Science Archaeology Ancient'dirty dishes' may have led archaeologists astray for decades A new study questions if Bronze Age dishes really do have traces of olive oil. Breakthroughs, discoveries, and DIY tips sent every weekday. As far as kitchen staples, you don't really get much better than olive oil . It can do it all--jazz up a salad, sauté vegetables, add a nice crisp to some noodles, and more. Humans have been using olive oil for about 8,000 years, so archeologists often report olive oil residue on excavated pottery.

This paper describes the experiences and lessons learned after the deployment of a country-scale environmental digital twin on the island of Cyprus for three years. This digital twin, called GAEA, contains 27 environmental geospatial services and is suitable for urban planners, policymakers, farmers, property owners, real-estate and forestry professionals, as well as insurance companies and banks that have properties in their portfolio. This paper demonstrates the power, potential, current and future challenges of geospatial analytics and environmental digital twins on a large scale.

Photovoltaic (PV) operators face substantial uncertainty in generation and short-term electricity prices. Continuous intraday markets enable producers to adjust their positions in real time, potentially improving revenues and reducing imbalance costs. We propose a feature-driven reinforcement learning (RL) approach for PV intraday trading that integrates data-driven features into the state and learns bidding policies in a sequential decision framework. The problem is cast as a Markov Decision Process with a reward that balances trading profit and imbalance penalties and is solved with Proximal Policy Optimization (PPO) using a predominantly linear, interpretable policy. Trained on historical market data and evaluated out-of-sample, the strategy consistently outperforms benchmark baselines across diverse scenarios. Extensive validation shows rapid convergence, real-time inference, and transparent decision rules. Learned weights highlight the central role of market microstructure and historical features. Taken together, these results indicate that feature-driven RL offers a practical, data-efficient, and operationally deployable pathway for active intraday participation by PV producers.

Emerging in December 2019, the COVID-19 pandemic caused widespread health, economic, and social disruptions. Rapid global transmission overwhelmed healthcare systems, resulting in high infection rates, hospitalisations, and fatalities. To minimise the spread, governments implemented several non-pharmaceutical interventions like lockdowns and travel restrictions. While effective in controlling transmission, these measures also posed significant economic and societal challenges. Although the WHO declared COVID-19 no longer a global health emergency in May 2023, its impact persists, shaping public health strategies. The vast amount of data collected during the pandemic offers valuable insights into disease dynamics, transmission, and intervention effectiveness. Leveraging these insights can improve forecasting models, enhancing preparedness and response to future outbreaks while mitigating their social and economic impact. This paper presents a large-scale case study on COVID-19 forecasting in Cyprus, utilising a two-year dataset that integrates epidemiological data, vaccination records, policy measures, and weather conditions. We analyse infection trends, assess forecasting performance, and examine the influence of external factors on disease dynamics. The insights gained contribute to improved pandemic preparedness and response strategies.

Abstract-- V ertical T otal Electron Content (vTEC) is an iono-spheric characteristic used to derive the signal delay impo sed by the ionosphere on near-vertical trans-ionospheric link s. The major aim of this paper is to design a prediction model based o n the main factors that influence the variability of this param eter on a diurnal, seasonal and long-term time-scale. The model should be accurate and general (comprehensive) enough for efficiently approximating the high variations of vTEC. Howe ver, good approximation and generalization are conflicting obje ctives. For this reason a Genetic Programming (GP) with Multi-objec tive Evolutionary Algorithm based on Decomposition characteri stics (GP-MOEA/D) is designed and proposed for modeling vTEC over Cyprus. Experimental results show that the Multi-Objectiv e GPmodel, considering real vTEC measurements obtained over a period of 11 years, has produced a good approximation of the modeled parameter and can be implemented as a local model to account for the ionospheric imposed error in positioning . Particulary, the GP-MOEA/D approach performs better than a Single Objective Optimization GP, a GP with Non-dominated Sorting Genetic Algorithm-II (NSGA-II) characteristics a nd the previously proposed Neural Network-based approach in most cases. The ionosphere is defined as a region of the earth's upper atmosphere where sufficient ionisation can exist to affect t he propagation of radio waves. It ranges in height above the surface of the earth from approximately 50 km to 1000 km.

Hezbollah chief Hassan Nasrallah has issued a stern warning to Israel, threatening a war with "no restraint and no rules and no ceilings" in case of a major Israeli offensive against Lebanon. Nasrallah's remarks on Wednesday come amid soaring tensions at the Lebanon-Israel border after Israeli officials reiterated that the country is ready for an all-out war against Hezbollah. "All what the enemy says and the threats and warnings the mediators bring – and what is being said in the Israeli media – about a war in Lebanon does not scare us," Nasrallah said in a speech via video feed. He said that Israel is the party that should be "scared". Israeli Foreign Israeli Foreign Minister Israel Katz on Tuesday raised the prospect of a major conflict with the Lebanese group after Hezbollah released surveillance drone footage showing major infrastructure and military sites in northern Israel.

Garbage disposal is a challenging problem throughout the developed world. In Cyprus, as elsewhere, illegal ``fly-tipping" is a significant issue, especially in rural areas where few legal garbage disposal options exist. However, there is a lack of studies that attempt to measure the scale of this problem, and few resources available to address it. A method of automating the process of identifying garbage dumps would help counter this and provide information to the relevant authorities. The aim of this study was to investigate the degree to which artificial intelligence techniques, together with satellite imagery, can be used to identify illegal garbage dumps in the rural areas of Cyprus. This involved collecting a novel dataset of images that could be categorised as either containing, or not containing, garbage. The collection of such datasets in sufficient raw quantities is time consuming and costly. Therefore a relatively modest baseline set of images was collected, then data augmentation techniques used to increase the size of this dataset to a point where useful machine learning could occur. From this set of images an artificial neural network was trained to recognise the presence or absence of garbage in new images. A type of neural network especially suited to this task known as ``convolutional neural networks" was used. The efficacy of the resulting model was evaluated using an independently collected dataset of test images. The result was a deep learning model that could correctly identify images containing garbage in approximately 90\% of cases. It is envisaged that this model could form the basis of a future system that could systematically analyse the entire landscape of Cyprus to build a comprehensive ``garbage" map of the island.

The concept of sustainable intensification in agriculture necessitates the implementation of management practices that prioritize sustainability without compromising productivity. However, the effects of such practices are known to depend on environmental conditions, and are therefore expected to change as a result of a changing climate. We study the impact of crop diversification on productivity in the context of climate change. We leverage heterogeneous Earth Observation data and contribute a data-driven approach based on causal machine learning for understanding how crop diversification impacts may change in the future. We apply this method to the country of Cyprus throughout a 4-year period. We find that, on average, crop diversification significantly benefited the net primary productivity of crops, increasing it by 2.8%. The effect generally synergized well with higher maximum temperatures and lower soil moistures. In a warmer and more drought-prone climate, we conclude that crop diversification exhibits promising adaptation potential and is thus a sensible policy choice with regards to agricultural productivity for present and future.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Cyprus authorities say they're taking extra efforts to ensure flight safety isn't compromised from Turkish warplanes and military drones flying inside Cypriot-monitored airspace without filing either flight plans or communicating with air traffic control. The issue over unregulated Turkish military flights again came to the fore earlier this month when Cypriot authorities said a Turkish warplane "illegally" flew low over a United Nations-controlled buffer zone that cuts across the ethnically-divided island nation on what was believed to be a surveillance mission. "Despite these illegal acts by Turkey, and the illegal operation of the self-styled air traffic control by the secessionist entity, the Department of Civil Aviation of Cyprus is doing its utmost to ensure the safe provision of air traffic services within the Nicosia FIR in its entirety," the Cyprus government told The Associated Press late Wednesday.

Zeal group of companies (collectively Zeal Group) is a business portfolio of the parent company Zeal Holdings Limited, comprising regulated financial institutions and fintech companies specializing in multi-asset liquidity solutions in regulated markets backed by proprietary technology. We are a people focused business and our team of 500 professionals globally are dedicated to maximizing the success of our employees and customers. Headquarter is located in the UK with global presence in 12 countries across Asia, Middle East, & Europe with 22 offices internationally. The estimated monthly trading volume in an average of USD 100B executed by 100,000 retail investors, professional traders, and financial institution clients.