Search Results (10,868)

To address the difficulty of directionally cutting thick, hard key strata in gob-side entry retaining using conventional blasting or hydraulic fracturing, this paper proposes a high-pressure water-jet slotting-induced pre-cracked weakening belt (PCWB) roof-cutting technology. Several finite-length PCWBs are arranged within the key stratum and designed to coalesce into a plane, inducing through-going roof failure along a pre-determined path. A fixed–fixed key strata beam model combined with linear elastic fracture mechanics shows that the double-belt configuration forces the bending moment and shear force to concentrate in a thin rock bridge, where bending and shear stresses are amplified by about 1.5–2.8 times and 1.2–1.7 times, respectively, for 2–4 m thick key strata, providing a mechanical basis for preferential tensile–shear failure. Two-dimensional RFPA2D simulations reveal “width-dominated, length-assisted” control of cutting performance and identify an optimal weakening belt geometry of about 400 mm in width and 200 mm in length. Three-dimensional numerical modeling of parallel slot pairs indicates that intra-pair spacing of about 40 mm produces a continuous, directional weakening belt, whereas smaller or larger spacing causes, respectively, destructive interference or loss of connectivity. High-pressure water-jet tests (320 MPa, 0.33 mm nozzle, 1.30 mm/s traverse speed) on limestone blocks confirm that single slots can penetrate the full thickness and that cracks from adjacent slots coalesce through the rock bridge, forming a wide, straight fracture band. Field application in the Dongjiang Mine (3.5 m limestone key stratum, ~400 m depth) shows that the first weighting is advanced from the 7th to the 3rd day, peak support resistance is reduced from 8.8 to 7.4 MPa, and periodic weighting becomes more frequent and smoother. The PCWB technology is therefore suitable for panels with 2–4 m thick hard key strata at similar depths, offering precise key stratum severance, active stress relief, and safe, controllable construction. Full article

Deep learning models have achieved high accuracy in forecasting problems, but at the cost of large computational energy demand. Brain-inspired spiking neural networks (SNNs) offer a promising, low-power alternative, yet their adoption for time-series forecasting has been limited by information loss from binary spikes and degraded performance in deeper networks. This paper proposes a fully spiking framework that bridges this gap by improving both the encoding and propagation of information in SNNs. The framework introduces a hybrid Delta-Rate encoding mechanism that captures both abrupt changes and gradual trends in time-series data, and a Mem-Spike mechanism that transmits analog membrane potential values to preserve fine-grained information between spiking layers. We further employ residual membrane connections to maintain signal flow in deep spiking networks. Using two public energy load datasets, our enhanced SNNs consistently outperform conventional spiking models, improving prediction accuracy by up to 61.6% and mitigating degradation in multi-layer networks. Notably, it narrows the gap to the selected deep learning baseline (LSTM), achieving comparable accuracy in some settings while requiring only about 10% of the estimated inference energy of that baseline under a common operation-level model. These results show that, within the empirical scope considered here, enhanced conventional SNNs can improve time-series forecasting accuracy while retaining favorable estimated efficiency. Full article

Naked oat (Avena nuda L.) is an important dual-purpose crop for grain and forage in cold regions; however, its high fatty acid content renders seeds prone to deterioration during storage. This study aimed to investigate the protective effects of zinc oxide nanoparticles (ZnO NPs) on artificially aged naked oat seeds and elucidate the underlying molecular mechanisms. Non-aged seeds (Naged) were subjected to artificial aging at 45 °C and 100% relative humidity for 24 h (Aged), followed by priming with 30 mg L⁻¹ ZnO NPs for 6 h (Daged). Antioxidant enzyme activities were determined spectrophotometrically, and transcriptome sequencing was performed on an Illumina platform to identify differentially expressed genes (DEGs) and enriched pathways. We found that ZnO NPs increased catalase (CAT), peroxidase (POD) and superoxide dismutase (SOD) activities by 3–4-fold, restored germination rate from 75% to 98%, and enhanced seed vigor index. A total of 21,403 DEGs were detected, with 15,841 stably expressed in response to nano-priming. Reactive oxygen species (ROS) burst rapidly induced up-regulation of AP2/EREBP transcription factor family members, which subsequently activated antioxidant enzyme genes to maintain cellular redox homeostasis. Metabolic pathway analysis demonstrated that the phenylpropanoid pathway was reprogrammed, characterized by down-regulated lignin biosynthesis and up-regulated flavonoid production, thereby enhancing ROS scavenging capacity. Additionally, the pentose phosphate pathway was activated to provide additional NADPH for antioxidant defense, and up-regulated ADP-glucose pyrophosphorylase (AGPase) facilitated starch accumulation. Notably, the 40S ribosomal protein S13 exhibited the highest connectivity in protein–protein interaction networks, was up-regulated 2.1-fold, and was enriched in post-translational modification processes. These findings suggest that nano-priming with ZnO NPs represents a promising biotechnological strategy for enhancing seed vigor and storability in naked oat, with potential applications in sustainable agriculture and the seed industry. Full article

There are some methods based on the railway power conditioner (RPC) that can address the imbalance issue, improve load fluctuation, and manage the regenerative braking energy (RBE) of the traction power supply system in high-speed railways. However, the coupling of imbalance compensation and energy storage is a problem in the RPC method. Therefore, a novel decoupling control method is proposed in this paper. The topology of the method is based on a three-phase converter, and the energy storage unit is connected to the DC side of the converter. A decoupling possibility and principle analysis is carried out. The mechanism of the proposed method in coping with different working conditions of high-speed railways is introduced in detail. Then, a capacity analysis and the control method are presented. According to the theoretical analysis, while the traditional RPC requires no extra capacity under single-task operations, its required capacity increases by 15.47% under typical hybrid conditions and can even surge by over 30% under severe coupling scenarios, to achieve the same effect as the proposed decoupled method. Finally, simulations and experiments are carried out to verify the effectiveness and flexibility of the novel method. Full article

Background: Pleuroparenchymal fibroelastosis (PPFE) is a rare fibroelastotic lung disease characterized histologically by dense pleural and subpleural fibrosis with upper-lobe predominance. In clinical practice, diagnosis often relies on characteristic radiologic findings, as surgical lung biopsy is rarely feasible. Unlike idiopathic pulmonary fibrosis, robust radiologic criteria validated against biopsy-proven cohorts remain limited, and the diagnostic performance of imaging alone is incompletely defined. Although initially described as idiopathic, PPFE is increasingly recognized in secondary settings, including connective tissue disease-associated interstitial lung disease (CTD-ILD), where it frequently overlaps with more common fibrotic patterns. Methods: We conducted a focused narrative review of the literature on PPFE in CTD-ILD, synthesizing evidence on morphology, epidemiology, clinical course, prognostic implications, and proposed pathobiological mechanisms, with emphasis on distinguishing true PPFE from PPFE-like lesions. Results: CTD-associated PPFE is associated with accelerated lung function decline, increased risk of pneumothorax, and poorer outcomes, particularly in systemic sclerosis and rheumatoid arthritis. However, distinguishing true PPFE from radiologic mimics remains challenging, and diagnostic approaches rely heavily on imaging without robust histopathologic validation. Proposed mechanisms include epithelial injury, immune dysregulation, and vascular or lymphatic abnormalities, although causal links remain unproven. Significant gaps persist regarding natural history and therapeutic responsiveness. Conclusions: Earlier identification of PPFE in CTD-ILD is important, as misclassification may delay risk stratification and management. Longitudinal imaging, multidisciplinary evaluation, and standardized diagnostic criteria are needed to improve clinical care and guide future research. Full article

The increasing prevalence of obesity and Alzheimer’s disease (AD) in the aging population underscores an urgent need to understand the common cellular and metabolic mechanisms they share. Accumulated evidence suggests that overlapping metabolic disturbances contribute to the pathogenesis of these two conditions. In this review, we highlight key underlying interconnecting metabolic pathways: (1) adipose-brain crosstalk mediated by adipokines and adipose tissue-derived extracellular vesicles that can modulate neuronal function and amyloid pathology, (2) dysregulated lipid metabolism affecting cholesterol, sphingolipids, and phospholipids and thereby promoting inflammation, amyloid precursor protein processing, and tau hyperphosphorylation, (3) impaired glycolysis and insulin resistance, which accelerate both obesity and neurodegenerative processes, (4) mitochondrial dysfunction marked by disrupted tricarboxylic acid cycle enzymes and electron transport chain complexes, leading to elevated reactive oxygen species and driving both obesity and AD pathology, and (5) gut microbiota dysbiosis, which can trigger inflammation as well as amyloid and tau aggregation. Together, these mechanisms show that metabolic alterations appear early, preceding clinical disease, and that understanding these underlying connections can provide strategies to protect metabolic health and prevent disease progression. Full article

Skin diseases frequently coexist with other disorders, such as metabolic syndrome, diabetes mellitus, depression, psoriatic arthritis, and cardiovascular disease. Altered levels of distinct chemokines, like CCL5/RANTES, CXCL12/SDF-1a, CCL7/MCP-3, CCL2/MCP-1, CXCL1/GROa, and the eotaxin family, contribute to the development and/or exacerbation of inflammation, which is a common feature of numerous skin diseases as well as metabolic syndrome. The pathological and molecular connections between chronic inflammatory skin diseases and metabolic syndrome are increasingly recognized as being driven by shared inflammatory pathways, oxidative stress, and adipokine dysregulation. While systemic inflammation acts as a common thread, the precise mechanisms for some conditions remain partially understood. Nevertheless, the exact pathological and molecular connections between skin diseases (i.e., psoriasis, atopic dermatitis, pemphigus vulgaris, acute and chronic spontaneous urticaria, bullous pemphigoid, squamous cell carcinoma, alopecia areata, systemic sclerosis, discoid lupus erythematosus, diffuse large B-cell lymphoma) and metabolic syndrome are not yet fully understood. This narrative review summarizes the robust association between various chronic inflammatory skin diseases and metabolic syndrome in the context of pro-inflammatory chemokines. Full article

The long-term sustainability of Australian agriculture is fundamentally constrained by the capacity, condition, availability, and governance of soil resources. Australian soils are among the oldest and most weathered globally, highly heterogeneous, and often slow or effectively irreversible to recover once degraded. Traditional approaches centred on soil health, while valuable at paddock scale, are insufficient to address national-scale challenges related to spatial variability, data continuity, economic valuation, and policy integration. This paper examines soil security as a policy-relevant framework for supporting more sustainable Australian agriculture. Building on the dimensions of soil security (capacity, condition, capital, connectivity, and codification), we synthesise recent Australian case studies to show how soil security extends beyond soil health to integrate biophysical properties, digital soil infrastructure, socio-economic value, and governance mechanisms. Drawing on recent Australian case studies, this review identifies advances in digital soil mapping, national soil assessments, economic valuation of soil capital, stakeholder connectivity, and emerging policy frameworks, while also identifying persistent gaps in regulation, data standardisation, and institutional coordination. The paper argues that soil security can help operationalise 3-N agriculture—Net-Zero, Nature-Positive, and Nutrient-Balanced systems—by translating sustainability goals into spatially explicit, place-based decisions grounded in soil realities. By explicitly accounting for soil capacity limits, condition trajectories, capital value, information flows, and codified rules, soil security can support more realistic climate mitigation strategies, targeted nature-positive interventions, and durable nutrient security outcomes. We conclude that embedding soil security more explicitly within Australian agricultural research, policy, and governance would strengthen efforts to deliver productive, resilient, and socially legitimate food and fibre systems. Without soil security, sustainability frameworks may remain difficult to operationalise consistently; with soil security, they can be translated more effectively into measurable, place-based, and durable decisions. Full article

In quantum time (QT) schemes, time is promoted to a degree of freedom, allowing Lorentz covariance to be made explicit for single particles. We ask whether this can be lifted to QFT so that Lorentz covariance becomes manifest at the Hilbert-space level, rather than being hidden as in the standard canonical formulation. We address this question by proposing a second-quantized approach in which the elementary particle is the QT particle itself, leading naturally to the notion of spacetime field algebras and of quantum action. We show, however, that a naive many-body construction runs into inconsistencies. To pinpoint their origin we introduce a classical counterpart of the second-quantized formalism, spacetime classical mechanics (SCM), and prove a no-go theorem: Dirac quantization of SCM collapses back to standard QFT and therefore hides covariance. We circumvent this problem by presenting a quantum-action-based quantization that yields a spacetime version of quantum mechanics (SQM), making covariance manifest for (interacting) QFTs. Finally, we show that this resolution is tied to a genuine spacetime generalization of the notion of a quantum state, required by causality and closely connected to recent “states over time” proposals and, in dS/CFT–motivated settings, to microscopic notions of timelike entanglement and emergent time. Full article

Tornado is a localized, small-scale severe convective weather phenomenon characterized by extreme destructiveness. Tornado detecting and warning mainly rely on Doppler weather radar, which identifies and tracks tornadoes by recognizing the tornado vortex signature and supercells in radar data. Artificial intelligence technology has been applied to tornado recognition in recent years. However, existing monitoring methods, especially those using unsupervised learning algorithms, still have limited recognition accuracy and timely warning, and usually struggle to strike a balance between detection accuracy and false alarm rate. A novel tornado detection algorithm TDA-DARKNet has been proposed to address the aforementioned issues. The algorithm integrates a dual attention mechanism, dense residual connections, and Kolmogorov–Arnold network (KAN). A tornado dataset suitable for deep learning has been formed, which utilizes features including radial velocity, reflectivity, velocity spectrum width, differential reflectivity, and correlation coefficient in radar data. The TDA-DARKNet algorithm was trained and tested using the tornado dataset, and evaluated in tornado cases. The experimental results show that TDA-DARKNet improves the detection probability and extends the lead time to a maximum of 42 min in strong tornado situations, while achieving 97.11% accuracy, 95.08% precision, indicating strong overall identification performance. In addition, by directly leveraging radar-based data for tornado identification, the algorithm eliminates the need for manual feature engineering, simplifies data processing, reduces complexity, and further enhances detection effectiveness. Full article

Hyperconnected IoT ecosystems have become crucial for organizational operations; yet, existing governance structures remain fragmented, are technology-centric, and not well-equipped to manage the risks, compliance pressures, and resilience needs of IoT. This paper presents an integrated, theory-based information security governance model that is tailored for IoT-driven organizations. A conceptual synthesis is performed through integrating five theoretical anchors: governance theory, socio-technical systems theory, risk governance theory, institutional/compliance theory, and resilience/adaptive capacity theory. These theoretical lenses are used to derive essential governance constructs and to develop a modular architecture tailored to IoT security needs. The model’s validity is grounded in theoretical integration rather than empirical testing, consistent with the nature of conceptual research. The integrated model provides six interdependent governance dimensions: strategic governance, operational governance, technical oversight, compliance alignment, risk governance, and resilience/adaptation, anchored by an ecosystem coordination layer. It provides structured decision rights, continuous risk monitoring, regulatory legitimacy, and native adaptive capabilities toward dynamic cyber-physical threats. This research addresses a known gap in the literature on IoT governance by providing an integrated, theoretically validated governance model that systematically connects the rationale and operational mechanisms of governance for resilient, future-proof IoT adoption. The model is further operationalized through a five-level maturity structure, enabling organizations to assess and progressively enhance governance capabilities. Full article

Energy-efficient and resilient decentralized unmanned aerial vehicles (UAV) swarm operation in global navigation satellite system (GNSS) denied environments remains challenging because propulsion demand, communication load, and onboard inference are tightly coupled at the mission level. Although prior studies have examined some of these components separately, their joint evaluation within adaptive decentralized swarms remains limited under degraded navigation conditions. This study proposes an energy-aware adaptive communication-topology framework integrated with lightweight edge artificial intelligence (AI)-assisted navigation for decentralized UAV swarms operating without reliable GNSS support. The approach combines a unified mission-level energy-accounting structure for propulsion, communication, and onboard inference, a residual-energy-aware topology adaptation mechanism for preserving swarm connectivity, and a convolutional neural network-long short-term memory (CNN–LSTM) based edge-AI navigation module for improving localization robustness. The framework was evaluated in 1200 s Robot Operating System 2 (ROS2)–Gazebo–PX4 simulation scenarios against fixed topology and extended Kalman filter (EKF)-based baselines. Under the adopted simulation assumptions, the proposed configuration achieved a 22.7% reduction in total energy consumption, with the largest decrease observed in the communication-energy component, while preserving positive algebraic connectivity across all evaluated runs. The edge-AI module yielded a 4.8% root mean square error (RMSE) reduction relative to the EKF baseline, indicating a modest but meaningful improvement in localization performance. These results support the feasibility of integrated energy-aware swarm coordination in GNSS-denied environments; however, they should be interpreted as simulation-based evidence under the adopted modeling assumptions, and further high-fidelity propagation modeling, broader learning validation, and hardware-in-the-loop studies remain necessary. Full article

Redundant robot manipulators are widely used in constrained operations and tasks in complex environments. However, when multiple task constraints and inequality constraints coexist, motion planning becomes significantly more difficult. In high-dimensional configuration spaces, conventional planners are prone to local minima and may generate trajectories that are difficult to execute in real time. To address these issues, this paper proposes a hierarchical, redundancy-driven real-time replanning framework. First, we perform Cartesian sampling on the task-constraint manifold to reduce the search dimension and generate multiple candidate joint configurations for each Cartesian sample via a redundancy mapping. During connection, manipulability and executability margin are used as evaluation metrics, so that redundant degrees of freedom are explicitly exploited in tree expansion and configuration selection. Second, at the local execution layer, we employ a null-space manipulability optimization strategy to continuously improve dexterity while keeping the primary task unchanged and combine it with a priority-based hard inequality constraint filtering mechanism to project the nominal motion onto the feasible set under joint limits, velocity bounds, and safety-distance constraints in real time. Unlike existing approaches that treat global planning and local control as loosely coupled modules, the proposed framework unifies redundancy reconfiguration, feasibility maintenance, and topological replanning within a single closed-loop structure, thereby reinterpreting local minima as event-triggered topology-switching conditions. To handle the mismatch between dynamic environments and real-time perception, we further introduce a feasibility-margin monitoring mechanism that triggers event-based replanning based on changes in manipulability, constraint scaling, and safety distance, enabling fast topology-level switching and escape from local minima. Simulation and experimental results show that the proposed method effectively restores manipulability through redundancy-driven configuration adjustment and achieves a higher success rate of local recovery under dynamic obstacle intrusion. In forced replanning scenarios, the framework further demonstrates faster environmental response and lower replanning overhead while maintaining better task-constraint stability compared with existing approaches. Full article

Obtaining multiple MRI contrasts for each patient prolongs scan acquisition time, increases healthcare costs, and may not always be feasible due to patient specific constraints. Deep learning-based MRI contrast synthesis offers a potential solution, yet most existing approaches are evaluated on preprocessed public benchmarks that do not reflect real-world clinical variability. In this study, we propose a fusion U-Net transformer framework for bidirectional T1-weighted ↔ T2-weighted brain MRI synthesis trained and evaluated exclusively on retrospectively acquired clinical data. The proposed architecture integrates multiscale convolutional feature extraction with axial attention mechanisms and a transformer bottleneck for efficient global context modeling. A fusion refinement block is incorporated to mitigate skip connection artifacts. An adversarial training strategy with the least squares GAN objective and a hybrid loss combining L1 reconstruction and structural similarity (SSIM) is employed to promote both pixel-level accuracy and perceptual fidelity. The model is evaluated using SSIM and PSNR metrics alongside qualitative expert assessment conducted by two board-certified radiologists. For both synthesis directions, the framework achieves competitive quantitative performance against baseline models under the challenging conditions of clinical data. Expert evaluation confirms high anatomical fidelity and clinically acceptable image quality across both synthesis directions. These results indicate that the proposed framework represents a promising approach for multi-contrast MRI synthesis in clinically heterogeneous data environments. Full article

The countries in the Belt and Road Initiative (BRI) significantly influence global carbon emissions, and the spatial correlation and driving mechanisms of their emissions are crucial for regional emission reduction and global climate governance. This study constructs a carbon emission spatial correlation network, where links represent pairwise spatial correlations derived from a modified gravity model, using data from 54 BRI countries (2011–2020). It applies social network analysis (SNA) to examine the network structure and uses the Temporal Exponential Random Graph Model (TERGM) to identify influencing factors. The main findings are as follows: (1) The BRI carbon emission network has become more interconnected and cohesive, with stronger regional connectivity and reduced inequality. (2) The network shows a core–periphery structure with notable spatial association patterns. Countries like Qatar, Israel, India, China, and the UAE have rapidly established carbon emission links, positioning them at the core due to their high connectivity and influence. (3) The network displays temporal dependence, with reciprocity associated with stronger mutual connections and transitivity associated with more cohesive network structures. Technological innovation and industrial structure optimization are positively associated with the formation of carbon emission connections, while energy structure and foreign investment are negatively associated with it. Economic development and technological innovation are associated with a country’s greater involvement in carbon emission connections, and countries with similar urbanization rates, energy, and industrial structures, but large economic disparities are more likely to form carbon emission associations, reflecting potential complementarities in the network structure. Full article