安全科学与韧性(英文)最新文献

The global health landscape has been persistently challenged by the emergence and re-emergence of infectious diseases. Traditional epidemiological models, rooted in the early 20th century, have provided foundational insights into disease dynamics. However, the intricate web of modern global interactions and the exponential growth of available data demand more advanced predictive tools. This is where AI for Science (AI4S) comes into play, offering a transformative approach by integrating artificial intelligence (AI) into infectious disease prediction. This paper elucidates the pivotal role of AI4S in enhancing and, in some instances, superseding traditional epidemiological methodologies. By harnessing AI's capabilities, AI4S facilitates real-time monitoring, sophisticated data integration, and predictive modeling with enhanced precision. The comparative analysis highlights the stark contrast between conventional models and the innovative strategies enabled by AI4S. In essence, AI4S represents a paradigm shift in infectious disease research. It addresses the limitations of traditional models and paves the way for a more proactive and informed response to future outbreaks. As we navigate the complexities of global health challenges, AI4S stands as a beacon, signifying the next phase of evolution in disease prediction, characterized by increased accuracy, adaptability, and efficiency.

The study critically examines the principles, mechanisms, and effectiveness of different damage control techniques in dealing with natural disasters, emphasizing their pivotal role in minimizing casualties and economic losses. Each of these damage control techniques is mapped based on their applications and relevance in the key areas of natural disaster management. By utilizing various real-world instances, the present study shows that the effective implementation of various innovative techniques is shaping the space of natural disaster management in a global context. The integration of different innovative techniques into the existing natural disaster management system has improved the survival rate, economic performance, and sustainable development. The study finds that innovative disaster financing models, clear strategies, and creating awareness among communities can improve the overall efficiency of innovative techniques that are currently used for damage control during natural disaster events. Despite the substantial advantages of these creative strategies, the study acknowledges challenges such as financial constraints, unclear policy goals, and community adaptation requirements. The study also indicates that in the future, automatic damage restoration, quick prototyping, and additive engineering will play a vital role in controlling damage from catastrophic events, while it acknowledges limitations in temporal scope, generalizability, and financial constraints.

In-vehicle communication has been optimized day to day to keep updated of the technologies. Control area network (CAN) is used as a standard communication method because of its efficient and reliable connection. However, CAN is prone to several network level attacks because of its lack in security mechanisms. Various methods have been introduced to incorporate this in CAN. We proposed an unsupervised method of intrusion detection for in-vehicle communication networks by combining the optimal feature extracting ability of autoencoders and more precise clustering using fuzzy C-means (FCM). The proposed method is light weight and requires less computation time. We performed an extensive experiment and achieved an accuracy of 75.51 % with the ML350 in-vehicle intrusion dataset. By experimental result, the proposed method also works better for other intrusion detection problems like wireless intrusion detection datasets such as WNS-DS with accuracy of 84.05 % and network intrusion detection datasets such as KDDCup with accuracy 60.63 % , UNSW_NB15 with accuracy 73.62 % and Information Security Center of Excellence (ISCX) with accuracy 74.83 %. Overall, the proposed method outperforms the existing methods and avoids labeled datasets when training an in-vehicle intrusion detection model. The results of the experiment of our proposed method performed on various intrusion detection datasets indicate that the proposed approach is generalized and robust in detecting intrusions and can be effectively deployed in real time to monitor CAN traffic in vehicles and proactively alert during attacks.

Airport tower control plays an instrumental role in ensuring airport safety. However, obtaining objective, quantitative safety evaluations is challenging due to the unavailability of pertinent human operation data. This study introduces a probabilistic model that combines aircraft dynamics and the peak-over-threshold (POT) approach to assess the safety performance of airport controllers. We applied the POT approach to model reaction times extracted from a radiotelephony dataset via a voice event detection algorithm. The model couples the risks of tower control and aircraft operation to analyze the influence of human factors. Using data from radiotelephony communications and the Base of Aircraft Data (BADA) database, we compared risk levels across scenarios. Our findings revealed heightened airport control risks under low demand (0.374) compared to typical conditions (0.197). Furthermore, the risks associated with coupling under low demand exceeded those under typical demand, with the final approach stage presenting the highest risk ($4.929\times {10}^{-7}$). Our model underscores the significance of human factors and the implications of mental disconnects between pilots and controllers for safety risks. Collectively, these consistent findings affirm the reliability of our probabilistic model as an evaluative tool for evaluating the safety performance of airport tower controllers. The results also illuminate the path toward quantitative real-time safety evaluations for airport controllers within the industry. We recommend that airport regulators focus on the performance of airport controllers, particularly during the final approach stage.

With the invention of Internet-enabled devices, cloud and blockchain-based technologies, an online voting system can smoothly carry out election processes. During pandemic situations, citizens tend to develop panic about mass gatherings, which may influence the decrease in the number of votes. This urges a reliable, flexible, transparent, secure, and cost-effective voting system. The proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system, and it is carried out in three phases: the registration phase, vote casting phase and vote counting phase. A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting phases. Using smart contracts, third-party interventions are eliminated, and the transactions are secured in the blockchain network. Finally, to provide accurate voting results, the practical Byzantine fault tolerance (PBFT) consensus mechanism is adopted to ensure that the vote has not been modified or corrupted. Hence, the overall performance of the proposed system is significantly better than that of the existing system. Further performance was analyzed based on authentication delay, vote alteration, response time, and latency.

The U.S. National Institute of Standards and Technology (NIST) published the Community Resilience Planning Guide in 2016. The NIST Guide advocates for a participatory process for developing a performance measurement framework for the jurisdiction's resilience against a scenario hazard. The framework centers around tables of expected and desired recovery times for selected community assets, such as electricity, water, and natural gas infrastructures. The NIST Guide does not provide a method for estimating the expected recovery times. However, building high-fidelity computer models for such estimations requires substantial resources that even larger jurisdictions cannot cost-justify. The most promising approach to recovery time estimation is to systematically use data elicited from people to tap into the wisdom of the (knowledgeable) crowd. This paper describes a novel research-through-design project to enable the computer-supported elicitation of recovery time series data. This work is the first in the literature to examine people's ability to estimate recovery curves and how design influences such estimations. Its main contribution to resilience planning is three-fold: development of a new elicitation tool called Restimate, understanding its potential user base, and providing insights into how it can facilitate resilience planning. Restimate is the first tool to enable evidence-based expert elicitation in any community with limited resources for resilience planning. Beyond resilience planning, those who facilitate high-stakes planning activities under large uncertainties (e.g., mission-critical system design and planning) will benefit from a similar research-through-design process.

Pedestrian safety evacuation in aircraft cabins has been a challenging problem because of the aircraft's unique characteristics, such as the diversity of passengers and the restricted evacuation environment. It is difficult to reproduce evacuation activities in aircraft cabin due to safety concerns and cost constraints. To fill this gap, an improved cellular automaton model of crowd evacuation for aircraft cabin is established by incorporating the characteristics of cabin space structures and passenger attributes. Passengers are divided into healthy individual passengers and disabled-healthy group passengers, whose movement mechanisms are quantified. Based on the constructed model, simulation experiments are conducted using the configuration cabin layout of B737-800 as an example. The results show that the evacuation time is prolonged with increased passenger density and the number of disabled passengers. Moreover, the overall evacuation time is insignificantly affected by whether disabled-healthy group passengers' seats are close to the aisle or window, and the evacuation efficiency is best when their seats are evenly distributed in the cabin. The evacuation time is the shortest when all cabin doors are open, and pedestrians are evacuated the slowest when the central emergency doors are closed. This study provides valuable insights into effective strategies for pedestrian evacuation and crowd emergency management of civil aircraft.

The exponential growth in communication networks, data technology, advanced libraries, and mainly World Wide Web services has played a pivotal role in facilitating the retrieval of various types of information as needed. However, this progress has also led to security concerns related to the transmission of confidential data. Nevertheless, safeguarding these data during communication through insecure channels is crucial for obvious reasons. The emergence of steganography offers a robust approach to concealing confidential information, such as images, audio tracks, text files, and video files, in suitable media carriers. A novel technique is envisioned based on back-propagation learning. According to the proposed method, a hybrid fuzzy neural network (HFNN) is applied to the output obtained from the least significant bit substitution of secret data using pixel value differences and exploiting the modification direction. Through simulation and test results, it has been observed that the proposed methodology achieves secure steganography and superior visual quality. During the experiments, we observed that for the secret image of the cameraman, the PSNR & MSE values of the proposed technique are 61.963895 and 0.041361, respectively.

Information and communication technologies enable the transformation of traditional energy systems into cyber-physical energy systems (CPESs), but such systems have also become popular targets of cyberattacks. Currently, available methods for evaluating the impacts of cyberattacks suffer from limited resilience, efficacy, and practical value. To mitigate their potentially disastrous consequences, this study suggests a two-stage, discrepancy-based optimization approach that considers both preparatory actions and response measures, integrating concepts from social computing. The proposed Kullback-Leibler divergence-based, distributionally robust optimization (KDR) method has a hierarchical, two-stage objective function that incorporates the operating costs of both system infrastructures (e.g., energy resources, reserve capacity) and real-time response measures (e.g., load shedding, demand-side management, electric vehicle charging station management). By incorporating social computing principles, the optimization framework can also capture the social behavior and interactions of energy consumers in response to cyberattacks. The preparatory stage entails day-ahead operational decisions, leveraging insights from social computing to model and predict the behaviors of individuals and communities affected by potential cyberattacks. The mitigation stage generates responses designed to contain the consequences of the attack by directing and optimizing energy use from the demand side, taking into account the social context and preferences of energy consumers, to ensure resilient, economically efficient CPES operations. Our method can determine optimal schemes in both stages, accounting for the social dimensions of the problem. An original disaster mitigation model uses an abstract formulation to develop a risk-neutral model that characterizes cyberattacks through KDR, incorporating social computing techniques to enhance the understanding and response to cyber threats. This approach can mitigate the impacts more effectively than several existing methods, even with limited data availability. To extend this risk-neutral model, we incorporate conditional value at risk as an essential risk measure, capturing the uncertainty and diverse impact scenarios arising from social computing factors. The empirical results affirm that the KDR method, which is enriched with social computing considerations, produces resilient, economically efficient solutions for managing the impacts of cyberattacks on a CPES. By integrating social computing principles into the optimization framework, it becomes possible to better anticipate and address the social and behavioral aspects associated with cyberattacks on CPESs, ultimately improving the overall resilience and effectiveness of the system's response measures.

Outdoor cameras play an important role in monitoring security and social governance. As a common weather phenomenon, haze can easily affect the quality of camera shooting, resulting in loss and distortion of image details. This paper proposes an improved multi-exposure image fusion defogging technique based on the artificial multi-exposure image fusion (AMEF) algorithm. First, the foggy image is adaptively exposed, and the fused image is subsequently obtained via multiple exposures. The fusion weight is determined by the saturation, contrast, and brightness. Finally, the image fused by a multi-scale Laplacian algorithm is enhanced with simple adaptive details to obtain a clearer defogging image. It is subjectively and objectively verified that this algorithm can obtain more image details and distinct picture colors without a priori information, effectively improving the defogging ability.