Search Results (1,012)

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

Early detection of agricultural fires with Unmanned Aerial Vehicles (UAVs) is important for environmental safety, yet it remains difficult because ignition cues are extremely small, smoke patterns vary widely, and farmland scenes often contain strong background interference such as specular reflections. Model development is further constrained by the scarcity of data from the early ignition stage. To address these challenges, we propose a joint data and model optimization framework. We first build a hybrid dataset through an ROI-guided synthesis pipeline, in which latent diffusion models are used to insert high-fidelity, carefully screened fire samples into real farmland backgrounds. We then introduce EF-YOLO, a detector designed for high sensitivity to small targets. The network uses SPD-Conv to reduce feature loss during spatial downsampling and includes a high-resolution P2 head to improve the detection of minute objects. To reduce background clutter, a Dual-Path Frequency–Spatial Enhancement (DP-FSE) module serves as a lightweight statistical surrogate that extracts global contextual cues and local salient features in parallel, thereby suppressing high-frequency noise. Experimental results show that EF-YOLO achieves an AP_s of 40.2% on sub-pixel targets, exceeding the YOLOv8s baseline by 15.4 percentage points. With a recall of 88.7% and a real-time inference speed of 78 FPS, the proposed framework offers a strong balance between detection performance and efficiency, making it well suited for edge-deployed agricultural fire early-warning systems. Full article

While Generative Artificial Intelligence technology empowers content production on user-generated content platforms, it also gives rise to novel risks of disinformation dissemination. The effective governance of these risks is critical to ensuring the cybersecurity of the online ecosystem and maintaining long-term social stability. To address the collaborative governance dilemma, this study constructs a tripartite “platform-user-government” evolutionary game model based on prospect theory. It explores the evolutionarily stable strategies and stability conditions of each actor, supplemented by numerical simulations and practical case validation. The results indicate that: (1) under specific conditions, the system can converge to an ideal equilibrium {active platform governance, engaged user participation, stringent government supervision}; (2) the government’s reward–penalty mechanisms can drive the system towards this ideal equilibrium; (3) users’ digital literacy is a key variable influencing the system’s evolutionary path; (4) both the risk preference coefficient (β) and loss aversion coefficient (λ) from prospect theory have a significant moderating effect on the system’s evolution. Finally, targeted recommendations are proposed for the three aforementioned stakeholders to accelerate the improvement of China’s collaborative governance of the content ecosystem. Full article

Against the dual backdrop of worsening air pollution and industrial intelligent transformation, industrial robot technology has become an important means to promote air pollution synergistic governance. This study innovatively incorporates public environmental satisfaction and industrial robot application as dynamic mechanism variables, constructing an evolutionary game model involving the government, industrial enterprises, and the public. Through theoretical analysis and numerical simulation, the study reveals the influence mechanism of key cost–benefit parameters on stakeholders’ strategic interaction and the system’s evolution path. The conclusions are as follows: (1) The government’s environmental supervision directly affects enterprises’ green transformation willingness, and enterprises’ behavior reversely impacts public satisfaction and supervision effectiveness, forming a “supervision–response–feedback” closed-loop. (2) The cost and benefit parameters related to industrial robots are crucial for the evolution of the game system, and there is significant heterogeneity in their impact on the strategic choices of the three parties. The robot adaptation transformation of enterprise industrial depends on the comprehensive consideration of the transformation cost and the green benefits. Public supervision is regulated by both the supervision cost and the incentive benefit. The government regulation takes into account both the regulatory cost and the loss of social reputation. Various parameters dynamically regulate the system’s equilibrium by altering the party’s cost–benefit structure. (3) The application of industrial robots and the feedback of public environmental satisfaction form a coupling effect, jointly determining the long-term evolution direction of the game system. When the cost benefit and supervision incentives are well-matched, enterprises will actively promote the green transformation of industrial robots in order to achieve intelligent pollution control. The effectiveness of public supervision has also been fully realized. The dynamic adaptation of the two components can lead the system towards an efficient and stable equilibrium in air pollution governance. Full article

Against the backdrop of accelerating climate change, extreme weather events have increasingly caused yield losses in agricultural crops. Meanwhile, they undermine the stability of production systems, posing an increasingly severe threat to agriculture. This study draws on the “diversity–stability” hypothesis to construct a country-level measure of agricultural production resilience in China (ARES). Using output time series for multiple agricultural products, we capture the co-movements of shocks and system resilience through output stability and volatility. By combining ARES with climate exposure measures, we assemble a panel dataset covering 1343 counties over the period 2000–2023 and employ a dynamic panel threshold model to jointly account for persistence in ARES and state-dependent nonlinearities in climate impacts. The results reveal significant path dependence in ARES and pronounced threshold effects across climate dimensions. In the full sample, extreme high-temperature days become significantly detrimental after crossing the threshold, whereas extreme low-temperature days become significantly beneficial in the high-exposure regime. Extreme rainfall days and extreme drought days generally exhibit positive effects that weaken markedly beyond their respective thresholds, indicating diminishing marginal gains in ARES under severe exposure. The comprehensive climate physical risk index significantly suppresses ARES when it is below the threshold value; however, after surpassing the threshold, its marginal effect becomes significantly weaker. Heterogeneity analyses across hilly, plain, and mountainous areas, as well as nationally designated key counties for poverty alleviation and development, further show that threshold locations and regime-specific effects differ substantially by terrain and development conditions. These findings highlight the need for “threshold-based” climate adaptation governance, emphasizing targeted investments and risk-financing instruments to prevent ARES collapse under tail-risk regimes. Full article

To address the challenge of accurately distinguishing UAVs and birds in a low-altitude detection field, this paper proposes a classification algorithm of UAVs and birds based on L/K dual-band micro-Doppler spectrograms and Mamba. We establish a dual-band radar detection model for unmanned aerial vehicles (UAVs) and birds, provide a method for characterizing the Doppler parameters of the echo signals, and research a UAV and bird target classification network model that integrates micro-Doppler and Mamba. Based on a dual-branch encoding framework, we use Patch block decomposition to design a classification model to serialize the two-dimensional spectrogram of the echo signal, and introduce the Mamba state-space backbone network to extract the long-term sequence features of the target’s micro-motion. The main breakthrough of the proposed classification algorithm lies in the feature fusion stage, where a late fusion strategy is adopted to integrate the dual-path high-level representation measures, fully leveraging the sensitivity of the K-band to high-frequency textures and the scale complementarity of the L-band. Then, according to the joint loss function of mutual learning and contrastive learning, we improve the model’s prediction consistency and representation discriminability. Through experimental testing, the results show that the proposed method can effectively classify UAVs and birds, and compared with other algorithms, the accuracy rate reaches 97.5%. Full article

Omnidirectional mobile robots are increasingly used in industrial and service applications due to their high maneuverability and ability to perform combined translational and rotational motions in confined spaces. However, these locomotion advantages are often accompanied by additional wheel–ground interaction losses, making power consumption an important design criterion in the design of efficient mobile platforms. This study presents a dynamic modeling and experimental-power benchmarking framework for a modular mobile robot equipped with three omnidirectional wheels, using a four-omni-wheel configuration as a baseline reference for comparison. A CAD-consistent multibody dynamic model of the three-wheel architecture is developed in the MATLAB/Simulink–Simscape Multibody R2024benvironment to estimate motor currents and electrical-power demand during motion. Experimental validation is carried out on the physical prototype using Hall-effect current sensors integrated into the drive modules, enabling real-time current acquisition for each motor. Both the simulation and experiments are performed on a standardized 1 m square-path benchmark at a constant 12 V supply. The results show that the proposed three-omni-wheel configuration reaches a total measured power of 14.43 W and a simulated power of 12.72 W, corresponding to a robot-level deviation of 11.85%. By comparison, the four-omni-wheel baseline exhibits a total measured power of 25.75 W and a simulated power of 24.92 W. Therefore, the proposed three-wheel architecture reduces the measured power demand by approximately 43.96% relative to the baseline, while the four-wheel configuration provides higher model fidelity. The proposed methodology supports power-oriented evaluation and informed design selection of omnidirectional locomotion architectures for modular mobile robots intended for industrial applications. Full article

Quasi-brittle structures modeled with softening constitutive laws may lose the uniqueness of equilibrium, producing bifurcation and multiple admissible crack evolutions even under symmetric loading. This paper develops a stability test and a constructive multiplicity procedure for finite element cracking analyses formulated as a Parametric Linear Complementarity Problem (PLCP) solved in tableau form. The approach exploits the pivot sequence of a complementary tableau to monitor stability by tracking the positive definiteness of the reduced active-mode Hessian $\widehat{\mathbf{A}}$ through a complement condition, without eigenvalue computations. A direct relationship between loss of positive definiteness and the sign of the incremental load factor $\Delta \dot{\mathsf{\alpha }}$ is established, providing an intrinsic indicator of transition to descending response. When degeneracy occurs, a “void pivot” mechanism is introduced to generate an alternative admissible tableau, enabling a systematic construction of multiple isolated solutions associated with competing crack patterns. The method is demonstrated on a two-notched direct tension specimen with cohesive softening, where symmetric and antisymmetric paths emerge at a critical step. The implementation is compatible with parallelized matrix operations and remains effective in the presence of non-holonomic constraints. Full article

Post-disaster emergency perishable material distribution is an essential part of emergency relief, which is of great significance to reducing disaster losses and casualties and improving rescue efficiency. However, in actual rescue, the demand information of disaster sites is often complex to determine, and the demand for emergency perishable materials needs to be clarified. Therefore, the single-cycle distribution makes it difficult to meet the demand for emergency perishable materials at disaster sites. To effectively improve the efficiency of emergency relief, this paper constructs a distribution optimization model with a multi-cycle vehicle path and the dual objectives of minimizing the distribution delay penalty and corruption cost and minimizing the unsatisfied demand. Initially, the fuzzy demand is addressed through the application of triangular fuzzy numbers and the most probable value weighting method. Following this, a two-stage optimization algorithm is devised by integrating the K-means++ algorithm with an enhanced Differential Evolutionary Whale Optimization Algorithm (DE-WOA). This algorithm operates by first clustering the affected points and subsequently solving the multi-objective model, thereby providing a robust methodology and strategic recommendations for the distribution of perishable materials across diverse scenarios. Our study reveals that the multi-objective model developed in this paper exhibits remarkable operability and practicality in the distribution of post-disaster emergency perishable materials, as evidenced by the verification via numerical examples. Through a comparison with the single-stage whale optimization algorithm, it is evident that the enhanced two-stage differential evolutionary whale optimization algorithm not only demonstrates a substantially faster convergence rate and a superior solution quality but also proves to be more suitably adapted to the proposed model. Significantly, the overall optimization performance has been augmented by 33%, thereby providing further substantiation of the efficacy of the designed improved algorithm. Full article

Background: Although digitalization is recognized to improve the food supply chain, its effect pathways have not been thoroughly researched, especially in the context of developing countries. This study examines the association of three digitalization practices: digital internal practice (DIP), digital integration with suppliers (DIS), and digital integration with customers (DIC) with nine supply chain performance metrics: efficiency, flexibility, food safety/quality, reliability, traceability, food loss, and sustainability, mediated by operational efficiency, trust, and transparency, using food processing company case in Ethiopia. Methods: Using an explanatory approach, data from 153 respondents were analyzed through mediation-based structural equation modeling (SEM) in JASP (v.0.95.4.0). The analysis involved 27 direct and 81 indirect effect paths. Results: The results demonstrated a fundamental comprehension that while digital practices manifest direct positive (improvement) effects, a purely direct-impact assessment is insufficient. Statistically, more than half of the suggested direct paths were not significant. The total effects, on the other hand, were significant for all 27 paths tested with much stronger positive associations. Conclusions: The mediation-based examination of the relationship of digitalization practices on food supply chain performance offers essential insight, indicating that the impact of digitalization on supply chain performance is primarily indirect, functioning through the enhanced capabilities it fosters. Full article

With the large-scale integration of renewable energy and the increasing use of power electronics, the issue of wide-band oscillations in power grids has become increasingly prominent. The scarcity and uneven distribution of oscillation samples pose significant challenges for training data-driven models, and traditional generative models struggle to ensure fidelity in both time and frequency domains. To address this, this paper proposes a Time–Frequency Fusion Generative Adversarial Network (TFF-GAN) for generating power grid oscillation risk scenarios. The method constructs a dual-path generation and discrimination framework, where the generator decomposes the signal using Short-Time Fourier Transform (STFT), with time-domain features extracted by a convolutional neural network (CNN) and frequency-domain features extracted from the STFT representation by a dedicated spectral network. These features are then fused using a U-Net structure. The discriminator simultaneously evaluates the authenticity of both the time-domain waveform and the frequency-domain spectrum. A composite loss function, incorporating time-domain loss, frequency-domain loss, and adversarial loss, is used for joint optimization. Experimental results demonstrate that the proposed method generates oscillation scenarios with high fidelity in both time-domain waveforms and frequency-domain spectra, effectively supporting power grid oscillation risk assessment and control strategy validation. Full article

Federated learning (FL) enables collaborative model training across distributed devices and robotic systems while preserving data privacy, making it well-suited for swarm robotics and edge-device-powered intelligence. However, FL remains vulnerable to adversarial behaviors such as data and model poisoning, particularly in real-world deployments where detection methods must operate under strict computational and communication constraints. This paper presents a practical, real-world federated learning framework that enhances robustness to adversarial agents in a ROS2-based edge-device swarm environment. The proposed system integrates the Federated Averaging (FedAvg) algorithm with a lightweight average cosine similarity-based filtering method to detect and suppress harmful model updates during aggregation. Unlike prior work that primarily evaluates poisoning defenses in simulated environments, this framework is implemented and evaluated on physical hardware, consisting of a laptop-based aggregator and multiple Raspberry Pi worker nodes. A convolutional neural network (CNN) based on the MobileNetV3-Small architecture is trained on the MNIST dataset, with one worker executing a sign-flipping model poisoning attack. Experimental results show that FedAvg alone fails to maintain meaningful model accuracy under adversarial conditions, resulting in near-random classification performance with a final global model accuracy of 11% and a loss of 2.3. In contrast, the integration of cosine similarity filtering demonstrates effective detection of sign-flipping model poisoning in the evaluated ROS2 swarm experiment, allowing the global model to maintain model accuracy of around 90% and loss around 0.37, which is close to baseline accuracy of 93% of the FedAvg algorithm only under no attack with a very minimal increase in loss, despite the presence of an attacker. The proposed method also maintains a false positive rate (FPR) of around 0.01 and a false negative rate (FNR) of around 0.10 of the global model in the presence of an attacker, which is a minimal difference from the baseline FedAvg-only results of around 0.008 for FPR and 0.07 for FNR. Additionally, the proposed method of FedAvg + cosine similarity filtering maintains computational statistics similar to baseline FedAvg with no attacker. Baseline results show an average runtime of about 34 min, while our proposed method shows an average runtime of about 35 min. Also, the average size of the global model being shared among workers remains consistent at around 7.15 megabytes, showing little to no increase in message payload sizes between baseline results and our proposed method. These results demonstrate that computationally lightweight cosine similarity-based detection methods can be effectively deployed in real-world, resource-constrained robotic swarm environments, providing a practical path toward improving robustness in real-world federated learning deployments beyond simulation-based evaluation. Full article

Electric vehicles (EVs) are promoted to decarbonise road transport, yet uptake remains slow in many emerging markets. This study examines consumer resistance to EV adoption in Sri Lanka by modelling status quo bias (SQB) using a Prospect Theory lens. An online survey of urban vehicle owners and near-term buyers yielded 157 responses; after screening and removing influential outliers, 151 cases were analysed using partial least squares structural equation modelling (PLS-SEM). The model tests five Prospect Theory-aligned antecedents, namely, loss aversion, reference dependence, risk perception, framing effects, and uncertainty aversion, and evaluates environmental concern as a moderator. Results indicate that loss aversion has a significant positive effect on SQB (β = 0.216, p = 0.005) and uncertainty aversion is the strongest predictor (β = 0.453, p < 0.001), while reference dependence, risk perception, and framing effects show positive but statistically non-significant direct effects. Moderation tests show that environmental concern significantly moderates the effects of reference dependence (β = 0.181, p = 0.039) and framing effects (β = 0.179, p = 0.037) on SQB, but does not significantly moderate the loss aversion, risk perception, or uncertainty aversion paths. Overall, perceived losses and—especially—ambiguity surrounding EV ownership appear to sustain reliance on internal combustion vehicles in this developing-country context, underscoring the need for interventions that reduce uncertainty (credible infrastructure signals, stable policy, service capability) and mitigate perceived losses (warranties, resale assurances) alongside carefully framed communications. Full article

To address the progressive collapse of mountainous stilted RC frames induced by debris flows, this study establishes a three-dimensional refined solid model using ABAQUS. The alternate path method (element removal method) is employed to simulate the failure of ground-floor columns under impact, revealing the underlying damage evolution mechanism. The results indicate that the loss of an edge column compromises structural stability significantly more than that of a corner column. Sequential multi-column failure leads to a nonlinear accumulation of damage; specifically, the simultaneous failure of a ‘corner column and its adjacent edge column’ completely severs the outer load-transfer paths, triggering a drastic inward load redistribution. Furthermore, under extreme scenarios, the maximum structural displacement and nodal stress surge to 66.67 mm and 40 MPa, respectively, while the axial force of the core central column jumps by nearly 150% (reaching 2.67 × 10⁶ N). The crushing of internal central columns due to overloading is identified as the critical mechanism triggering global collapse. Based on these findings, design recommendations are proposed, emphasizing the reinforcement of upstream edge columns and the construction of a ‘component-joint-global’ hierarchical defense system. Full article

Tubular flux-switching permanent-magnet linear machines (TFSPMLMs) are difficult to optimize using a single core material because conventional axial laminations suffer from severe in-plane eddy-current loss, whereas soft magnetic composites (SMCs) exhibit lower permeability and higher hysteresis loss. To address this trade-off, three hybrid SMC–laminated steel core configurations were investigated: H1, with radially laminated steel in the yoke; H2, with axially laminated steel in the tooth; and H3, with circumferential laminated steel segments. A reference SMC model (R1) and the three hybrid models were comparatively evaluated using three-dimensional finite element analysis (3D FEA). H1 and H2 showed degraded performance due to an interfacial micro-gap along the main flux path and additional in-plane eddy currents in the laminated steel regions. To mitigate these limitations, circumferential segmentation was applied to the laminated steel parts. With eight segments, H2 achieved a thrust force of 278.8 N, comparable to that of R1, while reducing iron loss by 22.5%; even a two-segment structure provided noticeable improvement. Among the investigated models, H3 showed the best overall performance by avoiding a micro-gap on the main flux path, achieving 285.5 N, and 3.9% higher thrust force and 18% lower iron loss than R1. These results indicate that H3 is the most effective hybrid-core configuration for maximizing both thrust force and loss reduction, whereas segmented H2 is an attractive practical option when manufacturability and low-loss operation are considered. Full article