Search Results (7,786)

Soils with an albic horizon (characterized by a bleached, nutrient-poor eluvial layer), classified primarily as Albic Planosols and associated groups (e.g., Albic Luvisols and Retisols) in the World Reference Base for Soil Resources (WRB), are widespread in Northeast China and suffer from inherent poor nutrient availability and low crop productivity. The present study aimed to evaluate the efficacy of novel microbial–enzyme composite biofertilizers in ameliorating Albic soils. This comprehensive assessment investigated their effects on soil nutrient availability, microbial community structure, and the activities of key enzymes involved in nutrient cycling (e.g., dehydrogenase and phosphatase). Concurrently, the impact on maize crop performance was determined by measuring changes in agronomic traits, including chlorophyll content, stem diameter, and final grain yield. A field experiment was conducted in Heilongjiang Province during the 2023 maize growing season using a randomized block design with six treatments: CF (conventional chemical fertilizer, 330 kg·ha⁻¹ NPK), OF (chemical fertilizer + 1500 kg·ha⁻¹ organic carrier), BF1 (OF + 75 kg·ha⁻¹ marine actinomycetes), BF2 (OF + 75 kg·ha⁻¹ actinomycetes + 45 kg·ha⁻¹ phytase), BF3 (OF + 75 kg·ha⁻¹ actinomycetes + 45 kg·ha⁻¹ mycorrhizal fungi + 45 kg·ha⁻¹ phytase), and BF4 (OF + 75 kg·ha⁻¹ actinomycetes + 45 kg·ha⁻¹ mycorrhizal fungi + 45 kg·ha⁻¹ phytase + 45 kg·ha⁻¹ β–glucosidase). The results showed that biofertilizers significantly increased microbial abundance and enzyme activity. The integrated treatment BF4 notably enhanced topsoil fungal abundance by 188.1% and dehydrogenase activity in the 0–20 cm layer, while also increasing available phosphorus by 92.6% at maturity. Although BF4 improved soil properties the most, BF3 produced the highest maize yield—boosting grain output by 18.3% over CF—and improved stem diameter and chlorophyll content. Strong correlations between microbial parameters and enzyme activities indicated a nutrient-cycling mechanism driven by microorganisms, with topsoil fungal abundance positively linked to alkaline phosphatase activity (r = 0.72) and subsoil bacterial abundance associated with available phosphorus (r = 0.65), demonstrating microbial–mediated carbon–phosphorus coupling. In conclusion, microbial–enzyme biofertilizers, particularly BF4, provide a sustainable strategy for enhancing Albic soil fertility and crop productivity. Full article

Multiple Unmanned Aerial Vehicle (UAV) collaborative coverage path planning is widely applied in fields such as regional surveillance. However, optimizing the trade-off between deployment costs and task execution efficiency remains challenging. To balance resource costs and execution efficiency with an uncertain number of UAVs, this paper analyzes the characteristics of irregular mission areas and formulates a bi-level optimization model for multi-UAV collaborative CPP. The model aims to minimize both the number of UAVs and the total path length. First, in the upper level, an improved Best Fit Decreasing algorithm based on binary search is designed. Straight-line scanning paths are generated by determining the minimum span direction of the irregular regions. Task allocation follows a longest-path-first, minimum-residual-range rule to rapidly determine the minimum number of UAVs required for complete coverage. Considering UAV’s turning radius constraints, Dubins curves are employed to plan transition paths between scanning regions, ensuring path feasibility. Second, the lower level transforms the problem into a Multiple Traveling Salesman Problem that considers path continuity, range constraints, and non-overlapping path allocation. This problem is solved using an Improved Biased Random Key Genetic Algorithm. The algorithm employs a variable-length master–slave chromosome encoding structure to adapt to the task allocation of each UAV. By integrating biased crossover operators with 2-opt interval mutation operators, the algorithm accelerates convergence and improves solution quality. Finally, comparative experiments on mission regions of varying scales demonstrate that, compared with single-level optimization and other intelligent algorithms, the proposed method reduces the required number of UAVs and shortens the total path length, while ensuring complete coverage of irregular regions. This method provides an efficient and practical solution for multi-UAV collaborative CPP in complex environments. Full article

Forecasting stock market crashes is difficult because such events are rare, highly nonlinear, and shaped by latent structural and behavioral forces. This study introduces a calibrated and interpretable Random Forest framework for detecting pre-crash conditions through structural feature engineering, early-warning calibration, and model explainability. Using daily data on global equity indices and major large-cap stocks from the U.S., Europe, and Asia, we construct a feature set that captures volatility expansion, moving-average deterioration, Bollinger Band width, and short-horizon return dynamics. Probability-threshold optimization significantly improves sensitivity to rare events and yields an operating point at a crash-probability threshold of 0.33. Compared with econometric and machine learning benchmarks, the calibrated model attains higher precision while maintaining competitive F1 and MCC scores, and it delivers meaningful early-warning signals with an average lead-time of around 60 days. SHAP analysis indicates that predictions are anchored in theoretically consistent indicators, particularly volatility clustering and weakening trends, while robustness checks show resilience to noise, structural perturbations, and simulated flash crashes. Taken together, these results provide a transparent and reproducible blueprint for building operational early-warning systems in financial markets. Full article

Similarity search on image embeddings is a common practice for image retrieval in machine learning and pattern recognition systems. Approximate nearest neighbor (ANN) methods enable scalable similarity search on large datasets, often approaching sub-linear complexity. Yet, little empirical work has examined how ANN neighborhood geometry differs from that of exact k-nearest neighbors (k-NN) search as the neighborhood size increases under constrained search effort. This study quantifies how approximate neighborhood structure changes relative to exact k-NN search as k increases across three experimental conditions. Using multiple random subsets of 10,000 images drawn from the STL-10 dataset, we compute ResNet-50 image embeddings, perform an exact k-NN search, and compare it to a Hierarchical Navigable Small World (HNSW)-based ANN search under controlled hyperparameter regimes. We evaluated the fidelity of neighborhood structure using neighborhood overlap, average neighbor distance, normalized barycenter shift, and local intrinsic dimensionality (LID). Results show that exact k-NN and ANN search behave nearly identically when $efSearch>k$. However, as the neighborhood size grows and $efSearch$ remains fixed, ANN search fails abruptly, exhibiting extreme divergence in neighbor distances at approximately $k\approx 2$–$3.5\times efSearch$. Increasing index construction quality delays this failure, and scaling search effort proportionally with neighborhood size ($efSearch=\alpha \times k$ with $\alpha \ge 1$) preserves neighborhood geometry across all evaluated metrics, including LID. The findings indicate that ANN search preserves neighborhood geometry within its operational capacity but abruptly fails when this capacity is exceeded. Documenting this behavior is relevant for scientific applications that approximate embedding spaces and provides practical guidance on when ANN search is interchangeable with exact k-NN and when geometric differences become nontrivial. Full article

This study examines the relationship between income and municipal waste generation within the Waste Kuznets Curve (WKC) framework, with a focus on selected disaggregated household waste fractions (paper and cardboard, glass, bulky waste, and biowaste). The aim is to assess whether increases in earnings per capita are associated with non-linear waste dynamics once spatial interactions and local socio-demographic characteristics are taken into account. The study employs a spatial panel dataset for 378 Polish counties over the period 2017–2024. Fixed-effects panel models, supplemented with random-effects panel models with Mundlak’s approach, are estimated alongside spatial panel specifications. Control variables include population ageing, urbanisation, and tourism, while spatial effects are decomposed into direct and indirect impacts. The results indicate that, in non-spatial models, an inverted U-shaped relationship between earnings and waste generation is observed for most waste fractions. However, once spatial dependence is explicitly incorporated, income effects weaken. In contrast, demographic structure—the share of retirement-age population—emerges as a robust and spatially persistent determinant of waste generation. Urbanisation and tourism exert only a limited influence across waste fractions. The paper advances WKC research by using spatial econometric methods and disaggregated waste fractions at the county level. The evidence suggests that conclusions about income-driven waste decoupling are sensitive to spatial dependence, emphasising the need for locally tailored waste management strategies. Full article

Fine segmentation of point clouds in hull structure junction regions is a key technology for achieving high-precision digital inspection. Conventional hard-segmentation methods frequently yield over- or under-segmentation in junction regions such as welds, compromising the reliability of subsequent inspections. This study presents a computational framework that combines the Gaussian-Uniform Mixture Model (GUMM) with the Hidden Markov Random Field (HMRF) and follows a “coarse segmentation–model construction–fine segmentation” pipeline. The framework jointly optimizes the sampling model, the probabilistic model, and the expectation–maximization (EM) inference procedure. By leveraging model simplification and dimensionality reduction, the algorithm simultaneously addresses initial value estimation, spatial distribution characterization, and continuity constraints. Experiments on representative structures, including wall corner, T-joint weld, groove, and flange, show that the proposed framework outperforms the conventional GMM-EM method by approximately 2.5% in precision and 1.5% in both accuracy and F1 score. In local segmentation tasks of complex hull structures, the method achieves a deviation of less than 0.2 mm relative to manual measurements, validating its practical utility in engineering contexts. Full article

Background: “Healthy Life Longevity” (a latent variable) is defined as the number of survival days, along with recommended subjective health and long-term care needs. This study aimed to clarify the structural relationships among several related factors. Methods: In September 2001, a postal survey using a self-administered questionnaire was conducted among 16,462 elderly residents of Tokyo. In a cohort study, 8162 individuals with confirmed survival after six years were examined. We analyzed data to evaluate the need for long-term care three years after the initial survey. Additionally, the number of days survived was calculated from the third year after the initial survey. Covariance structure analysis was used to explore the structural relationships. Results: The direct effects of lifestyle habits, including a healthy diet, dental care rather than physician care, and socioeconomic factors, were minimal in improving “Healthy Life Longevity.” However, a structural relationship was established: desirable lifestyles, including diet and dental care, were selected based on socioeconomic status, thereby influencing mental, physical, and social health and reducing disease incidence. This relationship ultimately enhanced “Healthy Life Longevity.” Socioeconomic factors were identified as confounders in the association between preferred lifestyle choices, including diet, and Healthy Life Longevity. The determination coefficient of “Healthy Life Longevity” is 83%. Conclusions: Although healthy longevity can be achieved by improving mental, physical, and social health, and reducing disease burden, the relevant structure is shaped by socioeconomic status. Additionally, socioeconomic status is associated with healthy longevity by facilitating the choice of a preferred lifestyle, including diet, and the selection of a dentist. Future randomized intervention studies focused on socioeconomic status should explore ways to promote healthy longevity. Full article

Non-AIDS-defining cancers (NADCs) have emerged as an increasingly prominent cause of non-AIDS-related morbidity and mortality among people living with HIV (PLWH). However, the scarcity of NADC clinical samples, compounded by privacy and security constraints, continues to present formidable obstacles to advancing pathological and clinical investigations. In this study, we adopted a joint analysis strategy and deeply integrated and analyzed transcriptomic data from 12,486 PLWH and cancer patients to systematically identify potential key regulators for 23 NADCs. This effort culminated in NADCdb—a database specifically engineered for NADC pathological exploration, structured around three mechanistic frameworks rooted in the interplay of immunosuppression, chronic inflammation, carcinogenic viral infections, and HIV-derived oncogenic pathways. The “rNADC” module performed risk assessment by prioritizing genes with aberrant expression trajectories, deploying bidirectional stepwise regression coupled with logistic modeling to stratify the risks for 21 NADCs. The “dNADC” module, synergized patients’ dysregulated genes with their regulatory networks, using Random Forest (RF) and Conditional Inference Trees (CITs) to identify pathogenic drivers of NADCs, with an accuracy exceeding 75% (in the external validation cohort, the prediction accuracy of the HIV-associated clear cell renal cell carcinoma model exceeded 90%). Meanwhile, “iPredict” identified 1905 key immune biomarkers for 16 NADCs based on the distinct immune statuses of patients. Importantly, we conducted multi-dimensional profiling of these key determinants, including in-depth functional annotations, phenotype correlations, protein–protein interaction (PPI) networks, TF-miRNA-target regulatory networks, and drug prediction, to deeply dissect their mechanistic roles in NADC pathogenesis. In summary, NADCdb serves as a novel, centralized resource that integrates data and provides analytical frameworks, offering fresh perspectives and a valuable platform for the scientific exploration of NADCs. Full article

Unsafe behaviors at construction sites often originate from cognitive failures such as lapses in memory and attention. This study proposes a novel, hybrid framework to systematically identify and predict the key contributors of cognitive failures among construction workers. First, a detailed literature review was conducted to identify 30 candidate factors related to cognitive failures and unsafe behaviors at construction sites. Thereafter, 10 construction safety experts ranked these factors to prioritize the most influential variables. A questionnaire was then developed and field surveys were conducted across various construction sites. A total of 500 valid responses were collected from construction workers involved in residential, highway, and dam projects in Pakistan. The collected data was first analyzed using conventional statistical analysis techniques like correlation analysis followed by multiple linear and binary logistic regression to estimate factor effects on cognitive failure outcomes. Thereafter, machine-learning models (including support vector machine, random forest, and gradient boosting) were implemented to enable a more robust prediction of cognitive failures. The findings consistently identified fatigue and stress as the strongest predictors of cognitive failures. These results extend unsafe behavior frameworks by highlighting the significant factors influencing cognitive failures. Moreover, the findings also imply the importance of targeted interventions, including fatigue management, structured training, and evidence-based stress reduction, to improve safety conditions at construction sites. Full article

This study evaluates the performance of several machine learning models in predicting dissolved oxygen concentration in the surface layer of the Mar Menor coastal lagoon. In recent years, this ecosystem has suffered a continuous process of eutrophication and episodes of hypoxia, mainly due to continuous influx of nutrients from agricultural activities, causing severe water quality deterioration and mortality of local flora and fauna. In this context, monitoring the ecological status of the Mar Menor and its watershed is essential to understand the environmental dynamics that trigger these dystrophic crises. Using field data, this study evaluates the performance of eight predictive modelling approaches, encompassing regularised linear regression methods (Ridge, Lasso, and Elastic Net), instance-based learning (k-nearest neighbours, KNN), kernel-based regression (support vector regression with a radial basis function kernel, SVR-RBF), and tree-based ensemble techniques (Random Forest, Regularised Random Forest, and XGBoost), under multiple experimental settings involving spatial variability and varying time lags applied to physicochemical and meteorological predictors. The results showed that incorporating time lags of approximately two weeks in physicochemical variables markedly improves the models’ ability to generalise to new data. Tree-based regression models achieved the best overall performance, with eXtreme Gradient Boosting providing the highest evaluation metrics. Finally, analysing predictions by sampling point reveals spatial patterns, underscoring the influence of local conditions on prediction quality and the need to consider both spatial structure and temporal inertia when modelling complex coastal lagoon systems. Full article

Background/Objectives: Prebiotics, which are non-digestible compounds that selectively modulate gut microbiota, are recognized for their potential to promote host health. Although their bifidogenic effect is well documented, a systematic synthesis of how this microbial modulation translates into clinical gastrointestinal (GI) and metabolic outcomes across diverse populations is needed. This review aims to integrate mechanistic insights with clinical evidence to elucidate the pathway from prebiotic structures to tangible health benefits. Methods: This comprehensive narrative review details the structural properties of major prebiotics (e.g., inulin, FOS, and GOS) that govern their fermentation and the production of short-chain fatty acids (SCFAs). To evaluate clinical efficacy, an analysis of 22 randomized controlled trials from the past decade was conducted, focusing on human studies that utilized ISAPP-recognized prebiotics as the sole intervention. Results: The analysis confirms that prebiotic supplementation consistently increased the abundance of beneficial bacteria (e.g., Bifidobacterium and Lactobacillus) and SCFA production. These changes are associated with significant clinical improvements, including enhanced stool frequency and consistency, strengthened intestinal barrier function, and modulated immune responses. Benefits have been documented in healthy individuals, children, the elderly, and those with conditions such as constipation, metabolic syndrome, and antibiotic-associated dysbiosis. However, significant inter-individual variability in response was evident, and the study designs showed notable heterogeneity in prebiotic type, dosage, and duration. Conclusions: Prebiotics are effective modulators of gut health, driving clinical benefits through selective microbial fermentation and SCFA production. The documented heterogeneity and variability highlight the need for future research to focus on personalized nutritional strategies. Key priorities include standardizing intervention protocols, elucidating dose–response relationships, integrating multi-omics data to link taxonomy to function, and exploring novel applications such as synbiotic formulations and gut–brain axis modulation. Full article

The urgency of research into innovation and digital marketing is driven by growing competition within the tourism industry, which demands greater destination visibility (DV) and tourist engagement (TE). At the same time, Areia Branca Beach, a prominent destination in Timor-Leste, has not been managed optimally to support sustainable tourism. Furthermore, the utilisation of governance innovation and digital marketing—particularly the integration of content marketing (CM), immersive technology (IT), and digital data analytics (DDA)—remains limited and has yet to be substantiated by robust empirical evidence at the scale of a developing destination. This study aims to investigate the role of DDA in the causality between CM and IT in influencing DV and TE. A quantitative approach was employed, using moderated regression analysis (MRA) to test the empirical relationships between the variables. Primary data were collected through face-to-face field surveys of tourists who had visited Areia Branca Beach, located northeast of Dili, Timor-Leste, on at least two occasions. The study adopted simple random sampling (SRS) with a finite population correction (FPC). A total of 364 tourists were selected to assess their perceptions using a structured questionnaire. The study reveals four main findings. First, CM significantly affects DDA and DV. Second, IT influences DDA, but not TE. Third, DDA significantly affects both DV and TE. Fourth, DDA moderates the effect of CM on DV and the effect of IT on TE. The findings underscore that the collaborative governance concept, through governance and marketing innovations, is not yet optimal for shaping sustainable tourism. Finally, future academic and practical policy implications require more in-depth exploration to emphasise the enhancement of resource management capacity genuinely needed in the subjects studied, beyond governance and digital marketing innovations within the sustainable tourism framework. Full article

Cancer and Alzheimer’s disease (AD) display an inverse relationship, and there is a need to further explore this interplay. One key genetic contributor to AD is SORL1, the loss of which is thought to be causally related to AD development. SORL1 also appears to be implicated in cancer. To examine SORL1 and its network, this article simulated SORL1 and its interactions via signal-flow Boolean modelling, including in silico knockouts (mirroring in vivo loss-of-function mutations). This model (SKM034) predicted a total of 29 key changes in molecular relationships following the loss of SORL1 or another highly connected protein (ERBB2). Literature validation demonstrated that 2 of these predictions were at least partially validated experimentally, whilst 27 were Potentially Novel Predictions (PNPs). Complementing the in-depth relationship analyses was signal flow analysis through the network’s structure, validated using cell line and cancer patient RNA-seq data. Correct prediction rates for these analyses reached 60% (statistically significant relative to a random model). This article demonstrates the clinical relevance of this Alzheimer’s-related network in a cancer context and, through the PNPs, provides a strong starting point for in vitro experimental validation. As with previously published models using similar methods, the model may be reanalysed in different contexts for further discoveries. Full article

This structured narrative review compared the efficacy, durability, and safety of anti-vascular endothelial growth factor (anti-VEGF) agents and intravitreal corticosteroids for the treatment of diabetic macular edema (DME), with the aim of identifying patient- and disease-specific factors to guide individualized therapy. A comprehensive search of PubMed, Embase, the Cochrane Library, and ClinicalTrials.gov was conducted for studies published between January 2009 and November 2025, including randomized controlled trials, meta-analyses, and large observational cohorts with at least six months of follow-up. Visual acuity, anatomical outcomes, treatment burden, durability, and safety were extracted, and evidence quality was assessed using the GRADE framework. Eleven studies encompassing 1341 eyes were included. Anti-VEGF therapy consistently produced greater improvements in best-corrected visual acuity, particularly in treatment-naïve eyes and in patients with worse baseline vision, whereas corticosteroids achieved larger reductions in central macular thickness and significantly reduced injection burden because of longer durability. However, corticosteroid therapy was associated with higher rates of intraocular pressure elevation and cataract progression. In pseudophakic patients and in chronic or refractory DME, functional and anatomical outcomes were generally comparable between the two therapeutic classes. Combination therapy resulted in the greatest anatomical improvement but at the cost of increased ocular adverse events. Overall, anti-VEGF agents remain the preferred first-line treatment for most patients with DME owing to superior visual outcomes and a more favorable safety profile, while corticosteroids represent valuable alternatives in pseudophakic eyes, chronic or anti-VEGF–refractory DME, and cases with prominent inflammatory features, provided that careful monitoring for ocular adverse events is maintained. Full article

As a core support for the integration of new energy and smart grids, Vehicle-to-Grid (V2G) networks face a core contradiction between user privacy protection and transaction security traceability—a dilemma that is further exacerbated by issues such as the quantum computing vulnerability of traditional cryptography, cumbersome key management in stateful ring signatures, and conflicts between revocation mechanisms and privacy protection. To address these problems, this paper proposes a post-quantum revocable linkable ring signature scheme based on SPHINCS+, with the following core innovations: First, the scheme seamlessly integrates the pure hash-based architecture of SPHINCS+ with a stateless design, incorporating WOTS+, FORS, and XMSS technologies, which inherently resists quantum attacks and eliminates the need to track signature states, thus completely resolving the state management dilemma of traditional stateful schemes; second, the scheme introduces an innovative “real signature + pseudo-signature polynomially indistinguishable” mechanism, and by calibrating the authentication path structure and hash distribution of pseudo-signatures (satisfying the Kolmogorov–Smirnov test with $D\le 0.05$), it ensures signer anonymity and mitigates the potential risk of distinguishable pseudo-signatures; third, the scheme designs a KEK (Key Encryption Key)-sharded collaborative revocation mechanism, encrypting and storing the $(I,pk,RID)$ mapping table in fragmented form, with KEK split into $KE{K}_1$ (held by the Trusted Authority, TA) and $KE{K}_2$ (held by the regulatory node), with collaborative decryption by both parties required to locate malicious users, thereby resolving the core conflict of privacy leakage in traditional revocation mechanisms; fourth, the scheme generates forward-secure linkable tags based on one-way private key updates and one-time random factors, ensuring that past transactions cannot be traced even if the current private key is compromised; and fifth, the scheme adopts hash commitments instead of complex cryptographic commitments, simplifying computations while efficiently binding transaction amounts to signers—an approach consistent with the pure hash-based design philosophy of SPHINCS+. Security analysis demonstrates that the scheme satisfies the following six core properties: post-quantum security, unforgeability, anonymity, linkability, unframeability, and forward secrecy, thereby providing technical support for secure and anonymous payments in V2G networks in the quantum era. Full article