Search Results (7,986)

Measuring the bioeconomy enables policymakers to monitor advancements in sustainable development goals, identify growth opportunities, comprehend the economic implications of bio-based products, assess environmental impacts, and shape policies that foster a sustainable economy reliant on renewable biological resources. For this purpose, this study measures the value of the bioeconomy in New Zealand using the latest published input–output table for the year 2020. This study estimates the size and economic significance of New Zealand’s bioeconomy by applying two complementary methodologies. Results indicate that, in 2020, the total value added by the bioeconomy ranged from NZD 48.8 billion to NZD 50.8 billion, representing 16.5% to 17.1% of the nation’s total value added. Agriculture emerged as the dominant contributor, accounting for approximately 89% of the sector’s total value added, followed by forestry and logging at around 11%. To identify potential growth areas, the analysis further disaggregated bioeconomy value added by economic subsectors. Among bio-based industries, food manufacturing was the largest contributor, generating 43.1% (NZD 21 billion) of total bioeconomy value added, followed by bio-based services at 12.9% (NZD 6.3 billion). The biotechnology sector contributed NZD 0.34 billion, equivalent to 0.7% of the total bioeconomy. Additional significant contributors included wood processing and manufacturing (3.3%; NZD 1.6 billion), construction (0.71%; NZD 0.35 billion), and textiles and clothing (0.58%; NZD 0.29 billion). These findings underscore the pivotal role of food manufacturing, services, wood processing, textiles and clothing, and construction in shaping the bioeconomy. They further highlight the importance of assessing the economic and environmental impacts of bio-based industries and formulating policy frameworks that support a sustainable, renewable resource-based economy. Full article

The Zigui Basin, located in the Three Gorges Reservoir Area, has developed numerous landslides due to its interlayering of sandstone and mudstone, geological structure, and reservoir operations. This study identifies a fourth type of landslide failure mode: an oblique-dip slope wedge (OdSW) landslide, based on the Wanshuitian landslide. Following four heavy rainfall events from 3 to 13 July 2024, this landslide exhibited significant deformation on the 17th and was completely destroyed within 40 min. The dimensions of the landslide were 350 m in length, 160 m in width, and 20 m in thickness, with a volume estimated at 8.0 × 10⁵ m³. The characteristics of landslide deformation and the changes in moisture content within the shallow slide body were ascertained using unmanned aerial vehicles, moisture meters, and mobile phone photography. The landslide was identified to have occurred within the weathered residual layer of mudstone, situated between two sandstone layers, with the eastern boundary defined by an inclined rock layer. Upon transitioning into the accelerated deformation stage, the landslide initially exhibited uniform overall sliding deformation, culminating in accelerated deformation destruction. The dip structure created terrain disparities, resulting in a step-like terrain on the left bank and gentler slopes on the right bank, with interbedded soil and rock in a shallow layer, because the interlayered soft and hard geological conditions caused varied weathering and erosion patterns on the riverbank slopes. The interbedded weak–hard stratum layer fostered the development of the oblique-dip slope wedge landslide. Based on the improved Green–Ampt model, we developed a stability prediction methodology for an oblique-dip slope wedge landslide and determined the rainfall infiltration depth threshold of the Wanshuitian landslide (9.8 m). This study aimed not merely to sharpen the evolution mechanism and stability prediction of the Wanshuitian landslide but also to formulate more effective landslide-monitoring strategies and emergency management measures. Full article

Mapping the oil palm (Elaeis guineensis), the globally leading oil-bearing crop and a crucial industrial commodity, is of vital importance for food security and raw material supply. However, existing remote sensing approaches for oil palm mapping present several methodological challenges including temporal resolution constraints, suboptimal feature parameterization, and limitations in age structure assessment. This study addresses these gaps by systematically optimizing temporal, spatial, and textural parameters for enhanced oil palm mapping and age structure analysis through integration of Landsat 4/5/7/8/9, Sentinel-2 multispectral, and Sentinel-1 radar data (LSMR). Analysis of oil palm distribution and dynamics in Malaysia revealed several key insights: (1) Methodological optimization: The integrated LSMR approach achieved 94% classification accuracy through optimal parameter configuration (3-month temporal interval, 3-pixel median filter, and 3 × 3 GLCM window), significantly outperforming conventional single-sensor approaches. (2) Age estimation capabilities: The adapted LandTrendr algorithm enabled precise estimation of the plantation establishment year with an RMSE of 1.14 years, effectively overcoming saturation effects that limit traditional regression-based methods. (3) Regional expansion patterns: West Malaysia exhibits continued plantation expansion, particularly in Johor and Pahang states, while East Malaysia shows significant contraction in Sarawak (3.34 × 10⁵ hectares decline from 2019–2023), with both regions now converging toward similar topographic preferences (100–120 m elevation, 6–7° slopes). (4) Age structure concerns: Analysis identified a critical “replanting gap” with 13.3% of plantations exceeding their 25-year optimal lifespan and declining proportions of young plantations (from 60% to 47%) over the past five years. These findings provide crucial insights for sustainable land management strategies, offering policymakers an evidence-based framework to balance economic productivity with environmental conservation while addressing the identified replanting gap in one of the world’s most important agricultural commodities. Full article

Assessing fishery resources is crucial for sustainable marine ecosystem management and the operation of fisheries. This study integrates trawl surveys, environmental DNA (eDNA) analysis, and scientific echo sounder techniques to analyze the fishery resource distribution of and seasonal variations in the East China Sea. Surveys were conducted in April, July, August, and November 2022, utilizing bottom trawl sampling, eDNA metabarcoding, and acoustic data collection. The results revealed temporal differences in species composition, with crustaceans dominating in terms of abundance and fish species in biomass. The integration of eDNA analysis provided broader species detection, including cryptic and pelagic species, while acoustic techniques enabled large-scale resource assessment. However, discrepancies between methods highlighted the need for methodological refinement. Dominant species exhibited seasonal variation, with Portunus trituberculatus prevailing in spring (April), Trachurus japonicus and Scomber japonicus in summer (July–August), and Pampus argenteus in late autumn (November). A comparative analysis revealed that eDNA is sensitive to pelagic and cryptic species, trawl surveys effectively detect demersal fish, and acoustics allow for broad-scale biomass estimation, highlighting the complementary value of method integration. Full article

Background/Objectives: Long-term obesity management consistently fails due to two major barriers: poor adherence, exacerbated by ultra-processed foods with addictive potential, and post-weight loss metabolic adaptation that reduces energy expenditure by approximately 500 kcal/day. Current paradigms—static diets and GLP-1 receptor agonists—address these barriers only partially. The objectives of this thesis-driven review are: (1) to conduct a focused evidence-mapping of Ketogenic–Mediterranean Diet (KMD) protocols; (2) to analyze why existing protocols have not explicitly countered metabolic adaptation; and (3) to present the Adaptive Ketogenic–Mediterranean Protocol (AKMP). Methods: Hybrid methodology—an argumentative narrative review anchored by a structured evidence-mapping search (PRISMA-style flow for transparency). Results: We identified 29 studies implementing KMD protocols with significant weight loss and superior adherence. However, none of the published protocols explicitly implement anti-adaptive strategies, despite an estimated ketogenic metabolic advantage (≈100–300 kcal/day), context-dependent and more consistently observed in longer trials and during weight-maintenance settings. Conclusions: Unlike GLP-1 receptor agonists—which primarily suppress appetite, require ongoing pharmacotherapy, and do not directly mitigate the decline in energy expenditure—the AKMP couples a Mediterranean foundation for adherence with a ketogenic metabolic advantage and a biomarker-guided adjustment system explicitly designed to counter metabolic adaptation, aiming to improve the durability of weight loss and patient self-management. As a theoretical construct, the AKMP requires confirmation in prospective, controlled studies; accordingly, we outline a pragmatic 24-week pilot design in “Pragmatic Pilot Trial to Validate the AKMP–Incretin Sequencing”. Full article

Digital transformation (DT) has become a strategic priority for public administrations, particularly due to the need to deliver more efficient and citizen-centered services and respond to societal expectations, ESG (Environmental, Social, and Governance) criteria, and the United Nations Sustainable Development Goals (UN SDGs). In this context, the main objective of this study is to propose an innovative methodology to automatically evaluate the level of digital transformation (DT) in public sector organizations. The proposed approach combines traditional assessment methods with Artificial Intelligence (AI) techniques. The methodology follows a dual approach: on the one hand, surveys are conducted using specialized staff from various public entities; on the other, AI-based models (including neural networks and transformer architectures) are used to estimate the DT level of the organizations automatically. Our approach has been applied to a real-world case study involving local public administrations in the Valencian Community (Spain) and shown effective performance in assessing DT. While the proposed methodology has been validated in a specific local context, its modular structure and dual-source data foundation support its international scalability, acknowledging that administrative, regulatory, and DT maturity factors may condition its broader applicability. The experiments carried out in this work include (i) the creation of a domain-specific corpus derived from the surveys and websites of several organizations, used to train the proposed models; (ii) the use and comparison of diverse AI methods; and (iii) the validation of our approach using real data. Based on the deficiencies identified, the study concludes that the integration of technologies such as the Internet of Things (IoT), sensor networks, and AI-based analytics can significantly support resilient, agile urban environments and the transition towards more effective and sustainable Smart City models. Full article

We have developed a generalisable machine learning framework for reservoir quality prediction in deeply buried clastic systems. Applied to the Lower Jurassic deltaic sandstones of the Tilje Formation (Halten Terrace, North Sea), the approach integrates sedimentological facies modelling with mineralogical and petrophysical prediction in a single workflow. Using supervised Extreme Gradient Boosting (XGBoost) models, we classify reservoir facies, predict permeability directly from standard wireline log parameters and estimate the abundance of porosity-preserving grain coating chlorite (gamma ray, neutron porosity, caliper, photoelectric effect, bulk density, compressional and shear sonic, and deep resistivity). Model development and evaluation employed stratified K-fold cross-validation to preserve facies proportions and mineralogical variability across folds, supporting robust performance assessment and testing generalisability across a geologically heterogeneous dataset. Core description, point count petrography, and core plug analyses were used for ground truthing. The models distinguish chlorite-associated facies with up to 80% accuracy and estimate permeability with a mean absolute error of 0.782 log(mD), improving substantially on conventional regression-based approaches. The models also enable prediction, for the first time using wireline logs, grain-coating chlorite abundance with a mean absolute error of 1.79% (range 0–16%). The framework takes advantage of diagnostic petrophysical responses associated with chlorite and high porosity, yielding geologically consistent and interpretable results. It addresses persistent challenges in characterising thinly bedded, heterogeneous intervals beyond the resolution of traditional methods and is transferable to other clastic reservoirs, including those considered for carbon storage and geothermal applications. The workflow supports cost-effective, high-confidence subsurface characterisation and contributes a flexible methodology for future work at the interface of geoscience and machine learning. Full article

In the Mexican Caribbean, the demand for tourism services led to the expansion of the hotel industry from the coast inland. This caused rural and urban communities in the region to become involved in tourism activities, initiating the formulation of an international model of sustainable development with a focus on cultural tourism. Considering the tourism potential that the study area can offer to nearby rural communities, as well as the limited number of studies aimed at estimating tourism carrying capacity (see examples of TCC for environmental management units in communal land areas like Baja California, Mexico and the Huagapo cave in Peru), the present research aims at estimating the tourism carrying capacity in the southern region of the Mexican Caribbean. A mixed methodological approach was adopted for the present study entailing a detailed description of flora and fauna in the study area using natural resource mapping tools, social diagnosis of the communities in the study area using the Participatory Action Research (PAR) technique in the communities of Caobas and San José de la Montaña and the estimation of tourism carrying capacity (TCC), Physical Carrying Capacity (PCC), Real Carrying Capacity (RCC), and Effective Carrying Capacity (ECC) using information gathered through fieldwork and bibliographic review. It was found that the area can support a tourism carrying capacity of 538.33 visits per day. In this initial assessment, it was estimated that the implementation of an ecotourism project in a rural community would not alter its environmental conditions. The estimated indicators provide appropriate tools for designing and planning long-term sustainable tourism proposals. Moreover, they integrate environmental, economic, and social aspects in a balanced manner, generating tangible and lasting benefits. Full article

This study introduces a novel family of probability distributions, termed the Pi-Power Logistic-G family, constructed through the application of the Pi-power transformation technique. By employing the Weibull distribution as the baseline generator, a new and flexible model, the Pi-Power Logistic Weibull distribution, is formulated. Particular emphasis is given to this specific member of the family, which demonstrates a rich variety of hazard rate shapes, including J-shaped, reverse J-shaped, and monotonic increasing patterns, thereby highlighting its adaptability in modeling diverse types of lifetime data. The paper examines the fundamental properties of this distribution and applies the method of maximum likelihood estimation (MLE) to determine its parameters. A Monte Carlo simulation was performed to assess the performance of the estimation method, demonstrating that both Bias and mean square error decline as the sample size increases. The utility of the proposed distribution is further highlighted through its application to real-world engineering datasets. Using model selection metrics and goodness-of-fit tests, the results demonstrate that the proposed model outperforms existing alternatives. In addition, a Bayesian approach was used to estimate the parameters of both datasets, further extending the model’s applicability. The findings of this study have significant implications for the fields of reliability modeling, survival analysis, and distribution theory, enhancing methodologies and offering valuable theoretical insights. Full article

Controlling nitrogen oxide (NO_x) emissions is a critical priority for the maritime industry, driven by increasingly stringent international maritime organization (IMO) Tier III regulations and the sector’s broader decarbonization efforts. Accurate prediction and minimization of NO_x emissions require well-calibrated engine models that reflect real-world operating behavior under varied conditions. This study presents a robust calibration methodology for the NO_x emissions model of a high-speed dual-fuel marine engine, using a 1D engine simulation platform (WAVE 2025.1) integrated with a nonlinear optimization algorithm (fmincon in MATLAB R2025a). The calibration focuses on tuning the extended Zeldovich mechanism by empirically adjusting the Arrhenius equation coefficients to achieve a weighted sum of NO_x and unburned hydrocarbon (HC) emissions below the 7.2 g/kWh regulatory threshold. The proposed approach reduces the need for extensive experimental data while maintaining high predictive accuracy. Simulation results confirm compliance with IMO regulations across multiple engine loads defined by the E3 test cycle. A sensitivity analysis further revealed that while the pre-exponent multiplier (ARC1) plays a critical role in influencing NO_x emissions at high loads, the exponent multiplier (AERC1) has an even more significant impact across the full load range, making its precise calibration essential for robust emissions modeling. The calibrated NO_x emissions model not only ensures realistic emissions estimation but also provides a reliable foundation for further research, such as dual-fuel performance studies, and can be effectively integrated into future engine optimization tasks under different operating conditions. Full article

Power flow analysis is essential for managing power systems, helping grid operators ensure reliability and efficiency. This paper explores the use of machine learning (ML) techniques as surrogates for computationally intensive power flow calculations to evaluate the effects of distributed energy resources, such as battery energy storage systems (BESSs), on grid performance. In this paper, a case study is presented where XGBoost (eXtreme Gradient Boosting) and Artificial Neural Networks (ANNs) are trained to simulate power flows in a medium-voltage grid in Norway. The impact of BESS units on line loading, transformer loading, and bus voltages is estimated across thousands of configurations, with results compared in terms of simulation time, error metrics, and robustness. In this paper it is proven that while ML models require considerable data and training time, they offer speed-up factors of up to 45×, depending on the predicted parameter. The proposed methodology can also be used to assess the impact of other grid-connected assets, such as small-scale solar plants and electric vehicle chargers, whose presence in distribution networks continues to grow. Full article

This paper presents a literature review of low-power hybrid wind–photovoltaic (PV) systems and introduces a 3 m diameter prototype rotor featuring twelve PV-coated pivoting blades stiffened by a peripheral rim. Existing solutions—foldable umbrella concepts, Darrieus rotors with PV-integrated blades, and morphing blades—are surveyed, and current gaps in simultaneous wind + PV co-generation on a single moving structure are highlighted. Key performance indicators such as power coefficient (C_p), DC ripple, cell temperature difference (ΔT), and levelised cost of energy (LCOE) are defined, and an integrated assessment methodology is proposed based on blade element momentum (BEM) and computational fluid dynamics (CFD) modelling, dynamic current–voltage (I–V) testing, and failure modes and effects analysis (FMEA) to evaluate system performance and reliability. Preliminary results point to moderate aerodynamic penalties (ΔC_p ≈ 5–8%), PV output during rotation equal to 15–25% of the nominal PV power (P_PV), and an estimated 70–75% reduction in blade–root bending moment when the peripheral ring converts each blade from a cantilever to a simply supported member, resulting in increased blade stiffness. Major challenges include the collective pitch mechanism, dynamic shading, and wear of rotating components (slip rings); however, the suggested technical measures—maximum power point tracking (MPPT), string segmentation, and redundant braking—keep performance within acceptable limits. This study concludes that the concept shows promise for distributed microgeneration, provided extensive experimental validation and IEC 61400-2-compliant standardisation are pursued. This paper has a dual scope: (i) a concise literature review relevant to low-Re flat-blade aerodynamics and ring-stiffened rotor structures and (ii) a multi-fidelity aero-structural study that culminates in a 3 m prototype proposal. We present the first evaluation of a hybrid wind–PV rotor employing untwisted flat-plate blades stiffened by a peripheral ring. Using low-Re BEM for preliminary loading, steady-state RANS-CFD (k-ω SST) for validation, and elastic FEM for sizing, we assemble a coherent load/performance dataset. After upsizing the hub pins (Ø 30 mm), ring (50 × 50 mm), and spokes (Ø 40 mm), von Mises stresses remain < 25% of the 6061-T6 yield limit and tip deflection ≤ 0.5%·R acrosscut-in (3 m s⁻¹), nominal (5 m s⁻¹), and extreme (25 m s⁻¹) cases. CFD confirms a broad efficiency plateau at λ = 2.4–2.8 for β ≈ 10° and near-zero shaft torque at β = 90°, supporting a three-step pitch schedule (20° start-up → 10° nominal → 90° storm). Cross-model deviations for C_p, torque, and pressure/force distributions remain within ± 10%. This study addresses only the rotor; off-the-shelf generator, brake, screw-pitch, and azimuth/tilt drives are intended for later integration. The results provide a low-cost manufacturable architecture and a validated baseline for full-scale testing and future transient CFD/FEM iterations. Full article

Fertility potential ever more diminishes due to the complex, multifactorial, and still not entirely clarified process of reproductive aging in women and men. Gamete quality and reproductive lifespan are compromised by biologic factors like mitochondrial dysfunction, increased oxidative stress (OS), and incremental telomere shortening. Clinically confirmed biomarkers, including follicle-stimulating hormone (FSH) and anti-Müllerian hormone (AMH), are used to estimate ovarian reserve and reproductive status, but these markers have limited predictive validity and an incomplete representation of the complexity of reproductive age. Recent advances in artificial intelligence (AI) have the capacity to address the integration and interpretation of disparate and complex sets of data, like imaging, molecular, and clinical, for consideration. AI methodologies that improve the accuracy of reproductive outcome predictions and permit the construction of personalized treatment programs are machine learning (ML) and deep learning. To promote fertility evaluations, here, as part of its critical discussion, the roles of mitochondria, OS, and telomere biology as latter-day biomarkers of reproductive aging are presented. We also address the current status of AI applications in reproductive medicine, promises for the future, and applications involving embryo selection, multi-omics set integration, and estimation of reproductive age. Finally, to ensure that AI technology is used ethically and responsibly for reproductive care, model explainability, heterogeneity of data, and other ethical issues remain as residual concerns. Full article

PM_2.5 is an air pollutant that has a direct link to increased cardiovascular and respiratory morbidity and mortality, which has been demonstrated in numerous studies. Existing research highlights species-specific variations in the capacity of trees to capture and retain particulate matter (PM). However, a critical gap remains regarding sensitivity analyses of i-Tree Eco model assumptions. Such analyses are crucial for validating the model’s PM deposition estimates against empirically derived efficiencies, a deficiency that the present study addresses. The study consisted of two steps: a tree inventory was carried out at three selected sites, based on which, an ecosystem service analysis was performed using i-Tree Eco, and samples were taken from the leaves of trees at the analysed sites, which were the basis for comparing the data from the i-Tree Eco method and laboratory methods. The study focused on comparing PM_2.5 and PM₁₀ removal estimates derived from both the model and laboratory measurements. The results revealed significant discrepancies between the modelled and laboratory values. A comparison of the average annual PM₁₀ accumulation measured using laboratory methods for individual tree species showed that Tilia sp. achieved 24%, Fraxinus sp. 47.6%, Aesculus sp. 50.77%, and Quercus robur 23.4% of the PM₁₀ uptake efficiency estimated by the i-Tree Eco model. For PM_2.5 uptake, the values obtained through both methods were more consistent. Furthermore, trees growing under more challenging environmental conditions exhibited smaller diameter at breast height (DBH) and lower PM₁₀ and PM_2.5 removal efficiency according to both methods. While I-Tree Eco incorporates tree biophysical characteristics and health status, its methodology currently lacks the resolution to reflect site-specific environmental conditions and local pollutant concentrations at the individual tree level. Therefore, laboratory methods are indispensable for calibrating, validating, and supplementing i-Tree Eco estimates, especially when applied to diverse urban environments. Only the combined application of empirical and model-based methods provides a comprehensive understanding of the potential of urban greenery to improve air quality. Full article

Modern culture, strongly influenced by the growth of sectors such as the fashion and textile industries, has generated an environmental trend that is difficult to reverse. It is estimated that between 60 and 70% of the dyes used in these sectors are synthetic, which offer great versatility, a low cost, and a broad spectrum of colors, making them indispensable in many sectors. Among these synthetic dyes, azo dyes stand out due to their excellent chromophoric properties, structural stability, and ease of synthesis. However, these compounds are considered xenobiotics with a strong recalcitrant potential. This review article comprehensively examines the biodegradation potential of azo contaminants by microorganisms, including bacteria, fungi, microalgae, and consortia, using the PRISMA 2020 methodology. In this regard, this study identified 720 peer-reviewed articles on this topic that are outstanding. The analysis of these studies focused on the effect of parameters such as pH, temperature, and exposure time, as well as the enzymatic degradation pathways associated with the degradation efficiency of these contaminants. For example, the results identified that microorganisms such as Meyerozyma guilliermondii, Trametes versicolor, Pichia kudriavzevi, Chlorella vulgaris, and Candida tropicalis possess significant potential for degrading azo dyes (up to 90%). This degradative efficiency was attributed to the high enzymatic activity that cleaves the azo bonds of these contaminants through specialized enzymes, such as azoreductases, laccases, and peroxidases. Furthermore, the results highlight synergistic effects or metabolic cooperation between species that enhance the biodegradation of these contaminants, suggesting an eco-friendly alternative for environmental remediation. Full article