1. "Israël is Forever" : "Le sionisme religieux le plus fanatique"… avant France-Israël, un gala organisé par des personnalités franco-israéliennes d’extrême droite fait polémique  L'Indépendant
2. Match France-Israël : Pourquoi le gala « Israël for ever » organisé ce soir à Paris fait polémique ?  20 Minutes
3. Qu’est-ce que le Betar, ce mouvement juif qui organise un rassemblement le 13 novembre à Paris ?  Sud Ouest
4. Qui est Bezalel Smotrich, le ministre d’extrême droite au cœur de la polémique du gala « Israël is forever » organisé à Paris  Le Parisien
5. "La justice a tranché": Bruno Retailleau confirme la tenue d'un gala pro-Israël à Paris  BFMTV

1. Notre-Dame de Paris : discours d'Emmanuel Macron, première messe, ouverture au public… Découvrez le calendrier de la réouverture de la cathédrale  franceinfo
2. Réouverture de Notre-Dame : Emmanuel Macron s’exprimera «sur le parvis» de la cathédrale  Le Figaro
3. Réouverture de Notre-Dame de Paris : le calendrier dévoilé, une bonne nouvelle pour les visiteurs  actu.fr
4. Notre-Dame : la cathédrale ouvrira au public du 8 au 14 décembre "jusqu'à 22 heures"  RTL
5. Cérémonies, huit jours de fête, visites: ce qu'il faut savoir sur la réouverture de Notre-Dame de Paris  BFM Paris Ile-de-France

1. B.C. ports lockout update: Union says it will challenge Ottawa’s intervention in work stoppages  Vancouver Sun
2. Union says it will challenge Ottawa's intervention in B.C. port work stoppages  CTV News
3. Labour minister forcing end of negotiations at Quebec ports marks 'dark day for workers' rights,' union says  CBC.ca
4. Canada orders ports to restart as labour disputes choke trade  Financial Post
5. Opinion: Stuck in the middle, and risking union support  The Globe and Mail

1. Ottawa businesses brace for possible Canada Post strike before the holiday season  CTV News Ottawa
2. Tougher work conditions for postal workers at the heart of labour dispute, union says  The Globe and Mail
3. Ottawa businesses worry about potential postal disruption  CBC.ca
4. Calgary workers 'ready to go' if Canada Post, CUPW unable to reach agreement by Friday: union president  Calgary Herald
5. ‘Feeling the pinch’: Potential postal strike may be first of many, Manitoba prof says  Global News Toronto

Le 20 avril 2018, un appel au boycott a été lancé sur les réseaux sociaux marocains contre trois entreprises leaders dans leurs secteurs d’activités. L’eau minérale de Sidi Ali, le lait de Centrale Danone et les stations de services Afriquia (pétrole) ont été victimes d’une guerre d’information, justifiée selon les internautes par des prix de vente élevés. Les appels au boycott ont été relayés par les internautes Marocains via des groupes et des pages ...

Ghislaine Maxwell, condamnée en juin à vingt ans de prison pour trafic sexuel de mineures, a affirmé dans une interview diffusée lundi qu'elle aurait aimé "ne jamais avoir rencontré" Jeffrey Epstein, dont elle se dit convaincue "qu'il a été assassiné".

L'ex-mondaine britannique a été condamnée en juin à New York pour trafic sexuel de mineures pour le compte du financier américain décédé, accusé d'exploitation sexuelle de dizaines de mineures. "J'aimerais honnêtement ne l'avoir jamais rencontré", a-t-elle affirmé à propos de son ancien compagnon, dans l'entretien accordé depuis sa prison aux Etats-Unis à la chaîne britannique TalkTV. 

"Clairement (...) le fait que je travaille avec lui et que je passe du temps avec lui et que je le connaisse a détruit ma vie et blessé énormément de gens qui me sont chers et que j'aime", a-t-elle affirmé. Elle a souligné qu'elle ne "savait pas" qu'Epstein était "aussi horrible" même si "évidemment maintenant, quand on regarde en arrière, bien sûr que oui".

Le financier américain, accusé d'avoir, entre 2002 et 2005 au moins, fait venir des mineures dans ses résidences "pour se livrer à des actes sexuels avec lui", a été retrouvé pendu dans sa cellule le 10 août 2019. Si l'autopsie confirme un suicide par pendaison, Mme Maxwell se dit elle convaincue "qu'il a été assassiné".

Déjà dimanche, des extraits de l'interview avaient été publiés et Mme Maxwell y prenait la défense du prince Andrew, affirmant qu'une photo montrant le frère du roi Charles III avec une jeune fille qui l'a ensuite accusé d'agressions sexuelles était "un faux". 

L'Américaine Virginia Giuffre, aujourd'hui âgée de 39 ans, accuse le prince de l'avoir agressée sexuellement à trois reprises en 2001, quand elle avait 17 ans. Elle a affirmé l'avoir rencontré par l'entremise d'Epstein.

Andrew, ami de Ghislaine Maxwell et Jeffrey Epstein, a scellé avec elle en février 2022 un accord à l'amiable, en payant des millions de dollars, ce qui lui a permis d'éviter un procès au civil à New York, qui aurait été extrêmement embarrassant pour la famille royale britannique.

Le prince de 62 ans, tombé en disgrâce après ces accusations, a toutefois toujours contesté les accusations. Selon des journaux britanniques, il étudie désormais les options légales pour tenter d'annuler l'accord avec Virginia Giuffre.

(Belga) Un Américain, qui avait été immortalisé les pieds sur une table dans le bureau de la cheffe démocrate Nancy Pelosi lors de l'assaut sur le Capitole, a été reconnu coupable lundi de plusieurs délits.

Après une courte délibération, les jurés ont déclaré Richard Barnett, 62 ans, coupable, entre autres, d'entrave à une procédure officielle, vol et intrusion dans un bâtiment officiel avec une arme dangereuse (un bâton de marche capable d'envoyer des décharges électriques). Le 6 janvier 2021, il avait envahi, comme des centaines de partisans de l'ex-président républicain Donald Trump, le siège du Congrès au moment où les élus certifiaient la victoire du démocrate Joe Biden à la présidentielle. Il avait été photographié par l'AFP dans le bureau de la cheffe de la chambre des représentants, Nancy Pelosi, les pieds sur un meuble. Le cliché avait fait le tour du monde et permis à la police de l'interpeller rapidement. Selon le dossier d'accusation, ce partisan de la mouvance complotiste Qanon avait laissé un message insultant à la démocrate et volé une enveloppe qu'elle avait signée. Pendant son procès, il s'était montré défiant, assurant avoir été "poussé à l'intérieur" du Capitole par la foule. Sa peine sera prononcée en mai. En attendant, il reste assigné à résidence avec un bracelet électronique. En deux ans d'enquête, plus de 950 participants à cette attaque ont été arrêtés, et près de 200 condamnés à des peines de prison. (Belga)

(Belga) Le conseil communal de la Ville de Charleroi a adopté lundi une motion faisant de Charleroi "une ville antifasciste" et consacrant l'existence d'une "coalition antifasciste" composée des partis politiques carolos, des syndicats, d'associations et de membres de la société civile.

Cette "coalition antifasciste" est le fruit de discussions entamées dans un contexte de montée générale des idées d'extrême droite et à la suite des incidents qui sont survenus le 25 janvier 2020 à Charleroi à l'occasion d'une mobilisation d'un front antifasciste contre la tenue dans la métropole d'une réunion d'un nouveau parti d'extrême droite. Ce jour-là, selon les manifestants antifascistes, la police avait fait usage contre eux de sprays, d'autopompes et de coups de matraques même pour les disperser. Ce qui avait provoqué un certain émoi, y compris au sein de la classe politique carolo. La motion donne à la coalition antifasciste quelques objectifs généraux, comme celui "d'empêcher par tous les moyens légaux la diffusion de propos incitant à la haine, au racisme, à l'antisémitisme, au sexisme, à la discrimination relative à l'orientation sexuelle, ouvertement fasciste et xénophobe, sur le territoire de Charleroi" ou celui de relayer l'information "lorsqu'elle concerne un événement susceptible d'inciter à la haine, au racisme, à l'antisémitisme, au sexisme, ouvertement fasciste et xénophobe". (Belga)

Elon Musk a tenté de montrer lundi au tribunal que ses fameux tweets de 2018, sur sa volonté de sortir Tesla de la Bourse, n'avaient rien de trompeurs ou de frauduleux, contrairement aux accusations d'investisseurs qui disent avoir perdu des millions de dollars à cause du milliardaire.

Le patron de Tesla -- et de Twitter, depuis fin octobre -- a assuré qu'il n'avait "jamais" cherché à tromper les investisseurs, et que l'accusation de fraude était "scandaleuse".

Il avait créé la stupeur le 7 août 2018 en affirmant qu'il voulait retirer son groupe automobile de la Bourse au prix de 420 dollars par action, puis en assurant que le financement était "sécurisé".

"Je ne disais pas que c'était fait, je disais simplement que je l'envisageais, que j'y pensais. Et qu'à mon avis le financement était sécurisé", a déclaré Elon Musk à la barre, dans le tribunal de San Francisco où a lieu le procès.

La semaine dernière, le principal avocat des plaignants, Nicholas Porritt, avait accusé le dirigeant d'avoir "menti" et d'être responsable des pertes des investisseurs.

Le titre avait bondi dans la foulée des tweets très inhabituels (et le Nasdaq avait temporairement suspendu le cours de l'action Tesla), avant de décliner les jours suivants. Des articles de presse avaient fini par révéler que le patron n'avait pas vraiment les fonds.

Tesla était restée cotée en Bourse.

A travers ses questions, Nicholas Porritt a cherché à montrer qu'Elon Musk n'avait pas réalisé les consultations appropriées, et ne disposait pas ni des éléments nécessaires, ni de l'autorité pour faire une annonce aussi fracassante, surtout sur Twitter, et surtout pendant que les marchés étaient ouverts.

- "M. Tweet" -

L'avocat a mis en avant des échanges acerbes le 12 août 2018 entre le milliardaire et Yasir Al-Rumayyan, le directeur du fonds souverain saoudien, qui s'était engagé "catégoriquement" et "sans hésitation" à financer l'opération, selon Elon Musk.

"Le financement n'était pas vraiment sécurisé, n'est-ce pas?", a demandé M Porritt.

Yasir Al-Rumayyan a fait du "rétropédalage", a rétorqué le patron de Tesla.

Il a assuré qu'il avait de toute façon la possibilité de vendre ses actions de son autre fleuron, SpaceX, "l'entreprise non cotée la plus valorisée des Etats-Unis".

"Cela m'aurait brisé le cœur (de les vendre), mais je l'aurais fait si besoin", a-t-il déclaré, évoquant comment il avait dû se séparer d'actions de Tesla pour racheter Twitter l'année dernière.

Costume sombre, chemise blanche et cravate, il est apparu hésitant, ne se souvenant pas de nombreux emails et détails, et répondant souvent à côté des questions pour répéter à l'envie les messages qu'il voulait faire passer au jury.

Au point de faire perdre patience à l'avocat des investisseurs. "Nous avons passé toute une journée ensemble à Austin, vous vous en souvenez M. Tweet?!", a lancé Nicholas Porritt, avant de corriger pour "M. Musk".

- "Karma" -

L'accusation est aussi revenue sur le prix proposé par Elon Musk, 420 dollars par action. Aux Etats-Unis, les chiffres 4 et 20 accolés sont associés à la consommation de cannabis. Quand le milliardaire a proposé de racheter Twitter au printemps dernier, il a choisi un prix de 54,20 dollars par action.

"Avez-vous arrondi à 420 en guise de blague à l'attention de votre petite amie?", a demandé Nicholas Porritt.

"Ce n'était pas une blague, cela représentait une prime de 20% au-dessus du prix de l'action", a répondu Elon Musk, reconnaissant cependant qu'il y a "un certain karma autour de 420".

"Pas sûr que ce soit un bon ou un mauvais karma à ce stade", a-t-il encore plaisanté.

Son avocat Alex Spiro l'a ensuite aidé à dresser le portrait d'un immigré parti de rien, venu aux Etats-Unis - "là où les grandes choses sont possibles" - après une enfance "malheureuse" en Afrique du Sud, selon les mots du milliardaire.

"On m'a traité de fou à de nombreuses reprises", a déclaré Elon Musk après avoir énuméré les entreprises qu'il a cofondées.

Mais "à ce stade je crois que j'ai levé plus d'argent que quiconque dans l'histoire", s'est-il vanté, attribuant sa réussite à son "honnêteté" à l'égard des investisseurs.

Le procès doit durer trois semaines. Dans une précédente décision liée à cette affaire, un juge avait estimé que le fameux tweet de 2018 pouvait être considéré comme "faux et trompeur".

Le gendarme boursier américain, la SEC, avait de son côté obligé Elon Musk à céder la présidence du conseil d'administration, à payer une amende et à faire pré-approuver par un juriste ses tweets directement liés à l'activité de Tesla.

(Belga) Quatre membres de la milice d'extrême droite "Oath Keepers" ont été reconnus coupables lundi de sédition pour leur rôle dans l'assaut du Capitole, à l'issue du second procès organisé sur ce chef d'accusation extrêmement rare.

Depuis l'attaque du 6 janvier 2021, plus de 950 partisans de l'ex-président républicain Donald Trump ont été arrêtés et inculpés pour avoir semé le chaos dans le siège de la démocratie américaine. Parmi eux, seuls 14 militants de groupuscules d'extrême droite - neuf membres des "Oath Keepers" et cinq "Proud Boys" - ont été accusés de "sédition", un chef passible de 20 ans de prison qui implique d'avoir planifié l'usage de la force pour s'opposer au gouvernement. Faute de place suffisante dans le tribunal fédéral de Washington, la justice a organisé le procès des Oath Keepers, accusés de s'être entraînés et armés pour l'occasion, en deux temps. Un premier procès s'est conclu fin novembre par un verdict mitigé: le fondateur de cette milice, Stewart Rhodes, et un responsable local ont été déclarés coupables de sédition, mais leurs trois co-accusés ont été acquittés sur ce chef. Lundi, à l'issue du second procès, les jurés ont jugé coupables les quatre derniers Oath Keepers, des hommes âgés de 38 à 64 ans décrits comme de dangereux "traîtres" par l'accusation, mais comme des "fanfarons" par leurs avocats. Le procès des Proud Boys, dont leur leader Enrique Tarrio, s'est ouvert en décembre et était toujours en cours lundi, dans le même tribunal. (Belga)

by Mitchell Longan[1] Introduction The European Parliament is currently considering a proposal to address problems of piracy and other forms of copyright infringement associated with the digital world.[2] Article 13 of the proposed Directive on Copyright in the Digital Single

Hasard du calendrier ou volontￜ de prￜserver le nouveau Premier ministre ? Selon Mediapart, une enquￜte judiciaire ouverte par le parquet de Perpignan, potentiellement gￜnante pour Jean Castex, a ￜtￜ...

A chaque jour, une nouvelle annonce. Cet ￜtￜ, le ministre de l'Intￜrieur, Gￜrald Darmanin, a multipliￜ les dￜplacements sur le terrain et les annonces. Pour lutter contre les pyromanes ￜ...

La politique est souvent guidée par des marqueurs idéologiques qui manquent de nuances. L'immigration illustre cette polarisation : la gauche prône une générosité aveugle, la droite une sévérité rigide. Le meurtre de Philippine de Carlan relance ce débat. La gestion de l'immigration doit équilibrer humanité et acceptabilité, mais sans sacrifier vérité ni lucidité.

In this paper, a second-order sliding mode adaptive controller with finite time stability is proposed for trajectory tracking of robotic systems. In order to reduce the chattering phenomenon in the response of the variable structure resistant controller, two dependent sliding surfaces are used. In the outer loop, a kinematic controller is used to compensate the geometric uncertainty of the robot, and in the inner loop, the proposed resistive control is used as the main loop. On the other hand, considering the dynamic uncertainty and disturbance of the robot, an adaptive strategy has been used to estimate the uncertainty limit during the control process in order to eliminate the need for basic knowledge to determine the uncertainty limit in the resistant structure. The proposed control method demonstrates significant enhancements in performance, with the linear velocity error improving by approximately 80%, leading to a more accurate and responsive system.

The confluence of the internet of things (IoT) with smart grids has ushered in a paradigm shift in energy management, promising unparalleled efficiency, economic robustness and unwavering reliability. However, this integrative evolution has concurrently amplified the grid's susceptibility to cyber intrusions, casting shadows on its foundational security and structural integrity. Machine learning (ML) and deep learning (DL) emerge as beacons in this landscape, offering robust methodologies to navigate the intricate cybersecurity labyrinth of IoT-infused smart grids. While ML excels at sifting through voluminous data to identify and classify looming threats, DL delves deeper, crafting sophisticated models equipped to counteract avant-garde cyber offensives. Both of these techniques are united in their objective of leveraging intricate data patterns to provide real-time, actionable security intelligence. Yet, despite the revolutionary potential of ML and DL, the battle against the ceaselessly morphing cyber threat landscape is relentless. The pursuit of an impervious smart grid continues to be a collective odyssey. In this review, we embark on a scholarly exploration of ML and DL's indispensable contributions to enhancing cybersecurity in IoT-centric smart grids. We meticulously dissect predominant cyber threats, critically assess extant security paradigms, and spotlight research frontiers yearning for deeper inquiry and innovation.

Image encryption is a critical and attractive issue in digital image processing that has gained approval and interest of many researchers in the world. A proposed hybrid encryption method was implemented by using the combination of the Nahrain chaotic map with a well-known optimised algorithm namely the grey wolf optimisation (GWO). It was noted from analysing the results of the experiments conducted on the new hybrid algorithm, that it gave strong resistance against expected statistical invasion as well as brute force. Several statistical analyses were carried out and showed that the average entropy of the encrypted images is near to its ideal information entropy.

Screen-shooting will lead to information leakage. Anti-screen-shooting watermark, which can track the leaking sources and protect the copyrights of images, plays an important role in image information security. Due to the randomness of shooting distance and angle, more robust watermark algorithms are needed to resist the mixed attack generated by screen-shooting. A robust digital watermarking algorithm that is resistant to screen-shooting is proposed in this paper. We use improved Harris-Laplace algorithm to detect the image feature points and embed the watermark into the feature domain. In this paper, all test images are selected on the dataset USC-SIPI and six related common algorithms are used for performance comparison. The experimental results show that within a certain range of shooting distance and angle, this algorithm presented can not only extract the watermark effectively but also ensure the most basic invisibility of watermark. Therefore, the algorithm has good robustness for anti-screen-shooting.

The video game industry (VGI) has evolved considerably, transitioning from a niche market to a substantial sector. The VGI's magnitude and the societal implications tied to video game consumption have naturally piqued the interest of scholars in marketing and consumer behaviour. This research serves a dual purpose: firstly, it consolidates existing VG literature by evaluating articles, concepts, and methodologies, systematically tracing their evolution; secondly, it outlines potential directions and implications for forthcoming research. Within this literature, a predominant focus lies on articles investigating purchase decisions concerning VGs, followed by those exploring the integration of video game consumption into broader social contexts. Notably, a limited number of articles delve into player-game interactions and experiences within gaming worlds. This imbalance can be attributed to the fact that such inquiries are often suited to psychology and multidisciplinary journals, while the marketing discipline has predominantly addressed the VGI from a marketing management standpoint.

Differential privacy is usually applied to location privacy protection scenarios, which confuses real data by adding interference noise to location points to achieve the purpose of protecting privacy. However, this method can result in a significant amount of redundant noisy data and impact the accuracy of the location. Considering the security and practicability of location data, an effective differential privacy protection method of location data based on perturbation loss constraint is proposed. After applying the Laplace mechanism under the condition of differential privacy to perturb the location data, the Savitzky-Golay filtering technology is used to correct the data with noise, and the data with large deviation and low availability is optimised. The introduction of Savitzky-Golay filtering mechanism in differential privacy can reduce the error caused by noise data while protecting user privacy. The experiments results indicate that the scheme improves the practicability of location data and is feasible.

In order to further improve the teaching quality of multimedia teaching in school daily teaching, a classroom facial expression emotion recognition model is proposed based on convolutional neural network. VGGNet and CliqueNet are used as the basic expression emotion recognition methods, and the two recognition models are fused while the attention module CBAM is added. Simulation results show that the designed classroom face expression emotion recognition model based on V-CNet has high recognition accuracy, and the recognition accuracy on the test set reaches 93.11%, which can be applied to actual teaching scenarios and improve the quality of classroom teaching.

To achieve psychological and emotional analysis of massive internet chats, researchers have used statistical methods, machine learning, and neural networks to analyse the dynamic tendencies of texts dynamically. For long readers, the author first compares and explores the differences between the two psychoanalysis algorithms based on the emotion dictionary and machine learning for simple sentences, then studies the expansion algorithm of the emotion dictionary, and finally proposes an extended text psychoanalysis algorithm based on conditional random field. According to the experimental results, the mental dictionary's accuracy, recall, and F-score based on the cognitive understanding of each additional ten words were calculated. The optimisation decreased, and the memory and F-score improved. An <i>F</i>-value greater than 1, which is the most effective indicator for evaluating the effectiveness of a mental analysis problem, can better demonstrate that the algorithm is adaptive in the literature dictionary. It has been proven that this scheme can achieve good results in analysing emotional tendencies and has higher efficiency than ordinary weight-based psychological sentiment analysis algorithms.

In the context of smart city development, the managed pressure drilling (MPD) drilling process faces many uncertainties, but the characteristics of the process are complex and require accurate wellbore pressure control. However, this process runs the risk of introducing un-modelled dynamics into the system. To this problem, this paper employs neural network control techniques to construct a dual-network system for throttle pressure control, the design encompasses both the controller and identifier components. The radial basis function (RBF) network and proportional features are connected in parallel in the controller structure, and the RBF network learning algorithm is used to train the identifier structure. The simulation results show that the actual wellbore pressure can quickly track the reference pressure value when the pressure setpoint changes. In addition, the controller based on neural network realises effective control, which enables the system to track the input target quickly and achieve stable convergence.

Elections hold immense significance in shaping the leadership of a nation or organisation, serving as a pivotal moment that influences the trajectory of the entity involved. Despite their centrality to modern democratic systems, elections face a significant hurdle: widespread mistrust in the electoral process. This pervasive lack of confidence poses a substantial threat to the democratic framework, even in the case of prominent democracies such as India and US, where inherent flaws persist in the electoral system. Issues such as vote rigging, electronic voting machine (EVM) hacking, election manipulation, and polling booth capturing remain prominent concerns within the current voting paradigm. Leveraging blockchain for electronic voting systems offers an effective solution to alleviate the prevailing apprehensions associated with e-voting. By incorporating blockchain into the electoral process, the integrity and security of the system could be significantly strengthened, addressing the current vulnerabilities and fostering trust in democratic elections.

This paper examines the growing aspect of perceive organisational politics (POPs) in organisations by understanding their employee engagement with mediating effect of emotional intelligence. This study is cross-sectional, wherein a survey is conducted on executives of different sectors holding strategic positions. The purposive sampling technique is applied to find the 117 most suitable executives for this survey. The survey is self-administered, and a questionnaire is used as an instrument with 43 measurement scale items adopted from previous similar studies. Construct's reliability and validity followed by PLS-SEM is performed using JASP statistical application. The result revealed that the dimensionality support and validation of POP based on a new set of measures centred on generalised beliefs of the application and abuse of power, infrastructure, credibility, choice making, and line-of-sight. In line with previous findings, the current findings also showed that POP works as a barrier to individual behavioural demand and can negatively affect work efficiency. Existence of perceive organisational politics due to the normative belief of the situation happing in the organisation, disengagement of employees, and also evaluates new empirical insight into the organisation by mediating emotional intelligence.

This paper reports on the outcomes from a pilot study targeted at mothers of school children in Melbourne, Australia. The aim of the study was to engender a positive view of technology in the participants and to introduce the concept of Information Technology (IT) as a potential career. Mothers were given the opportunity to develop basic IT skills and learn about different IT career pathways for their children with an emphasis on their daughters’ choices. Mothers were offered an evening course over a four week period that was designed to introduce them to a range of social media and Web 2.0 tools. Their opinions were documented using both questionnaires and informal discussions. It explored whether their attitudes towards IT can be changed by up-skilling and introducing them to the technologies their children commonly use. The findings of the pilot study suggest that addressing this demographic has the potential to make the participants question their pre-conceptions about IT careers for women.

Prior work demonstrates the importance of introducing young children to programming and engineering content before gender stereotypes are fully developed and ingrained in later years. However, very little research on gender and early childhood technology interventions exist. This pilot study looks at N=45 children in kindergarten through second grade who completed an eight-week robotics and programming curriculum using the KIWI robotics kit. KIWI is a developmentally appropriate robotics construction set specifically designed for use with children ages 4 to 7 years old. Qualitative pre-interviews were administered to determine whether participating children had any gender-biased attitudes toward robotics and other engineering tools prior to using KIWI in their classrooms. Post-tests were administered upon completion of the curriculum to determine if any gender differences in achievement were present. Results showed that young children were beginning to form opinions about which technologies and tools would be better suited for boys and girls. While there were no significant differences between boys and girls on the robotics and simple programming tasks, boys performed significantly better than girls on the advanced programming tasks such as, using repeat loops with sensor parameters. Implications for the design of new technological tools and curriculum that are appealing to boys and girls are discussed.

Aim/Purpose: This research aims to describe and demonstrate the results of an intervention through educational robotics to improve the computational thinking of student teachers. Background: Educational robotics has been increasing in school classrooms for the development of computational thinking and digital competence. However, there is a lack of research on how to prepare future teachers of Kindergarten and Elementary School in the didactic use of computational thinking, as part of their necessary digital teaching competence. Methodology: Following the Design-Based Research methodology, we designed an intervention with educational robots that includes unplugged, playing, making and remixing activities. Participating in this study were 114 Spanish university students of education. Contribution: This research helps to improve the initial training of student teachers, especially in the field of educational robotics. Findings: The student teachers consider themselves digital competent, especially in the dimensions related to social and multimedia aspects, and to a lesser extent in the technological dimension. The results obtained also confirm the effectiveness of the intervention through educational robotics in the development of computational thinking of these students, especially among male students. Recommendations for Practitioners: Teacher trainers could introduce robotics following these steps: (1) initiation and unplugged activities, (2) gamified activities of initiation to the programming and test of the robots, (3) initiation activities to Scratch, and (4) design and resolution of a challenge. Recommendation for Researchers: Researchers could examine how interventions with educational robots helps to improve the computational thinking of student teachers, and thoroughly analyze gender-differences. Impact on Society: Computational thinking and robotics are one of the emerging educational trends. Despite the rise of this issue, there are still few investigations that systematize and collect evidence in this regard. This study allows to visualize an educational intervention that favors the development of the computational thinking of student teachers. Future Research: Researchers could evaluate not only the computational thinking of student teachers, but also their didactics, their ability to teach or create didactic activities to develop computational thinking in their future students.

Aim/Purpose: This study examines the use of the Emergency Remote Teaching (ERT) platform by undergraduates of the University of Ibadan, Nigeria, during the COVID-19 pandemic using the constructs of the UTAUT2 model. Five constructs of the UTAUT2 model were adopted to investigate the use of the ERT platform by undergraduates of the university. Background: The Coronavirus (COVID-19) outbreak disrupted academic activities in educational institutions, leading to an unprecedented school closure globally. In response to the pandemic, higher educational institutions adopted different initiatives aimed at ensuring the uninterrupted flow of their teaching and learning activities. However, there is little research on the use of ERT platforms by undergraduates in Nigerian universities. Methodology: The descriptive survey research design was adopted for the study. The multi-stage random sampling technique was used to select 334 undergraduates at the University of Ibadan, Nigeria, while a questionnaire was used to collect data from 271 students. Quantitative data were collected and analyzed using frequency counts, percentages, mean and standard deviation, Pearson Product Moment Correlation, and regression analysis. Contribution: The study contributes to understanding ERT use in the educational institutions of Nigeria – Africa’s most populous country. Furthermore, the study adds to the existing body of knowledge on how the UTAUT2 Model could explain the use of information technologies in different settings. Findings: Findings revealed that there was a positive significant relationship between habit, hedonic motivation, price value, and social influence on the use of ERT platforms by undergraduates. Hedonic motivation strongly predicted the use of ERT platforms by most undergraduates. Recommendations for Practitioners: As a provisional intervention in times of emergencies, the user interface, navigation, customization, and other aesthetic features of ERT platforms should be more appealing and enjoyable to ensure their optimum utilization by students. Recommendation for Researchers: More qualitative research is required on users’ satisfaction, concerns, and support systems for ERT platforms in educational institutions. Future studies could consider the use of ERT by students in different countries and contexts such as students participating in English as a Foreign Language (EFL) and the English for Speakers of other languages (ESOL) programs. Impact on Society: As society faces increased uncertainties of the next global pandemic, this article reiterates the crucial roles of information technology in enriching teaching and learning activities in educational institutions. Future Research: Future research should focus on how different technology theories and models could explain the use of ERT platforms at different educational institutions in other geographical settings and contexts.

Aim/Purpose: The study focused on learner sentiments and experiences after using the Moodle assessment module and trained a machine learning classifier for future sentiment predictions. Background: Learner assessment is one of the standard methods instructors use to measure students’ performance and ascertain successful teaching objectives. In pedagogical design, assessment planning is vital in lesson content planning to the extent that curriculum designers and instructors primarily think like assessors. Assessment aids students in redefining their understanding of a subject and serves as the basis for more profound research in that particular subject. Positive results from an evaluation also motivate learners and provide employment directions to the students. Assessment results guide not just the students but also the instructor. Methodology: A modified methodology was used for carrying out the study. The revised methodology is divided into two major parts: the text-processing phase and the classification model phase. The text-processing phase consists of stages including cleaning, tokenization, and stop words removal, while the classification model phase consists of dataset training using a sentiment analyser, a polarity classification model and a prediction validation model. The text-processing phase of the referenced methodology did not utilise tokenization and stop words. In addition, the classification model did not include a sentiment analyser. Contribution: The reviewed literature reveals two major omissions: sentiment responses on using the Moodle for online assessment, particularly in developing countries with unstable internet connectivity, have not been investigated, and variations of the k-fold cross-validation technique in detecting overfitting and developing a reliable classifier have been largely neglected. In this study we built a Sentiment Analyser for Learner Emotion Management using the Moodle for assessment with data collected from a Ghanaian tertiary institution and developed a classification model for future sentiment predictions by evaluating the 10-fold and the 5-fold techniques on prediction accuracy. Findings: After training and testing, the RF algorithm emerged as the best classifier using the 5-fold cross-validation technique with an accuracy of 64.9%. Recommendations for Practitioners: Instead of a closed-ended questionnaire for learner feedback assessment, the open-ended mechanism should be utilised since learners can freely express their emotions devoid of restrictions. Recommendation for Researchers: Feature selection for sentiment analysis does not always improve the overall accuracy for the classification model. The traditional machine learning algorithms should always be compared to either the ensemble or the deep learning algorithms Impact on Society: Understanding learners’ emotions without restriction is important in the educational process. The pedagogical implementation of lessons and assessment should focus on machine learning integration Future Research: To compare ensemble and deep learning algorithms

Aim/Purpose: To conduct a needs assessment and subsequently create micro-storytelling media aimed at enhancing the Digital Intelligence Quotient (DQ) skills of young individuals. Background: In today's digital society, DQ has emerged as a vital skill that elevates individuals in all aspects of life, from daily living to education. To empower Thai youth, this study seeks to innovate DQ content by adapting it into a digital format known as micro-storytelling. This unique approach combines the art of storytelling with digital elements, creating engaging and effective micro-learning media Methodology: The methodology comprises three phases: 1) assessing the need for digital micro-storytelling development; 2) developing digital micro-storytelling; and 3) evaluating the DQ skills among young individuals. The sample group consisted of 55 higher education learners for needs assessment and 30 learners in the experiment group. Data analysis involves PNI modified, mean, and standard deviation. Contribution: This research contributes by addressing the urgent need for DQ skills in the digital era and by providing a practical solution in the form of digital micro-storytelling, tailored to the preferences and needs of Thai youth. It serves as a valuable resource for educators and policymakers seeking to empower young learners with essential digital competencies. Findings: The findings demonstrate three significant outcomes: 1) The learners wanted to organize their own learning experience with self-paced learning in a digital landscape, and they preferred digital media in the form of video. They were most interested in developing DQ to enhance their understanding of digital safety, digital security, and digital literacy; 2) according to a consensus of experts, digital micro-storytelling has the greatest degree of quality in terms of its development, content, and utilization, with an overall average of 4.86; and 3) the overall findings of the assessment of DQ skills indicate a favorable level of proficiency. Recommendations for Practitioners: Align materials with micro-learning principles, keeping content concise for effective knowledge retention. Empower students to personalize their digital learning and promote self-paced exploration based on their interests. Recommendation for Researchers: Researchers should continuously assess and update digital learning materials to align with the evolving digital landscape and the changing needs of students and investigate the long-term effects of DQ improvement, especially in terms of online safety and digital literacy in students' future lives and careers. Impact on Society: This study's impact on society is centered around fostering a DQ, promoting innovative educational approaches, and elevating Thai youth with essential digital skills. It contributes to a safer, more informed, and digitally literate generation prepared for the challenges of the digital era. Future Research: Undertake comparative studies to analyze the effectiveness of different digital learning formats and methodologies. Comparing micro-storytelling with other approaches can help identify the most efficient and engaging methods for enhancing DQ.

Aim/Purpose: This mixed-methods study aims to examine factors influencing academicians’ intentions to continue using AI-based chatbots by integrating the Task-Technology Fit (TTF) model and social network characteristics. Background: AI-powered chatbots are gaining popularity across industries, including academia. However, empirical research on academicians’ adoption behavior is limited. This study proposes an integrated model incorporating TTF factors and social network characteristics like density, homophily, and connectedness to understand academics’ continuance intentions. Methodology: A qualitative study involving 31 interviews of academics from India examined attitudes and the potential role of social network characteristics like density, homophily, and connectedness in adoption. Results showed positive sentiment towards chatbots and themes on how peer groups accelerate diffusion. In the second phase, a survey of 448 faculty members from prominent Indian universities was conducted to test the proposed research model. Contribution: The study proposes and validates an integrated model of TTF and social network factors that influence academics’ continued usage intentions toward AI chatbots. It highlights the nuanced role of peer networks in shaping adoption. Findings: Task and technology characteristics positively affected academics’ intentions to continue AI chatbot usage. Among network factors, density showed the strongest effect on TTF and perceived usefulness, while homophily and connectedness had partial effects. The study provides insights into designing appropriate AI tools for the academic context. Recommendations for Practitioners: AI chatbot designers should focus on aligning features to academics’ task needs and preferences. Compatibility with academic work culture is critical. Given peer network influences, training and demonstrations to user groups can enhance adoption. Platforms should have capabilities for collaborative use. Targeted messaging customized to disciplines can resonate better with academic subgroups. Multidisciplinary influencers should be engaged. Concerns like plagiarism risks, privacy, and job impacts should be transparently addressed. Recommendation for Researchers: More studies are needed across academic subfields to understand nuanced requirements and barriers. Further studies are recommended to investigate differences across disciplines and demographics, relative effects of specific network factors like size, proximity, and frequency of interaction, the role of academic leadership and institutional policies in enabling chatbot adoption, and how AI training biases impact usefulness perceptions and ethical issues. Impact on Society: Increased productivity in academia through the appropriate and ethical use of AI can enhance quality, access, and equity in education. AI can assist in mundane tasks, freeing academics’ time for higher-order objectives like critical thinking development. Responsible AI design and policies considering socio-cultural aspects will benefit sustainable growth. With careful implementation, it can make positive impacts on student engagement, learning support, and research efficiency. Future Research: Conduct longitudinal studies to examine the long-term impacts of AI chatbot usage in academia. Track usage behaviors over time as familiarity develops. Investigate differences across academic disciplines and roles. Requirements may vary for humanities versus STEM faculty or undergraduate versus graduate students. Assess user trust in AI and how it evolves with repeated usage, and examine trust-building strategies. Develop frameworks to assess pedagogical effectiveness and ethical risks of conversational agents in academic contexts.

This study focuses on the importance of monitoring student attendance in education and the challenges faced by educators in doing so. Existing methods for attendance tracking have drawbacks, including high costs, long processing times, and inaccuracies, while security and privacy concerns have often been overlooked. To address these issues, the authors present a novel internet of things (IoT)-based self-lecture attendance system (SLAS) that leverages smartphones and QR codes. This system effectively addresses security and privacy concerns while providing streamlined attendance tracking. It offers several advantages such as compact size, affordability, scalability, and flexible features for teachers and students. Empirical research conducted in a live lecture setting demonstrates the efficacy and precision of the SLAS system. The authors believe that their system will be valuable for educational institutions aiming to streamline attendance tracking while ensuring security and privacy.

Smart home devices are vulnerable to various attacks, including distributed-denial-of-service (DDoS) attacks. Current detection techniques face challenges due to nonlinear thought, unusual system traffic, and the fluctuating data flow caused by human activities and device interactions. Identifying the baseline for 'normal' traffic and suspicious activities like DDoS attacks from encrypted data is also challenging due to the encrypted protective layer. This work introduces a concept called synoptic crow search with recurrent transformer network-based DDoS attack detection, which uses the synoptic weighted crow search algorithm to capture varying traffic patterns and prioritise critical information handling. An adaptive recurrent transformer neural network is introduced to effectively regulate DDoS attacks within encrypted data, counting the historical context of the data flow. The proposed model shows effective performance in terms of low false alarm rate, higher detection rate, and accuracy.

This paper explores innovative ideas and strategies for promoting environmental sustainability within the hotel industry, with the goal of streamlining these concepts for practical application in the industry and facilitating future academic research. The research methodology encompassed extensive online desk research, yielding a collection of 87 articles that were subject to thorough analysis. Additionally, personal consultations were conducted with industry experts to align their insights with the identified innovative ideas. To facilitate comprehension, appropriate terminology was assigned to these concepts. Subsequently, a post-discussion phase was conducted, engaging in one-on-one sessions with five industry experts to distil these insights into four distinct directions. This paper holds potential value for both industry stakeholders and academics, serving as a structured compendium of ideas and innovations crucial for advancing sustainability in the hotel sector. Moreover, it provides a solid foundation for further academic research.

The traditional numerical simulation method of financial fluctuation cycle does not focus on the study of non-linear financial fluctuation but has problems such as high numerical simulation error and long time. To solve this problem, this paper introduces the non-linear equation of motion to optimise the numerical simulation method of financial fluctuation cycle. A comprehensive analysis of the components of the financial market, the establishment of a financial market network model and the acquisition of relevant financial data under the support of the model. Based on the collection of financial data, set up financial volatility index, measuring cycle, the financial wobbles, to establish the non-linear equations of motion, the financial wobbles, the influence factors of the financial volatility cycle as variables in the equation of motion, through the analysis of different influence factors under the action of financial volatility cycle change rule, it is concluded that the final financial fluctuation cycle, the results of numerical simulation. The simulation results show that, compared with the traditional method, the numerical simulation of the proposed method has high precision, low error and short time, which provides relatively accurate reference data for the stable development of regional economy.

This article presents a case study that outlines the potential of DigiBot technology, an interactive automated response program, in mobile open learning (MOL) for business subjects. The study, which draws on a project implemented in Sub-Saharan Africa, demonstrates the applications of DigiBots delivered via WhatsApp to over 650,000 learners. Employing a mixed-methods approach, the article reports on live event tracking, qualitative observations from facilitators and learning technologists, and a learner survey (<i>N</i> = 304,000). The research offers practical recommendations and proposes a model for scalable DigiBot learning. Findings reveal that in this case, DigiBot MOL had the potential to effectively address two key obstacles in open learning: accessibility and scalability. Leveraging mobile platforms such as WhatsApp mitigates accessibility restrictions, particularly in resource-constrained contexts, while tailored micro-learning enhances scalability.

The value and utility of traditional knowledge in conserving and commercialising genetic resources are increasingly becoming apparent due to advances in biotechnology and bioprospecting. However, the absence of an international legally binding instrument within the WIPO system means that traditional knowledge associated with genetic resources is not sufficiently protected like other forms of intellectual property. This means that indigenous peoples and local communities (IPLCs) do not benefit from the commercial exploitation of these resources. The efficacy of domestic tools to protect traditional knowledge and in balancing the rights of IPLCs and intellectual property rights (IPRs) is still debated. This paper employs a doctrinal research methodology based on desktop research of international and regional law instruments and the Constitution of the Republic of South Africa, 1996, to determine the basis for balancing the protection of genetic resources and associated traditional knowledge with competing interests of IPLCs and IPRs in South Africa.

Intellectual property rights face new obstacles and possibilities as a result of the emergence of the Metaverse, a simulation of the actual world. This paper explores the current status of intellectual property rights in the Metaverse and examines the challenges and opportunities for enforcement. The article describes virtual assets and investigates their copyright and trademark protection. It also examines the protection of user-generated content in the Metaverse and the potential liability for copyright infringement. The article concludes with a consideration of the technological and jurisdictional obstacles to enforcing intellectual property rights in the Metaverse, as well as possible solutions for stakeholders. This paper will appeal to lawyers, policymakers, developers of virtual assets, platform owners, and anyone interested in the convergence of technology and intellectual property rights.

Dynamic nature of the FMCG sector perpetually provides a tricky challenge for organisational leaders to nurture their employees. High demand for products, less shelf life and tough competitors always challenge the leaders to uphold their products in the market. Due to technology and e-commerce, many new competitors have joined the market, vying with the industry's veterans. Due to their unique business models that match client needs, these firms are expected to boost FMCG industry income in the future. Managers' leadership styles depend primarily on emotional intelligence. This quantitative study examines how emotional intelligence influences West Bengal FMCG senior managers' leadership styles. 500 FMCG managers were selected. PLS-SEM is used to study. Emotionally competent leaders choose transactional and transformational leadership styles depending on the occasion. Managers' transactional leadership style is strongly influenced by their sympathetic awareness, as shown by a path coefficient of 0.755. Transformational leadership style has a path coefficient of 0.693, indicating that managers' empathy affects their organisational management. Thus, sympathetic awareness and emotion regulation predict good management leadership.

The EU 'General Data Protection Regulation' (GDPR) marked the most important step towards reforming data privacy regulation in recent years, as it has brought about significant changes in data process in various sectors, ranging from healthcare to banking and beyond. Various concerns have been raised, and as a consequence of these, certain parts of the text of the GDPR itself have already started to become questionable due to rapid technological progress, including, for example, the use of information technology, automatisation processes and advanced algorithms in individual decision-making activities. The road to GDPR compliance by all European Union members may prove to be a long one and it is clear that only time will tell how GDPR matters will evolve and unfold. In this paper, we aim to offer a review of the practical, ethical and constitutional aspects of the new regulation and examine all the controversies that the new technology has given rise to in the course of the regulation's application.

The patient must follow a good diet to lessen the risk of health conditions. The body needs vitamins, minerals, and nutrients for illness prevention. When the human body does not receive the right amount of nutrients, nutritional disorders can develop, which can cause a number of different health issues. Chronic diseases like diabetes and hypertension can be brought on by dietary deficiencies. The human body receives the nutrients from a balanced diet to function properly. This research has a prototype that enables patients to find nutritious food according to their health preferences. It suggests meals based on their preferences for nutrients such as protein, fibre, high-fibre, low-fat, etc., and diseases such as pregnancy and diabetes. The process implements the recommendation based on the patient's profile (content-relied, K-NN), recommendation relied on patients with similar profiles, and recommendation based on the patient's past or recent activity.

International universities are changing with their style of operation, mode of teaching and learning operations. This change is noticeable rapidly in India and also in international contexts due to healthy and innovative methods, educational strategies, and nomenclature throughout the world. Technologies are changing rapidly, including ICT. Different subjects are developed in the fields of IT and computing with the interaction or applications to other fields, viz. health informatics, bio informatics, agriculture informatics, and so on. Agricultural informatics is an interdisciplinary subject dedicated to combining information technology and information science utilisation in agricultural sciences. The digital agriculture is powered by agriculture informatics practice. For teaching, research and development of any subject educational methods is considered as important and various educational programs are there in this regard viz. Bachelor of Education, Master of Education, PhD in Education, etc. Degrees are also available to deal with the subjects and agricultural informatics should not be an exception of this. In this context, Doctor of Education (EdD or DEd) is an emerging degree having features of skill sets, courses and research work. This paper proposed on EdD program with agricultural informatics specialisation for improving healthy agriculture system. Here, a proposed model core curriculum is also presented.

Numerous Internet of Things (IoT) applications require brain-empowered intelligence. This necessity has caused the emergence of a new area called cognitive IoT (CIoT). Reasoning, planning, and selection are typically involved in decision-making within the network bandwidth limit. Consequently, data minimisation is needed. Therefore, this research proposes a novel technique to extract conscious data from a massive dataset. First, it groups the data using k-means clustering, and the entropy is computed for each cluster. The most prominent cluster is then determined by selecting the cluster with the highest entropy. Subsequently, it transforms each cluster element into an informative element. The most informative data is chosen from the most prominent cluster that represents the whole massive data, which is further used for intelligent decision-making. The experimental evaluation is conducted on the 21.25 years of environmental dataset, revealing that the proposed method is efficient over competing approaches.