Early Iterations Research Articles

“Californian ‘Start-’Em-Young’ System”: The Golden State and US Swimming, 1954–1964

Each Olympics, the American swimming team is comprised of swimmers from universities and club teams from across the country. California has produced elite swimmers since the first Olympics where US swimmers won a medal (1904), and its clubs and teams developed into national and international swimming powers in the post-World War II rejuvenation of the Olympics. This article investigates the ways the California Age Group swimming program shaped the American swimming culture as it rose to prominence in global competitions in the 1950s and 1960s, identifying the aquatic enterprise as an early iteration of youth sport as a serious, commercialized, and commodified pastime. Additionally, we explore how California Age Group swimming served as a site for young girls (and boys) to be a part of the American nationalist project in the post-war pre-Title IX era, an era when there were otherwise few sporting opportunities for girls.

Revisiting social workers in schools (SWIS) – making the case for safeguarding in context and the potential for reach

PurposeRecently, there have been renewed calls to place social workers in schools. Although these are not unchartered waters, contextual understandings of safeguarding have reaffirmed the centrality of schools in the lives of young people and keeping them safe. Yet, schools can only do so much to support young people. Safeguarding practice reviews continue to highlight the shortcomings of contextless assessment. This paper aims to make the case for a broader approach to safeguarding practice by placing social workers in schools.Design/methodology/approachA scoping review was undertaken to elicit social workers in schools (SWIS) literature from the UK, as well as international examples. Keyword searches revealed a lack of consensus on shared/agreement terminology for SWIS. The literature was organised thematically, as a mechanism to open up the extent, range and nature of research activity in relation to SWIS.FindingsFindings are presented in three themes: misunderstandings of SWIS – what do they actually do?; micro versus macro interventions; and the concept of reach.Research limitations/implicationsThe main implications of this study are to sharpen the focus on the centrality of schools in the lives of children and young people; to expand school-based initiatives as a way to reach young people at risk; to re-centre practice to local, community orientation with an emphasis on early help; and to bring together pockets of good practice and learn from successful partnership models.Originality/valueLittle attempt has been made to contemplate the past and present and rethread school-based initiatives. There is an absence of attention afforded to the theoretical foundations of SWIS. This paper identifies a gap in interest from the early iterations of SWIS, with a recent upsurgence in attention.

No more jokes: Comic complexity, Adult Swim and a political aesthetic model of humour

For such a complex cultural form, the politics of humour have historically been understood in highly reductive terms: either as an abstract political function (e.g. carnival or ridicule) or as a simple formal flourish that can be pressed into the service of any cause. Drawing on the work of Raymond Williams and Jacques Rancière, I argue instead for a ‘political aesthetic’ model that grasps humour as a cultural formation, the politics of which cannot be determined in advance or in the abstract but only understood in relation to the political, economic and social elements of a wider conjuncture. This political aesthetic approach will be illustrated through a case study of the historical development of the internationally distributed Adult Swim programming block: an example of how shifts in economic and technological context can lead to shifts in the political meaning of a persistent comic aesthetic. At the forefront of an emergent comic formation in the early 2000s, Adult Swim’s once niche comic aesthetic now informs dominant models of online humour in ways that threaten to mitigate, or even reverse, the critical cultural politics of its earlier iterations.

A Modified Grey Wolf Optimization Algorithm for an Intrusion Detection System

Cyber-attacks and unauthorized application usage have increased due to the extensive use of Internet services and applications over computer networks, posing a threat to the service’s availability and consumers’ privacy. A network Intrusion Detection System (IDS) aims to detect aberrant traffic behavior that firewalls cannot detect. In IDSs, dimension reduction using the feature selection strategy has been shown to be more efficient. By reducing the data dimension and eliminating irrelevant and noisy data, several bio-inspired algorithms have been employed to improve the performance of an IDS. This paper discusses a modified bio-inspired algorithm, which is the Grey Wolf Optimization algorithm (GWO), that enhances the efficacy of the IDS in detecting both normal and anomalous traffic in the network. The main improvements cover the smart initialization phase that combines the filter and wrapper approaches to ensure that the informative features will be included in early iterations. In addition, we adopted a high-speed classification method, the Extreme Learning Machine (ELM), and used the modified GWO to tune the ELM’s parameters. The proposed technique was tested against various meta-heuristic algorithms using the UNSWNB-15 dataset. Because the generic attack is the most common attack type in the dataset, the primary goal of this paper was to detect generic attacks in network traffic. The proposed model outperformed other methods in minimizing the crossover error rate and false positive rate to less than 30%. Furthermore, it obtained the best results with 81%, 78%, and 84% for the accuracy, F1-score, and G-mean measures, respectively.

Jointly Optimizing Client Selection and Resource Management in Wireless Federated Learning for Internet of Things

Federated learning (FL) has been proposed to efficiently and privacy-preserving distributed machine learning architecture for the Internet of Things (IoT). In a wireless FL system, clients in IoT devices train their local models over the local data sets. The derived local models are uploaded to an FL server to generate a global model, broadcasted to the clients in the next global iteration for further training. Owing to the heterogeneous feature of the clients, client selection is critical to determine the overall training time. Traditionally, the objective of client selection is to select the maximum number of clients who can derive and upload their local models before the deadline in each global iteration. However, selecting more clients increases the energy consumption of the clients. Moreover, selecting the maximum number of clients is unnecessary as having fewer clients in early global iterations and more clients in later global iterations have been proved to achieve higher model accuracy. Hence, this article proposes to dynamically adjust and optimize the tradeoff between maximizing the number of selected clients and minimizing the total energy consumption of the clients by selecting suitable clients and allocating appropriate resources in terms of CPU frequency and transmission power. We formulate the joint client selection and resource management problem and design the energy and latency-aware resource management and client selection (ELASTIC) algorithm to efficiently solve the problem. Extensive simulations are conducted to demonstrate the performance of ELASTIC.

Impact of population topology on particle swarm optimization and its variants: An information propagation perspective

Particle swarm optimization is one of the most effective optimization algorithms motivated by bird flocking behaviours. Population topology is a key aspect of particle swarm optimization research. However, after more than twenty years of research, the effects of the population topology are still poorly understood. Previous research has established that the information propagation speed determined by the population topology has an important impact on the algorithm performance; however, the impact of information propagation speed on particle swarm optimization and its variants has not yet been investigated. In this paper, information propagation in particle swarms is described and, hence, a method of simulating information propagation in particle swarms is introduced, which is used to obtain the information propagation speed. The correlation between the information propagation speed and algorithm performance is clarified through numerical simulation. The results show that the information propagation speed has a strong negative correlation with the population diversity of particle swarm optimization and its variants in the early iterations, regardless of the adopted test function and population diversity measure. The results also show that when optimizing problems with the same property, the impact of population topology on the optimization results of particle swarm optimization and variant algorithms is similar. Further more, this study provides some guidance on the population topology selection for particle swarm optimization and its variants. These findings contribute to our understanding of the impact of population topology on particle swarm optimization and its variants, and provide a basis for population topology selection for particle swarm optimization and its variants.

Marking the Tides of Nova Scotia’s Elastic History: Margaret Conrad’s At the Ocean’s Edge

Marking the Tides of Nova Scotia’s Elastic History: Margaret Conrad’s At the Ocean’s Edge Thomas Peace (bio) MARGARET CONRAD IS THE BEST PROFESSOR I NEVER HAD. Teaching at Acadia during the first few years of my undergraduate career, she had a reputation as an engaged scholar who expected her students not just to know the details about their chosen area of study but also the context in which it was situated. Though I never took a course with her, at that time her influence on me was three-fold. First, she once gave me a good ribbing for my poor trivia knowledge at a faculty-student mixer. Second, she was the author of my Canadian history textbook – History of the Canadian Peoples (affectionately known by the names of its two co-authors “Conrad and Finkel”).1 And third, just before she left Acadia I caught her talking about history on a national television program as she modeled civically engaged scholarship. From these experiences I learned that good history carefully balances detail and context with an eye to its significance in shaping the present and future. Without taking a class with her, Conrad had taught me several important lessons about what it means to be a historian; they continue to shape my professional life today. Conrad’s At the Ocean’s Edge: A History of Nova Scotia to Confederation exemplifies these qualities of Conrad’s scholarship.2 The book begins with Sylvia Hamilton’s evocative poem “Keep On Keepin’ On,” a poem about persistence, exclusion, and inclusion in Nova Scotia. Conrad uses Hamilton’s work to suggest that the foundation for the province’s cultural, social, and political developments lies somewhere between the region’s awkward geography (her exact words are on xiv) and the diverse communities that made this place home before 1867. Over the book’s 12 chapters, Conrad provides a beautiful narrative that well balances her careful eye for detail with an explanation of the significance of her arguments about Nova Scotia placed in national and [End Page 96] international contexts. Her book carefully weaves together local events into their broader regional and global significance. Though its title implies that its subject matter will be geographically and chronologically bound, At the Ocean’s Edge is expansive in its scope. The first chapter tackles “Ancient History” while the “Afterword” brings the reader through a nuanced discussion of the province’s post-Confederation history. Though we might quibble about the necessity of including the region’s distant past as part of a book that explains Nova Scotia’s early history, a reader would be mistaken if they dismissed the book’s powerful “Afterword.” It is here where Conrad’s argument – that this period matters significantly if we want to understand the province today – is at its strongest. The watchword for this part of the book is “continuity” as the post-Confederation Nova Scotian world built upon the substantial moments of change that occurred over the two centuries preceding the colony’s entering Confederation: “It is not that Nova Scotians have resisted making adjustments to national and global trends since 1867, but rather that continuity as much as change has defined the results of the adjustment process [to the industrial age]” (336). Conrad’s chronological scope is matched by her geographical framing. This is a book that forcefully grapples with Nova Scotia’s fluid identities and elastic borders. Though the province is a place that we might easily define today by its political and geographical boundaries, Conrad recognizes the legacy of its earlier iterations. Following the work of John Reid (among others), Conrad considers Nova Scotia as a place with multiple meanings – identifying Mi’kma’ki and Acadie as relatively coterminous geopolitical spaces that remain meaningful for many people living in this place today. In taking this approach, Conrad draws attention to aspects of the region’s history often neglected in sweeping overviews of colonial or provincial histories written in the mid-to-late 20th century or earlier. One captivating strategy that she uses is biography. As the reader makes their way through this book, they will be introduced to dozens of the people whose lives shaped, or...

An Adaptive Planewave Method for Electronic Structure Calculations

We propose an adaptive planewave method for eigenvalue problems in electronic structure calculations. The method combines a priori convergence rates and accurate a posteriori error estimates into an effective way of updating the energy cut-off for planewave discretizations, for both linear and nonlinear eigenvalue problems. The method is error controllable for linear eigenvalue problems in the sense that for a given required accuracy, an energy cut-off for which the solution matches the target accuracy can be reached efficiently. Further, the method is particularly promising for nonlinear eigenvalue problems in electronic structure calculations as it shall reduce the cost of early iterations in self-consistent algorithms. We present some numerical experiments for both linear and nonlinear eigenvalue problems. In particular, we provide electronic structure calculations for some insulator and metallic systems simulated with Kohn--Sham density functional theory (DFT) and the projector augmented wave (PAW) method, illustrating the efficiency and potential of the algorithm.

Reactor Physics Considerations for Use of Yttrium Hydride Moderator

Recent developments in manufacturing large metal hydrides are enabling their use as a moderator for advanced reactor designs. Yttrium hydride (YHx) is particularly attractive for small reactor designs because of its ability to retain a high hydrogen density at elevated temperatures. Design iteration for the Transformational Challenge Reactor (TCR), which uses a YH1.85 moderator, revealed positive moderator temperature coefficients. A positive temperature coefficient for YHx is expected regardless of the core design, however, the positive moderator coefficient exceeded that of the negative fuel temperature coefficient in some early TCR design iterations. The cause of the positive moderator coefficient is analyzed, and conditions for which positive temperature coefficients would be expected are identified for a number of fuel and moderator geometry layouts for dense tristructural isotropic/silicon carbide fuel and UO2 fuel.

Evolution of total hip arthroplasty in patients younger than 30 years of age: A systematic review and meta-analysis.

While surgical technique and implant technology for total hip arthroplasty (THA) has improved over the years, it is unclear whether recent progress has translated to improved clinical outcomes for young patients. The goal of this study is to determine trends in (1) indications, (2) surgical technique (3) clinical and radiographic outcomes, and (4) survivorship for THA in patients younger than 30years of age. MedLine, Cochrane, EMBASE, and Google Scholar were searched using several key phrases for articles focusing on THA performed on patients younger than 30years of age between 1971 and 2020. A total of 34 qualifying articles were identified and stratified into three groups according to operative years and compared to one another on the basis of (1) indications; (2) fixation technique; (3) implant design; (4) clinical and radiographic outcomes; and (7) survivorship. The mean patient age at index THA were 20.5 (9-30), 22.1 (11-30) and 21.5 (10-30) years, respectively, for each study group. Over time, patients underwent fewer THAs for JRA (Juvenile Rheumatoid Arthritis) (p < 0.001) but more for post-treatment and iatrogenic avascular necrosis (p < 0.001; p < 0.001). Early THAs primarily used metal on UHMWPE (Ultra high molecular weight polyethylene) (71.7%, p < 0.001), modern THA predominantly use ceramic on HXLPE (Highly cross-linked polyethylene) (42.5%, p < 0.001). Early fixation methods used cement (60.4%, p < 0.001), and modern fixation primarily use press fit technology (95.9%, p < 0.001). Prevalence of radiographic loosening decreased significantly (p < 0.001) over time. There was no significant difference in clinical improvement on HHS. Lastly, fewer patients required THA revision in recent decades (p < 0.001). Advances in surgical technique and technology have served to improve implant longevity. Surprisingly, subjective clinical scores showed no significant improvement over time, suggesting that early iterations of THA were extremely successful.

An Enhanced Grey Wolf Optimizer with a Velocity-Aided Global Search Mechanism

This paper proposes a novel variant of the Grey Wolf Optimization (GWO) algorithm, named Velocity-Aided Grey Wolf Optimizer (VAGWO). The original GWO lacks a velocity term in its position-updating procedure, and this is the main factor weakening the exploration capability of this algorithm. In VAGWO, this term is carefully set and incorporated into the updating formula of the GWO. Furthermore, both the exploration and exploitation capabilities of the GWO are enhanced in VAGWO via stressing the enlargement of steps that each leading wolf takes towards the others in the early iterations while stressing the reduction in these steps when approaching the later iterations. The VAGWO is compared with a set of popular and newly proposed meta-heuristic optimization algorithms through its implementation on a set of 13 high-dimensional shifted standard benchmark functions as well as 10 complex composition functions derived from the CEC2017 test suite and three engineering problems. The complexity of the proposed algorithm is also evaluated against the original GWO. The results indicate that the VAGWO is a computationally efficient algorithm, generating highly accurate results when employed to optimize high-dimensional and complex problems.

AUV launch & recovery handling simulation on a rough sea

Launch & recovery operations performed on a rough sea are challenging from many perspectives. Not only the risk of equipment damage is high, but also waiting for an acceptable weather window can be costly and may affect the installation or investment plans. This work concentrates on a LARS and AUV handling system operated from a vessel subjected to motions due to waves. A mathematical model developed uses joint coordinates and homogeneous transformations to derive the equations of motion. Then a numerical model is developed and implemented, forming an own, dedicated code. The model and computer programme allows to simulate a launch and recovery operation. Hydrodynamic loading is proposed with an own modelling approach, which should suit an early project design stage. The aim of developed tool is to support with decisions such as vessel pre-selection, LARS location on deck, possible operational weather limits, system loads and performance and to assist with many other related problems. Simulations presented in this work are examples of practical analyses performed when a design of handling equipment enters an early engineering stage and fast variant calculations are required to advance the project. The tool developed may be applied in early discussions and design iterations as the time needed for analyses do not influence any other technical or commercial processes.

A Two-Step Optimization Framework for Low Sidelobe NLFM Waveform Using Fourier Series

The nonlinear frequency modulation (NLFM) waveform can achieve low sidelobes without loss of signal-to-noise ratio (SNR) by utilizing a nonlinear time-frequency structure. This letter presents a novel two-step progressive optimization framework for the NLFM waveform using Fourier series by exploiting the advantages of random searches and deterministic modifications. The modified GA is used for the preliminary design of the NLFM waveform, where random searches with waveform diversity and consideration of the signal’s characteristic provide a rapid decrease of sidelobes in early iterations. When the descent speed of the sidelobes slows down, deterministic modifications in the gradient-based tuning are followed to further reduce the sidelobes. In the simulation, the peak-to-sidelobe level ratio (PSLR) is reduced by at least 5 dB with an almost equivalent normalized impulse response width (IRW) under the same time-bandwidth product (TBP) compared with other NLFM waveforms. Finally, the optimized NLFM waveform is implemented on hardware, which verifies its performance.

Computational Methods for Scheduling the Charging and Assignment of an On-Site Shared Electric Vehicle Fleet

We investigate a fleet scheduling problem arising when a company has to manage its own fleet of electric vehicles. Aim is to assign given usage reservations to these vehicles and to devise a suitable charging plan for all vehicles while minimizing a cost function. We formulate the problem as a compact mixed integer linear program, which we strengthen in several ways. As this model is hard to solve in practice, we perform a Benders decomposition, which separates the problem into a master problem and a subproblem and solves them iteratively in an alternating manner. We perform the decomposition in two different ways. First we follow a more classical way, then we enrich the master problem making it stronger but also more complex and the subproblem smaller and simpler to solve. To improve the overall performance, we propose a problem-specific General Variable Neighborhood Search metaheuristic for solving the master problem in earlier iterations. Experimental results show that directly solving the complete mixed integer linear program usually performs well for small to some medium sized problem instances. For larger instances, however, it is not able to find any reasonable primal solutions anymore, while the Benders decomposition scales much better. Especially the variant with the heuristic delivers high quality solutions in reasonable time. The Benders decomposition with the more complex master problem also yields reasonable dual bounds and thus practically relevant quality guarantees for the larger instances.

Hybrid Evolutionary Algorithm Based Relevance Feedback Approach for Image Retrieval

Searching images from the large image databases is one of the potential research areas of multimedia research. The most challenging task for nay CBIR system is to capture the high level semantic of user. The researchers of multimedia domain are trying to fix this issue with the help of Relevance Feedback (RF). However existing RF based approaches needs a number of iteration to fulfill user's requirements. This paper proposed a novel methodology to achieve better results in early iteration to reduce the user interaction with the system. In previous research work it is reported that SVM based RF approach generating better results for CBIR. Therefore, this paper focused on SVM based RF approach. To enhance the performance of SVM based RF approach this research work applied Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) before applying SVM on user feedback. The main objective of using these meta-heuristic was to increase the positive image sample size from SVM. Firstly steps PSO is applied by incorporating the user feedback and secondly GA is applied on the result generated through PSO, finally SVM is applied using the positive sample generated through GA. The proposed technique is named as Particle Swarm Optimization Genetic Algorithm- Support Vector Machine Relevance Feedback (PSO-G A-SVM-RF). Precisions, recall and F-score are used as performance metrics for the assessment and validation of PSO-GA-SVM-RF approach and experiments are conducted on coral image dataset having 10908 images. From experimental results it is proved that PSO-GA-SVM-RF approach outperformed then various well known CBIR approaches.

A Fast and Scalable Polyatomic Frank-Wolfe Algorithm for the LASSO

We propose a fast and scalable Polyatomic Frank-Wolfe (P-FW) algorithm for the resolution of high-dimensional LASSO regression problems. The latter improves upon traditional Frank-Wolfe methods by considering generalized greedy steps with polyatomic (i.e. linear combinations of multiple atoms) update directions, hence allowing for a more efficient exploration of the search space. To preserve sparsity of the intermediate iterates, we re-optimize the LASSO problem over the set of selected atoms at each iteration. For efficiency reasons, the accuracy of this re-optimization step is relatively low for early iterations and gradually increases with the iteration count. We provide convergence guarantees for our algorithm and validate it in simulated compressed sensing setups. Our experiments reveal that P-FW outperforms state-of-the-art methods in terms of runtime, both for FW methods and optimal first-order proximal gradient methods such as the Fast Iterative Soft-Thresholding Algorithm (FISTA).

‘Who could have expected such a disaster?’ How responses to the 1892 cyclone determined institutional trajectories of vulnerability in Mauritius

On the April 29, 1892 an intense cyclone directly struck the island of Mauritius. The resulting devastation was considerable with over 1200 people killed, making it by far the deadliest cyclone in recorded Mauritian history. While this cyclone is commemorated in multiple places, the responses chosen (and rejected) at the time, their long term impacts, and their antecedent factors have never before been detailed.This research draws from archive data in Mauritius to place the 1892 cyclone in its historical context. A context that includes uncovering the emergence of vulnerability within the early iterations of the cyclone early warning system in Mauritius (which is also revealed to likely be the world's first) before the 1892 cyclone and the failure of this warning system in 1892. This research details the consequences of the popular and prevalent discourses of the cyclone being ‘unprecedented’, ‘unpredictable’ and without warning signs, that were central to the way the cyclone was experienced, responded to, and the long term recovery.This article goes on to detail the institutional decision not to address vulnerability in the early warning system in 1892 (shaped by competing interests, a number of misunderstandings, and a lack of memory) and how this decision determined one element of the experience of cyclones and the trajectories of vulnerability in Mauritius over the long term and up to today. This article therefore adds to calls to consider the long term nature of disasters as fundamental to understanding the creation and perpetuation of vulnerability (referred to as trajectories) over time, and that decisions made in the distant past may be critical to understanding vulnerability today.

Migration-Based Moth-Flame Optimization Algorithm

Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO cannot provide competitive results for complex optimization problems. The algorithm sinks into the local optimum due to the rapid dropping of population diversity and poor exploration. Hence, in this article, a migration-based moth–flame optimization (M-MFO) algorithm is proposed to address the mentioned issues. In M-MFO, the main focus is on improving the position of unlucky moths by migrating them stochastically in the early iterations using a random migration (RM) operator, maintaining the solution diversification by storing new qualified solutions separately in a guiding archive, and, finally, exploiting around the positions saved in the guiding archive using a guided migration (GM) operator. The dimensionally aware switch between these two operators guarantees the convergence of the population toward the promising zones. The proposed M-MFO was evaluated on the CEC 2018 benchmark suite on dimension 30 and compared against seven well-known variants of MFO, including LMFO, WCMFO, CMFO, CLSGMFO, LGCMFO, SMFO, and ODSFMFO. Then, the top four latest high-performing variants were considered for the main experiments with different dimensions, 30, 50, and 100. The experimental evaluations proved that the M-MFO provides sufficient exploration ability and population diversity maintenance by employing migration strategy and guiding archive. In addition, the statistical results analyzed by the Friedman test proved that the M-MFO demonstrates competitive performance compared to the contender algorithms used in the experiments.

Population initialization techniques for evolutionary algorithms for single-objective constrained optimization problems: Deterministic vs. stochastic techniques

The population initialization step is a common step in the majority (or even all) of evolutionary algorithms (EAs). There are many population initialization techniques. Due to the limited population size and the high dimensionality of many problems, there is little chance to cover the promising regions in the search space. From different perspectives, this paper compares the stochastic and deterministic population initialization techniques through comparing five of the well-known population initializers: Random number generator (RNG), Latin Hypercube, Sobol, Halton, and Kronecker. Due to the presence of many constraints in real-world applications, in this paper, we are focusing only on single-objective constrained optimization problems. Specifically, the goal is to investigate if there is a significant difference between these population initialization methods. In this paper, we explain theoretically and mathematically these different population initialization techniques. Moreover, different illustrative examples and visualizations are introduced to explain the behavior of each technique and compare different techniques from different perspectives. The results show that due to the high uniformity of the low-discrepancy sequences such as the Halton and Sobol sequences, the generated points using these sequences are more evenly distributed over the space than RNG, which is the commonly used technique for initializing the populations in EAs. Practically, using a set of benchmark functions, we investigate the use of each population initialization technique for initializing different population-based evolutionary algorithms. The results of our experiments prove that with sufficient numbers of iterations, the EAs are not sensitive to the initialization methods and there are no significant differences between the mentioned population initialization methods. Further, the low discrepancy methods enhance the exploration ability of EAs in early iterations.

Sanitary Passports and the Birth of the Immunized Self

Abstract This article sketches a short history of the Covid-19 passport by examining its earlier iterations, including the “sanitary passport” (passeport sanitaire), an epidemiological tool officially introduced on the global stage by the French delegation during the 1893 International Sanitary Conference in Dresden. The sanitary passport shares with the Covid-19 passport two features. First, a similar aim, that of controlling the movement of potentially infected individuals across borders. Second, a similar condition of possibility, that of being the product of a pandemic crisis. The article identifies key characteristics as well as departures with the reinvention of the Covid-19 vaccine or immunity passport. The paper also situates the birth of the sanitary passport within a security context of increasing use of national passports as a means for the continuous surveillance of criminals and vagabonds as well as a scientific context marked by a key mutation: the birth of the immunized self.