Search Results (32,436)

With the extensive integration of renewable energy sources (RESs), power systems face challenges in load frequency control (LFC) due to RES intermittency. While energy storage systems (ESSs) aid frequency regulation, existing strategies are limited—single-type ESSs fail in multi-ESS scenarios, and hybrid ESSs lack state-of-charge (SoC) consistency control. This paper proposes an LFC framework combining energy storage aggregators (ESAs), leader–follower finite-time consensus control, and DDPG-RNN (Deep Deterministic Policy Gradient with Recurrent Neural Networks). ESAs aggregate small distributed ESSs for scalable regulation; consensus control ensures finite-time ESS power tracking and SoC balancing; and DDPG-RNN adaptively tunes control gains to handle RES fluctuations and load changes. Simulations on a high-RES power system with hybrid ESSs (SCES, LABES, VRFBES, LIPBES) show that the framework outperforms traditional proportional–integral–derivative (PID) control and basic leader–follower control: it reduces frequency deviation peaks, shortens recovery time, achieves SoC synchronization, and alleviates conventional generator power fluctuations. Full article

This study examines how tourism development in Tunisia responds to environmental degradation, renewable energy consumption, technological innovation, and foreign direct investment. Using annual data for 1990–2023, we apply the Autoregressive Distributed Lag (ARDL) bounds approach to identify long-run equilibria and short-run dynamics and validate the results with Fully Modified Ordinary Least Squares (FMOLS). The bounds tests confirm stable long-run relationships among tourism development and its structural determinants—environmental degradation, renewable energy, technological innovation, and foreign direct investment. The empirical results show that environmental degradation depresses tourism development in the long run, whereas renewable energy and technological innovation promote it. Foreign direct investment provides the strongest positive contribution. Complimentary Granger causality tests confirm unidirectional causality from environmental degradation, renewable energy, and technological innovation to tourism development, and bidirectional causality between tourism and foreign direct investment, validating the robustness and direction of influences among variables. Short-run effects appear weaker and occasionally mixed; however, the negative and highly significant error-correction term indicates convergence toward equilibrium. The FMOLS estimates closely match the ARDL results, providing further confidence in the results. Accordingly, policymakers should bolster environmental management, increase renewable energy as part of tourism infrastructure, advance digital and eco-innovation, and attract FDI in cleaner technologies and higher standards of services. This study fills conceptual and regional evidence gaps by integrating environmental, technological, and financial dimensions within a unified framework. It offers practical guidance consistent with the Sustainable Development Goals; specifically, Goals 7 (clean energy), 8 (sustainable growth and jobs), and 13 (climate action). Full article

Panax notoginseng is a valuable medicinal herb, but its sustainable production is constrained by long cultivation cycles and continuous cropping obstacles. Adventitious root culture presents a viable alternative. This study establishes a robust and sustainable platform for the efficient production of ginsenosides in P. notoginseng adventitious root cultures. We first systematically optimized the culture system, identifying leaf segments as the optimal explant due to their high callus induction rate (95.77%), low contamination, and renewable nature. Combined with optimized bioprocess parameters (3 g/L inoculation density, 50 g/L sucrose), this strategy addressed key practical bottlenecks. Beyond methodological advancement, our research provides novel mechanistic insights into the action of methyl jasmonate (MeJA). A key finding is the discovery that MeJA functions as a ‘precision metabolic switch,’ differentially regulating a critical branch point in the saponin pathway. It coordinately upregulates the protopanaxadiol (PPD)-type gene CYP716A47 while downregulating the protopanaxatriol (PPT)-type gene CYP716A53v2 that genetically explains the directed enrichment of specific ginsenosides. This integrated approach not only advances the fundamental understanding of elicitor action but also provides a scalable and controllable system for the industrial production of P. notoginseng phytopharmaceuticals with tailored saponin profiles. Full article

Conventionally, tomography is an inspection technique in which tomographic images are intended for human perception and interpretation. In this work, we shift this paradigm by transforming tomography into an autonomous estimator of industrial reactor states, enabling fully automated process control. Alcoholic fermentation was employed as an example of a controlled process in the current study. The work presents an original concept utilizing transfer learning in conjunction with a ResNet-type artificial neural network, which converts electrical measurements into a sequence of values correlated with the conductivity of pixels constituting the cross-section of the examined biochemical reactor. The conductivity vector is transformed into a parameter determining substrate concentration, enabling dynamic process regulation in response to signals generated from EIT (Electrical Impedance Tomography). Within the scope of the described research, calibration of the conductivity vector against substrate concentrations was performed, and a Matlab/Simulink-based dynamic Monod kinetics model was developed. The obtained results demonstrate high accuracy in substrate concentration estimation relative to reference values throughout a forty-six-hour process. The same signals enable energy-efficient process control, in which cooling and mixing intensity are regulated according to energy prices and renewable energy availability. This strategy may possess particular application in facilities where fermentation installations are co-located with bioenergy production units. Full article

The multifunctional role of forests in supplying renewable biomaterials and delivering ecosystem services (ESs) is often overlooked in standard life cycle assessment (LCA) methodologies, despite its relevance for sustainable construction. This study developed the BioCons Impact Compensation Model (ICM), which integrates ES into life cycle inventory (LCI) databases and quantifies proprietary BioCons Mitigation Indicators, capturing additional environmental information, ensuring transparency, and preventing greenwashing. Using structural Scots pine in Spain as a case study, the GWP_-luluc-roots indicator was found to be 226.84 kg CO_2-eq/FU, representing 36% of the biogenic carbon (616.45 kg CO_2-eq/FU), highlighting the contribution of root-derived carbon to long-term soil carbon storage. The BioCons Mitigation Indicators demonstrate that mitigation generally exceeds environmental impacts, except for HTP-nc-inorganics, with surplus ES available as biocredits to offset emissions in other life cycle stages. Integrating these indicators into environmental product declarations (EPDs) provides a transparent and accurate view of environmental performance. The results validate the hypothesis that forest bio-based construction products (FBCPs) act as carriers of ESs embedded in derived products, supporting more comprehensive and robust sustainability assessments. Full article

Current power systems are facing noticeable power quality (PQ) performance deterioration, which has been attributed to nonlinear loads, distributed generation, and extensive renewable energy infiltration (REI). These conditions cause voltage sags, harmonic distortion, flicker, and disadvantageous power factors. The traditional PI/PID-based scheme of control, when applied to Flexible AC Transmission Systems (FACTSs), demonstrates low adaptability and low anticipatory functions, which are required to operate a grid in real-time and dynamic conditions. Artificial Intelligence (AI) opens proactive, reactive, or adaptive and self-optimizing control schemes, which reformulate FACTS to thoughtful, data-intensive power-system objects. This literature review systematically studies the convergence of AI and FACTS technology, with an emphasis on how AI can improve voltage stability, harmonic control, flicker control, and reactive power control in the grid formation of various types of grids. A new classification is proposed for the identification of AI methodologies, including deep learning, reinforcement learning, fuzzy logic, and graph neural networks, according to specific FQ goals and FACTS device categories. This study quantitatively compares AI-enhanced and traditional controllers and uses key performance indicators such as response time, total harmonic distortion (THD), precision of voltage regulation, and reactive power compensation effectiveness. In addition, the analysis discusses the main implementation obstacles, such as data shortages, computational time, readability, and regulatory limitations, and suggests mitigation measures for these issues. The conclusion outlines a clear future research direction towards physics-informed neural networks, federated learning, which facilitates decentralized control, digital twins, which facilitate real-time validation, and multi-agent reinforcement learning, which facilitates coordinated operation. Through the current research synthesis, this study provides researchers, engineers, and system planners with actionable information to create a next-generation AI-FACTS framework that can support resilient and high-quality power delivery. Full article

Conventional plastics are widely utilised across industrial sectors and in consumer products. However, the growing use of plastics has led to plastic pollution, including the formation of nanoplastics (NPs), which are harmful to aquatic organisms. Bioplastics are emerging alternatives. They are renewable and/or biodegradable and are supposed to be more environmentally friendly. However, the toxicity and environmental fate of bioplastics are not yet fully understood. This study evaluated the toxicity and fate of commercially available plain or fluorescent polylactic acid (PLA) NPs (250 nm) on aquatic organisms. Confocal microscopy demonstrated the uptake of fluorescent PLA NPs by the test organisms, marine rotifers (Brachionus plicatilis), brine shrimps (Artemia salina) and zebrafish (Danio rerio) embryos. However, the results of the bioassays indicate that plain PLA NPs did not induce acute toxicity in either of the two zooplankton species and did not cause substantial mortality, malformations, or hatching delays in zebrafish embryos at the tested concentrations (up to 100 mg/L). However, brine shrimp showed a significant decrease in ingestion capability. The biochemical biomarkers, catalase activity induction, as an indicator of oxidative stress, and acetylcholinesterase inhibition, as a marker of neurotoxicity, showed no significant alterations compared to the control of both zooplankton species and that of zebrafish embryos. Overall, the findings suggest a pattern of no acute and low sublethal toxicity for the tested plain PLA NPs in the studied organisms. Nonetheless, further research is imperative to comprehensively assess the environmental fate of bioplastics found in various consumer products, as these may contain harmful chemical additives, as well as the effects of prolonged exposure and their impact on physiological parameters, ensuring informed decisions before their widespread commercialisation and presence in the environment. Full article

The targeting of environmentally conscious German car drivers through product aesthetics can foster the acceptance of sustainable cars. No guidelines are currently available to designers to create product personality based on environmentally friendly design cues (EFDCs). The aim of this paper was to explore EFDCs for car interiors through bio-based materials. To address this topic, Study 1 examined a collection of bio-based material samples and samples featuring specific colors and reactions to light to determine their potential for the creation of an environmentally friendly product personality. Study 2 built on the implications of the former to examine the contribution of blue as a color and glowing attribute on the development of EFDCs. Wood veneer, cork, and cotton fabric were perceived as sustainable, natural, and renewable by most of the subjects. Brown and white leather was also perceived as sustainable. Moreover, the perception of the naturalness of materials in direct contact with blue light was reduced. Visual texture features for EFDC design are visible fibers or a wooden look. Haptic features include soft, warm, and rough surfaces, while glare, shimmer, a shiny surface, and smoothness should be avoided. The color brown should be considered, while blue, green, and yellow should be avoided. Full article

Accurate short-term wind speed forecasting is essential for the sustainable operation and planning of coastal wind farms. This study develops an improved BP-PSO hybrid model that integrates particle-swarm optimization, time-ordered walk-forward validation, and uncertainty quantification through block-bootstrap confidence intervals and Monte-Carlo dropout prediction intervals. Using multi-year and seasonal datasets from four coastal stations in China—from Bohai Bay (LHT, XCS, ZFD) to Zhejiang Province (SSN)—the proposed model achieves high predictive accuracy, with RMSE values between 1.09 and 1.54 m/s, MAE between 0.79 and 1.10 m/s, and R² exceeding 0.70 at most sites. The multi-year configuration provides the most stable and robust results, while autumn at ZFD yields the highest errors due to intensified turbulence. XCS and SSN exhibit the most consistent performance, confirming the model’s spatial adaptability across distinct climatic regions. Compared with the ARIMA and persistence baselines, BP-PSO reduces RMSE by over 50%, demonstrating improved efficiency and generalization. These results highlight the potential of intelligent data-driven forecasting frameworks to enhance renewable energy reliability and sustainability by enabling more accurate wind-power scheduling, grid stability, and coastal energy system resilience. Full article

This study develops an interpretable and calibrated XGBoost framework for probabilistic slope stability assessment using a 627-case database of circular-mode failures. Six predictors, namely, unit weight (γ), cohesion (c), friction angle (φ), slope angle (β), slope height (H), and pore-pressure ratio (r_u), were used to train a gradient-boosted tree model optimized through Bayesian hyperparameter search with five-fold stratified cross-validation. Physically based monotone constraints ensured that failure probability (P_f) decreases as c and φ increase and increases with β, H, and r_u. The final model achieved strong performance (AUC = 0.88, Accuracy = 0.80, MCC = 0.61) and reliable calibration, confirmed by a Brier score of 0.14 and ECE/MCE of 0.10/0.19. A 1000-iteration bootstrap quantified both epistemic and aleatoric uncertainties, providing 95% confidence bands for P_f-feature curves. SHAP analysis validated physically consistent influence rankings (φ > H ≈ c > β > γ > r_u). Predicted probabilities were classified into Low (P_f < 0.01), Medium (0.01 ≤ P_f ≤ 0.10), and High (P_f > 0.10) risk levels according to geotechnical reliability practices. The proposed framework integrates calibration, uncertainty quantification, and interpretability into a comprehensive, auditable workflow, supporting transparent and risk-informed slope management for infrastructure, mining, and renewable energy projects. Full article

The use of semi-transparent photovoltaic (Solar PV) glass in buildings is an effective strategy for integrating renewable energy generation, solar control, and thermal comfort. However, conventional simulation models rely on global optical properties, neglecting spectral radiation and its propagation within the material. This limits the accurate assessment of thermal comfort, light distribution, and performance in complex systems such as multi-layer glazing. This study presents the development, implementation, and experimental validation of a numerical model that reproduces the thermal, electrical, and optical behaviour of semi-transparent Solar PV glass, explicitly incorporating radiative transfer. The model simultaneously solves the conduction, convection, and electrical generation equations together with the radiative transfer equation, solved via the finite volume method across two spectral bands. The refractive index and extinction coefficient, derived from manufacturer-provided optical data, were used as inputs. Experimental validation employed 10% semi-transparent a-Si glass, comparing surface temperatures and electrical power generation. The model achieved average relative errors of 3.8% for temperature and 3.3% for electrical power. Comparisons with representative literature models yielded errors between 6% and 21%. Additionally, the proposed model estimated a solar factor of 0.32, closely matching the manufacturer’s 0.29. Full article

Symbiosis and synergy among urban uses are key determinants of spatial quality, liveability, and resilience. While symbiosis denotes the coexistence of users and functions within specific places, synergy refers to the collective benefits emerging from their interaction. These dynamics are especially relevant in city centres and main streets, which serve as structural and social backbones of urban life. This article applies the SyM_SyN Method to Zwycięstwa Street in Gliwice, Poland, to assess the intensity and distribution of symbiotic and synergistic relations. The analysis identified significant spatial deficiencies that weaken the coherence and attractiveness of the street. The results demonstrate how a systematic, data-driven evaluation can expose hidden weaknesses in urban structures. Importantly, from the perspective of the smart city paradigm, liveability and responsiveness of urban spaces cannot be reduced to technology-driven systems of sensors and devices. They must also be understood in terms of human-scale interactions and the ability of urban form to support them. Beyond its methodological contribution, the study emphasises the practical implications for urban renewal: reinforcing positive interactions between adjacent uses enhances street vitality, improves social inclusiveness, and supports more sustainable development strategies. The SyM_SyN Method thus provides both an analytical framework and a decision-support tool for designing user-oriented, high-quality urban spaces within the broader smart and sustainable city paradigm. Full article

The growing demand for renewable energy requires efficient technologies to maximize solar resource utilization. This study presents the development and validation of a novel dual-axis solar tracking system that integrates kinematic modeling, embedded control, and a monocular vision algorithm. Unlike fixed photovoltaic systems, the proposed design dynamically aligns solar panels with the sun’s position using a Denavit–Hartenberg-based model and real-time image analysis. The system was experimentally validated in the Isthmus of Tehuantepec, Mexico, a high-irradiance region. Results showed reliable sensor calibration with errors below 3%, and an 18% increase in energy capture compared to a fixed panel system. The prototype achieved a maximum output of 800 W using four 205 Wp modules. This work contributes an innovative, replicable approach to enhance solar energy harvesting under real operating conditions. Full article

Due to the large emissions of greenhouse gases from the burning of fossil fuels and people’s demand for green materials and energy, the development of environmentally friendly triboelectric nanogenerators (TENGs) is becoming increasingly significant. Silk fibroin (SF) is considered an ideal biopolymer candidate for fabricating green TENGs due to its biodegradability and renewability. However, its intrinsic brittleness and relatively weak triboelectric performance severely limit its practical applications. In this study, SF was physically blended with poly(ethylenimine) (PEI), a polymer rich in amino groups, to fabricate SF/PEI composite films. The resulting films were employed as tribopositive layers and paired with a poly(tetrafluoroethylene) (PTFE) tribonegative layer to assemble high-performance TENGs. Experimental results revealed that the incorporation of PEI markedly enhanced the flexibility and electron-donating capability of composite films. By optimizing the material composition, the SF/PEI-based TENG achieved an open-circuit voltage as high as 275 V and a short-circuit current of 850 nA, with a maximum output power density of 13.68 μW/cm². Application tests demonstrated that the device could serve as an efficient self-powered energy source, capable of lighting up 66 LEDs effortlessly through simple hand tapping and driving small electronic components such as timers. In addition, the device can function as a highly sensitive self-powered sensor, capable of generating rapid and distinguishable electrical responses to various human motions. This work not only provides an effective strategy to overcome the intrinsic limitations of SF-based materials but also opens up new avenues for the development of high-performance and environmentally friendly technologies for energy harvesting and sensing. Full article

As urban boundaries continue to expand and core city areas undergo optimization, megacities such as New York, London, Beijing, Shanghai, and Guangzhou exert a siphon effect on surrounding regions, intensifying population concentration and land demand. However, the imperative for coordinated production-living-ecological space development has placed limits on uncontrolled urban sprawl, highlighting the need for connotative, high-quality urban growth. Recent initiatives in urban village renewal and regeneration aim to enhance land-use efficiency but face persistent challenges—including preserving indigenous settlements and cultural heritage, while creating livable and friendly communities within high-density contexts. Utilizing a mixed-methods approach—combining bibliometrics analysis, questionnaire surveys, and enterprise interviews—this research investigates core challenges to urban renewal. Results indicate that multi-party collaborative governance integrating policy innovation, cultural preservation, human-centered planning, smart technologies, and sustainable development is essential for advancing “people-industry-city integration” in renewal models. Full article