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Il existe des similitudes linguistiques et culturelles entre le créole et certaines populations du Sud Bénin, notamment celles parlant le gungbe et le fongbe. Les deux territoires sont des anciennes colonies de la France, caractérisés par le multilinguisme et le multiculturalisme. Mieux, il existe une forte similarité des pratiques endogènes de soins de santé impliquant l’usage des plantes médicinales. Certaines désignations de plantes sont par ailleurs identiques, comme le terme « ama/aman ». There are linguistic and cultural similarities between Creole and certain populations in southern Benin, notably those Gungbe and Fongbe speakers. Both territories are former colonies of France, characterised by multilingualism and multiculturalism. What's more, there is a strong similarity in endogenous healthcare practices involving the use of medicinal plants. Some plant designations are also identical, such as the term "ama/aman".

This paper delves into the transformative intersection of emerging technologies and digital libraries, illuminating a path toward an enriched and accessible knowledge landscape. Focusing on Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Augmented Reality (AR), and Virtual Reality (VR), the study explores how these technologies redefine digital library experiences. AI and ML algorithms empower intuitive content curation and recommendation, reshaping the way users interact with digital resources. NLP bridges the gap between human language intricacies and digital systems, enhancing search functionalities and making information retrieval seamless. AR overlays digital information onto the physical world, expanding interactive learning possibilities, while VR immerses users in virtual realms, revolutionizing educational paradigms. The paper critically examines the practical integration of these technologies, ensuring digital libraries not only preserve vast knowledge repositories but also present information in engaging and accessible formats. Through AI-driven metadata generation and content tagging, digital libraries are systematically organized and enriched, amplifying search accuracy. These innovations not only preserve the past but also illuminate a future where knowledge is universally accessible, fostering curiosity, learning, and exploration. The study not only theoretically explores the potential of these technologies but also delves into the perceptions of practical library users, ensuring a user-centric approach in shaping the digital libraries of tomorrow. This research contributes significantly to the evolving landscape of digital libraries, paving the way for inclusive, immersive, and engaging knowledge experiences for diverse users worldwide.

The intersection of Ψ and ψn+1 is obtained by playing the game throughthe infinity geometries above. The game is played by finding the intersectionbetween the vector elements ⃗ Ωθ ∃ and ∂ ⃗Xl and then using the boundary ∂∇⟨∂ ⃗Yθto find the intersection of Ψ and ψn+1. Finally, the ˆΓ∞ can be used to verify ifthe intersection is non-empty or not.Show iterations of playing the game "Quantum Game Theory and Infinity Geometries" \section{Global Game} General Case: $$\tau = \mathcal{F} \left[\Psi\ \vee\ \Psi_{0 \to \delta} \Rightarrow \delta[\psi^{\mathcal{A}}]\cup\left(\psi_1\wedge \psi_2 \leftrightarrow \Psi_1 \Rightarrow \left( \xrightarrow{\Rightarrow \mathcal{F} \left[\Psi \cap \left\{ \bigcup_{\infty_{d=1}^{\infty} \psi_i} \psi_i \Rightarrow \delta\left[\psi^{\mathcal{A}}_{d \cdot \psi}\right] \right] \cup \left(\psi_1 \wedge \psi_2 \leftrightarrow \Psi_1 \RTimes \left({\Psi \downarrow}_{\mathcal{K}_{\mathbf{3}}} \cap\bigcup_{\emptyset =_1 y_{st_1} + y_{st_2} + \ldots + y_{st_l} \atop \exists (p',k) \in I_{y_{s2}l \overrightarrow{y}2}_{(p+\#I_{y_{s1}2,{(p,k)}},\ k-1)}} \left(I_\ind{y_{s1^d},p'_1,k+1}\right)\cap\displaystyle\bigoplus_{i=1}^l \vec{u}_i \wedge \vec{x}_{i+1} \right)\cap\displaystyle\bigoplus_{i=j}^{n+1} q_{\alpha_i}^{n_i-1} \right)\cap \displaystyle\bigoplus_{i=1}^l \aleph_m \wedge \aleph_{\zeta^{\prime}}\left(\aleph_l,\ldots, \zeta_m\right)\left(\begin{array}{c} q^{3n_1+n_2+\ldots+n_t} \circ \aleph_k^2 \circ \\ \aleph_{n_1}(\bigoplus_{j=1}^N\aleph_i) \circ \aleph_{n_2}(\bigoplus_{j=1}^{\infty}\aleph_i) \circ \aleph_{n_3}( \bigoplus_{j=0}^{N_{n_3}}\alpha_j x^{2+j}) \\ \vdots \\ \aleph_{n_t}(q^{2^{t-1}}) \end{array}\right)\cap {\bigoplus}^{\infty}_{j=k}\aleph_{k+j} \wedge \aleph_j \left( \begin{array}{c} 2 \circ \\ 3 \circ \\ \vdots \\ 2^{n_j} \end{array}\right) \bigoplus_{j=1}^{{n}_{\gamma_p}}F\left(l,\bar{n},\bigoplus_{i=1}^t \bigoplus_{k=0}^{\alpha_k^k} n_k^{t_k} P_k ,n\right)\cap\displaystyle\bigoplus_{\Gamma = 0}^{1}\left[ \Gamma \cup \ \Delta \cup \dagger \Xi^{\mathfrak{V}}\Omega \right] \cap\bigcup_{\Gamma = 0}^{1}\left[ \Gamma \cup \ \Delta \cup \dagger \Xi^{\mathfrak{V}}\Omega \right] \right] \cap \mathcal{R}^Q \cap\left\{\psi_{n+1}\right\} \bigg] \neq \emptyset$$ $$\left\{\bigcup_{\emptyset =_1 y_{st_1} + y_{st_2} + \ldots + y_{st_l} \atop \exists (p',k) \in I_{y_{s2}l \overrightarrow{y}2}_{(p+\#I_{y_{s1}2,{(p,k)}},\ k-1)}} \left(I_\ind{y_{s1^d},p'_1,k+1}\right)\cap\displaystyle\bigoplus_{i=1}^l \vec{u}_i \wedge \vec{x}_{i+1} \right)\cap\displaystyle\bigoplus_{i=j}^{n+1} q_{\alpha_i}^{n_i-1} \right)\cap \displaystyle\bigoplus_{i=1}^l \aleph_m \wedge \aleph_{\zeta^{\prime}}\left(\aleph_l,\ldots, \zeta_m\right)\left(\begin{array}{c} q^{3n_1+n_2+\ldots+n_t} \circ \aleph_k^2 \circ \\ \aleph_{n_1}(\bigoplus_{j=1}^N\aleph_i) \circ \aleph_{n_2}(\bigoplus_{j=1}^{\infty}\aleph_i) \circ \aleph_{n_3}( \bigoplus_{j=0}^{N_{n_3}}\alpha_j x^{2+j}) \\ \vdots \\ \aleph_{n_t}(q^{2^{t-1}}) \end{array}\right)\cap{\bigoplus}^{\infty}_{j=k}\aleph_{k+j} \wedge \aleph_j \left( \begin{array}{c} 2 \circ \\ 3 \circ \\ \vdots \\ 2^{n_j} \end{array}\right) \bigoplus_{j=1}^{{n}_{\gamma_p}}F\left(l,\bar{n},\bigoplus_{i=1}^t \bigoplus_{k=0}^{\alpha_k^k} n_k^{t_k} P_k ,n\right)\cap\displaystyle\bigoplus_{\Gamma = 0}^{1}\left[ \Gamma \cup \ \Delta \cup \dagger \Xi^{\mathfrak{V}}\Omega \right] \cap\bigcup_{\Gamma = 0}^{1}\left[ \Gamma \cup \ \Delta \cup \dagger \Xi^{\mathfrak{V}}\Omega \right] \right]\bigcap\left\{\psi_{n+1}\right\}$$ $$\forall \Psi\in\mathcal{V}\ \forall \psi_n \in \Psi \ \forall\ \psi_{n+1} \in \mathcal{V}\left(\psi_n \in \Psi \wedge\ \psi_{n+1} \notin \Psi\right)\to\ \tau = \mathcal{F} \left[\Psi\ \vee\ \Psi_{0 \to \delta}\right] \neq \emptyset$$ $...$ $$\tau = \mathcal{F} \left[\Psi^{m} \Rightarrow \Psi^{m+1} \wedge \Psi^{m-1} \wedge \Psi^{m-2} \wedge \ldots \wedge \Psi^{m-m} \cup \Psi^{m+2} \vee \Psi^{m+1} \vee \Psi^{m-1} \vee \Psi^{m-2} \wedge \ldots \wedge \Psi^{m-m} \right] \neq \emptyset$$ $$\forall \Psi\in\mathcal{V}\ \forall \psi_n \in \Psi \ \forall\ \psi_{n+1} \in \mathcal{V}\left(\psi_n \in \Psi \wedge\ \psi_{n+1} \notin \Psi\right)\to\ \tau = \mathcal{F} \left[\Psi^{m} \Rightarrow \Psi^{m+1} \wedge \Psi^{m-1} \wedge \Psi^{m-2} \wedge \ldots \wedge \Psi^{m-m} \cup \Psi^{m+2} \vee \Psi^{m+1} \vee \Psi^{m-1} \vee \Psi^{m-2} \wedge \ldots \wedge \Psi^{m-m} \right] \neq \emptyset$$ \begin{displaymath} \tau \sim \mathcal{G}_Q \left( \Gamma, \Lambda, \left\{\varphi_i\right\}_{i=1}^{m_q}, \left\{\psi_j\right\}_{j=1}^{n_q}\right) = \left\{\gamma\vert \gamma \in \Gamma\wedge \forall \lambda \in \Lambda (\gamma \in \lambda\iff \left\{\forall i \in [n_m], i \in \overrightarrow{\mathbf{M}}_{p_q[n_m]} \wedge \forall j \in [n_m], j \in \overrightarrow{\mathbf{\Phi}}_{m_q[n_m]}$, with the pseudo-affinity $\varphi_i$ and then to a symmetric $j \in \overrightarrow{\mathbf{\Psi}}_{n_q[n_m]}$ \ $\varphi_i \wedge \dots \wedge \psi_j$ and the ever finalist symmetric $\Psi = \kappa_{\dot\mu}^{nu}$}, ends at the diagonal 1/2, that is Diode and Expressions should be to connect a gate tp the element $S$ and $\sum_{\phi_f = 1}^{\phi_f + 1}$, in our case within the complexity of logic setup, is equivalent to being in $k^{\alpha_l}$. In general, a directed graph can be generated from an arbitrary natural number $d_l$ and some set of numbers $\left\{\gamma_i^l = l\right\}$ such that \begin{itemize} \item $n^l,i,j\neq 0$, where $(\lambda_{\alpha(i_0)} := 1)$. \item $n^{j+1} = \begin{array}{c} (n_{i_l}-1)n_{i_l}\\ 0 \end{array}$ $$...$$

Ce travail a bénéficié d'une aide de l'État gérée par l'Agence Nationale de la Recherche au titre du programme d'Investissements d'avenir portant la référence « ANR-21-ESRE-0005 » (ÉquipEx Biblissima+) Programme des journées 2023 du cluster 6 Biblissima+ (3-4 octobre 2023, Bibliothèque musicale La Grange-Fleuret, Paris)

Εθνικό Μετσόβιο Πολυτεχνείο. Μεταπτυχιακή εργασία. Διεπιστημονικό - Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) "Υλικά και Επεμβάσεις Συντήρησης - Προστασία Μνημείων (Κατ. B')"

Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at the local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost. The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.Occupation data for 2021 and 2022The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022APS Well-Being DatasetsFrom 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.APS disability variablesOver time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage. End User Licence and Secure Access APS dataUsers should note that there are two versions of each APS dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes Government Office Region geography, banded age, 3-digit SOC and industry sector for main, second and last job. The Secure Access version contains more detailed variables relating to: age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family nationality and country of origin geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district health: including main health problem, and current and past health problems education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from system variables: including week number when interview took place and number of households at address The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS. Multi-stage stratified random sample Face-to-face interview Telephone interview

Abstract copyright UK Data Service and data collection copyright owner.Background The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation. Longitudinal data The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary. LFS Documentation The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.Occupation data for 2021 and 2022 data filesThe ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.2022 WeightingThe population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust. Main Topics:The five-quarter longitudinal datasets include a subset of the most commonly used variables from the Quarterly Labour Force Survey (QLFS), covering the main areas of the survey. See documentation for details Compilation or synthesis of existing material the datasets were created from existing QLFS data. They do not contain all records, but only those of respondents of working age who have responded to the survey in all the periods being linked. The data therefore comprise approximately one third of all QLFS variables. Cases were linked using the QLFS panel design.

What happens when AI goes to theatre with human audiences? Generative AI's natural-language conversational interface has frequently been cast as an anthropomorphic interface. In performance, digital screen as interface has evolved from a vehicle for dramatic messages to a meaning-making agent with an anthropomorphic presence. While the tendency to anthropomorphize this technology is problematic, ChatGPT can be seen as a ghost of the publics, a synthetic version of the publics, or a shadow public. In her keynote lecture, "Screen as Anthropomorphic Interface: Remediated Performance and Generative Artificial Intelligence," Alexa Alice Joubin argued that screens are a site where cultural and performative meanings are generated and negotiated. She uses interface theories and performance studies methods to analyze the outputs of AI. :::: YouTube: https://youtu.be/Bi9a9TBtrow