Search Results (1,950)

Intangible cultural heritage (ICH) is deeply embedded in everyday social life, yet its officially recognized spatial distribution reflects both the independent influences of cultural traditions, development trajectories, and governance practices, and the complex interactions among them. Focusing on 494 national-level ICH items across ten categories in Jiangsu(J), Zhejiang(Z), and Shanghai(H), this study adopts a social-geographical perspective to examine both the spatio-temporal evolution and the driving mechanisms of ICH recognition in one of China’s most developed regions. After rigorous verification of point-based ICH locations, we combine kernel density estimation and the average nearest neighbor index to trace changes across five batches of national designation, and then employ the univariate and interaction detectors of the Geodetector model to assess the effects of 28 natural, socioeconomic, and cultural-institutional variables. The results show, first, that ICH exhibits significant clustering along river corridors and historical cultural belts, with a persistent high-density core in the Shanghai–southern Jiangsu–northern Zhejiang zone and a clear shift over time from highly concentrated to more dispersed and territorially balanced recognition. Second, human-environment factors—especially factors such as urban and rural income and consumption; residents’ education and cultural expenditures; and public education and cultural facilities—have far greater explanatory power than natural conditions, while different ICH categories embed distinctively in urban and rural socio-economic contexts. Third, bivariate interactions reveal that natural and macroeconomic “background” variables are strongly amplified when combined with demographic and cultural factors, whereas interactions among strong human variables show bivariate enhancement with diminishing marginal returns. In summary, these findings enrich international debates on the geography of ICH by clarifying how recognition processes align with regional development and social equity agendas, and they provide a quantitative basis for category-sensitive, place-based strategies that coordinate income policies, public cultural services, and the joint safeguarding of tangible and intangible heritage in both urban renewal and rural revitalization planning. Full article

Farmland soil water-soluble organic carbon (WSOC), serving as a labile carbon substrate for microbial utilization, demonstrates pronounced sensitivity to land-use modifications and agricultural management practices. This study systematically investigated the impacts of long-term straw incorporation frequencies—including annual (S-1), biennial (S-2), and triennial (S-3) return patterns—on WSOC distribution across 0–20 cm and 20–40 cm soil profiles. Through the integration of three-dimensional excitation–emission matrix (EEM) fluorescence spectroscopy with parallel factor analysis (PARAFAC), we elucidated structural characteristics and humification dynamics associated with different incorporation regimes. The results showed a depth-dependent WSOC distribution pattern with higher concentrations in surface soils (0–20 cm: 261.2–368.9 mg/kg) compared to subsurface layers (20-40 cm: 261.8–294 mg/kg). Straw incorporation significantly increased WSOC content in the 0–20 cm of 16.9%~21.7% and 20–40 cm soil layers of 6.2%~12.3%. Biennial return had the lowest WSOC/SOC ratio, indicating enhanced stability of the soil organic carbon pool. Spectral indices—including the fluorescence index (FI, 1.59~1.69), biological index (BIX, 0.90~0.95), and humification index (HIX, 0.64~0.74)—collectively indicated that WSOC predominantly consisted of microbially processed organic matter with a low degree of humification. PARAFAC modeling resolved two fluorescent components: C1 (humic acid-like substances, 47.4–50.4%), C2 (soluble microbial metabolites, 49.6–52.6%). This systematic investigation provides mechanistic insights into how straw management temporality regulates both quantity and quality of labile carbon pools in agricultural ecosystems. Full article

As a form of long-term asset allocation, pension fund investment necessitates accurate estimation of both asset returns and associated risks over extended time horizons. However, long-term asset returns are significantly influenced by macroeconomic factors, whereas variance-based risk measures cannot account for the directional nature of deviations from expected returns. To address these issues, we propose a novel CVaR-based Black–Litterman model incorporating macroeconomic cycle views (CVaR-BL-MCV) for optimal asset allocation of pension funds. This approach integrates macroeconomic cycle dynamics to quantify their impact on asset returns and utilizes Conditional Value-at-Risk (CVaR) as a coherent measure of downside risk. We employ a Markov-switching model to identify and forecast the phases of economic and monetary cycles. By analyzing the economic cycle with PMI and CPI, economic conditions are categorized into three distinct phases: stable, transitional, and overheating. Similarly, by analyzing the monetary cycle with M2 and SHIBOR, monetary conditions are classified into expansionary and contractionary phases. Based on historical asset return data across these cycles, view matrices are constructed for each cycle state. CVaR is used as the risk measure, and the posterior distribution of the Black–Litterman (BL) model is derived via generalized least squares (GLS), thereby extending the traditional BL framework to a CVaR-based approach. The experimental results demonstrate that the proposed CVaR-BL-MCV model outperforms the benchmark models. When the risk aversion coefficient is 1, 1.5, and 3, the Sharpe ratio of pension asset allocation using the CVaR-BL-MCV model is 21.7%, 18.4%, and 20.5% higher than that of the benchmark models, respectively. Moreover, the BL model incorporating CVaR improves the Sharpe ratio of pension asset allocation by an average of 19.7%, while the BL model with MCV achieves an average improvement of 14.4%. Full article

This paper develops a robust portfolio optimization framework that integrates Weighted Shannon Entropy (WSE) into the classical mean–variance paradigm, offering a distribution-free approach to diversification suited for volatile and heavy-tailed markets. While traditional variance-based models are highly sensitive to estimation errors and instability in covariance structures—issues that are particularly acute in cryptocurrency markets—entropy provides a structural mechanism for mitigating concentration risk and enhancing resilience under uncertainty. By incorporating informational weights that reflect asset-specific characteristics such as volatility, market capitalization, and liquidity, the WSE model generalizes classical Shannon entropy and allows for more realistic, data-driven diversification profiles. Analytical solutions derived from the maximum entropy principle and Lagrange multipliers yield exponential-form portfolio weights that balance expected return, variance, and diversification. The empirical analysis examines two case studies: a four-asset cryptocurrency portfolio (BTC, ETH, SOL, and BNB) over January–March 2025, and an extended twelve-asset portfolio over April 2024–March 2025 with rolling rebalancing and proportional transaction costs. The results show that WSE portfolios achieve systematically higher entropy scores, more balanced allocations, and improved downside protection relative to both equal-weight and classical mean–variance portfolios. Risk-adjusted metrics confirm these improvements: WSE delivers higher Sharpe ratios and less negative Conditional Value-at-Risk (CVaR), together with reduced overexposure to highly volatile assets. Overall, the findings demonstrate that Weighted Shannon Entropy offers a transparent, flexible, and robust framework for portfolio construction in environments characterized by nonlinear dependencies, structural breaks, and parameter uncertainty. Beyond its empirical performance, the WSE model provides a theoretically grounded bridge between information theory and risk management, with strong potential for applications in algorithmic allocation, index construction, and regulatory settings where diversification and stability are essential. Moreover, the integration of informational weighting schemes highlights the capacity of WSE to incorporate both statistical properties and market microstructure signals, thereby enhancing its practical relevance for real-world investment decision-making. Full article

In recent years, honeycomb sandwich structures have seen continuous development due to their excellent structural performance and design flexibility in heat dissipation. However, their complex heat transfer mechanisms and diverse modes of thermal exchange necessitate research on the air flow behavior and temperature distribution characteristics of micro-channels and lattice pores. This study investigates the internal flow field within a ventilated honeycomb sandwich structure through numerical simulation. The spatial flow characteristics and temperature distribution are analyzed, with a focus on the effects of turbulent kinetic energy, heat flux distribution on the heated surface, and varying pressure drop conditions on the thermal performance. The results indicate that the micro-channels inside the honeycomb core lead to a strong correlation between temperature distribution, flow velocity, and turbulence intensity. Regions with higher flow velocity and turbulent kinetic energy exhibit lower temperatures, confirming the critical role of flow motion in heat transfer. Heat flux analysis further verifies that heat is primarily removed by airflow, with superior heat exchange occurring inside the honeycomb cells compared to the solid regions. The intensive mixing induced by highly turbulent flow within the small cells enhances contact with the solid surface, thereby improving heat conduction from the solid to the flow. Moreover, as the inlet pressure increases, the overall temperature gradually decreases but exhibits a saturation trend. This indicates that beyond a certain pressure level, further increasing the inlet pressure yields diminishing returns in heat dissipation enhancement. Full article

This work presents an assessment focused on technical, economic, and resilience-related aspects applied to a crude palm oil production process using the FP²O methodology, considering a capacity of 30 tons of fresh fruit bunches (FFB) per hour and an annual production of 54,056 tons of oil per year. Operating parameters, capital and input costs, as well as the total investment, which amounts to approximately US$43 million, distributed between fixed capital, working capital, and start-up costs, were established. The analysis identified annual operating costs of US$24.7 million, with a majority share of raw materials. Economic and financial indicators showed positive values, higher than previous studies, highlighting a gross profit of over US$23 million, an after-tax profitability of US$13.7 MM, and an internal rate of return of 25.29%, which demonstrates the economic viability of the process. A simple payback period of 1.62 years and a discounted payback period of 4.88 years were determined, in addition to a positive net present value of $58.74 million, confirming the project’s profitability over a 15-year horizon. Using the FP²O methodology, the technical and economic resilience of the process to variations in product price, raw material costs, processing capacity, and normalized operating costs was evaluated. The results showed sensitivity to reductions in the oil sales price, while also demonstrating high resilience to increases in palm bunch costs and decreases in processing capacity. Furthermore, the break-even analysis revealed that the plant can operate 36.59% below its maximum capacity and maintain positive margins, requiring a minimum of 87,825 tons of raw material per year and a sales price of $482.35 per ton to avoid losses. This research highlights the applicability of the FP²O methodology as a strategic tool for scaling up crude palm oil production processes, guiding investment decisions, and supporting policies that promote more resilient and sustainable agro-industrial systems. Full article

This study assessed the economic and financial viability of representative smallholder dairy farms (RSDFs) by analyzing two farm types: (1) RSDFs that rely exclusively on family labor and milk receipts, and (2) RSDFs that employ hired labor and obtain income from milk in addition to sales of crops and agricultural by-products. A stochastic simulation based on empirical distributions derived from 44 years of historical data was used to project a 10-year horizon. Results indicate a low-to-minimal probability of decapitalization, an overall outlook of economic and financial viability, and a return on assets between 12% and 22%. Net present value (NPV) was positive for all RSDFs except one; however, in every case, NPV was lower than the opening asset value. Under current economic and policy conditions, RSDFs in the highlands of Mexico appear economically and financially viable through 2033. Family labor was associated with stronger economic and financial outcomes among the small-scale dairy farms evaluated. Full article

Traditional species richness estimators often assume spatial homogeneity in species distribution, which can lead to underestimating biodiversity, especially in large, ecologically complex areas. The Total–Species (T–S) curve may provide an accurate framework for estimating γ-diversity by accounting for compositional variation across spatial subunits. Our study tested the T–S curve model, modified to account for species rarity and patterns of β-diversity, to estimate macroalgal richness in the northeast Adriatic (Mediterranean Sea), an area where the total macroalgal diversity is known and a comprehensive reference list is available (487 species). Uncertainty in species richness estimates from T–S curves was quantified as 95%CI based on bootstrapping, and a sensitivity analysis was also carried out to quantify changes in estimates under different settings. Other parametric and non-parametric estimators, including the classic T–S curve, largely under- or overestimated the total species richness if compared to the refined T–S model, which returned a realistic estimate of 393 species in total. Our results demonstrate that the T–S curve modified to consider species rarity, and refined for potential biases associated with erroneous quantification of small-scale patchiness and spatial variations in assemblage composition, allowed for more realistic extrapolations of γ-diversity over large areas. Full article

We introduce the Symmetric-$\mathbb{Z}$ (Sy-$\mathbb{Z}$) family, a unified class of symmetric discrete distributions on the integers obtained by multiplying a three-point symmetric sign variable by an independent non-negative integer-valued magnitude. This sign-magnitude construction yields interpretable, zero-centered models with tunable mass at zero and dispersion balanced across signs, making them suitable for outcomes, such as differences of counts or discretized return increments. We derive general distributional properties, including closed-form expressions for the probability mass and cumulative distribution functions, bilateral generating functions, and even moments, and show that the tail behavior is inherited from the magnitude component. A characterization by symmetry and sign–magnitude independence is established and a distinctive operational feature is proved: for independent members of the family, the sum and the difference have the same distribution. As a central example, we study the symmetric Poisson model, providing measures of skewness, kurtosis, and entropy, together with estimation via the method of moments and maximum likelihood. Simulation studies assess finite-sample performance of the estimators, and applications to datasets from finance and education show improved goodness-of-fit relative to established integer-valued competitors. Overall, the Sy-$\mathbb{Z}$ framework offers a mathematically tractable and interpretable basis for modeling symmetric integer-valued outcomes across diverse domains. Full article

Flooding is one of Africa’s most impactful natural disasters, significantly affecting human lives, infrastructure, and economies. This study examines the spatial and temporal distribution of historical flood events across the continent from 1927 to 2020, with a focus on fatalities, affected populations, and economic damage. Data from the Emergency Events Database (EM-DAT), the fifth generation of bias-corrected European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), and the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) observational datasets were used to calculate extreme precipitation indices—Consecutive Wet Days (CWD), annual precipitation on very wet days (R95PTOT), and Annual Maximum Precipitation (AMP). Spatial analysis tools and the Mann–Kendall test were used to assess trends in flood occurrences, while Pearson correlation analysis identified key meteorological drivers across 16 African capital cities for 1981–2019. A flood frequency analysis was conducted using Weibull, Gamma, Lognormal, Gumbel, and Logistic probability distribution models to compute flood return periods for up to 100 years. Results reveal a significant upward trend with a slope above 0.50 floods per year in flood frequency and impact over the period, particularly in regions such as West Africa (Nigeria, Ghana), East Africa (Ethiopia, Kenya, Tanzania), North Africa (Algeria, Morocco), Central Africa (Angola, Democratic Republic of Congo), and Southern Africa (Mozambique, Malawi, South Africa). Positive trends (at 99% significance level with slopes ranging between 0.50 and 0.60 floods per year) were observed in flood-related fatalities, affected populations, and economic damage across Regional Economic Communities (RECs), individual countries, and cities of Africa. The CWD, R95PTOT, and AMP indices emerged as reliable predictors of flood events, while non-stationary return periods exhibited low uncertainties for events within 20 years. These findings underscore the urgency of implementing robust flood disaster management strategies, enhancing flood forecasting systems, and designing resilient infrastructure to mitigate growing flood risks in Africa’s rapidly changing climate. Full article

Analyzing the spatial patterns of vitality in historic urban areas and their influencing elements is essential for improving the vitality of historic and cultural cities and fostering sustainable urban development. This research investigated the historic urban area of Kaifeng City. Employing Baidu Huiyan population location data, it assessed the spatial distribution of vitality on weekdays and weekends. A built environment indicator system was developed using multi-source data, and the CatBoost-SHAP model was applied to examine the nonlinear relationship between the built environment and the vitality of a historic urban area, along with the interactions among different factors. The study systematically explored the spatiotemporal dynamics of vitality and the influence mechanisms of the built environment. The results showed the following: (1) The vitality of Kaifeng’s historic urban area demonstrated significant spatiotemporal heterogeneity, exhibiting an “inner-hot, outer-cold” spatial pattern. Overall vitality levels were higher on weekends than on weekdays, with a progressive decline from morning to night. (2) Built environment factors dynamically influenced vitality across time periods. The impacts of POIM and BD shifted markedly, indicating temporal variations in vitality-driving mechanisms. (3) Synergistic interactions among built environment factors exerted nonlinear effects on urban vitality. Within reasonable threshold ranges, BSD, POID, and BD promoted vitality but exhibited diminishing marginal returns under high-density conditions. Notably, BSD played a core moderating role in multi-factor interactions. These findings reveal the complex and dynamic relationship between the built environment and historic urban vitality. They indicate that spatial governance should prioritize the synergistic integration of transportation, functions, ecology, and culture to achieve dual improvements in urban vitality and environmental quality, thereby providing important theoretical support and practical guidance for planning and spatial optimization in historic urban areas. Full article

This study investigates the effects of different tillage practices on soil quality and maize yield in black soil farmland. Based on an eight-year continuous field plot experiment initiated in 2017, we examined the impacts of five tillage methods: conventional tillage (CT), no-tillage with straw mulching (NTS), subsoiling tillage with straw mulching (STS), harrow tillage with straw mulching and incorporation (HTS), and moldboard plowing tillage with straw incorporation (MPS). The focus was on soil structure, hydrothermal characteristics, organic matter, and nutrient content within the 0–40 cm soil layer, as well as maize dry matter accumulation and grain yield. The results indicate that, in 2023, compared to CT, STS significantly improved the soil structure and hydrothermal characteristic quality index (SHQI) in the 0–40 cm soil layer. Additionally, NTS, STS, HTS, and MPS significantly enhanced the soil organic matter and nutrient quality index (ONQI) in the 0–40 cm soil layer. NTS and STS increased the soil quality index (SQI) by 9.0% to 16.6% compared to the other treatments. Additionally, NTS, STS, HTS, and MPS significantly enhanced the soil organic matter and nutrient quality index (ONQI) in the 0–40 cm soil layer. In 2024, NTS and STS increased the soil quality index (SQI) by 9.0% to 16.6% compared to the other treatments. Furthermore, NTS and MPS significantly improved the SHQI in the 0–40 cm soil layer compared to CT. NTS and STS also significantly enhanced the ONQI in the 0–40 cm soil layer, while NTS, STS, and MPS increased the SQI by 7.3% to 22.6% compared to the other treatments. STS and MPS treatments significantly increased both hundred-kernel weight and grain yield compared to CT and NTS. Correlation and redundancy analyses revealed that SHQI in the 10–40 cm soil layer is a crucial factor affecting dry matter accumulation, yield, and its components in maize. In summary, in the semi-humid region of Northeast China, STS and MPS are cultivation techniques that optimize black soil quality and enhance maize grain yield. Full article

The optimal allocation of funds within a portfolio is a central research focus in finance. Conventional mean-variance models often concentrate a significant portion of funds in a limited number of high-risk assets. To promote diversification, Shannon Entropy is widely applied. This paper develops a portfolio optimization model that incorporates Shannon Entropy alongside a risk diversification principle aimed at minimizing the maximum individual asset risk. The study combines empirical analysis with numerical simulations. First, empirical data are used to assess the theoretical model’s effectiveness and practicality. Second, numerical simulations are conducted to analyze portfolio performance under extreme market scenarios. Specifically, the numerical results indicate that for fixed values of the risk balance coefficient and minimum expected return, the optimal portfolios and their return distributions are similar when the risk is measured by standard deviation, absolute deviation, or standard lower semi-deviation. This suggests that the model exhibits robustness to variations in the risk function, providing a relatively stable investment strategy. Full article

County-wide distributed rooftop photovoltaic (DRPV) systems, as an emerging form of renewable energy development, constitute a critical component for the low-carbon energy transition and carbon reduction. However, the pilot implementation in China has faced many challenges, with resistance from rural residents being a key issue requiring urgent resolution. This study aimed to investigate the underlying factors influencing their participation in DRPV and identify the key determinants. The topic modeling and evolutionary analysis were first conducted based on the multi-platform online textual data. The theoretical model was constructed combining the antecedent variables identified by the online textual analysis and the classic Unified Theory of Acceptance and Use of Technology (UTAUT) framework. This model was validated through questionnaire surveys and structural equation modeling. The results revealed that facilitating conditions were the core determinant of rural residents’ participation in DRPV systems. Government-led safeguard mechanisms served as the primary enhancer of perceived convenience. Additionally, effort expectancy (0.301), performance expectancy (0.253), and social influence (0.424) all positively correlated with participation intention, with social influence exhibiting the strongest impact. Notably, rural residents equally prioritize environmental benefits and economic returns from DRPV systems. These findings provided policy insights for promoting DRPV projects in the future. Full article

Deposit Refund Systems (DRS) are widely adopted in many European countries as effective mechanisms for increasing recycling rates and promoting circular-economy practices. Greece is currently preparing for the introduction of a national DRS for beverage containers, a transition expected to reshape existing waste-management structures. This study investigates the systemic challenges that may hinder the successful implementation of the upcoming Greek DRS. Focusing exclusively on polyethylene terephthalate (PET), aluminum, and glass beverage containers, this study adopts a multi-stakeholder qualitative approach involving 28 semi-structured interviews with beverage producers, retailers, recyclers, logistics actors, consumer representatives, and regulatory authorities. Thematic analysis reveals four interdependent barriers: restricted consumer accessibility due to uneven distribution of return infrastructure; fragmented governance and unclear institutional responsibilities; weak coordination and operational misalignment among supply-chain actors; and low consumer participation shaped by behavioral and cultural factors. These findings underscore that Greece’s DRS readiness is constrained not by technological limitations but by systemic gaps in governance, infrastructure planning, and stakeholder collaboration. This study contributes to the DRS literature by providing one of the first pre-implementation, multi-actor assessments in a Southern European context and offers policy-relevant insights to support an effective, equitable, and transparent rollout of the national DRS. Full article