Search Results (6,371)

Antarctica’s extreme cryospheric conditions impose severe thermodynamic constraints on the natural attenuation of hydrocarbon pollutants. Despite the Antarctic Treaty System’s protections, the footprint of human logistics has left persistent reservoirs of petroleum hydrocarbons that threaten endemic biodiversity. This review critically synthesizes the state-of-the-art in Antarctic bioremediation, moving beyond traditional culture-dependent studies to integrate recent multi-omics breakthroughs (2020–2025). We analyze the molecular mechanisms limiting bioavailability in frozen soils and highlight the adaptive strategies of psychrophilic consortia, including the modification of membrane fluidity and the expression of cold-active enzymes (e.g., RHDs, AlkB). Notably, we discuss emerging findings on novel long-chain alkane degradation genes (almA, ladA) identified in 2025, which challenge previous assumptions about recalcitrance. Furthermore, the review evaluates the engineering bottlenecks of in situ versus ex situ strategies, emphasizing the synergistic potential of bacterial–fungal co-cultures and the ecological necessity of “climate-smart” remediation to mitigate methane emissions from thawing permafrost. By bridging the gap between fundamental microbial genetics and applied field engineering, we propose a roadmap for the next generation of biotechnological solutions in the warming polar environment. Full article

The incorporation of artificial intelligence, multi-sensor perception, and cyber-physical control into mining operations offers tremendous opportunities for increasing productivity, safety, and sustainability. However, present frameworks focus on discrete subsystems rather than providing a unified, safety-constrained optimization method that has been verified in both surface and underground environments. This paper describes a scalable, hierarchical autonomous mining architecture that incorporates sensor fusion, edge intelligence, fleet coordination, and digital twin-based decision support. It is designed to operate in GNSS-denied conditions and extreme climatic constraints common to Nordic mining environments. A mathematical modeling approach formalizes vehicle dynamics, drilling mechanics, and multi-agent fleet coordination inside a safety-constrained multi-objective optimization formulation. The framework is validated using Monte Carlo simulation with uncertainty measurement, sensitivity analysis, and statistical hypothesis testing. The preliminary results show improvements over a typical baseline, with productivity increasing by approximately 24.3% ± 3.2%, energy consumption decreasing by 12.8% ± 2.5%, and safety risk decreasing by 48.6% ± 4.1%. A sensitivity study identifies localization accuracy, communication delay, and optimization weighting as the primary system performance drivers. The suggested framework serves as a reproducible and transferable reference model for next-generation intelligent mining systems, having direct applications to both industrial deployment and future research in autonomous resource extraction. Full article

Ceramics are widely evaluated for their extreme hardness, high-temperature stability, and corrosion resistance, which enable applications in harsh service environments. However, these same properties, high melting points, brittleness, and low thermal shock resistance, make conventional manufacturing of complex ceramic components difficult and expensive. Traditional processes often require costly diamond tooling or energy-intensive sintering and tend to produce only simple geometries, with significant waste material and risk of defects. Additive manufacturing (AM) has recently emerged as a promising route to fabricate intricate, near-net-shape ceramic parts without these drawbacks. By building components layer by layer, AM reduces the need for extensive machining and enables the fabrication of geometrically complex, near-net-shape ceramic structures with reduced material waste, although challenges such as porosity, interlayer defects, and cracking during post-processing remain. Nonetheless, ceramic AM technologies lag behind their metal and polymer counterparts, and significant challenges remain in achieving fully dense parts with reliable mechanical properties. This review provides an in-depth overview of the state of the art in ceramics and ceramic composite additive manufacturing. We detail the most widely used AM processes (stereolithography, binder jetting, material extrusion, powder bed fusion, inkjet printing, and direct energy deposition) and typical feedstock formulations for each technique. We examine the resulting mechanical properties (strength, toughness, hardness, wear resistance) and functional properties (thermal stability, dielectric behavior, biocompatibility) of additively manufactured ceramics, and discuss their current and potential engineering applications in the aerospace, defense, automotive, biomedical, and energy sectors. Persistent challenges, including porosity, shrinkage and cracking during sintering, achieving uniform microstructures, high process costs, and scalability issues, are analyzed, and we highlight promising future directions such as multi-material grading, integration of machine learning for process optimization, and sustainable manufacturing approaches. Despite significant progress, challenges remain in achieving fully dense structures, improving process reliability, and scaling ceramic AM for industrial applications, highlighting the need for further research in process optimization, material design, and multi-material integration. Full article

Sapphire, with excellent optical properties and high hardness, has become a key hard and brittle material component in extreme environments like aviation equipment and infrared detection systems. Its processing quality directly determines the performance of various equipment systems. To address processing defects, technologies such as multi-wire cutting, magnetorheological polishing, chemical mechanical polishing, femtosecond laser processing, and ion beam etching have been developed and studied to improve the surface quality of sapphire components. This paper focuses on key technologies, including sapphire’s nano-scale surface morphology control, intrinsic nano-surface atomic-level defect control, and combined process systems for precision and shape control. These technologies lay the foundation for sapphire components’ process chain manufacturing to achieve high-precision shape and surface quality control. Full article

Intelligent bearing fault diagnosis is essential for maintaining the reliability of rotating machinery. However, deploying deep learning models in industrial environments is often constrained by a lack of labeled data, environmental noise, and strict hardware limits. To address these connected challenges, this paper proposes 1D-CNN-Trans, a flexible and resource-efficient hybrid framework. Designed for supervised diagnosis with restricted data, the configurable model combines a compact one-dimensional convolutional neural network (1D-CNN) for local feature extraction, a Transformer encoder for capturing long-range temporal dependencies, and an optional squeeze-and-excitation (SE) module for channel recalibration under favorable conditions. The method is evaluated on two standard mechanical benchmarks under limited sample conditions, controlled additive white Gaussian noise (AWGN), and dynamic non-stationary interference. Experimental results indicate that 1D-CNN-Trans shows improved robustness under interference compared to selected baselines, notably improving accuracy against a standard CNN backbone. Furthermore, findings indicate that while the Transformer ensures noise robustness, channel recalibration (via SE) introduces optimization instability under extreme sparsity and noise. Consequently, we reposition the architecture as a configurable framework where recalibration is conditionally activated. Finally, theoretical complexity analysis is provided to validate the model’s low computational burden, indicating its general feasibility for resource-constrained scenarios. Full article

Extreme low-light image enhancement (ELLIE) targets the restoration of visual quality under ultra-dim environments (<0.1 lux). Conventional image signal processing (ISP) pipelines often fail in such scenarios due to the limitations of heuristic, hand-crafted algorithms. While deep learning has advanced the field via end-to-end mapping, existing models suffer from constrained generalization and suboptimal perceptual fidelity, primarily stemming from the scarcity of large-scale, high-diversity datasets. To bridge this gap, we present the Day and Night (DaN) dataset, a semi-synthetic benchmark synthesized through a rigorous physics-based noise model. This approach effectively captures authentic noise characteristics while enabling the scalable generation of paired samples across multifaceted illumination conditions and scenes. Furthermore, we propose No Longer Vigil (NLV), a fully differentiable AI-ISP framework. By replacing traditional rigid blocks with adaptive non-linear networks, NLV facilitates scene-dependent transformations without requiring manual priors. Comprehensive evaluations demonstrate that our method significantly outshines state-of-the-art approaches, yielding a 4.15 dB gain in PSNR and a 0.026 improvement in SSIM. Full article

Robust robotic autonomy remains challenging in complex environments, where loss of stability on uneven or slippery terrain can induce extreme accelerations and angular velocities. Such motions corrupt sensor measurements and degrade state estimation, motivating the need for improved algorithmic robustness. To investigate this issue, we introduce the Tumbling-Induced Gyroscope Saturation (TIGS) dataset, which consists of recordings from a mechanical lidar and an Inertial Measurement Unit (IMU) tumbling down a hill. The dataset contains angular speeds up to four times higher than those in similar datasets and is publicly available. We then propose two complementary methods to improve Simultaneous Localization And Mapping (SLAM) robustness and evaluate them on TIGS. First, Saturation-Aware Angular Velocity Estimation (SAAVE) estimates angular velocities when gyroscope measurements become saturated during aggressive motions, reducing angular speed estimation error by 83.4%. Second, Stretch-ICP, a novel registration and deskewing algorithm, enables reconstruction of smoother 6-Degrees Of Freedom (DOF) trajectories under aggressive motions compared to classical Iterative Closest Point (ICP). Stretch-ICP reduces linear and angular velocity errors by 95.2% and 94.8%, respectively, at scan boundaries. Together, these contributions improve the robustness and consistency of lidar-inertial state estimation under aggressive motions. Full article

The semiconductor industry has long relied on Statistical Process Control (SPC) for yield and reliability management. In early technology nodes, classic univariate tools such as Shewhart charts, cumulative sums (CUSUM), exponentially weighted moving averages (EWMA), and the Cp/Cpk exponent could effectively monitor a finite set of key variables. However, sub-5nm and emerging 3 nm technologies have fundamentally changed the statistical environment. Advanced patterning, high-aspect-ratio etching, atomic layer deposition (ALD), chemical-mechanical polishing (CMP), and novel materials have drastically narrowed the process window. At these scales, nanometer-level deviations in critical dimensions (CD), overlay, or surface roughness can significantly impact yield. Simultaneously, modern wafer fabs generate massive amounts of high-frequency sensor data and high-dimensional metrology data. Traditional SPC assumptions—such as independence, normality, low dimensionality, and stationarity—often do not hold. Semiconductor data exhibits: (i) extremely high-dimensionality and strong intervariate correlations; (ii) a hierarchical structure encompassing fab → tooling → chamber → recipe → batch → wafer → field; and (iii) metrological delays and sampling limitations leading to incomplete and asynchronous observations. To address these challenges, this paper reviews advanced statistical methods applicable to wafer fabrication. These methods include multivariate statistical process control (MSPC) approaches such as Hotelling T² statistics, PCA/PLS combining T² and Q statistics, contribution diagnostics, time-series drift and change point detection, and Bayesian hierarchical modeling for uncertainty-aware monitoring in data-limited scenarios. Furthermore, we discuss how to integrate these methods with fault detection and classification (FDC), line-to-line monitoring (R2R), advanced process control (APC), and manufacturing execution systems (MES). This paper focuses on scalable, interpretable, and maintainable implementations that transform statistical analysis from a passive monitoring tool into an active component of data-driven fab control. Full article

This study addresses a critical methodological gap in evaluating building envelope performance in hot, arid climates, the overreliance on annual energy indicators, which fail to capture transient thermal behavior during peak-load periods. In such environments, instantaneous heat gains, their intensity, and temporal distribution are decisive factors for cooling demand, occupant comfort, and grid stability. To overcome this limitation, a dynamic evaluation framework—the Thermal Adaptation Rating (TAC) system—is proposed. TAC integrates three interrelated indices—peak temperature reduction (ΔT_peak), relative peak cooling load reduction (ΔP_peak, %), and peak thermal delay (Δt_delay), representing thermal damping, load intensity mitigation, and temporal redistribution, respectively. A typical residential building in Karbala was modeled in DesignBuilder using the EnergyPlus engine, with inputs documented and calibration performed against real consumption data following ASHRAE standards (MBE and CV(RMSE)) to ensure reliability. The study examined advanced envelope systems, including thermochromic glass (TG), phase-change materials (PCMs), aerogel materials (AMs), and hybrid combinations. Results revealed that while AM achieved the greatest annual energy savings, its impact on instantaneous cooling load was limited. PCM, by contrast, effectively mitigated and delayed peak loads, enhancing thermal comfort (PMV/PPD). Hybrid systems, particularly TG-PCM, delivered the most balanced performance, simultaneously reducing peak cooling load and shifting its occurrence to reshape the cooling demand curve during critical periods. These findings demonstrate that annual indices alone are insufficient for evaluating envelope performance in extreme climates. Peak-condition analysis, expressed in terms of instantaneous cooling load, as operationalized through TAC, provides a more accurate representation of thermal behavior and offers a practical tool to guide envelope design decisions in hot, dry regions. Full article

Silicon carbide (SiC) has emerged as a highly attractive material for microelectromechanical systems (MEMS) operating in harsh environments, owing to its outstanding mechanical, thermal, and chemical properties. This review provides a comprehensive overview of the advantages and limitations of SiC-based MEMS, with particular emphasis on the strong interdependence between material structure, mechanical properties, and epitaxial growth processes. The role of defects, residual stress, and crystal quality is discussed in relation to device performance and reliability. Special attention is devoted to cubic SiC grown on silicon substrates, highlighting how growth-induced features influence the mechanical response of micromachined structures. Furthermore, a detailed analysis of the quality factor (Q-factor) is presented for 3C-SiC (111)/Si resonators, including the development of analytical models and their validation through numerical simulations performed using COMSOL Multiphysics (Version 6.1). The necessity of incorporating anisotropic loss factors in numerical modeling is demonstrated to be essential for accurately describing the experimentally observed behavior. This review aims to provide design guidelines and modeling strategies for the optimization of SiC MEMS, supporting their further development for high-performance and extreme-environment applications, including pressure sensors, mechanical resonators and high-stress-tolerant sensors. Full article

The Chinese giant salamander (Andrias davidianus) is a nationally protected species in China, and its respiratory behavior serves as a key indicator of its physiological state, health status, and biological rhythm. However, research on intelligent monitoring of its respiratory behavior remains limited due to several challenges, including the species’ nocturnal habits, resulting in low image contrast and poor quality in dark environments; extremely subtle breathing movements; and high-cost manual annotation, leading to a scarcity of high-quality annotated visual data. These factors severely constrain the application of deep learning techniques in this field. To support research on respiratory behavior monitoring in the Chinese giant salamander, this study constructs and releases the CGS-BR dataset, which is the first vision-based dataset dedicated specifically to respiratory behavior detection in this species. The dataset was collected under controlled simulated breeding conditions and consists of 1732 images extracted from 215 high-definition video clips. Following a standardized procedure, each complete respiratory cycle is manually annotated into four stages: head-up, diving, exhalation, and inhalation. To validate the effectiveness of this dataset, this study selects YOLOv8n as the baseline model, which balances detection accuracy, speed, and parameter count, enabling efficient giant salamander respiratory detection under limited resources. By comparing it with several representative models, we provide a reliable evaluation of the dataset’s applicability. CGS-BR aims to provide fundamental data support for research on respiratory monitoring in the Chinese giant salamander, laying the foundation for subsequent applications in conservation management, captive breeding, health monitoring, and early disease warning. Full article

Glomeruli are signal-processing units of the olfactory bulb (OB) that play a key role in many OB computations, including contrast enhancement, gain control, and odorant-selective habituation. In awake mice, we unveil an extremely stable, inhomogeneous map of basal glomerulus-specific activity that serves as the background against which olfactory signal processing occurs. This activity is strongly driven by centrifugal cholinergic inputs; endogenous and airflow-evoked spiking of olfactory sensory neurons; and, to a minor extent, by the odor environment. Moreover, it is brain-state dependent and suppressed under various forms of anesthesia, and is therefore likely attenuated during sleep. These results reveal an important layer in the OB signal-processing network, likely increasing the system’s variance and dynamic range via glomerulus-specific functional inhomogeneity. Full article

This paper presents a collaborative optimization design methodology aimed at improving heat dissipation efficiency through the modulation of microstructural variations. The approach addresses the thermal protection requirements of high-temperature components, such as ceramic matrix composite turbine blades, which are subjected to complex and elevated thermal loads. Through the integration of numerical simulation and experimental validation, a bidirectional mapping model linking carbon nanotube (CNT) content with the macroscopic anisotropic thermal conductivity of the material was developed. Furthermore, a thermal conduction analysis and optimization framework for Ceramic Matrix Composite (CMC) high-temperature components under non-uniform thermal loads was established. This study expands the adjustable range of the material’s thermal conductivity by allowing flexible modulation of carbon nanotube content. The results demonstrate that this methodology effectively enhances the heat dissipation capacity of CMC materials in extreme thermal environments: the maximum surface temperature of the optimized flat plate is reduced by 8.96%, the peak temperature gradient is lowered by 46.64%, and the maximum thermal stress is decreased by 38.17%. This research provides new insights into the comprehensive integration of thermal dissipation requirements for CMC hot components. Full article

Copper is a crucial cofactor that sustains multiple cellular electron-transfer reactions, making it an essential element for life. However, cytotoxic levels of copper can cause structural damage and cell death through the production of reactive oxygen species (ROS) and nonspecific attacks on proteins. Moreover, immune cells, including neutrophils and macrophages, accumulate copper to induce oxidative bursts that kill engulfed pathogens. Therefore, a well-regulated copper homeostasis system is required for the human commensal fungus Candida albicans to thrive in extreme host environments. Remarkably, C. albicans exhibits higher copper tolerance than the nonpathogenic model yeast Saccharomyces cerevisiae, suggesting the presence of a specific copper tolerance mechanism that supports its adaptability to copper stress. Ndt80 is a versatile transcription factor that regulates several biological processes in C. albicans, ranging from morphological control to drug resistance. This study further reveals that Ndt80 may contribute to copper tolerance by regulating copper transporters and copper-dependent superoxide dismutases (Sods). Additionally, RNA sequencing and complementary approaches uncovered the involvement of Ndt80 in plasma membrane integrity and mitochondrial respiration under copper stress, further linking Ndt80 to copper tolerance. Together, these results broaden our understanding of Ndt80 functions and provide new insights into copper tolerance in C. albicans. 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