Search Results (1,027)

Background/Objectives: Diabetes is a chronic metabolic disorder affecting global health, where early prediction can significantly reduce disease severity. Methods: This research proposes an interpretable multi-metric fuzzy distance-based ensemble (MMFDE) that integrates multi-variant gradient-boosting classifiers (GBM, LightGBM, XGBoost, and AdaBoost) through a novel fuzzy fusion mechanism designed for intrinsic interpretability. Unlike conventional ensembles relying on opaque averaging or voting, MMFDE transforms base classifier predictions into a high-dimensional fuzzy space quantified via a weighted hybrid distance incorporating Euclidean, Manhattan, Chebyshev, and cosine metrics against ideal diabetic and non-diabetic reference vectors. These distances are translated into membership degrees with the help of exponentially decaying functions, which give clinicians calibrated confidence scores for every prediction. Comprehensive SHAP analysis identifies important clinical risk factors (glucose, BMI, and diabetes pedigree function), which show concordance with the medical literature, thereby giving greater clinical trust. Results: Experimental evaluations on two publicly available datasets, Hospital Frankfurt Germany Diabetes Dataset (HFGDD) and Pima Indians Diabetes Dataset (PIDD), show that MMFDE outperforms all base models with a significant accuracy of 94.83% and Area Under the Curve (AUC) of 97.66% on HFGDD and three different levels of interpretability: geometric transparency via distance-based decisions, confidence-calibrated uncertainty estimates, and feature-level explanations via SHAP. The confidence thresholds enabled in the framework support risk stratification clinical workflows with high-confidence predictions for automated screening and cases with moderate/low confidence flagged out for review by the clinician. Conclusions: By demonstrating that high performance and interpretability need not be mutually exclusive, MMFDE advances trustworthy AI for clinical decision support, addressing the critical need for transparent and clinically actionable diabetes prediction systems. Full article

Availability of phosphorus (P) is thought to limit bole growth in wet tropical forests, raising concern that removing P through repeated logging in P-limited stands may be unsustainable. Motivated by a study in Indonesia, we analyzed Olsen extractable and total soil P in the upper 10 cm in paired samples we collected under vs. near decaying boles of two contrasting species in a wet tropical forest in Puerto Rico. Guarea guidonia had higher wood and leaf P concentrations than Dacryodes excelsa. G. guidonia colonized valleys with higher soil P concentrations than ridge sites dominated by D. excelsa. We used two age cohorts of trees > 30 cm diameter, felled by hurricanes Hugo in 1989 (11 years old) and Georges in 1998 (1.5 years old), but soil P did not differ with age. Soil Olsen P concentrations were significantly higher under versus away from boles of both species. Paradoxically, augmentation of soil P was greater under boles of D. excelsa than G. guidonia despite having lower wood P. Soil % C and Olsen P were strongly positively correlated in D. excelsa but not in G. guidonia, suggesting that regulation of soil P-availability differs between ridges and valleys. Both soil C and P may be critical for maintaining soil fertility on ridges in a wet tropical forest. Our results are discussed in the context of prior experiments at our site, including two where bole growth increased with wood addition and/or decreased after removal of woody debris. These studies in Puerto Rico, together with others elsewhere, suggest that reduced forest productivity could potentially result from repeated logging of forest stands on ridges with low P-availability in humid tropical areas since decaying wood could directly and indirectly maintain P-availability in sites with low soil P-availability. We suggest several hypotheses on P-cycling in montane humid tropical forests that need further research to elucidate mechanisms controlling soil P-availability and identify sites where repeated logging is likely to be unsustainable. Full article

Cs₃Cu₂I₅ single crystals are regarded as promising next-generation scintillators due to their large Stokes shift and low self-absorption characteristics. However, the cost-effective solution growth method faces critical challenges: the instability of colloidal precursors in solutions and the severe oxidation of Cu⁺ during crystal growth. This study innovatively introduces yttrium chloride (YCl₃) as a dual-functional additive to address both issues simultaneously. The hydrolysis of YCl₃ creates a controlled acidic environment, effectively suppressing the oxidation of Cu⁺; meanwhile, it enhances the stability of colloidal precursors by significantly increasing their surface charge and narrowing the particle size distribution. These synergistic effects enable the rapid growth (approximately 100 h) of near-centimeter-sized Cs₃Cu₂I₅ single crystals with high crystallinity, without the need for inert gas protection. The optimized crystals exhibit exceptional performance: a photoluminescence quantum yield (PLQY) of 93.22% ± 0.47%, a scintillation decay time of 210.04 ns, and a light yield of ~738.14 pe/MeV. This YCl₃-mediated growth strategy establishes an efficient approach for the solution-based synthesis of high-quality Cs₃Cu₂I₅ single crystals, holding great significance for advancing high-sensitivity, environment-stable radiation detection applications such as medical diagnostics and nuclear safety monitoring. Full article

Accurate forecasts of tropical cyclone (TC) track and intensity with a sufficient lead time are critical for disaster preparedness and risk mitigation. Traditional numerical weather prediction models, while fundamental to operational forecasting, often exhibit systematic errors due to limitations in observations, physical parameterizations, and model resolution. In recent years, machine learning (ML) and deep learning (DL) approaches have emerged as promising data-driven alternatives for improving TC forecasts. This study presents a comparative evaluation of six ML and DL models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN)—for forecasting TC track and intensity in the North Atlantic basin. The models are trained using the National Hurricane Center’s (NHC) HURDAT2 best-track dataset for storms from 1990 to 2019 and evaluated on an independent test set from the 2020 season. Model performance is compared across all models and benchmarked against the 2020 mean Decay-SHIFOR5 intensity error, CLIPER5 track errors, and the NHC official forecast (OFCL) errors. Forecast skill is assessed using mean absolute error (MAE) with 95% bootstrap confidence intervals and the coefficient of determination (R²) across lead times of 6, 12, 18, 24, 48, and 72 h. The results show that: (1) several ML and DL models achieve intensity forecast performance that is broadly comparable in magnitude to the 2020 mean OFCL benchmarks, with an average error reduction of 5–11% at the 24 h lead time; (2) among the ML models, XGBoost and CatBoost slightly outperform LightGBM and RF in accuracy, while LightGBM demonstrates the highest computational efficiency; and (3) among the DL models, CNNs outperform ANNs in predictive accuracy and intensity forecasting efficiency, while ANNs exhibit lower computational cost for track forecast. Bootstrap confidence intervals indicate relatively low variability in model errors, supporting the statistical stability of the results within the 2020 season. However, these results reflect within-season variability and do not necessarily generalize across different years or climatological conditions. Overall, the findings demonstrate the potential of ML/DL-based approaches to complement existing operational forecast systems and enhance TC track and intensity forecasting in the North Atlantic basin. Full article

This systematic review examines the effect of pre-incident information on public preparedness prior to an emergency or disaster. Preparing members of the public for adverse events can improve self-sufficiency and improve health outcomes, particularly during periods when emergency responders are not immediately available. Twenty-three studies were identified, addressing both natural and human-influenced events. All the studies investigated pre-incident training targeting members of the public rather than specialist responders. The synthesis considered training content, delivery approaches and evaluation methods. The studies included preparation, personal safety, triage, first aid and evacuation in scenarios involving terrorism, fire, earthquake, flood and CBRN events. Pre-incident education generally improves knowledge and intention to act, with higher-intensity and interactive training yielding greater engagement and response. Due to the difficulty of simulating emergencies and disasters, several studies used self-reporting and hypothetical testing, while others attempted to create real-life scenarios. The immediate effects of pre-incident education were generally positive, although many studies tested outcomes theoretically or within a classroom environment. It was also noted that few studies considered retention over the medium to long term; this is a concern as temporal decay may reduce preparedness. This review provides a basis for continued development of public-facing pre-incident education to increase resilience to both terrorist attacks and natural disasters. Full article

This paper introduces a q-deformed extension of the Lindley distribution. This extension is obtained by replacing the classical exponential with the q-exponential function from Tsallis non-extensive statistical techniques. This transformation offers more control over the tail behavior of the distribution, providing a transition between exponential and power-law decay patterns. Such flexibility is particularly useful when modeling right-skewed data with excess kurtosis, where classical models may not adequately describe heavy-tailed and highly skewed data. We derive several key properties, including the quantile function, expressed by the Lambert–Tsallis function $W_q$, the raw and incomplete moments, the probability-weighted moments, and the Tsallis entropy. The distribution of order statistics is also investigated. For parameter estimation, we employ several frequentist methods and conduct extensive Monte Carlo simulation studies to assess and compare their performance. Finally, applications to real-world datasets show that the q-deformed Lindley model is practically superior and more flexible than the classical Lindley distribution and other well-known models. Full article

Flexible deformation sensors inevitably suffer from sensitivity degradation and severe measurement errors during long-term cyclic stretching due to structural fatigue. Traditional material-level optimizations are costly and lack dynamic adaptability. Herein, we propose an artificial intelligence (AI)-driven predict-and-compensate framework for the online calibration of flexible sensors. To overcome training sample scarcity, a generative adversarial network (GAN) performs temporal data augmentation. Subsequently, a hybrid deep learning framework integrating long short-term memory (LSTM) networks and a Sequence Attention mechanism is employed. This architecture accurately captures both local signal fluctuations and multiscale long-term decay trends, enabling precise multi-step prediction and output compensation. Experimental evaluations validate that this strategy significantly suppresses sensor response drift. Under cyclic loading, an initially substantial relative measurement error of 48.63% plummets to 7.16% post-calibration, with typical errors consistently reduced to the ~1% level. Furthermore, when deployed in a smart glove gesture recognition system, this method successfully restores the recognition accuracy from a fatigue-induced low of 75.73% (after 200 stretch cycles) back to 97.70%. This generative and attention-based deep learning paradigm offers robust, real-time error calibration, providing a highly viable solution for extending the long-term reliability and stability of flexible sensor systems. Full article

²⁰Na is a well-known $\beta$-delayed $\alpha$ emitter, owing to the large decay energy of ²⁰Na above the α + ¹⁶O threshold in the $A=5\alpha$ daughter nucleus ²⁰Ne. In this work, the decay property of ²⁰Na is investigated in detail via the β-γ β-α and β-γ-α coincidence spectroscopy. As the day-one experiment of the Beijing Rare Isotope Facility (BRIF), the intense ²⁰Na beam was produced using the Isotope Separator On Line (ISOL) technique through the 100 MeV proton bombarding a stack of MgO as a thick target. Specific interest was focused on the exotic decay mode of ²⁰Na; the previously reported low-energy α lines at 713 and 846 keV were confirmed, and several weak β-γ-α decay sequences were clearly identified for the first time, thanks to the strong resolving power of α-γ coincidence spectroscopy. The decay properties of ²⁰Na are compared to the shell model calculation, which agree reasonably well with the allowed β transition strengths and subsequent electro-magnetic transitions with the use of the sd shell-model space with the USDB interaction. Full article

The global rise in the incidence and severity of invasive fungal infections, particularly among immunocompromised and immunodeficient patients, has created an urgent need for rapid and accurate diagnostic techniques. Therefore, fungal-specific positron emission tomography imaging agents are increasingly in demand, as they offer the potential for early-stage detection of fungal infections. Recently, 2-deoxy-2-[¹⁸F]fluorocellobiose ([¹⁸F]FCB), a fluorine-18-labeled analog of cellobiose that is selectively metabolized by fungal pathogens possessing cellulose-degrading mechanisms (cellulolytic), was developed for the targeted imaging of Aspergillus infections. However, the final [¹⁸F]FCB contained less than 2% unreacted 2-deoxy-2-[¹⁸F]fluoroglucose ([¹⁸F]FDG), which can potentially interfere with image interpretation. Accordingly, this study aims to eliminate residual [¹⁸F]FDG from the final product by enzymatically converting it to [¹⁸F]FDG-6-phosphate through hexokinase-mediated phosphorylation. A Trasis AllInOne (Trasis AIO) module was used to automate the radiolabeling procedure. The reagent vials contain [¹⁸F]FDG, glucose-1-phosphate, cellobiose phosphorylase, adenosine triphosphate (ATP), and hexokinase. A Sep-Pak cartridge was used to purify the tracer. The overall radiochemical yield was 45–50% (n = 3, decay-corrected) in a 40 min synthesis time, with a radiochemical purity of >99% (no detectable [¹⁸F]FDG). This is a highly reliable protocol to produce current good manufacturing practice (cGMP)-compliant [¹⁸F]FCB for clinical PET imaging. Full article

To clarify cultivar differences in growth performance, yield formation, and bioactive characteristics at the sprout and fresh seed stages, twelve pea cultivars were evaluated. Growth traits, yield formation, and changes in phenolic compounds and antioxidant activity during sprouting were assessed, and the nutritional quality and mineral element composition of fresh seeds were also determined. The results showed that cultivars 24-164 and 24-510 exhibited low germination rates and severe cotyledon decay, making them unsuitable for sprout production. Significant differences were observed among the remaining cultivars in growth traits, edible ratio, and yield efficiency, with cultivars 24-724 and 24-486 showing superior processing efficiency and utilization value. During sprouting, total phenolic and total flavonoid contents, as well as 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging activity and ferric reducing antioxidant power (FRAP), were significantly influenced by both cultivar and light exposure stage. Root length and root diameter were significantly and positively correlated with phenolic accumulation and antioxidant activity. Analysis of fresh seed quality revealed marked inter-cultivar variation in nutritional and health-related traits. Cultivar 24-486 exhibited the highest values for phenolic content, antioxidant capacity, vitamin C, vitamin E, and Fe and Se accumulation, whereas cultivar 24-013 showed advantages in calcium and potassium contents. These results identify cultivars 24-724 and 24-486 as promising candidates for sprout production and highlight cultivar 24-486 as a dual-purpose genotype for both sprout and fresh seed utilization. Full article

Postharvest losses in Pacific Island Countries remain a significant challenge, affecting food security and farmers’ livelihoods. Limited research exists on horticultural handling practices in the region, particularly on taro corm bruising. This study characterised defects in taro corms caused by poor physical handling using a simulated laboratory drop test with two drop heights (1 m and 2 m), two drop frequencies (1 and 4 drops), and three storage durations (3, 5, and 7 days). It examined the combined effects of the drop test on external bruising, internal bruise depth, bruise severity scores, and visible decay incidence. Data were collected using the laboratory drop test, samples of farmer-handled taro, and farmer interviews. The results showed that the increased drop height and repeated impacts significantly increased severity, depth, and length over time. Corms subjected to the higher drop height (2 m) exhibited greater tissue breakdown; by day 7, corms dropped from 2 m had approximately 47% greater bruise depth than those dropped from 1 m. Statistical analysis confirmed that the drop height, the storage duration, and the drop frequency were key determinants of postharvest deterioration (p < 0.05). Mechanical stress also weakened corm integrity, increasing susceptibility to infection and decay. These findings underscore the need for improved postharvest handling practices, such as minimising free-fall distances, using padded storage and adopting better sorting methods to reduce mechanical injury. Enhancing these practices could substantially reduce food loss, extend taro shelf life and improve marketability, thereby supporting more resilient and sustainable food systems and contributing to food security and economic stability for taro farmers in the Pacific. Full article

Cavity ring-down spectroscopy has significant potential for detecting trace volatile organic compounds, owing to its long absorption path and high sensitivity. However, in practical measurements, noise severely decreases the accuracy of decay curves and the reliability of concentration retrieval. To address this, we developed a deep learning-based denoising model called decay-upsampling FC-Net. Experimental results showed that the model improved the signal-to-noise ratio from 13.86 dB to 26.79 dB and processed a single decay curve in only 0.000207 s on average. Moreover, under high-noise conditions, it determined the ring-down time more accurately than conventional methods. This study provides an effective signal processing solution to enhance the practical reliability of Cavity ring-down spectroscopy gas detection systems. Full article

Marine concrete engineering faces severe service environment challenges, including high hydraulic pressure, large stress, and serious penetration. The evaluation of the durability and safety of these structures depends directly on the damage mechanism of concrete materials submitted to high hydraulic pressures. This paper introduced the experimental research on the mechanical properties and the damage mechanism of concrete submitted to high hydraulic pressures. The permeability tests were carried out on concrete specimens under the effect of different hydraulic pressures (1.2 MPa, 2.4 MPa, 3.6 MPa) and durations (10 d, 20 d, 30 d), after which the compressive strength, micro-cracks, and the ultrasonic velocity were obtained and analyzed. The results show that under the effect of sustained high hydraulic pressure, the micro-cracks in concrete increase, the density decreases, and the harmful pores expand, resulting in a degradation in the mechanical properties of concrete. The damage to concrete is more severe at the near end of the hydraulic head than at the far end. The pore water pressure decays gradually with depth inside the concrete and expands inward when the outer layer of concrete is damaged. The conclusions will provide a scientific basis for the safety evaluation of marine concrete engineering. Full article

The continuous and rapid spread of African swine fever (ASF) still poses a significant threat to Eurasian wild boar and domestic pigs, leading to substantial economic losses in all associated sectors annually. In Europe, including Germany, affected wild boar populations have become an important driver and host of ASF virus (ASFV), and monitoring and surveillance is key to tailor control measures that impede viral spread. While molecular methods are used to confirm the disease and to monitor viral evolution, serology gains importance in endemically affected regions as it provides insights into disease dynamics and possible attenuation of ASFV strains. Frontline serological diagnosis is done using ELISA assays, of which several are commercialized. However, accurate performance of ELISA assays is key for correct interpretation of wild boar samples. Due to the various stages of hemolysis and decay, field samples from wild boar can be challenging for ELISA assays. To assess which indirect or competitive ELISA kit performs best when dealing with such samples, we compared the sensitivity and specificity of four commercially available ELISA kits that are licensed in Germany, as well as three unlicensed but commercially available kits. For this comparison, we used all wild boar samples submitted to the National Reference Laboratory (NRL) for ASF in years 2021 and 2022, as well as samples from domestic pigs to have a control cohort where sample quality is optimal. We observed that wild boar samples, varying in stage of hemolysis and decay, were challenging for all kits included in this study. However, samples of domestic pigs were largely interpreted correctly by ELISA, using immunoperoxidase test as verification method. Additionally, the comparability of results obtained by regional laboratories was high. Our study provides data that highlights the importance of careful kit selection, assessment of sample quality, and data interpretation for effective ASFV surveillance and control. Full article

Industrial heritage in resource-depleted mining towns faces the dual challenge of physical decay and social severance. To achieve sustainable urban revitalization, adaptive reuse strategies must align with local collective memory and emerging experiential consumption trends. Adopting a Scene Theory perspective, this study constructs a multi-level analytical framework using Meitanba Town (Hunan, China) and its power plant as a case study. A mixed-methods approach was employed, combining semantic network analysis of 1582 online user comments with 61 offline questionnaires distributed to local residents to quantitatively diagnose current scene elements, functions, and features. The quantitative results reveal a significant imbalance: while “Functional Media” achieved the highest comprehensive score (10.0) due to strong historical recognition, “Diverse Groups” scored the lowest (3.4), indicating a lack of social inclusivity. Specifically, residents expressed the highest demand for sports facilities (31.2%) and cultural spaces (23.7%), identifying the main workshop (26.4%) and chimney as core carriers of industrial identity. Responding to these findings, the paper proposes three targeted strategies: (1) Activate: creating open-access recreation scenes to satisfy urgent sports demands; (2) Link: constructing immersive cultural scenes to narrate the “coal–electricity–life” history; and (3) Enhance: developing industry-powered commercial scenes to avoid homogenization. This study enriches the localized application of Scene Theory and provides a data-driven, context-adjustable analytical and strategic model that can inform the sustainable renewal of mining towns globally, with its specific implementation requiring adaptation to local social, economic, and cultural characteristics. Full article