Search Results (2,368)

Fracture systems critically govern coal seam permeability, influencing hydrocarbon migration pathways and well placement strategies. We established a predictive framework for fracture characterization in thin-interbedded coal reservoirs by integrating seismic response analysis with multi-domain validation. Utilizing borehole log statistics and staggered-grid wave equation modeling, we first decode azimuthal amplitude anisotropy patterns in fractured coal seams under varying lithological contexts. Key findings reveal that (1) isotropic thick surrounding rocks yield distinct fracture symmetry axis alignment (ellipse long-axis orientation shifts with layer velocity), while (2) anisotropic thin-interbedded host strata amplify azimuthal anisotropy ratios at mid–far offsets but induce prediction ambiguity under comparable fracture intensities. By applying azimuthally partitioned OVT data with optimized mid–long offset stacking, our amplitude ellipse fitting method demonstrates unique fracture solutions validated against structural, logging, and production data. This workflow resolves the multi-solution challenges in thin-layered systems, enabling precise fracture parameter prediction to optimize coalbed methane development in geologically complex basins. Full article

We synthesized emeraldine salts of poly(aniline-co-aniline-2-sulfonic acid) capped with camphorsulfonic acid (CSA), forming spherical nanoparticles (NPs), i.e., CSA-capped P(ANi-co-ASNi), and demonstrated their efficacy as gas sensor elements. The synthesized core–shell spherical NPs, averaging 265 nm in diameter, feature a CSA shell with a porous thin-film morphology, characterized by the uneven distribution of fine particulate domains across the outer surface of the positively charged P(ANi-co-ASNi) cores. This uniquely heterogeneous shell architecture facilitates stable charge transport at the core–shell interface, enhances resistance to ambient humidity, and promotes efficient interaction with organic gas molecules. The CSA-capped P(ANi-co-ASNi) sensors reliably detected low concentrations of acetone (1–5 ppm) and water vapor (1–28% RH) under ambient conditions. Furthermore, the sensors exhibited superior stability across varying temperature, humidity, and cyclic performance, outperforming conventional pure PANiNi. Full article

Wood is a renewable, carbon-sequestering, and structurally versatile material that has supported human civilization for millennia and continues to play a central role in advancing sustainable development. Although its low density, high specific strength, and esthetic appeal make it highly attractive, its intrinsic flammability presents significant challenges for safety-critical uses. This review offers a comprehensive analysis that uniquely integrates three key domains, covering advanced processing technologies, flame-retardant functionalization strategies, and multifunctional applications. Clear connections are drawn between processing approaches such as delignification, densification, and nanocellulose extraction and their substantial influence on improving flame-retardant performance. The review systematically explores how these engineered wood substrates enable more effective fire-resistant systems, including eco-friendly impregnation methods, surface engineering techniques, and bio-based hybrid systems. It further illustrates how combining processing and functionalization strategies allows for multifunctional applications in architecture, transportation, electronics, and energy devices where safety, durability, and sustainability are essential. Future research directions are identified with a focus on creating scalable, cost-effective, and environmentally compatible wood-based materials, positioning engineered wood as a next-generation high-performance material that successfully balances structural functionality, fire safety, and multifunctionality. Full article

In this paper, we present a unique method to determine the level of bot contamination of web-based user agents. It is common practice for bots and robotic agents to masquerade as human-like to avoid content and performance limitations. This paper continues our previous work, using over 600 million web log entries collected from over 4000 domains to derive and generalize how the prominence of specific web browser versions progresses over time, assuming genuine human agency. Here, we introduce a parametric model capable of reproducing this progression in a tunable way. This simulation allows us to tag human-generated traffic in our data accurately. Along with the highest confidence self-tagged bot traffic, we train a Transformer-based classifier that can determine the bot contamination—a botness metric of user-agents without prior labels. Unlike traditional syntactic or rule-based filters, our model learns temporal patterns of raw and heuristic-derived features, capturing nuanced shifts in request volume, response ratios, content targeting, and entropy-based indicators over time. This rolling window-based pre-classification of traffic allows content providers to bin streams according to their bot infusion levels and direct them to several specifically tuned filtering pipelines, given the current load levels and available free resources. We also show that aggregated traffic data from multiple sources can enhance our model’s accuracy and can be further tailored to regional characteristics using localized metadata from standard web server logs. Our ability to adjust the heuristics to geographical or use case specifics makes our method robust and flexible. Our evaluation highlights that 65% of unclassified traffic is bot-based, underscoring the urgency of robust detection systems. We also propose practical methods for independent or third-party verification and further classification by abusiveness. Full article

Atopic dermatitis (AD) is the most common chronic inflammatory skin disease, characterized by pruritic and eczematous lesions. Skin barrier dysfunction and aberrant inflammatory responses are hallmark features of AD. Recent genome-wide association studies have implicated NLRP10, a unique member of the NOD-like receptors (NLRs) lacking a leucine-rich repeat (LRR) domain, in AD susceptibility. Unlike other NLRs, the physiological role of NLRP10 in skin remains incompletely understood. Emerging evidence shows that NLRP10 regulates keratinocyte survival and differentiation, acts as a molecular sensor for mitochondrial damage, enhances anti-microbial response and contributes to skin barrier function. This review summarizes current insights into NLRP10′s functions in skin homeostasis, its interplay with cell death pathways, and its role in maintaining skin barrier function. Furthermore, therapeutic opportunities to target NLRP10 as a novel strategy for modulating epidermal cell death and restoring barrier function in AD are highlighted. Full article

In recent years, inverse reinforcement learning algorithms have garnered substantial attention and demonstrated remarkable success across various control domains, including autonomous driving, intelligent gaming, robotic manipulation, and automated industrial systems. Nevertheless, existing methodologies face two persistent challenges: (1) finite or non-optimal expert demonstration and (2) ambiguity in which different reward functions lead to same expert strategies. To improve and enhance the expert demonstration data and to eliminate the ambiguity caused by the symmetry of rewards, there has been a growing interest in research on developing inverse reinforcement learning based on the maximum entropy method. The unique advantage of these algorithms lies in learning rewards from expert presentations by maximizing policy entropy, matching expert expectations, and then optimizing the policy. This paper first provides a comprehensive review of the historical development of maximum entropy-based inverse reinforcement learning (ME-IRL) methodologies. Subsequently, it systematically presents the benchmark experiments and recent application breakthroughs achieved through ME-IRL. The concluding section analyzes the persistent technical challenges, proposes promising solutions, and outlines the emerging research frontiers in this rapidly evolving field. Full article

Background: Primary hepatocellular carcinoma (HCC) and liver metastases differ in origin, progression, and therapeutic response, yet a direct high-resolution spatial comparison of their tumor microenvironments (TMEs) within the liver has not previously been performed. Methods: We applied high-definition spatial transcriptomics to fresh-frozen specimens of one HCC and one liver metastasis (>16,000 genes per sample, >97% mapping rates) as a proof-of-principle two-specimen study, cross-validated in human proteomics and patients’ survival datasets. Transcriptional clustering revealed spatially distinct compartments, rare cell states, and pathway alterations, which were further compared against an independent systemic dataset. Results: HCC displayed an ordered lineage architecture, with transformed hepatocyte-like tumor cells broadly dispersed across the tissue and more differentiated hepatocyte-derived cells restricted to localized zones. By contrast, liver metastases showed two sharply compartmentalized domains: an invasion zone, where proliferative stem-like tumor cells occupied TAM-rich boundaries adjacent to hypoxia-adapted tumor-core cells, and a plasticity zone, which formed a heterogeneous niche of cancer–testis antigen–positive germline-like cells. Across both tumor types, we detected a conserved metabolic program of “porphyrin overdrive,” defined by reduced cytochrome P450 expression, enhanced oxidative phosphorylation gene expression, and upregulation of FLVCR1 and ALOX5, reflecting coordinated rewiring of heme and lipid metabolism. Conclusions: In this pilot study, HCC and liver metastases demonstrated fundamentally different spatial architectures, with metastases uniquely harboring a germline/neural-like plasticity hub. Despite these organizational contrasts, both tumor types converged on a shared program of metabolic rewiring, highlighting potential therapeutic targets that link local tumor niches to systemic host–tumor interactions. Full article

The core challenge of the Unique Continuity (UC) problem lies in inferring solutions across an entire domain using limited observational data, holding significant practical implications for multiphysics coupled models. Recently, physics-informed neural networks (PINNs) have shown considerable promise in addressing the UC problem. However, the reliance on a fixed activation function and a fixed weighted loss function prevents PINNs from adequately representing the multiphysics characteristics embedded in coupled models. To overcome these limitations, we propose a novel dual adaptive neural network (DANN) algorithm. This approach integrates trainable adaptive activation functions and an adaptively weighted loss scheme, enabling the network to dynamically balance the observational data and governing physics. Our method is applicable not only to the UC problem but also to general forward problems governed by partial differential equations. Furthermore, we provide a theoretical foundation for the algorithm by deriving a generalization error estimate, discussing the potential causes of neural networks solving this problem. Extensive numerical experiments including 3D demonstrate the superior accuracy and effectiveness of the proposed DANN framework in solving the UC problem compared to standard PINNs. Full article

GpsB is a conserved cell-cycle regulator in the Firmicute clade of Gram-positive bacteria that coordinates multiple aspects of envelope biogenesis. Recent studies demonstrate interactions between GpsB and the key division cytoskeleton FtsZ, suggesting that GpsB links cell division to various aspects of cell envelope biogenesis in Staphylococcus aureus and potentially other Firmicutes. We determined a 1.7 Å resolution crystal structure of the N-terminal domain of Staphylococcus aureus GpsB, revealing an asymmetric dimer with a bent conformation. This conformation is nearly identical to one of two conformations reported by Sacco et al., confirming the unique conformation of S. aureus GpsB compared to other Gram-positive bacteria. This structural agreement provides strong validation of the S. aureus GpsB fold and supports its proposed role in organizing the cell division machinery. Full article

The use of machine learning methods for the development of robust and flexible visual inspection systems has shown promising results. However, their performance is highly dependent on the large amount and diversity of training data, which is difficult to obtain in practice. Recent developments in synthetic dataset generation have seen increasing success in overcoming these problems. However, the prevailing work revolves around the usage of generative models, which suffer from data shortages, hallucinations, and provide limited support for unobserved edge-cases. In this work, we present the first synthetic data generation pipeline that is capable of generating large datasets of physically realistic textures exhibiting sophisticated structured patterns. Our framework is based on procedural texture modelling with interpretable parameters, uniquely allowing us to guarantee precise control over the texture parameters as we generate a high variety of observed and unobserved texture instances. We publish the dual dataset used in this paper, presenting models of sandblasting, parallel, and spiral milling textures, which are commonly present on manufactured metal products. To evaluate the dataset quality, we go beyond final model performance comparison by measuring different image similarities between the real and synthetic domains. This uncovered a trend, indicating these metrics could be used to predict downstream detection performance, which can strongly impact future developments of synthetic data. Full article

This paper expands the Mallows model for use in combinatorial domains. The Mallows model is a popular distribution used to sample permutations around a central tendency but requires a unique normalizing constant for each distance metric used in order to be computationally efficient. In this paper, closed-form expressions for the Mallows model normalizing constant are derived for the Hamming distance, symmetric difference, and the similarity coefficient in combinatorial domains. Additionally, closed-form expressions are derived for the normalizing constant of the weighted Mallows model in combinatorial domains. The weighted Mallows model increases the versatility of the Mallows model by allowing granular control over likelihoods of individual components in the domain. The derivation of the closed-form expression results in a reduction of the order of calculations required to calculate probabilities from exponential to constant. Full article

As research on triboelectric nanogenerators (TENGs) continues to advance, their applications are becoming increasingly diverse and sophisticated. This paper aims to provide future researchers with a concise yet comprehensive understanding of the four fundamental operational principles of TENGs, enabling them to fully appreciate the unique characteristics and application scenarios of each mode. In doing so, researchers can make informed and well-grounded choices in selecting the most suitable operational mode for exploration and innovation, tailored to their specific fields and requirements. Furthermore, this paper aligns closely with the current research frontiers and development trends of TENGs by systematically reviewing the literature and analyzing recent developments in the field from three key perspectives: the expansion of application domains, innovations in structural design, and optimizations in material properties. Through this multidimensional framework, it not only highlights the broad potential and practical prospects of TENGs but also uncovers the latest advancements and future directions in technological breakthroughs and performance enhancement. Full article

Bridge health monitoring is critical for infrastructure safety, especially with the growing deployment of IoT sensors. This work addresses the challenge of securely storing large volumes of sensor data and extracting actionable insights for timely damage detection. We propose BIONIB, a novel framework that combines an unsupervised machine learning approach called the Novelty Index (NI) with a scalable blockchain platform (EOSIO) for secure, real-time monitoring of bridges. BIONIB leverages EOSIO’s smart contracts for efficient, programmable, and secure data management across distributed sensor nodes. Experiments on real-world bridge sensor data under varying loads, climatic conditions, and health states demonstrate BIONIB’s practical effectiveness. Key findings include CPU utilization below 40% across scenarios, a twofold increase in storage efficiency, and acceptable latency degradation, which is not critical in this domain. Our comparative analysis suggests that BIONIB fills a unique niche by coupling NI-based detection with a decentralized architecture, offering real-time alerts and transparent, verifiable records across sensor nodes. Full article

The advancement of sports science has heightened demands for precise monitoring of athletes’ technical movements, physiological status, and performance. Optical fiber sensing (OFS) technology, with its unique advantages including high sensitivity, immunity to electromagnetic interference, capability for distributed sensing, and strong biocompatibility, demonstrates significant application potential in sports science. This review systematically examines the technical principles, innovative breakthroughs, and practical application cases of optical fiber sensors in various domains: monitoring key human physiological parameters such as respiration, heart rate, and body temperature; capturing motion and analyzing movement covering muscle activity, joint angles, and gait; integrating within smart sports equipment and protective gear; and monitoring sports apparatus and environments. The value of OFS technology is further analyzed in areas including sports biomechanics analysis, training load monitoring, injury prevention, and rehabilitation optimization. Concurrently, current technical bottlenecks such as the need for enhanced sensitivity, advancements in flexible packaging technologies, cost control, system integration, and miniaturization are discussed. Future development trends involving the integration of OFS with artificial intelligence, the Internet of Things, and new materials are explored, aiming to provide a theoretical foundation for sports medicine and training optimization. Full article

Distance learning has become essential to higher education, yet its application in military officer training presents unique academic, operational, and security challenges. For Lithuania’s future officers, remote education must foster not only knowledge acquisition but also decision-making, leadership, and operational readiness—competencies traditionally developed in immersive, in-person environments. This study addresses these challenges by integrating System Dynamics Modelling, Contemporary Risk Management Standards (ISO 31000:2022; Dynamic Risk Management Framework), and Learning Analytics to evaluate the interdependencies among twelve critical factors influencing the system resilience and effectiveness of distance military education. Data were collected from fifteen domain experts through structured pairwise influence assessments, applying the fuzzy DEMATEL method to map causal relationships between criteria. Results identified key causal drivers such as Feedback Loop Effectiveness, Scenario Simulation Capability, and Predictive Intervention Effectiveness, which most strongly influence downstream outcomes like learner engagement, risk identification, and instructional adaptability. These findings emphasize the strategic importance of upstream feedback, proactive risk planning, and advanced analytics in enhancing operational readiness. By bridging theoretical modelling, contemporary risk governance, and advanced learning analytics, this study offers a scalable framework for decision-making in complex, high-stakes education systems. The causal relationships revealed here provide a blueprint not only for optimizing military distance education but also for enhancing overall system resilience and adaptability in other critical domains. Full article