Search Results (2,780)

Aiming at the problem of low signal-to-noise ratio of the projectile signal output by the sky screen sensor, the symmetrical characteristics of the projectile signal and the noise sources were analyzed, and a joint denoising method of variational mode decomposition (VMD) and wavelet threshold based on the whale optimization algorithm (WOA) was proposed. This method employs the whale optimization algorithm (WOA) to globally optimize the key parameters of variational mode decomposition (VMD), namely the number of modes $K$ and the penalty factor $\alpha$, to obtain the optimal parameter combination that minimizes the envelope entropy. The original projectile signal is adaptively decomposed through the optimal VMD parameters. The variance contribution rate is used to screen the decomposed intrinsic mode function to retain the IMF component containing the projectile signal information and improve the signal-to-noise ratio of the projectile signal. Then, a wavelet threshold function is introduced to conduct secondary denoising processing on the selected modal components, further improving the signal-to-noise ratio of the projectile signal. Through noise reduction experiments on the measured projectile signals, it is proved that the signal-to-noise ratio of the signals has been significantly improved, indicating that this method can suppress noise while retaining the effective signal of the projectile to the greatest extent, laying a foundation for the recognition of projectile signals of the sky screen target. Full article

Assessing Mine Emergency Rescue Capability (MERC) is critical for ensuring mining safety and advancing sustainable development. However, existing MERC assessments often lack a holistic sustainability perspective. To bridge this gap, this study develops a MERC assessment model grounded in the Triple Bottom Line (TBL) framework, integrating the relative difference function (RDF) to address the fuzziness and subjectivity in evaluation processes. A hierarchical indicator system is constructed, comprising 5 primary factors and 25 sub-indicators across environmental, economic, and social dimensions, reflecting both immediate rescue effectiveness and long-term sustainability performance. Indicator weights are derived from a hybrid approach that combines the subjective G1 method with the objective entropy weight method. RDF is employed to compute membership degrees, and the final MERC level is determined by level characteristic values. The model is validated through an empirical study of six green mines in China. Results demonstrate robust performance and consistency with alternative methods and reveal the environmental dimension as the dominant driver within the TBL framework. This finding supports the ecology-first principle of green mining and underscores the alignment of high-level emergency preparedness with sustainable development objectives. By explicitly embedding sustainability principles into safety assessment, the proposed model provides a scientifically grounded tool to guide the green transformation of the mining industry. Future work will adapt the model to diverse mining contexts and refine the indicators to better support global sustainability goals. Full article

Plastic pollution, driven by the durability and widespread use of polyolefins such as polypropylene (PP) and high-density polyethylene (HDPE), poses a formidable environmental challenge. To address this issue, we have developed an integrated multiscale framework that combines thermocatalytic experimentation, process-scale simulation, and molecular-level modeling to optimize the catalytic pyrolysis of PP and HDPE waste. Under the identified optimal conditions (300 °C, 10 wt % HMOR zeolite), liquid-oil yields of 60.8% for PP and 87.3% for HDPE were achieved, accompanied by high energy densities (44.2 MJ/kg, RON 97.5 for PP; 43.7 MJ/kg, RON 115.2 for HDPE). These values significantly surpass those typically reported for uncatalyzed pyrolysis, demonstrating the efficacy of HMOR in directing product selectivity toward valuable liquids. Above 400 °C, the process undergoes a pronounced shift toward gas generation, with gas fractions exceeding 50 wt % by 441 °C, underscoring the critical influence of temperature on product distribution. Gas-phase analysis revealed that PP-derived syngas contains primarily methane (20%) and ethylene (19.5%), whereas HDPE-derived gas features propylene (1.9%) and hydrogen (1.5%), highlighting intrinsic differences in bond-scission pathways governed by polymer architectures. Aspen Plus process simulations, calibrated against experimental data, reliably predict product distributions with deviations below 20%, offering a rapid, cost-effective tool for reactor design and scale-up. Complementary density functional theory (DFT) calculations elucidate the temperature-dependent energetics of C–C bond cleavage and radical formation, revealing that system entropy increases sharply at 500–550 °C, favoring the generation of both liquid and gaseous intermediates. By directly correlating catalyst acidity, molecular reaction mechanisms, and process-scale performance, this study fills a critical gap in plastic-waste valorization research. The resulting predictive platform enables rational design of catalysts and operating conditions for circular economy applications, paving the way for scalable, efficient recovery of fuels and chemicals from mixed polyolefin waste. Full article

Cyber defense has evolved into an algorithmically intensive discipline where mathematical rigor and adaptive computation underpin the robustness and continuity of digital infrastructures. This review consolidates the algorithmic spectrum that supports modern cyber defense, from cryptographic primitives that ensure confidentiality and integrity to behavioral intelligence algorithms that provide predictive security. Classical symmetric and asymmetric schemes such as AES, ChaCha20, RSA, and ECC define the computational backbone of confidentiality and authentication in current systems. Intrusion and anomaly detection mechanisms range from deterministic pattern matchers exemplified by Aho-Corasick and Boyer-Moore to probabilistic inference models such as Markov Chains and HMMs, as well as deep architectures such as CNNs, RNNs, and Autoencoders. Malware forensics combines graph theory, entropy metrics, and symbolic reasoning into a unified diagnostic framework, while network defense employs graph-theoretic algorithms for routing, flow control, and intrusion propagation. Behavioral paradigms such as reinforcement learning, evolutionary computation, and swarm intelligence transform cyber defense from reactive automation to adaptive cognition. Hybrid architectures now merge deterministic computation with distributed learning and explainable inference to create systems that act, reason, and adapt. This review identifies and contextualizes over 50 foundational algorithms, ranging from AES and RSA to LSTMs, graph-based models, and post-quantum cryptography, and redefines them not as passive utilities, but as the cognitive genome of cyber defense: entities that shape, sustain, and evolve resilience within adversarial environments. Full article

As the core component of pumped storage stations (PSS), pumped storage units (PSU) require a scientific and comprehensive evaluation method to guide the selection of optimal units and support the development of the new-type power system (NPS). This paper aims to address the symmetry issues in PSU evaluation methods by proposing an innovative approach based on evolutionary combination weighting and cloud model theory, thereby adapting to the long-term asymmetric evolution of the power system. First, the subjective and objective weights of indicators at all levels for PSU are obtained using the analytic hierarchy process (AHP) and the entropy weight method (EWM). Then, the optimal combination coefficients for subjective and objective weights are determined through game theory, achieving symmetry and balance between the subjective and objective weights. Subsequently, dynamic correction of the indicator weights is realized using a designed evolutionary response function, enabling the weights to evolve dynamically in response to the asymmetric development of the power system. Finally, the cloud model is employed to characterize the randomness and fuzziness of evaluation boundaries, which enhances the adaptability of the evaluation process and the interpretability of results. The simulation results show that, when considering the long-term asymmetric evolution of the power system, the expected score deviations of secondary indicators are approximately 4.7%, 1.3%, 3.5%, and 7.7%, respectively, with an overall score deviation of about 6.4%. The proposed method not only achieves symmetry and balance between subjective and objective factors in traditional evaluation but also accommodates the asymmetric evolution requirements of the power system. Full article

With the growing global demand for renewable energy, the pump as turbine (PAT) exhibits significant potential in the micro-hydropower sector. To reveal its internal unsteady flow characteristics and energy loss mechanisms, this study analyzes the internal flow field of an ultra-low specific speed pump as turbine (USSPAT) by employing a combined approach of entropy generation theory and dynamic mode decomposition (DMD). The results indicate that the outlet pressure pulsation characteristics are highly dependent on the flow rate. Under low flow rate conditions, pulsations are dominated by low-frequency vortex bands induced by rotor-stator interaction (RSI), whereas at high flow rates, the blade passing frequency (BPF) becomes the absolute dominant frequency. Energy losses within the PAT are primarily composed of turbulent and wall dissipation, concentrated in the impeller and volute, particularly at the impeller inlet, outlet, and near the volute tongue. DMD reveals that the flow field is governed by a series of stable modes with near-zero growth rates, whose frequencies are the shaft frequency (25 Hz) and its harmonics (50 Hz, 75 Hz, 100 Hz). These low-frequency modes, driven by RSI, contain the majority of the fluctuation energy. Therefore, this study confirms that RSI between the impeller and the volute is the root cause of the dominant pressure pulsations and periodic energy losses. This provides crucial theoretical and data-driven guidance for the design optimization, efficient operation, and stability control of PAT. Full article

This paper presents an integrated approach to social network analysis that combines graph theory, social network analysis (SNA), and Shannon’s information theory, applied to a real-world Twitter network built around the political hashtag Zandberg. Unlike studies based on synthetic data, our analysis leverages empirical content from a live political discourse. We employ classical centrality measures (degree, betweenness, closeness), local clustering coefficients, and community detection using the Louvain algorithm. A key theoretical contribution is the introduction of a novel metric: the Structural Entropy Index of a Community (SEIC), which quantifies internal decentralization of communication independently of community size. The analysis reveals significant variation in community structures and entropy levels. Larger communities tend to be decentralized (SEIC > 0.8), while smaller groups are often dominated by single influential nodes. These findings have practical implications for influencer identification, disinformation resilience assessment, and communication strategy optimization. The proposed methodological framework provides a robust tool for studying the structural and informational dynamics of real-world social networks in digital environments. Full article

Accurate abdominal multi-organ segmentation is essential for disease diagnosis and treatment planning. Although numerous deep-learning models have been proposed, current methods still struggle to balance segmentation accuracy with computational efficiency, particularly for images exhibiting inhomogeneous intensity distributions and complex anatomical structures. To address these challenges, we present a hybrid framework that integrates an Efficient Vision Mamba (EViM) module into a Transformer-based encoder. The EViM module leverages hidden-state mixer-based state-space duality to enable efficient global context modelling and channel-wise interactions. In addition, a weighted combination of cross-entropy and Jaccard loss is employed to improve boundary delineation. Experimental results on the Synapse dataset demonstrate that the proposed model achieves an average Dice score of 82.67% and an HD95 of 16.36 mm, outperforming current state-of-the-art methods. Further validation on the ACDC cardiac MR dataset confirms the generalizability of our approach across imaging modalities. The results indicate that the proposed framework achieves high segmentation accuracy while effectively integrating global and local information, offering a practical and robust solution for clinical abdominal multi-organ segmentation. Full article

This study aims to develop a non-contact automated impact-acoustic measurement system (AIAMS) for real-time detection of manufacturing defects in automotive brake calipers, a key component of the Electric Parking Brake (EPB) system. Calipers hold brake pads in contact with discs, and defects caused by repeated loads and friction can lead to reduced braking performance and abnormal vibration and noise. To address this issue, an automated impact hammer and a microphone-based measurement system were designed and implemented. Feature extraction was performed using Fast Fourier Transform (FFT) and Principal Component Analysis (PCA), followed by defect classification through machine learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbor (KNN), and Decision Tree (DT). Experiments were conducted on five normal and six defective caliper specimens, each subjected to 200 repeated measurements, yielding a total of 2200 datasets. Twelve statistical and spectral features were extracted, and PCA revealed that Shannon Entropy (SE) was the most discriminative feature. Based on SE-centric feature combinations, the SVM, KNN, and DT models achieved classification accuracies of at least 99.2%/97.5%, 98.8%/98.0%, and 99.2%/96.5% for normal and defective specimens, respectively. Furthermore, GUI-based software (version 1.0.0) was implemented to enable real-time defect identification and visualization. Field tests also demonstrated an average defect classification accuracy of over 95%, demonstrating its applicability as a real-time quality control system. Full article

Rural tourism in China is advancing rapidly, with cultural and tourism integration (CTI) becoming a vital pathway for sustainability. Mountain–water towns, given their special geographical conditions, face numerous challenges in CTI development, which need to enhance landscape resilience. This study proposes the theoretical framework of landscape resilience in mountain–water towns from the perspective of CTI. Taking Yinji Town of Wugang City as an example, it constructs a resilience evaluation system including three dimensions: cultural landscape, natural landscape, and social systems. The study uses the AHP–Entropy Weight combined method to determine indicator weights. Indicator scores are obtained through field research and GIS analysis, which are substituted into the preparedness–vulnerability resilience model to calculate resilience level, and the Jenks Natural Breaks method is used for level classification. Finally, the Obstacle Degree Model is applied to identify the primary obstacle factors affecting landscape resilience. The results indicate the following: (1) The average landscape resilience (RI) score of the 19 villages in Yinji Town is 0.84 (RI < 1), indicating a generally low level. Two villages are in the high-level range, while four villages are in the low-level range. (2) Cultural landscape resilience is the primary weakness, with the lowest average score (0.70), while natural landscape resilience is the highest (1.03). (3) Major obstacles include such as the number of cultural inheritors, the degree of susceptibility to natural disasters, and the distance to core mountain–water resources. The study contributes a CTI-based evaluation framework and methodology for assessing landscape resilience, offering enhancement strategies through increased preparedness and reduced vulnerability. Full article

Pine wilt disease (PWD), caused by the invasive nematode Bursaphelenchus xylophilus, poses a growing threat to East Asian coniferous forests, which is further exacerbated by climate change. While studies have successfully applied Maximum Entropy (MaxEnt) models to map the potential spread of PWD, they have primarily focused on broad spatial scales and climatic factors. This highlights the need for fine-scale, integrative modeling approaches that also account for environmental and anthropogenic factors. Therefore, we applied the MaxEnt model combined with change vector analysis to evaluate the spatial risk and potential future spread of PWD in Andong-si, Republic of Korea, under the SSP1-2.6 climate scenario. We integrated forest structure, soil conditions, topography, climate variables, and anthropogenic factors to generate high-resolution risk maps and identify the most influential environmental drivers. Notably, we demonstrated that historical infection proximity and isothermality strongly influence habitat suitability. We also, for the first time, projected an eastward shift of high-risk areas in Andong-si under future climate conditions. These findings provide timely insights for designing proactive surveillance networks, implementing risk-based monitoring, and developing climate-resilient management strategies. Our integrative modeling framework offers decision-support tools that can enhance early detection and targeted interventions against invasive forest pests under environmental change. Full article

Building community resilience is essential for ensuring that communities can not only survive but also thrive in the face of various challenges and uncertainties. However, existing research has deficiencies in the comprehensive evaluation framework and systematic analysis of different types of urban communities within high-density Chinese cities. This study constructed a comprehensive urban community resilience assessment system (UCRA) that covers four dimensions: environmental, service, social, and governance resilience. In a case study of the Chinese megacity of Tianjin, urban communities were categorized into three physical types and three regional categories. The UCRA contained 40 detailed indicators, and the weighting of indicators was was determined using a mixed approach combining the AHP and entropy methods. The findings revealed that tower apartments in urban Chinese communities demonstrated relatively high resilience, whereas older residential complexes exhibited the lowest resilience performance. Furthermore, central urban communities generally displayed high resilience, in contrast to peripheral urban areas, where low levels of resilience were often discovered. Building upon these findings, this study discusses the characteristics and challenges associated with the resilience of various community types. By establishing a theoretical basis for creating intelligent assessment and monitoring systems, we advocate for targeted community development strategies, thereby promoting smart transformation of community resilience. Full article

Objectives: This study proposes a systematic strategy for optimizing sustained-release formulations using mixture experiments. Methods: Model variables were identified and screened via LASSO regression, Smoothly Clipped Absolute Deviation (SCAD), and Minimax Concave Penalty (MCP), leading to the construction of a quadratic inference function-based objective model. Using this model, three multi-objective optimization algorithms—NSGA-III, MOGWO, and NSWOA—were employed to generate a Pareto-optimal solution set. Solutions were further evaluated through the entropy weight method combined with TOPSIS to reduce subjective bias. Results: The MCP-screened model demonstrated strong fit (AIC = 19.8028, BIC = 45.2951) and suitability for optimization. Among the Pareto-optimal formulations, formulation 45, comprising HPMC K4M (38.42%), HPMC K100LV (13.51%), MgO (6.28%), lactose (17.07%), and anhydrous CaHPO₄ (7.52%), exhibited superior performance, achieving cumulative release rates of 22.75%, 64.98%, and 100.23% at 2, 8, and 24 h, respectively. Compared with the original formulation, drug release was significantly improved across all time points. Conclusions: This integrated workflow effectively accounted for component interactions and repeated measurements, providing a robust and scientifically grounded approach for optimizing multi-component sustained-release formulations. Full article

Ectropis grisescens Warren (Lepidoptera: Geometridae) is a destructive pest that has severely impacted major tea-growing regions in recent years; as such, it is vital to determine how climate change influences its areas of distribution. In this study, we employed a parameter-optimized maximum entropy (MaxEnt) model, integrating 170 E. grisescens occurrence records and seven selected environmental variables, to predict the pest’s current and future potential distribution in China. Parameter optimization was conducted with the ENMeval package in R, identifying the optimal feature combination as “linear—L, quadratic—Q” and the regularization multiplier as 0.5. These results indicated that the mean diurnal range (bio2), precipitation of driest month (bio14), and elevation were the key variables contributing to the suitable area for E. grisescens. Currently, the total potential suitable area for E. grisescens in China spans approximately 1.969 × 10⁶ km², covering 20.51% of the country’s land area, of which 5.121 × 10⁵ km², 7.385 × 10⁵ km², and 7.185 × 10⁵ km² possess low, medium, and high suitability, respectively. Notably, the high-suitability regions are predominantly concentrated in southeastern China, encompassing the provinces and municipalities of Zhejiang, Anhui, Hunan, Jiangsu, Chongqing, Jiangxi, Guangxi, Hubei, and Sichuan. Under future climate scenarios, it is projected that the suitable habitats for this pest will undergo varying degrees of change. Specifically, under the SSP1-2.6 scenario, the suitable habitat area is estimated to increase by up to 12.21% by the 2070s. Under the SSP2-4.5 scenario, the centroid of the suitable habitat will be displaced northwest by up to 238.4 km by the 2030s. Our findings provide valuable insights into the future management of E. grisescens and will aid in mitigating its ecological and economic impacts. Full article

Enterprise document management faces a significant challenge: traditional clustering methods focus solely on content similarity while ignoring organizational context, such as priority, workflow status, and temporal relevance. This paper introduces FLACON (Flag-Aware Context-sensitive Clustering), an information-theoretic approach that captures multi-dimensional document context through a six-dimensional flag system encompassing Type, Domain, Priority, Status, Relationship, and Temporal dimensions. FLACON formalizes document clustering as an entropy minimization problem, where the objective is to group documents with similar contextual characteristics. The approach combines a composite distance function—integrating semantic content, contextual flags, and temporal factors—with adaptive hierarchical clustering and efficient incremental updates. This design addresses key limitations of existing solutions, including context-aware systems that lack domain-specific intelligence and LLM-based methods that require prohibitive computational resources. Evaluation across nine dataset variations demonstrates notable improvements over traditional methods, including a 7.8-fold improvement in clustering quality (Silhouette Score: 0.311 vs. 0.040) and performance comparable to GPT-4 (89% of quality) while being ~7× faster (60 s vs. 420 s for 10 K documents). FLACON achieves $O(m log n)$ complexity for incremental updates affecting $m$ documents and provides deterministic behavior, which is suitable for compliance requirements. Consistent performance across business emails, technical discussions, and financial news confirms the practical viability of this approach for large-scale enterprise document organization. Full article