安全科学与韧性(英文)最新文献

{"title":"Early fire detection technology based on improved transformers in aircraft cargo compartments","authors":"Hong-zhou Ai ,&nbsp;Dong Han ,&nbsp;Xin-zhi Wang ,&nbsp;Quan-yi Liu ,&nbsp;Yue Wang ,&nbsp;Meng-yue Li ,&nbsp;Pei Zhu","doi":"10.1016/j.jnlssr.2024.03.003","DOIUrl":"https://doi.org/10.1016/j.jnlssr.2024.03.003","url":null,"abstract":"<div><p>The implementation of early and accurate detection of aircraft cargo compartment fire is of great significance to ensure flight safety. The current airborne fire detection technology mostly relies on single-parameter smoke detection using infrared light. This often results in a high false alarm rate in complex air transportation environments. The traditional deep learning model struggles to effectively address the issue of long-term dependency in multivariate fire information. This paper proposes a multi-technology collaborative fire detection method based on an improved transformers model. Dual-wavelength optical sensors, flue gas analyzers, and other equipment are used to carry out multi-technology collaborative detection methods and characterize various feature dimensions of fire to improve detection accuracy. The improved Transformer model which integrates the self-attention mechanism and position encoding mechanism is applied to the problem of long-time series modeling of fire information from a global perspective, which effectively solves the problem of gradient disappearance and gradient explosion in traditional RNN (recurrent neural network) and CNN (convolutional neural network). Two different multi-head self-attention mechanisms are used to classify and model multivariate fire information, respectively, which solves the problem of confusing time series modeling and classification modeling in dealing with multivariate classification tasks by a single attention mechanism. Finally, the output results of the two models are fused through the gate mechanism. The research results show that, compared with the traditional single-feature detection technology, the multi-technology collaborative fire detection method can better capture fire information. Compared with the traditional deep learning model, the multivariate fire prediction model constructed by the improved Transformer can better detect fires, and the accuracy rate is 0.995.</p></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"5 2","pages":"Pages 194-203"},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666449624000197/pdfft?md5=ae8ef9b8111b0ce9fd4d6ed8f06a3a5e&pid=1-s2.0-S2666449624000197-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140650168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dynamic exploratory hybrid modelling framework for simulating complex and uncertain system","authors":"Gangqiao Wang ,&nbsp;Han Xing ,&nbsp;Yongqiang Chen ,&nbsp;Yi Liu","doi":"10.1016/j.jnlssr.2024.03.001","DOIUrl":"https://doi.org/10.1016/j.jnlssr.2024.03.001","url":null,"abstract":"<div><p>Complex disaster systems involve various components and mechanisms that could interact in complex ways and change over time, leading to significant deep uncertainty. Due to deep uncertainty, decision-makers have severe inadequacy of knowledge and often encounter unpredictable surprises that may emerge in the future, thus making it difficult to specify appropriate models and parameters to describe the system of interest. In this paper, we propose a dynamic exploratory hybrid modeling framework that fits data, models, and computational experiments together to simulate complex systems with deep uncertainty. In the framework, one needs to develop multiple plausible models from a hybrid modeling perspective and perform enormous computational experiments to explore the diversity of future scenarios. Real-time data is then incorporated into diverse forecasts to dynamically adjust the simulation system. This ultimately enables an ongoing modeling and analysis process in which deep uncertainty would be gradually mitigated. Our approach has been applied to a human-involved car-following system simulation under complex traffic conditions. The results show that the proposed approach can improve the prediction accuracy while enhancing the sensitivity of the simulation system to uncertain changes in the system of interest.</p></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"5 2","pages":"Pages 167-178"},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666449624000203/pdfft?md5=81390dfc9db37ae6f4389e10c3a77d2f&pid=1-s2.0-S2666449624000203-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140606684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An indicator model for assessing community resilience to the COVID-19 pandemic and its validation: A case study in Hong Kong","authors":"Nan Liao,&nbsp;Muhammad Nawaz","doi":"10.1016/j.jnlssr.2023.12.005","DOIUrl":"10.1016/j.jnlssr.2023.12.005","url":null,"abstract":"<div><p>The COVID-19 outbreak had a significant negative impact on the world, and the fifth wave of COVID-19 in Hong Kong brought a considerable shock to Chinese society. There is a growing call for more resilient cities. However, empirical evidence and validation of modeling studies of resilience indicators for urban community responses to the COVID-19 pandemic still need to be provided. In this study, a resilience assessment indicator model comprising 4 subsystems, 7 indicators, and 12 variables was developed to assess the resilience of Hong Kong communities in response to COVID-19 (i.e., Resilience Index). Furthermore, this study utilized regression models such as geographically weighted regression (GWR) and multiscale GWR (MGWR) to validate the resilience model proposed in this study at the model and variable levels. In the regression model, the Resilience Index and the individual variables in the resilience model are explanatory variables, and the outcomes of the COVID-19 pandemic (confirmed cases, confirmation rate, discharged cases, discharge rate) are dependent variables. The results showed that: (i) the resilience of Hong Kong communities to the COVID-19 pandemic was not strong in general and showed some clustered spatial distribution characteristics; (ii) the validation results at the model level showed that the Resilience Index did not explain the consequences of the COVID-19 pandemic to a high degree; (iii) the validation results at the variable level showed that the MGWR model was the best at identifying the relationships between explanatory variables and the dependent variable; and (iv) compared with the model-level assessment results, the variable-level assessment explained the consequences of the COVID-19 pandemic better than the model level assessment results. The above analysis and the spatial distribution maps of the resilience variables can provide empirically based and targeted insights for policymakers.</p></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"5 2","pages":"Pages 222-234"},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666449624000185/pdfft?md5=30fbcd9b6e3118101c1dc386d741cc38&pid=1-s2.0-S2666449624000185-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140795815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic risk assessment of gas pipeline operation process by fusing visual and olfactory monitoring","authors":"Denglong Ma ,&nbsp;Weigao Mao ,&nbsp;Guangsen Zhang ,&nbsp;Chaoyi Liu ,&nbsp;Yi Han ,&nbsp;Xiaoming Zhang ,&nbsp;Hansheng Wang ,&nbsp;Kang Cen ,&nbsp;Wan Lu ,&nbsp;Denghui Li ,&nbsp;Hanyue Zhang","doi":"10.1016/j.jnlssr.2024.02.003","DOIUrl":"https://doi.org/10.1016/j.jnlssr.2024.02.003","url":null,"abstract":"<div><p>With the rapid increase in urban gas consumption, the frequency of maintenance and repair of gas pipelines has escalated, leading to a rise in safety accidents during these processes. The traditional manual supervision model presents challenges such as inaccurate monitoring results, incomplete risk factor analysis, and a lack of quantitative risk assessment. This research focuses on developing a dynamic risk assessment technology for gas emergency repair operations by integrating the monitoring outcomes of artificial olfactory for gas leakage information and video object recognition for visual safety factor monitoring data. To quantitatively evaluate the risk of the operation process, a three-dimensional risk assessment model combining gas leakage with risk-correlated sensitivity was established as well as a separate three-dimensional risk assessment model integrating visual risk factors with predictable risk disposition. Furthermore, a visual risk quantification expression mode based on the risk matrix-radar map method was introduced. Additionally, a risk quantification model based on the fusion of visual and olfactory results was formulated. The verification results of simulation scenarios based on field data indicate that the visual-olfactory fusion risk assessment method can more accurately reflect the dynamic risk level of the operation process compared to simple visual safety factor monitoring. The outcomes of this research can contribute to the identification of safety status and early warning of risks related to personnel, equipment, and environmental factors in emergency repair operations. Moreover, these results can be extended to other operational scenarios, such as oil and gas production stations and long-distance pipeline operations.</p></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"5 2","pages":"Pages 156-166"},"PeriodicalIF":0.0,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666449624000161/pdfft?md5=9adfe514fc88f341a408e4a7855e67fd&pid=1-s2.0-S2666449624000161-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140539229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI for science: Predicting infectious diseases","authors":"Alexis Pengfei Zhao ,&nbsp;Shuangqi Li ,&nbsp;Zhidong Cao ,&nbsp;Paul Jen-Hwa Hu ,&nbsp;Jiaojiao Wang ,&nbsp;Yue Xiang ,&nbsp;Da Xie ,&nbsp;Xi Lu","doi":"10.1016/j.jnlssr.2024.02.002","DOIUrl":"10.1016/j.jnlssr.2024.02.002","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"5 2","pages":"Pages 130-146"},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266644962400015X/pdfft?md5=e98d804486d0967444d73fd9de22a294&pid=1-s2.0-S266644962400015X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140281542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring innovative techniques for damage control during natural disasters","authors":"Moinak Maiti ,&nbsp;Parthajit Kayal","doi":"10.1016/j.jnlssr.2024.02.004","DOIUrl":"10.1016/j.jnlssr.2024.02.004","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"5 2","pages":"Pages 147-155"},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666449624000173/pdfft?md5=d9f27cf83cda5df06f88a07917a4a9a9&pid=1-s2.0-S2666449624000173-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140271564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised intrusion detection system for in-vehicle communication networks","authors":"Kabilan N ,&nbsp;Vinayakumar Ravi ,&nbsp;V Sowmya","doi":"10.1016/j.jnlssr.2023.12.004","DOIUrl":"https://doi.org/10.1016/j.jnlssr.2023.12.004","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"5 2","pages":"Pages 119-129"},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666449624000070/pdfft?md5=c270f1be76c12ff19f65027e63889cd9&pid=1-s2.0-S2666449624000070-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140328614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A probabilistic model based on the peak-over-threshold approach for risk assessment of airport controllers' performance","authors":"Lili Zu ,&nbsp;Yijie Lu ,&nbsp;Min Dong","doi":"10.1016/j.jnlssr.2024.02.001","DOIUrl":"https://doi.org/10.1016/j.jnlssr.2024.02.001","url":null,"abstract":"<div><p>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 (<span><math><mrow><mn>4.929</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>7</mn></mrow></msup></mrow></math></span>). 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.</p></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"5 1","pages":"Pages 110-118"},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666449624000057/pdfft?md5=9d6786b81f15945e52ed0553f0807e58&pid=1-s2.0-S2666449624000057-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139986215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"E-voting system using cloud-based hybrid blockchain technology","authors":"Beulah Jayakumari ,&nbsp;S Lilly Sheeba ,&nbsp;Maya Eapen ,&nbsp;Jani Anbarasi ,&nbsp;Vinayakumar Ravi ,&nbsp;A. Suganya ,&nbsp;Malathy Jawahar","doi":"10.1016/j.jnlssr.2024.01.002","DOIUrl":"https://doi.org/10.1016/j.jnlssr.2024.01.002","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"5 1","pages":"Pages 102-109"},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666449624000069/pdfft?md5=3f8ecb21cf6c18772a6fba45e0bdbc41&pid=1-s2.0-S2666449624000069-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139985765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Restimate: Recovery Estimation Tool for Resilience Planning","authors":"Scott Miles ,&nbsp;Megan Ly ,&nbsp;Nick Terry ,&nbsp;Youngjun Choe","doi":"10.1016/j.jnlssr.2024.01.001","DOIUrl":"https://doi.org/10.1016/j.jnlssr.2024.01.001","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"5 1","pages":"Pages 47-63"},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666449624000045/pdfft?md5=efcbd9e2f5868035b756e8a77d1fd347&pid=1-s2.0-S2666449624000045-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139714984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}