1. Apparition en plein concert ! Cameron Hallyday se montre pour son père David Hallyday, sa tante Laura Smet conquise  Purepeople
2. Duo virtuel avec Johnny, Sylvie Vartan, Laura et Santa invitées… David Hallyday a frappé fort à Paris  Le Parisien
3. David Hallyday et Laura Smet, le frère et la sœur réunis sur scène après 7 ans  Paris Match
4. Rendez-vous culture : show, sang pour sang Hallyday !  TF1+
5. « C’est si émouvant» : David Hallyday rejoint sur scène par sa mère Sylvie Vartan et sa sœur Laura Smet  La Voix du Nord

1. Un iPhone dans sa version Pro reconditionné pour moins de 300 € et c'est grâce à ce code promo exclusif au Black Friday  Lesmobiles
2. iPhone 13 : c’est le moment idéal pour obtenir le smartphone Apple à prix réduit sur Amazon  Capital.fr
3. Ce n’est pas une hallucination, l’iPhone 16 est en promotion dès 799 €, c’est le Black Friday avant l’heure !  Le Figaro
4. C’est le moment ou jamais de vous offrir l’iPhone 15 au meilleur prix : 629 euros seulement  20 Minutes
5. iPhone 14 Pro Max : même en 2024, son prix continue de chuter sans prévenir  Ouest-France

1. Sauvetage de Casino: les syndicats retirent leur plainte en appel après avoir conclu un accord avec la direction  Le Figaro
2. Plan de sauvetage de Casino : accord avec la direction, les représentants du personnel retirent leur plainte en appel  France 3 Régions
3. Sauvetage de Casino : un accord social enfin trouvé pour clore le dossier  Capital.fr
4. Casino: les représentants du personnel retirent leur appel formulé à l'encontre du plan de sauvegarde  Investir
5. Plan de sauvegarde de Casino : la direction trouve un accord avec le personnel  La Tribune.fr

1. Premiers résultats de la grande enquête nationale « Contexte des sexualités en France 2023  Inserm (salle de presse)
2. La sexualité des Français a connu de gros changements en dix ans  L'Union
3. Plus de partenaires, pratiques variées, numérique... Une grande étude dévoile les dessous de la sexualité...  BFMTV
4. La vie sexuelle des Français, une journée spéciale sur France Inter, jeudi 14 novembre 2024  France Inter

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. Teenager in critical condition with Canada’s first human case of bird flu  The Guardian
2. B.C. teen with avian flu is in critical condition, provincial health officer says  CBC News
3. What do you do when a goose dies in your backyard, amid concerns about avian flu?  CityNews Kitchener
4. B.C. teen with Canada's first human case of avian flu in critical condition, Dr. Bonnie Henry says  CTV News Vancouver
5. B.C. teen diagnosed with bird flu in critical condition, with source of virus likely to remain unknown  The Globe and Mail

J'ai lu les titres et articles de presse à propos d'Asia Argento et de sa supposée agression contre Jimmy Bennett. Ça sent le traquenard à plein nez. 

Le type assure avoir été agressé sexuellement par l'actrice à l'âge de 17 ans. Il se fout de la gueule de qui, le petit traumatisé ? La prétendue agression ne l'empêchait pas de titrer quelques semaines après que "Maman" lui manquait. Pour la petite histoire, les deux acteurs jouent à la mère et au fils depuis qu'ils ont tourné ensemble dans un film alors que Jimmy avait 7 ans et qu'Asia incarnait sa ...

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) 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)

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.

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.

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 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.

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.

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.

The prevalence of social media transforms e-business into social commerce and facilitates consumers' engagement in cross-cultural social commerce. However, social commerce operations encounter unpredictable challenges in cross-cultural business environment. It is vital to further investigate how contextual elements affect consumers' trust and their engagement when they are exposed to the complexity of cross-cultural business environment. The stimuli-organism-response paradigm is employed to examine how the two dimensions of social media usage influence consumers' engagement in cross-cultural social commerce. The current study surveyed 2,058 samples from 135 countries, and the regression analysis results illustrate the mechanism whereby informational and socialising usage of social media positively influences consumers' engagement in social commerce through consumers' trust toward social commerce websites. Additionally, the associations between two aspects of social media usage and consumers' trust towards social commerce are negatively moderated by cultural distance. Both theoretical and practical implications are also discussed.

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.

The performance of a building's internal environment, which includes the air temperature, lighting and acoustics, is what determines the quality of the environment inside the building. We present a thermal model for achieving thermal comfort in buildings that makes use of a multimodal analytic framework as a solution to this challenge. In this study, a multimodal combination is used to evaluate several temperature and humidity sensors as well as an area image. Additionally, a CNN and LSTM combination is used to process the image and sensor data. The results show that heating setback and interior set point temperatures, as well as mechanical ventilation based on real people's presence and CO<SUB align=right>2 levels, are all consistently reduced when ICT-driven intelligent solutions are used. The CNN-LSTM model has a goodness of fit that is 0.7258 on average, which is much higher than both the CNN (0.5291) and LSTM (0.5949) models.

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.

Constrained programming is the concept used to select possible alternatives from an incredibly diverse range of candidates. This paper proposes an AI-assisted Backtracking Scheme (AI-BS) by integrating the generic backtracking algorithm with Artificial Intelligence (AI). The detailed study observes that the extreme dual ray associated with the infeasible linear program can be automatically extracted from minimum unfeasible sets. Constraints are used in artificial intelligence to list all possible values for a group of variables in a given universe. To put it another way, a solution is a way of assigning a value to each variable that these values satisfy all constraints. Furthermore, this helps the study reach a decreased search area for smart backtracking without paying high costs. The evaluation results exhibit that the IB-BC algorithm-based smart electricity schedule controller performs better electricity bill during the scheduled periods than comparison approaches such as binary backtracking and binary particle swarm optimiser.

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.

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 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.

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.

With the expansion of business scope, the environment faced by enterprises has also changed, and competition is becoming increasingly fierce. Traditional financial systems are increasingly difficult to handle complex tasks and predict potential financial risks. In the context of the digital economy era, the booming financial sharing services have reduced labour costs and improved operational efficiency. This paper designs and implements an intelligent financial sharing platform, establishes a fund payment risk early warning model based on an improved support vector machine algorithm, and tests it on the Financial Distress Prediction dataset. The experimental results show that the effectiveness of using F2 score and AUC evaluation methods can reach 0.9484 and 0.9023, respectively. After using this system, the average financial processing time per order decreases by 43%, and the overall financial processing time decreases by 27%. Finally, this paper discusses the role of intelligent financial sharing platform in accounting transformation and optimisation of production.

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.

With the development of urban construction, its spatial evolution is also influenced by behavioural actors such as enterprises, residents, and environmental factors, leading to some decision-making behaviours that are not conducive to urban public space and environmental design. At the same time, some cities are vulnerable to various factors such as distance factors, transportation factors, and human psychological factors during the construction of public areas, resulting in a decline in the quality of urban human settlements. Urban public space is the guarantee of urban life. For this, in order to standardise urban public space and improve the quality of urban living environment, the standardisation of the environment of urban public space is required. The rapid development of intelligent algorithms and fuzzy control provides technical support for the environmental design of urban public spaces. Through the modelling of intelligent algorithms and the construction of fuzzy space, it can meet the diverse.

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 face of the hospitality sector in India is continuously changing and in times of career transitiveness, it is important to know the factors that support a successful career. The current research aims to explore the relationship between career planning, employee optimism, career adaptability and career satisfaction in the Indian hospitality sector. The study included 283 employees from Indian hospitality sector. Additionally, the study used SEM and bootstrap method to measure the dual mediating relationship between career planning, employee optimism dimensions, career adaptability dimensions, and career satisfaction in Indian setting. The results indicated that optimism dimensions and career adaptability dimensions partially mediate the relationship between career planning and career satisfaction in Indian hospitality sector. The study suggests useful implications for academia and industrial purpose. The limitations and future research avenues have been discussed. The study would contribute to the sparse literature on employee optimism, career planning, career adaptability and subjective career success. It would contribute to the social cognitive career theory (SCCT).

Teaching advanced programming can be a challenge, especially when the students are pursuing different majors with diverse analytical and problem-solving capabilities. The purpose of this paper is to explore the efficacy of using a particular problem as a vehicle for imparting a broad set of programming concepts and problem-solving techniques. We present a classic brain teaser that is used to communicate and demonstrate advanced software development concepts and techniques. Our results show that students with varied academic experiences and goals, assuming at least one procedural/structured programming pre-requisite, can benefit from and also be challenged by such an exercise. Although this problem has been used by others in the classroom, we believe that our use of this problem in imparting such a broad range of topics to a diverse student population is unique.

Aim/Purpose : To examine the use of a flipped classroom in the English Language subject in secondary classrooms in Hong Kong. Background: The research questions addressed were: (1) What are teachers’ perceptions towards the flipped classroom pedagogy? (2) How can teachers transfer their flipped classroom experiences to teaching other classes/subjects? (3) What are students’ perceptions towards the flipped classroom pedagogy? (4) How can students transfer their flipped classroom experiences to studying other subjects? (5) Will students have significant gain in the knowledge of the lesson topic trialled in this study? Methodology: A total of 57 students from two Secondary 2 classes in a Band 3 secondary school together with two teachers teaching these two classes were involved in this study. Both quantitative and quantitative data analyses were conducted. Contribution: Regarding whether the flipped classroom pedagogy can help students gain significantly in their knowledge of a lesson topic, only one class of students gained statistically significantly in the subject knowledge but not for another class. Findings: Students in general were positive about the flipped classroom. On the other hand, although the teachers considered that the flipped classroom pedagogy was creative, they thought it may only be useful for teaching English grammar. Recommendations for Practitioners: Teachers thought that flipping a classroom may only be useful for more motivated students, and the extra workload of finding or making suitable pre-lesson online videos is the main concern for teachers. Recommendations for Researchers: Both quantitative and qualitative analyses should be conducted to investigate the effectiveness of a flipped classroom on students’ language learning. Impact on Society : Teachers and students can transfer their flipped classroom experiences in English Language to teaching and studying other subjects. Future Research: More classes should be involved and a longer period of time should be spent on trial teaching in which a flipped classroom can be implemented in different lesson topics, not only teaching grammar. Teachers also need to determine if students can use the target language item in a task.

Table of Contents of the Journal of Information Technology Education: Innovations in Practice, Volume 17, 2018

Aim/Purpose: This paper presents a data mining approach for analyzing responses to advanced declarative programming questions. The goal of this research is to find a model that can explain the results obtained by students when they perform exams with Constructed Response questions and with equivalent Multiple-Choice Questions. Background: The assessment of acquired knowledge is a fundamental role in the teaching-learning process. It helps to identify the factors that can contribute to the teacher in the developing of pedagogical methods and evaluation tools and it also contributes to the self-regulation process of learning. However, better format of questions to assess declarative programming knowledge is still a subject of ongoing debate. While some research advocates the use of constructed responses, others emphasize the potential of multiple-choice questions. Methodology: A sensitivity analysis was applied to extract useful knowledge from the relevance of the characteristics (i.e., the input variables) used for the data mining process to compute the score. Contribution: Such knowledge helps the teachers to decide which format they must consider with respect to the objectives and expected students results. Findings: The results shown a set of factors that influence the discrepancy between answers in both formats. Recommendations for Practitioners: Teachers can make an informed decision about whether to choose multiple-choice questions or constructed-response taking into account the results of this study. Recommendation for Researchers: In this study a block of exams with CR questions is verified to complement the area of learning, returning greater performance in the evaluation of students and improving the teaching-learning process. Impact on Society: The results of this research confirm the findings of several other researchers that the use of ICT and the application of MCQ is an added value in the evaluation process. In most cases the student is more likely to succeed with MCQ, however if the teacher prefers to evaluate with CR other research approaches are needed. Future Research: Future research must include other question formats.

Aim/Purpose: The study examined types of errors made by novice programmers in different Java concepts with students of different ability levels in programming as well as the perceived causes of such errors. Background: To improve code writing and debugging skills, efforts have been made to taxonomize programming errors and their causes. However, most of the studies employed omnibus approaches, i.e. without consideration of different programing concepts and ability levels of the trainee programmers. Such concepts and ability specific errors identification and classifications are needed to advance appropriate intervention strategy. Methodology: A sequential exploratory mixed method design was adopted. The sample was an intact class of 124 Computer Science and Engineering undergraduate students grouped into three achievement levels based on first semester performance in a Java programming course. The submitted codes in the course of second semester exercises were analyzed for possible errors, categorized and grouped across achievement level. The resulting data were analyzed using descriptive statistics as well as Pearson product correlation coefficient. Qualitative analyses through interviews and focused group discussion (FGD) were also employed to identify reasons for the committed errors. Contribution:The study provides a useful concept-based and achievement level specific error log for the teaching of Java programming for beginners. Findings: The results identified 598 errors with Missing symbols (33%) and Invalid symbols (12%) constituting the highest and least committed errors respec-tively. Method and Classes concept houses the highest number of errors (36%) followed by Other Object Concepts (34%), Decision Making (29%), and Looping (10%). Similar error types were found across ability levels. A significant relationship was found between missing symbols and each of Invalid symbols and Inappropriate Naming. Errors made in Methods and Classes were also found to significantly predict that of Other Object concepts. Recommendations for Practitioners: To promote better classroom practice in the teaching of Java programming, findings for the study suggests instructions to students should be based on achievement level. In addition to this, learning Java programming should be done with an unintelligent editor. Recommendations for Researchers: Research could examine logic or semantic errors among novice programmers as the errors analyzed in this study focus mainly on syntactic ones. Impact on Society: The digital age is code-driven, thus error analysis in programming instruction will enhance programming ability, which will ultimately transform novice programmers into experts, particularly in developing countries where most of the software in use is imported. Future Research: Researchers could look beyond novice or beginner programmers as codes written by intermediate or even advanced programmers are still not often completely error free.

Table of Contents of the Journal of Information Technology Education: Innovations in Practice, Volume 18, 2019

Aim/Purpose: The purpose of this study is to develop an efficient methodology that can assist the evaluators in assessing a variable number of individuals that are working in groups and guarantee that the assessment is dependent on the group members’ performance and contribution to the work developed. Background: Collaborative work has been gaining more popularity in academic settings. However, group assessment needs to be performed according to each individual’s performance. The problem rests on the need to distinguish each member of the group in order to provide fair and unbiased assessments. Methodology: Design Science Research methodology supported the design of a framework able to provide the evaluator with the means to distinguish individuals in a workgroup and deliver fair results. Hevner’s DSR guidelines were fulfilled in order to describe WebAVALIA. To evaluate the framework, a quantitative study was performed and the first results are presented. Contribution: This paper provides a methodological solution regarding a fair evaluation of collaborative work through a tool that allows its users to perform their own assessment and peer assessment. These are made accordingly to the user’s perspectives on the performance of each group member throughout the work development. Findings: The first analysis of the results indicates that the developed method provides fairness in the assessment of group members, delivering a distinction amongst individuals. Therefore, each group member obtains a mark that corresponds to their specific contribution to the workgroup. Recommendations for Practitioners: For those who intend to apply this workgroup assessment method, it is relevant to raise student awareness about the methodology that is going to be used. That is, all the functionalities and steps in WebAVALIA have to be thoroughly explained before beginning of the project. Then, the evaluators have to decide about the students’ intermediate voting, namely if the evaluator chooses or not to publish student results throughout the project’s development. If there is the decision to display these intermediate results, the evaluator must try to encourage collaboration among workgroup members, instead of competition. Recommendation for Researchers: This study explores the design and development of an e-assessment tool – WebAVALIA. In order to assess its feasibility, its use in other institutions or contexts is recommended. The gathering of user opinions is suggested as well. It would then be interesting to compare the findings of this study with the results from other experimentations Impact on Society: Sometimes, people develop a rejection of collaborative work because they feel exploited due to the biased evaluation results. However, the group members assessment distinction, according to each one’s performance, may give each individual a sense of fairness and reward, leading to an openness/willingness towards collaborative work. Future Research: As future work, there are plans to implement the method in other group assessment contexts – such as sports and business environments, other higher education institutions, technical training students – in other cultures and countries. From this myriad of contexts, satisfaction results would be compared. Other future plans are to further explore the mathematical formulations and the respective WebAVALIA supporting algorithms.

Table of Contents of the Journal of Information Technology Education: Innovations in Practice, Volume 19, 2020