British Prime Minister Keir Starmer has discussed the war in Ukraine with leaders of the United States, Italy, France and Germany, a 10 Downing Street spokesperson has said, in advance of US President Donald Trump's planned call with his Russian counterpart, Vladimir Putin, on Monday. The flurry of diplomacy comes shortly after inconclusive direct Russia-Ukraine talks in Istanbul, Turkiye on Friday. The leaders discussed the need for an unconditional ceasefire and for Putin to take peace talks seriously, the spokesperson said late on Sunday, adding that they also raised the use of sanctions if Russia failed to engage seriously in a ceasefire and concerted peace talks. In remarks to reporters earlier on Sunday, German Chancellor Friedrich Merz said he discussed the issue with US Secretary of State Marco Rubio while the two men were attending the inaugural mass of Pope Leo XIV at the Vatican. Merz said he also spoke at length at the Vatican with Ukraine's President Volodymyr Zelenskyy.

With anti-government protests sweeping across Turkey, the authorities have used all technological means to try to curb them, from restricting internet access to using facial recognition to identify protesters, who have been forced to adapt. Amid a ban on protests, nearly 2,000 people have been arrested in connection with the demonstrations that erupted on March 19 following the detention of Istanbul's mayor Ekrem Imamoglu on graft charges. As well as those apprehended in the streets, many others have been arrested in predawn raids at their homes after being identified from footage or photos taken by the police during the demonstrations.

Muslims around the world have begun celebrating Eid al-Fitr, one of the biggest celebrations in the Islamic calendar. Eid al-Fitr - which means "festival of the breaking of the fast" - is celebrated at the end of Ramadan, a month of fasting for many adults, as well as spiritual reflection and prayer.ReutersHere in Moscow, worshippers are seen preparing for prayer.ReutersHundreds took part in prayers at Tononoka grounds, in Mombasa, KenyaGetty ImagesPrayers were also observed at a stadium in Port Sudan in the east of the countryGetty ImagesLittle children joined adults at the Moskee Essalam in Rotterdam, NetherlandsGetty ImagesGifts are handed out to Muslim children in Lviv, Ukraine, as Russia's war on the country continuesReuters Palestinians in Jabaliya in the northern Gaza Strip pray amidst the rubble of a mosque destroyed in the current war between Israel and HamasGetty ImagesFamilies gather at al-Aqsa mosque in Jerusalem - the third holiest site in IslamReutersA boy yawns during prayers at a stadium in QatarEPAMuslims greet each-other at Martim Moniz Square in Lisbon, PortugalGetty ImagesWomen worshippers gather in Burgess Park, London, for an outdoor prayerEPAThere were also worshippers gathered outside Plebiscito Square in Naples, ItalyReutersSome women took pictures after attending prayers at the Hagia Sophia Grand Mosque in Istanbul, TurkeyGetty ImagesAfghan refugees pray at a mosque on the outskirts of Peshawar, PakistanMiddle EastEuropeEid al-FitrReligionIslamRelated'I was afraid for my life': At the scene of the attack on Palestinian Oscar winner 5 days agoMiddle EastMore8 hrs ago'In Bradford, families spend thousands on new clothes for Eid' Muslims spend large amounts in Bradford's supermarkets, clothes shops and other services before Eid.8 hrs agoEngland1 day ago The tourist has received an award from the city's mayor after restraining a man during a stabbing.1 day agoEurope1 day ago Another 21 people are injured, as a restaurant and several buildings are set ablaze in the city, local officials say.1 day agoWorld1 day ago Town's successful Ramadan lights project expanded A Scunthorpe community group says it has seen an "amazing" response to its lights display.1 day agoLincolnshire1 day ago Bishop says school that changed Easter events'valued' The BBC is not responsible for the content of external sites.

When Turkish lender Denizbank paid more than 350 million for the building that houses its headquarters in April, the purchase cemented the bank's place in Istanbul's most expensive commercial district. Yet, almost as soon as the ink had dried on the deal for the imposing 34-story tower, the same institution was quietly scouting for an alternative premises in another city. The reason wasn't because of a seismic shift in its business, it was the prospect of seismic activity beneath it. Earthquakes are unpredictable but also inevitable, and experts say Istanbul might be due one. After the devastation last year in the southeast of the country and criticism the authorities weren't prepared, banks are now getting ahead on contingency planning. That includes establishing teams in shadow offices in the capital Ankara to ensure continuity should disaster strike.

Istanbul, Turkey – As nationwide local elections approach on March 31, there are concerns in Turkey about the growing threat of disinformation and fake media created through artificial intelligence. Earlier this year, a video spread across social media purportedly showing Istanbul's opposition mayor praising President Recep Tayyip Erdogan's ruling Justice and Development Party (AK Party). Ekrem Imamoglu, of the Republican People's Party (CHP), is seen in the video commending the "great steps" taken in public transport projects when the AK Party controlled Istanbul. While the video was widely discredited due to the substance of Imamoglu's "comments", it raised fears about media manipulation in an election where the AK Party is trying to retake cities won by the opposition in 2019. Political scandals over "leaked" recordings are nothing new in Turkey.

The performance of an artificial neural network (ANN) in forecasting crash risk is shown in this paper. To begin, some traffic and weather data are acquired as raw data. This data is then analyzed, and relevant characteristics are chosen to utilize as input data based on additional tree and Pearson correlation. Furthermore, crash and non-crash time data are separated; then, feature values for crash and non-crash events are written in three four-minute intervals prior to the crash and non-crash events using the average of all available values for that period. The number of non-crash samples was lowered after calculating crash likelihood for each period based on accident labeling. The proposed CNN model is capable of learning from recorded, processed, and categorized input characteristics such as traffic characteristics and meteorological conditions. The goal of this work is to forecast the chance of a real-time crash based on three periods before events. The area under the curve (AUC) for the receiver operating characteristic curve (ROC curve), as well as sensitivity as the true positive rate and specificity as the false positive rate, are shown and compared with three typical machine learning and neural network models. Finally, when it comes to the error value, AUC, sensitivity, and specificity parameters as performance variables, the executed model outperforms other models. The findings of this research suggest applying the CNN model as a multi-class prediction model for real-time crash risk prediction. Our emphasis is on multi-class prediction, while prior research used this for binary (two-class) categorization like crash and non-crash.

Radio Frequency Energy Harvesting (RF-EH) networks are key enablers of massive Internet-ofthings by providing controllable and long-distance energy transfer to energy-limited devices. Relays, helping either energy or information transfer, have been demonstrated to significantly improve the performance of these networks. We first obtain the optimal solution to the scheduling and power control problem for the given relay selection. Then, the relay selection problem is formulated as a classification problem, for which two convolutional neural network (CNN) based architectures are proposed. While the first architecture employs conventional 2D convolution blocks and benefits from skip connections between layers; the second architecture replaces them with inception blocks, to decrease trainable parameter size without sacrificing accuracy for memory-constraint applications. To decrease the runtime complexity further, teacher-student learning is employed such that the teacher network is larger, and the student is a smaller size CNN-based architecture distilling the teacher's knowledge. A novel dichotomous searchbased algorithm is employed to determine the best architecture for the student network. Our simulation results demonstrate that the proposed solutions provide lower complexity than the state-of-art iterative approaches without compromising optimality. A. G. Onalan and S. Coleri are with the Department of Electrical and Electronics Engineering, and B.Kopru is with Department of Computer Engineering, Koc University, 34450, Istanbul, Turkey e-mail: {aonalan17, bkopru17, scoleri}@ku.edu.tr. Sinem Coleri acknowledges the support of the Scientific and Technological Research Council of Turkey 2247-A National Leaders Research Grant #121C314. Energy Harvesting (EH) is a promising alternative to fixed power supplies (e.g., batteries) for energy-constrained Internet-of-Things (IoT) applications, such as smart cities and wearable devices [1], by enabling the net-zero energy objective of sixth generation (6G) mobile networks [2]. The use cases and feasibility of wireless-powered IoT have been investigated in many standardization bodies, including IEEE 802.11 [3] and 3GPP [4]. Radio Frequency (RF) signals are preferred over other energy sources such as vibration and sun due to their independence from climate conditions, accessibility, and more predictable nature [5]. RF-EH network design follows two main protocols: Simultaneous Information and Power Transfer (SWIPT), in which power and information are sent simultaneously, and Wireless Powered Communication Networks (WPCN), in which power and information are sent consecutively in downlink (DL) and uplink (UL) [6].

This study presents an innovative approach for automatic road detection with deep learning, by employing fusion strategies for utilizing both lower-resolution satellite imagery and GPS trajectory data, a concept never explored before. We rigorously investigate both early and late fusion strategies, and assess deep learning based road detection performance using different fusion settings. Our extensive ablation studies assess the efficacy of our framework under diverse model architectures, loss functions, and geographic domains (Istanbul and Montreal). For an unbiased and complete evaluation of road detection results, we use both region-based and boundary-based evaluation metrics for road segmentation. The outcomes reveal that the ResUnet model outperforms U-Net and D-Linknet in road extraction tasks, achieving superior results over the benchmark study using low-resolution Sentinel-2 data. This research not only contributes to the field of automatic road detection but also offers novel insights into the utilization of data fusion methods in diverse applications.

Tripadvisor has a new tool that uses OpenAI's generative AI technology to create custom travel itineraries. Using the tool, you can choose a destination, outline specifics of your trip, and having a comprehensive itinerary within minutes. The tool leverages reviews and insights from Tripadvisor users, so you're essentially getting an automated experience of the research you might normally do on the crowd-sourced site. Let's plan a seven-day trip to Istanbul, Turkey to see how it works. On a desktop or mobile browser, go to the Tripadvisor site and click on the Trips section at the top of the homepage. In the "Create a trip box," choose "Use AI" to get started.

While Jamal Khashoggi was being carefully slaughtered in the Saudi consulate in Istanbul, a (clumsy and not much alike) man was trying out his shoes and clothes. The plan was for the imposter to appear on CCTV cameras while exiting the consulate and walk back to Khashoggi's residence. The plan eventually blew up, because the Turkish intelligence had already bugged the consulate and recorded exactly what had happened. This was one of the first attempts by state actors to manipulate other states (or publics) through CCTV footage. However, recent actions of the Iranian state television have taken this type of information warfare to a different level.