Search Results (23,720)

The accelerating energy demands from artificial intelligence (AI) deployment introduce systemic challenges for achieving carbon neutrality. Large language models (LLMs) represent a dominant driver of AI energy consumption, with inference operations constituting 80–90% of total energy usage. Current energy benchmarks report aggregate metrics without domain-level breakdowns, preventing accurate carbon footprint estimation for workloadspecific operations. This study addresses this critical gap by introducing a carbon-aware framework centered on the carbon cost of intelligence (CCI), a novel metric enabling workload-specific energy and carbon calculation that balances accuracy and efficiency across heterogeneous domains. This paper presents a comprehensive cross-domain energy benchmark using the massive multitask language understanding (MMLU) dataset, measuring accuracy and energy consumption in five representative domains: clinical knowledge (medicine), professional accounting (finance), professional law (legal), college computer science (technology), and general knowledge. Empirical analysis of GPT-4 across 100 MMLU questions, 20 per domain, reveals substantive variations: legal queries consume 4.3× more energy than general knowledge queries (222 J vs. 52 J per query), while energy consumption varies by domain due to input length differences. Our analysis demonstrates the evolution from simple ratio-based approaches (weighted accuracy divided by weighted energy) to harmonic mean aggregation, showing that the harmonic mean, by preventing bias from extreme values, provides more accurate carbon usage estimates. The CCI metric, calculated using weighted harmonic mean (analogous to P/E ratios in finance, where A/E represents accuracy-to-energy ratio), enables practitioners to accurately estimate energy and carbon emissions for specific workload mixes (e.g., 80% medicine + 15% general + 5% law). Results demonstrate that the domain workload mix significantly impacts carbon footprint: a law firm workload (60% law) consumes 96% more energy per query than a hospital workload (80% medicine), representing 49% potential savings through workload optimization. Carbon footprint analysis using US Northeast grid intensity (320 gCO₂e/kWh) shows domain-specific emissions ranging from 0.0046–0.0197 gCO₂ per query. CCI is validated through comparison with simple weighted average, demonstrating differences up to 12.1%, confirming that the harmonic mean provides more accurate and conservative carbon estimates essential for carbon reporting and neutrality planning. Our findings provide a novel cross-domain energy benchmark for GPT-4 and establish a practical carbon calculator framework for sustainable AI deployment aligned with carbon neutrality goals. Full article

The continued performance scaling of AI gigafactories requires the development of energy-efficient devices to meet the rapidly growing global demand for AI services. Emerging materials offer promising opportunities to reduce energy consumption in such systems. In this work, we propose an electro-optic microring modulator that exploits a graphene (Gr) and transition-metal dichalcogenide (TMD) interface for phase modulation of data-bit signals. The interface is configured as a capacitor composed of a top Gr layer and a bottom WSe${}_2$ layer, separated by a dielectric Al${}_2$O${}_3$ film. This multilayer stack is integrated onto a silicon (Si) waveguide such that the microring is partially covered, with coverage ratios varying from 10% to 100%. In the design with the lowest power consumption, the device operates at 26.3 GHz and requires an energy of 5.8 fJ/bit under 10% Gr–TMD coverage while occupying an area of only 20 $\mathsf{\mu }$m${}^2$. Moreover, a modulation efficiency of $V_{\pi }L=$ 0.203 V$·$cm and an insertion loss of 6.7 dB are reported for the 10% coverage. The Gr-TMD-based microring modulator can be manufactured with standard fabrication techniques. This work introduces a compact microring modulator designed for dense system integration, supporting high-speed, energy-efficient data modulation and positioning it as a promising solution for sustainable AI gigafactories. Full article

Research on road transport infrastructures is rapidly evolving as electrification, automation, and digital connectivity reshape how systems are designed, operated, and managed. This study presents a combined bibliometric, scientometric, and network analysis of 2755 publications published between 2021 and 2025 to map the intellectual structure, main contributors, and dominant technological themes shaping contemporary road transport research. Using data from the Web of Science Core Collection, co-occurrence mapping, thematic analysis, and collaboration networks were generated using Bibliometrix and VOSviewer. The results reveal strong growth in research output, with China, the United States, and Europe forming the core of high-impact publication and collaboration networks. Six bibliometric clusters were identified and consolidated into three overarching domains: road transport systems, emphasizing vehicle dynamics, control, and real-time computational frameworks; energy and efficiency-oriented mobility research, focusing on electrification, optimization, and infrastructure integration; and emerging digital technologies, including IoT, AI, and autonomous vehicles. The analysis highlights persistent research gaps related to interoperability, cybersecurity, large-scale deployment, and governance of intelligent transport infrastructures. Overall, the findings provide a data-driven overview of current research priorities and structural patterns shaping next-generation road transport systems. Full article

The rapid integration of unmanned aerial vehicles (UAVs) into next-generation wireless systems demands seamless and reliable handover (HO) mechanisms to ensure continuous connectivity. However, frequent topology changes, high mobility, and dynamic channel variations make traditional HO schemes inadequate for UAV-assisted 6G networks. This paper presents a comprehensive review of existing HO optimization studies, emphasizing artificial intelligence (AI) and machine learning (ML) approaches as enablers of intelligent mobility management. The surveyed works are categorized into three main scenarios: non-UAV HOs, UAVs acting as aerial base stations, and UAVs operating as user equipment, each examined under traditional rule-based and AI/ML-based paradigms. Comparative insights reveal that while conventional methods remain effective for static or low-mobility environments, AI- and ML-driven approaches significantly enhance adaptability, prediction accuracy, and overall network robustness. Emerging techniques such as deep reinforcement learning and federated learning (FL) demonstrate strong potential for proactive, scalable, and energy-efficient HO decisions in future 6G ecosystems. The paper concludes by outlining key open issues and identifying future directions toward hybrid, distributed, and context-aware learning frameworks for resilient UAV-enabled HO management. Full article

Driven by the global energy transition, the rapid expansion of photovoltaic (PV) capacity—particularly in China’s “sand-Gobi-desert” mega-bases—demands highly efficient collection technologies. DC collection, offering low losses, compactness, and high reliability, is emerging as a critical solution for large-scale integration. This paper provides a comprehensive review of PV DC step-up collection systems. First, it analyzes typical network architectures, compares AC versus DC schemes, and examines design constraints imposed by DC bus voltage levels. Second, control strategies are summarized across device, equipment, and system levels. Third, based on engineering practices in ultra-large-scale bases, key challenges regarding fault detection, efficiency optimization, economic viability, and grid code compatibility are identified alongside representative solutions. Finally, future trends in high-voltage hardware maturation, protection bottlenecks, real-time artificial intelligence, and specialized standardization are proposed. This study serves as a vital reference for the topology design and engineering standardization of PV DC collection systems. Full article

Spinel LiNi_0.5Mn_1.5O₄ (LNMO) has emerged as a highly competitive cobalt-free cathode material for higher-energy-density lithium-ion batteries. However, its practical application is hindered by severe capacity degradation, particularly under high-voltage operation. To solve this problem, we put forward a surface modification strategy employing a Li_0.4La_0.54TiO₃ (LLTO) coating. The LLTO coating forms a protective cathode–electrolyte interphase that helps to inhibit interfacial side reactions, enabling enhanced electrochemical performance up to 5 V. As a result, the optimized 1 wt% LLTO-coated LNMO exhibits a remarkable capacity retention of 96.5% after 200 cycles at 0.1 C and delivers a high-rate capacity of 103.5 mAh g⁻¹ at 2 C, significantly outperforming its pristine counterpart (86.8% and 89.6 mAh g⁻¹, respectively). This work provides a viable and efficient surface modification approach for achieving robust high-voltage LNMO cathode material, underscoring its great potential for next-generation energy storage systems. Full article

In Internet of Things (IoT) systems, data collected by geographically distributed sensors is often incomplete due to device failures, harsh deployment conditions, energy constraints, and unreliable communication. Such data gaps can significantly degrade downstream data processing and decision-making, particularly when failures result in the loss of all locally redundant sensors. Conventional imputation approaches typically rely on historical trends or multi-sensor fusion within the same target environment; however, historical methods struggle to capture emerging patterns, while same-location fusion remains vulnerable to single-point failures when local redundancy is unavailable. This article proposes a correlation-aware, cross-location data fusion framework for data imputation in IoT networks that explicitly addresses single-point failure scenarios. Instead of relying on co-located sensors, the framework selectively fuses semantically similar features from independent and geographically distributed gateways using summary statistics-based and correlation screening to minimize communication overhead. The resulting fused dataset is then processed using a lightweight KNN with an Iterative PCA imputation method, which combines local neighborhood similarity with global covariance structure to generate synthetic data for missing values. The proposed framework is evaluated using real-world weather station data collected from eight geographically diverse locations across the United States. The experimental results show that the proposed approach achieves improved or comparable imputation accuracy relative to conventional same-location fusion methods when sufficient cross-location feature correlation exists and degrades gracefully when correlation is weak. By enabling data recovery without requiring redundant local sensors, the proposed approach provides a resource-efficient and failure-resilient solution for handling missing data in IoT systems. Full article

The transition to low-carbon energy systems requires scalable and energy-efficient routes for producing liquid biofuels that are compatible with existing fuel infrastructures. This review focuses on bio-oil production from phototrophic microorganisms, highlighting their high biomass productivity, rapid growth, and inherent capacity for carbon dioxide fixation as key advantages over conventional biofuel feedstocks. Recent progress in thermochemical conversion technologies, particularly hydrothermal liquefaction (HTL) and fast pyrolysis, is critically assessed with respect to their suitability for wet and dry algal biomass, respectively. HTL enables direct processing of high-moisture biomass while avoiding energy-intensive drying, whereas fast pyrolysis offers high bio-oil yields from lipid-rich feedstocks. In parallel, catalytic upgrading strategies, including hydrodeoxygenation and related hydroprocessing routes, are discussed as essential steps for improving bio-oil stability, heating value, and fuel compatibility. Beyond conversion technologies, innovative biological and biotechnological strategies, such as strain optimization, stress induction, co-cultivation, and synthetic biology approaches, are examined for their role in tailoring biomass composition and enhancing bio-oil precursors. The integration of microalgal cultivation with wastewater utilization is briefly considered as a supporting strategy to reduce production costs and improve overall sustainability. Overall, this review emphasizes that the effective coupling of advanced thermochemical conversion with targeted biological optimization represents the most promising pathway for scalable bio-oil production from phototrophic microorganisms, positioning algal bio-oil as a viable contributor to future low-carbon energy systems. Full article

This study presents a first-of-its-kind investigation into retrofitting domestic vessels with a novel hybrid system integrating a Solid Oxide Fuel Cell (SOFC) and an Internal Combustion Engine (ICE). Using a Lake Ferry and an Island Ferry as case studies, three power-sharing scenarios (10–20% SOFC contribution) were examined for cruise and port operations. The results show that increasing the SOFC power share enhances overall system efficiency, reducing daily fuel energy consumption by up to 9% while achieving SOFC efficiencies of 58–60% in port. The design analysis confirms the physical retrofit feasibility for both vessels, with all scenarios occupying 72–92% of available machinery space. However, increasing the SOFC share from 10% to 15–20% raised total system weight by 10–20% and volume by 12–27%. Economically, the system demonstrates strong viability for high-utilization vessels, with Levelized Cost of Energy (LCOE) values of 236–248 EUR/MWh, while the sensitivity analysis highlights the SOFC capital cost as the dominant economic driver. Environmentally, the hybrid system achieves annual CO₂ reductions of 46–51% and NOx reductions of 51–62% compared to conventional diesel systems, with zero NOx emissions in port. The SOFC-ICE hybrid system proves to be a robust transitional pathway for maritime decarbonization, particularly for vessels with significant port-side operating hours. Full article

The article presents an extensive review of modern technological solutions for pulverized coal combustion, with emphasis on combustion modifiers and biomass co-firing. It highlights the role of coal in the national energy system and the need for its sustainable use in the context of energy transition. The pulverized coal combustion process is described, along with factors influencing its efficiency, and a classification of modifiers that improve combustion parameters. Both natural and synthetic modifiers are analyzed, including their mechanisms of action, application examples, and catalytic effects. Special attention is given to the synergy between transition metal compounds (Fe, Cu, Mn, Ce) and alkaline earth oxides (Ca, Mg), which enhances energy efficiency, flame stability, and reduces emissions of CO, SO₂, and NO_x. The article also examines biomass-coal co-firing as a technology supporting energy sector decarbonization. Co-firing reduces greenhouse gas emissions and increases the reactivity of fuel blends. The influence of biomass type, its share in the mixture, and processing methods on combustion parameters is discussed. Finally, the paper identifies directions for further technological development, including nanocomposite combustion modifiers and intelligent catalysts integrating sorption and redox functions. These innovations offer promising potential for improving energy efficiency and reducing the environmental impact of coal-fired power generation. Full article

Background: Achieving deep decarbonization in global supply chains is essential for advancing net-zero objectives; however, the integrative role of artificial intelligence (AI) and robotics in this transition remains insufficiently explored. This study examines how these technologies support carbon-emission reduction across supply chain operations. Methods: A curated corpus of 83 Scopus-indexed peer-reviewed articles published between 2013 and 2025 is analyzed and organized into six domains covering supply chain and logistics, warehousing operations, AI methodologies, robotic systems, emission-mitigation strategies, and implementation barriers. Results: AI-driven optimization consistently reduces transport emissions by enhancing routing efficiency, load consolidation, and multimodal coordination. Robotic systems simultaneously improve energy efficiency and precision in warehousing, yielding substantial indirect emission reductions. Major barriers include the high energy consumption of certain AI models, limited data interoperability, and poor scalability of current applications. Conclusions: AI and robotics hold substantial transformative potential for advancing supply chain decarbonization; nevertheless, their net environmental impact depends on improving the energy efficiency of digital infrastructures and strengthening cross-organizational data governance mechanisms. The proposed framework delineates technological and organizational pathways that can guide future research and industrial implementation, providing novel insights and actionable guidance for researchers and practitioners aiming to accelerate the low-carbon transition. Full article

Bamboo-derived biochar (BC) is promising for high-salinity wastewater treatment through photothermal evaporation. This study systematically evaluated BCs synthesized at 400–800 °C with residence times of 40 or 70 min. Pyrolysis temperature proved dominant, with 600 °C representing a critical threshold. Below this temperature, BCs maintained high carbon content and polar functional groups but exhibited limited porosity. Above it, structural reorganization enhanced pore development and aromaticity while reducing polar surface groups. Residence time primarily influenced volatile retention, and prolonged pyrolysis led to pore collapse. The optimal BC—produced at 800 °C for 40 min—combined hierarchical porosity with balanced surface chemistry, achieving an evaporation rate of 1.21 kg/m²·h and a photothermal efficiency of 70.45% under high-salinity conditions. Mechanistic analysis indicates that short, high-temperature pyrolysis preserves structural integrity and interfacial activity with minimal energy input. These results establish a thermal processing approach that reconciles carbon stability with surface functionality, offering practical guidance for scaling efficient and sustainable biochar-based wastewater treatment systems. Full article

The grid-forming static var generator (SVG) is a key device that supports the stable operation of power grids with a high penetration of renewable energy. The cooling efficiency of its forced water-cooling system directly determines the reliability of the entire unit. However, existing wired monitoring methods suffer from complex cabling and limited capacity to provide a full perception of the water-cooling condition. To address these limitations, this study develops a wireless monitoring system based on multi-source information fusion for real-time evaluation of cooling efficiency and early fault warning. A heterogeneous wireless sensor network was designed and implemented by deploying liquid-level, vibration, sound, and infrared sensors at critical locations of the SVG water-cooling system. These nodes work collaboratively to collect multi-physical field data—thermal, acoustic, vibrational, and visual information—in an integrated manner. The system adopts a hybrid Wireless Fidelity/Bluetooth (Wi-Fi/Bluetooth) networking scheme with electromagnetic interference-resistant design to ensure reliable data transmission in the complex environment of converter valve halls. To achieve precise and robust diagnosis, a three-layer hierarchical weighted fusion framework was established, consisting of individual sensor feature extraction and preliminary analysis, feature-level weighted fusion, and final fault classification. Experimental validation indicates that the proposed system achieves highly reliable data transmission with a packet loss rate below 1.5%. Compared with single-sensor monitoring, the multi-source fusion approach improves the diagnostic accuracy for pump bearing wear, pipeline micro-leakage, and radiator blockage to 98.2% and effectively distinguishes fault causes and degradation tendencies of cooling efficiency. Overall, the developed wireless monitoring system overcomes the limitations of traditional wired approaches and, by leveraging multi-source fusion technology, enables a comprehensive assessment of cooling efficiency and intelligent fault diagnosis. This advancement significantly enhances the precision and reliability of SVG operation and maintenance, providing an effective solution to ensure the safe and stable operation of both grid-forming SVG units and the broader power grid. Full article

In recent years, metal nanoclusters (NCs) with atomic-scale precision have emerged as novel photosensitizers for light energy conversion in metal cluster-sensitized semiconductor (MCSS) systems. However, conventional NCs often suffer from photodegradation after binding with semiconductors, limiting their long-term catalytic stability. Modifying NCs via single-atom doping provides an effective strategy to tailor their interfacial charge transfer behavior. In this study, PtAg₂₈ NCs were synthesized by doping Pt single atoms into Ag₂₉ NCs and subsequently loaded onto TiO₂ via electrostatic adsorption to construct composite photocatalysts. Systematic investigations revealed that Pt doping significantly enhances light absorption and promotes the formation of a direct Z-scheme heterojunction. The optimized PtAg₂₈/TiO₂ composite exhibits effective suppression of charge recombination. This enhanced charge separation efficiency, driven by pronounced band bending at the interface, leads to a remarkable hydrogen evolution rate of 14,564 μmol g⁻¹ h⁻¹. This work demonstrates the critical role of single-atom doping in regulating the photophysical properties of metal NCs and offers a feasible approach for designing highly efficient and stable metal-cluster-based photocatalytic systems. Full article

The increasing deployment of Internet of Things (IoT) sensors in precision agriculture has created critical challenges related to wireless communication range, energy efficiency, and data transmission latency, particularly in large-scale rural operations. This systematic survey, conducted following the PRISMA 2020 guidelines, investigates how drones, acting as mobile data collectors and communication gateways, can enhance the performance of agricultural wireless sensor networks (WSNs). The literature search was carried out in the Scopus and IEEE Xplore databases, considering peer-reviewed studies published in English between 2014 and 2025. After duplicate removal, 985 unique articles were screened based on predefined inclusion and exclusion criteria related to relevance, agricultural application, and communication technologies. Following full-text evaluation, 64 studies were included in this review. The survey analyzes how drones can be efficiently integrated with WSNs to improve data collection, addressing technical and operational challenges such as energy constraints, communication range limitations, propagation losses, and data latency. It further examines the primary applications of drone-based data acquisition supporting efficiency and sustainability in agriculture, identifies the most relevant wireless communication protocols and Technologies and discusses their trade-offs and suitability. Finally, it considers how drone-assisted data collection contributes to improved prediction models and real-time analytics in digital agriculture. The findings reveal persistent challenges in energy management, coverage optimization, and system scalability, but also highlight opportunities for hybrid architectures and the use of intelligent reflecting surfaces (IRSs) to improve connectivity. This work provides a structured overview of current research and future directions in drone-assisted agricultural communication systems. Full article