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.

La Bourse de New York a conclu en hausse lundi, tirée par la technologie, à l'entame d'une semaine chargée en résultats d'entreprises où les investisseurs semblent enclins à davantage d'optimisme.

L'indice Dow Jones a avancé de 0,76% à 33,629,56 points, le Nasdaq, à dominante technologique, a grimpé de 2,01% à 11.364,41 points et l'indice élargi S&P 500 a pris 1,19%, repassant au-dessus des 4.000 points, à 4.019,81 points.

"Les marchés se focalisent sur les résultats d'entreprises et même si jusqu'ici ils sont, à mon avis, un peu décevants, les actions se comportent bien", a noté Hugh Johnson, de la firme de conseil économique Hugh Johnson Economics, soulignant les meilleures performances des secteurs de la technologie et des dépenses facultatives.

Pas moins de 11 sociétés membres de l'indice Dow Jones, soit un tiers d'entre elles, vont publier cette semaine leurs résultats trimestriels et souvent annuels.

Dès mardi sont attendus notamment Johnson and Johnson, 3M, General Electric et Microsoft. Mercredi, les investisseurs guetteront Boeing et Tesla.

Mais, selon M. Johnson, c'est surtout l'attitude à venir de la banque centrale américaine (Réserve fédérale ou Fed) qui motivait l'humeur du marché. Les investisseurs "penchent vers l'idée que la Fed va lever le pied sur les hausses des taux d'intérêt, ce qui est synonyme de meilleurs temps économiques, de meilleurs résultats d'entreprises et de meilleurs cours des actions", a-t-il résumé.

La Fed, qui réunit son Comité monétaire la semaine prochaine, se dirige, à en croire plusieurs de ses membres, vers un relèvement moindre des taux d'un quart de point de pourcentage, contre un demi-point en décembre.

"La grande attente désormais, pour la Réserve fédérale, est qu'elle relève ses taux de seulement un quart de point de pourcentage en février mais aussi en mars", a commenté M. Johnson.

"De plus, d'après l'évolution des produits à terme basés sur les fonds fédéraux, les investisseurs commencent à penser que la Fed va envisager une baisse des taux au dernier trimestre 2023", a-t-il assuré.

Selon lui, le marché penche donc "légèrement vers l'optimisme et cela se voit dans la performance des actions".

Du côté des valeurs, Salesforce a été recherchée (+3,09%), après l'annonce d'une forte augmentation de la participation dans le groupe informatique du fonds d'investissement activiste Elliott Management.

Elliott dispose désormais d'une participation de "plusieurs milliards de dollars" au capital du groupe de logiciels, a-t-on précisé de source proche, ce qui représenterait un investissement majeur en comparaison de sa participation jusqu'ici.

Spotify, le numéro un mondial des plateformes audio, groupe suédois coté à Wall Street, a gagné 2,08% à 99,95 dollars après avoir annoncé la suppression de 600 emplois, soit 6% de ses effectifs, dernier épisode d'une série de grands licenciements chez les géants du Net pour réduire leurs coûts.

Le titre du site de ventes en ligne d'ameublement Wayfair s'est envolé de 26,86% à 59,36 dollars, après que sa décision de réduire ses coûts et ses effectifs a entraîné la publication d'une note favorable de la part d'analystes bancaires.

Le groupe, très prospère aux Etats-Unis pendant la pandémie, avait annoncé vendredi qu'il allait se défaire de 10% de son personnel, soit 1.750 emplois. L'action avait déjà gagné 20% dans la foulée de cette annonce.

Les investisseurs ont modestement réagi à l'annonce d'un élargissement d'un partenariat entre Microsoft et le spécialiste de l'intelligence artificielle OpenAI, créateur du robot conversationnel ChatGPT, moyennant un investissement de "plusieurs milliards de dollars". L'action Microsoft a avancé de 0,98% à 242,58 dollars.

Tesla a gagné 7,74% à 143,75 dollars, dans l'attente de ses résultats mercredi et alors que son patron Elon Musk est revenu lundi à la barre à San Francisco au procès où il est accusé de fraude par des investisseurs pour avoir tweeté il y a plus de quatre ans qu'il comptait sortir le constructeur automobile de la Bourse.

Le fabricant de semi-conducteurs AMD a bondi de presque 9,22% grâce à une bonne note d'analystes bancaires.

Sur le marché obligataire, les rendements sur les bons du Trésor à dix ans se tendaient légèrement à 3,52% contre 3,47% vendredi.

(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)

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) Deux personnes ont été tuées et une troisième a été gravement blessée lundi dans un centre pour jeunes à Des Moines, dans l'État américain de l'Iowa (centre), a annoncé la police.

Après un appel d'urgence, les forces de l'ordre qui se sont rendues à l'établissement "Starts Right Here", un programme d'aide aux jeunes en difficulté, ont découvert trois personnes blessées par balle, dont deux "très grièvement". "Ces deux personnes, deux élèves, sont mortes (...). La troisième personne, qui est employée par l'établissement, est dans un état grave", a dit à la presse Paul Parizek, un porte-parole de la police de Des Moines. La police n'était pas encore en mesure de préciser l'âge exact des deux victimes. "Je ne sais pas s'ils sont adultes ou (...) adolescents mineurs", a dit M. Parizek. Sur la foi de descriptions de témoins, la police a arrêté un véhicule et détenu "trois suspects potentiels". L'enquête se poursuit, a-t-elle dit dans un communiqué. La gouverneure de l'Iowa, Kim Reynolds, s'est dite "choquée et attristée" par la fusillade. (Belga)

(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

by Dr Kim Barker (University of Stirling) & Dr Olga Jurasz (Open University) The UK Government has recently published an Online Harms White Paper: initial consultation response. It is the cornerstone of the Government’s ongoing reform package which aims to

By Hasan Basri Cifci[1] In the world of 2014, the Commission of Facebook/WhatsApp merger case[2] concluded that integration and interoperation of Facebook and WhatsApp were unfeasible. However, Facebook integrated its three subsidiaries (WhatsApp, Instagram, and Facebook) under its brand in

By Lucas Miotto Lopes and Jiahong Chen The future of real-time appeal Knowing when to say or do something is often just as important as knowing what to say or do. The right advice at the wrong time is not

The current advertising push methods have low accuracy and poor advertising conversion effects. Therefore, a Weibo marketing advertising push method based on social network data mining is studied. Firstly, establish a social network graph and use graph clustering algorithm to mine the association relationships of users in the network. Secondly, through sparsisation processing, the association between nodes in the social network graph is excavated. Then, evaluate the tightness between user preferences and other nodes in the social network, and use the TF-IDF algorithm to extract user interest features. Finally, an attention mechanism is introduced to improve the deep learning model, which matches user interests with advertising domain features and outputs push results. The experimental results show that the push accuracy of this method is higher than 95%, with a maximum advertising click through rate of 82.7% and a maximum advertising conversion rate of 60.7%.

In order to solve the problems of long prediction consumption time and many prediction iterations existing in traditional prediction models, an e-commerce growth prediction model based on grey Markov chain is proposed. The Scrapy crawler framework is used to collect a variety of e-commerce data from e-commerce websites, and the feedforward neural network model is used to clean the collected data. With the cleaned e-commerce data as the input vector and the e-commerce growth prediction results as the output vector, an e-commerce growth prediction model based on the grey Markov chain is built. The prediction model is improved by using the background value optimisation method. After training the model through the improved particle swarm optimisation algorithm, accurate e-commerce growth prediction results are obtained. The experimental results show that the maximum time consumption of e-commerce growth prediction of this model is only 0.032, and the number of iterations is small.

With the rapid development and application of driverless technology, the number and location of vehicles, the channel and bandwidth of wireless network are time-varying, which leads to the increase of offloading delay and energy consumption of existing algorithms. To solve this problem, the vehicle terminal task offloading decision problem is modelled as a Markov decision process, and a task offloading algorithm based on DDQN is proposed. In order to guide agents to quickly select optimal strategies, this paper proposes an offloading mechanism based on task feature. In order to solve the problem that the processing delay of some edge server tasks exceeds the upper limit of their delay, a task scheduling mechanism based on buffer delay is proposed. Simulation results show that the proposed method has greater performance advantages in reducing delay and energy consumption compared with existing algorithms.

To achieve the benefits as much as possible, it is required to identify the available PEV capacity and prepare scheduling plans based on that. The analysis revealed that the risk-based scheduling of the microgrid could reduce the financial risk completely from $9.89 to $0.00 and increases the expected operation cost by 24% from $91.38 to $112.94, in turn. This implies that the risk-averse decision-maker tends to spend more money to reduce the expected risk-in-cost by using the proposed downside risk management technique. At the end, by the help of fuzzy satisfying method, the suitable risk-averse strategy is determined for the studied case.

Smart cities research is growing all over the world seeking to understand the effect of smart cities from different angles, domains and countries. The aim of this study is to analyse how the smart city ICTs (e.g., big data analytics, AI, IoT, cloud computing, smart grids, wireless communication, intelligent transportation system, smart building, e-governance, smart health, smart education and cyber security) are related to government. services and society welfare from the perspective of China. This research confirmed a positive correlation of smart city ICTs to e-Govt. Services (e-GS). On the other hand, the research showed a positive influence of smart city ICTs on society's welfare. These findings about smart cities and ICTs inform us how the thought paradigm to smart technologies can cause the improvement of e-GS through economic development, job creation and social welfare. The study offers different applications of the theoretical perspectives and the management perspective which are significant to building a society during recent technologised era.

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.

In the present scenario, the security of medical images is an important aspect in the field of image processing. Support vector machines (SVMs) are a supervised machine learning technique used in image classification. The roots of SVM are from statistical learning theory. It has gained excellent significance because of its robust, accurate, and very effective algorithm, even though it was applied to a small set of training samples. SVM can classify data into binary classification or multiple classifications according to the application's needs. Discrete wavelet transform (DWT) and singular value decomposition (SVD) transform techniques are utilised to enhance the image's security. In this paper, the image is first classified using SVM into ROI and RONI, and thereafter, to enhance the images diagnostic capabilities, the DWT-SVD-based hybrid watermarking technique is utilised to embed the watermark in the RONI region. Overall, our work makes a significant contribution to the field of medical image security by presenting a novel and effective solution. The results are evaluated using both perceptual and imperceptibility testing using PSNR and SSIM parameters. Different attacks were introduced to the watermarked image, which shows the efficacy and robustness of the proposed algorithm.

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 current study has outlined slow fashion (SF) research trends and created a future research agenda for this field. It is a thorough analysis of the literature on slow fashion. Numerous bibliometric features of slow fashion have been discussed in the paper. This study comprises 182 research articles from the Scopus database. The database was utilised for bibliometric analysis. To identify certain trends in the area of slow fashion, a bibliometric study is done. For bibliometric analysis, the study employed R-software (the Biblioshiny package). Here, VOSviewer software is used to determine the co-occurrence of authors, countries, sources, etc. The study has outlined the gap that still exists in the field of slow fashion. Here, the research outcome strengthens the domain of slow fashion for sustainable consumption. The study findings will be useful for policymakers, industry professionals, and researchers.

Access to quality healthcare is a fundamental right but unfortunately, India suffers from gender disparities in healthcare access. Telemedicine has the potential to improve access to healthcare services for women by eliminating traditional barriers. Therefore, our research aims to investigate the factors influencing the adoption of telemedicine among Indian women. This study has collected 442 responses and analysed them through structural equation modelling. The result indicates a strong and positive connection between the willingness to adopt telemedicine services and factors like performance expectancy, perceived benefits, e-health literacy, and perceived reliability. Notably, perceived reliability emerges as the most impactful predictor, closely followed by perceived benefits, while factors like effort expectancy and user resistance show no significant influence. This underscores the pivotal role of reliability and perceived benefits in shaping women's inclination toward adopting telemedicine. The study provides practical insights for telemedicine providers and policymakers to customise strategies and policies for effective promotion.

This systematic literature study employed bibliometric analysis to identify workplace mindfulness-related methods and practices in literature published from 2000 to 2020 by leading nations, institutions, journals, authors, and keywords. We also assessed the impact of workplace mindfulness research papers. Scopus analysis tools provided a literature report for 638 Scopus articles used in the study. Using VOSviewer, leading nations, institutions, articles, authors, journals, and keyword co-occurrence network maps were constructed. PRISMA was used to identify 56 publications to recognise workplace mindfulness literature's significant achievements. The research's main contribution is a deep review of neurological mindfulness and psychological measuring tools as workplace mindfulness tool categories. The study is the first to use the PRISMA technique to capture the essential contributions of workplace mindfulness papers from 2000 to 2020.

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.

Although artificial intelligence provides a new method to gather, process, analyse data, generate insights, and offer customised solutions, such methods could change how marketers deal with customers, and there is a lack of literature to portray the application of artificial intelligence in marketing. This study aims to recognise and portray the use of artificial intelligence from a marketing standpoint, as well as to provide a conceptual framework for the application of artificial intelligence in marketing. This study uses a systematic literature review analysis as a research method to achieve the aims. Data from 142 articles were extracted from the Scopus database using relevant search terms for artificial intelligence and marketing. The systematic review identified significant usage of artificial intelligence in conversational artificial intelligence, content creation, audience segmentation, predictive analytics, personalisation, paid ads, sales forecasting, dynamic pricing, and recommendation engines and the bibliometric analysis produced the trend in co-authorship, citation, bibliographic coupling, and co-citation analysis. Practitioners and academics may use this study to decide on the marketing area in which artificial intelligence can be invested and used.

This study investigates the effects of social media marketing efforts (SMME) on smartphone brand identification, attachment, association, and willingness to pay premium prices. A survey of 320 smartphone users who followed official social media handles managed by smartphone companies was conducted for this purpose. PLS-SEM was used to analyse the collected data. The findings demonstrated importance of SMME dimensions. According to the study's findings, the smartphone brand's SMMEs had significant impact on brand identification, brand association, and brand attachment. The results revealed that SMMEs had significant impact on willingness to pay the premium price. The findings also show that brand identification, attachment, and association mediated the relationship between SMMEs and willingness to pay a premium price. The findings of this study will be useful in developing social media marketing strategies for smartphones. This study demonstrates the use of social media marketing to promote mobile phones, particularly in emerging markets.

Word of mouth (WOM) and electronic word of mouth (eWOM) are very effective and important communication tools to persuade consumers for purchasing the products/services. These become more significant with products that are difficult to assess before consumption, e.g., hospitality. The tourism industry is reviving, and the consumer is conscious when booking a particular destination. Thus, it is important to understand how WOM and eWOM are impacting the various factors in distinct ways while choosing the tourist destination. The seven factors identified, for the present study, are channel engagement, expertise, homophily, resource helpfulness, source credibility, tie-strength, and trustworthiness. The PLS-SEM was used to test the theoretical model of this study. The study shows that both WOM and eWOM impact an individual in different ways. The expertise of the reviewer is the most important factor in the case of WOM and channel engagement is the most significant factor for eWOM. Resource helpfulness is common for both WOM and eWOM.

Despite the considerable growth potential predicted for mobile games, little research explored what motivates users to be sticky and make purchases in the mobile game context. Drawing on uses and gratifications theory (UGT), this study evaluates the influencing effects of players' characteristics (i.e., individual gratification and individual situation) and the mobile game structure (i.e., presence and governance) on players' mobile game behaviour (i.e., stickiness and purchase intention). Specifically, the model was extended with factors of the individual situation and governance. After surveying 439 samples, the research model was examined using the Partial least squares structural equation modelling (PLS-SEM) approach. The results indicate that stickiness is a crucial antecedent for users' in-game purchase intention. The individual situation plays an essential role in influencing user gratification, and individual gratification is the most vital criterion affecting stickiness. Finally, except for incentives, presence, and integration positively affect stickiness. This study provides further insights into both mobile game design and governance strategies.

A metaverse is a virtual shared space connected to the real world, an alternative reality that enables economic activities, exchanges, and transactions as well as formation of relationships between user avatars and non-player characters (NPCs). Initial experiences of the metaverse were not very satisfactory; new virtual world metaverses may or may not survive as information services or platforms. The purpose of this empirical study is to identify the characteristics of a virtual world metaverse and their effects on intention to continue usage of the platform. Considering the metaverse as a new type of user experience and a powerful mode of communication, we examine the mediating role of these characteristics according to Pine and Gilmore's (1998) experience economy theory, which enriches our understanding of the factors affecting the success of a metaverse. In addition, since social interaction is important in metaverses, we extend Pine and Gilmore's experience economy model by including Schmitt's (2011) relate experience for better understanding.

TCP BBR is one of the most powerful congestion control algorithms. In this article, we provide a comprehensive review of BBR analysis, expanding on existing knowledge across various fronts. Utilising ns3 simulations, we evaluate BBR's performance under diverse conditions, generating graphical representations. Our findings reveal flaws in the probe's RTT phase duration estimation and unequal bandwidth sharing between BBR and CUBIC protocols. Specifically, we demonstrated that the probe's RTT phase duration estimation algorithm is flawed and that BBR and CUBIC generally do not share bandwidth equally. Towards the end of the article, we propose a new improved version of TCP BBR which minimises these problems of inequity in bandwidth sharing and corrects the inaccuracies of the two key parameters RTprop and cwnd. Consequently, the BBR' protocol maintains very good fairness with the Cubic protocol, with an index that is almost equal to 0.98, and an equity index over 0.95.

This paper presents a Scalable Video Coding (SVC) system over multicast Software-Defined Networking (SDN), which focuses on, transmission management for the sender-receiver model. Our approach reduces bandwidth usage by allowing the receiver to select various video resolutions in a multicast group, which helps avoid a video freezing issue during bandwidth congestion. Moreover, the SVC Multiple Sessions Transmission Bandwidth thresholds Quantised Level Balance (SVC-MST BWQLB) routes different layers of the SVC stream using distinct paths and reduces storage space and bandwidth congestion problems in different video resolutions. The experimental results show that the proposed model provides better display quality than the traditional Open Shortest Path First (OSPF) routing technique. Furthermore, it reduced transmission delays by up to 66.64% for grouped resolutions compared to SVC-Single Session Transmission (SVC-SST). Additionally, the modified Real-time Transport Protocol (RTP) header and the sorting buffer for SVC-MST are proposed to deal with the defragmentation problem.

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.

Food quality is defined as a collection of properties that differentiate each unit and influences acceptability degree of food by users or consumers. Owing to the nature of food, food quality prediction is highly significant after specific periods of storage or before use by consumers. However, the accuracy is the major problem in the existing methods. Hence, this paper presents a BEFA_DRNN approach for accurate food quality prediction using dairy products. Firstly, input data is fed to data normalisation phase, which is performed by min-max normalisation. Thereafter, normalised data is given to feature fusion phase that is conducted employing DNN with Canberra distance. Then, fused data is subjected to data augmentation stage, which is carried out utilising oversampling technique. Finally, food quality prediction is done wherein milk is graded employing DRNN. The training of DRNN is executed by proposed BEFA that is a combination of BES and FA. Additionally, BEFA_DRNN obtained maximum accuracy, TPR and TNR values of 93.6%, 92.5% and 90.7%.

We propose that technological, service-dominant and design logics must interplay for an IT artefact to succeed. Based on data from a project aiming at a B2B platform for manufacturing small and medium enterprises (SMEs) in Europe, we explore these three logics in an agile software design context. By using an inductive approach, we theorise about what is needed for the alignment of the three logics. We contribute with a novel theoretical lens, the Framework for Adaptive Space. We offer insights into the importance of continuously reflecting on all three logics during the agile software design process to ensure mutual understanding among the agile team and the B2B platform end-users involved.

The banking sector has experienced a substantial technological shift that has opened up new and better opportunities for its customers. Based on their technological expenditures, the study assessed the two biggest public Indian banks and the two biggest private Indian banks. The most crucial statistical techniques used to demonstrate the aims are realistic are bivariate correlations and ordinary least squares. This work aims to establish a connection between research and a technology index that serves as a proxy for operational efficiency. The results show that for both public and private banks, the technology index positively influences operational efficiency metrics like IT costs, marketing costs, and compensation costs. This suggests that when the technology index increases, so do IT, marketing, and compensation costs, even though it has been shown that the technology index favourably improves operational efficiency measures like depreciation and printing. This means that the cost to banks is high despite greater investment in technology for these activities.

In order to further improve the public health assurance system and the infectious diseases early warning system to give play to their positive roles and enhance their collaborative capacity, this paper, based on the big and thick data analytics technology, designs a 'rolling-type' data synergy model. This model covers districts and counties, municipalities, provinces, and the country. It forms a data blockchain for the public health assurance system and enables high sharing of data from existing system platforms such as the infectious diseases early warning system, the hospital medical record management system, the public health data management system, and the health big and thick data management system. Additionally, it realises prevention, control and early warning by utilising data mining and synergy technologies, and ideally solves problems of traditional public health assurance system platforms such as excessive pressure on the 'central node', poor data tamper-proofing capacity, low transmission efficiency of big and thick data, bad timeliness of emergency response, and so on. The realisation of this technology can greatly improve the application and analytics of big and thick data and further enhance the public health assurance capacity.

Emergency disaster management presents substantial risks and obstacles to shield tunnel building projects, particularly in the event of water leakage accidents. Contemporary water leak detection is critical for guaranteeing safety by reducing the likelihood of disasters and the severity of any resulting damages. However, it can be difficult. Deep learning models can analyse images taken inside the tunnel to look for signs of water damage. This study introduces a unique strategy that employs deep learning techniques, generative adversarial networks (GAN) with long short-term memory (LSTM) for water leakage detection i shield tunnel construction (WLD-STC) to conduct classification and prediction tasks on the massive image dataset. The results demonstrate that for identifying and analysing water leakage episodes during shield tunnel construction, the WLD-STC strategy using LSTM-based GAN networks outperformed other methods, particularly on huge data.

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.

As IOT develops drastically these years, the application of cloud computing in many fields has become possible. In this paper, we take low-voltage current transformers in power systems as the research object and propose a TCN-BI-GRU power measurement method that incorporates the signal characteristics based on the transformer input and output. Firstly, the basic signal enhancement extraction of input and output is completed by using EMD and correlation coefficients. Secondly, multi-dimensional feature extraction is completed to improve the data performance according to the established TCN network. Finally, the power prediction is completed by using BI-GRU, and the results show that the RMSE of this framework is 5.69 significantly lower than other methods. In the laboratory test, the device after being subjected to strong disturbance, its correlation coefficient feature has a large impact, leading to a large deviation in the prediction, which provides a new idea for future intelligent prediction.

Intelligent digital virtual technology has become an indispensable part of modern construction, but there are also some problems in its practical application. Therefore, it is necessary to strengthen the design of intelligent building image processing systems from many aspects. Starting from image digital processing methods, this paper studies the digital twin virtual design scene construction method and related algorithms, converts the original image into a colour digital image through a greyscale algorithm, and then combines morphological knowledge and feature point extraction methods to complete the construction of a three-dimensional virtual environment. Finally, through the comparison of traditional image processing effects with smart building images based on digital twins and BIM technology, the results show that the optimised image processing results have higher clarity, sharper contrast, and a sensitivity increased by 5.84%, presenting better visual effects and solving the risk of misjudgement caused by inaccurate image recognition.

Since its birth, the stock market has received widespread attention from many scholars and investors. However, there are many factors that affect stock prices, including the company's own internal factors and the impact of external policies. The extent and manner of fundamental impacts also vary, making stock price predictions very difficult. Based on this, this article first introduces the research significance of the stock market prediction framework, and then conducts academic research and analysis on two key sentences of stock market prediction and artificial intelligence in stock market prediction. Then this article proposes a constructive algorithm theory, and finally conducts a simulation comparison experiment and summarises and discusses the experiment. Research results show that the neural network prediction method is more effective in stock market prediction; the minimum training rate is generally 0.9; the agency's expected dilution rate and the published stock market dilution rate are both around 6%.

Based on a survey of 2548 American adults conducted by Pew Research Center in 2021, this study finds that trust in the accuracy of news circulated on X (former Twitter) is positively correlated with following news sites on X, underscoring the crucial role of trust in news accuracy in shaping news-seeking behaviour. Trust in news accuracy also positively relates to political participation via X. Those who trust in news accuracy are more likely to perceive X as an effective tool for raising public awareness about political and social issues, as well as a positive force for democracy. However, exposure to misinformation weakens the connection between trust in news accuracy and users' perception about X as an effective tool for raising public awareness about political or social issues and as a positive driver for democracy.

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.

Digital Transformation seeks to improve the processes of an organisation by integrating digital technology in all its areas, this is inevitable due to technological evolution that generates new demands, new habits and greater demands on customers and users, therefore Digital Transformation is important. In organisations to maintain competitiveness. In this context, universities are no strangers to this reality, but they find serious problems in their execution, it is not clear how to deal with an implementation of this type. The work seeks to identify tools that can be used in the implementation of Digital Transformation in universities, for this a systematic review of literature is carried out with a method based on three stages, 23 models, 13 frameworks and 8 roadmaps are identified. The elements found are analysed, obtaining eight main components with their relationships and dependencies, which can be used to generate more optimal models for universities.

The aim of the study is to examine the determinants of digitalisation in public sector. This research is particularly relevant as digital transformation has become a crucial factor in modernising public sector and enhancing service delivery to citizens. The method of the systematic literature review (SLR) was implemented by searching documents on the Scopus database. The initial research reached the 7902 documents and after specifying the keywords the authors found 207 relevant documents. Finally; after the careful read of their abstracts and the use of inclusion and exclusion criteria; the most cited and relevant 32 papers constituted the final sample. Findings highlighted the focus of the literature on technological factors such as the sense of trust and safety as well as the ease of use in the adoption of digital governance; emphasising the need for effective; trustworthy and user-friendly digital services. The most discussed internal factors were leadership and organisational culture. The study offers a deeper understanding of the factors that shape the successful implementation of digital governance initiatives.

This study investigates the well-being of healthcare professionals in India, with a specific focus on the detrimental effects of the pandemic on their mental and physical health, including stress, burnout, and fatigue. This research examines the roles played by calling, mindfulness, and compassionate love as essential resources in promoting the well-being of healthcare professionals. Utilising structural equation modelling (SEM), the results reveal a significant cause and effect relationship between calling, mindfulness, and compassionate love and their influence on overall well-being. Furthermore, the study identifies a noteworthy parallel mediation effect, demonstrating that mindfulness and compassionate love serve as mediators in the relationship between calling and well-being. This research offers practitioners invaluable insights into the effective utilisation of mindfulness and compassionate love practices to enhance the overall well-being of healthcare professionals.

This empirical study investigates the mediating role of two components of psychological capital (PsyCap), namely self-efficacy and optimism, in the context of the relationship between transformational leadership (TL), work engagement (WE), and innovative work behaviour (IWB). The study was conducted among IT professionals with a minimum of three years of experience employed in Chennai, India. Data collection was executed using a Google Form, and both measurement and structural models were examined using SPSS 25.0 and AMOS 23.0. The findings of this study reveal several significant relationships. Firstly, transformational leadership (TL) demonstrates a robust positive association with work engagement (WE). Furthermore, work engagement (WE) positively correlates substantially with innovative work behaviour (IWB). Notably, the study underscores that two crucial components of psychological capital, specifically self-efficacy and optimism, mediate the relationship between transformational leadership (TL) and work engagement (WE). These findings carry valuable implications for IT company managers. Recognising that transformational leadership positively influences both work engagement and employees' innovative work behaviour highlights the pivotal role of leaders in fostering a productive and innovative work environment within IT organisations.

The purpose of this research is to investigate how genuine leaders impact the creativity and innovative behaviour (IWB) of information technology (IT) employees. It also examines the impact of perceived organisational support as a mediator in the correlations between authentic leadership as well as innovative behaviours. This study explores the influence of authentic leadership via the employee's IWB using aspects from social exchange theory as well as social cognitive theory. The data was collected from a sample of 487 employees of the IT sector in India. The partial least square method is applied to test the structural relationship of the research framework. Findings reveal that authentic leadership positively impact innovative work behaviour and perceived organisation support mediates authentic leadership and IWB. Additionally, when organisations and leaders support the employees and value their creative thinking then the employee replicates IWB in the organisation. The practical and theoretical implications are discussed.