Search Results (327)

The field of electrochemical devices, encompassing energy storage, fuel cells, electrolysis, and sensing, is fundamentally reliant on the electrode materials that govern their performance, efficiency, and sustainability. Traditional materials, while foundational, often face limitations such as restricted reaction kinetics, structural deterioration, and dependence on costly or scarce elements, driving the need for continuous innovation. Emerging electrode materials are designed to overcome these challenges by delivering enhanced reaction activity, superior mechanical robustness, accelerated ion diffusion kinetics, and improved economic feasibility. In energy storage, for example, the shift from conventional graphite in lithium-ion batteries has led to the exploration of silicon-based anodes, offering a theoretical capacity more than tenfold higher despite the challenge of massive volume expansion, which is being mitigated through nanostructuring and carbon composites. Simultaneously, the rise of sodium-ion batteries, appealing due to sodium’s abundance, necessitates materials like hard carbon for the anode, as sodium’s larger ionic radius prevents efficient intercalation into graphite. In electrocatalysis, the high cost of platinum in fuel cells is being addressed by developing Platinum-Group-Metal-free (PGM-free) catalysts like metal–nitrogen–carbon (M-N-C) materials for the oxygen reduction reaction (ORR). Similarly, for the oxygen evolution reaction (OER) in water electrolysis, cost-effective alternatives such as nickel–iron hydroxides are replacing iridium and ruthenium oxides in alkaline environments. Furthermore, advancements in materials architecture, such as MXenes—two-dimensional transition metal carbides with metallic conductivity and high volumetric capacitance—and Single-Atom Catalysts (SACs)—which maximize metal utilization—are paving the way for significantly improved supercapacitor and catalytic performance. While significant progress has been made, challenges related to fundamental understanding, long-term stability, and the scalability of lab-based synthesis methods remain paramount for widespread commercial deployment. The future trajectory involves rational design leveraging advanced characterization, computational modeling, and machine learning to achieve holistic, system-level optimization for sustainable, next-generation electrochemical devices. Full article

The rapid evolution of wireless communication toward Fifth Generation (5G) networks has enabled unprecedented performance improvement in terms of data rate, latency, reliability, sustainability, and connectivity. Recent years have witnessed an excessive deployment of new 5G networks worldwide. This deployment lead to an exponential growth in traffic flow and a massive number of connected devices requiring a new generation of energy-hungry base stations (BSs). This results in increased power consumption, higher operational costs, and greater environmental impact, making energy efficiency (EE) a critical research challenge. This paper presents a comprehensive survey of EE optimization strategies in 5G networks. It reviews the transition from traditional methods such as resources allocation, energy harvesting, BS sleep modes, and power control to modern artificial intelligence (AI)-driven solutions employing machine learning, deep reinforcement learning, and self-organizing networks (SON). Comparative analyses highlight the trade-offs between energy savings, network performance, and implementation complexity. Finally, the paper outlines key open issues and future directions toward sustainable 5G and beyond-5G (B5G/Sixth Generation (6G)) systems, emphasizing explainable AI, zero-energy communications, and holistic green network design. Full article

This perspective paper aims to set the stage for current development in the field of energy consumption and environmental impacts in two major digital industries: cryptocurrency mining and artificial intelligence (AI). To better understand current developments, this paper uses a comparative analytical framework of life-cycle assessment principles and high-resolution grid modeling to explore the energy impacts from academic and industry data. On the one hand, while both sectors convert energy into digital value, they operate according to completely different logics, in the sense that cryptocurrencies rely on specialized hardware (application-specific integrated circuits) and seek cheap energy, where they can function as “virtual batteries” for the network, quickly shutting down at peak times, with increasing hardware efficiency. On the other hand, AI is a much more rigid emerging energy consumer, in the sense that it needs high-quality, uninterrupted energy and advanced infrastructure for high-performance Graphics Processing Units (GPUs). The training and inference stages generate massive consumption, difficult to quantify, and AI data centers put great pressure on the electricity grid. In this sense, the transition from mining to AI is limited due to differences in infrastructure, with the only reusable advantage being access to electrical capacity. Regarding competition between the two industries, this dynamic can fragment the energy grid, as AI tends to monopolize quality energy, and how states will manage this imbalance will influence the energy and digital security of the next decade. Full article

Background: Maternal aging progressively compromises oocyte competence, yet the precise molecular trajectory across the reproductive lifespan remains insufficiently defined. Methods: Here, we mapped the transcriptomic landscape of mouse germinal vesicle (GV) oocytes across three distinct reproductive stages: post-pubertal peak fertility (Young, 8 weeks), fertility decline (Middle, 12 months), and reproductive senescence (Old, 18 months). Results: Our bioinformatic analyses reveal that oocyte aging follows a biphasic nonlinear trajectory. The transition from Young to Middle age marked the most profound period of transcriptional destabilization, characterized by 1197 DEGs and extensive perturbation of metabolic and signaling networks. To elucidate the regulatory drivers of this early drift, we performed transcription factor binding site (TFBS) analysis, which identified massive regulatory potential involving master regulators such as LHX8, MYC, and GATA4. Interestingly, despite the predicted extensive TF–target interactions, the mRNA expression levels of these TFs remained stable across age groups. This discrepancy suggests that the observed transcriptional dysregulation is likely associated by age-associated epigenetic modifications that alter chromatin accessibility or binding efficiency, rather than TF depletion. In the subsequent transition from Middle to Old age, the landscape shifted from active perturbation to systemic collapse. This late stage was characterized by mitochondrial respiratory dysfunction and severe proteostatic stress. Conclusions: Colectively, our findings define oocyte aging as a biphasic transition from compensatory resistance to systemic collapse. We identify midlife as the critical inflection point of regulatory remodeling, followed by terminal network exhaustion in senescence. This framework provides a molecular foundation for therapeutic and rejuvenation strategies aimed at mitigating age-associated infertility. Full article

Deep learning (DL) methods have revolutionized natural language processing (NLP), enabling industrial documentation systems to process and generate text with high accuracy and fluency. Modern deep learning models, such as transformers and recurrent neural networks (RNNs), learn contextual relationships in text, making them ideal for analyzing and creating complex industrial documentation. Transformer-based architectures, such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), are ideally suited for tasks such as text summarization, content generation, and question answering, which are crucial for documentation systems. Pre-trained language models, tuned to specific industrial datasets, support domain-specific vocabulary, ensuring the generated documentation complies with industry standards. Deep learning-based systems can use sequential models, such as those used in machine translation, to generate documentation in multiple languages, promoting accessibility, and global collaboration. Using attention mechanisms, these models identify and highlight critical sections of input data, resulting in the generation of accurate and concise documentation. Integration with optical character recognition (OCR) tools enables DL-based NLP systems to digitize and interpret legacy documents, streamlining the transition to automated workflows. Reinforcement learning and human feedback loops can enhance a system’s ability to generate consistent and contextually relevant text over time. These approaches are particularly effective in creating dynamic documentation that is automatically updated based on data from sensors, registers, or other sources in real time. The scalability of DL techniques enables industrial organizations to efficiently produce massive amounts of documentation, reducing manual effort and improving overall efficiency. NLP has become a fundamental technology for automating the generation, maintenance, and personalization of industrial documentation within the Industry 4.0, 5.0, and emerging Industry 6.0 paradigms. Recent advances in large language models, search-assisted generation, and multimodal architectures have significantly improved the accuracy and contextualization of technical manuals, maintenance reports, and compliance documents. However, persistent challenges such as domain-specific terminology, data scarcity, and the risk of hallucinations highlight the limitations of current approaches in safety-critical manufacturing environments. This review synthesizes state-of-the-art methods, comparing rule-based, neural, and hybrid systems while assessing their effectiveness in addressing industrial requirements for reliability, traceability, and real-time adaptation. Human–AI collaboration and the integration of knowledge graphs are transforming documentation workflows as factories evolve toward cognitive and autonomous systems. The review included 32 articles published between 2018 and 2025. The implications of these bibliometric findings suggest that a high percentage of conference papers (69.6%) may indicate a field still in its conceptual phase, which contextualizes the article’s emphasis on proposed architecture rather than their industrial validation. Most research was conducted in computer science, suggesting early stages of technological maturity. The leading countries were China and India, but these countries did not have large publication counts, nor were leading researchers or affiliations observed, suggesting significant research dispersion. However, the most frequently observed SDGs indicate a clear health context, focusing on “industry innovation and infrastructure” and “good health and well-being”. Full article

This study provides the first comprehensive characterization and classification of organic microfacies within the globally significant Jurassic hydrocarbon source rocks of Iraqi Kurdistan. This study aims to resolve the knowledge gap in the Jurassic source rocks of northern Iraq by establishing the first organic microfacies classification scheme, utilizing an integrated petrographic and geochemical approach to reconstruct the regional paleoenvironmental evolution and confirm the source rock’s petroleum potential. The Middle–Late Jurassic Sargelu, Naokelekan, and Barsarin formations were investigated using samples from the Mangesh-1 and Sheikhan-8 wells. Using cluster analysis, we identified five distinct organic microfacies (A–E). Microfacies A (highly laminated bituminite), B (laminated/groundmass bituminite), C (laminated rock/lamalginite), and D (massive organic-matter-rich) show the highest hydrocarbon generation potential. The findings reveal a clear paleoenvironmental evolution: the Sargelu Formation was deposited in anoxic open marine conditions (microfacies C, D); the Naokelekan Formation represents a progressively restricted silled basin with intense anoxia leading to condensed sections dominated by microfacies A, which shows the highest source rock potential; and the Barsarin Formation reflects increasing restriction and hypersalinity, showing diverse microfacies (B, C, D, E) that captured variations in marine productivity and terrigenous influx. Principal component analysis (PCA) quantitatively modeled these paleoenvironmental gradients, aligning the distinct organic microfacies and their transitions with conceptual basin models. Geochemical analysis confirms that the organic matter is rich, predominantly Type II kerogen, and thermally mature, falling within the oil window. The presence of solid bitumen, both in situ and as evidence of migration (microfacies E), confirms effective hydrocarbon generation and movement. This integrated approach confirms the significant hydrocarbon potential of these Jurassic successions and highlights the critical role of specific organic microfacies in the region’s petroleum system, providing crucial guidance for future hydrocarbon exploration in northern Iraq. Full article

The Dazhuyuan Formation (northern Guizhou) is the host stratum for bauxite deposits and enriched with critical metals like Li. We investigated sedimentary processes of the formation using detrital zircon geochronology and whole-rock geochemistry. From bottom to top, the formation comprises iron-rich claystone (IC), clastic bauxite (CB), massive bauxite (MB; where Li is enriched (1555–4210 ppm)), and clastic claystone (CC). From lower part to upper part of the formation, the sedimentary environment becomes more reducing, transitioning from continental to marine–continental facies. The P₁d exhibit rare-earth-element (REE) distributions similar to the Hanjiadian Formation. The Hanjiadian Formation detrital-zircon U–Pb ages reach ~960 and ~760 Ma; the IC and CB layers show similar results. The dominant peak of detrital-zircon ages for the MB and CC layers occurs at ~960 Ma, while the ~760-Ma dominant peak disappears. Numerous zircons are aged 1030–1150 Ma, which substantially diverges from the Hanjiadian Formation. All layers exhibit different REE distributions and detrital-zircon age distributions than the Huanglong Formation, indicating that the formation is the primary source for the Dazhuyuan Formation. The MB and CC layers receive contributions from other sources. Full article

Stance detection identifies the attitude or stance toward specific targets and has a wide range of applications across various domains. Implicit mention of the target makes it difficult to establish connections between the target and the text. Existing approaches focus on integrating external knowledge but overlook the complex associations and stance information within it, which is crucial for maintaining reasoning consistency. To solve this problem, we propose a logical stance detection framework based on the principle of stance transitivity. Our framework achieves stance reasoning by leveraging symbolic and natural language reasoning. Specifically, we extract a list of targets related to the specific target from unlabeled data and use LLMs to construct a target-stance graph. This allows us to examine complicated interactions between targets and integrate stance information across related targets. We conducted massive experiments to validate the effectiveness of our proposed method. The experimental results indicate that our framework significantly improves the performance of stance detection tasks, offering a robust solution to the challenges posed by implicit targets. Full article

The transition from Fifth Generation (5G) New Radio (NR) systems to Beyond 5G (B5G) and Sixth Generation (6G) networks requires innovative architectures capable of supporting ultra-high data rates, sub-millisecond latency, and massive connection densities in dense urban environments. This paper proposes a comprehensive design methodology for a mini-cluster architecture operating in sub-THz (0.1–0.3 THz) and THz (0.3–3 THz) frequency bands. The proposed framework aims to enhance existing 5G infrastructure while enabling B5G/6G capabilities, with a particular focus on hotspot coverage and mission-critical applications in dense urban environments. The architecture integrates mini Base Stations (mBS), Distributed Edge Computing Units (DECUs), and Intelligent Reflecting Surfaces (IRS) for coverage enhancement and blockage mitigation. Detailed link budget analysis, coverage and capacity planning, and propagation modeling tailored to complex urban morphologies are performed for representative case study cities, Quito and Guayaquil (Ecuador). Simulation results demonstrate up to 100 Gbps peak data rates, sub 100 μs latency, and tenfold energy efficiency gains over conventional 5G deployments. Additionally, the proposed framework highlights the growing importance of THz communications in the 5G evolution towards B5G and 6G systems, where ultra-dense, low-latency, and energy-efficient mini-cluster deployments play a key role in enabling next-generation connectivity for critical and immersive services. Beyond the studied cities, the proposed framework can be generalized to other metropolitan areas facing similar propagation and capacity challenges, providing a scalable pathway for early-stage sub-THz/THz deployments in B5G/6G networks. Full article

Zinc excess (Zn stress) could lead to deleterious effects in plants such as enhanced ROS production, inhibition of photosynthetic machinery, and impairment of nutrient uptake. Hence, we aimed to investigate the complexity of metabolic responses to Zn stress in Amaranthus caudatus young and mature leaves, as well as in roots by means of proteomics. Our previous metabolomics research has indicated potential involvement of gluconate and salicylate in Zn tolerance mechanisms. However, proteomics study of metabolic adjustments underlying Zn stress tolerance can give additional insight to the issue, as a lot of enzymes are known to be affected by the excess of transitional metals. The results obtained through bottom-up proteomics were complementary to our earlier metabolomics data and, furthermore, enlightened other important details in the metabolic response of A. caudatus plants to the applied Zn stress. In particular, the significant involvement of redox-related enzymes was shown, especially for the roots, and their possible interactions with salicylate and jasmonate signaling could be proposed. Furthermore, Zn²⁺-induced changes in roots and young leaves strongly affected sugar metabolism, enhanced protein quality control system, while mature leaves were characterized by remarkable decrease in subunits of photosynthetic electron transport complexes. Thus, this work emphasizes massive metabolic reprogramming aimed to reinforce root defense responses while supporting young leaves with sugar metabolites. Mass spectrometry proteomics data are available via ProteomeXchange with identifier PXD069557. Full article

Efficient public transportation systems are fundamental to achieving sustainable urban development. As the backbone of urban mobility, the coordinated development of rail transit and bus systems is crucial. The opening of a new rail transit line inevitably reshapes urban travel patterns, posing significant challenges to the existing bus network. Understanding passenger switch behavior is key to optimizing the competition and cooperation between these two modes. However, existing methods on the switch behavior of bus passengers along the newly opened rail transit line cannot balance the predictive accuracy and model interpretability. To bridge this gap, we propose a CART (classification and regression tree) decision tree-based switch behavior model that incorporates both predictive and interpretive abilities. This paper uses the massive passenger swiping-card data before and after the opening of the rail transit to construct the switch dataset of bus passengers. Subsequently, a data-driven predictive model of passenger switch behavior was established based on a CART decision tree. The experimental findings demonstrate the superiority of the proposed method, with the CART model achieving an overall prediction accuracy of 85%, outperforming traditional logit and other machine learning benchmarks. Moreover, the analysis of factor significance reveals that ‘Transfer times needed after switch’ is the dominant feature (importance: 0.52), and the extracted decision rules provide clear insights into the decision-making mechanisms of bus passengers. Full article

The transition to 5G networks brings unprecedented speed, ultra-low latency, and massive connectivity. Nevertheless, it introduces complex traffic patterns and broader attack surfaces that render traditional intrusion detection systems (IDSs) ineffective. Existing rule-based methods and classical machine learning approaches struggle to capture the temporal and dynamic characteristics of 5G traffic, while many deep learning models lack interpretability, making them unsuitable for high-stakes security environments. To address these challenges, we propose Bidirectional Temporal Anomaly Detector (BiTAD), a deep temporal learning architecture for anomaly detection in 5G networks. BiTAD leverages dual-direction temporal sequence modelling with attention to encode both past and future dependencies while focusing on critical segments within network sequences. Like many deep models, BiTAD’s faces interpretability challenges. To resolve its “black-box” nature, a dual-perspective explainability module, coined TwinLens, is proposed. This module integrates SHAP and TimeSHAP to provide global feature attribution and temporal relevance, delivering dual-perspective interpretability. Evaluated on the public 5G-NIDD dataset, BiTAD demonstrates superior detection performance compared to existing models. TwinLens enables transparent insights by identifying which features and when they were most influential to anomaly predictions. By jointly addressing the limitations in temporal modelling and interpretability, our work contributes a practical IDS framework tailored to the demands of next-generation mobile networks. Full article

The Antas North mine, located in the southeastern Amazonian Craton within the Carajás Mineral Province, is hosted by mafic and felsic metavolcanic rocks that have undergone extensive hydrothermal alteration. Field and petrographic data reveal a hydrothermal sequence comprising sodic (albite), potassic (biotite + scapolite), calcic (amphibole + apatite), silicification (quartz), and propylitic (chlorite + epidote + calcite) assemblages. Copper–gold mineralization, spatially associated with calcic alteration, occurs as massive sulfide lenses, breccia zones, and vein networks dominated by chalcopyrite, pyrrhotite, and pyrite. The absence of magnetite/hematite and the dominance of sulfides and ilmenite classify Antas North as an Iron Sulfide–Copper–Gold (ISCG) system, representing a reduced endmember within the broader IOCG spectrum. New U–Pb titanite geochronology yields two concordant age populations at ca. 2476.6 ± 15.9 Ma Ga and 2162.9 ± 28.1 Ma Ga, recording a late Archean mineralizing stage and subsequent Paleoproterozoic reactivation during the Transamazonian orogeny. These ages parallel the multistage evolution recognized in other Carajás IOCG deposits, where copper–gold-related mineralization was repeatedly overprinted by later tectono-hydrothermal events. The reduced character of Antas North, marked by ilmenite and sulfide dominance with scarce magnetite, demonstrates that reduced IOCG styles were already established in the Neoarchean–Paleoproterozoic transition and underscores the diversity of mineralizing processes within the Carajás IOCG–IOA spectrum. Full article

Chile’s National Electric System is one of the countries in South America with the greatest potential for the development of solar–wind generation, allowing for the acceleration of the energy transition with the definitive withdrawal of conventional fossil fuel thermal generation. However, the integration of the market of ancillary services requires security, stability, and quality of service to the electricity system. In this context, the primary frequency control (PFC) is considered as the first line of defense of an electric power system, due to its immediate action in severe frequency variations when they exceed ±0.7 Hz with respect to the nominal operating frequency of 50.00 Hz, allowing the safe and efficient integration of large blocks of solar–wind renewable generation in spite of the uncertainty or forecast errors that could cause its massive dispatch. The principal contribution of this work is the implementation of a technical-economic mathematical model that minimizes the total costs of real-time power reserve reallocations for primary frequency control, using the dynamic factors of stationarity in those conventional and renewable solar–wind generation plants. The validation of the model is consolidated through real scenarios, specifically the deficit of power reserves, which necessitates a dynamic response in primary frequency control over 10 s and 5 min. In terms of expected results, the proposed model contributes to the Supra-/Infra-Marginal methodology, reducing the total costs of power reallocation reserves for primary frequency control, compared to other inefficient methods, such as the Maximum Power Method, the Minimum Technical Method, and the Random Direct Instruction Method. Full article

Cities worldwide are transitioning from demolition–redevelopment-driven expansion to high-quality regeneration centered on stock upgrading, cultural continuity, and long-term operations. Against the backdrop of China’s high-quality urban renewal phase guided by the “anti-massive demolition and construction” policy, this study constructs a calibrated tripartite evolutionary game among government, investors, and residents. By embedding culture–economy parameters—cultural renovation intensity (k), operational profit-sharing ratio between investors and residents (j), cultural identification coefficient (i), and cost-sharing coefficient (w)—we establish a behavioral interaction mechanism of “cultural value conversion–benefit-sharing–cultural identification–cost-sharing.” Simulations based on replicator dynamics demonstrate that sustained tripartite cooperation requires four conditions: cultural intensity surpasses the cost threshold (k ∈ [0.6, 0.7]); the profit-sharing ratio preserves market incentives (j ∈ [0.25, 0.35]); cultural identification reaches a minimum threshold (i ≥ 0.4); and residents’ cost-sharing does not exceed their benefit capacity (w ≤ 0.2). These findings reveal the core tension in China’s high-quality urban renewal stage—namely, the challenge of instituting sustainable operational mechanisms under cultural protection constraints—and globally provide a quantifiable policy toolbox for culture-led urban regeneration. Full article