Search Results (636)

This paper examines the interconnection and wavelet coherence between the green cryptocurrency market and the green conventional market, utilizing daily data. The research period covers 1 July 2020 to 30 September 2024. Employing the time-varying parametric vector autoregression (TVP-VAR) model and wavelet coherence analysis, we capture both short- and long-term spillovers across markets. The results show that cryptocurrencies, particularly Binance and Litecoin, act as dominant transmitters of volatility and return shocks, while green conventional indices function mainly as receivers with strong self-dependence. Spillover intensity is highly time-varying, with peaks during periods of systemic stress, particularly during the COVID-19 pandemic, and troughs indicating diversification opportunities. These findings advance the literature on systemic risk and portfolio design by showing that crypto assets can simultaneously amplify vulnerabilities and enhance diversification when combined with green finance instruments. For policy, the results highlight the need for regulatory frameworks that integrate sustainability taxonomies, mandate environmental disclosures for digital assets, and incentivize energy-efficient blockchain adoption to align crypto markets with sustainable finance objectives. This research enhances our understanding of the interrelationship between green investments and cryptocurrencies, providing valuable insights for investors and policymakers on risk management and diversification strategies in an increasingly sustainable financial landscape. Full article

Over the past several decades, old industrialized regions worldwide have faced immense pressure to adapt to global economic shifts. Using one of China’s major industrial provinces, Hebei, as a representative case study, this study examines how the evolution of one of China’s old industrial provinces, Hebei’s industrial structure has influenced its economic growth from 1990 to 2023. Drawing on theories of structural transformation and endogenous growth, we argue that the reallocation of resources from lower-productivity sectors (e.g., agriculture) to higher-productivity sectors (manufacturing and services) can act as an engine of growth. We employ a shift-share analysis (SSA) to decompose Hebei’s economic growth into components attributable to national trends, industrial structure, and regional competitive performance. The results reveal a globally relevant pattern of stagnation: while Hebei’s growth largely benefited from nationwide economic expansion (national effect), its heavy industrial structure initially posed a drag on growth (negative structural effect) and its regional competitive advantage in steel and energy sectors has eroded over time (weakening competitive effect). Our regression analysis further shows that growth was overwhelmingly dependent on capital accumulation while the contribution of labor was statistically insignificant, pointing to a low-productivity trap common in such regions. By integrating these methods, this study provides a robust diagnostic framework for identifying the root causes of economic distress in legacy industrial regions both within and outside China. These findings underscore the importance of structural upgrading for sustainable growth and offer critical lessons for policymakers globally, highlighting the necessity of moving beyond extensive, capital-driven growth toward an intensive model focused on industrial diversification, innovation, and human capital to ensure the sustainable revitalization of post-industrial economies. Full article

This study investigates whether green assets can serve as safe havens for dirty assets in the context of carbon and energy futures markets. Using daily data from April 2021 to June 2025, the analysis focuses on four key instruments: carbon emissions futures and crude oil futures, EUA futures, and natural gas futures. The study applies two main approaches—a conditional value-at-risk (CVaR)-based relative risk ratio (RRR) analysis and dynamic conditional correlation (DCC-GARCH) modeling—to assess tail risk mitigation and time-varying correlations. The results show that while green assets do not consistently act as safe havens during extreme market downturns, they can reduce the portfolio tail risk beyond certain allocation thresholds. Natural gas futures demonstrate significant volatility but offer diversification benefits when their portfolio weight exceeds 40%. EUA futures, although highly correlated with carbon emissions futures, show limited safe haven behavior. The findings challenge the assumption that green assets inherently provide downside protection and highlight the importance of strategic allocation. This research contributes to the literature by extending safe haven theory to environmental futures and offering empirical insights into the risk dynamics between green and dirty assets. Full article

Monoculture involves growing the same crop on the same land over at least two crop cycles. Continuous monoculture can increase the population density of pests and pathogens over time, thereby reducing agricultural yields and increasing dependence on chemical inputs. Crop rotation is an agricultural practice that involves systematically and sequentially planting different crops in the same field over multiple growing seasons. This review explores the advantages of crop rotation and its contribution to promoting sustainable farming practices, such as legume integration and cover cropping. It is based on a thematic literature review of peer-reviewed studies published between 1984 and 2025. We found that crop rotation can significantly improve soil structure and organic matter content and enhance nutrient cycling. Furthermore, soil organic carbon increased by up to 18% when legumes were included in rotations compared to monoculture systems in Europe, while also mitigating greenhouse gas emissions, enhancing carbon sequestration, and decreasing nutrient leaching and pesticide runoff. Farmers can adopt several strategies to optimise crop rotation benefits, such as diversification of various crops, legume integration, cultivation of cover crops, and rotational grazing. These practices ensure agricultural sustainability and food security and support climate resilience. Full article

Franchising operates as a proven business model that drives substantial growth for small and medium-sized enterprises (SMEs) worldwide. The franchise ecosystem in Saudi Arabia lacks sufficient research, despite established frameworks for success in markets such as the United States, the United Kingdom, and Australia. This research investigates the elements that lead to franchise success in Saudi Arabia through a combination of qualitative and quantitative data. This research evaluates franchise performance through metrics such as Average Revenue Per Unit (ARPU), Return on Investment (ROI), Franchise Success Rate, Time to Break Even, and Market Growth Rate, comparing Saudi Arabia with the U.S., the U.K., and India to identify essential success determinants. The research reveals that franchise success depends on regulatory frameworks, cultural alignment, economic diversification, and supply chain efficiency. The U.S. and U.K. enjoy established legal protections, whereas Saudi Arabia faces regulatory complexities and resource limitations. The research proposes three strategic recommendations: government incentives, locally adapted business models, and carefully selected locations to boost franchise success. The analysis provides essential information to policymakers, franchisors, and entrepreneurs seeking to expand their businesses in Saudi Arabia. The implementation of Vision 2030 growth barrier solutions and market opportunities will enable Saudi Arabia to build up its franchising sector and enhance market performance. This research adds new knowledge to the franchising literature in emerging markets and its impact on sustainable business growth. Full article

The plant-specific TCP transcription factor family originated before the emergence of land plants. However, the timing of the appearance of their specific transcriptional repressor family, the TCP Interactor containing EAR motif protein (TIE), remains unknown. Here, through phylogenetic analyses, we traced the origin of the TIE family to the early evolution of the embryophyte, while an earlier diversification in algae cannot be ruled out. Strikingly, we found that the number of TIE members is highly constrained compared to the expansion of TCPs in angiosperms. We used co-expression data to identify potential TIE-TCP regulatory targets across Arabidopsis thaliana and rice. Notably, the expression pattern between these species is remarkably similar. TCP Class I and Class II genes formed two distinct clusters, and TIE genes cluster within the TCP Class I group. This study provides a comprehensive evolutionary analysis of the TIE family, shedding light on its conserved role in the regulation of gene transcription in flowering plant development. Full article

With the diversification of space applications, the tracking of maneuvering targets has gradually gained attention. Issues such as their wide range of movement and observation outliers caused by human operation are worthy of in-depth discussion. This paper presents a novel distributed diffusion multi-noise Interacting Multiple Model (IMM) filter for maneuvering target tracking in heavy-tailed noise. The proposed approach leverages parallel Gaussian and Student-t filters to enhance robustness against non-Gaussian process and measurement noise. This hybrid filter is implemented as a node within a distributed network, where the diffusion algorithm leads to the global state asymptotically reaching consensus as the filtering time progresses. Furthermore, a fusion of multiple motion models within the IMM algorithm enables robust tracking of maneuvering targets across the distributed network and process outlier caused by maneuver compared to previous studies. Simulation results demonstrate the effectiveness of the proposed filter in tracking maneuvering targets. Full article

Background/Objectives: Scutiger ningshanensis (Fang, 1985) is an endemic Chinese amphibian species within the genus Scutiger (Megophryidae). Despite its ecological significance, its mitochondrial genome architecture and evolutionary relationships remain poorly understood. Given the high structural variability in Megophryidae mitogenomes and unresolved phylogenetic patterns in Scutiger, this study aims to (1) characterize the complete mitogenome of S. ningshanensis, (2) analyze its molecular evolution, and (3) clarify its phylogenetic position and divergence history within Megophryidae. Methods: The complete mitochondrial genome was sequenced and annotated, followed by analyses of nucleotide composition, codon usage bias, and selection pressures (Ka/Ks ratios). Secondary structures of rRNAs and tRNAs were predicted, and phylogenetic relationships were reconstructed using maximum likelihood and Bayesian methods. Divergence times were estimated using molecular clock analysis. Results: The mitogenome of S. ningshanensis is 17,282 bp long, encoding 13 protein-coding genes (PCGs), 22 tRNAs, 2 rRNAs, and a control region, with a notable AT bias (61.05%) with nucleotide compositions of T (32.51%), C (24.64%), G (14.3%), and A (28.54%). All tRNAs exhibited cloverleaf structures except trnS1, which lacked a DHU stem. Phylogenetic analysis confirmed the monophyly of Scutiger, forming a sister clade to Oreolalax and Leptobrachium, and that S. ningshanensis and S. liubanensis are sister species with a close evolutionary relationship. Positive selection was detected in Atp8 (Ka/Ks > 1), suggesting adaptation to plateau environments, while other PCGs underwent purifying selection (Ka/Ks < 1). Divergence time estimation placed the origin of Megophryidae at~47.97 MYA (Eocene), with S. ningshanensis diverging~32.67 MYA (Oligocene). Conclusions: This study provides the first comprehensive mitogenomic characterization of S. ningshanensis, revealing its evolutionary adaptations and phylogenetic placement. The findings enhance our understanding of Megophryidae’s diversification and offer a genomic foundation for future taxonomic and conservation studies. Full article

This comprehensive review maps the fast-evolving landscape in which artificial intelligence (AI) and deep-learning (DL) techniques converge with the Internet of Things (IoT) to manage energy, comfort, and sustainability across smart environments. A PRISMA-guided search of four databases retrieved 1358 records; after applying inclusion criteria, 143 peer-reviewed studies published between January 2019 and April 2025 were analyzed. This review shows that AI-driven controllers—especially deep-reinforcement-learning agents—deliver median energy savings of 18–35% for HVAC and other major loads, consistently outperforming rule-based and model-predictive baselines. The evidence further reveals a rapid diversification of methods: graph-neural-network models now capture spatial interdependencies in dense sensor grids, federated-learning pilots address data-privacy constraints, and early integrations of large language models hint at natural-language analytics and control interfaces for heterogeneous IoT devices. Yet large-scale deployment remains hindered by fragmented and proprietary datasets, unresolved privacy and cybersecurity risks associated with continuous IoT telemetry, the growing carbon and compute footprints of ever-larger models, and poor interoperability among legacy equipment and modern edge nodes. The authors of researches therefore converges on several priorities: open, high-fidelity benchmarks that marry multivariate IoT sensor data with standardized metadata and occupant feedback; energy-aware, edge-optimized architectures that lower latency and power draw; privacy-centric learning frameworks that satisfy tightening regulations; hybrid physics-informed and explainable models that shorten commissioning time; and digital-twin platforms enriched by language-model reasoning to translate raw telemetry into actionable insights for facility managers and end users. Addressing these gaps will be pivotal to transforming isolated pilots into ubiquitous, trustworthy, and human-centered IoT ecosystems capable of delivering measurable gains in efficiency, resilience, and occupant wellbeing at scale. Full article

This study examines the impact of geopolitical risk (GPR) on the connectedness dynamics among the sovereign bonds of the emerging seven (E7) and the Group of Seven (G7) countries. Initially, a quantile-based vector-autoregressive (Q-VAR) connectedness approach is used to calculate the total connectedness index (TCI) among sovereign bonds under different market states. Then, the impact of GPR on the TCI at the median and tails is estimated to examine if GPR affects the TCI among sovereign bonds. Using daily yields from 30 January 2012, to 17 June 2024, the findings show that the GPR is one of the significant determinants of the TCI among sovereign bonds during normal and extreme market conditions. Other determinants of the TCI include yields on Treasury bills (T-bills), the exchange rate, and the financial market volatility index. The impact of GPR on the TCI varies significantly during different GPR episodes and bond market conditions. The effect of GPR on the TCI among sovereign bonds yields is higher during war times and when bond yields are average. These findings can be utilized by investors seeking to achieve international diversification and policymakers aiming to mitigate the effects of heightened geopolitical risk on financial stability. Furthermore, GPR can be used as an early signal tool for systematic tail risk spillovers among sovereign bonds. Full article

This paper explores the effectiveness of an innovative FinTech risk-scoring model to predict the risk-appropriate return for short-term credit sales. The risk score serves to mitigate the information asymmetry between the seller of receivables (“Seller”) and the purchaser (“Funder”), at the same time providing an opportunity for the Funder to earn returns as well as to diversify its portfolio on a risk-appropriate basis. Selling receivables/credit to potential Funders at a risk-appropriate discount also helps Sellers to maintain their short-term financial liquidity and provide the necessary cash flow for operations and other immediate financial needs. We use 18,304 short-term credit-sale transactions between 23 April 2020 and 30 September 2022 from the private FinTech startup Crowdz and its Sustainability, Underwriting, Risk & Financial (SURF) risk-scoring system to analyze the risk/return relationship. The data includes risk scores for both Sellers of receivables (e.g., invoices) along with the Obligors (firms purchasing goods and services from the Seller) on those receivables and provides, as outputs, the mutual gains by the Sellers and the financial institutions or other investors funding the receivables (i.e., the Funders). Our analysis shows that the SURF Score is instrumental in mitigating the information asymmetry between the Sellers and the Funders and provides risk-appropriate periodic returns to the Funders across industries. A comparative analysis shows that the use of SURF technology generates higher risk-appropriate annualized internal rates of return (IRR) as compared to nonuse of the SURF Score risk-scoring system in these transactions. While Sellers and Funders enter into a win-win relationship (in the absence of a default), Sellers of credit instruments are not often scored based on the potential diversification by industry classification. Crowdz’s SURF technology does so and provides Funders with diversification opportunities through numerous invoices of differing amounts and SURF Scores in a wide range of industries. The analysis also shows that Sellers generally have lower financing stability as compared to the Obligors (payers on receivables), a fact captured in the SURF Scores. Full article

As part of its modernisation and economic diversification policies, Saudi Arabia is building smart, sustainable cities intended to improve quality of life and meet environmental goals. However, involving the public in urban planning remains complex, with traditional methods often proving expensive, time-consuming, and inaccessible to many groups. Integrating artificial intelligence (AI) into public participation may help to address these limitations. This study explores whether Saudi residents are ready to engage with AI-driven tools in urban planning, how they prefer to interact with them, and what ethical concerns may arise. Using a quantitative, survey-based approach, the study collected data from 232 Saudi residents using non-probability stratified sampling. The survey assessed demographic influences on AI readiness, preferred engagement methods, and perceptions of ethical risks. The results showed a strong willingness among participants (200 respondents, 86%)—especially younger and university-educated respondents—to engage through AI platforms. Visual tools such as image and video analysis were the most preferred (96 respondents, 41%), while chatbots were less favoured (16 respondents, 17%). However, concerns were raised about privacy (76 respondents, 33%), bias (52 respondents, 22%), and over-reliance on technology (84 respondents, 36%). By exploring the intersection of generative AI and participatory urban governance, this study contributes directly to the discourse on inclusive smart city development. The research also offers insights into how AI-driven public engagement tools can be integrated into urban planning workflows to enhance the design, governance, and performance of the built environment. The findings suggest that AI has the potential to improve inclusivity and responsiveness in urban planning, but that its success depends on public trust, ethical safeguards, and the thoughtful design of accessible, user-friendly engagement platforms. Full article

One of the most challenging problems in data analysis is visualizing patterns and extracting insights from multi-dimensional datasets that vary over time. The complexity of data and variations in the correlations between different features adds further difficulty to the analysis. In this paper, we provide a framework to analyze the temporal dynamics of such datasets. We use machine learning clustering techniques and examine the time evolution of data patterns by constructing the corresponding cluster trajectories. These trajectories allow us to visualize the patterns and the changing nature of correlations over time. The similarity and correlations of features are reflected in common cluster membership, whereas the historical dynamics are described by a trajectory in the corresponding (cluster, time) space. This allows an effective visualization of multi-dimensional data over time. We introduce several statistical metrics to measure duration, volatility, and inertia of changes in patterns. Using the Hamming distance of trajectories over multiple time periods, we propose a novel metric, the Hamming diversification index, to measure the spread between trajectories. The novel metric is easy to compute, has a simple machine learning implementation, and provides additional insights into the temporal dynamics of data. This parsimonious diversification index can be used to examine changes in pattern similarities over aggregated time periods. We demonstrate the efficacy of our approach by analyzing a complex multi-year dataset of multiple worldwide economic indicators. Full article

Avian metapneumovirus subtype B (aMPV/B) is an economically significant pathogen in poultry, causing respiratory and reproductive disorders. In this study, 167 clinical samples were collected from commercial poultry farms across Thailand to investigate the prevalence, genetic diversity, and evolutionary dynamics of aMPV/B. Nested RT-PCR targeting the G gene revealed a positivity rate of 34.13% (57/167). Phylogenetic and Median-joining network analyses of sequenced amplicons identified two distinct Thai lineages: one genetically similar to vaccine strains and another of unknown origin. Divergence time analysis using a Bayesian framework estimated the time to the most recent common ancestor (tMRCA) of these lineages around 2006, with further sub-lineage diversification occurring around 2009 and 2016. These findings suggest that the circulating Thai aMPV/B strains likely stem from limited introduction events followed by local evolution. Lineage-specific amino acid substitutions within the G gene were identified, which may affect antigenic properties and immune recognition. This study highlights the molecular heterogeneity and ongoing diversification of aMPV/B in Thailand and underscores the need for sustained genomic surveillance and regionally tailored vaccination strategies. Full article

This study conducted a spatiotemporal review of the coelacanth fossil record and explored its distribution and diversity patterns. Coelacanth research can be divided into two distinct periods: the first period, which is based solely on the fossil record, and the second period following the discovery of extant taxa, significantly stimulating research interest. The distribution and research intensity of coelacanth fossils exhibit marked spatial heterogeneity, with Europe and North America being the most extensively studied regions. In contrast, Asia, South America, and Oceania offer substantial potential for future research. Temporally, the coelacanth fossil record also demonstrates significant variation across geological periods, revealing three diversity peaks in the Middle Devonian, Early Triassic, and Late Jurassic, with the Early Triassic peak exhibiting the highest diversity. With the exception of the Late Devonian, Carboniferous, and Late Cretaceous, most periods remain understudied, particularly the Permian, Early Jurassic, and Middle Jurassic, where the record is notably scarce. Integrating the fossil record with phylogenetic analyses enables more robust estimations of coelacanth diversity patterns through deep time. The diversity peak observed in the Middle Devonian is consistent with early burst models of diversification, whereas the Early and Middle Triassic peaks are considered robust, and the Late Jurassic peak may be influenced by taphonomic biases. The low population abundance and limited diversity of coelacanths reduce the number of specimens available for fossilization. The absence of a Cenozoic coelacanth fossil record may be linked to their moderately deep-sea habitat. Future research should prioritize addressing gaps in the fossil record, particularly in Africa, Asia, and Latin America; employing multiple metrics to mitigate sampling biases; and integrating a broader range of taxa into phylogenetic analyses. In contrast to the widespread distribution of the fossil record, extant coelacanths exhibit a restricted distribution, underscoring the urgent need to increase conservation efforts. Full article