Search Results (7,646)

Atomic Layer Deposition (ALD) is a key thin-film fabrication technique that enables the growth of ultra-thin, conformal, and compositionally controlled layers for applications in nanoelectronics, optoelectronics, and energy devices. However, the high cost and operational complexity of commercial ALD systems limit their accessibility in academic and emerging research environments. In this work, a low-cost, automated thermal ALD system is designed, assembled, and experimentally validated for the deposition of zinc oxide (ZnO) thin films. The developed system enables precise control of precursor dosing, purge sequences, and substrate temperature via an integrated LabVIEW–Arduino control architecture, allowing reproducible and stable thin-film growth. The design allows the use of various precursors through high-precision three-way diaphragm valves. In addition, the system allows continuous purge gas flow in the reaction chamber, which enhances the drag velocity of the precursor gas, reducing dosage requirement, accelerating chamber saturation time and lowering the total consumption of precursors per deposition cycle. ZnO thin films were successfully grown on silicon and glass substrates at 200 °C using diethylzinc (DEZ) as the metal precursor and hydrogen peroxide (H₂O₂) as the oxidant. The process exhibited self-limiting growth characteristics typical of ALD, yielding a growth per cycle of approximately 0.8 Å. The deposited ZnO films exhibited optical transparency of 70–80% in the visible region, a refractive index of approximately 1.9, and an optical bandgap close to 3.4 eV, which are consistent with values reported for high-quality ZnO films grown in commercial ALD systems. These results demonstrate that the proposed low-cost platform is capable of producing functional ZnO thin films with properties comparable to those obtained with conventional commercial reactors. Overall, this work presents an accessible and scalable thermal ALD system that significantly reduces equipment costs while maintaining reliable process control and film quality, offering a practical framework for expanding thin-film research capabilities across microelectronics, optoelectronics, and nanotechnology laboratories. Full article

Camellia oleifera, a woody oil crop unique to China, plays a crucial role in alleviating the global pressure of edible oil supply and maintaining ecological security. However, it remains challenging to accurately forecast Camellia oleifera yield in complex landscapes using only remote sensing data. The aim of this study is to develop an interpretable deep learning model, namely Shapley Additive Explanations–guided Attention–long short-term memory (SALSTM), for estimating Camellia oleifera yield by integrating an improved spectral bloom index and environmental parameters. The study area is located in Hengyang City in Hunan Province. Sentinel-2 imagery, meteorological observation from 2019 to 2023, and topographic data were collected. First, an improved spectral bloom index (ISBI) was constructed as a proxy for flowering density, while average temperature, precipitation, accumulated temperature, and wind speed were selected to represent environmental regulation variables. Second, a SALSTM model was designed to capture temporal dynamics from multi-source inputs, in which the LSTM module extracts time-dependent information and an attention mechanism assigns time-step-wise weights. Feature-level importance derived from SHAP analysis was incorporated as a guiding prior to inform attention distribution across variable dimensions, thereby enhancing model transparency. Third, model performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²). The result show that the constructed SALSTM model achieved strong predictive performance in predicting Camellia oleifera yield in Hengyang City (RMSE = 0.5738 t/ha, R² = 0.7943). Feature importance analysis results reveal that ISBI weight > 0.26, followed by average temperature and precipitation from flowering to fruit stages, these features are closely associated with C. oleifera yield. Spatially, high-yield zones were mainly concentrated in the central–southern hilly regions throughout 2019–2023, In contrast, low-yield zones were predominantly distributed in the northern and western mountainous areas. Temporally, yield hotspots exhibited a gradual increasing while low-yield zones showed mild fluctuations. This framework provides an effective and transferable approach for remote sensing-based yield estimation of flowering and fruit-bearing crops in complex landscapes. Full article

Droughts are recognized as one of the most devastating extreme climate events, leading to severe socioeconomic losses and ecological degradation globally under climate change. With global warming, the frequency and intensity of extreme droughts are increasing, posing critical challenges to water resource management. The Standardized Precipitation Conversion Index (SPCI) has demonstrated potential in drought monitoring; however, its applicability across diverse climatic zones and multiple temporal scales remains inadequately validated. This study addresses this gap by establishing a novel multi-scale inversion analysis using ERA5-based precipitable water vapor (PWV) and precipitation data. SPCI is selected for its advantage in eliminating climatic background biases through probability normalization, overcoming limitations of traditional indices such as the Standardized Precipitation Index (SPI) and Standardized Precipitation-Evapotranspiration Index (SPEI). We systematically evaluated the spatiotemporal evolution of Precipitation Efficiency (PE) and SPCI across four climatic zones in China. Results show that the first two principal components explain over 85% of the spatiotemporal variability of PE, with PC1 independently contributing from 82.05% to 83.80%. This high variance contribution underscores that the spatiotemporal patterns of PE are dominated by a few key climatic drivers, validating the robustness of the principal component analysis. SPCI exhibits strong correlation with SPI, exceeding 0.95 in the Tropical Monsoon Zone (TMZ) at scales of 1–6 months, indicating its utility for short-to-medium-term drought monitoring. Distinct zonal differentiation in PE patterns is revealed, such as the bimodal annual cycle in the Tropical-Subtropical Monsoon Composite Zone (TSMCZ). This study evaluates the performance of the SPCI against the widely used SPI and SPEI across four major climatic zones in China. It validates the SPCI’s applicability across China’s complex climates, providing a scientific basis for region-specific drought early warning and water resource optimization. Full article

A central challenge in reconfigurable photonics based on quasi bound states in the continuum (quasi-BICs) is to move beyond binary switching toward multistate and polarization-aware programmability. Here we propose a dual-phase-change material (PCM) metasurface that enables four-state nonvolatile switching and polarization-divergent dispersion rewriting within a single unit cell. Two independently switchable PCM layers provide four addressable configurations (0-0, 0-1, 1-0, 1-1) at a fixed geometry, allowing the resonance landscape to be reprogrammed through complex-index rewriting without structural modification. Angle-resolved transmission maps reveal fundamentally different evolution pathways for orthogonal polarizations. For p polarization, the quasi-BIC exhibits strong state sensitivity with dispersion reshaping and multi-branch features near normal incidence; the resonance red-shifts from ~1331 nm to ~1355 nm while the quality factor decreases from ~6.7 × 10⁴ to ~4.0 × 10⁴. In contrast, for s polarization, a single weakly dispersive branch translates coherently across states, producing a much larger shift from ~1635 nm to ~1790 nm while the quality factor increases from ~9.0 × 10³ to ~1.8 × 10⁴. The opposite quality-factor trajectories, together with the polarization-contrasting tuning ranges, demonstrate that dual-PCM programming reconfigures polarization-selective radiative coupling rather than imposing a uniform resonance shift. This compact two-bit metasurface platform provides multistate, high-Q control with active dispersion engineering, enabling polarization-multiplexed reconfigurable filters, state-addressable sensors, and other programmable photonic devices. Full article

Educators’ epistemological experience of facilitating medical students’ active learning is under-researched, especially concerning non-biomedical learning in integrated curricula. Longitudinal, qualitative research on problem-based learning (PBL) tutors’ long-term insights is rare. Therefore, this study explores the following question: How do tutors conceptualise knowledge and knowing, particularly non-biomedical, after substantial experience in an integrated, problem-based medical curriculum and how does that relate to the student perspective? In a mixed-method study (pragmatism paradigm), sixteen years after semi-structured interviews with inaugural PBL tutors, follow-up interviews with the remaining ten revisited their replies about the population health knowledge theme. Via e-questionnaire, two years later, 9/10 tutors discussed student comments about their own knowledge base from four historical surveys (two student-cohorts, Years 1 and 5). Those surveys also provided a backdrop of comments on the public health knowledge theme, including threshold concepts and reducing health inequalities, plus Moore’s Cognitive Complexity Index (CCI). Each survey found mean CCI in Perry position 3–4 transition (multiplicity-to-relativism). Uncertainty or concern, especially about feared basic science gaps, prevailed across CCI scores. Public health knowledge appeared ‘worthy’ but unappealing for students’ professional identity, but tutors now appreciated its ‘ways of knowing’ and were more reflective, flexible, and accommodating about their own and students’ knowledge. Persistent challenges were student uncertainty or concern about knowledge gaps, particularly basic science, and conflict between knowledge types, for which staff and student epistemological support should be explicitly anticipated. Further research should explore staff–student epistemologies about other types of knowledge. Full article

Lignin is a complex aromatic polymer that constitutes a major fraction of plant biomass and represents a valuable renewable carbon resource. Naturally decaying wood serves as an environmental reservoir of microorganisms capable of degrading lignin. In this study, we isolated and characterized sixteen bacterial strains from decaying Tilia cordata wood using an enrichment culture technique with lignin as the sole carbon source. Taxonomic identification via 16S rRNA gene sequencing revealed microbial diversity spanning the genera Bacillus, Pseudomonas, Stenotrophomonas, and several members of the Enterobacteriaceae family, including Raoultella terrigena isolates. Metagenomic sequencing of the wood substrate revealed an exceptionally rich and balanced bacterial community (Shannon index H′ = 5.07), dominated by Streptomyces, Bradyrhizobium, Bacillus, and Pseudomonas, likely reflecting a specialized consortium adapted to lignin rich late-stage decay. Functional phenotyping demonstrated that all isolates possess ligninolytic potential, evidenced by peroxidase/laccase-type activity through methylene blue decolorization. Dynamic Light Scattering (DLS) and HPLC analyses showed that some isolates, such as Raoultella terrigena MGMM806, effectively depolymerized lignosulfonate into low molecular weight fragments (1.23 nm), while others accumulated intermediate metabolites or completely mineralized the substrate. Growth profiling on monolignol substrates revealed a broad spectrum of catabolic specialization in lignin monomer degradation. The results demonstrate a complex system of metabolic partitioning within a natural bacterial consortium. This collection represents a foundational genetic resource for developing engineered biocatalysts and synthetic microbial communities aimed at the efficient conversion of lignin into valuable aromatic compounds. Full article

Dissolved organic matter (DOM) in salt lake brines comprises organic compounds dissolved in high-salinity aquatic systems. With complex composition and diverse sources, DOM significantly influences biogeochemical cycles, mineral formation, and resource development in salt lakes. However, few studies have investigated the characteristics and sources of DOM in salt lake brines. In this study, a DOM sample (YC-4) from brine of Shanxi Yuncheng Salt Lake was isolated and characterized using FT-ICR-MS, nuclear magnetic resonance spectroscopy, three-dimensional fluorescence spectroscopy, and parallel factor analysis. The results demonstrate that YC-4 DOM exhibits rich chemical diversity, primarily composed of lignin/CRAM-like compounds (54.26%), tannins (16.75%) and proteins (13.43%). The predominant carbon forms in YC-4 DOM were aliphatic C-O bonded compounds (33.74%), aliphatic compounds (24.31%), and carboxylic acid compounds (23.95%). YC-4 DOM consists of five fluorescent components: marine-like humic substances, two types of humic-like substances, fulvic-like substances, and one protein-like substance. The fluorescence signature, characterized by high fluorescence index (FI 1.99), low humification index (HIX 0.66), and high biological index (BIX 1.27), collectively indicates that the DOM in Yuncheng Salt Lake brine is predominantly autochthonous, weakly humified, and highly bioavailable. This study reveals the DOM feature within the “human–environment coupled system” of Yuncheng Salt Lake. The findings provide a scientific basis for the sustainable utilization of its brine DOM resources and further enrich the theoretical system of DOM biogeochemical cycle in high-salinity lake system. Full article

Accurate prediction of individual treatment effects in survival analysis is often complicated by the presence of competing risks and the inherent unobservability of counterfactual outcomes. While machine learning models offer improved discriminative power, they typically lack rigorous guarantees for uncertainty quantification, which are essential for safety-critical clinical decision-making. In this paper, we introduce CONFIDE (CONFormal Inference for Distribution-free Estimation), a novel framework that bridges causal inference and conformal prediction to construct valid prediction sets for cause-specific cumulative incidence functions. Unlike traditional confidence intervals for population-level parameters, CONFIDE provides individual-level prediction sets for time-to-event outcomes, which are more clinically actionable for personalized treatment decisions by directly quantifying uncertainty in future patient outcomes rather than uncertainty in population averages. By integrating semi-parametric hazard estimation with targeted bias correction strategies, CONFIDE generates calibrated prediction sets that cover the true potential outcome with a user-specified probability, irrespective of the underlying data distribution. We empirically validate our approach on four diverse medical datasets, demonstrating that CONFIDE achieves competitive discrimination (C-index up to 0.83) while providing robust finite-sample marginal coverage guarantees (e.g., 85.7% coverage on the Bone Marrow Transplant dataset). We note two key limitations: (1) coverage may degrade under heavy censoring (>40%) unless inverse probability of censoring weighted (IPCW) conformal quantiles are used, as demonstrated in our sensitivity analysis; (2) while the method guarantees marginal coverage averaged over the covariate distribution, conditional coverage for specific covariate values is theoretically impossible without structural assumptions, though practical approximations via locally-adaptive calibration can improve conditional performance. Our framework effectively enables trustworthy personalized risk assessment in complex survival settings. Full article

The phaeozem in Northeast China is rich in soil organic carbon (SOC). However, the excessive and inefficient application of chemical fertilizers, particularly nitrogen fertilizers, has primarily led to a decrease in soil pH in this region. Currently, the relationship between soil pH and the stability of soil organic carbon (SOC) remains ambiguous. This study, conducted over 13 years of field experiments, focused on soils exhibiting varying degrees of pH resulting from different nitrogen application rates. The research employed aggregate classification, ¹³C nuclear magnetic resonance spectroscopy, and analysis of microbial community composition to investigate the alterations in the SOC stabilization mechanisms under varying nitrogen application levels. Our results demonstrated that the decline in soil pH led to reductions in macroaggregates (>2 mm) and the soil aggregate destruction rate (PAD) by 4.8–14.6%, and in soil aggregate unstable agglomerate index (E_LT) by 9.7–13.4%. The mean weight diameter (MWD) and geometric mean diameter (GMD) exhibited significant declines (p < 0.05) with decreasing pH levels. According to the ¹³C NMR analysis, the SOC was predominantly composed of O-alkyl carbon and aromatic carbon. At a pH of 5.32, the Alip/Arom values decreased, while the molecular structure of SOC became more complex under different levels of pH. In addition, the increase in [Fe(Al)-OC] (31.4–71.9%) complex indicates a shift in the stability of organic carbon from physical protection to organic mineral binding. Declining soil pH significantly reduced the diversity of soil microbial communities and promoted a shift toward copiotrophic microbial groups. Overall, declining soil pH resulted in a decline in soil aggregate stability and an increase in SOC aromaticity. This drove the shift in the stabilization mechanism of SOC in the black soil ecosystem of meadows in Northeast China from physical protection to chemical stability. Full article

Beach nourishment has become a globally adopted “soft” engineering measure to mitigate coastal erosion and sustain beach functions. This study conducts a systematic comparative analysis of beach nourishment practices between China and the United States, focusing on extensive project data and historical records. The research examines differences in historical development trajectories, spatial distribution patterns, restoration philosophies, funding mechanisms, and key technologies. The results reveal that the U.S., with over a century of experience, exhibits large-scale, high-frequency nourishment projects supported by diversified funding and long-term maintenance strategies. In contrast, China, despite a later start (circa 1992), has achieved rapid progress in both project scale and technical innovation, though its approach remains more government-led and structurally oriented. This study also identifies converging trends in resource concentration between the two countries, as measured by a proposed “beach nourishment primacy” index. Based on these findings, the work offers strategic recommendations for the coastal management of China, including the establishment of a national nourishment database, adoption of Regional Sediment Management, and greater integration of ecological engineering principles. This comparative analysis provides valuable insights for coastal nations seeking to optimize beach nourishment strategies in the face of growing climatic and anthropogenic pressures; to further advance these efforts, future research could explore the integration of interdisciplinary approaches and intelligent technologies, aiming to deepen our understanding of coastal system complexity and support the development of dynamic adaptive management. Full article

In regions with limited meteorological monitoring systems, spatial drought modeling is of importance for efficient water resource management. This study recommends an alternative drought modeling strategy for Standardized Precipitation Evapotranspiration Index (SPEI) prediction at multiple target stations using data from neighboring stations. The Multi-Output Random Forest (MORF) model is implemented in this study to consider the spatial correlations among stations for the simultaneous prediction of SPEI for multiple stations instead of training independent models for each station. The efficiency of MORF is further compared to Multi-Output Support Vector Regression (MOSVR) and three baselines; a single-output RF, a monthly climatology model, and a persistence model. In addition to statistical performance criteria, drought characteristics are evaluated using intensity–duration–frequency analysis for three temporal scales (SPEI-3, SPEI-6, and SPEI-12). Results demonstrate that MORF outperformed MOSVR and RF in approximating observed drought intensity, duration, and frequency under moderate, severe, and extreme drought scenarios. Furthermore, spatial analysis reveals that MORF accurately captured the seasonal evolution of drought conditions including onset and recovery phases. The remarkable success of MORF in contrast to MOSVR and three traditional baselines can be explained by its ability to detect nonlinear and complex interactions of drought condition among various neighboring stations. This study emphasizes the promise of multi-output machine learning algorithms for drought monitoring in water resource management and climate adaptation planning in data-scarce regions. Full article

Accurate characterization of ore grindability is essential for optimizing mill throughput, reducing energy consumption, and predicting mill performance under varying ore conditions. However, the standard Bond work index (BWI) test remains time-consuming, costly, and requires a large amount of sample. This study evaluates the effectiveness of several rapid, low-cost alternatives, Leeb rebound hardness (LRH), Cerchar abrasivity Index (CAI), portable X-ray fluorescence (pXRF), and hyperspectral imaging (HSI), as proxies for grindability in gold-bearing ores. Sixty-two hand-size rock samples collected from two adjacent Canadian open-pit mines were analyzed using these techniques and subsequently grouped into ten ore groups for BWI testing. LRH and CAI effectively differentiated moderate (<15 kWh/t) from hard (>15 kWh/t) grindability classes, while geochemical features and HSI-based mineralogical attributes also showed strong predictive capability. HSI, in particular, provided non-destructive, spatially continuous data that are advantageous for complex geology and large-scale operational deployment. A conceptual workflow integrating HSI with complementary field measurements is proposed to support comminution planning and optimization, enabling more responsive and timely decision-making. While BWI testing remains necessary for circuit design, the results highlight the value of combining rapid proxy measurements with advanced analytics to enhance geometallurgical modelling, reduce operational risk, and improve overall mine-to-mill performance. Full article

Economic diversification is one state method used to best utilize national resources and contribute to economic and sustainable development. This paper examines the impact of governance on economic diversification in a selected number of countries (114) using both governance and economic diversification indicators from 1996 to 2023. The intended outcome of this paper is to determine whether the improvement in the quality of governance, measured by the aggregated WGI index, is positively and statistically associated with an increase in the Economic Complexity Index (ECI). A general linear mixed model (GLMM) was constructed to address the research question by evaluating fixed and random effects based on the analysis of repeated measures. However, the study has some limitations such as using an aggregate governance index rather than each indicator by itself and differences among country groups in development and institutional quality level. The findings reveal that economic diversification is linked to the quality of a country’s institutions. The result shows that (coefficient β = 0.283) with 95% CI, which means that on average, the ECI increased by 0.283 for every one-unit increase in the WGI. Moreover, the increase in ECI exceeded 0.1 for every one-unit increase in WGI 95% of the time. Countries with advanced administrative, economic, and institutional structures are better positioned to achieve their desired economic diversification goals. Thus, decision-makers and legislators, especially in countries with low-levels of institutional quality, need to balance ensuring good governance practices with supporting the country’s economic development. Full article

Background: Irritable Bowel Syndrome (IBS) is a heterogeneous gastrointestinal disorder characterized by complex brain–gut interactions. Patient Similarity Networks (PSNs) offer a novel approach for exploring this heterogeneity and identifying clinically relevant patient subgroups. Methods: We analyzed data from 78 participants (49 IBS patients and 29 healthy controls) with 36 brain morphometric measures (FreeSurfer v7.4.1) and 6 measures of cognitive functions (5 RBANS domain indices plus a Total Scale score). PSNs were constructed using multiple similarity measures (Euclidean, cosine, correlation-based) with Gaussian kernel transformation. We performed community detection (Louvain algorithm), centrality analyses, feature importance analysis, and correlations with symptom severity. Statistical validation included bootstrap confidence intervals and permutation testing. Results: The PSN comprised 78 nodes connected by 469 edges, with four communities detected. These communities did not significantly correspond to diagnostic groups (Adjusted Rand Index = 0.011, permutation $p=0.212$), indicating IBS patients and healthy controls were intermixed. However, each community exhibited distinct neurobiological profiles: Community 1 (oldest, preserved cognition) showed elevated intracranial volume but reduced subcortical gray matter; Community 2 (youngest, most severe IBS symptoms) had elevated cortical volumes but reduced white matter; Community 3 (most balanced IBS/HC ratio, mildest IBS symptoms) showed the largest subcortical volumes; Community 4 (lowest cognitive performance across multiple domains) displayed the lowest RBANS scores alongside high IBS prevalence. Top network features included subcortical structures, corpus callosum, and cognitive indices (Language, Attention). Conclusions: PSN identifies brain–cognition communities that cut across diagnostic categories, with distinct feature profiles suggesting different hypothesis-generating neurobiological patterns within IBS that may inform personalized treatment strategies. Full article

Given global efforts to promote sustainable energy transitions, this study investigates how the deployment of renewable energy technologies (RETs) relates to multidimensional societal welfare and provides empirical evidence on these linkages in Lithuania. The purpose of the study is to provide an integrated, Lithuania-specific assessment of how economic, social, and environmental determinants associated with RET deployment are related to multiple dimensions of societal welfare. Drawing on scientific literature, an integrated indicator framework is developed that links the economic, social, and environmental determinants of renewable energy technology (RET) deployment to six societal welfare dimensions, as defined by the Lithuanian Quality of Life Index. Using official Lithuanian statistics for 2020–2024, a standardized panel dataset is constructed and Pearson correlation analysis and multiple linear regression are applied using aggregated determinant categories, with model assumptions verified using the Breusch–Pagan and Durbin–Watson tests. Correlation results show very strong positive links between RET intensity indicators and key economic welfare measures (for example, wages, GDP per capita, foreign direct investment, disposable income), with absolute correlation coefficients typically between 0.90 and 0.99 (p < 0.05), and strong negative correlations between air-pollution indicators and GDP, income, FDI, and education (correlation coefficients between −0.96 and −0.90; p < 0.05). The results indicate that RET-related economic determinants have a statistically significant positive effect on the societal welfare dimensions of material living conditions; entrepreneurship/business competitiveness; and public infrastructure, living-environment quality/safety. Social factors also significantly support the societal welfare dimensions of entrepreneurship/business competitiveness and public infrastructure, living-environment quality/safety. In the retained regression models, explanatory power is very high (R² between 0.91 and 0.999), with positive and statistically significant coefficients for the economic determinant (regression coefficients between 0.43 and 0.96; p < 0.05) and negative, statistically significant coefficients for the environmental determinant in the entrepreneurship and public-infrastructure dimensions (regression coefficients between −1.13 and −1.51; p < 0.05). Environmental determinants are associated with lower air pollution but show negative effects on the societal welfare dimensions of entrepreneurship/business competitiveness and public infrastructure, living-environment quality/safety. Overall, the findings suggest that RET deployment is an important correlate of the economic aspects of societal welfare, while environmental and social dimensions display more complex, domain-specific impacts. Full article