Mitigation Capability Research Articles

Distributed Denial of Service Attack Detection and Prevention Using Pipit Fox-Attentional Deep Learning in Software-Defined Networking

The Software-Defined Network (SDN) remains the innovative model that assists in satisfying the new application requirements of future networks. However, destructive attacks, particularly Distributed Denial of Service (DDoS) attacks, are aimed primarily at the SDN control panel, presenting a significant risk to network security. The traditional approaches for DDoS attack detection exhibit several limitations including interpretability challenges, overfitting problems, and false predictions. To mitigate these drawbacks, the research proposed a Pipit Flying Fox Optimized Deep Neural Network (PPF-DNN) and Intelligent Pipit Forage Optimized-Attentional Convolutional Neural Network (IPFO-ACNN) model. The channel attention is employed to enable the network to focus selectively on relevant information, improving the model’s sensitivity to subtle anomalies associated with DDoS attacks. By dynamically adjusting the attention weights across channels, the mechanism enhances the capability of the model to discriminate the normal network traffic and malicious patterns, which results in improved precision and reduced false positives. This approach harnesses the power of CNN to automatically extract hierarchical features from network data, enhancing the efficacy of the ACNN model in attack detection. IPFO and PPF algorithms integrate the unique strengths of bio-inspired algorithms, creating a synergistic approach for efficient model parameter tuning. In the case of Training Percentage (TP) 80, the accuracy, sensitivity, and specificity of the IPFO-ACNN model are evaluated as 98.87%, 99.24% and 97.91%, respectively. Similarly, the PPF-DNN model’s performance is assessed as 98.49%, 92.50% and 98.55%, which presents a promising avenue for bolstering network security infrastructure, ensuring more robust DDoS detection and mitigation capabilities in an increasingly complex cyber threat landscape.

Research on a High-Dynamics Acquisition Algorithm for New Binary Offset Carrier Signal in UAV Communication

As unmanned aerial vehicles (UAVs) are widely used in various fields, there is an increasing demand for UAV anti-jamming, multipath mitigation, and covert secrecy. Frequency-hopping binary offset carrier (FH-BOC) signals possess higher anti-jamming and multipath mitigation capabilities than direct-sequence spread spectrum (DSSS) and binary offset carrier (BOC) signals. A prerequisite for constructing communication links between UAVs using FH-BOC signals is the design of efficient acquisition algorithms to capture the signals successfully. In this paper, the modulation and characteristics of the FH-BOC signal are introduced. The maximum relative velocity between UAVs is 5.5 km/s, the maximum acceleration is 50 g, and the maximum plus acceleration is 20 g/s. In this high dynamic environment, the parameters for the parallel code phase and Partial Matched Filter–Fast Fourier Transform (PMF-FFT) acquisition algorithms targeting FH-BOC(10,1) signals are designed, and the acquisition performance of these algorithms is comparatively analyzed. The acquisition time for the first and second algorithms is 4.3317 s and 6.137 s. The number of real additions required by the first and second algorithms is approximately 10.9×109 and 8.9×109, and the number of real multiplications is approximately 7.6×109 and 6.7×109. This helps in selecting the acquisition algorithm when FH-BOC signals are used to build inter-UAV communication links.

Assessment of disaster mitigation capability oriented to typhoon disaster chains: A case study of Fujian Province, China

Typhoon disasters are the most frequent and severe natural disasters in China’s southeastern coastal region. The strong wind and rainstorms during typhoons result in secondary disasters, such as storm surges, floods, and landslides. This phenomenon is referred to as a typhoon disaster chain that causes significant loss of life and property every year. Accurately assessing the disaster mitigation capacity and reducing limitations are crucial for improving typhoon disaster risk prevention. However, assessing the capacity of mitigating typhoon disaster chains requires further study. In this study, we select Fujian Province, China, as a case study to identify typical typhoon disaster chains, drawing on historical disasters and the sensitivity of the disaster-forming environment. We establish a county-based framework to assess the disaster mitigation capabilities for typhoon disaster chains. The mitigation capacities of 84 counties within Fujian Province are evaluated using the Random Forest (RF) algorithm, which determines the weights of various indicators. The results show the following. 1) The dominant types of typhoon disaster chains in Fujian Province are typhoon-rainstorm-urban waterlogging (TRU), typhoon-rainstorm-flooding (TRF), typhoon-rainstorm-landslide (TRL), and typhoon-strong wind-storm surge (TWS). Significant spatial differences are observed. 2) The assessment framework, which includes disaster prevention, disaster relief, and government management capabilities, accurately reflects the spatial differences in the disaster chain. The results can be extended to other regions or other disaster chains. 3) Significant spatial heterogeneity is observed in the disaster prevention, relief, and disaster mitigation capacity for typhoon disaster chains in the counties in Fujian Province. The eastern coastal areas have high mitigation capacity, whereas the northwest has low capacity. The disaster prevention capacity is very high in Changle City and Xiuyu District (2.4 %), and the disaster relief capacity is very high in Gulou District, Taijiang District, Changle City, and Huli District (4.8 %). The government disaster management capacity is very high in 7 counties (e.g., Longhai City, Minhou County, and Hui’an City (8.3 %)). The comprehensive disaster mitigation capabilities are very high in Changle City, Longhai City, and Xiuyu County (3.6 %). This study provides a scientific reference for assessing disaster chain mitigation capabilities and enhancing grassroots disaster mitigation efforts.

Dynamic Response of Gradient Aluminum Foam Sandwich Tubes under External Explosive Loads.

In this paper, we numerically investigate the dynamic response and explosion resistance of gradient aluminum foam sandwich tubes subjected to external blast loads. Based on 3D-Voronoi technology, we construct density-graded aluminum foam cores to systematically explore the influence of core density distribution, density gradient, and average relative density on the protective performance of these structures. Our primary objective is to identify optimal design parameters that maximize explosion mitigation capabilities while balancing energy absorption and specific energy absorption capacities. The research results show that a positive gradient core configuration exhibits superior anti-explosion performance, significantly outperforming its uniform and negatively graded counterparts, particularly when the gradient value is substantial. For the positive gradient cores, an increase in the gradient value leads to a corresponding enhancement in explosion resistance. Conversely, in negatively graded cores, a higher gradient value diminishes the anti-explosion performance. Furthermore, while augmenting the relative density of the core layer does improve the overall explosion resistance of the sandwich tube, it comes at the cost of reduced energy absorption and specific energy absorption capabilities, highlighting the need for a delicate balance among these competing factors.

Earthquake damage mitigation methods for buried pipelines under compressive loads: A case study of the Thames water pipeline

Promoting tensile failure by providing a proper orientation angle between the pipe axis and the fault line is the main seismic design philosophy for buried steel pipelines. However, most of the severe damage and failures experienced by pipelines are mainly due to negative crossing angle and thus compressive loads acting along the pipeline. This paper investigates different earthquake damage mitigation methods such as Carbon Fiber Reinforced Polymer (CFRP) wrapped pipes, Steel Pipes for Fault Crossing (SPF), and corrugated pipes for buried steel pipelines which are mainly subjected to compressive loads. Therefore, the Thames water transmission pipeline, which is a well-known case study, that suffered major and minor damage due to compressive forces in the 1999 Kocaeli earthquake, is considered to simulate and compare the earthquake damage mitigation capabilities of these countermeasures. The numerical studies are performed by using a three-dimensional nonlinear finite element model. The results show that the use of CFRP composites in buried pipelines, regardless of their thickness, wrapping length, or layer orientation, does not have the expected damage reduction effect, but does increase the effective length between major wrinkles or change the type of pipe failure. On the other hand, SPFs and corrugated pipes are more effective in earthquake damage reduction due to their high axial and rotational capabilities.

Guessing Human Intentions to Avoid Dangerous Situations in Caregiving Robots

The integration of robots into social environments necessitates their ability to interpret human intentions and anticipate potential outcomes accurately. This capability is particularly crucial for social robots designed for human care, as they may encounter situations that pose significant risks to individuals, such as undetected obstacles in their path. These hazards must be identified and mitigated promptly to ensure human safety. This paper delves into the artificial theory of mind (ATM) approach to inferring and interpreting human intentions within human–robot interaction. We propose a novel algorithm that detects potentially hazardous situations for humans and selects appropriate robotic actions to eliminate these dangers in real time. Our methodology employs a simulation-based approach to ATM, incorporating a “like-me” policy to assign intentions and actions to human subjects. This strategy enables the robot to detect risks and act with a high success rate, even under time-constrained circumstances. The algorithm was seamlessly integrated into an existing robotics cognitive architecture, enhancing its social interaction and risk mitigation capabilities. To evaluate the robustness, precision, and real-time responsiveness of our implementation, we conducted a series of three experiments: (i) A fully simulated scenario to assess the algorithm’s performance in a controlled environment; (ii) A human-in-the-loop hybrid configuration to test the system’s adaptability to real-time human input; and (iii) A real-world scenario to validate the algorithm’s effectiveness in practical applications. These experiments provided comprehensive insights into the algorithm’s performance across various conditions, demonstrating its potential for improving the safety and efficacy of social robots in human care settings. Our findings contribute to the growing research on social robotics and artificial intelligence, offering a promising approach to enhancing human–robot interaction in potentially hazardous environments. Future work may explore the scalability of this algorithm to more complex scenarios and its integration with other advanced robotic systems.

TECXY simulations of the power exhaust in the multi-impurity plasma of DTT reactor

Reduction of the heat load to plasma-facing components is a crucial problem for future fusion reactors like Divertor Test Tokamak (DTT). Mitigation of the power load via increased plasma radiation with the use of puffed ions of impurities would be one way to mitigate power in the scrape-off layer. This paper presents a numerical investigation of the impact of seeded impurities on the radiation pattern and the power load to the divertor plates of the high-field DTT reactor in the single null (SN) configuration. The simulations have been done with the use of the TECXY code, which solves multi-species plasma transport equations for multiple impurity species simultaneously and all associated ionization stages in a two-dimensional poloidal geometry. TECXY represents the model of plasma transport in the scrape-off layer region by a classical set of transport equations of multi-species plasma derived by Braginskii. The paper aims to compare the mitigation capabilities of neon and argon impurities seeded in the plasma of the DTT device and to obtain a significant energy flux reduction to the target plate at the smallest possible impurity concentration. Performed investigations showed the effects of neon and argon impurities seeding separately for constant electron density at the separatrix. It has been found that the decrease in electron temperature on the divertor plates up to 3 eV at the outer and the inner divertor plates and the peak power load below 15 MW m−2 at the outer divertor plate can be achieved with argon seeding and much lower impurity concentration than that in the case of neon impurity seeding. Studies have shown the complexity of the effect of neon and argon impurities on the boundary plasma. It was found that the reduction of temperature and the power on both divertor plates was the most effective for the high upstream plasma densities. The results show also that diffusive perpendicular transport strongly affects impurity radiation and thus plasma condition at divertor plates.

Application of a microalga, Tetradesmus obliquus PF3, for NO and CO2 removal from actual flue gas via cultivating in wastewater.

Greenhouse gas (GHG) emissions, particularly anthropogenic emissions, are the primary drivers of climate change. The cultivation of microalgae represents a highly promising strategy for mitigating atmospheric GHG levels. The growth characteristics and GHG mitigation capabilities of Tetradesmus obliquus PF3 were investigated in domestic wastewater at a thermal power plant. The maximum cell density and productivity were 1.52 ± 0.01 g L-1 and 0.33 ± 0.01 g L-1 day-1, respectively. Utilizing a serial configuration of two reactors, the elimination efficiency of NO and CO2 attained values of 78 ± 4% and 14 ± 4%, respectively. NO concentration at the outlet was less than 24.6 ± 2.9 mg m-3, meeting the latest Chinese discharge limits. Besides, the recovery efficiency of NO and CO2 increased to 77 ± 8% and 2.24 ± 0.04%, respectively, compared to that of the single reactor (40 ± 3%, 0.9 ± 0.0%). A removal efficiency of over 90% was achieved for TN and TP in domestic wastewater. The concentrations of COD (76.5 mg L-1), NH4+-N (0.9 mg L-1), TN(6.31 mg L-1), and TP (0.35 mg L-1) in effluent were below the thresholds of 100 mg L-1, 25 mg L-1, none data, and 3 mg L-1, respectively, complying with the Chinese Discharge Standard (Class II criteria set forth) for Municipal Wastewater Treatment Plants Pollutants. The harvested biomass exhibited a high content of carbohydrates and proteins, making it a viable feedstock for biofuels and bio-fertilizers. Our results demonstrate that Tetradesmus obliquus PF3-based flue gas treatment technology can simultaneously realize GHG removal, wastewater bio-remediation, and biomass recovery.

Metabolic-methane mitigation by combination of probiotic Escherichia coli strain Nissle 1917 and biochar in rumen fluid in vitro fermentation of dairy cow

In response to climate change, there have been various trials to reduce methane emissions from ruminant animals in the livestock industry, and one of the typical candidates is probiotics used as feed additives. We employed Escherichia coli Nissle 1917 (EcN) as a promising probiotic for the reduction of methane emissions and investigated the molecular mechanism of methane-reducing effects using CRISPR/Cas9. The anaerobic culture of EcN with biochar (BC) could significantly increase acetate and hydrogen production. Additionally, RNA-seq analysis revealed an upregulation in the expression of acetate synthesis genes, namely, pta, poxB, and ackA. Subsequently, treatment of rumen fluid with EcN significantly decreased methane and increased acetate and propionate production, as observed through the analysis of short-chain fatty acids, and these effects could accelerate with additional supplementation of BC. The observation of changes in the microbial composition of rumen fluid following treatment with EcN and BC was made through rumen metagenomic analysis and RT-qPCR. The results revealed an increase in acetogen and propionate producing bacteria abundance and a decrease in methanogen abundance. Based on these findings, the CRISPR/Cas9 system was employed to elucidate the molecular mechanism of the methane-reducing capability of EcN. Gene deletion was performed targeting the genes poxB, ackA, and pta as key factors in acetate pathway in E. coli. The knockout of poxB, ackA, and pta could lead to the elimination of the methane-reduction of EcN as well as acetate-producing abilities. Collectively, the methane reduction ability of EcN in rumen fluid is associated with its acetate production capability, and the addition of BC significantly enhances methane mitigation capability of EcN.

A compact quasi-zero stiffness metamaterial for energy absorption and impact protection

A compact mechanical metamaterial for impact protection is proposed in this study. During compression, the unit cell of the metamaterial exhibits quasi-zero stiffness (QZS) and quasi-zero Poisson’s ratio (QZPR) characteristics, along with large densification strain, making it suitable for protection against low-speed impacts. Firstly, the parametric geometric model of the metamaterial is established, with sinusoidal thin-walled structures serving as the primary load-bearing units. The grid structures are employed to connect the thin-walled structures into a unified system. Secondly, to shorten the design cycle, numerical methods for the mechanical properties of the buffer structure are developed. The motion equation of the thin-walled structure are expressed in terms of arc length coordinates and solved using the Runge–Kutta method and the shooting method. The grid structure deformation and the effect of material plastic reinforcement on the structural mechanical response are analyzed, and the energy absorption (EA) of the buffer structure is calculated. Both simulation and experimental results demonstrate the high accuracy of the proposed calculation method. Lastly, drop hammer experiments are conducted to evaluate the impact mitigation performance of the designed buffer structure. The results indicate that the structure exhibits excellent impact mitigation capabilities, highlighting its potential for practical applications.

Optimization strategies for carbon neutrality in a maize-soybean rotation production system from farm to gate

Agricultural food production stands as a primary source of global greenhouse gas emissions, with residue management widely acknowledged as an effective avenue to achieve carbon neutrality. However, there is a lack of comprehensive assessment of the carbon footprint of agricultural residue production, processing, and recycling from farm to gate. Here we conducted a hybrid approach that integrated the DeNitrification-DeComposition (DNDC) model and life cycle carbon footprint to co-optimize water and residue management, targeting carbon neutrality and yield increase. The results revealed that controlled drainage with sub-irrigation (CDSI) served as a potent water management strategy to mitigate greenhouse gas emissions (15.65 %) while increasing yield (24.34 %) compared to free drainage. Regardless of the type of bioproduct, incorporating CDSI with downstream residue utilization exhibited substantial potential for carbon-negative emissions, reducing the average farm carbon footprint to −1538.15 kg CO2 eq yr−1. Among those scenarios, the CDSI with bioethanol scenario particularly stands out with its robust carbon mitigation capability (−1994.94 kg CO2 eq yr−1), driven by the enormous energy demand throughout the agricultural production process and the environmental friendliness of bioethanol, which substantially exceeds that of gasoline. However, challenges such as high plant construction costs and extended investment payback periods associated with residue conversion underscore the need for the urgent establishment of national subsidies and market mechanisms to foster carbon neutrality in agricultural production.

A Novel Method for Heat Haze-Induced Error Mitigation in Vision-Based Bridge Displacement Measurement.

Vision-based techniques have become widely applied in structural displacement monitoring. However, heat haze poses a great threat to the precision of vision systems by creating distortions in the images. This paper proposes a vision-based bridge displacement measurement technique with heat haze mitigation capability. The properties of heat haze-induced errors are illustrated. A dual-tree complex wavelet transform (DT-CWT) is used to mitigate the heat haze in images, and the speeded-up robust features (SURF) algorithm is employed to extract the displacement. The proposed method is validated through indoor experiments on a bridge model. The designed vision system achieves high measurement accuracy in a heat haze-free condition. The proposed mitigation method successfully corrects 61.05% of heat haze-induced errors in static experiments and 95.31% in dynamic experiments.

Torsional vibration suppression of cantilever beams system with the PNESI

Considering the intricate design of the conventional inerter structures, the compressive-torsional coupling effects of chiral metamaterial are used to achieve the torsional movement alongside linear movement, forming a simplified inerter mechanism. This simplified design is then integrated with the nonlinear energy sink (NES). In this study, a novel NES with the chiral metamaterial inerter structure is suggested to mitigate vibrations in the cantilever beam system. The introduction includes both the piecewise linear beam, providing nonlinear stiffness, and the chiral metamaterial inerter structure. These components are combined to form the piecewise nonlinear NES with inerter (PNESI) structure. Subsequently, the PNESI structure is integrated with the cantilever beam system, and a corresponding dynamic model is formulated to evaluate its vibration mitigation capabilities. The theoretical results are verified through experiments. The findings indicate that under steady-state vibration conditions, PNESI achieves peak vibration reductions of 88.6% in simulations and 62.7% in experiments for the cantilever beam system.

Design of a Radiation-Hardened SRAM Cell using 16nm Technology Node

Electronic circuits are exposed to very high energy radiation in the harsh conditions of outer space. This leads to soft errors such as single-event upsets (SEU), double-event upsets (DEU) and single-event transients (SET). The memory circuits are the most susceptible to these soft errors resulting in severe data loss. This paper proposes the design for an SRAM cell that is radiation hardened by design (RHBD). A comparative study of the standard SRAM cell and the RHBD SRAM cell indicates that the proposed design is resilient to SEUs and DEUs. The proposed design is an improvement on the 8T SRAM cell design and includes a 20T triple interlocked cell (TICE) design. The error correction capabilities of both designs are compared by manually injecting bit upsets at crucial nodes in the circuit. It is observed that the TICE design provides a 96.75% and 98.5% improvement over the standard 8T cell for 1-0 and 0-1 bit upsets respectively. The proposed design has been implemented using the 16nm model from the Arizona State University’s Predictive Technology Model (ASU-PTM), and the LTSpice software was used to carry out the simulations. Tools used: LTSpice v17 is the tool used to design the schematic circuits and to obtain the simulation results. The library file used to design the schematic is the 16nm technology node from the library of the Arizona State University’s Predictive Technology Model (ASU-PTM). Methods: To perform a comparative study of the 8T SRAM and 20T TICE, the schematic circuits were designed using the 16nm technology node in LTSpice. A baseline for the expected voltages of logic 0 and logic 1 is established before injecting errors at the sensitive voltage nodes of the circuit. The next step is to manually inject bit upsets at the crucial nodes of the memory cell to induce either a 0-1 upset or a 1-0 upset. The read/write operation is carried out along with the error injection to compare the error mitigation capacity of the circuits. Finally, the results are tabulated to prove that the 20T TICE is resilient to soft errors arising from exposure to high radiation. Results: During the normal operation of the 8T SRAM cell, the observed output voltage corresponding to logic 0 and logic 1 are 25mV and 780mV respectively. The voltage levels increased to 400mV during a 0-1 upset and dropped to 25mV during a 0-1 bit upset. This indicates that the cell is not reliable. In case of the 20T RHBD cell, the voltage levels for the normal operation were 5mV (logic 0) and 780mV (logic 1). The major improvement is shown in the case where the 20T TICE is injected with bit upsets, since it delivers a voltage of just 6mV during a 0-1 bit upset and maintains a voltage of 770mV during a 1-0 bit upset. Conclusion: The work performs the schematic designs for the comparison of the error mitigation capabilities of a 8T SRAM cell and a 20T TICE which is radiation hardened by design. The study proves that the 20T cell is capable of successfully mitigating both a 1-0 bit upset and a 0-1 bit upset

Comprehensive evaluation and optimal management of extreme disaster risk in Chinese urban agglomerations by integrating resilience risk elements and set pair analysis

This study comprehensively assesses the extreme disaster risk faced by Chinese urban agglomerations and optimizes disaster risk management strategies to enhance their disaster resilience and sustainable development. The focus is specifically on droughts, floods, and earthquakes. The integration of resilience risk elements and set pair analysis (SPA) is employed to obtain risk assessment results for different urban agglomerations, including hazard, exposure, vulnerability, and disaster prevention and mitigation capabilities. The study explores the contributions of important indicators, such as exposure, vulnerability, and disaster prevention and mitigation capabilities, in disaster risk assessment, as well as their performance in various risk-prone urban areas. Comparative analysis of the assessment results for different urban agglomerations under various disaster types reveals varying levels of risk in droughts, floods, and earthquakes across different regions. Some areas exhibit higher hazard, exposure, or vulnerability levels, necessitating strengthened disaster prevention and mitigation measures. Regions with high exposure, low vulnerability, and strong disaster prevention and mitigation capabilities may face lower earthquake disaster risk, while regions with low exposure, high vulnerability, and weak disaster prevention and mitigation capabilities may face higher earthquake disaster risk. Moreover, the Comprehensive SPA model and Interval-valued SPA model outperform traditional SPA methods in assessing the existing flood risk level in Chinese urban agglomerations. The integrated model plays a significant role in predicting earthquake disasters, demonstrating the best predictive performance. This study provides essential reference data for urban disaster prevention and mitigation efforts, aiming to enhance urban resilience and disaster resilience capabilities.

Detection and Assessment of Seismic Response of High-Speed Railway Bridges Based on Smartphone Public Participation

The seismic response detection and operational safety assessment of high-speed railway (HSR) bridges play a crucial role in ensuring HSR systems’ operational safety and reliability. Smartphones have introduced intelligent inspection tools for structural health detection, becoming a new tool for intelligent structural inspection. Combining the public and smartphones is the key to public participation in structural health detection. This study utilizes smartphone-based structural seismic response inspection technology to investigate the framework of public participation in earthquake response inspection and assessment. This system comprises the Smart Bridge Brain (SBB), which integrates data from multiple sources and systems, an assigning mechanism for public participation inspection tasks, and smartphone-based HSR bridge structural seismic response inspection technology. At the same time, the Unreal Engine 5.0 software is used to create a mixed-reality virtual simulation experimental environment to validate the feasibility of this framework. The results indicate that the intelligent optimization of task allocation by the SBB successfully assigns detection tasks to each public participant. Public participants can promptly reach predefined damage structure detection targets and rapidly inspect bridge structural seismic response indicators using smartphones. In addition, this paper also conducts a comprehensive evaluation and analysis of the detection of the work efficiency index (WEI) within the system. Furthermore, optimization strategies for the efficient execution of detection tasks are proposed based on WEI variations influenced by different factors. The system framework is expected to enhance cluster-based HSR bridges’ intelligent disaster prevention and mitigation capabilities.

Cyberattack, cyber risk mitigation capabilities, and firm productivity in Kenya

Cyberattack, cyber risk mitigation capabilities, and firm productivity in Kenya

Hydrogel Extinguishants.

The exploitation of clean and efficient fire extinguishing materials has substantial implications for improving disaster prevention, mitigation, and relief capabilities, maintaining public safety, and protecting people's lives and property as well as the natural environment. Natural polymer hydrogel with high water containment, excellent film formation, high heat insulation, ecofriendliness, and degradability has huge potential in achieving new breakthroughs for developing clean and efficient fire extinguishing materials and products. In recent years, the exploitation of hydrogel extinguishing materials and the fabrication of products has attracted great attention, gradually replacing traditional fire extinguishing products. In this perspective, an in-depth review of the evolution of hydrogels applied for fire extinguishing and prevention is presented. Firstly, the extinguishing principles of hydrogel extinguishants are explained. Secondly, the preparation strategies and evaluation system of the hydrogel extinguishants are emphatically discussed. Although great progress has been made in developing high-performance hydrogel extinguishants, it remains challenging to develop cost-effective, degradable, and easy-to-use hydrogel extinguishants. Additionally, we highlight the importance of considering the commercial aspects of hydrogel extinguishants. Looking into the future, hydrogel extinguishants are promising, but continued investment in research and development is necessary to overcome the challenges.

Bio-inspired mechanical metamaterial with ultrahigh load-bearing capacity for energy dissipation

Mechanical metamaterials with energy-dissipating properties can provide impact mitigation in the field of engineering. However, current energy-dissipating metamaterials frequently face a tradeoff between energy-dissipation performance and load-bearing capability, severely limiting their practicality in high-intensity impact scenarios. Here, inspired by mushroom gills, we propose a mechanism for the snap-through buckling induced by geometric frustration, and we construct a snap-through metamaterial (STM) to address this problem. By analyzing the bifurcation buckling phenomenon, the STM is improved with higher energy-dissipation efficiency. Experiments demonstrate that the STM adaptively dissipates energy and mitigates impacts, achieving up to 33% reduction, in a reusable, self-recoverable, and rate-independent manner, leading to comprehensive performance. Employing a preloading strategy further enhances its impact mitigation capability as required. Notably, the STM exhibits a remarkable load-bearing capacity of up to 55 times higher than those of previous designs. The proposed design strategy of STMs paves the way for the development of interaction-based metamaterials, enabling applications in advanced dampers, mechanical waveguides, soft robotics, and low-frequency energy harvesters.

Research on the coupling test and organizational collaboration relationship between emergency plans and emergency responses—Based on the social network analysis method

Emergency plan is the action guide of emergency response, and the high coupling level and smooth organizational collaboration between emergency plan and emergency response are the necessary conditions for successful response to emergencies. Based on the case of “4–18″ fire accident in Beijing Changfeng Hospital, use social network analysis to quantify and visualize the coupling level and organizational collaboration relationship between emergency plan and emergency response from the overall network and individual central network. The results of the study found that in the overall network, the emergency plan and emergency response organization collaborative relationship network, has a high level of coupling and organizational collaboration. In the individual central network, the network of emergency plan and emergency response organization collaborative relationship has a low level of coupling and organizational collaboration. The results of this study reveal a noteworthy finding that the function of media organizations is unusually prominent in the network of emergency response organization collaboration, while it is not found in the network of emergency plan organization collaboration. As global risks continue to evolve, the results of the study provide valuable insights for other countries and regions seeking to establish emergency plan networks and emergency response networks with high levels of coupling and organizational collaboration, which are essential for enhancing global disaster prevention, mitigation, and response capabilities.