Search Results (35,238)

Ornamental horticulture represents one of the dominant pathways for the introduction of alien plant species and has played a central role in shaping current and future invasion dynamics. Many ornamental plants escape cultivation after long lag phases, driven by high propagule pressure, human-mediated selection of functional traits, and increasing climatic suitability. As a result, ornamental species contribute substantially to Europe’s invasion debt, with many future invasions already “locked in” under ongoing global change. In this review, we synthesize current knowledge on the invasive risk of ornamental plants in Europe, examining introduction pathways, biological traits promoting invasiveness, the role of climate change, and the ecological, economic, and social impacts associated with ornamental plant invasions. We highlight that beyond biodiversity loss, invasive ornamental plants pose underappreciated threats to agriculture and related activities, including increased management costs, weed problems in managed landscapes, and disruption of water management and irrigation infrastructure, particularly through invasive aquatic species. We further review tools for risk assessment and prevention, including weed risk assessment frameworks, green lists, horizon scanning, and climate-informed spatial forecasting, emphasizing the importance of proactive, pathway-based approaches. Where prevention fails, management of established invasive ornamentals relies on integrated strategies combining mechanical, chemical, and biological control, often generating large quantities of biomass and long-term economic costs. We discuss the emerging but still limited potential of invasive plant biomass valorization as a complementary management option, highlighting both opportunities and constraints. Finally, we discuss implications for horticultural practices, policy development, and future research, arguing that reconciling ornamental horticulture with biodiversity conservation and sustainable agriculture will require anticipatory governance, stakeholder engagement, and climate-aware decision-making. By aligning horticultural innovation with invasion risk awareness, it may be possible to reduce future invasions while maintaining the social and economic benefits of ornamental plant use in Europe. Full article

For nations heavily dependent on fossil-fuel exports, hydrogen is emerging as a promising solution to reduce carbon emissions while preserving economic stability and promoting countries’ energy independence. This research study examines hydrogen potential as a renewable energy source to facilitate the transition toward a sustainable economy with a special focus on Middle East and North Africa (MENA) countries. The analysis delves into policy frameworks, technological advancements, and infrastructure adaptations to build a reliable green hydrogen supply chain for a scalable and bankable future. The role played by other renewable energies like solar and wind, together with the risk related to the high demand for water resources to achieve the green hydrogen transition, has also been assessed. Furthermore, key challenges have been highlighted, including the repurposing of the existing pipelines into the energy networks, public–private partnerships to secure investment, and legislation requirements to encourage the adoption of novel hydrogen applications. In order to do that, a SWOT-PESTEL analysis has been carried out to identify the main decarbonization strategies for achieving a replicable framework. Moreover, a multi-criteria decision analysis was performed, applying 11 indicators across supply-side (e.g., solar/wind potential, LCOE, and water stress), demand-pull/logistics (e.g., maritime connectivity, steel production, and LNG export capacity), and risk/regulation dimensions (e.g., governance effectiveness, regulatory quality, and fossil rent dependence). The Analytic Hierarchy Process (AHP) was used for weighting, the entropy method for weighting variability (hybrid 50/50 combined weights), min–max normalization for costs, 5% Winsorization for outliers, and TOPSIS for aggregation following OECD-JRC composite indicator guidelines. Results have been validated through a multiple scenario analysis (base, supply-led, and risk-aware) and sensitivity testing via Dirichlet bootstrapping (5000 iterations) with ±20% weight perturbations. Six countries of the MENA region have been studied. The multi-criteria decision analysis outcomes rank Egypt (composite score 0.518), Algeria (0.482), and Oman (0.479) as the most suitable countries for large-scale green hydrogen and ammonia production/export, while Saudi Arabia, Qatar, and Kuwait achieved lower supply scores in the base case due to higher perceived risks. Full article

Rural households’ risky financial asset selection (RFAS) is the foundation of households’ diversified asset allocation, which in turn helps expand their sources of property income. However, rural households rarely participate in risky financial markets due to limited participation access and a lack of financial knowledge and market information. Digital Village Construction (DVC) has brought new opportunities for a change in this phenomenon. This study determines the impact of DVC on RFAS using data on 5593 rural households from the 2020 China Family Panel Studies and County Digital Village Index. The findings show that DVC significantly increases the likelihood, amount, and rate of rural households’ RFAS. However, the impact of DVC varies across its different dimensions. Specifically, the development of digital infrastructure, digital economy, and digital lifestyles each exerts a significant positive effect on RFAS, whereas digital governance does not show a statistically significant impact—likely due to the underdevelopment or inefficiencies of current digital governance platforms. Mechanism analyses reveal that DVC promotes rural households’ RFAS by improving farmers’ access to information and expanding their market participation opportunities, while rural households’ education expenditure and pension income uncertainties weaken this positive effect. Heterogeneity analyses suggest that the impact of DVC on rural households’ RFAS is more pronounced among young families, those with lower education levels, and high-income families. With the projected advancement of digital villages in China, this study offers crucial guidance for implementing policies, such as the Digital Village Construction Guidelines, by guiding rural households toward more rational and inclusive participation in risky financial markets. Full article

Water systems experience increasing sustainability challenges from climate variability, aging infrastructure, and energy and chemical intensity demands, but AI has typically been assessed against prediction accuracy rather than demonstrated operational success. This PRISMA 2020 systematic review analyzed the role of AI solutions on sustainability in distribution, treatment, and basin management. The database search identified 920 records; after deduplication (n = 185), screening was conducted on n = 735 titles/abstracts and examination of the full text for n = 85, providing a total of n = 41 included peer-reviewed studies for qualitative synthesis and n = 38 for quantitative/bibliometric synthesis with the additional analysis of seven grey-literature sources. Evidence mapping reveals high growth post-2020, and distribution and wastewater operations are dominated by a few companies. The most deployable evidence is found with monitoring, anomaly/leak detection, and short-term forecasting, while optimization and reinforcement-learning control are primarily simulation validated with limited field applications. While accuracy metrics are often reported, transformation into water saved, kWh/m³, chemicals, compliance/reliability/resilience/equity measures are inconsistently and less frequently operationalized. In general, AI is most believable when it is part of analysis-ready workflows, bounded decision support, and measurement-and-verification. Full article

Digital transformation is central to circular economy (CE) strategies, yet the intersection between digital innovation and women’s entrepreneurship remains underexplored. We examine how IoT, AI, blockchain, data analytics and platform technologies are represented in CE-oriented management research and assess the visibility of gender-inclusive and women entrepreneurship perspectives. We merged Scopus and Web of Science records (2015–2025), removed duplicates, screened for relevance, and mapped themes and networks using bibliometrix (R) and VOSviewer. Digital-CE scholarship was found to rise after 2018, dominated by smart manufacturing, circular supply chains, digital product passports and blockchain traceability. Four clusters emerged: digital circular manufacturing, circular business model innovation, waste and resource management, and policy–social aspects. Gender-related terms appear in only 1.35% of the corpus, revealing a gap between academic research and EU policy priorities for inclusive digital and circular transitions. We integrate a gender-inclusive lens and outline an agenda positioning women entrepreneurs as critical yet overlooked actors in digital circular ecosystems. As a bibliometric review, this study maps scholarly attention rather than the prevalence of women-led circular ventures. Beyond mapping, we advance the paper’s primary contribution by proposing a governance-oriented synthesis that frames digital infrastructures as administrative mechanisms shaping who can participate in, benefit from, and influence digital circular ecosystems. Full article

Water utilities continue to lose a lot of Non-Revenue Water (NRW) because of leaks that go undetected. This makes it necessary to find accurate but easy-to-use monitoring solutions. This paper presents FiT-WST+, a wavelet-guided Frequency-Informed Transformer (FiT) designed for the classification of five distinct leak types utilising accelerometer measurements. The proposed architecture combines the spectral modelling ability of a FIT with the stable translation-invariant representation of the Wavelet Scattering Transform (WST). The model uses a guided attention mechanism to combine spectral and scattering cues that work well together to make classes more distinct, especially for fault types that are similar. On the held-out test set, FiT-WST+ achieves 99.6% accuracy, 99.6% balanced accuracy, and a 99.6% macro-averaged F1-score. Comparative benchmarking against recent methods tested on the same dataset shows that this method works at a low sampling rate (1 kHz), which greatly lowers bandwidth needs and allows for scalable deployment on edge devices with limited resources for real-time monitoring of important water infrastructure. Full article

Rapid urban air mobility (UAM) developments and new classes of vertical takeoff and landing (eVTOL) aircraft have changed the safety paradigm in urban airspace. eVTOL aircraft operations in dense urban environments are characterized by increased variability of external factors, highly dynamic flight scenarios, and an increased likelihood of rare but potentially critical events. Traditional safety assessment approaches do not capture the specific features of eVTOL designs, power plants, autonomy algorithms, and urban air traffic characteristics; this results in low threat prediction accuracy and limited development of modern incident prevention systems. Herein, the risk profile of eVTOL aircraft is analyzed, accounting for the multifactorial nature of urban environments and the complexity of integrating such vehicles into existing UAM infrastructure. The need for quantitative methods for assessing the probability of critical situation risks is also substantiated. These methods provide a statistically accurate description of extreme events and enable the identification of hidden dependencies in complex technical and organizational systems. Approaches based on probabilistic models, extreme value analysis, and systemic Full article

Visible Light Positioning (VLP) is widely regarded as a promising technology for high-precision indoor localization due to its immunity to radio-frequency interference and compatibility with existing Light-Emitting Diode (LED) lighting infrastructure. Despite recent progress, current VLP systems remain fundamentally limited by nonlinear received signal strength (RSS) characteristics, unknown transmitter orientations, and dynamic indoor disturbances. Existing solutions typically address these challenges in isolation, resulting in limited robustness and scalability. This paper proposes SCENE-VLP (Sequential Deep Learning with Feature Compression and Optimal State Estimation), a structured positioning framework that integrates feature compression, temporal sequence modeling, and probabilistic state refinement within a unified estimation pipeline. Specifically, SCENE-VLP combines Principal Component Analysis (PCA) and Denoising Autoencoders (DAE) for linear and nonlinear observation conditioning, Gated Recurrent Units (GRU) for modeling temporal dependencies in RSS sequences, and Kalman-based filtering (KF/EKF) for recursive state-space refinement. The framework is formulated as a hierarchical approximation of the nonlinear observation model, linking data-driven measurement learning with Bayesian state estimation. A systematic ablation study across multiple scenarios, including same-dataset evaluation and cross-dataset generalization, demonstrates that each component provides complementary benefits. Feature compression reduces redundancy while preserving dominant signal structure; GRU significantly improves robustness over static regression; and recursive filtering consistently reduces positioning error compared to unfiltered predictions. While both KF and EKF improve performance, EKF provides incremental refinement under mild nonlinearities. Extensive simulations conducted on an indoor dataset collected from a realistic deployment with eight ceiling-mounted LEDs and a single photodetector (PD) show that SCENE-VLP achieves sub-decimeter localization accuracy, with P50 and P95 errors of 1.84 cm and 6.52 cm, respectively. Cross-scenario evaluation further confirms stable generalization and statistically consistent improvements. These results demonstrate that the structured integration of observation conditioning, temporal modeling, and Bayesian refinement yields measurable gains beyond partial pipeline configurations, establishing SCENE-VLP as a robust and scalable solution for next-generation indoor visible light positioning systems. Full article

Fresh fish is a highly nutritious and widely consumed product that remains highly perishable due to its chemical composition. Conventional preservation methods, such as chilling and freezing, are effective at inhibiting microbial growth but often compromise nutritional and organoleptic quality. Advanced thermal techniques, including supercooling and cryogenic storage, can extend shelf life to approximately 180 days but involve high infrastructure costs and potential sensory alterations. In response, non-thermal technologies have emerged as promising alternatives capable of minimizing microbial and enzymatic deterioration while reducing oxidative and sensory damage. These include high-pressure processing, cold plasma, gamma irradiation, advanced packaging systems (e.g., modified atmospheres, edible coatings), and natural antioxidants. However, such methods face limitations such as lipid oxidation, flavor changes, and scalability issues, highlighting the need for integrated preservation strategies. This study addresses a critical gap in the application of synergistic, multi-hurdle approaches that combine non-thermal technologies to enhance shelf life without compromising nutritional or sensory quality. It is essential to propose tailored and scalable solutions specific to fishery products to advance the development of sustainable and effective preservation systems that meet the practical needs of the seafood industry. Full article

Many developing country governments would like to attract investment and create jobs in manufacturing and high-tech industries. Heavy and unpredictable laws and regulations, frequent demands for bribes, high taxes, poor-quality roads, slow and inefficient ports, and unreliable power, however, deter private investors. Moreover, political opposition and fiscal constraints prevent governments from resolving the numerous issues. Rather than attempting to solve everything everywhere, many governments have tried to fix problems in only small regions. These special economic zones (SEZs) often have lower taxes, more liberal regulation, and better infrastructure. This paper asks whether firms located in African and South Asian SEZs report less regulation and corruption than other firms in the same countries. We find, on average, being located in an SEZ is associated with lower burdens due to corruption and regulation. Firms in the zones are less likely to report paying bribes than firms outside the zones and report spending less time dealing with inspections and regulations. However, this is not true in Africa; firms in African zones report that corruption and regulation are as troublesome as for similar firms outside the zones. Full article

To promote the resource utilization of oilfield solid waste and facilitate the green and low-carbon transformation of transportation infrastructure, this study employed drilling cuttings from the Maye area of the Xinjiang oilfield and coal-fired furnace ash as primary raw materials. NaOH, Na₂O·nSiO₂, and Ca(OH)₂ were used as alkali activators to prepare alkali-activated solidification materials for oilfield road base applications. The optimal curing system identified in this study (4 wt.% NaOH + 20 wt.% furnace ash) falls within the commonly reported dosage ranges for alkali-activated solid-waste materials, where NaOH contents are typically 3%–8% and furnace ash contents 15%–30%. Considering the distinct chemical characteristics of the Xinjiang oilfield solid wastes, a targeted optimization strategy was adopted to achieve a balance between mechanical performance and economic feasibility. Based on mix-proportion experiments, macroscopic mechanical properties were evaluated. In combination with X-ray diffraction (XRD), laser particle size analysis, simultaneous thermal analysis (TG–DSC), and scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM–EDS), the influence of activator type on both mechanical performance and microstructural evolution was systematically investigated. The results indicate that the system containing 4 wt.% NaOH + 20 wt.% furnace ash exhibits the best overall performance, achieving a 28-day compressive strength of 4.81 MPa and a splitting tensile strength of 0.41 MPa, which are significantly higher than those of the Na₂O·nSiO₂ system (3 wt.% Na₂O·nSiO₂ + 20 wt.% furnace ash) and the Ca(OH)₂ system (4 wt.% Ca(OH)₂ + 15 wt.% furnace ash). The primary hydration products were identified as C-(N)-A-S-H and C-S-H gels. The type of alkali activator plays a decisive role in regulating hydration reaction kinetics and the spatial distribution of Ca and Si elements, thereby governing the hierarchical differences in macroscopic mechanical properties. In particular, NaOH generates a highly alkaline environment that promotes the dissolution of active Si/Al components in both drilling cuttings and furnace ash, enhances gel polymerization, and results in a denser microstructure. This study provides theoretical and technical support for the high-value utilization of oilfield solid wastes in highway base engineering. Full article

This paper presents a sensitivity analysis of positioning accuracy in a localization system based on signal time-of-flight measurements, intended for operation in underground roadway workings. The underground environment is characterized by limited installation space, numerous obstacles causing multipath propagation, and the presence of sections with non-uniform geometry, which in practice leads to a “flattening” of the transmitter constellation and a deterioration of the conditioning of the trilateration problem. As a result, even small changes in input parameters (e.g., related to infrastructure geometry, distance-measurement quality, or the adopted model) may cause a significant change in the position-estimation error, thereby reducing the reliability of roadheader localization across the entire working area. In this study, a local sensitivity analysis is employed to identify the parameters that dominate the positioning outcome. Sensitivity coefficients are defined in a normalized form and are determined numerically using a perturbation approach (changing a given input parameter by a prescribed percentage), which avoids analytical differentiation of the complex relationships arising from the trilateration equations. The analysis is performed for a roadway scenario supported by an ŁP10 steel arch yielding support, with transmitters installed under the support arch and the roadheader trajectory represented by a sequence of consecutive position vectors. The obtained results allow the solution’s susceptibility to errors and uncertainties in the parameters to be assessed and indicate which parameters require priority control in practical implementation. On this basis, recommendations are formulated for the design and maintenance of the localization infrastructure, including transmitter placement and reconfiguration rules (relocation or adding an additional transmitter), to maintain stable positioning quality under operational mining conditions. Full article

Accurate prediction of wear behavior in polymer nanocomposites remains challenging due to the coupled influence of operating conditions and evolving surface morphology. In this study, a surface-aware triboinformatics framework is proposed to predict the dry sliding wear behavior of multi-walled carbon nanotube (MWCNT) reinforced epoxy composites by integrating operating parameters with run-wise atomic force microscopy (AFM) surface descriptors. Wear experiments were conducted using a Taguchi L16 design by varying CNT content (0–0.75 wt.%), applied load (10–40 N), sliding speed (183–458 rpm), and sliding distance (500–1250 m). AFM-derived parameters, including Ra, Rq, Z-range, and surface area difference, were extracted from the worn surface corresponding to each experimental run. Multiple regression-based machine learning models were evaluated using leave-one-out cross-validation, with ensemble-based models providing the best predictive performance (R² > 0.85 with low RMSE and MAE). Feature importance and partial dependence analyses identified CNT content as the dominant factor controlling wear reduction, followed by Z-range and Ra, highlighting the critical role of surface damage severity. Neat epoxy exhibited a maximum wear loss of 0.444 mg, whereas the 0.75 wt.% CNT composite showed values as low as 0.003 mg under comparable conditions, corresponding to a reduction of approximately 99%. The proposed framework enables mechanistically interpretable wear prediction and supports the design of durable polymer composites, contributing to SDG 9 (Industry, Innovation and Infrastructure) and SDG 12 (Responsible Consumption and Production). Full article

Monitoring vegetation and land cover changes over time in oil-impacted regions is crucial for assessing ecological degradation and informing remediation options. This study aimed to identify the challenges encountered when using Landsat imagery to detect changes in vegetation health and land cover in Bodo, a hydrocarbon-impacted community in the Niger Delta region of Nigeria, over a 20-year period. Landsat 7 ETM+ and Landsat 8 OLI imagery were used to derive the Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), and Normalised Difference Built-up Index (NDBI) from 2003 to 2023. Data continuity was affected by the Landsat 7 Scan Line Corrector malfunction in the 2008 images and by high cloud coverage in the Landsat 8 OLI 2013 images. Hence, 2008 and 2013 were excluded from the analysis, limiting multi-year comparisons. Results from the available years indicated that NDBI values increased gradually, suggesting minor urban expansion. Stable but low NDWI levels suggest water stress, while changing NDVI values indicate alterations in vegetative health. However, this study highlights observable environmental changes and the challenges involved in using satellite imagery for environmental monitoring in oil-impacted regions, underscoring the need for improved cloud-masking methodologies and radar datasets to enhance long-term environmental assessment. Full article

With the development of polar regions and the deepening utilization of cold region resources, a large number of infrastructure projects are continuously being carried out. The freezing temperature of unsaturated soil is a critical factor governing the freezing depth and stability of foundations in cold regions or seasons. Concurrently, the supercooling state of soil significantly influences the assessment of its phase composition and physico-mechanical properties. This study employed physical experiments, theoretical formulas, and numerical simulations to reveal the influencing factors and underlying mechanisms of supercooling characteristics in unsaturated soils under controlled low-rate continuous cooling conditions. The results demonstrate that a reduced temperature gradient between the sample surface and the ambient environment correlates with a lower supercooling limit temperature and an extended supercooling duration. An excessively high cooling rate suppresses the supercooling phenomenon in the sample core due to boundary effects. In contrast, neither the temperature difference nor the external cooling rate exhibit a negligible influence on the freezing temperature. Analysis of the temperature–time curves reveals that the freezing process of silty clay is more stable, exhibiting fewer stepwise temperature declines during the phase change plateau, whereas mudstone shows heightened sensitivity to variations in the thermal gradient. Compared to conventional thermocouple measurements, the proposed methodology achieves an optimal balance between temporal efficiency and measurement accuracy. It not only enhances experimental controllability and data reliability, but also provides more scientific theoretical support and technical pathways for predicting freezing depth, designing foundation thermal systems, and preventing frozen ground disasters in cold region engineering. Full article