Search Results (205)

Global warming (GW) contributions from feedbacks and feedback loops are projected to rise from ≈54% (loops: 29%) in 2024 to ≈71% (loops: 50%) under faltering RCP pathways without Solar Geoengineering (SG) by about 2100. A critical threshold, RCP_Critical, defined as the point at which feedback loops account for more than half of GW, is projected to occur between 2075 and 2125. Beyond this point, reversing warming becomes severely constrained, and climate tipping points become more likely. From these trends, an average mitigation difficulty and cost increase rate (MDCR) of ≈1.33–1.5% per year is estimated. By 2100, absent mitigation, the effort required to offset global warming would roughly double relative to today, approaching an unsustainable mitigation critical threshold. Current feedback levels may already be driving nonlinear warming behavior. These diagnostic estimates align with three key indicators: a minimum-feedback baseline from 1870, an equilibrium climate sensitivity (ECS) range of 3.1 °C–4.3 °C (potentially reached by ≈2082), and consistency with IPCC AR6 confidence bounds. In response, this study proposes Annual Solar Geoengineering-PLUS pathways (ASG+Ps) as supplemental measures. These include Earth Brightening, targeted Arctic Stratospheric Aerosol Injection (SAI), and feasible L1 Space Sunshade systems designed to reduce feedback amplification and extend mitigation timelines. The “PLUS” component refers to the use of increased mitigation levels with a focus on high-amplification regions, particularly the Arctic and the tropics, to help reverse local feedbacks and promote negative feedback loops. These moderate ASG+P pathways directly address AR6 concerns while avoiding many governance challenges of full-scale SG. ASG+Ps are less controversial and provide ≈14× stronger cooling potential per Wm⁻² than Carbon Dioxide Removal (CDR), while allowing variable regional targeting. Meanwhile, RCP2.6 has already been missed, placing RCP4.5 and RCP6 at risk. In 2024, atmospheric CO₂ rose by ≈23 Gt (≈3 ppm), while forest tree losses exceeded afforestation gains by 2×, yielding a 2 GtCO₂ sink loss, further diminishing CDR’s effectiveness. Declines in planetary albedo since 1998 continue to amplify warming. Urbanization accounts for roughly 13% of total surface GW, affecting 60% of the population, underscoring the mitigation potential of urban Earth Brightening. New results here also show major Space Sunshading area reductions, at ≈32× less than prior flawed estimates (detailed here) and ≈1600× less under the ASG+P method, substantially improving feasibility and the importance of space agencies’ needed mitigation role. A coordinated global ASG+P strategy, supported by IPCC working groups and space agencies like NASA/SpaceX, are needed to provide a critical supplemental pathway for climate stabilization. Given the shrinking intervention window, rising MDCR, and the escalating risks to civilization, prioritizing timely work in this area is essential; the investment is minor compared to the trillions in climate financial damages that could be avoided. Full article

Reliable Internet connectivity is essential for latency-sensitive services such as video conferencing, media streaming, and online gaming. Round-trip time (RTT) is a key indicator of network performance and is central to setting retransmission timeout (RTO); inaccurate RTT estimates may trigger unnecessary retransmissions or slow loss recovery. This paper proposes an Enhanced Regularized Extreme Learning Machine (RELM) for RTT estimation that improves generalization and efficiency by interleaving a bidirectional log-step heuristic to select the regularization constant C. Unlike manual tuning or fixed-range grid search, the proposed heuristic explores C on a logarithmic scale in both directions ($\times 10$ and /10) within a single loop and terminates using a tolerance–patience criterion, reducing redundant evaluations without requiring predefined bounds. A custom RTT dataset is generated using Mininet with a dumbbell topology under controlled delay injections (1–1000 ms), yielding 1000 supervised samples derived from 100,000 raw RTT measurements. Experiments follow a strict train/validation/test split (6:1:3) with training-only standardization/normalization and validation-only hyperparameter selection. On the controlled Mininet dataset, the best configuration (ReLU, 150 hidden neurons, $C={10}^2$) achieves $R^2=0.9999$, $MAPE=0.0018$, $MAE=966.04$, and $RMSE=1589.64$ on the test set, while maintaining millisecond-level runtime. Under the same evaluation pipeline, the proposed method demonstrates competitive performance compared to common regression baselines (SVR, GAM, Decision Tree, KNN, Random Forest, GBDT, and ELM), while maintaining lower computational overhead within the controlled simulation setting. To assess practical robustness, an additional evaluation on a public real-world WiFi RSS–RTT dataset shows near-meter accuracy in LOS and mixed LOS/NLOS scenarios, while performance degrades markedly under dominant NLOS conditions, reflecting physical-channel limitations rather than model instability. These results demonstrate the feasibility of the Enhanced RELM and motivate further validation on operational networks with packet loss, jitter, and path variability. Full article

Underground hydrogen storage (UHS) in depleted oil and gas reservoirs is regarded as a highly promising subsurface option due to its large storage capacity. In such reservoirs, the pore structure provides the primary space for hydrogen storage and governs matrix flow and diffusion. Tree-shaped fracture networks generated by hydraulic fracturing or cycling injection–production typically exhibit much higher transmissivity and serve as the dominant pathways. In this study, the geometry of multilevel branching fractures was parameterized, and two classes of tree-shaped fracture configurations were constructed, including point–line-type (PLTSF) and disc-shaped (DSTSF) networks. Analytical models were developed to evaluate the equivalent permeability of tree-shaped fracture networks with either elliptical or rectangular cross-sections. The Klinkenberg slip correction and a gas-type factor associated with molecular kinetic diameter were incorporated. The apparent equivalent permeability of hydrogen (k_app,H2) was quantified and compared with those of nitrogen and methane under identical conditions. The main findings were as follows: (1) the fracture width ratio (β) was identified as the primary factor controlling network conductivity, while the height ratio (α) amplified or attenuated this effect at a given β; (2) as the main-fracture aspect ratio, the branching order (n) or branching angle (θ) increased, the rectangular cross-sections were more favorable for maintaining higher permeability compared to the elliptical cross-section; (3) under typical operating pressures of 5–30 MPa, the apparent permeability of hydrogen was approximately 2–9% higher than that of methane and nitrogen; and (4) by introducing the fracture volume fraction, the REV-scale equivalent-permeability expression was derived for fractured rock masses containing tree-shaped fracture networks. The proposed framework provides a theoretical basis and parametric support for quantifying fracture flow capacity for UHS in depleted reservoirs. Full article

Pine wilt disease (PWD), caused by Bursaphelenchus xylophilus, is driven by a tri-component system involving the pinewood nematode, Monochamus spp. beetle vectors, and susceptible pine hosts. Chemical control remains a scenario-dependent option for emergency suppression and high-value protection, but its deployment is constrained by strong regional regulatory and practical differences. In Europe (e.g., Portugal and Spain), field chemical control is generally not practiced; post-harvest phytosanitary treatments for wood and wood packaging rely mainly on heat treatment, and among ISPMs only sulfuryl fluoride is listed for wood treatment with limited use. This review focuses on recent progress in PWD chemical control, summarizing advances in nematicide discovery and modes of action, greener formulations and delivery technologies, and evidence-based, scenario-oriented applications (standing-tree protection, vector suppression, and infested-wood/inoculum management). Recent studies highlight accelerated development of target-oriented nematicides acting on key pathways such as neural transmission and mitochondrial energy metabolism, with structure–activity relationship (SAR) efforts enabling lead optimization. Formulation innovations (water-based and low-solvent products, microemulsions and suspensions) improve stability and operational safety, while controlled-release delivery systems (e.g., micro/nanocapsules) enhance penetration and persistence. Application technologies such as trunk injection, aerial/Unmanned aerial vehicle (UAV) operations, and fumigation/treatment approaches further strengthen scenario compatibility and operational efficiency. Future research should prioritize robust target–mechanism evidence, resistance risk management and rotation strategies, greener formulations with smart delivery, and scenario-based exposure and compliance evaluation to support precise, green, and sustainable integrated control together with biological and other sustainable approaches. Full article

Injecting chemical agents into tree trunks is a key method for preventing pine wilt disease (PWD). However, the long-term use of conventional trunk injection agents such as emamectin benzoate (EB) and avermectin (AVM) may lead to nematode resistance. Therefore, it is crucial to evaluate the potential of new-generation nematicides, including fluopyram (FLU) and its compound formulations, as alternatives to EB and AVM in PWD prevention. In this study, four trunk injection agents, i.e., 5% FLU microemulsion (ME), 2% AVM + 6% FLU ME, 5% EB ME, and 5% AVM emulsifiable concentrate (EC), were injected into Pinus massoniana trunks, and their residual dynamics over time and preventive effects on PWD were compared. Results showed that all agents were transported to various parts of the trees within 90 days post-injection, with FLU showing significantly stronger translocation compared with EB and AVM. At 660 days post-injection, the active ingredient levels of 5% FLU ME in apical branches remained significantly higher than those of the other three agents at both tested doses (30 and 60 mL). Artificial inoculation with 10,000 Bursaphelenchus xylophilus nematodes per tree at 90 days post-injection showed that trees injected with 5% FLU ME and 2% AVM + 6% FLU ME had nearly 100% disease prevention rates at both doses, outperforming 5% EB ME and 5% AVM EC. A second nematode inoculation at 480 days post-injection showed that 2% AVM + 6% FLU ME showed 50% efficacy, outperforming 5% EB ME (25% efficacy). These findings offer a foundation for developing alternative trunk injection strategies for future PWD management in China. Full article

Coronary artery disease (CAD) remains a leading cause of mortality worldwide, and accurate coronary artery segmentation in X-ray coronary angiography (XCA) is challenged by low contrast, structural ambiguity, and anisotropic vessel trajectories, which hinder quantitative coronary angiography. We propose HR-UMamba++, a U-Mamba-based framework centered on a rotation-aligned multi-directional state-space scan for modeling long-range vessel continuity across multiple orientations. To preserve thin distal branches, the framework is equipped with (i) a persistent high-resolution bypass that injects undownsampled structural details and (ii) a UNet++-style dense decoder topology for cross-scale topological fusion. On an in-house dataset of 739 XCA images from 374 patients, HR-UMamba++ is evaluated using eight segmentation metrics, fractal-geometry descriptors, and multi-view expert scoring. Compared with U-Net, Attention U-Net, HRNet, U-Mamba, DeepLabv3+, and YOLO11-seg, HR-UMamba++ achieves the best performance (Dice 0.8706, IoU 0.7794, HD95 16.99), yielding a relative Dice improvement of 6.0% over U-Mamba and reducing the deviation in fractal dimension by up to 57% relative to U-Net. Expert evaluation across eight angiographic views yields a mean score of 4.24 ± 0.49/5 with high inter-rater agreement. These results indicate that HR-UMamba++ produces anatomically faithful coronary trees and clinically useful segmentations that can serve as robust structural priors for downstream quantitative coronary analysis. Full article

LoRaWAN is widely used for IoT environmental monitoring, but its lightweight security mechanisms leave the physical layer vulnerable to availability attacks such as jamming and battery-depletion. These risks are particularly critical in mission-critical environmental monitoring systems. This paper proposes a multi-attribute physical-layer authentication (PLA) framework that supports uplink legitimacy assessment by jointly exploiting radio, energy, and temporal attributes, specifically RSSI, altitude, battery_level, battery_drop_speed, event_step, and time_rank. Using publicly available Brno LoRaWAN traces, we construct a device-aware semi-synthetic dataset comprising 230,296 records from 1921 devices over 13.68 days, augmented with energy, spatial, and temporal attributes and injected with controlled jamming and battery-depletion anomalies. Five classifiers (Random Forest, Multi-Layer Perceptron, XGBoost, Logistic Regression, and K-Nearest Neighbours) are evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. The Multi-Layer Perceptron achieves the strongest detection performance (F1-score = 0.8260, AUC-ROC = 0.8953), with Random Forest performing comparably. Deployment-oriented computational profiling shows that lightweight models such as Logistic Regression and the MLP achieve near-instantaneous prediction latency (below 2 µs per sample) with minimal CPU overhead, while tree-based models incur higher training and storage costs but remain feasible for Network Server-side deployment. Full article

SCADA systems, widely used in critical infrastructure, are becoming increasingly vulnerable to complex cyber threats, which can compromise national security. This study presents an artificial intelligence-based approach aimed at the early and reliable detection of cyberattacks against SCADA systems. The study physically scaled the SCADA communication architecture of a hydroelectric power plant and created a suitable test environment. In this environment, in addition to the benign normal state, attack scenarios such as Man-in-the-Middle (MITM), Denial-of-Service (DoS), and Command Injection were implemented while the process created for the system’s operation was running continuously. While the scenarios were being implemented, the SCADA system was monitored, and network data flow was collected and stored for later analysis. Basic machine learning algorithms, including KNN, Naive Bayes, Decision Trees, and Logistic Regression, were applied to the obtained data. Also, different combinations of these methods have been tested. The analysis results showed that the hybrid model, consisting of a Decision Tree and Logistic Regression, achieved the most successful results, with a 98.29% accuracy rate, an Area Under the Curve (AUC) value of 0.998, and a reasonably short detection time. The results demonstrate that the proposed approach can accurately classify various types of attacks on SCADA systems, providing an effective early warning mechanism suitable for field applications. Full article

Heavy oil reservoirs often enter a high-water-cut and low-production stage after multiple cycles of steam stimulation. Converting to steam flooding can enhance recovery, yet the reliable prediction of incremental production potential and optimal design of injection–production parameters remain limited. In this study, a real heavy oil reservoir block was selected to develop a hybrid modeling framework integrating numerical simulation and machine learning for predicting steam flooding performance. A conceptual model was established on a numerical simulation platform to reproduce the transition from cyclic stimulation to continuous steam flooding, analyzing temperature, oil saturation, and recovery evolution under different geological, operational, and process conditions. Sensitive parameters were identified through single- and multi-factor analyses, and mathematical models for multiple injection–production schemes—continuous, cyclic, and asynchronous—were constructed for optimization. A comprehensive multi-scenario dataset combining simulation and field data was used to train and validate several machine learning models, including artificial neural networks, gradient boosting decision trees, XGBoost, and LightGBM. Among them, the LightGBM model achieved the highest predictive accuracy (R² = 0.99) and computational efficiency. The proposed framework enables the rapid and reliable prediction of incremental oil potential and provides a robust tool for optimizing steam flooding parameters, offering significant value for field-scale heavy oil development. Full article

Tree endotherapy has risen to prominence in the field of precision agriculture as an innovative and sustainable method of tree care, being respectful of both environmental protection and consumer health needs. A comprehensive review of the state of the art of research in this field has made it possible to spotlight the main advantages of tree infusion, which has undergone significant progress in step with technological innovation and an increased understanding of tree anatomy and physiology. The major criticalities associated with this technique, as well as the biological and technical–operational obstacles that still hinder its wider use, are also highlighted. What emerges is an innovative and rapidly expanding technique in tree care, in both the cultivation and phytosanitary management of fruit and ornamental trees. Some of the strengths of the endotherapy technique, such as the next-to-no water consumption, the strong reduction in the use of fertilizers and pesticides, the possibility of using biological control agents (BCAs) or other products of natural origin, the precision administration of the product inside the xylem of the tree, and the efficacy (20–90%) and persistence (1–2 years) of treatments, make it one of the cornerstones of sustainable tree protection at present. With a very low consumption of the “active ingredient”, endotherapy has a negligible impact on the external environment, minimizing the drift and dispersal of the active ingredient and thus limiting the exposure of non-target organisms such as beneficial insects, birds, and wildlife. The large-scale application of the technique would therefore also help to achieve an important goal in “climate-smart agriculture”, the saving of water resources, significantly contributing to climate change mitigation, especially in those areas of the planet where water is a precious resource. Full article

The Attribute-Based Access Control (ABAC) model provides access control decisions based on subject, object (resource), and contextual attributes. However, the use of sensitive attributes in access control decisions poses many security and privacy challenges, particularly in cloud environment where third parties are involved. To address this shortcoming, we present a novel privacy-preserving Dummy-ABAC model that obfuscates real attributes with dummy attributes before transmission to the cloud server. In the proposed model, only dummy attributes are stored in the cloud database, whereas real attributes and mapping tokens are stored in a local machine database. Only dummy attributes are used for the access request evaluation in the cloud, and real data are retrieved in the post-decision mechanism using secure tokens. The security of the proposed model was assessed using a simulated threat scenario, including attribute inference, policy injection, and reverse mapping attacks. Experimental evaluation using machine learning classifiers (“DecisionTree” DT, “RandomForest” RF), demonstrated that inference accuracy dropped from ~0.65 on real attributes to ~0.25 on dummy attributes confirming improved resistance to inference attacks. Furthermore, the model rejects malformed and unauthorized policies. Performance analysis of dummy generation, token generation, encoding, and nearest-neighbor search, demonstrated minimal latency in both local and cloud environments. Overall, the proposed model ensures an efficient, secure, and privacy-preserving access control in cloud environments. Full article

The global citrus industry faces unprecedented challenges due to Huanglongbing (HLB), which is associated with the bacterial pathogen Candidatus Liberibacter asiaticus (CLas). This study evaluates the field performance of 11 rootstocks, grafted with ‘Valencia’ orange (Citrus sinensis), under Florida’s HLB-endemic production conditions, while also examining the impact of systemic applications of oxytetracycline (OTC) via trunk injection. Mature trees received annual OTC injections and were assessed over two production seasons. In year 1, OTC-treated trees exhibited significant improvements regardless of the rootstock, including a 36% increase in yield, an 11% increase in juice TSS, and reduced leaf bacterial titers. During year 2, the positive effects of OTC were sustained, or even enhanced. CLas titers were reduced in both leaves and roots; yield increased by 70%; and fruit weight, juice color, and TSS also improved significantly. Moreover, OTC-injected trees exhibited a larger percentage of finer roots compared to non-injected trees. US-1688 and US-1672, both hybrids of C. maxima ‘Hirado’ and C. reticulata ‘Cleopatra’, emerged as the most productive rootstocks. These results demonstrate the importance of rootstock selection for sustainable citrus cultivation while highlighting the benefits of integrating the systemic delivery of OTC to manage HLB and maximize the resilience of citrus. Full article

Single Event Transients (SETs) in clock-distribution networks are a major source of soft errors in synchronous systems. We present a practical framework that assesses SET risk early in the design cycle, before layout and parasitics, using a Vulnerability Function (VF) derived from Verilog fault injection. This framework guides targeted Engineering Change Orders (ECOs), such as clock-net remapping, re-routing, and the selective insertion of SET filters, within a reproducible open-source flow (Yosys, OpenROAD, OpenSTA). A new analytical Soft Error Rate (SER) model for clock trees is also proposed, which decomposes contributions from the root, intermediate levels, and leaves, and is calibrated by SPICE-measured propagation probabilities, area, and particle flux. When coupled with throughput, this model yields a frequency-aware system-level Bit Error Rate (${BER}_{sys}$). The methodology was validated on a First-In First-Out (FIFO) memory, demonstrating a significant vulnerability reduction of approximately 3.35× in READ mode and 2.67× in WRITE mode. Frequency sweeps show monotonic decreases in both clock-tree vulnerability and ${BER}_{sys}$ at higher clock frequencies, a trend attributed to temporal masking and throughput effects. Cross-node SPICE characterization between 65 nm and 28 nm reveals a technology-dependent effect: for the same injected charge, the 28 nm process produces a shorter root-level pulse, which lowers the propagation probability relative to 65 nm and shifts the optimal clock-tree partition. These findings underscore the framework’s key innovations: a technology-independent, early-stage VF for ranking critical clock nets; a clock-tree SER model calibrated by measured propagation probabilities; an ECO loop that converts VF insights into concrete hardening actions; and a fully reproducible open-source implementation. The paper’s scope is architectural and pre-layout, with extensions to broader circuit classes and a full electrical analysis outlined for future work. Full article

Populus species are highly susceptible to wetwood formation, which adversely affects tree growth, timber quality, and wood processing. In this study, 28 aerobic and 7 anaerobic bacterial strains were isolated and purified from I-69 poplar trees infected with wetwood using tissue-based pathogen isolation techniques. Preliminary screening identified three highly pathogenic isolates, including two aerobic strains (AB4 and AB14) and one anaerobic strain (ANAB1), all of which induced wetwood symptoms in 100% of inoculated seedlings with pronounced severity. Through comprehensive characterization, including morphological analysis, physiological–biochemical profiling, and 16S rRNA gene sequencing, these strains were taxonomically classified as Pantoea agglomerans (AB4), Escherichia fergusonii (AB14), and Enterobacter hormaechei (ANAB1). These 35 strains were subsequently inoculated into one-year-old healthy poplar seedlings through three distinct methods, including stem injection, root infection, and leaf infection. Experimental results demonstrated that only stem injection successfully induced wetwood symptoms, while root and leaf infection failed to produce pathological manifestations. For stem-inoculated specimens, pathogenicity was evaluated based on three diagnostic parameters, including heartwood discoloration length, pigmentation intensity, and affected tissue area ratio. Significant variability in symptom severity was observed among different bacterial strains. These findings expand the known diversity of bacterial species associated with wetwood development and provide valuable insights for understanding its etiology and for guiding future disease management strategies. Full article

Targeting the classification and evaluation of chemical flooding well groups in medium-thick sandstone reservoirs (single-layer thickness: 5–15 m), this study proposes a multi-level classification model based on decision trees. Through the comprehensive analysis of key static factors influencing chemical flooding efficiency, a four-tier classification index system was established, comprising: interlayer/baffle development frequency (Level 1), thickness-weighted permeability rush coefficient (Level 2), reservoir rhythm characteristics (Level 3), and pore-throat radius-based reservoir connectivity quality (Level 4) as its core components. The model innovatively transforms common reservoir physical parameters (porosity and permeability) into pore-throat radius parameters to enhance guidance for polymer molecular weight design, while employing a thickness-weighted permeability rush coefficient to simultaneously characterize heterogeneity impacts from both permeability and thickness variations. Unlike existing classification methods primarily designed for thin-interbedded reservoirs—which consider only connectivity or apply fuzzy mathematics-based normalization—this model specifically addresses medium-thick reservoirs’ unique challenges of interlayer development and intra-layer heterogeneity. Furthermore, its decision tree architecture clarifies classification logic and significantly reduces data preprocessing complexity. In terms of engineering practicality, the classification results are directly linked to well-group development bottlenecks, as validated in the J16 field application. By implementing customized chemical flooding formulations tailored to the study area, the production performance in the expansion zone achieved comprehensive improvement: daily oil output dropped from 332 tons to 243 tons, then recovered to 316 tons with sustained stabilization. Concurrently, recognizing that interlayer barriers were underdeveloped in certain well groups during production layer realignment, coupled with strong vertical heterogeneity posing polymer channeling risks, targeted profile modification and zonal injection were implemented prior to flooding conversion. This intervention elevated industrial replacement flooding production in the study area from 69 tons to 145 tons daily post-conversion. This framework provides a theoretical foundation for optimizing chemical flooding pilot well-group selection, scheme design, and dynamic adjustments, offering significant implications for enhancing oil recovery in medium-thick sandstone reservoirs through chemical flooding. Full article