1. Haute-Savoie Trois enfants tués à Taninges : qui est Deborah Pel, la mère de famille recherchée  Le Dauphiné Libéré
2. Ce que l’on sait de la mère de famille recherchée après des infanticides en Haute-Savoie  Le HuffPost
3. Triple infanticide en Haute-Savoie : institutrice, dépressive... que sait-on de Déborah, la mère de famille en fuite  Midi Libre
4. Trois enfants retrouvés morts, la mère de famille recherchée : ce que l’on sait du triple infanticide en Haute-Savoie  France 3 Régions
5. Triple infanticide en Haute-Savoie : institutrice, dépressive… Que sait-on sur la mère de famille toujours recherchée ?  Sud Ouest

1. What is walking pneumonia? As cases rise in Canada, the symptoms to look out for  The Globe and Mail
2. Walking pneumonia on the rise in Kingston, but treatable  The Kingston Whig-Standard
3. What parents need to know about walking pneumonia in kids  FingerLakes1.com
4. Pediatric pneumonia is surging in Central Ohio  MSN
5. Walking Pneumonia is spiking right now. How do you know you have it?  CBS 6 News Richmond WTVR

1. Management of opioid use disorder: 2024 update to the national clinical practice guideline  CMAJ
2. New set of guidelines set to combat opioid usage  CTV News Toronto

Faster flowing glaciers could help predict nearby volcanic activity  Phys.org

Running and longevity: Sub-four-minute milers increase their lifespan by up to 9 years  Genetic Literacy Project

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

1. Niagara Health offering free parking after delays reported  News Talk 610 CKTB
2. Implementation of new Niagara Health patient info system resulting in long wait times  St. Catharines Standard
3. Temporary delays impacting registration at emergency departments  Thorold News
4. Niagara Health Working Through Delays  101.1 More FM
5. Niagara Health experiencing temporary delays impacting registration and EDs  Niagara Health

1. Bear encounter recorded in Whistler Village prompts conservation officer warning  CTV News Vancouver
2. Tourists 'shocked' to see man approach black bear in Whistler  Pique Newsmagazine

1. UN chief warns COP29 summit to pay up or face climate-led disaster for humanity  The Globe and Mail
2. Climate Summit, in Early Days, Is Already on a ‘Knife Edge’  The New York Times
3. At COP29 summit, nations big and small get chance to bear witness to climate change  The Globe and Mail
4. Terence Corcoran: COP29 hit by political ‘dunkelflaute’  Financial Post
5. COP29: Albania PM goes off script to ask 'What on Earth are we doing?'  Euronews

(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)

“Fifteen Years of Evolution of Law, Technology and Society” To celebrate SCRIPTed’s 15th birthday, we are hosting a conference on Monday 28 January (3pm-7pm) at Evolution House here in Edinburgh. For more details, the programme, and (free) registration, see our

by Bashar H. Malkawi Copyright is an engine for knowledge. Although copyright creates monopoly, it should not be considered as a good in itself, but as a tool which can be used to achieve desirable objectives in society. Against the

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

Voilￜ un "petit oubli" bien embￜtant. Selon Mediapart, "le garde des Sceaux, Eric Dupont-Moretti, a oubliￜ de dￜclarer au fisc et ￜ la Haute Autoritￜ pour la transparence de la vie publique...

Le retour de Politique.net mￜritait bien de commencer par une fￜte. Selon Le Canard enchaￜnￜ, "le 17 juin, Pￜle emploi Il-de-France s'est offert un sￜminaire managers au Stade de France pour communier...

C'est malin. A peine nommￜ ministre de la Santￜ, Franￜois Braun croyait avoir trouvￜ une astuce pour dￜsengorger les services d'urgences des hￜpitaux : demander aux patients d'appeler le 15,...

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

Through the unparalleled espousal of theory of planned behaviour, this study intends to significantly add to the current knowledge on social networking sites (SNS) in <i>eWOM</i> information and its role in defining intentions to buy green products. In specie, this study seeks to first investigate the part played by <i>attitude towards SNS information</i> in influencing the <i>acceptance of SNS information</i> and then by <i>acceptance of SNS information</i> in effecting the <i>green purchase intention</i>. Besides this, it also aims to analyse the influence exerted by first <i>credibility of SNS information</i> on <i>acceptance of SNS information</i> and then by <i>acceptance of SNS information</i> on <i>green purchase intention</i>. In doing so, it also examines how well the age of the SNS users moderates all these four associations.

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%.

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.

Image forgery detection techniques refer to the process of detecting manipulated or altered images, which can be used for various purposes, including malicious intent or misinformation. Image forgery detection is a crucial task in digital image forensics, where researchers have developed various techniques to detect image forgery. These techniques can be broadly categorised into active, passive, machine learning-based and hybrid. Active approaches involve embedding digital watermarks or signatures into the image during the creation process, which can later be used to detect any tampering. On the other hand, passive approaches rely on analysing the statistical properties of the image to detect any inconsistencies or irregularities that may indicate forgery. In this paper for the detection of scaling and cropping attack a deep learning method has been proposed using ResNet. The proposed method (Res-Net-Adam-Adam) is able to achieve highest amount of accuracy of 99.14% (0.9914) while detecting fake and real images.

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.

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.

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.

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.

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.

Numerous Indian economic sectors have been impacted by the COVID-19 epidemic, with many being forced to the verge of extinction. As a result, this essay analyses the importance of supply chains for grapes and the manufactured goods made from them, including beverages and currants, in a specific state that happens to be India's top grape-producing region. In order to identify the sites of rupture brought on by the pandemic and to recommend policy changes to create a resilient system, a value chain analysis is performed. Value chain management has emerged as one of the key strategies businesses use today to boost productivity and costs when they are up against greater rivalry in the marketplace, however, with several new challenges, such as concerns over security, environmental protection, compensation, and business accountability. According to the value chain study, the level of value addition for intermediary agents, such as pre-harvest contractors, has increased after COVID-19 at the expense of farmers. Various policy approaches are explained to enhance the grape value chain using the knowledge gained from Porter's value chain results and supply and demand shocks.

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.

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.

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

In order to reduce the challenges encountered by learners and educators in engaging in educational activities, this paper classifies learners' roles in virtual learning communities, and explores the role of behaviour characteristics and their positions in collaborative knowledge construction networks in promoting the process of knowledge construction. This study begins with an analysis of the relationship structure among learners in the virtual learning community and then applies the FCM algorithm to arrange learners into various dimensional combinations and create distinct learning communities. The test results demonstrate that the FCM method performs consistently during the clustering process, with less performance oscillations, and good node aggregation, the ARI value of the model is up to 0.90. It is found that they play an important role in the social interaction of learners' virtual learning community, which plays a certain role in promoting the development of artificial intelligence.

This article studies the application of artificial intelligence technology in natural resource planning and management. This article first introduces the background of NR and AI intelligent technology, then conducts academic research and summary on NR planning management and AI intelligent technology. Then, an algorithm model based on multi-objective intelligent planning algorithm is established. Finally, simulation experiments are conducted, and experiments summary and discussion are provided. The experimental results show that the average efficiency value of the four stages of NR planning and management before use is 5.25, and the average efficiency value of the four stages of NR planning and management after use is 7. The difference in the average efficiency value before and after use is 1.75. It can be seen that the use of AI intelligent technology can effectively improve the efficiency of natural resource planning and management.

Physiological indicators are an important basis for reflecting the physiological health status of the human body and play an important role in medical practice. Association rules have also been one of the important research hotspots in recent years. This study aims to create a data mining system of association rules and artificial intelligence in biochemical indicators of sports training. This article uses Markov logic for network creation and system training, and tests whether the Markov logic network can be associated with the training system. The results show that the accuracy and recall rate obtained are about 90%, which shows that it is feasible to establish biochemical indicators of sports training based on Markov logic network, and the system has universal, guiding and constructive significance, ensuring that the construction of training system indicators will not go in the wrong direction.

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.

Physical and social aspects each make up a separate part of servicescape. Together, these make up the servicescape. Although previous research has frequently investigated these aspects separately, the purpose of this study is to simultaneously find out the impact of both aspects within the casual dining restaurants' context. In total, 462 customers in Delhi were polled for this study, and structural equation modelling was used to analyse the data. According to the results, both the social and physical parts of the servicescape have the ability to affect how satisfied customers are, which in turn can affect how they behave in the future.