Search Results (20,105)

Recirculating aquaculture systems (RASs) improve water-use efficiency in fish production but generate nutrient-rich effluents requiring management. Integrating aquatic biomass cultivation into RASs offers a promising approach to nutrient recovery, CO₂ utilization, and biomass production. This study evaluates the technical and economic feasibility of integrating Wolffia globosa cultivation with RASs through process simulation and techno-economic analysis (TEA). A pilot-scale system in Thailand was modeled using SuperPro Designer, comparing static and suspended aeration cultivation. The suspended configuration required only ~10–12 m² for 28.80 m³, whereas static cultivation required 131 m² for 32.80 m³, corresponding to about a 12-fold reduction in land area. The suspended system achieved higher annual biomass production (1056 kg dry weight (DW) yr⁻¹) than the static system (690 kg DW yr⁻¹), corresponding to CO₂ fixation of ~1.50 and ~0.98 t CO₂ yr⁻¹, respectively. The static system achieved higher nutrient removal efficiencies (97% N and 99.66% P), while the suspended system showed lower removal (64% N and 65.30% P) but higher productivity. Economic analysis confirmed feasibility, with the suspended system achieving higher return on investment (17.56% vs. 12.89%) and a shorter payback period (5.70 vs. 7.76 years). These results demonstrate the potential of RAS–Wolffia integration as a circular approach for resource recovery and sustainable aquaculture. Full article

The strain on the global food sector to reconcile environmental sustainability with operational efficiency has been intensifying. In a growing economy, this study investigates the revolutionary potential of integrated digital ecosystems that include blockchain, big data analytics, and IoT-enabled real-time monitoring on the performance of Green Supply Chain Management (GSCM). The research, that relies on the Technology–Organization–Environment (TOE) framework, utilizes a rigorous mixed-methods approach which utilizes Fuzzy-Set Qualitative Comparative Analysis (fsQCA) and Structural Equation Modeling (SEM) on data from food-processing firms in Pakistan. Green innovation is an important moderating catalyst, and SEM results confirm that digital integration significantly enhances waste reduction and energy efficiency, explaining 62% of performance variance. A further configurational analysis indicates causal equifinality and reveals 3 distinct paths to superior sustainability, from “Innovation-Driven Institutionalization” to “Government-Supported Scaling.” It demonstrates that various combinations of external support and internal readiness may ultimately contribute to success. The findings are supported by post-implementation evaluations, which show a 29% decrease in energy consumption and a 55% reduction in cold-chain losses. These findings offer novel insights for practitioners and policymakers, validating that environmental stewardship and commercial profitability are mutually reinforcing objectives in the digital age. Full article

To address the poor adaptability and rigid initiation modes of the loitering munition fuze in complex environments and the inadequacy of single fuzzy control against strong interference, this paper proposes a dual-loop adaptive reconfiguration control method. The architecture integrates the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm with fuzzy logic. The inner loop uses TD3 to dynamically optimize fuzzy scaling factors based on real-time interference and state deviations. Concurrently, the outer loop utilizes a Fuze Readiness Index (FRI) and a finite state machine to manage real-time multi-modal mission switching (e.g., proximity, delay, and airburst) and reverse safety-state conversions. Co-simulations under non-stationary composite interference show that the proposed method reduces the burst height RMSE by 82.4% and 61.6% compared with the fixed-threshold and standard fuzzy baselines under the considered non-stationary composite interference setting, respectively. The false alarm rate (FAR) is reduced to 0.15%, and the reconfiguration response time under sudden interference is shortened to 12 ms. Even under extreme conditions, such as 400 ms sensor signal loss, the relative error remains within 5%. These simulation results demonstrate the potential of the proposed architecture to improve precision, responsiveness, and robustness under dynamic interference conditions and show good robustness to intermittent observation loss within the simulated operating envelope. Full article

Climate change poses an escalating threat to global water resources, with semi-arid regions such as Morocco being particularly vulnerable due to high climatic variability and limited adaptive capacity. In these regions, including the Tassaoute watershed in central Morocco, data scarcity and uncertainties related to data availability and quality frequently hinder robust assessments of climate change impacts. Recent advances in data science and remote sensing offer promising alternatives to overcome these limitations. This study investigates the potential of the PERSIANN-CDR satellite-derived precipitation product for assessing climate change impacts on water resources. The capability of PERSIANN-CDR to reproduce observed precipitation patterns and associated hydrological responses is evaluated through a comparative analysis using observed precipitation data. Results indicate that PERSIANN-CDR generally underestimates peak precipitation events and total rainfall amounts compared to in situ observations. Runoff is simulated using two hydrological models: GR2M (Génie Rural 2 parameters Mensuel) and the Thornthwaite water balance method, both driven by observed meteorological data and PERSIANN-CDR precipitation. The future water availability was assessed using 5 climate models, under two scenarios: RCP4.5 and RCP8.5 for the periods 2030–2060 and 2061–2090. Results show a marked temperature increase of 2–3 °C across all models, accompanied by a general decline in precipitation ranging from −30% to −60% under RCP4.5 and −20% to −80% under RCP8.5. These climatic changes translate into substantial reductions in runoff, with stronger decreases projected under the high-emission scenario and during the dry season. Monthly analyses reveal pronounced seasonal contrasts, highlighting the increased sensitivity of low-flow periods to climate forcing. Overall, runoff is projected to decrease by 50–90%, with model and data-source differences highlighting the importance of multi-model and satellite-derived approaches in data-sparse regions. These results emphasize the utility of satellite precipitation datasets in guiding climate-adaptive water management strategies. Full article

Smart power management practices are needed for a sustainable EV charging infrastructure due to the fast use of renewable energy resources. An innovative power management structure for a small grid-connected solar PV system-based AC and DC charging station, combined with a backup purpose battery energy system (BESS), is demonstrated in this paper’s study. The sustainability transition is associated with integrating renewable energy resources with a battery storage system, providing a helpful solution for managing large power-demanding entities (EV, microgrid, etc.). In this study, a solar PV system takes 500 datasets (based on data availability or to prevent overfitting) of PV voltage, solar irradiance, and air temperature, and the performance of controlling for the maximum power point tracker by training these datasets using Levenberg–Marquardt (LM), which was implemented in the ANN toolbox and created this technique in MATLAB 2016 or Simulink. Also, using this technique for the estimation and forecasting of the datasets of solar PV systems and EVs obtains better results for achieving further targets. To enhance decision-making capability through optimized technique, we have to find it before forecasting PV power generation and EV datasets throughout the day (24 h). The optimized power flows among solar PV power generation, EV charging demand (including AC charging and DC fast charging), the BESS, and the utility/small grid under several priority operating scenarios. A famous technique for optimization, mixed-integer linear programming (MILP), is applied. In this technique, the objective function is used for the solution of problem formation and compliance with system constraints such as the power balancing equation, charging/discharging limits, SOC limits, and grid export/import exchange limits: basically, equality, inequality, and bounds limits. Optimized results show that the coordinated power flow operations are consented to by EV users, by prioritizing some key points, such as solar PV use at the maximum, reducing the grid power dependency, and the first power flow towards EV charging demand. The verified MILP-based solutions boost the maximum utilization of renewable energy resources, feasible EV charging demand, and scaling power flow among these entities. The key contribution of this study is suitable for different powered EV charging stations based on both AC and DC, with different ratings of EVs (including fast and slow charging). Most solar PV-based generation supports the EVCS and backup for ranking-wise BESS, and grid support for the EVCS. Also, the key contribution of hybrid techniques in this article is divided into two stages: in the first stage, an artificial neural network (ANN) is utilized for estimating the PV voltage at the maximum point and forecasting, while in the second stage, mixed-integer linear programming (MILP) employs optimal power management. Full article

Global climate change is rapid and poses an alarming threat to agricultural production, significantly impacting economies. Modern agriculture has strongly emphasized improving nutrient availability in crops to address rising malnutrition and contribute to global food security. However, abiotic stresses, including warmer temperatures, drought, waterlogging stress, and elevated CO_2, have critical direct and indirect effects on nutrient availability in plants. This systematic review was conducted in accordance with the PRISMA guidelines. The literature survey followed a time period of 2–5 months, during which the conceptualization, analysis, writing, and editing of the article were conducted. In the present era, it is essential to adopt measures to improve the nutritional value [enhance Nutrient Use Efficiency (NUE)] and nutrient management of plant-based foods. Plant-associated microbiomes have co-evolved with their plant counterparts and perform essential functions in nutrient acquisition, including microbial sensing and cross-talk with the plant host, nutrient uptake and sharing, and signaling mechanisms. In natural and agricultural ecosystems, plant microbiomes offer major opportunities and can be harnessed to sustainably supply essential plant nutrients and improve NUE in crops of global importance. Crop-associated microbiomes can be precisely tailored to achieve targeted outcomes, enhancing nutrient acquisition and utilization via microbiome engineering. However, bridging knowledge gaps, understanding microbial colonization, plant–microbiome dynamics, and adopting precise editing approaches are crucial to boost sustainable outcomes and crop productivity. By elucidating plant microbiome crosstalk and microbe–microbe signaling, a better understanding of microbe-mediated nutrient acquisition in plants can be achieved, defining key implications in global food security. Full article

This study applies Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to investigate surface deformation dynamics in the hyper-arid Eastern Province of Saudi Arabia, with emphasis on quantifying sand dune migration and identifying areas susceptible to wind erosion. Utilizing Sentinel-1 SAR data and the MintPy toolbox, ground deformation was quantified with millimeter-scale precision. Results reveal significant subsidence, up to 15 cm/year in landfills, linked to waste compaction and groundwater depletion. Localized uplift of ~4 cm/year on northern peripheries is directly attributed to aeolian sand accumulation from seasonal Shamal winds, providing quantitative evidence of dune migration. While direct measurement of wind erosion (net deflation) remains challenging due to the dominance of depositional signals and the spatial heterogeneity of erosion processes, areas of potential erosion are inferred from negative displacement patterns outside landfill zones and from coherence characteristics indicative of surface instability. The integration of SBAS-InSAR with GPS and ERA5 wind reanalysis resolves the combined influence of aeolian deposition, hydrogeological changes, and anthropogenic activity, offering insights into both components of aeolian dynamics and a replicable model for sustainable land management in arid environments. Full article

Against the backdrop of accelerating low-carbon transformation in the global energy system and decarbonization in the transportation sector, the widespread adoption of electric vehicles has intensified grid load imbalances and highlighted challenges in integrating intermittent renewable energy generation. Vehicle-to-Grid (V2G) technology has emerged as a key solution to these challenges. This paper systematically traces the global evolution of V2G technology from conceptualization to large-scale deployment, focusing on localized practices in China’s scaled V2G applications. It dissects the logic behind policy evolution, identifies three distinct Chinese V2G models—centralized, distributed, and battery-swapping—and validates the practical outcomes of representative pilot projects. Research reveals three core constraints hindering China’s large-scale V2G adoption: the absence of battery capacity degradation management mechanisms, fragmented standardization systems, and rigid market mechanisms. Based on this, the paper proposes recommendations for scaling V2G in China across three dimensions: power battery second-life utilization, standardization system construction, and market mechanism optimization. Furthermore, aligning with the global demand for large-scale V2G implementation, this paper proactively proposes innovative market models. These include establishing a coordinated trading mechanism between green power and V2G, developing a digitally driven distributed trust and transaction system, and exploring financialization and risk hedging models for battery assets. These concepts provide theoretical foundations and decision-making references for achieving high-quality, large-scale V2G applications worldwide. Full article

Underground utilities form an essential part of urban infrastructure, yet their importance often becomes apparent only when service disruptions occur. Excavation activities for maintenance, relocation, or new construction carry considerable risks, including utility strikes, project delays, worker injuries, and even fatalities. These risks are largely driven by incomplete or inaccurate information about the location, depth, or material properties of buried utilities. To address this challenge, this study proposes a comprehensive Building Information Modeling (BIM)-based framework for managing underground utilities throughout their lifecycles. The framework is structured into five key stages: data acquisition, data processing, modeling, system application, and data updating. A highway project was used as a case study to validate the proposed approach. The study involved the integrated modeling and visualization of the highway corridor, underground gas pipelines, and overground high-voltage transmission pylons using Autodesk Civil 3D, InfraWorks, and Navisworks. The developed model and workflow were subsequently reviewed with the client department. Application of the framework to a 5 km highway corridor identified five utility-road conflict points (three subsurface gas pipeline intersections and two overground pylon encroachments) that were not detectable from existing 2D records. Expert review by the client department confirmed that the BIM-based visualization and 4D simulation improved construction planning clarity and supported proactive utility relocation decisions. By simplifying information workflows and enabling collaboration among stakeholders, the proposed framework demonstrates strong potential to improve excavation safety, enhance decision-making, and support the wider adoption of BIM for underground utility management. Full article

The release of heavy metals into the environment due to human activities is increasing, and this has led to concern about heavy-metal contamination on farmland. Prior studies have primarily focused on short-term investigations or specific pollution sources, lacking systematic monitoring of cadmium’s long-term input-output fluxes and their mass balance at the scale of a complete farmland ecosystem. This study clarified the cadmium (Cd) pollution trends for a typical paddy system in southern China. A six-year long-term monitoring study (2019–2024 inclusive) of a Cd-contaminated paddy system in Ningxiang City, Hunan Province, China, was conducted. The Cd flux dynamics for three input pathways (atmospheric deposition, irrigation water, and fertilizer) and three output pathways (crop harvesting, surface runoff, and subsurface infiltration) were investigated. The results showed that atmospheric deposition is the primary source of Cd input, accounting for 76% of total inputs, and leads to persistent net accumulation of soil Cd. Straw removal serves as the dominant output mechanism, facilitating substantial Cd removal, representing 77% of total Cd exports, while straw retention significantly reduces export fluxes. The study found that the net Cd fluxes from 2019 to 2024 were 1.994, 2.624, 8.984, 11.299, 9.944, and 20.162 g·(hm²·a)⁻¹, straw removal was primarily adopted during the period. A net flux analysis showed that progressive soil Cd accumulation had occurred over the study period. The results suggest that science-based straw management is critical when attempting to mitigate soil Cd pollution and enhance safe land utilization. These findings can be used to improve region-specific pollutant source control strategies and soil management policies. Full article

Optogenetic modulation employs light-sensitive proteins known as opsins to regulate cellular activity. A unique therapeutic application of this technique involves modulating pain perception by selectively targeting neural pathways within the spinal cord. Multi-Characteristic Opsin (MCO) represents an innovative optogenetic actuator capable of activation across a broad spectrum of light wavelengths, exhibiting a slow depolarizing phase that resembles natural photoreceptors. This study examines the current advancements in spinal optogenetic modulation utilizing MCO for pain management. Due to its high sensitivity, MCO facilitates minimally invasive, remotely controlled optogenetic modulation of spinal neurons. This approach enables the regulation of extensive spatial regions, provided the MCO channel receives sufficient light intensity to surpass the activation threshold. Nano-enhanced optical delivery (NOD) successfully transfected spinal neurons with the GAD67-MCO2-mCherry construct, as confirmed by membrane-localized mCherry fluorescence with DAPI-labeled nuclei. Using this platform, 5 Hz spinal optogenetic stimulation produced a significant reduction in formalin-evoked pain behaviors, demonstrating frequency-specific modulation of spinal pain circuits. Neither 2 Hz nor 10 Hz stimulation yielded comparable analgesic effects, underscoring the importance of precise stimulation parameters. The therapeutic impact also depended on transfection efficiency: reducing the fGNR–plasmid concentration diminished MCO expression and weakened the analgesic response. Together, these results show that effective spinal optogenetic pain modulation requires both optimal stimulation frequency and robust gene delivery. Full article

Background: Thoracentesis is pivotal in managing pleural effusion (PE), particularly in invasive mechanical ventilation (IMV), with documented improvements in respiratory mechanics, oxygenation, and hemodynamic parameters. However, its efficacy may vary based on effusion type and drained volume. Methods: A retrospective longitudinal study was conducted at a high-complexity care center in Cali, Colombia (2019–2024), including 93 (IMV) patients who underwent therapeutic thoracentesis (TT). Respiratory and hemodynamic parameters were assessed before and up to 24 h post-procedure. Stratified analysis was performed by drained volume, fluid type, and left ventricular ejection fraction (LVEF). Results: TT yielded significant improvements in fraction of inspired oxygen (FiO₂) (−4%), positive end expiratory pressure (PEEP) (−0.5 cmH₂O), and Oxygen arterial Pressure Index/Inspired Oxygen Fraction (PaO₂/FiO₂-ratio) (+27.1), with greater impact for volumes ≥500 mL and transudative PE. Patients with LVEF ≤ 40% showed increased mean arterial pressure (MAP) and PaO₂. Complication rates were low (<4%). Conclusions: TT is safe and effective in critically ill IMV patients, particularly for transudative PE and drained volumes ≥500 mL, as well as in subjects with LVEF ≤ 40%. Its positive impact on oxygenation and ventilation supports its therapeutic utility in critical care. Full article

The accurate anticipation of passenger demand across specific origin–destination (OD) airport routes is a cornerstone of strategic and operational decision-making within the global aviation sector, including airlines optimizing fleet and route management, airports planning infrastructure development, and regulatory bodies overseeing airspace efficiency. However, conventional forecasting techniques frequently encounter limitations when confronted with the inherent complexities and non-linear interdependencies that characterize air travel demand patterns. These patterns are shaped by an array of dynamic variables, including macroeconomic trends, population dynamics, distinct seasonal variations, and emergent phenomena. This investigation evaluates the utility of Artificial Intelligence (AI) paradigms in constructing predictive models for monthly passenger volumes between international OD airport pairs. This work highlights the ongoing transformative impact of AI methodologies on forecasting tasks within the aviation industry. Full article

Ground-level ozone (GLO₃) poses a critical threat to public health and the success of the Saudi Green Initiative, yet its long-term spatiotemporal evolution across the Arabian Peninsula remains poorly constrained. Utilizing CAMS-derived mixing ratios (1000–850 hPa) from 2003 to 2024, this study identifies a major systemic regime shift occurring in 2016–2017, marking a transition toward a more O₃-enriched atmospheric state across Saudi Arabia. While the early study period was characterized by pronounced spatial heterogeneity, post-2017 diagnostics reveal a synchronized intensification of GLO₃, particularly within the urban industrial belts of the Eastern and Western Provinces. Statistical trend metrics, including Mann–Kendall and regime-shift detection, show a persistent upward trend in GLO₃ concentrations, most significantly during winter and over the southwestern highlands. These trends are robustly coupled with increasing boundary-layer height, temperature, and UV-B radiation, alongside shifting precursor stoichiometry (CO, VOCs, NO_x) that separates titration-dominated from production-dominated regimes. Our results suggest that this mid-decade intensification reflects a convergence of anthropogenic forcing under Saudi Vision 2030 and shifting regional climatic drivers. By uncovering the transition from localized variability to kingdom-wide synchronization, this research provides a process-based foundation for targeted air quality management and the safeguarding of regional sustainability frameworks. Full article

Alternanthera philoxeroides is a highly invasive alien species in China that causes waterway blockages, agricultural yield loss, biodiversity decline, and ecosystem degradation. This study assessed the invasion risk and environmental drivers of A. philoxeroides in Guizhou Province, a karst mountainous region in Southwest China. Occurrence records were obtained from field surveys and the Chinese Virtual Herbarium. The genetic algorithm for rule-set production (GARP) model and the jackknife method were employed to identify 13 key environmental indicators for predicting invasion risk. The global invasion risk index (GIRI) was applied to quantify the overall invasion risk. Additionally, the Geodetector model was utilized to analyze the spatially differentiated effects of six environmental factors. The results showed that A. philoxeroides poses a high invasion risk in Guizhou Province, and the invasion risk in the Yangtze River Basin within Guizhou is higher than that in the Pearl River Basin. The environmental factors influencing invasion risk, in order of impact, were slope, elevation, land use, river density, rocky desertification, and soil pH. Moreover, interactions among these factors further amplify the invasion risk. These findings provide valuable insights for developing targeted management strategies for A. philoxeroides in karst mountainous regions and support biodiversity preservation and regional ecological sustainability. Full article