Browse Articles

Spatial structure and influencing factors of agricultural civilization heritage in the Yellow River Basin

Dosimetry study of 3D-printed noncoplanar template-assisted CT-guided 125I seed implantation for the treatment of recurrent and metastatic tumors in the head and neck

Objective Recurrent and metastatic tumors of the head and neck pose significant treatment challenges due to their proximity to critical structures and prior radiation exposure. This study aimed to evaluate the consistency between preoperative and postoperative dosimetric parameters in CT-guided 3 D-printed noncoplanar template (3DPNCT)-assisted radioactive iodine-125 seed implantation (RISI). Methods Twenty-six patients with recurrent or metastatic head and neck cancer were retrospectively analyzed. Gross tumor volume (GTV) coverage and dosimetric parameters such as D90 (dose covering 90% of the GTV), conformity index (CI), and homogeneity index (HI) were compared before and after implantation. The Shapiro-Wilk test was used to assess data normality. Results There were no significant differences between pre- and postoperative D90, V100, V150, or CI values (P > 0.05). Bland–Altman analysis showed high agreement for key metrics. Conclusions 3DPNCT-assisted RISI demonstrated accurate dose delivery and high reproducibility. This approach may enhance local control while minimizing radiation to organs at risk in complex head and neck anatomies. These results suggest that this technique has promising clinical applicability in complex head and neck cases; however, further validation through larger prospective studies is warranted to confirm long-term efficacy and safety.

Tensor decomposed distinguishable cluster. I. Triples decomposition

We present a cost-reduced approach for the distinguishable cluster approximation to coupled cluster with singles, doubles, and iterative triples (DC-CCSDT) based on a tensor decomposition of the triples amplitudes. The triples amplitudes and residuals are processed in the singular-value-decomposition (SVD) basis. Truncation of the SVD basis according to the values of the singular values together with the density fitting (or Cholesky) factorization of the electron repulsion integrals reduces the scaling of the method to N6, and the DC approximation removes the most expensive terms of the SVD triples residuals and at the same time improves the accuracy of the method. The SVD basis vectors for the triples are obtained from the approximate CC3 triples two-electron density matrices constructed in an intermediate SVD basis of doubles amplitudes. This allows us to avoid steps that scale higher than N6 altogether. Tests against DC-CCSDT and CCSDT(Q) on a benchmark set of chemical reactions with closed-shell molecules demonstrate that the SVD-error is very small already with moderate truncation thresholds, especially so when using a CCSD(T) energy correction. Tests on alkane chains demonstrate that the SVD-error grows linearly with system size, confirming the size extensivity of SVD-DC-CCSDT within a chosen truncation threshold.

Development and validation of a competency-based ladder pathway for AI literacy enhancement among higher vocational students

Abstract The rapid integration of artificial intelligence across industries necessitates systematic AI literacy development in higher vocational education to prepare students for AI-driven professional environments. This study develops and validates a comprehensive competency-based ladder development pathway specifically designed to enhance AI literacy among vocational students. The research employs a mixed-methods approach combining theoretical framework construction, empirical investigation, and practical implementation validation. The three-tier pathway model integrates foundational cognitive, skills application, and comprehensive innovation layers to address diverse learning needs while maintaining progression standards. Through empirical investigation involving 2850 students across 15 institutions, the study identifies distinct learner profiles and competency deficits, informing personalized development strategies. The validation experiment with 420 participants demonstrates significant improvements across all competency dimensions, with overall AI literacy gains of 56.0% and sustained retention rates exceeding 85% at six-month follow-up. The innovative pedagogical approaches incorporate project-driven learning, experiential methodologies, and hybrid delivery models to optimize competency development. The comprehensive evaluation framework provides robust assessment tools that balance formative and summative approaches while maintaining alignment with industry standards. Results indicate that students in the ladder pathway intervention achieved 34.7% higher cognitive assessment scores, 42.3% superior performance on skills application tasks, and 28.9% better innovation competency outcomes compared to traditional instruction. This research contributes to the theoretical understanding of competency-based AI education while providing practical implementation guidance for enhancing workforce readiness in the artificial intelligence era.

Exploring the interplay between BMI, subjective body image perception, and health behaviors: A cross-sectional study

Background This study explored the impact of discrepancies between Body Mass Index (BMI) and Subjective Body Image Perception (SBIP) on metabolic health indicators, physical activity (PA), sedentary behavior (SB), sleep time (ST), and stress levels in Korean adults. Methods Data from 8,634 participants in the 8 th Korea National Health and Nutrition Examination Survey (KNHANES, 2019–2021) were analyzed. Participants were categorized into three groups: Group A (SBIP = BMI), Group B (SBIP < BMI), and Group C (SBIP > BMI). Chi-square tests, ANOVA, and multinomial logistic regression were used to evaluate associations among discrepancies in SBIP and BMI and health behaviors. Results Group B exhibited higher BMI levels (26.04 kg/m 2 ) and adverse metabolic indicators, including elevated fasting glucose (102.11 mg/dL) and triglycerides (161.74 mg/dL), compared to the other groups ( p  < 0.05). Group C had better High-Density Lipoprotein (HDL) cholesterol (59 mg/dL) and lower prevalence rates of hyperlipidemia (9.7%) and hypertension (5.5%) than Group B (hyperlipidemia: 11.6%; hypertension: 5.1%) and Group A (hyperlipidemia: 13.2%; hypertension: 7.6%). Moderate-to-Vigorous PA (MVPA) was significantly lower in Group C (97.88 min/week) than Group A (133.18 min/week; p  < 0.05) and Group B (169.64 min/week; p  < 0.05). SBIP discrepancies had a stronger effect on PA and SB than BMI alone, with Group C being 1.30 times more likely not to meet PA guidelines. Stress levels were significantly higher in those with lower BMI or higher SBIP (Odds Ratio [OR] = 1.93, p  < 0.01). Conclusions SBIP has a stronger influence on health behaviors, particularly PA patterns, than BMI alone. Including SBIP in health promotion strategies may improve interventions for improving PA and addressing metabolic health disparities.

Large riverbed sediment flux sustained for a decade after an earthquake 

Effective spin Hamiltonians for the quantum-rotor tunneling problem in pulse EPR

We analyzed the spin-tunneling Hamiltonian of a quantum rotor coupled to an electron spin. Even under conditions where the rotor’s nuclei are magnetically inequivalent, the symmetry between the rotor’s state exchange and relabeling of nuclei holds exactly; hence, the Hamiltonian can be simplified with the help of a group theoretical approach. We demonstrated this principle on methyl-type and methane-type rotors, both in protonated and deuterated forms. We showed that the spin-tunneling problem in these cases is equivalent to solving a few spin-only problems where the tunneling interaction appears in the form of an effective spin Hamiltonian. We derived spin-operator forms of the effective Hamiltonians and discussed the application to the two-pulse electron spin echo envelope modulation experiment.

Application of construal level theory in identifying factors affecting individual decision-making in implementing flood protection measures in rural areas of Iran

Evaluating chatbots in psychiatry: Rasch-based insights into clinical knowledge and reasoning

Chatbots are increasingly being recognized as valuable tools for clinical support in psychiatry. This study systematically evaluated the clinical knowledge and reasoning of 27 leading chatbots in psychiatry. Using 160 multiple-choice questions from the Taiwan Psychiatry Licensing Examinations and Rasch analysis, we quantified performance and qualitatively assessed reasoning processes. OpenAI’s ChatGPT-o1-preview emerged as the top performer, achieving a Rasch ability score of 2.23, significantly surpassing the passing threshold (p < 0.001). While it excelled in diagnostic and therapeutic reasoning, it also demonstrated notable limitations in factual recall, niche topics, and occasional reasoning biases. Our findings indicate that while advanced chatbots hold significant potential as clinical decision-support tools, their current limitations underscore that rigorous human oversight is indispensable for patient safety. Continuous evaluation and domain-specific training are crucial for the safe integration of these technologies into clinical practice.

Just how bad will climate change get? The only way to know is to fund basic research

A machine learning assisted identification of optimum set of order parameters for study of gas hydrate nucleation in a molecular simulation

The study of crystalline materials is of scientific and technological importance. In this regard, tools such as molecular simulations are widely used to characterize their structure and study their mechanisms of formation. In this work, we develop models for identification and classification of crystal polymorphs during a molecular simulation. The models are based on the XGBoost algorithm, which is a scalable, distributed gradient-boosted decision tree model. The inputs to the model are a set of generic order parameters that have been identified from a large pool using machine learning techniques. This study focuses on gas hydrates, which are naturally occurring crystalline compounds of light gases and water. These materials have tremendous scientific and technological importance, and their formation mechanisms under natural/laboratory conditions are areas of active scientific research. The XGBoost models developed in this work are able to accurately classify gas hydrate polymorphs and also compute nucleation rates using the mean first passage time technique. The novelty of this work is to demonstrate that the use of machine learning techniques mitigates the need for considerable expertise in crystallography while identifying crystal order parameters for polymorph classification.

A novel hybrid extreme learning machine-based diagnosis model for sensor node faults in aquaculture

Resolving Acuticulata (Metridioidea: Enthemonae: Actiniaria), a clade containing many invasive species of sea anemones

Acuticulata is a globally distributed group in the actiniarian superfamily Metridioidea comprised of taxa with ecological, economic, and scientific significance. Prominent members such as Exaiptasia diaphana and Diadumene lineata serve as model organisms for studying coral symbiosis, bleaching phenomena, and ecological invasions. Despite their importance, unresolved phylogenetic relationships and outdated taxonomic frameworks hinder a full understanding of the diversity and evolution of the taxa in this clade. In this study, we employ a targeted sequence-capture approach to construct a robust phylogeny for Acuticulata, addressing long-standing questions about familial monophyly and comparing the results to results from a more conventional five-gene dataset. Specimens from previously underrepresented families and global regions, including the Falkland Islands, were included to elucidate evolutionary interrelationships and improve resolution. Our results support the monophyly of Aliciidae, Boloceroididae, Diadumenidae, Gonactiniidae, and Metridiidae. Our results reiterate the need for taxonomic revision within the family Sagartiidae, as the specimens we included from this family were recovered in four distinct clades. Based on our results, we transfer Paraiptasia from Aiptasiidae to Sagartiidae. These findings emphasize the utility of genome-scale data for resolving phylogenetic ambiguities for morphologically problematic taxa and suggest a framework for future integrative taxonomic and ecological studies within Acuticulata.

Rubber planting and deforestation

State-to-state dynamics of H + LiHe+ (<i>v</i> = 0, <i>j</i> = 0) → LiH+ + He reaction

This work reports the new and up-to-date dynamical investigation of an exoergic reaction, H + LiHe+ (v = 0, j = 0) → LiH+ + He, carried out using the Coriolis coupled time-dependent quantum mechanical and quasi-classical trajectory methods up to the collision energy of 1.0 eV. The potential energy surface used in this work was recently developed in our group [Rawat et al., J. Chem. Phys. 161, 124308 (2024)]. Various reaction observables, such as reaction probability, integral cross sections (ICSs), rate constants, and energy disposal mechanism, are investigated at both state-selected and state-to-state levels. Attempts are made to analyze the reaction mechanism by combining the observed results. Total reaction probability for different J (three-body total angular momentum) collisions and ICSs at state-selected and state-to-state levels show rich resonances, a consequence of the well present in the underlying potential energy surface. The low-energy (∼0.074 96 eV) collision-induced dissociation channel affects the reaction, showing visible effects on reaction observables. These results are significantly different from the previously reported results, which likely improves the understanding of lithium chemistry in the early universe.

Multi-sensor remote sensing captures geometry and slow-to-fast sliding transition of the 2017 Mud Creek landslide

Abstract Landslides pose a significant hazard worldwide. Despite advances in landslide monitoring, predicting their size, timing, and location remains a major challenge. We revisit the 2017 Mud Creek landslide in California using radar interferometry, pixel tracking, and elevation change measurements from satellite and airborne radar, lidar, and optical data. Our analysis shows that pixel tracking of optical imagery captured the transition from slow motion to runaway acceleration starting ~ 1 month before catastrophic failure—an acceleration undetected by satellite InSAR alone. Strain rate maps revealed a new slip surface formed within the landslide body during acceleration, likely a key weakening mechanism. Failure forecast analysis indicates the acceleration followed a hyperbolic trend, suggesting failure time could have been predicted at least 6 days in advance. We also inverted for the landslide thickness during the slow-moving phase and found variations from &lt; 1 to 36 m. While thickness inversions provide important first-order information on landslide size, more work is needed to better understand how landslide subsurface properties and deforming volumes may evolve during the transition from slow-to-fast motion. Our findings underscore the need for integrated remote sensing techniques to improve landslide monitoring and forecasting. Future advancements in operational monitoring systems and big data analysis will be critical for tracking slope instability and improving regional-scale failure predictions.

Seed quality drives grain yield in Ethiopian and Senegalese sorghum: Insights from machine learning

Accurately predicting grain yield remains a major challenge in sorghum breeding, particularly across genetically and geographically diverse germplasm. To address this, we applied a phenotype-informed machine learning (PIML) framework to analyze nine phenotypic traits in 179 Ethiopian and Senegalese accessions. Using hierarchical clustering and oversampling with ADASYN, we achieved high classification accuracy (0.99) for phenotypic group assignment. Grain yield prediction was most effective with a Neural Boosted model (NTanH(3)NBoost(8)), achieving a mean R2  of 0.36 and RASE (equivalent to RMSE) of 4.87. Feature importance analysis consistently identified seed weight and germination rate as the strongest predictors of grain yield, while disease resistance traits showed limited predictive value. These findings suggest that early selection based on seed quality traits may provide a practical strategy for improving sorghum yield under field conditions, especially in resource-limited environments.

Exact quantum dynamics of methanol: Full-dimensional <i>ab initio</i> potential energy surface of spectroscopic quality and variational vibrational states

The methanol molecule is a sensitive probe of astrochemistry, astrophysics, and fundamental physics. The first-principles elucidation and prediction of its rotational–torsional–vibrational motions are enabled in this work by the computation of a full-dimensional, ab initio potential energy surface (PES) and numerically exact quantum dynamics. An active-learning approach is used to sample explicitly correlated coupled-cluster electronic energies, and the datapoints are fitted with permutationally invariant polynomials to obtain a spectroscopic-quality PES representation. Variational vibrational energies and corresponding tunneling splittings are computed up to the first overtone of the C–O stretching mode by direct numerical solution of the vibrational Schrödinger equation with optimal internal coordinates and efficient basis and grid truncation techniques. As a result, the computed vibrational band origins finally agree with experiment within 5 cm−1, allowing for the exploration of the large-amplitude quantum mechanical motion and tunneling splittings coupled with the small-amplitude vibrational dynamics. These developments open the route toward simulating rovibrational spectra used to probe methanol in outer space and in precision science laboratories, as well as for probing interactions with external magnetic fields.

Sustainable water treatment using thermally stable natural clay: dual adsorption–thermolysis approach for organic pollutants and nitrate removal

Enhancement of antiphotoaging properties of Cannabis sativa stem water extracts by fermentation with Lacticaseibacillus casei

Skin photoaging, driven primarily by UVB radiation, leads to collagen degradation and oxidative stress, contributing to the visible signs of aging, such as wrinkles and loss of skin elasticity. This process is mediated by the upregulation of matrix metalloproteinase-1 (MMP-1), which is triggered by reactive oxygen species, and the activation of photoaging-related signaling pathways, including ERK, JNK, and p65. In the present study, we evaluated the antiphotoaging potential of fermented and non-fermented Cannabis sativa stem water extracts, focusing on their ability to suppress MMP-1 expression and reduce oxidative stress in UVB-irradiated human dermal fibroblasts. Unlike previous studies that have primarily focused on leaves or flowers, our study highlights the stems of C. sativa as a novel and underutilized source of bioactive compounds for skin protection. Using Lacticaseibacillus casei for fermentation, we observed enhanced bioactivity in the fermented extracts, particularly in terms of a 6.6% greater inhibition of MMP-1 expression and 68.3% increased flavonoid content, compared to the non-fermented extracts. Fermented water extract demonstrated the most potent suppression of UVB-induced signaling pathways and collagen breakdown. Our findings suggest that fermentation enhances the antiphotoaging properties of C. sativa stems, offering a promising potential for natural, plant-based skin care solutions aimed at preventing UVB-induced skin aging.