract Financial analysis has a long history of capturing the stochasticity of real-world phenomena. For informed investment decisions, it is crucial to understand and quantify uncertainty propagation from financial model input to output. Yet to that end, in the photovoltaics sector one has so far relied on coarse-grained approximations or extensive simulations. Here we present a numerically inexpensive approach that exactly traces uncertainty propagation on the level of probability distributions. It leverages analytic shortcuts through switching between different distribution representations, and only assumes independent input variables. With the financial analysis of a typical photovoltaic system as a case study, we use this approach to compute key financial metrics and demonstrate that their values can differ significantly from those obtained by a standard approximation. Moreover, we show with both frameworks that input uncertainty alone can significantly impact the outcome of financial analysis.
New insights into the molecular basis of gametogenesis in the hybridogenetic water frog Pelophylax esculentus
Abstract The molecular mechanisms underlying genome exclusion and clonal gamete formation in the germline of hybridogenetic water frog hybrids are poorly understood. Here, we characterize for the first time the coding sequences of 160 gametogenic genes from the European water frog species Pelophylax lessonae (LL) and Pelophylax ridibundus (RR). In addition, single nucleotide polymorphisms (SNPs) in 52 of these genes were analyzed in both parental species (60 LL, 252 RR) and in 340 diploid hybrids, Pelophylax esculentus (LR), sampled from population systems differing in genotypic composition, sex ratio, and inheritance modes. Ten of these genes showed associations with the population system and may therefore be related to the mode of inheritance, namely the exclusion of the ridibundus (R) genome in the lessonae–esculentus system and/or the lessonae (L) genome in the ridibundus–esculentus system. These genes are involved in diverse cellular processes, including spindle formation and chromosome movement during mitosis and meiosis, epigenetic silencing, cell cycle regulation, double-strand break repair and homologous recombination, including transposable element silencing. Our results are consistent with the clonal inheritance patterns described for P. esculentus and suggest that genome exclusion is governed by complex genomic networks involving multiple genes and molecular factors. Furthermore, our data indicate that bidirectional gene flow between the L and R gene pools has played an important role in the evolution of the different clonal inheritance modes in P. esculentus , thereby contributing to the emergence of distinct population systems.
High- and low-fidelity modal and mechanical analysis of architected strut-based lattice structures with auxetic topologies
Leveraging wearable haptics for guidance in virtual rehabilitation: effects on motor control from an immersive VR setting
Abstract Immersive Virtual Reality (iVR) interventions have emerged as effective complements to conventional rehabilitation, with advantages in terms of flexible parametrization, extended data recording and patient engagement. Home-rehabilitation is another impactful, yet unexplored potential. A key requirement is ensuring that patients execute tasks with proper motor coordination and postural awareness: to this end, tactile sensory feedback can be introduced to enhance both immersion and motor guidance. We investigate here the efficacy and the effects on motor coordination of tactile guidance provided within an iVR rehabilitation serious game. A wearable haptic armband, delivering directional continuous and vibrotactile feedback, has been developed to guide grasping and pronosupination tasks. We evaluated the interaction on motor control and task execution using objective performance metrics, biomechanical responses, and subjective assessments, in a group of 12 healthy subjects, and a preliminary feasibility study with two participants with stroke. Findings show that haptic guidance in iVR statistically significantly enhances movement precision, reduces variability, and induces adaptive changes in muscle coordination. Feedback from clinical tests further indicates preliminary indications of usability and acceptability of the developed technology as part of a rehabilitation program. These results underscore the promise of haptic-enriched iVR systems for advancing both clinical and home-based motor rehabilitation.
A frequency-spatial dual perception network for efficient and accurate medical image segmentation
MDI-YOLO a lightweight transformer-CNN-based multidimensional feature fusion model for small object detection
A lightweight YOLOv8n-based method for human abnormal posture detection
On certain novel numerical and analytical solutions for the pure-cubic Schrödinger equation in optical fibers with Kerr nonlinearity
Multimodality medical image fusion using directional total variation based linear spectral clustering in NSCT domain
STTORM-CD low-demand and high-impact disaster monitoring onboard satellites using change detection
Abstract Satellite imagery can play a crucial role in disaster management, but critical images often take hours or even days to reach end-users, and upgrading hardware to improve transmission speed is prohibitively expensive for many small satellite missions. This article thus explores onboard change detection methods as a cost-effective alternative to reduce reaction time. Building on RaVAEn, we introduce STTORM-CD, a framework that combines a Variational Autoencoder (VAE) with a triplet loss, specifically designed for change detection. The triplet loss improves detection accuracy while maintaining the computational and storage efficiency of VAE, making it suitable for deployment on resource-constrained satellite hardware. To support training and evaluation, we present a new dataset, STTORM-CD-Floods, annotated with a custom strategy optimized for flood detection, along with new metrics, AURC and RDP, designed to address limitations of RaVAEn evaluation strategies, which are influenced more by dataset composition than model performance. Our experiments show that STTORM-CD outperforms existing flood detection methods, achieving an increase of 35 percentage points (pp) in custom AURC and standard AUPRC metrics against RaVAEn on the presented STTORM-CD-Floods dataset, while showing negligible changes in AURC (approximately -4 to +0.1 pp) for landslides and wildfires. This demonstrates that improvements on one disaster type do not necessarily compromise performance on others and highlights the potential for a universal and accurate real-time disaster detection system.