Advancing Open Science

In marine hydrodynamics, the core of the boundary element method (BEM) lies in the numerical calculation of the free-surface Green’s function. With the rise of artificial intelligence, using neural networks to fit Green’s function has become a new trend, yet most existing studies are confined to fitting Green’s function in infinite water depth. In this paper, a neural network fitting method for a finite-depth Green’s function is proposed. The classical Multilayer Perceptron (MLP) network and the emerging Kolmogorov–Arnold Network (KAN) are employed to conduct global and partition-based fitting experiments. Experiments indicate that the partition-based KAN fitting model achieves higher fitting accuracy, with most regions reaching 4D fitting precision. For large-scale data input, the average time for the model to calculate a single Green’s function value is 0.0868 microseconds, which is significantly faster than the 0.1120 s required by the traditional numerical integration method. These results demonstrate that the KAN can serve as an accurate and efficient model for finite-depth Green’s functions. The proposed KAN-based fitting method not only reduces the computational cost of numerical evaluation of Green’s functions but also maintains high prediction precision, providing an alternative approach to accelerate BEM calculations for floating body hydrodynamic analysis.

We determined the in vivo absorption coefficient (μ_a) for 82 test subjects, all classified as Fitzpatrick skin phototypes II, III, IV, and V. Measurements were conducted using the integrating-sphere technique on the dorsal and palmar surfaces of the hand and the forearm. The reflectance data obtained were processed using the Inverse Adding Doubling algorithm to calculate the absorption coefficient. The mean values for this parameter ranged from 0.0132 mm⁻¹ to 0.1021 mm⁻¹ at a central wavelength of 624 nm. It was found that these parameters may be grouped into a distinct cohort, paving the way for studies and the design of light-based diagnostics and treatments better suited to the population in Mexico and Latin America.

Background: The Chinese dwarf cherry (CDC) has been valued for over 2000 years for its medicinal and nutritional properties, particularly its kernels. Despite its recognition as a rich source of oil, the potential health benefits of CDC kernel oil remain unclear. Method: Initially, we evaluated the preventive effectiveness of CDC in a mouse model of constipation induced by loperamide. Results: The findings indicated that CDC kernel oil alleviated constipation by reducing the first black fecal defecation time and increasing the fecal number, wet weight, water content and gastrointestinal transit rate in model mice. Additionally, CDC kernel oil reduced inhibitory neurotransmitters and increased excitability neurotransmitters, two anti-oxidases’ activity and fecal short-chain fatty acid (SCFA) content. Histological analysis revealed an improved mucus cell morphology in the intestinal tract. Furthermore, CDC kernel oil increased the abundance of some beneficial bacteria. It was identified that the gut microbiota was associated with neurotransmitters, mediators of inflammation and SCFAs. Conclusion: The findings offer a scientific foundation for considering CDC kernel oil as a potential functional food for the alleviation of constipation.

To elucidate the effect of cold stress on Dendroctonus valens larvae, a study was conducted under controlled laboratory conditions to examine the physiological and biochemical mechanisms associated with cold stress, coupled with transcriptome sequencing. Physiological and biochemical assessments indicated stable water content in larvae during cold stress initiation, with triglycerides and fats serving as primary energy reserves that decreased over cold stress progression. Glycogen and trehalose were identified as energy sources for larval energy metabolism, with their levels increasing as cold stress duration extended. Superoxide dismutase (SOD) activity exhibited an initial decline followed by an increase, while peroxidase (POD) activity initially rose before decreasing over induction time, and catalase (CAT) activity decreased during cold stress induction. Transcriptome sequencing at various time points revealed 4630 upregulated and 1554 downregulated genes, predominantly involved in metabolic pathways such as carbohydrate, amino acid, and lipid metabolism. Quantitative polymerase chain reaction (qPCR) results validated the transcriptome data accuracy. This investigation delineated the physiological, biochemical, and transcriptome alterations during cold stress, offering a theoretical framework for the rational prediction of Dendroctonus valens outbreaks.

This study presents a multiscale, uncertainty-aware hybrid deep learning approach addressing the short-term wind speed prediction problem, which is critical for the reliable planning and operation of wind energy systems. Wind signals are decomposed using adaptive variational mode decomposition (VMD), and the resulting wind components are processed together with meteorological data through a dual-stream CNN–BiLSTM architecture. Based on this multiscale representation, probabilistic forecasts are generated using quantile regression to capture best- and worst-case scenarios for decision-making purposes. Unlike fixed prediction intervals, the proposed approach produces adaptive prediction bands that expand during unstable wind conditions and contract during calm periods. The developed model is evaluated using four years of meteorological data from the Afyonkarahisar region of Türkiye. While the proposed model achieves competitive point forecasting performance (RMSE = 0.700 m/s and MAE = 0.54 m/s), its main contribution lies in providing reliable probabilistic forecasts through well-calibrated uncertainty quantification, offering decision-relevant information beyond single-point predictions. The proposed method is compared with a classical CNN–LSTM and several structural variants. Furthermore, SHAP-based explainability analysis indicates that seasonal and solar-related variables play a dominant role in the forecasting process.

The detection of trace nitrogen dioxide (NO₂) is critical for environmental monitoring and industrial safety. Among various sensing technologies, chemiresistive sensors based on semiconducting metal oxides are prominent due to their high sensitivity and fast response. However, their application is hindered by inherent limitations, including low selectivity and elevated operating temperatures, which increase power consumption. Two-dimensional metal oxysulfides have recently attracted attention as room-temperature sensing materials due to their unique electronic properties and fully reversible sensing performance. Meanwhile, their combination with optoelectronic gas sensing has emerged as a promising solution, combining higher efficiency with minimal energy requirements. In this work, we introduce non-layered 2D indium oxysulfide (In₂S_xO_3−x) synthesized via a two-step process: liquid metal printing of indium followed by thermal annealing of the resulting In₂O₃ in a H₂S atmosphere at 300 °C. The synthesized material is characterized by a micrometer-scale lateral dimension with 6.3 nm thickness and remaining n-type semiconducting behavior with a bandgap of 2.53 eV. It demonstrates a significant response factor of 1.2 toward 10 ppm NO₂ under blue light illumination at room temperature. The sensor exhibits a linear response across a low concentration range of 0.1 to 10 ppm, alongside greatly improved reversibility, selectivity, and sensitivity. This study successfully optimizes the application of 2D metal oxysulfide and presents its potential for the development of energy-efficient NO₂ sensing systems.

Background/Objectives: Neonates with hypoxic–ischemic encephalopathy (HIE) are born in delivery facilities with different levels of neonatal care. The objective of this study was to investigate differences in the incidence of HIE and postnatal management between different levels of neonatal care in delivery facilities. Methods: This is a retrospective, multi-center cohort study of neonates with moderate-to-severe HIE receiving therapeutic hypothermia (TH) in the Canton of Zurich, Switzerland, registered in the Swiss National Asphyxia and Cooling Register between 2015 and 2023. Incidences of HIE receiving TH were calculated for all delivery facilities according to the national levels of neonatal care on site (Level I—basic; Level IIB—intermediate (no Level IIA facility in the Canton of Zurich); Level III—intensive neonatal care). Perinatal characteristics and variables on transport and outcomes were compared between neonates born in Level I and Level IIB facilities (the majority of the HIE population) and reported for neonates born in all other facilities (for completeness). Results: A total of 173 neonates (79 (45.7%) born in Level I; 80 (46.2%) in Level IIB; 9 (5.2%) in Level III; 5 (2.9%) in birthing centers) were admitted to a neonatal cooling center to receive TH. The average number of annual cases of HIE receiving TH per facility was 0.67 (0.11–1.50) in Level I and 2.22 (0.22–3.11) in Level IIB facilities (p = 0.088), respectively. There was no difference in Apgar score, worst pH (within 60 min after birth) and the severity of encephalopathy between neonates born in Level I and Level IIB facilities. Neonatal transport team requests were initiated earlier in Level I facilities (median 12 vs. 34 min of life, p < 0.001). There was no difference in age at initiation of TH (median 3 vs. 3 h, p = 0.431) and the time when target temperature was reached (median 4 vs. 4 h, p = 0.431) between neonates born in Level I and Level IIB facilities. Conclusions: The level of neonatal care available in delivery facilities influenced the management of neonates with HIE receiving TH.

This study aims to examine the methodological applicability of the Theory of Inventive Problem Solving (TRIZ) in the conservation and revitalization of traditional military settlements. Using Zhenjing Village in Jingbian County as a case, the research constructs a systematic framework for contradiction identification and strategy generation. Methods: Through preliminary surveys, data integration, and system modeling, the study identifies major conflicts among authenticity preservation, ecological carrying capacity, and community vitality in Zhenjing Village. Technical contradiction matrices, separation principles, and the Algorithm of Inventive Problem Solving (ARIZ) are employed for structured analysis. Further, system dynamics modeling is used to simulate the effectiveness of strategies and to evaluate the dynamic impacts of various conservation interventions on authenticity maintenance, ecological stress, and community vitality. The research identifies three categories of core technical contradictions and translates the 39 engineering parameters into an indicator system adapted to the cultural heritage conservation context. ARIZ is used to derive the Ideal Final Result (IFR) for Zhenjing Village, which includes self-maintaining authenticity, self-regulating ecology, and self-activating community development, forming a systematic strategy. System dynamics simulations indicate that, compared with “inertial development,” TRIZ-oriented strategies reduce the decline in heritage authenticity by approximately 40%, keep ecological pressure indices below threshold levels, and significantly enhance the sustainability of community vitality. TRIZ enables a shift in the conservation of traditional military settlements from experience-driven approaches toward systematic problem solving. It strengthens conflict-identification capacity and improves the logical rigor of strategy generation, providing a structured and scalable innovative method for heritage conservation in arid and ecologically fragile regions in northern China and similar contexts worldwide.