Search Results (1,979)

Oxidative stress-induced mitochondrial dysfunction has been identified as a central driver of cellular senescence and age-related degeneration. The present study investigated the potential of vutiglabridin, a paraoxonase 2 (PON2) agonist, to mitigate reactive oxygen species (ROS)-induced senescence in human LO2 hepatocytes. The process of senescence was induced by the administration of hydrogen peroxide, followed by the recovery of the cells in fresh medium. The levels of intracellular ROS, the senescence-associated β-galactosidase staining, the p16/p21 expression, and the mitochondrial morphology were the focus of a comprehensive assessment utilizing a range of analytical techniques, including microscopy, quantitative PCR, and Western blotting. The present study demonstrated that the administration of vutiglabridin resulted in a dose-dependent reduction in attenuation of the expression of senescence markers. Transmission electron microscopy (TEM) and stimulated emission depletion (STED) imaging revealed the preservation of mitochondrial structure and network connectivity in cells treated with vutiglabridin. These effects were absent in PON2 knockout cells, confirming that vutiglabridin’s action requires functional PON2. The present study demonstrates that vutiglabridin alleviates oxidative stress-induced cellular senescence by preserving mitochondrial integrity and redox balance via a PON2-dependent mechanism. This study lends further support to the investigation of the PON2 pathway as a therapeutic target in age-related cellular dysfunction. Full article

With the continuous development of cities, the network connections between Chinese cities have rapidly strengthened, and cities are gradually transforming from traditional production bases into economic platforms within dynamic spaces. In this process, urban building carbon emissions are not only determined by the city’s own resource and industrial advantages but are increasingly influenced by its position within the urban economic space network. This study constructs an urban economic spatial network using the gravity model, and based on dynamic data of building carbon emissions in Chinese cities from 2008 to 2020, develops a new analytical framework from the perspective of dynamic network evolution to examine the impact mechanisms of urban network position and residential activity intensity on building carbon emissions. The findings indicate that both residents’ activity intensity and city’s network position have a significant positive impact on per capita building carbon emissions, The impact coefficient between residential activity intensity and per capita building carbon emissions is 0.278 (p < 0.01). This conclusion remains valid after robustness and endogeneity tests. The city’s network position can mitigate the detrimental impact that residents’ activity intensity has on per capita building carbon emissions, particularly in the dynamic decision-making process, where cities can adjust their strategies based on their network position. The influence of city’s network position on per capita building carbon emissions exhibits multidimensional heterogeneity, with its effect being more significant in megalopolis and metropolis compared to large city and medium & small city. Specifically, in megalopolis, the network position impact coefficient is 0.22, significantly higher than 0.039 in medium & small city. These findings provide new perspectives for reducing building carbon emissions at the urban-level in the context of dynamic spatial mobility. Full article

Promoting cycling in mixed-traffic environments remains a global challenge, hinging on the development of well-connected, low-stress networks. However, existing evaluation frameworks often lack comprehensiveness, overlooking the multifaceted nature of cyclists’ experiences. This study addresses this gap by proposing a novel multidimensional evaluation framework for assessing the effective connectivity of urban cycling networks. The framework integrates four critical dimensions: (1) structural connectivity of the basic road network, (2) dynamic interference from mixed traffic, (3) comfort of the cycling environment, and (4) cross-barrier connectivity. Using Nanjing, China, as a case study, we applied a hybrid Analytic Hierarchy Process (AHP)–Grey Clustering method to derive objective indicator weights and conduct a comprehensive evaluation. The results yield a composite score of 3.2568 (on a 0–4 scale), classifying Nanjing’s cycling network connectivity at the “Four-Star” level, indicating a generally positive developmental trajectory. Nevertheless, spatial disparities persist: the urban core faces intense traffic interference, while peripheral areas are hindered by network fragmentation and poor permeability. Key challenges include frequent vehicle–cyclist conflicts at intersections, inadequate nighttime illumination, suboptimal pavement conditions, and excessive detours caused by natural barriers such as the Yangtze River. This study provides urban planners and policymakers with a robust and systematic diagnostic tool to identify deficiencies and prioritize targeted interventions, ultimately contributing to sustainable urban mobility by enhancing the resilience, equity, and attractiveness of cycling networks in complex mixed-traffic settings. Full article

The Qianlong Tripitaka preserved in the Capital Museum is a distinctive large-scale wood block printing plates of the Qing Dynasty. It represents a unique type of Chinese documentary wooden heritage preserved in a dry museum environment, which has rarely been subjected to comprehensive physicochemical analysis, resulting in an inadequate understanding of their deterioration processes. This study applied a comprehensive multi-analytical method to investigate the deterioration of the scripture plates. The findings indicate that the Qianlong Tripitaka shows typical structural deformation, chemical depolymerization, and a decline in structural integrity and stability. Scanning Electron Microscopy (SEM) and Computed Tomography (CT) revealed thinning and the distortion of cell walls, reduced density, and partial collapse of tissue structures. Thermogravimetric Analysis (TGA) indicated lower decomposition temperatures and higher inorganic residues, while a Brunauer–Emmett–Teller surface area analyzer (BET) showed diminished surface area, expanded pores, and compromised connectivity. Moisture content analyses verified significant water loss, contributing to brittleness and susceptibility to microbial degradation. Fourier Transform Infrared Spectroscopy (FTIR) and X-ray Diffraction (XRD) analyses revealed considerable hemicellulose degradation, the disruption of cellulose crystallinity, and relatively stable lignin. This study highlights the value of a multi-analytical strategy for assessing the deterioration of wooden cultural heritage, providing a transferable framework for similar documentary wooden artifacts. Full article

This paper presents a systematic approach for rapid assessment of the performance and robustness of linear control systems through geometric analysis in the complex plane. By combining indirect performance indices within a defined zone of desired performance in the complex s-plane, a connection is established with direct performance indices, forming a foundation for the synthesis of control algorithms that ensure root placement within this zone. Analytical relationships between the complex variables s and z are derived, thereby defining an equivalent zone of desired performance for discrete-time systems in the complex z-plane. Methods for verifying digital algorithms with respect to the desired performance zone in the z-plane are presented, along with a visual assessment of robustness through radii describing robust stability and robust performance, representing performance margins under parameter variations. Through parametric modeling of controlled processes and their projections in the complex s- and z-domains, the influence of the discretization method and sampling period, as forms of a priori uncertainty, is analyzed. This paper offers original derivations for MISO systems, facilitating the analysis, explanation, and understanding of the dynamic behavior of real-world controlled processes in both the continuous and discrete-time domains, and is aimed at integration into expert systems supporting control strategy selection. The practical applicability of the proposed methodology is related to discrete control systems in energy, electric drives, and industrial automation, where parametric uncertainty and choice of method and period of discretization significantly affect both robustness and control performance. Full article

The Kennedy model provides a flexible and mathematically consistent framework for modeling the term structure of interest rates, leveraging Gaussian random fields to capture the dynamics of forward rates. Building upon our earlier work, where we developed both theoretical results—including novel proofs of the martingale property, connections between the Kennedy and HJM frameworks, and parameter estimation theory—and practical calibration methods, using maximum likelihood, Radon–Nikodym derivatives, and numerical optimization (stochastic gradient descent) on simulated and real par swap rate data, this study extends the analysis in several directions. We derive detailed formulas for the volatilities implied by the Kennedy model and investigate their asymptotic properties. A comprehensive sensitivity analysis is conducted to evaluate the impact of key parameters on derivative prices. We implement an industry-standard Monte Carlo method, tailored to the conditional distribution of the Kennedy field, to efficiently generate scenarios consistent with observed initial forward curves. Furthermore, we present closed-form pricing formulas for various interest rate derivatives, including zero-coupon bonds, caplets, floorlets, swaplets, and the par swap rate. A key advantage of these results is that the formulas are expressed explicitly in terms of the initial forward curve and the original parameters of the Kennedy model, which ensures both analytical tractability and consistency with market-observed data. These closed-form expressions can be directly utilized in calibration procedures, substantially accelerating multidimensional nonlinear optimization algorithms. Moreover, given an observed initial forward curve, the model provides significantly more accurate pricing formulas, enhancing both theoretical precision and practical applicability. Finally, we calibrate the Kennedy model to market-observed caplet prices. The findings provide valuable insights into the practical applicability and robustness of the Kennedy model in real-world financial markets. Full article

Urban horticulture as a segment of urban agriculture can take various forms: home gardens, allotment farming, community gardens, community-supported agriculture, vertical farming, etc. After the COVID-19 pandemic in Croatia and neighboring countries, growing horticultural plants in urban and suburban areas became increasingly popular. The aim of the study was to investigate citizens’ attitudes towards the benefits and support of urban horticulture, its relationship to the environment, and needs and relevance in studies in the cities of Šibenik, Split, Mostar and Skopje. The research methods used for the purpose of this study were theoretical analysis method, survey and analytical descriptive and statistical method. The research was conducted online during the first half of 2024 on a sample of 506 respondents. The main goal of the paper was to examine the views of citizens on urban horticulture. With specific objectives, the views of citizens were examined on the benefits of urban horticulture, the relationship between urban horticulture and the environment, urban horticulture and plant protection, support for urban horticulture, and the needs and trends of urban horticulture. and plant protection, support for urban horticulture, needs and trends of urban horticulture. The results showed that citizens are mostly positive towards growing horticultural plants in urban and suburban areas without pollution. In urban horticulture, respondents prefer using ecological principles and products. Female respondents expressed more positive attitudes towards the fashionability and need for urban horticulture. Respondents from Skopje showed the most positive attitudes towards the benefits of urban horticulture and its relationship to the environment. Also, there is no statistically significant difference in attitudes towards urban horticulture with regard to the location of residence. The research contributes to the trend of development and promotion of urban horticulture with a special emphasis on the importance of environmental preservation. It also contributes to the development of an interdisciplinary method that connects natural and social sciences, and develops an empirical approach that can improve urban culture with the aim of preserving the environment. Full article

Renewable energy sources can be of significant help to rural communities with inadequate electricity access. This study presents a comprehensive techno-economic assessment of a 500 kWp solar Photovoltaic (PV) energy system designed for Ibadan, Nigeria. A novel hybrid modeling framework was developed in which technical performance analysis was employed using PVSyst, whereas economic and optimization analysis was carried out using HOMER. Simulation outputs from PVSyst were integrated as inputs into HOMER, enabling a more accurate and consistent cross-platform assessment. Nigeria’s enduring energy crisis, marked by persistent grid unreliability and limited electricity access, necessitates need for exploration of sustainable alternatives. Among these, solar photovoltaic (PV) technology offers significant promise given the country’s abundant solar irradiation. The proposed system was evaluated using meteorological and load demand data. PVSyst simulations projected an annual energy yield of 714,188 kWh, with a 25-year lifespan yielding a performance ratio between 77% and 78%, demonstrating high operational efficiency. Complementary HOMER Pro analysis revealed a competitive levelized cost of energy (LCOE) of USD 0.079/kWh—substantially lower than the baseline grid-only cost of USD 0.724/kWh, and a Net Present Cost (NPC) of USD 6.1 million, reflecting considerable long-term financial savings. Furthermore, the system achieved compelling environmental outcomes, including an annual reduction of approximately 160,508 kg of CO₂ emissions. Sensitivity analysis indicated that increasing the feed-in tariff (FiT) from USD 0.10 to USD 0.20/kWh improved the project’s financial viability, shortening payback periods to just 5.2 years and enhancing return on investment. Overall, the findings highlight the technical robustness, economic competitiveness, and environmental significance of deploying solar-based energy solutions, while reinforcing the urgent need for supportive energy policies to incentivize large-scale adoption. Full article

The influence of traditional Chinese ritual culture on courtyard spatial sequences is widely acknowledged. However, quantitative analytical methods, such as space syntax, have rarely been applied in studies of ritual–residential space relations. This study uses space syntax, specifically Visibility Graph Analysis (VGA) and axial maps, to conduct a quantitative study of the spatial relationship between ritual and residential areas in Prince Kung’s Mansion. The VGA results indicate a distinct gradient of visual integration, which decreases progressively from the outward-oriented ritual areas, such as the palace gate and halls, through the transitional domestic ritual areas to the inward-oriented residential areas, such as Xijin Zhai and Ledao Tang. This pattern demonstrates a positive correlation between spatial visibility and ritual hierarchy. The axial map results confirm that the central axis and core ritual spaces exhibit the highest spatial connectivity, reflecting their supreme ritual status. More importantly, spatial connectivity is intensified during ritual activities compared to in daily life, indicating that enhanced spatial connectivity is required during rituals. Ritual spaces are characterized by extroversion, high visibility, and connectivity, while residential spaces prioritize introversion and minimal exposure. The deliberately designed ritual–residential architectural spatial sequence of Prince Kung’s Mansion articulates Confucian ideological principles, such as centrality as orthodoxy, gender segregation, and hierarchy. This study visually and quantitatively illustrates the harmony between ritual and residential spaces in Prince Kung’s Mansion. It enhances our understanding of the mechanisms of expression of courtyard ritual cultural spaces, providing evidence-based guidance for functional adaptive transformations in heritage conservation practices. It also offers a fresh perspective on the analysis of courtyard ritual spaces. Full article

Big data analytics has become a cornerstone of modern industries, driving advancements in business intelligence, competitive intelligence, and data-driven decision-making. This study applies Neural Topic Modeling (NTM) using the BERTopic framework and N-gram-based textual content analysis to examine job postings related to big data analytics in real-world contexts. A structured analytical process was conducted to derive meaningful insights into workforce trends and skill demands in the big data analytics domain. First, expertise roles and tasks were identified by analyzing job titles and responsibilities. Next, key competencies were categorized into analytical, technical, developer, and soft skills and mapped to corresponding roles. Workforce characteristics such as job types, education levels, and experience requirements were examined to understand hiring patterns. In addition, essential tasks, tools, and frameworks in big data analytics were identified, providing insights into critical technical proficiencies. The findings show that big data analytics requires expertise in data engineering, machine learning, cloud computing, and AI-driven automation. They also emphasize the importance of continuous learning and skill development to sustain a future-ready workforce. By connecting academia and industry, this study provides valuable implications for educators, policymakers, and corporate leaders seeking to strengthen workforce sustainability in the era of big data analytics. Full article

With the designation of the first cohort of national parks and the continued operation of remaining pilots, China’s national park reform has entered a critical stage requiring consolidation and adaptive improvement. A key challenge lies in the ambiguous status of five pilot zones, which lack a standardized evaluation mechanism to guide decisions on future inclusion or exit. This study develops a comprehensive indicator system specifically tailored to assess the construction and development of national park pilots, thereby supporting evidence-based governance beyond initial entry criteria. Drawing on relevant theories and China’s institutional context, the framework employs Analytic Hierarchy Process, expert consultation, and fuzzy scoring to determine indicator weights and evaluation standards. The resulting system integrates three dimensions—ecological protection system, management system, and public service system. Nanshan National Park was selected as a case study, scoring 87.77 in 2024 (Class II, “Proficient”), with strong overall performance but notable weaknesses in landscape connectivity, recreational product diversity, and regional integration. These findings suggest the need for targeted improvements in ecological corridors, service enrichment, and community benefit-sharing. Overall, the proposed framework provides a replicable tool for evaluating pilot zones, offering practical insights for refining China’s national park development and enhancing governance effectiveness. Full article

This research develops and applies the IoTRIM model to assess the economic and operational implications of IoT integration in postal and courier enterprises in Slovakia. Combining a multi-criteria evaluation framework with an extended Cobb–Douglas production function, the analysis captures both readiness levels and their translation into output performance. The IoTRIM assessment reveals heterogeneous distributions of strengths across four strategic and technical pillars, with notable disparities between connectivity, data analytics, and interoperability capacities. Monte Carlo simulations under pessimistic, realistic, and optimistic scenarios highlight divergent digital trajectories among enterprises, with some demonstrating accelerated gains from IoT readiness while others face structural bottlenecks in infrastructure and process integration. Hypothesis testing indicates that while a positive and statistically significant relationship between IoT readiness and output is observed in selected cases, this effect is not universal across all enterprises and scenarios. However, the inclusion of IoT readiness consistently improves the explanatory power of the production function models. The findings underline that digital transformation outcomes depend not only on investment scale but also on systemic absorption capacity, including interoperability, data governance, and organizational alignment. The proposed approach offers both a methodological contribution for measuring digital readiness impacts and practical insights for strategic planning in the postal and courier sector. Full article

Mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer’s disease (AD), comprises three potential trajectories: reversion, stability, or progression. Accurate prediction of these trajectories is crucial for disease modeling and early intervention. We propose a novel analytical framework that integrates a healthy control–AD difference template (HAD) with a large-scale Granger causality algorithm based on long short-term memory networks (LSTM-lsGC) to construct effective connectivity (EC) networks. By applying principal component analysis for dimensionality reduction, modeling dynamic sequences with LSTM, and estimating EC matrices through Granger causality, the framework captures both symmetrical and asymmetrical connectivity, providing a refined characterization of the network alterations underlying MCI progression and reversion. Leveraging graph-theoretical features, our method achieved an MCI subtype classification accuracy of 84.92% (AUC = 0.84) across three subgroups and 90.86% when distinguishing rMCI from pMCI. Moreover, key brain regions, including the precentral gyrus, hippocampus, and cerebellum, were identified as being associated with MCI progression. Overall, by developing a symmetry-aware effective connectivity framework that simultaneously investigates both MCI progression and reversion, this study bridges a critical gap and offers a promising tool for early detection and dynamic disease characterization. Full article

Amid the deep integration of China’s “dual-carbon” goals with regional coordinated development strategies, this study develops a multidimensional analytical framework of regional integration based on panel data from 41 prefecture-level cities in the Yangtze River Delta urban agglomeration from 2009 to 2023. The framework encompasses five dimensions: urban–rural integration, innovation coordination, infrastructure connectivity, ecological co-governance, and public service sharing. Using structural equation modeling (SEM), the study empirically investigates the mechanisms and pathways through which regional integration shapes low-carbon development. The results indicate that different dimensions exert differentiated impacts: urban–rural integration and infrastructure connectivity significantly promote low-carbon development, whereas public service sharing has an adverse effect due to a phenomenon known as “carbon lock-in”. By contrast, the impact of innovation coordination and ecological co-governance is not statistically significant. Moreover, substantial regional heterogeneity exists: Jiangsu Province demonstrates the leading performance in the manifest development level; Zhejiang Province shows strong systemic capacity level, but limited conversion into manifest outcomes. At the same time, most cities in Anhui Province lag in both aspects. Coordination analysis further identifies four typical development patterns: dual-high, system-driven, performance-dominant, and dual-low. Drawing on these findings, this study proposes policy recommendations across four dimensions—regional coordination, low-carbon pathway optimization, targeted empowerment, and collaborative governance—to facilitate the green and low-carbon transition of the Yangtze River Delta urban agglomeration. Full article

Understanding the effectiveness of engineering measures in mitigating surface erosion is crucial for sustainable land management. However, studies explicitly quantifying the combined effects of large-scale engineering measures and environmental factors remain limited. In this study, multisource remote sensing data were integrated with interpretable machine learning to quantify and analyze the regional influence of erosion control measures. We constructed a comprehensive indicator system encompassing spectral, textural, and topographic variables derived from high-resolution satellite imagery and DEM data. To address model transparency and enhance the interpretability of the results, we employed an interpretable machine learning framework capable of both accurate prediction and explicit attribution of feature importance. The results indicate that the implementation of engineering measures substantially reduces erosion intensity across the study area. Spatial heterogeneity in erosion mitigation effectiveness was closely associated with the distribution patterns of engineering measures and site-specific environmental conditions. Basins with a high proportion of check dams showed average elevation gains of up to 2.5 m compared with those without check dams, and terraces contributed to elevation increases of ~1.9 m in typical loess hilly regions. The interpretable machine learning model achieved R² = 0.62 at Basin 1 (average area ~100 km²) and R² = 0.73 at Basin 2 (~600 km²), demonstrating reliable predictive capability. The findings not only validate the role of engineering interventions in erosion mitigation but also provide a transparent analytical framework that connects remote sensing analytics with process-based geomorphological understanding. Full article