Search Results (9,379)

Artificial Intelligence (AI) is a key technological enabler for the transition of agricultural production and management from experience-driven to data-driven, continuously advancing modern agriculture toward smart agriculture. This evolution ultimately aims to achieve a precise agricultural production model characterized by low resource consumption, high safety, high quality, high yield, and stable, sustainable development. Although machine learning, deep learning, computer vision, Internet of Things, and other AI technologies have made significant progress in numerous agricultural production applications, most studies focus on singular agricultural scenarios or specific AI algorithm research, such as object detection, navigation, agricultural machinery maintenance, and food safety, resulting in relatively limited coverage. To comprehensively elucidate the applications of AI in agriculture and provide a valuable reference for practitioners and policymakers, this paper reviews relevant research by investigating the entire agricultural production process—including planting, management, and harvesting—covering application scenarios such as seed selection during the cultivation phase, pest and disease identification and intelligent management during the growth phase, and agricultural product grading during the harvest phase, as well as agricultural machinery and devices like fault diagnosis and predictive maintenance of agricultural equipment, agricultural robots, and the agricultural Internet of Things. It first analyzes the fundamental principles and potential advantages of typical AI technologies, followed by a systematic and in-depth review of the latest progress in applying these core technologies to smart agriculture. The challenges faced by existing technologies are also explored, such as the inherent limitations of AI models—including poor generalization capability, low interpretability, and insufficient real-time performance—as well as the complex agricultural operating environments that result in multi-source, heterogeneous, and low-quality, unevenly annotated data. Furthermore, future research directions are discussed, such as lightweight network models, transfer learning, embodied intelligent agricultural robots, multimodal perception technologies, and large language models for agriculture. The aim is to provide meaningful insights for both theoretical research and practical applications of AI technologies in agriculture. Full article

Under China’s diversified food supply strategy and the accelerated modernization of its fisheries sector, marine ranches have become vital food sources and production bases. Their digital–intelligent transformation now represents a key pathway to improve resource efficiency, ensure food security, and promote sustainable marine economic development. Adopting a qualitative research design, this study examines China’s marine ranches using the TOE framework and a systemic grounded theory approach to identify key elements and evolutionary logic of their digital–intelligent transformation from multi-source qualitative data. It constructs a three-stage evolutionary model comprising “Technology and Facility Capacity Building Phase–Digital Resource Integration and Application Deepening Phase–Multi-stakeholder Collaboration and Systemic Governance Phase,” revealing the dynamic coupling mechanism among technological progress, organizational change, and environmental adaptation. Results indicate that the digital–intelligent transformation of marine ranches represents a systemic transition from technology-driven to collaborative governance, characterized by platform-based collaboration, factor restructuring, and institutional linkage. Based on these findings, this study proposes tiered policy and practice recommendations emphasizing institutional guidance by governments, innovation investments by enterprises, and ecological support from third-party platforms. The research not only expands the application scope of the TOE framework but also provides an applicable theoretical framework and policy reference for digital governance and sustainable development in marine fisheries. Full article

Quantitative seismic characterization of transitional shale gas resources in the Da Ning–Ji Xian area, Ordos Basin, is severely hampered by complex coal-measure stratigraphy and rapid lithological variations. These challenges are critically exacerbated by severe signal attenuation from a thick loess overburden and multiple coal seams, which significantly degrades vertical resolution and undermines the reliability of quantitative interpretation. To surmount these obstacles, this study proposes an integrated, attenuation-centric inversion workflow that systematically rectifies attenuation effects as a foundational pre-conditioning step. The novelty of this study lies in establishing a systematic workflow where a data-driven, spatially variant Q-estimation is used as a crucial pre-conditioning step to guide a robust inverse Q-filtering, enabling a high-fidelity quantitative inversion for shale gas parameters in a geological setting with severe attenuation. The proposed workflow begins with a data-driven estimation of a spatially variant quality factor (Q) volume using the Local Centroid Frequency Shift (LCFS) method. This crucial Q-volume then guides a robust post-stack inverse Q-filtering process, engineered to restore high-frequency signal components and correct phase distortions, thereby substantially broadening the effective seismic bandwidth. With the seismic data now compensated for attenuation, high-resolution shale gas parameters, including Total Organic Carbon (TOC), are quantitatively derived through post-stack simultaneous inversion. Application of the workflow to field data yields an inverted volume characterized by improved structural clarity, sharply defined stratigraphic boundaries, and more robust lithological discrimination, highlighting its practical effectiveness. This attenuation-compensated inversion framework thus establishes a robust and transferable methodology for unlocking high-fidelity quantitative interpretation in geological settings previously deemed intractable due to severe seismic attenuation. Full article

The industrial motor systems account for 45% of global electricity consumption. A life cycle model is established to quantify the potential environmental benefits of typical adjustable permanent magnet drives (APMDs, 1250 kW) versus variable frequency drives (VFDs) in China. The model covers mining of metals, manufacturing, operation, and recycling phases of APMDs, incorporating empirical data from China’s 3232 coal-fired units. Four scenarios are set up: business-as-usual, moderate, aggressive, and full-retrofit. Key findings demonstrate that APMDs reduce operational energy consumption by 94.5% compared to VFDs through significantly declining frequency conversion losses and cooling requirements. The life cycle carbon emissions of APMDs (29.7 tonnes CO₂_eq) represent merely 5% of VFDs emissions (570 tonnes CO₂_eq), achieving a 95% reduction. Within APMDs’ footprint, recycling contributes a 45% emission offset (−13.3 tonnes CO₂-eq), while operational efficiency drives the majority of the reduction. Sensitivity analysis identifies electricity emission factors, NdFeB production emissions, and metal recycling rates as primary sensitivity drivers (sensitivity index ST = 0.80). Scenario simulations confirm that the aggressive retrofit strategy (covering high- and moderate-potential units) could reduce annual GHG emissions of 3.12 million tonnes CO₂_eq., with corresponding 89% decreases in particulate matter (PM). This research demonstrates that APMDs are an effective pathway for the low-carbon transition in coal power systems. Their large-scale implementation can potentially necessitate breakthroughs in tiered retrofit policies, thereby providing crucial technological support for industrial carbon neutrality. Full article

Background: Chronic Brain Injury (CBI) is a lifelong condition requiring continuous adaptation by patients, families, and healthcare professionals. Transitioning rehabilitation toward patient-centered and self-management approaches is essential, yet remains limited in Spain. Methods: We conducted a two-phase consensus study in collaboration with the Spanish Society of Physical Medicine and Rehabilitation (SERMEF) and the Spanish Federation of Brain Injury (FEDACE). In Phase 1, surveys were distributed to patients (214 invited; 95 complete responses, 44.4%) and physiatrists (256 invited; 106 valid responses, 41.4%) to capture perceptions of current rehabilitation practices, including tele-rehabilitation. Differences and convergences between groups were analyzed using a Synthetic Factor (F). In Phase 2, a panel of 21 experts applied a real-time eDelphi process (SmartDelphi) to assess the feasibility of proposed innovations, rated on a six-point Likert scale. Results: Patients and professionals showed both alignment and divergence in their views. Patients reported lower involvement of rehabilitation teams and expressed more reluctance toward replacing in-person care with telemedicine. However, both groups endorsed hybrid models and emphasized the importance of improved communication tools. Expert consensus prioritized feasible interventions such as online orthopedic renewal services, hybrid care models, and educational video resources, while less feasible options included informal communication platforms (e.g., WhatsApp) and bidirectional teleconsultations. Recommendations were consolidated into five domains: (R1) systemic involvement of rehabilitation teams in chronic care, (R2) patient and caregiver education, (R3) self-management support, (R4) communication tools, and (R5) socialization strategies. Conclusions: This study demonstrates the value of combining patient and professional perspectives through digital Delphi methods to co-design innovation strategies in CBI rehabilitation. Findings highlight the need to strengthen communication, provide structured education, and implement hybrid care models to advance patient-centered rehabilitation. The methodology itself fostered engagement and consensus, underscoring its potential as a tool for participatory healthcare planning. Full article

In recent years, the number of satellites in space has experienced explosive growth, and the number of non-cooperative satellites requiring close attention and precise tracking has also increased rapidly. Despite this, the world’s satellite precision tracking equipment is constrained by factors such as a slower growth in numbers and a scarcity of available deployment sites. To rapidly and efficiently identify satellites with potential new anomalies among the large number of cataloged non-cooperative satellites currently transiting, we have constructed a Bi-Directional Minimal GRU deep learning network model incorporating an attention mechanism based on Minimal GRU. This model is termed the Attention-based Bi-Directional Minimal GRU model (ABMGRU). This model utilizes tracking data from relatively inexpensive satellite observation equipment such as phased array radars, along with catalog information for non-cooperative satellites. It rapidly detects anomalies in target satellites during the initial phase of their passes, providing decision support for the subsequent deployment, scheduling, and allocation of precision satellite tracking equipment. The satellite tracking observation data used to support model training is predicted through Satellite Tool Kit simulation based on existing catalog information of non-cooperative satellites, encompassing both anomaly free data and various types of data containing anomalies. Due to limitations imposed by relatively inexpensive observation equipment, satellite tracking data is restricted to the following categories: time, azimuth, elevation, distance, and Doppler shift, while incorporating realistic noise levels. Since subsequent precision tracking requires utilizing more satellite pass time, the duration of tracking data collected during this phase should not be excessively long. The tracking observation time in this study is limited to 1000 s. To enhance the efficiency and effectiveness of satellite anomaly detection, we have developed an Attention-based Bi-Directional Minimal GRU deep learning network model. Experimental results demonstrate that the proposed method can detect non-cooperative anomalous satellites more effectively and efficiently than existing lightweight intelligent algorithms, outperforming them in both completion efficiency and detection performance. It exhibits superiority across various non-cooperative satellite anomaly detection scenarios. Full article

Saudi Arabia has a large part of the country’s power consumption in the building area, mainly operated by cooling demands under extreme climatic conditions, where the summer temperature is more than 45 °C and solar radiation peaks are more than 1200 W/MIC. Facing this challenge, this research examines the translation of biometric principles in the design of adaptive building construction for dry areas. We present a comprehensive, four-phase method structure: removing thermoregulatory and shading strategies from desert vegetation; computer display simulation using EnergyPlus 9.7.0 and CFD (ANSYS Fluent 2022 R2); and the development of an implementation guideline. Our findings achieve three central insights. First, the dynamic factor system, such as the electrochromic glazing tested in our student project, reduced the use of HVAC energy by 30%, while advanced materials, such as the polycarbonate panel, demonstrated notable thermal stability. Secondly, the synergy between cultural knowledge and technical performance proved to be decisive; vernacular-inspired Mushrabias improved generic louver not only in thermal efficiency but also in user acceptance, which increased the 97% approval rate in post-acquisition surveys. Finally, we demonstrate that scalability is economically viable, indicating a seven-year payback period for simulation, phase-transit material (PCM), which aligns with the budgetary realities of public and educational projects. By fusing the plant-induced strategies with rigorous computational modeling and real-world applications, the work provides actionable guidelines for permanent failure design in the warm-dry climate. It underlines that maximizing energy efficiency requires the cohesion of thermodynamic principles with the craft traditions of local architecture, an approach directly aligned with the Saudi Green Initiative and the ambitions of global carbon neutrality goals. Full article

Piecewise modeling of power amplifiers (PAs) typically involves assembling different polynomials to capture nonlinear behavior across different operating regions. However, recombining these sub-models can introduce discontinuities at segment boundaries, degrading prediction accuracy and potentially impacting digital predistortion (DPD) performance. This work addresses this issue by introducing a statistical framework to detect discontinuities through localized variations in the conditional mean and variance of amplitude and phase responses. Using the Vector-Switched Generalized Memory Polynomial (VS-GMP) as a case study, we propose a low-complexity post-processing smoothing technique based on a raised cosine weighting function applied at model transition regions. Unlike structural approaches, the method requires no retraining and integrates seamlessly into existing workflows as a post-processing tool. Experimental validation across two PA architectures (Doherty and Single-Stage) and multiple 5G/LTE signals (20–200 MHz bandwidth, up to 11 dB PAPR, including carrier aggregation) demonstrates consistent improvements: up to a 3 dB NMSE reduction and notable spectral error suppression. Full article

Background/Objectives: The inherent low aqueous solubility of lipophilic drugs, belonging to Class II based on Biopharmaceutical classification system, negatively impacts their oral bioavailability. However, the manufacturing of pharmaceutical dosage forms for these drugs faces challenges related to environmental impact and production complexity. Herein, the surfactant-enriched cross-linked scaffold addresses the limitations of conventional approaches, such as the use of organic solvents, energy-intensive processing, and the demand for sophisticated equipment. Methods: Scaffold former (Pluronic F68) and scaffold trigger agent (propylene glycol) were used to prepare cross-linked scaffold loaded with candesartan cilexetil as a model for lipophilic drugs. Moreover, surfactants were selected based on the measured solubility to enhance formulation loading capacity. Design-Expert was used to study the impact of Tween 80, propylene glycol, and Pluronic F68 concentrations on the measured responses. In addition, in vitro dissolution study was implemented to investigate the drug release profile. The current approach was assessed against the limitations of conventional approach in terms of environmental and manufacturing feasibility. Results: The optimized formulation (59.27% Tween 80, 30% propylene glycol, 10.73% Pluronic F68) demonstrated a superior drug loading capacity (19.3 mg/g) and exhibited a solid-to-liquid phase transition at 35.5 °C. Moreover, it exhibited a rapid duration of solid-to-liquid transition within about 3 min. In vitro dissolution study revealed a remarkable enhancement in dissolution with 92.87% dissolution efficiency compared to 1.78% for the raw drug. Conclusions: Surfactant-enriched cross-linked scaffold reduced environmental impact by eliminating organic solvents usage and reducing energy consumption. Moreover, it offers significant manufacturing advantages through simplified production processing. Full article

Effective thermal management is essential in metal additive manufacturing to ensure process stability and desirable material properties. Directed energy deposition using a laser beam and wire (DED-LB/w) enables the production of large, high-performance components but remains sensitive to adverse thermal effects during multi-layer deposition due to heat accumulation. While prior studies have investigated interlayer temperature control and substrate preheating in DED modalities, including laser-powder and arc-based systems, the influence of substrate preheating in DED-LB/w has not been thoroughly examined. This study employs substrate preheating to simulate heat accumulation and assess its effects on melt pool geometry, wire–melt pool interaction, and the microstructural evolution of Alloy 718. Experimental results demonstrate that increased substrate temperatures lead to a gradual expansion of the melt pool, with a notable transition occurring beyond 400 °C. Microstructural analysis reveals that elevated preheat temperatures promote coarser secondary dendrite arm spacing and the development of wider columnar grains. Moreover, Nb-rich secondary phases, including the Laves phase, exhibit increased size but relatively unchanged area fractions. Observations from electrical conductance measurements and coaxial visual imaging show that preheat temperature significantly affects the process dynamics and microstructural evolution, providing a basis for advanced process control strategies. Full article

La³⁺/Bi³⁺ co-doped BaTiO₃ ceramics were synthesized via ball milling followed by heat treatment at 1200 °C according to the Ba_1−3xLa_2xTi_1−3xBi_4xO₃ formula, with dopant levels ranging from x = 0.0 to 0.006. X-ray diffraction and Rietveld refinement confirmed a ferroelectric tetragonal phase for all compositions, with the highest tetragonality (c/a = 1.009) observed for x = 0.001 exceeding that of pure BaTiO₃ (1.0083). High-resolution electron microscopy analysis revealed faceted particles with mean sizes between 362.5 nm and 488.3 nm. Low-doped samples (x = 0.001 and 0.002) exhibited higher permittivity than undoped BaTiO₃, with the maximum dielectric constant (ε_r = 2469.0 at room temperature and 7499.7 at the Curie temperature) recorded for x = 0.001 at 1 kHz. At x = 0.006, minimal permittivity variation indicated a stable dielectric response. A decrease in the Curie temperature was observed with increasing doping levels, indicating a progressive tendency toward the cubic phase. Critical exponent γ values (0.94–1.56) indicated a sharp phase transition for low-doped samples and a diffuse transition for highly doped BaTiO₃. These results showed that La³⁺/Bi³⁺ co-doping effectively tunes the structural and dielectric properties of BaTiO₃ ceramics. Full article

Enhancing the high-temperature tribological performance of protective claddings is crucial for demanding industrial applications. This study focuses on developing hexagonal boron nitride (hBN)-reinforced Ni-based composite claddings to improve wear resistance over a wide temperature range. Ni/WC/CeO₂ cladding layers with varying hBN contents (0.25 wt% and 0.75 wt%) were fabricated on 45 steel substrates via vacuum cladding. Their microstructure, mechanical properties, and tribological behavior under thermal cycling (25–600 °C) were systematically evaluated. Results reveal that the in situ formation of a hard Cr₂B phase, coupled with hBN addition, was key to achieving optimal overall properties. The composite with 0.25 wt% hBN (NWB25) demonstrated optimal overall properties, featuring the lowest porosity (0.1813%) and the highest H/E ratio (0.0405), leading to the best overall tribological performance. A distinct transition from mild to severe wear was observed during the 300 °C-2 stage, resulting from the fracture of a high-temperature tribo-oxidative layer. An hBN content of 0.25 wt% is identified as optimal for balancing solid lubrication and matrix cohesion, thereby achieving superior thermal cycling wear resistance. Higher hBN concentrations promote grain coarsening and increased porosity, which degrade performance. Full article

Oxygen diffusion in (Pu_xTh_1−x)O₂ mixed oxide crystals was investigated using molecular dynamics simulation. The model systems were isolated nanocrystals of 5460 and 15,960 particles, featuring a free surface. The oxygen diffusion coefficient D increased with decreasing thorium content, in accordance with the decrease in the melting temperature of (Pu_xTh_1−x)O₂ as x varied from 0 to 1. The temperature dependences D(T) exhibited non-linearity in the Arrhenius coordinates lnD = f(1/kT). The three linear segments of the plots corresponded to the superionic state, a transitional region, and the low-temperature crystalline phase. The transitional region was characterized by maximum values of the effective diffusion activation energy E_D(PuO₂) = 3.47 eV, E_D(ThO₂) = 5.24 eV and a complex collective mechanism of oxygen migration, which involved the displacement of anions into interstitial sites. At lower temperatures, an interstitialcy mechanism of oxygen diffusion was observed. The temperature dependence of D(PuO₂) showed quantitative agreement with low-temperature experimental data. Full article

This study proposes and empirically validates a spatiotemporal life cycle assessment (LCA) framework for hydroelectric power generation applied to the Sinop Hydroelectric Power Plant in Brazil. Unlike conventional LCA, which assumes spatial and temporal homogeneity, the framework incorporates annual temporal discretisation and geographically differentiated impacts across all phases of assessment. The methodology combines the Enhanced Structural Path Analysis (ESPA) method with temporal modeling and region-specific inventory data. The results indicate that environmental impacts peak in the fourth year of the ‘Construction and Assembly’ stage, primarily due to the intensive production of concrete and steel. A spatial analysis shows that these impacts extend beyond Brazil, with notable contributions from international supply chains. By identifying temporal and geographical hotspots, the framework offers a refined understanding of impact dynamics and drivers. Uncertainty analysis further demonstrates that temporal discretisation significantly affects impact attribution, with the ‘Construction and Assembly’ stage results varying by up to ±15%, depending on scheduling assumptions. Overall, the study advances the LCA methodology while offering robust empirical evidence to guide sustainable decision-making in Brazil’s power sector and to inform global debates on low-carbon energy transitions. Full article

In the context of intensifying climate change, it is particularly important to assess the transformation of spring floods as a key phase of the hydrological regime of rivers. This study provides a comprehensive analysis of the characteristics of maximum runoff in the Zhaiyk–Caspian basin for the modern period and projected changes for 2030, 2040, and 2050 based on CMIP6 climate scenarios (SSP3-7.0 and SSP5-8.5). Analysis of observations at 34 hydrological stations showed a reduction in spring runoff by up to 35%, a decrease in the duration of high water and a reduction in maximum water discharge on some rivers by up to 45%. It has been established that those rising temperatures, more frequent thaws, and reduced autumn moisture lead to lower maximum water discharge and a redistribution of the seasonal flow regime. Scenario projections revealed significant spatial heterogeneity: some rivers are expected to experience an increase in maximum discharge of up to 72%, while others will see a steady decline in maximum discharge of up to 35%. The results obtained indicate the need to transition to an adaptive water management system focused on the regional characteristics of river basins and the sensitivity of small- and medium-sized watercourses to climate change. Full article