Search Results (5,653)

Information infrastructure is essential for digital transformation and AI-enabled services, but its operation also involves high electricity consumption and carbon emissions. This study develops a tripartite evolutionary game model involving the government, information-infrastructure operators and the public, and integrates it with system dynamics to examine how regulatory mechanisms influence operators’ net-zero behaviours. The model focuses on operational-stage information infrastructure. Initial parameters are calibrated using the 2023 China Statistical Yearbook on Resources and Environment and expert consultation, with key variables measured by operational revenue, net-zero costs, regulatory costs, incentives, penalties, public scrutiny costs and environmental losses. The results show that operators’ net-zero behaviours may fluctuate under weak or static regulation. Government incentives, penalties and public scrutiny can promote net-zero operations, while dynamic reward–penalty mechanisms are more effective in stabilising behavioural evolution. This study extends evolutionary game theory and system dynamics to the net-zero governance of information infrastructure and provides an adaptive regulatory framework for coordinating government regulation, operator behaviour and public participation. Full article

This paper develops a unified asymptotic theory for conditional single-index U-statistics and the associated conditional U-processes in the setting of locally stationary functional time series subject to missing-at-random response mechanisms. The proposed framework addresses, within a single nonparametric inferential architecture, three major sources of complexity in modern functional data analysis: infinite-dimensional covariates, smoothly time-varying stochastic dynamics, and incomplete response observations. The methodology is based on a class of kernel-type estimators combining temporal localization, functional single-index smoothing, and inverse-propensity correction. Temporal localization captures the gradual evolution of the underlying regression structure, the single-index projection provides an effective dimension-reduction mechanism for functional covariates, and the propensity adjustment restores the target conditional functional under the MAR sampling scheme. The principal contribution of the paper is the establishment of weak convergence, in a suitable space of bounded functions, for the resulting propensity-adjusted conditional U-process indexed by a general class of measurable kernels. Under absolute regularity conditions, local stationarity assumptions, small-ball probability requirements, entropy restrictions of VC type, and uniform consistency of the propensity-score estimator, the normalized process is shown to converge weakly to a tight centered Gaussian process. The limiting covariance structure explicitly reflects the interaction between temporal smoothing, functional concentration, dependence, and the random loss of responses. In parallel, uniform convergence rates are derived for the associated conditional single-index U-statistic estimators, thereby quantifying the respective contributions of smoothing bias, stochastic fluctuation, local-stationarity approximation error, and missingness-induced variance inflation. A substantial part of the analysis is devoted to the technical difficulties created by the simultaneous presence of dependence, nonstationarity, functional covariates, and incomplete observations. The proofs combine Hoeffding-type decompositions adapted to weighted incomplete data, blocking and coupling arguments for absolutely regular triangular arrays, refined entropy bounds for kernel-indexed function classes, and small-ball probability techniques for functional covariates. The MAR mechanism is incorporated via inverse-propensity weighting, and its effects on the effective sample size, asymptotic variance, and bias structure are made explicit. The theory also provides a rigorous foundation for bandwidth selection through blocked, propensity-adjusted cross-validation and clarifies its relation to the corresponding oracle risk. The proposed framework encompasses a broad class of statistical learning and inference problems involving pairwise or higher-order functionals of functional time series. In particular, it applies to conditional Kendall-type functionals, discrimination problems, metric learning with incomplete labels, and conditional independence testing under local stationarity. A simulation study illustrates the finite-sample behavior of the proposed estimators and supports the theoretical findings across varying regimes of temporal nonstationarity, serial dependence, functional concentration, and response missingness. Overall, the results provide a mathematically rigorous and methodologically flexible foundation for inference from evolving functional data when dependence, infinite dimensionality, and incomplete observation are present simultaneously. Full article

Koji production is a critical process that determines the flavor and quality of the final soy sauce product. However, the complex mechanisms underlying microbial metabolism and the evolution of the physicochemical environment still require further analysis. This study focuses on three parallel koji rooms in an industrialized koji fermentation process. This work tracked the dynamics of physicochemical indices, volatile flavor compounds, and microbial communities over a full 40 h cycle. Data integration and correlation analysis elucidated the close linkage between the microbial community, the fermentation environment, and flavor formation. Koji moisture declined gradually, with faster losses at later fermentation stages. This physiological dehydration arose from microbial metabolic heat, forced aeration and structural loosening of koji, not simple physical evaporation. System pH displayed a typical U-shaped trend across fermentation. Values dropped early, most likely driven by accumulating organic acids, before rising from mid to late fermentation. This pH rebound was tentatively attributed to ammonia release from proteolytic breakdown, which may neutralize acidic compounds. These observations cast doubt on the conventional assumption that organic acid levels may be reliably estimated solely from pH measurements. Physicochemical analysis showed continuous accumulation of amino acid nitrogen (0.6–0.9 g/100 g) and total acidity throughout fermentation. By contrast, reducing sugar concentrations differed across individual koji rooms, presumably owing to divergent microbial adaptation in early fermentation. A total of 77 common compounds were identified, among which 13 key odor-active compounds with OAV ≥ 1, such as 4-vinylguaiacol and 3-methylbutyraldehyde, constitute the characteristic flavor profile of soy sauce starter culture. High-throughput sequencing uncovered a distinct ecological pattern: eukaryotic communities, dominated by Aspergillus oryzae, converged under controlled regulation. While prokaryotic communities differentiated dynamically, driven by spatial heterogeneity in the semi-open fermentation environment. Spearman correlation analysis further indicated potential functional partitioning: high-abundance taxa (e.g., Aspergillus oryzae, Weissella) were predominantly associated with macromolecular substrate degradation, whereas rare low-abundance taxa (e.g., Alternaria) displayed significant correlations with the biosynthesis of key characteristic flavor compounds. This study clarifies the synergistic regulatory mechanisms linking physicochemical conditions, microbial metabolism, and flavor precursor formation during industrial koji production. The findings establish a scientific foundation for optimizing process parameters and achieving standardized quality control in soy sauce manufacturing. Full article

Constrained multi-objective optimization problems (CMOPs) widely exist in scientific research and industrial applications. In Type IV CMOPs, where the constrained Pareto front (CPF) is significantly separated from the unconstrained Pareto front (UPF) by large infeasible barriers, traditional single-population evolutionary algorithms often suffer from severe search reachability difficulties. Moreover, while existing dual-population coevolutionary frameworks can exploit auxiliary populations to provide global guidance for obstacle crossing, they typically adopt a constant knowledge transfer intensity, which may introduce negative transfer and interfere with fine-grained CPF convergence in later evolutionary stages. To address these challenges, this paper proposes a Dual-Population Constrained Multi-Objective Evolutionary Algorithm with Adaptive Knowledge Migration (ADCMO). The algorithm constructs a main–auxiliary dual-population coevolutionary framework: the main population pursues feasible convergence under the original constraints, while the auxiliary population explores the unconstrained objective landscape to maintain global awareness. A linearly decaying migration control factor is introduced to dynamically regulate the intensity of cross-population knowledge transfer. Specifically, a dual-defense mechanism is established by simultaneously controlling the auxiliary participation ratio in mating pool construction and the auxiliary offspring injection scale in environmental selection, thereby achieving the synergistic effect of enhanced obstacle crossing in early evolution and progressive interference suppression in later stages. Extensive experiments on two benchmark suites comprising 23 test problems and ten representative real-world constrained multi-objective optimization problems demonstrate that ADCMO shows clear advantages on several large-barrier Type IV-like CMOPs, especially on the LIR-CMOP suite, while maintaining feasible and competitive behavior on most remaining instances. Ablation studies further verify the non-negligible contributions of the auxiliary population, the adaptive migration factor, and the dual-defense mechanism to the overall performance. Full article

Pavement damages along the Qinghai–Xizang Highway (QXH) in permafrost regions are jointly controlled by geographical and engineering factors, leading to higher damage rates than in non-permafrost regions. However, the overall development trend of these damages and the spatial–temporal patterns have not been systematically quantified. To analyze the spatial distribution of different pavement damages, reveal the spatial–temporal associations, and analyze the spatial heterogeneity of the driving factors, three field surveys were conducted in 2014, 2019 and 2024, with records of seven major pavement damages. Statistical analyses were used to examine the relationships among single and co-occurring damages. Then, a novel geographical model, combining a graph convolutional network with multi-scale geographically weighted regression (GCN-MGWR), was further developed to treat the QXH as a linear geographic unit and to assess the spatial heterogeneity and relative contribution of different influencing factors. The results show that the mean pavement damage ratios in permafrost regions during the three surveys are 4.21%, 6.82%, and 4.74%, respectively, with crack-type damages (transverse, longitudinal, and block cracking) exhibiting the highest occurrence rates. The three strongest pairs of correlations are transverse and longitudinal cracking (0.584), transverse and block cracking (0.570), and waving and rutting (0.622). The primary factors influencing crack-type damages are embankment thickness, mean annual ground surface temperature (MAGST), elevation and existing damages. Transverse and longitudinal cracking show a pronounced increase with rising MAGST, and embankment thickness below 1 m or above 4 m significantly contribute to the development of both crack types (SHAP > 0.5). Overall, the evolution of crack-type damages has shifted from being primarily controlled by geographical factors to being controlled by the combined influence of engineering and geographical factors during 2014–2024. The factor contributions identified by the GCN-MGWR model provide quantitative support for the regional adaptive design and specific maintenance of roadway in permafrost regions. Full article

Extreme weather conditions impose significant challenges on distribution network inspection because UAV flight safety, energy consumption, vehicle mobility, and task coverage are strongly coupled under wind disturbances. To improve inspection efficiency and operational robustness, this paper proposes a weather-aware asynchronous vehicle–UAV cooperative scheduling method based on bi-level MODDPG–NSGA-II optimization. First, a dynamic wind field model and a wind-sensitive UAV energy model are established to describe the effects of background wind, vertical wind shear, and local gust disturbances on UAV motion and state-of-charge evolution. Then, an asynchronous vehicle–UAV collaboration mechanism is developed, allowing the vehicle to move toward downstream parking sites after UAV deployment while UAVs perform inspection and cross-site recovery under rendezvous and energy safety constraints. On this basis, a bi-level optimization framework is constructed, in which NSGA-II searches global coordination parameters and MODDPG learns adaptive multi-UAV scheduling policies in continuous decision spaces. Controlled wind-factor experiments show that, with the task scale fixed at 52 inspection tasks, the proposed method maintains 100% task coverage under 0–10 m/s wind conditions. As the reference wind speed increases from 0 m/s to 10 m/s, the mission completion time increases from 40.97 min to 70.24 min, while the minimum residual SOC decreases from 50.32% to 13.82%, which remains above the predefined safety threshold. Repeated stochastic trials and statistical significance analysis further indicate that the proposed method achieves shorter mission time and more stable task coverage than representative baselines under the same experimental conditions. The scope of this study is simulation-level validation; real-world flight tests and hardware-in-the-loop verification will be further investigated in future work. Full article

Background: Extrachromosomal DNA (ecDNA) amplification represents a distinct mechanism of genomic instability in cancer, increasingly recognized for its role in aggressive disease progression. This review examines how ecDNA drives tumour evolution and assesses its potential as both a prognostic marker and therapeutic target. Methods: The authors integrate findings from multiple detection platforms—including FISH, whole-genome sequencing, and specialized reconstruction algorithms—and present data across diverse cancer types; no preregistration is noted, and no animal studies are included. Results: ecDNA consists of circular, acentric DNA elements carrying high-copy oncogene amplifications (such as EGFR, MYC, MDM2, and CDK4). Unlike chromosomal DNA, ecDNA segregates unevenly during cell division, generating intratumoral heterogeneity, accelerating adaptation to selective pressures, and promoting resistance to therapy. Pan-cancer surveys summarized here reveal ecDNA in a significant subset of tumours, with particularly high frequencies in liposarcoma, glioblastoma, and HER2-positive breast cancer, and consistent associations with worse clinical outcomes. Conclusions: The authors conclude that ecDNA amplification serves as a credible adverse prognostic indicator and holds promise for refining risk stratification and guiding treatment strategies. However, they stress that clinical adoption remains constrained by the absence of standardized, scalable, and reproducible detection. Full article

Water scarcity is increasingly recognized as a slow-onset ecological crisis with major environmental, socio-economic and governance effects, yet systematic assessments of how research on this topic has evolved remain limited. This study addresses this gap through a bibliometric and thematic analysis of water-scarcity publications from 2000 to 2025, using VOSviewer (version 1.6.20), Biblioshiny™ (Bibliometrix version 4.3.1) and RStudio (version 2024.12.1 + 563) to map research trends, conceptual clusters and leading contributing countries, institutions and authors. The analysis shows that water scarcity research is organized around four dominant themes: adaptive water management and climate resilience, plant physiological responses to drought and water stress, ecosystem resilience and biodiversity under water scarcity, and water-limited agriculture and food security. Early scholarship focused heavily on biophysical processes such as drought tolerance and hydraulic conductivity, while recent studies increasingly incorporate socio-ecological, governance and policy dimensions, reflecting a shift toward holistic, solution-oriented approaches. Overall, the study provides a comprehensive overview of the evolution and global distribution of water scarcity research, highlighting the importance of integrating biophysical knowledge with human-centered strategies to support evidence-based decision-making, strengthen inclusive water governance, and enhance socio-ecological resilience in the face of a changing climate. Full article

Human activity recognition—HAR—plays a crucial role in the lives of patients battling neurodegenerative diseases. These debilitating conditions, such as Alzheimer’s or Parkinson’s, affect individuals’ ability to perform daily tasks autonomously and safely. HAR technology offers an invaluable solution by enabling real-time monitoring and assistance, helping to maintain independence and quality of life for patients. Additionally, this technology provides a valuable data source for doctors and caregivers, allowing for more precise and personalized care, which can make a difference in managing and treating these neurodegenerative diseases. The objective of this review is to identify the contribution of Transfer Learning and Reinforcement Learning in supporting the processes of daily activity recognition, thus enhancing the quality of life for patients. As this is a trending topic, the literature surrounding it is quite dispersed, which is why this review aims to present the current line of research in this field. To carry out this analysis, the science tree paradigm was used, which establishes two fundamental stages of analysis. The first is delimited by scientometrics, where the leading countries in the application of such technologies can be identified. This review highlights the evolution in the use of transfer learning and reinforcement learning in HAR in the healthcare field, where these techniques have significantly improved the accuracy and adaptability of real-time monitoring systems. The studies reviewed indicate that transfer learning has allowed models to adapt to data variations without requiring large volumes of manual labeling, which is essential in clinical and patient monitoring contexts. Additionally, reinforcement learning has optimized decision-making in complex scenarios, enabling activity recognition systems to dynamically adjust monitoring parameters, enhancing detection and response to critical or unusual activities in multi-user environments. These advances demonstrate that, by integrating these approaches, greater personalization and robustness can be achieved in human activity recognition, thereby improving the quality of life for patients in clinical settings. Full article

Advanced-stage classic Hodgkin lymphoma (cHL) represents one of the major success stories in modern oncology, with long-term survival now exceeding 80% for most patients. In this review, we examine the evolution of frontline therapy for advanced-stage cHL, tracing the transition from empiric combination chemotherapy to contemporary, biologically informed treatment strategies. We begin by revisiting the early development of multiagent chemotherapy regimens, including MOPP and ABVD. These regimens established, for the first time, that advanced lymphoma could be cured with systemic therapy. We then discuss efforts to improve outcomes through treatment intensification, which culminated in the development of BEACOPP-based approaches that improved disease control at the cost of substantial acute and long-term toxicity. Subsequently, the incorporation of functional imaging ushered in the era of PET-adapted therapy, enabling dynamic treatment modification based on early response and providing a framework to better balance efficacy with toxicity reduction. Finally, we review the integration of novel agents, including brentuximab vedotin and PD-1 blockade, which have reshaped the frontline treatment landscape and further improved outcomes for high-risk patients while challenging historical chemotherapy paradigms. Collectively, the treatment history of advanced-stage cHL reflects a broader evolution in oncology: from maximizing cytotoxic intensity toward increasingly personalized strategies designed to optimize cure while minimizing long-term harm. Ongoing efforts focused on biomarker-driven risk stratification and the utilization of circulating tumor DNA are poised to further refine this balance in the coming decade. Full article

The misuse of antibiotics is causing widespread antibiotic resistance, creating an urgent need for new treatment options such as nanoparticle-based therapies. This study aimed to compare silver nanoparticles (AgNPs) produced via green synthesis methods with those made through traditional chemical processes. Furthermore, the study investigated and contrasted the bacterial responses to these two types of AgNPs over a 21-day period of selection pressure using experimental evolution techniques. Analysis using scanning electron microscopy and transmission electron microscopy revealed a consistent, uniform morphology among the AgNPs produced via chemical methods. In contrast, AgNPs synthesized through green methods displayed an irregular morphology. Despite these morphological differences, all nanoparticles from both synthesis approaches were under 100 nm in diameter. These findings were further supported by the absorption spectrum data, which showed a maximum absorption peak between the 400 and 500 nm wavelength range. E. coli exposed to green synthesized AgNPs for 21 days adapted to their presence, exhibiting both enhanced resistance to the green synthesized AgNPs themselves and the development of cross-resistance to ionic silver, a pattern not observed in chemically synthesized AgNP-selected populations. Populations selected using chemical synthesized AgNPs did not develop increased resistance to either chemically or green synthesized AgNPs; however, they showed a slight increase in resistance to ionic silver. Genomics analysis identified polymorphism in genes in a green synthesized AgNP-resistant line including but not limited to the multidrug efflux transporter system (EmrAB), DUF4756 family protein (D1792_RS05680), putative zinc-binding protein YnfU/cold shock-like protein (ynfU/cspB) and imcF-related family protein (D1792_RS10035). Bacterial resistance to chemical AgNPs involves specific polymorphisms in key bacterial components like the RNA polymerase sigma factor (RpoE) and the EmrAB efflux pump. Collectively, the method used to synthesize the AgNPs influences their antibacterial efficacy and the likelihood of bacteria developing resistance. Understanding this interaction is vital for developing effective and resistance-controlled applications of AgNPs across medicine, environmental science, and industry. Full article

Colorectal cancer (CRC) is a major global health burden characterized by progressive genetic and metabolic alterations, with iron metabolism being increasingly recognized as a key contributor to tumorigenesis. This review provides an integrated synthesis of current evidence on iron metabolism across the continuum of colorectal cancer development, from preneoplastic lesions to advanced disease. We analyzed data from epidemiological, experimental, and mechanistic studies addressing systemic and cellular iron homeostasis, including the hepcidin–ferroportin axis, as well as iron handling within tumor cells and the tumor microenvironment. Available data indicate that colorectal epithelial cells progressively develop an iron-retentive phenotype, characterized by increased iron uptake and reduced export, leading to expansion of the intracellular labile iron pool. This imbalance contributes to oxidative stress, DNA damage, metabolic adaptation, and activation of oncogenic signaling pathways while also influencing immune responses. However, epidemiological findings on dietary iron and CRC risk remain inconsistent, highlighting the context-dependent nature of iron-related effects. In conclusion, iron metabolism represents a dynamic regulator of CRC progression and a mechanistic framework for understanding stage-specific tumor evolution, although further studies are needed to clarify how iron-dependent pathways differ across colorectal tumor subtypes and microenvironmental contexts. Full article

Qinghai Lake, the largest endorheic saline lake in China, has undergone a pronounced hydrological regime shift from a multi-decadal decline to a rapid post-2004 recovery, reflecting strong hydroclimatic non-stationarity in the northeastern Tibetan Plateau (TP). This paper supplements the current water level and lake area status of Qinghai Lake to provide basic background for future prediction. Reliable forecasting of such climate sensitive lake systems remains difficult because conventional statistical models often fail to capture non-linear fluctuations, whereas standalone deep learning models may overlook long-term deterministic evolution. To address this challenge, we developed a serial decomposition GeoAI framework that integrates autoregressive integrated moving average (ARIMA), one-dimensional convolutional neural networks (1D-CNNs), and long short-term memory (LSTM) networks for non-stationary water level forecasting. Using annual water level observations from 1960 to 2025, the ARIMA component was first used to extract the low-frequency deterministic trend, after which the CNN-LSTM module reconstructed the nonlinear residual variability. The model was trained on the 1960–2012 period and validated over 2013–2025, which represents the most dynamic expansion stage of Qinghai Lake. The hybrid framework outperformed the benchmark models, achieving a Root Mean Square Error (RMSE) of 0.2033 m, Mean Absolute Error (MAE) of 0.1727 m, and Mean Squared Error (MSE) of 0.0413 m² during validation. The decomposition strategy effectively reduced phase lag and amplitude attenuation, improving both predictive accuracy and process interpretability. Multi-step forecasting for 2026–2056 suggests that Qinghai Lake will continue to rise, reaching approximately 3204.08 m by 2056, although the growth rate is projected to slow as negative hydrological feedback strengthen. By explicitly separating deterministic climate scale signals from nonlinear short-term variability, the proposed framework provides a robust and transferable geoinformation based tool for forecasting water level dynamics and supporting adaptive management in climate sensitive, data scarce lake basins. Full article

This study proposes an experimental data-driven hybrid prediction framework for hydraulic turbine performance using a Particle Swarm Optimization-enhanced Extreme Learning Machine (PSO-ELM). The performance of three hydraulic turbines, namely Pelton, Kaplan, and Francis turbines, was experimentally investigated under different jet-opening and guide-vane conditions. The experimental results showed that the Pelton turbine (PT) achieved its highest efficiency at low jet opening, whereas the Kaplan and Francis turbines performed better at higher guide-vane openings. The measured data includes 36 tests, which were then used to evolve and evaluate hybrid ML models for predicting hydraulic power and efficiency. Jet-opening or guide-vane position (25%, 50%, 75% and 100%) and rotational speed were used as input variables, while brake power and efficiency were used as output variables. The proposed PSO-ELM model was compared with other optimized ELM models, including Genetic Algorithms Extreme Learning Machine (GA-ELM), Differential Evolution Extreme Learning Machine (DE-ELM), and Whale Optimization Algorithm Extreme Learning Machine (WOA-ELM), as well as Particle Swarm Optimization–Adaptive Neuro-Fuzzy Inference System (PSO-ANFIS) and Particle Swarm Optimization–Multi-Layer Perceptron (PSO-MLP) models. The suggested method presents a hopeful structure for tackling the difficulties linked to performance evaluation, thus enabling a more dependable and effective use of energy resources. The main findings validate that a PSO-based structure reaching an R² value of 0.997 is more efficient in predictive tool performance optimization for hydropower systems. Full article

Malassezia are commensal lipid dependent yeasts which can cause opportunistic skin infection. Topical imidazole antifungals such as clotrimazole and ketoconazole are the frontline treatment. However, the tendency of fungal infections to recur, combined with the emergence of multi-azole-resistant Malassezia isolates means that many patients have used these antifungal treatments repeatedly or for extended durations with limited efficacy. While the impact of single azole treatments has been studied, the ability of specific azoles to induce cross-resistance is unclear. Understanding the effect of prior exposure of one treatment on susceptibility to other antifungals is important in the selection of the appropriate treatment to avoid driving the evolution of greater resistance. We previously identified drug transporters from the ATP-Binding Cassette (ABC) and Major Facilitator Superfamily (MFS) to be upregulated on extended exposure to clotrimazole. In this study, we investigated the effect of extended clotrimazole, ketoconazole and fluconazole exposure on antifungal cross-resistance profiles and examined the expression of the MFS transporters OPT1 and FLR1 in resistance emergence. We observed that treatment with clotrimazole was associated with increased cross-resistance to other antifungals. Ketoconazole treatment caused elevated MICs in all tested antifungals that did not decrease after drug removal. These findings advance our understanding of fungal adaptive resistance mechanisms and inform improved antifungal strategies to mitigate resistance development. Full article