(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)

(Belga) La reine Mathilde et la princesse Élisabeth effectueront une visite de travail en Égypte du 14 au 16 mars, a indiqué le Palais royal dans un communiqué lundi soir.

"Cette visite marquera l'intérêt historique de la famille royale pour l'Égypte antique et rendra hommage à la reine Élisabeth, dont l'intérêt et la passion sont à l'origine de l'épanouissement de l'égyptologie en Belgique. La Reine et la Princesse visiteront plusieurs sites que la reine Élisabeth a elle-même visités lors de ses voyages en Égypte, notamment le tombeau de Toutankhamon. Au Caire, elles assisteront également au vernissage d'une exposition consacrée à la reine Élisabeth et à l'égyptologie belge", peut-on lire dans le communiqué. La Reine et la Princesse visiteront aussi différents sites archéologiques à Louxor et ses environs, où des institutions et des universités belges effectuent des fouilles. Cette visite de travail commémore par ailleurs plusieurs anniversaires célébrés en 2022 et 2023: le 200e anniversaire du déchiffrement des hiéroglyphes par Jean-François Champollion, les centenaires de la découverte du tombeau de Toutankhamon et de sa visite par la reine Élisabeth, le 125e anniversaire de l'émergence de l'égyptologie belge et le 75e anniversaire de la mort de l'égyptologue belge Jean Capart. (Belga)

(Belga) Le taux de cellules commerciales vides dans le centre-ville carolo est tendanciellement à la baisse, a indiqué lundi soir lors du conseil communal de la Ville de Charleroi Babette Jandrain, l'échevine carolo du Commerce en réponse à une question qui lui était adressée.

Selon l'élue, qui a indiqué reprendre les chiffres de l'Association de Management du Centre-Ville (AMCV), le taux actuel de cellules commerciales vides dans l'intra-ring carolo est de 29% contre 33,6% en 2020. Et ce malgré le Covid et la crise énergétique qui ont mis en difficulté l'activité commerciale dans les grandes villes. Si le taux de cellules commerciales vides reste élevé à Charleroi, il n'en reste pas moins que la situation a tendance à s'améliorer, a indiqué Babette Jandrain. Les chiffres donnés par l'échevine ont été constatés par la conseillère communale du PTB Sofie Merckx, évoquant une situation de "désolation" concernant le quartier de la Ville Basse et interrogeant par ailleurs la volonté de la Ville de faire de certaines cellules vides des logements. (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) Sept personnes ont été tuées lundi lors d'une double fusillade près de San Francisco, en Californie, ont indiqué les médias américains sur la base des déclarations de la police locale.

Le suspect a été arrêté, a annoncé sur Twitter le bureau du shérif du comté de San Mateo, qui comprend la ville de Half Moon Bay où ont eu lieu les drames. "Il n'y a plus de danger pour la population à cette heure", a-t-il assuré. Les deux fusillades sont intervenues dans des exploitations agricoles proches l'une de l'autre, ont précisé les médias. Cette nouvelle tuerie intervient moins de 48 heures après qu'un tireur a tué 11 personnes dans un club de danse près de Los Angeles. (Belga)

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L'information est passï¿œe inaperï¿œue mais elle ne manque pas de sel. Alors que la gestion de la crise du covid-19 par le gouvernement est trï¿œs contestï¿œe (au retard dans la livraison de masques s'ajoute...

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Aiming at the problems of high error rate, low evaluation accuracy and low investment return in traditional methods, a random forest-based e-bank investment risk evaluation method is proposed. First, establish a scientific e-bank investment risk evaluation index system. Then, G1-COWA combined weighting method is used to calculate the weights of each index. Finally, the e-bank investment risk evaluation index data is taken as the input vector, and the e-bank investment risk evaluation result is taken as the output vector. The random forest model is established and the result of e-banking investment risk evaluation is obtained. The experimental results show that the maximum relative error rate of this method is 4.32%, the evaluation accuracy range is 94.5~98.1%, and the maximum return rate of e-banking investment is 8.32%. It shows that this method can accurately evaluate the investment risk of electronic banking.

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%.

This paper aims to analyse the effectiveness of cost control oriented enterprise financial risk management. Firstly, it analyses the importance of enterprise financial risk management. Secondly, the position of cost control in enterprise financial risk management was analysed. Cost control can be used to reduce the operating costs of enterprises, improve their profitability, and thus reduce the financial risks they face. Finally, a corporate financial risk management strategy is constructed from several aspects: establishing a sound risk management system, predicting and responding to various risks, optimising fund operation management, strengthening internal control, and enhancing employee risk awareness. The results show that after applying the proposed management strategy, the enterprise performs well in cost control oriented enterprise financial risk management, with a cost accounting accuracy of 95% and an audit system completeness of 90%. It can also help the enterprise develop emergency plans and provide comprehensive risk management strategy coverage.

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.

To reduce the location cost of cross-border e-commerce logistics sites, this article proposes a multi-logistics site location method based on return uncertainty. Firstly, a site selection model is established with the objective function of minimising site construction costs, transportation costs, return costs, and operating costs, and the constraint conditions of return recovery costs and delayed pick-up time; Then, using the Monte Carlo method to simulate the number of returned items, and using an improved chicken swarm algorithm based on simulated annealing, the cross-border e-commerce multi-logistics site location model is solved to complete the location selection. Experimental results show that this method can effectively reduce the related costs of cross-border e-commerce multi-logistics site selection. After applying this method, the total cost of multi-logistics site selection is 19.4 million yuan, while the total cost of the five comparative methods exceeds 20 million yuan.

Alors qu'une fois encore des révélations aussi pénibles qu'indignes sont faites, cela pourrait-il ne pas nous toucher ? Pour certains, tout cela ne change rien. L'important, ce sont les fins dernières et la sanctification personnelle. Pour d'autres, cela change tout, ce qui est le cas du Fr. Philippe Lefebvre, qui a développé un propos qui peut être aussi perturbant que (re)fondateur.

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.

The problem of dispatching is key to management of shared bicycles. Considering the number of borrowing and returning events during the dispatching period, optimisation plans of shared bicycles dispatching are studied in this paper. Firstly, the dispatching model of shared bicycles is built, which regards the dispatching cost and lost demand as optimised objectives. Secondly, the solution algorithm is designed based on non-dominated Genetic Algorithm. Finally, a case is given to illustrate the application of the method. The research results show that the method proposed in the paper can get optimised dispatching plans, and the model considering borrowing and returning during dispatching period has better effects with a 39.3% decrease in lost demand.

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.

Several challenges exist in disseminating multi-level secure (MLS) data in multi-domain environments. First, the security domains participating in data dissemination generally use different MLS labels and lattice structures. Second, when MLS data objects are transferred across multiple domains, there is a need for an agreed security policy that must be properly applied, and correctly enforced for the data objects. Moreover, the data sender may not be able to predetermine the data recipients located beyond its trust boundary. To address these challenges, we propose a new framework that enables secure dissemination and access of the data as intended by the owner. Our novel framework leverages simple public key infrastructure and active bundle, and allows domains to securely disseminate data without the need to repackage it for each domain.

File transfer is always handled by a separate service, sometimes it is a third-party service in videoconferencing. When sending files during a video session, file data flow and video stream are independent of each other. Encryption is a mature method to ensure file security. However, it still has the chance to leave footprints on the intermediate forwarding machines. These footprints can indicate that a file once passed through, some protocol-related logs give clues to the hackers' later investigation. This work proposes a file-sending scheme through the video stream using blockchain and steganography. Blockchain is used as a file slicing and linkage mechanism. Steganography is applied to embed file pieces into video frames that are continuously generated during the session. The scheme merges files into the video stream with no file transfer protocol use and no extra bandwidth consumed by the file to provide trackless file transmission during the video communication.

At present, there is a need for an authentic and fast approach to certificate verification. Which verifies and authenticates the certificates to reduce the extent of duplicity and time. An academic certificate is significant for students, the government, universities, and employers. Academic credentials play a vital role in the career of students. A few people manipulate academic documents for their benefit. There are cases identified where people produced fake academic certificates for jobs or higher education admission. Various research works are developing a secure model to verify genuine academic credentials. This research article proposed a new security model which contains several security algorithms such as timestamps, hash function, digital signature, steganography, and blockchain. The proposed model issues secure digital academic certificates. It enhanced security measures and automated educational certificate verification using blockchain technology. The advantages of the proposed model are automated, cost-effective, secured, traceable, accurate, and time-saving.

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 emergence of digital technology and the increasing prevalence of smartphones have promoted innovations in payment options available in finance and consumption markets. Banks providing mobile payment must ensure the information security. Inadequate security control leads to information leakage, which severely affects user rights and service providers' reputations. This study uses control objectives for Information and Related Technologies 4.1 as the mobile payment security control framework to examine the emergent field of mobile payment. A literature review is performed to compile studies on the safety risk, regulations, and operations of mobile payments. In addition, the Delphi questionnaire is distributed among experts to determine the practical perspectives, supplement research gaps in the literature, and revise the prototype framework. According to the experts' opinions, 59 control objectives from the four domains of COBIT 4.1 are selected. The plan and organise, acquire and implement, deliver and support, and monitor and evaluate four domains comprised 2, 5, 10, and 2 control objectives that had mean importance scores of > 4.50. Thus, these are considered the most important objectives by the experts, respectively. The results of this study can serve as a reference for banks to construct secure frameworks in mobile payment services.

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.

Drawing on the MOA framework, this study establishes a research model that explains the usage intention of robo-advisors. In the model, three predictors that consist of technology relative advantage, technology herding, and technology familiarity influence usage intention of robo-advisors directly and indirectly via the partial mediation of trust. At the same time, the effects of the three predictors on trust are hypothetically moderated by learning goal orientation and perceived performance risk respectively. Statistical analyses are provided using the data of working professionals from the insurance industry in Taiwan. Based on its empirical findings, this study discusses important theoretical and practical implications.

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.

Aiming at the problem of aggravation of traffic congestion caused by unstable signal control of traffic signal control system, the Multi-Agent Deep Deterministic Policy Gradient-based Traffic Cyclic Signal (MADDPG-TCS) control algorithm is used to control the time and data dimensions of the signal control scheme. The results show that the maximum vehicle delay time and vehicle queue length of the proposed algorithm are 11.33 s and 27.18 m, which are lower than those of the traditional control methods. Therefore, this method can effectively reduce the delay of traffic signal control and improve the stability of signal control.

To improve the effectiveness of online training for music education, it was investigated how to improve the pulse-coupled neural network in image processing for spectral image segmentation. The study proposes a two-scale descent method to achieve oblique spectral correction. Subsequently, a convolutional neural network was optimised using a two-channel feature fusion recognition network for music theory notation recognition. The results showed that this image segmentation method had the highest accuracy, close to 98%, and the accuracy of spectral tilt correction was also as high as 98.4%, which provided good image pre-processing results. When combined with the improved convolutional neural network, the average accuracy of music theory symbol recognition was about 97% and the highest score of music majors was improved by 16 points. This shows that the method can effectively improve the teaching effect of online training in music education and has certain practical value.

Physical Layer Security (PLS) and relay technology have emerged as viable methods for enhancing the security of wireless networks. Relay technology adoption enhances the extent of coverage and enhances dependability. Moreover, it can improve the PLS. Choosing relay and jammer nodes from the group of intermediate nodes effectively mitigates the presence of powerful eavesdroppers. Current methods for Joint Relay and Jammer Selection (JRJS) address the optimisation problem of achieving near-optimal secrecy. However, most of these techniques are not scalable for large networks due to their computational cost. Secrecy will decrease if eavesdroppers are aware of the relay and jammer intermediary nodes because beamforming can be used to counter the jammer. Consequently, this study introduces a multi-agent Q-learning-based PLS-enhanced secured joint relay and jammer in dual-hop wireless cooperative networks, considering the existence of several eavesdroppers. The performance of the suggested algorithm is evaluated in comparison to the current algorithms for secure node selection. The simulation results verified the superiority of the proposed algorithm.

It is inevitable for basketball players to have physical injury in sports. Reducing basketball injury is one of the main aims of the study of basketball. In view of this, this paper proposes a monocular vision and FMS injury prediction model for basketball players. Aiming at the limitations of traditional FMS testing methods, this study introduces intelligent machine learning methods. In this study, random forest algorithm was introduced into OpenPose network to improve model node occlusion, missed detection or false detection. In addition, to reduce the computational load of the network, the original OpenPose network was replaced by a lightweight OpenPose network. The experimental results show that the average processing time of the proposed model is about 90 ms, and the output video frame rate is 10 frames per second, which can meet the real-time requirements. This study analysed the students participating in the basketball league of the College of Sports Science of Nantong University, and the results confirmed the accuracy of the injury prediction of college basketball players based on FMS scores. It is hoped that this study can provide some reference for the research of injury prevention of basketball players.

We introduce a unique inductive bias to improve the reconstruction quality of Neural Radiance Fields (NeRF), NeRF employs the Fourier transform to map 3D coordinates to a high-dimensional space, enhancing the representation of high-frequency information in scenes. However, this transformation often introduces significant noise, affecting NeRF's robustness. Our approach allocates attention effectively by segregating channels within NeRF using attention-based gating units. We conducted experiments on an open-source data set to demonstrate the effectiveness of our method, which leads to significant improvements in the quality of synthesised new-view images compared to state-of-the-art methods. Notably, we achieve an average PSNR increase of 0.17 compared to the original NeRF. Furthermore, our method is implemented through a carefully designed special Multi-Layer Perceptron (MLP) architecture, ensuring compatibility with most existing NeRF-based methods.

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%.

5G mobile communication systems are an in-depth fusion of multi-radio access technologies characterised with frequent handover between cells. Handover management is a particularly challenging issue for 5G networks development. In this article, a novel optimised handover framework is proposed to find the optimal network to connect with a good quality of service in accordance with the user's preferences. This framework is based on an extension of IEEE 802.21 standard with new components and new service primitives for seamless handover. Moreover, the proposed vertical handover process is based on an adaptive heuristic model aimed at achieving an optimised network during the decision-making stage. Simulation results demonstrate that, compared to other existing works, the proposed framework is capable of selecting the best network candidate accurately based on the quality-of-service requirements of the application, network conditions, mobile terminal conditions and user preferences. It significantly reduces the handover delay, handover blocking probability and packet loss rate.

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.

Along with amazing advancements in the field of digital payments, this article seeks to provide a summary of research undertaken over the last four decades and to suggest areas in need of additional study. This study employs a two-pronged technique for analysing its data. The first is concerned with performance analysis, and the second with science mapping. The study uses the apps VOS viewer and R-studio to do bibliometric data analysis. From 1982 until May 2022, the most trustworthy database, Scopus, is used to compile a database of 923 publications The findings of this study identify the scope of current research interest, which is explored with critical contributions from a variety of authors, journals, countries, affiliations, keyword analysis, citation analysis, co-citation analysis, and bibliometric coupling, as well as a potential research direction for further investigation in this emerging field.

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.

In the era of big data, enterprises have accumulated a large amount of research and development data. Effective management of their precipitated data and safe sharing of data can improve the collaboration efficiency of research and development personnel, which has become the top priority of enterprise development. This paper proposes to use blockchain technology to assist the collaboration efficiency of enterprise R&D personnel. Firstly, the multi-chain blockchain platform is used to realise the data sharing of internal data of enterprise R&D data department, project internal data and enterprise data centre, and then the process of construction of multi-chain structure and data sharing is analysed. Finally, searchable encryption was introduced to achieve data retrieval and secure sharing, improving the collaboration efficiency of enterprise research and development personnel and maximising the value of data assets. Through the experimental verification, the multi-chain structure improves the collaboration efficiency of researchers and data security sharing.

The utilisation of big data presents significant opportunities for businesses to create value and gain a competitive edge. This capability enables firms to anticipate and uncover information quickly and intelligently. The author introduces a human resource scheduling optimisation strategy using a parallel network fusion structure model. The author's approach involves designing a set of network structures based on parallel networks and streaming media, enabling the macro implementation of the enterprise parallel network fusion structure. Furthermore, the author proposes a human resource scheduling optimisation method based on a parallel deep learning network fusion structure. It combines convolutional neural networks and transformer networks to fuse streaming media features, thereby achieving comprehensive identification of the effectiveness of the current human resource scheduling in enterprises. The result shows that the macro and deep learning methods achieve a recognition rate of 87.53%, making it feasible to assess the current state of human resource scheduling in enterprises.

The intelligent financial sharing platform in the online realm is capable of collecting, storing, processing, analysing and sharing financial data through the integration of AI and big data processing technologies. However, as data volume grows exponentially, the cost of financial data storage and processing increases, and the asset accounting and financial profit data sharing analysis structure in financial sharing platforms is inadequate. To address the issue of data security sharing in the intelligent financial digital sharing platform, this paper proposes a data-sharing framework based on blockchain and edge computing. Building upon this framework, a non-separable task distribution algorithm based on data sharing is developed, which employs multiple nodes for cooperative data storage, reducing the pressure on the central server for data storage and solving the problem of non-separable task distribution. Multiple sets of comparative experiments confirm the proposed scheme has good feasibility in improving algorithm performance and reducing energy consumption and latency.