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The regeneration of bone and the repair of large segmental bone defects represent critical challenges in regenerative medicine. Natural bone tissue is an anisotropic material characterized by an intricate gradient distribution in structure, mechanical properties, and biochemical composition; this multi-dimensional heterogeneity is crucial for maintaining its physiological functions and guiding regeneration. Although tissue engineering scaffolds have demonstrated significant potential in the treatment of bone defects, homogeneous or single-gradient scaffolds often struggle to precisely recapitulate the high degree of heterogeneity and anisotropy of natural bone from the macroscopic to the microscopic level, thereby limiting their capability in repairing complex bone defects. In recent years, biomimetic gradient scaffolds—particularly those employing multi-gradient synergistic designs that integrate physical structure, biochemical composition, and mechanical properties—have emerged as a research frontier in this field due to their ability to accurately mimic the natural bone microenvironment and regulate cellular behavior. This research aims to systematically review the latest research progress in gradient scaffolds for bone tissue engineering. First, gradient characteristics of biomimetic gradient bone scaffolds are summarized; second, the design strategies for gradient scaffolds are discussed in depth, with a focus on the applications and advantages of advanced fabrication techniques, such as additive manufacturing, in constructing multi-dimensional gradient structures; finally, based on current research findings, the emerging development trends and future research directions of biomimetic gradient bone scaffolds are outlined to provide a reference for innovative breakthroughs in the field of bone tissue engineering. Full article

Background/Objectives: Teacher burnout represents a complex cognitive-emotional syndrome characterized by the interplay between mental exhaustion and emotional dysregulation, threatening educational sustainability during crisis periods. This study employed comprehensive behavioral data analysis to investigate burnout syndrome patterns among Greek teachers during the COVID-19 educational crisis, aiming to identify risk factors and resilience patterns through multiple analytical approaches that capture the syndrome’s multidimensional nature. Methods: A cross-sectional study examined primary and secondary school teachers in Western Greece during the autumn of 2021. Stratified random sampling ensured representativeness across school levels, geographic locations, and employment types. Participants completed the Greek-adapted Maslach Burnout Inventory for Educators, which measured emotional exhaustion, depersonalization, and personal accomplishment. Behavioral data analysis integrated traditional statistical methods with advanced pattern recognition techniques, including classification trees for non-linear relationships, association analysis for behavioral patterns, and cluster analysis for profile identification. Results: The majority of teachers experienced high stress with inadequate coping capabilities. Classification analysis achieved high accuracy in predicting burnout severity, identifying emotional exhaustion as the primary predictor. Deputy teachers demonstrated severe cognitive-emotional strain compared to permanent colleagues across all dimensions, with dramatically reduced personal accomplishment and minimal resources. Association analysis revealed that combined low support and high workload more than doubled burnout risk. Three distinct profiles emerged: Resilient teachers, characterized by older age and permanent employment; At-Risk teachers, showing early warning signs; and Burned Out teachers, predominantly young and in precarious employment. Remote teaching, exceeding half of the workload, significantly increased strain. Multiple regression confirmed emotional exhaustion as the dominant syndrome predictor. Conclusions: Behavioral data analysis revealed complex cognitive-emotional patterns constituting burnout syndrome during educational crisis. Employment precarity emerged as the fundamental vulnerability factor, with young deputy teachers facing dramatically higher syndrome probability compared to supported senior permanent teachers. The syndrome manifests through cascading processes where cognitive overload triggers emotional exhaustion, subsequently reducing personal accomplishment. These findings provide an evidence-based framework for early syndrome identification and targeted interventions addressing both cognitive and emotional dimensions of teacher burnout. Full article

Marine ports located in regions of moderate seismicity can face high Natech (natural hazard-triggered technological) risk because large inventories of hazardous materials are stored near dense urban populations. This study proposes and applies a Natech risk framework to a representative port on the Saint-Laurence River in Quebec, Canada. Site-specific peak ground accelerations (PGA) are first estimated for 12 earthquake scenarios using regional ground motion prediction equations adjusted for local site conditions. These hazard levels are combined with a damage probability matrix to estimate Hazardous Release Likelihood Index (HRLi) scores for atmospheric steel storage tanks. Offsite consequences are then evaluated to obtain Maximum Distances of Effect (MDEs) for different types of hazardous materials. MDE footprints are intersected with block-level demographic data and complemented by a domino-effect based on inter-tank spacing, yielding a tank-level Natech Risk Index $NR{\mathrm{I}}_{\mathrm{i},\mathrm{s}}$ for each storage tank ($\mathrm{i}$) and seismic scenario ($\mathrm{s}$). These values are then averaged over all tanks to obtain a scenario-level mean Natech Risk Index ($\overline{NRI}$) for each tank substance. Regression equations relating $\overline{NRI}$ to PGA are provided as a practical tool for defining critical intensity thresholds for seismic Natech risk management in marine ports. Full article

This explanatory mixed-methods study explored the relationship of teachers’ early informal teaching and facilitation experiences on the development of a teacher identity and decisions to pursue careers in teaching. Science teachers were surveyed (n = 160) about informal experiences they had prior to teaching that influenced their decision to enter a teaching career. Ten teachers were randomly selected to participate in a follow-up interview designed to gain insight into the context and nature of the experiences. Results showed that activities involving facilitation, such as tutoring, camp counselling, and volunteering at museums, were perceived as significant influences on career choice more than participatory experiences. Facilitating roles allowed individuals to practice and develop leadership and teaching skills. Experiences such as museum volunteering were reported as particularly impactful. The results suggest that some types and contexts of experiences play differential roles in the development of science teachers’ career aspirations. The findings underscore the potential of informal, facilitation-based experiences as tools to cultivate future science educators. This study examined reported experiences that contribute to science teachers’ identity and career aspirations, and the results can inform our understanding of the teacher pipeline by supporting opportunities for youth to be engaged in experiences where they can facilitate learning for others. Full article

Background: Following gonadotropin-releasing hormone (GnRH) agonist trigger and “freeze all” in order to prevent ovarian hyper-stimulation syndrome (OHSS), patients are usually anxious to continue immediately with a frozen embryo transfer (FET). Currently, the preferred FET protocol in based on natural or induced ovulation. Objectives: Do ovarian stimulation and GnRH-a, used to trigger final oocyte maturation, affect the reproductive axis in the next natural cycle? Design: An observational case series of 100 subsequent in vitro fertilization (IVF) patients to whom GnRH-a (Triptorelin 0.2 mg) was given for final oocyte maturation in the context of ovarian hyper-stimulation syndrome prevention, followed by embryos “freeze all”. Methods: In the next natural cycle, patients were followed to detect a dominant follicle (≥17 mm), at which time ovulation was triggered with human chorionic gonadotropin (hCG, 250 µg), and FET was scheduled according to embryo’s age on freezing day. Results: Whereas natural ovulation according to pre-IVF treatment was predicted to be on cycle day 14, the actual hCG-scheduled ovulation in our patients was on day 21. In eight patients, follicular activity was not detected after 15–28 days; therefore, the natural cycle frozen embryo transfer approach was abandoned. Conclusions: Ovarian stimulation and GnRH-a used to trigger final oocyte maturation in IVF patients inhibits the reproductive axis for days. Therefore, natural ovulation in the subsequent cycle may be deferred for about one week relative to the patient’s pre-IVF menstrual cycle pattern. This may help schedule clinic visits to optimize monitoring efficiency. Full article

A Monte Carlo based fractal prediction model is proposed to describe the evolution of surface morphology during the running-in process. The model accounts for the random and fractal characteristics of worn surfaces. The Weierstrass–Mandelbrot function is employed to simulate rough surfaces and establish the correlation between fractal dimension and surface roughness. By integrating traditional sliding wear models with surface effect functions, a unified prediction framework is developed. Experiments are conducted to obtain worn surface parameters and calculate fractal dimensions at different running-in stages. Model parameters are optimized by minimizing the variance between experimental and predicted results. Monte Carlo simulations are then introduced to represent the stochastic nature of the friction system, thereby improving prediction accuracy and objectivity. The proposed model reveals locally random yet globally convergent patterns, which are consistent with experimental observations. It effectively captures the stochastic evolution of surface morphology and provides a reliable approach for predicting worn surface behavior during running-in. Full article

Mucilage extracted from cladodes of Opuntia ficus-indica (L.) Mill. has attracted growing interest as a natural food additive due to its gelling and nutritional properties. In this study, the chemical characteristics of Opuntia ficus-indica mucilage were comparatively evaluated against commercial pectin, with particular emphasis on volatile compounds, mineral composition, and monosaccharide profiles by ¹³C-NMR spectroscopic analysis. The volatile components were analysed using gas chromatography–mass spectrometry (GC-MS), revealing distinct aromatic profiles between the two matrices, with the mucilage showing a significant presence of methoxypyrazines, but not detected in the powdered pectin studied. These compounds could negatively affect the sensory perception of mucilage. Mineral analysis demonstrated significantly higher levels of calcium, magnesium, and potassium, supporting its potential contribution to nutritional enrichment. The spectroscopic analysis, used to identify monosaccharide composition of polysaccharide chains, highlighted the presence of arabinose, galactose, glucose, and rhamnose in the mucilage sample compared to the predominantly glucose/galacturonic acid-based structure of pectin. Overall, the results indicate that Opuntia ficus-indica mucilage represents a promising alternative to pectin, offering unique chemical properties that may expand its application as a multifunctional, natural food additive. Full article

Obstacle negotiation during locomotion depends on the integration of exteroceptive information about the environment, proprioceptive signals from the body, and exproprioceptive visual feedback about the limbs. This study examined how removing visual limb information and introducing VR-specific sensory uncertainty affect overground obstacle-crossing behavior. Participants walked under three conditions: natural environment with full vision, natural environment with lower-limb occlusion, and immersive VR without a lower-limb representation. Removing limb vision in the real world selectively increased toe clearance while leaving baseline gait unchanged, demonstrating the role of exproprioceptive feedback in fine-tuning foot trajectory. VR amplified these adaptations, yielding slower speeds, wider bases of support, and even greater clearance margins, reflecting compounded uncertainty from altered exteroceptive cues. Yet obstacle location effects were consistent across environments, suggesting preserved underlying control and the potential for a scaling relationship between VR and real-world performance. Findings highlight key design considerations for VR-based gait assessment and rehabilitation. Full article

Earth Observation (EO) and Remote Sensing (RS) data are widely used in various fields, including weather, environment, and natural disaster modeling and prediction. EO and RS done through geostationary satellite constellations in fields such as these are limited to a smaller region, while sun synchronous satellite constellations have discontinuous spatial and temporal coverage. This limits the ability of EO and RS data for near-real-time weather, environment, and natural disaster applications. To address these limitations, we introduce Now Observation Assemble Horizon (NOAH), a multi-modal, sensor fusion dataset that combines Ground-Based Sensors (GBS) of weather stations with topography, vegetation (land cover, biomass, and crown cover), and fuel types data from RS data sources. NOAH is collated using publicly available data from Environment and Climate Change Canada (ECCC), Spatialized CAnadian National Forest Inventory (SCANFI) and United States Geological Survey (USGS), which are well-maintained, documented, and reliable. Applications of the NOAH dataset include, but are not limited to, expanding RS data tiles, filling in missing data, and super-resolution of existing data sources. Additionally, Generative Artificial Intelligence (GenAI) or Generative Modeling (GM) can be applied for near-real-time model-generated or synthetic estimate data for disaster modeling in remote locations. This can complement the use of existing observations by field instruments, rather than replacing them. UNet backbone with Feature-wise Linear Modulation (FiLM) injection of GBS data was used to demonstrate the initial proof-of-concept modeling in this research. This research also lists ideal characteristics for GM or GenAI datasets for RS. The code and a subset of the NOAH dataset (NOAH mini) are made open-sourced. Full article

This paper investigates tail-risk transmission and asymmetric dependence in commodity markets using an asymmetric fuzzy vine copula framework applied to gold, crude oil, natural gas, and silver from 1 January 2015 to 1 January 2025, extracted from Yahoo Finance. Bootstrap-based trapezoidal fuzzy numbers are used to estimate fuzzy tail dependence, VaR, and CoVaR, capturing both sampling variability and parameter uncertainty. Results show generally weak and symmetric dependence among commodities, except for strong lower-tail dominance between crude oil and natural gas, indicating downside contagion within the energy sector. Adding the SKEW index as a market-implied tail-risk proxy has negligible effects on dependence and spillovers, revealing that equity-market tail-risk sentiment does not influence commodity markets. Systemic risk remains localized within energy and precious-metal linkages, underscoring the need for sector-specific monitoring. Full article

This data descriptor presents two fully synthetic corpora for sentiment analysis and named entity recognition (NER) in Uzbek. The first corpus contains 12,000 hybrid synthetic sentences generated from templates with lexical randomization, automatic insertion of named entities (PER/ORG/LOC), lexicon-based polarity scoring, and a controlled emoji distribution. The second corpus includes 3000 “manual-style” sentences designed to resemble short, naturally structured messages. Although the manual-style subset was initially intended to be emoji-free, the released version includes a 39.6% emoji presence (sentences containing at least one emoji) to maintain comparability in emotional markers across corpora. Both corpora are released in CSV, XLSX, and JSONL formats and share a unified schema (id, text, sentiment, entities, entity_type, polarity_score, polarity_source, token_count, emojis, emoji_position, emoji_sentiment, conflict_flag, sentiment_from_polarity_score, split). The dataset is publicly available via Mendeley Data (DOI: 10.17632/y2d5pcyrzz.3). Full article

Table question answering has been studied using datasets drawn from a variety of tabular sources and task formats. However, most publicly available resources have been created in high-resource languages such as English. For low-resource languages, researchers are often required to construct new datasets or translate existing ones, which incurs substantial time, effort, and financial cost. In contrast to natural language text, table data consists of structured entries whose interpretation is less affected by language-specific syntax or word order. In this work, we present a cost-effective strategy for multilingual table QA that relies on selectively translating only the questions of existing datasets. Leveraging the language-agnostic structure of tables, our approach maintains the original table content while translating queries into multiple target languages. To address possible performance drops caused by using table data in the source language rather than the target language, we apply cross-lingual adaptation techniques using contrastive learning and adversarial training. In addition, to strengthen reasoning ability while avoiding degradation in languages not seen during pre-training, we perform supplementary pre-training of a RoBERTa-based multilingual encoder with SQL-derived table data. Finally, we extend our investigation beyond encoder-based architectures and evaluate decoder-only large language models under the same multilingual table QA setting. The experiments show that LLaMA-3 models exhibit strong cross-lingual generalization even without using translated table context and often achieve competitive performance using only Korean table data. Moreover, the performance gap among training configurations such as translated queries or translated datasets is notably smaller compared to encoder-based models, highlighting the inherent multilingual robustness of modern LLMs. We further evaluate LLaMA-3 models on domain-specific table datasets and observe that domain knowledge acquired from Korean tables transfers effectively across languages even without multilingual supervision, underscoring the potential of LLMs for specialized multilingual table reasoning. These findings demonstrate that LLMs can serve as an effective alternative for multilingual table QA, particularly in low-resource or partially translated environments. Full article

Uranium production tailing ponds in Kamyanske (Ukraine) are objects of increased radioecological danger. Violation of the stability and integrity of containment dams threatens the uncontrolled spread of radionuclides. The purpose of this study is to comprehensively assess the factors affecting the technical condition and environmental safety of the Sukhachivske tailing dam. The study included a visual inspection and detailed geophysical work using the natural pulse electromagnetic field of the Earth (NPEMFE) method. This method was chosen to identify hidden filtration paths and stress zones in the body of the earth dam. An analysis of the spatial distribution of waterlogging, filtration, and fissuring in the hydraulic structure was performed. Based on the results of the NPEMFE survey, six zones with varying degrees of waterlogging and stress–strain states of the structure were identified. The presence of externally unmanifested filtration paths and suffusion areas was established, and a tectonic scheme of fracture development in the dam body was compiled. A correlation was found between the dominant azimuths of crack extension (70–79° and 350–359°) and the directions of regional tectonic lineament zones, at the intersection of which the tailing pond is located. It has been established that modern tectonic movements along fault zones create zones of permeability, which serve as primary pathways for water filtration and further development of suffusion. This conclusion introduces a new tectonic feature for risk diagnosis and monitoring of similar hydraulic structures. Full article

This study presents the design, fabrication, and performance evaluation of a solar dryer capsule cabinet equipped with a parabola reflector, developed to enhance drying efficiency through the reflection of sunlight onto both the upper and lower surfaces of the product. Conventional solar drying exposes only the upper surface, resulting in uneven heating and the need for manual turning. The proposed system integrates a parabolic reflector and IoT-based monitoring sensors (BH1750 light sensor and DHT22 temperature-humidity sensor) to optimize heat distribution and record real-time environmental parameters. Dry experiments were conducted using Citrus hystrix DC. (Makrut lime) peels under natural sunlight from 9:00 a.m. to 5:00 p.m. The moisture loss achieved with the proposed dryer (P-DSD) was 45.66%, compared with 6.79% for direct solar drying (DSD). The drying rate increased from 3.05 g h⁻¹ (DSD) to 20.50 g h⁻¹ (P-DSD), while the specific energy consumption (SEC) decreased from 3519.75 kWh kg⁻¹ to 523.67 kWh kg⁻¹, representing an 85.13% energy reduction. Economic analysis showed a system cost of $1384 and a return on investment of 30.0%. These results demonstrate that the proposed solar dryer capsule cabinet with a parabola reflector offers a low-cost, eco-friendly, and high-efficiency solution for drying agricultural and herbal products, significantly shortening the drying time and improving product quality. Full article

Natural gas hydrates (NGHs) currently lack economic feasibility; efficient exploitation methods must be continuously explored to increase their production capacity. Drawing on field data from China’s first offshore NGH trial production, a numerical simulation method was used to evaluate a comprehensive development strategy that combines a horizontal well with boundary sealing, near-wellbore stimulation, and step-wise depressurization to improve the recovery of Class 1 NGH reservoirs. The results indicated that boundary sealing has a strong enhancement effect: it inhibits water invasion and thus concentrates the energy for hydrate dissociation. The use of high-pressure water jets for near-wellbore stimulation generates highly permeable channels, greatly accelerating hydrate dissociation and gas flow; step-wise depressurization optimizes the production behavior by controlling water production. The combined application of these technologies significantly improves development performance, with cumulative gas production (V_g) increasing to 220.1% and the gas-to-water ratio (R_gw) increasing to 102.6% compared to the base case, providing an effective strategy for the development of Class 1 NGH reservoirs. Full article