Search Results (18)

Accurate estimation of the mean-reversion speed $\alpha$ in the AR(1) process $X_{t+1}=(1-\alpha )X_t+{\epsilon }_t$ is central to energy-commodity modelling. Classical estimators such as GARCH, jump-diffusion, and regime-switching produce model-conditioned estimates by embedding $\alpha$ within distributional assumptions, so that different model choices yield different $\widehat{\alpha }$ values from the same series without a principled criterion to adjudicate. We propose a distribution-free neural estimator based on a Temporal Convolutional Network (TCN) trained on synthetic AR(1) series with Sinh-ArcSinh (SAS) innovations. Distribution-free here means that no parametric family is assumed for the innovation distribution at inference time: the estimator imposes no distributional hypothesis when processing a new series. The SAS family serves as a training vehicle—not a model for the real data—chosen for its ability to span a broad range of tail weights and asymmetry profiles. The theoretical foundation is spectral invariance: the Yule–Walker equations establish that the autocorrelation structure ${\rho }_k={(1-\alpha )}^k$ depends on $\alpha$ alone, provided innovations are uncorrelated across lags—a condition satisfied not only by i.i.d. innovations but also by conditionally heteroscedastic processes such as GARCH. The TCN therefore generalises to volatility-clustering environments without modification, learning to extract $\alpha$ from temporal dependence alone, independently of the marginal innovation distribution and of the temporal variance structure. On held-out test series the estimator outperforms all classical competitors, with the advantage growing monotonically with non-Gaussianity. A robustness analysis on three out-of-distribution innovation families and on AR(1)-GARCH(1,1) processes empirically validates the spectral invariance guarantee across both marginal and temporal variance structure, including near-integrated GARCH processes where innovation kurtosis far exceeds the training range. The distribution-free $\widehat{\alpha }$ enables a two-stage pipeline in which $\alpha$ and the innovation distribution are characterised independently—a decoupling structurally impossible in classical likelihood-based approaches. Once trained, the TCN acts as a universal mean-reversion estimator applicable to any price series without re-fitting. Applied to four energy markets—Italian natural gas (PSV price), Italian electricity (PUN price), US Henry Hub, and US PJM West Hub—spanning log-return kurtosis from near-Gaussian to strongly heavy-tailed, the TCN yields robust, distribution-free estimates of mean-reversion speed. Full article

Fairness and minority exclusion have emerged as the central concerns of contemporary Artificial Intelligence (AI) ethics. However, standard auditing and documentation practices often fail to capture harms affecting edge cases and marginalized groups. This article argues that this failure is structural: the act of “discretization”—converting continuous reality into discrete governance categories—inevitably produces a “residual.” Drawing on German Idealism (Kant, Fichte, Schelling) and continental philosophy (Dilthey, Gadamer, Merleau-Ponty), we reconceptualize residuals not as mere noise but as “surprising facts” that should trigger abductive hypothesis revision. We critique checklist-centered governance as a form of proceduralized auditing that can obscure these residuals. This article makes three key contributions: (i) a structural diagnosis of residual production using systems theory and topology; (ii) a philosophical reconstruction of abductive revision as a hermeneutic necessity; and (iii) an institutional design proposal—specifically, the Residual Ledger and Category Revision Protocols—to operationalize “Open Schema” governance. Full article

Green innovation efficiency (GIE) serves as a key indicator of urban development toward “dual carbon” goals and sustainable growth. However, systematic evidence remains scarce regarding the impact of the digital-real economy integration (DRI) in urban green innovation efficiency (UGIE). Based on the dual institutional perspectives of government environmental regulation (ER) and intellectual property protection (IPP), this paper proposes an integrated theoretical framework that incorporates integration level, institutional environment, and green innovation. Leveraging panel data from 281 prefecture-level and higher-administered cities in China spanning 2013 to 2023, this paper explores the underlying mechanism and the observed threshold effect of DRI on UGIE. The primary findings are summarized below: (1) DRI promotes UGIE, which is mediated significantly through the institutional roles of ER and IPP. (2) The influence of DRI on GIE is characterized by a threshold effect at a value of 0.9657. Beyond this threshold, the marginal effect rises from 0.47463 to 0.52555, thereby providing evidence for the positive feedback hypothesis between integration level and institutional response. (3) A more significant effect of DRI on GIE could be observed in non-resource-based cities, such as the central cities, southern cities. This paper expands the interdisciplinary research on digital economy and urban sustainability, providing micro-level evidence for the tailored development of digital–green institutional combinations. Full article

This study examines how national electricity market structures condition the impact of carbon pricing on green innovation within the European Union. Using two-way fixed-effects panel models, we uncover a central paradox: although liberalized, price-signal markets exhibit the highest baseline levels of green innovation, the marginal effect of carbon pricing in these markets is weakest and often negative. This pattern points to an innovation-substitution effect, whereby market flexibility facilitates short-term compliance strategies, such as fuel switching, that crowd out investment in fundamental research and development (R&D) when carbon prices remain moderate. By identifying this mechanism, the study establishes electricity market structure as a pivotal moderating factor in the carbon pricing–innovation nexus and highlights a critical boundary condition for the Porter Hypothesis. The findings provide important insights for the design of sustainability policy mixes, demonstrating that institutional context plays a decisive role in translating economic instruments into sustained technological change. Effective climate policy therefore cannot be context-blind; instead, it must combine carbon pricing with tailored market design and direct support for long-term R&D to coherently advance the sustainability transition. Full article

Tajikistan, a mountainous country in Central Asia, holds considerable potential for the development of ecotourism, particularly through community-based ecotourism (CBE), as a means of enhancing community resilience. The Pamir region, situated in the eastern part of the country, serves as a compelling case study for examining this potential. This study hypothesizes that ecotourism, especially CBE, can contribute to increased resilience among geographically and economically marginalized communities. To evaluate this hypothesis, we pose two guiding research questions: (1) In what ways is ecotourism contributing to community resilience in the Pamirs? and (2) How is CBE being integrated into ecotourism to further promote community resilience? To investigate these questions, 31 in-depth interviews were conducted with individuals directly engaged in the ecotourism sector. Findings indicate that stakeholders acknowledge the conceptual complexity of ecotourism. Regarding the current extent of CBE, the sector is still developing, with support from non-governmental organizations, but with potential for future expansion. Respondents reported that CBE initiatives have contributed to enhanced economic and community stability, increased participation in decision-making processes, and greater awareness of environmental and cultural conservation. These outcomes suggest that ecotourism and CBE contribute meaningfully to enhancing community resilience for participating communities. Nonetheless, several challenges persist, including inadequate infrastructure and insufficient government policy support, which may impede the development of this sector. Full article

Accurate assessment and prediction of water shortage risk are essential prerequisites for the rational allocation and risk management of water resources. However, previous water shortage risk assessment models based on copulas have strict requirements for data distribution, making them unsuitable for extreme conditions such as insufficient data volume and indeterminate distribution shapes. These limitations restrict the applicability of the models and result in lower evaluation accuracy. To address these issues, this paper proposes a water shortage risk assessment model based on kernel density estimation (KDE) and copula functions. This approach not only enhances the robustness and stability of the model but also improves its prediction accuracy. The methodology involves initially utilizing kernel density estimation to quantify the random uncertainties in water supply and demand based on historical statistical data, thereby calculating their respective marginal probability distributions. Subsequently, copula functions are employed to quantify the coupled interdependence between water supply and demand based on these marginal probability distributions, thereby computing the joint probability distribution. Ultimately, the water shortage risk is evaluated based on potential loss rates and occurrence probabilities. This proposed model is applied to assess the water shortage risk of the Yuxi water receiving area in the Central Yunnan Water Diversion Project, and compared with existing models through experimental contrasts. The experimental results demonstrate that the model exhibits evident advantages in terms of robustness, stability, and evaluation accuracy, with a rejection rate of 0 for the null hypothesis of edge probability fitting and a smaller deviation in joint probability fitting compared to the most outstanding model in the field. These findings indicate that the model presented in this paper is capable of adapting to non-ideal scenarios and extreme climatic conditions for water shortage risk assessment, providing reliable prediction outcomes even under extreme circumstances. Therefore, it can serve as a valuable reference and source of inspiration for related engineering applications and technical research. Full article

The convergence of digitization and greening is an unavoidable path of modern economic progress. Nonetheless, the digital economy does not consistently align with the principles of green development, potentially leading to a rebound effect in urban digitalization initiatives. To investigate the correlation between the digital rebound effect and urban green development, this study utilizes panel data from Chinese prefecture-level cities spanning from 2011 to 2019. By examining the dual impact of the digital economy on green development, the paper posits a theoretical hypothesis regarding the nonlinear marginal effect of the digital economy. This research demonstrates an inverted U-shaped correlation between the digital economy and urban green development via empirical analyses employing the random forest algorithm and partial dependency plots. It supports the existence of a moderate digital resiliency effect, which eventually reaches a state of stability rather than greatly diminishing the degree of green development in urban areas. In addition, the heterogeneity analysis reveals that the positive effects of the digital economy are more popular in cities located in the eastern and central regions, as well as in the National Comprehensive Pilot Zone for Big Data. However, these effects do not vary significantly among different ranks of cities. The mechanism test found that the information effect and the capital allocation effect are the mechanisms by which the digital economy affects green development, and there is a “U-shaped” relationship between the digital economy and information asymmetry and capital mismatch. According to the study’s results, improving the digital economy’s governance structure continues to make more sense than merely increasing the number of digital inputs. Full article

The Essaouira Basin, located in central western Morocco, faces a significant threat of water shortage due to both the substantially reduced rainfall caused by climate change and the continuously increasing demand for this essential resource. Groundwater resources are being increasingly exploited to meet the needs of the population, whether for agricultural or domestic purposes. Therefore, it has become necessary to intensify investigations across the entire basin, particularly through indirect methods such as geophysical techniques, to accurately delineate the productive zones. In this context, the present study was undertaken to investigate the deep structure of this basin with the aim of comprehending the functioning of its aquifer system. This study is based on the interpretation of gravity data covering the Essaouira Basin. In addition to their qualitative analysis, these data underwent a methodological approach involving transformations to extract meaningful insights. The observed anomalies were interpreted in terms of (i) thickness variations within the slightly folded sedimentary series of the basin; (ii) Paleozoic basement topography; and (iii) the presence of salt deposits. In fact, among the negative anomalies, some coincide with evaporitic deposits that are known either from the geological outcrops or the seismic surveys carried out in the Essaouira Basin within the framework of petroleum exploration programs, while others coincide with areas of increased thickness of sedimentary sequences. The latter include synclines and basement depressions, where the accumulation of groundwater tends to occur; as a result, they constitute suitable zones for the drilling of water extraction wells. Groundwater flows observed in some existing wells are consistent with this hypothesis. The results of the contact analysis approach implemented within the framework of the study reveals the Essaouira Basin is affected by a fault network whose main direction is parallel to the Atlantic margin (i.e., NNE–SSW). This implies that the extensional tectonic phase responsible for initiating the rifting of the Central Atlantic in the Triassic era has primarily impacted the structural configuration of this basin. This study demonstrates the strong potential of the gravity method as a tool to delineate the deep structure of sedimentary basins and to identify potentially productive groundwater zones. The final results will provide important support to decision makers in sustainable groundwater management, especially in vulnerable areas. Full article

This article aims to contribute to the limited literature on traditional gastronomic knowledge concerning acorn-based bread by ethnographically documenting the ingredients, preparation techniques and consumption practices of baked goods made from acorn seeds and flour that are still used today or at least still present in living memory. A qualitative comparative case method was adopted, and ethnographic data were gathered from 67 people in six selected Mediterranean, Central Asian and Middle Eastern countries. The analysis highlighted distinct trajectories in the development of acorn-based bread, showing some differences in terms of ingredients, preparation techniques and baking methods in the two cultural and geographical macro-regions. By exploring the evolution of the alimentary role of acorn bread in the past century, our findings also support the hypothesis that the product, at least during the last two centuries, has mostly been used as a famine food. By acknowledging the cultural importance of acorn fruits and acorn-based products, this study suggests that the rediscovery of acorn-based products and associated traditional knowledge may foster the sustainable development of rural and marginal regions in the Mediterranean, Middle East and Central Asia. This could help to reinforce the resilience of local communities and thus increase food security. Furthermore, reassessing acorns as a foodstuff may aid in developing innovative products in line with emerging trends in the food sector, which is looking for new non-cereal-based bakery products and other novel culinary applications. Full article

In many real-world networks of interest in the field of remote sensing (e.g., public transport networks), nodes are associated with multiple labels, and node classes are imbalanced; that is, some classes have significantly fewer samples than others. However, the research problem of imbalanced multi-label graph node classification remains unexplored. This non-trivial task challenges the existing graph neural networks (GNNs) because the majority class can dominate the loss functions of GNNs and result in the overfitting of the majority class features and label correlations. On non-graph data, minority over-sampling methods (such as the synthetic minority over-sampling technique and its variants) have been demonstrated to be effective for the imbalanced data classification problem. This study proposes and validates a new hypothesis with unlabeled data over-sampling, which is meaningless for imbalanced non-graph data; however, feature propagation and topological interplay mechanisms between graph nodes can facilitate the representation learning of imbalanced graphs. Furthermore, we determine empirically that ensemble data synthesis through the creation of virtual minority samples in the central region of a minority and generation of virtual unlabeled samples in the boundary region between a minority and majority is the best practice for the imbalanced multi-label graph node classification task. Our proposed novel data over-sampling framework is evaluated using multiple real-world network datasets, and it outperforms diverse, strong benchmark models by a large margin. Full article

The United Arab Emirates (UAE) has developed rapidly into one of the highest per capita income nations globally. The travel and tourism sector is a central contributor to the Gross Domestic Product (GDP), employment, foreign exchange earnings, and the country’s economic diversification strategy. However, the rapid growth of the sector and increase in international tourist arrivals are also major contributors to carbon emissions and long-term environmental challenges. In this context, we employed a tourism-induced Environmental Kuznets Curve (EKC) model for the UAE from 1984 to 2019. The study applied an Autoregressive Distributed Lag (ARDL) model to determine the marginal impact of tourist arrivals and related variables, namely, bank credits to the private sector, urbanization, and energy use, on CO₂ emissions. The Pesaran bounds test indicated redundancy of short run estimates. The long-run coefficients confirmed the EKC hypothesis of inverted U-shape for carbon emissions and per capita income, along with environmental degradation due to tourist arrivals and financial development. Notably, urbanization and energy use highlighted the positive steps taken by the government. Granger causality tests indicated a unidirectional association from GDP, bank credits, and energy consumption to carbon emissions. Importantly, tourist arrivals and urbanization had bidirectional causality with carbon dioxide levels. This study is the first to apply the tourism-induced EKC model to the UAE, and the findings have important implications for policymakers and practitioners. The causality results highlight the need to balance tourism targets and sustainable economic growth through the adoption of ‘green’ standards. The results also indicate the potential importance of financial sector efforts to boost green investments and implement clean energy-related technologies. Full article

Background and objective: Diabetes mellitus (DM) refers to a group of metabolic disorders characterized by hyperglycemia resulting from impaired secretion or action of insulin. The high levels of glucose in the blood can negatively affect the healing processes through alterations in vascularization, bone remodeling, and with increased susceptibility to infections. Diabetes mellitus is therefore a risk factor not only for many systemic diseases, but also for localized problems such as peri-implantitis. The objective of this systematic review was to identify a clear relationship between peri-implant inflammation indices and glycemic levels, through the investigation of prospective studies that report data on a short-term follow-up period. Our hypothesis was that peri-implant inflammatory indices may already present themselves in a statistically significant way as altered in patients with DM compared to patients without DM. Materials and methods: This review was reported according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA). Results: More than 992 records were identified in the PubMed, Scopus, and Cochrane Central Register of Controlled Trial electronic databases and only seven studies were included in the meta-analysis. The results of the meta-analysis report worse outcomes in patients with DM, even in the short period of six months, for peri-implatitis inflammation indices, such as Marginal bone loss (standardized (Std). mean difference (MD) 12\6 months 0.81 [0.45, 1.17]\1.82 [0.53, 3.10]), Bleeding on probing (Std. MD 12\6 months 2.84 [1.34, 4.34]\3.44 [1.41, 5.50]), Probing depth (Std. MD 12\6 months 1.14 [0.60, 1.68]\2.24 [0.66, 3.83]), and the plaque index (Std. MD 12 months 2.83 [0.09, 5.57]). Conclusion: The literature linking glycaemic control to peri-implant disease is highly heterogeneous due to lack of consistency of the definition of peri-implantitis and its clinical indicators among studies. Therefore, interpretation of finding and relevance to clinical practice should be considered on individual bases. In the era of personalized medicine, the clinician should utilize individualized information from translational researches and analyze all risk factors to provide the patient with evidence-based treatment options. Full article

Rear-edge populations are of significant scientific interest because they can contain allelic variation not found in core-range populations. However, such populations can differ in their level of genetic diversity and divergence reflecting variation in life-history traits, demographic histories and human impacts. Using 13 EST-microsatellites, we investigated the genetic diversity and differentiation of rear-edge populations of the Japanese endemic conifer Thuja standishii (Gordon) Carr. in southwest Japan from the core-range in northeast Japan. Range-wide genetic differentiation was moderate (Fst = 0.087), with northeast populations weakly differentiated (Fst = 0.047), but harboring high genetic diversity (average population-level Ar = 4.76 and Ho = 0.59). In contrast, rear-edge populations were genetically diverged (Fst = 0.168), but contained few unique alleles with lower genetic diversity (Ar = 3.73, Ho = 0.49). The divergence between rear-edge populations exceeding levels observed in the core-range and results from ABC analysis and species distribution modelling suggest that these populations are most likely relicts of the Last Glacial Maximum. However, despite long term persistence, low effective population size, low migration between populations and genetic drift have worked to promote the genetic differentiation of southwest Japan populations of T. standishii without the accumulation of unique alleles. Full article

Using a dendrochronological approach, we determined the resistance, recovery and resilience of the radial stem increment towards episodes of growth decline, and the accompanying variation of ¹³C discrimination against atmospheric CO₂ (Δ¹³C) in tree rings of two palaeotropical pine species. These species co-occur in the mountain ranges of south–central Vietnam (1500–1600 m a.s.l.), but differ largely in their areas of distribution (Pinus kesiya from northeast India to the Philippines; P. dalatensis only in south and central Vietnam and in some isolated populations in Laos). For P. dalatensis, a robust growth chronology covering the past 290 years could be set up for the first time in the study region. For P. kesiya, the 140-year chronology constructed was the longest that could be established to date in that region for this species. In the first 40 years of the trees’ lives, the stem diameter increment was significantly larger in P. kesiya, but levelled off and even decreased after 100 years, whereas P. dalatensis exhibited a continuous growth up to an age of almost 300 years. Tree-ring growth of P. kesiya was negatively related to temperature in the wet months and season of the current year and in October (humid transition period) of the preceding year and to precipitation in August (monsoon season), but positively to precipitation in December (dry season) of the current year. The P. dalatensis chronologies exhibited no significant correlation with temperature or precipitation. Negative correlations between BAI and Δ¹³C indicate a lack of growth impairment by drought in both species. Regression analyses revealed a lower resilience of P. dalatensis upon episodes of growth decline compared to P. kesiya, but, contrary to our hypothesis, mean values of the three sensitivity parameters did not differ significantly between these species. Nevertheless, the vigorous growth of P. kesiya, which does not fall behind that of P. dalatensis even at the margin of its distribution area under below-optimum edaphic conditions, is indicative of a relatively high plasticity of this species towards environmental factors compared to P. dalatensis, which, in tendency, is less resilient upon environmental stress even in the “core” region of its occurrence. Full article

To test the previous hypothesis that upper Paleozoic sediments in the eastern Sino-Korean Block were mostly derived from the paleo-orogen located to the east, we compared published and new U–Pb age data of detrital zircons from Pennsylvanian strata distributed in the Sino-Korean Block (SKB). The age distributions of detrital zircons from different localities of Pennsylvanian strata in North China reflect varying contributions from the Inner Mongolia Paleo-uplift in the north and the Central China Orogenic Belt in the south. The supply of detritus from the northern source to distant areas, however, appears to have been limited during the Pennsylvanian times. The age distributions of detrital zircons from Korean Pennsylvanian strata located in the east of the SKB are characterized by a dense cluster of 1.84–1.90 Ga and differ from those of North China. The Korean age characteristic is best explained by strong influences of the detritus derived from the Paleoproterozoic Yeongnam Massif in southeastern Korea. Along with the significant number of zircons that record syn- to near-depositional magmatic activities, this observation supports the hypothesis of the existence of an active continental margin setting in the east of the SKB. Full article