Addressing energy challenges in Iraq: Forecasting power supply and demand using artificial intelligence models.

The global surge in energy demand, driven by technological advances and population growth, underscores the critical need for effective management of electricity supply and demand. In certain developing nations, a significant challenge arises because the energy demand of their population exceeds their capacity to generate, as is the case in Iraq. This study focuses on energy forecasting in Iraq, using a previously unstudied dataset from 2019 to 2021, sourced from the Iraqi Ministry of Electricity. The study employs a diverse set of advanced forecasting models, including Linear Regression, XGBoost, Random Forest, Long Short-Term Memory, Temporal Convolutional Networks, and Multi-Layer Perceptron, evaluating their performance across four distinct forecast horizons (24, 48, 72, and 168 hours ahead). Key findings reveal that Linear Regression is a consistent top performer in demand forecasting, while XGBoost excels in supply forecasting. Statistical analysis detects differences in models performances for both datasets, although no significant differences are found in pairwise comparisons for the supply dataset. This study emphasizes the importance of accurate energy forecasting for energy security, resource allocation, and policy-making in Iraq. It provides tools for decision-makers to address energy challenges, mitigate power shortages, and stimulate economic growth. It also encourages innovative forecasting methods, the use of external variables like weather and economic data, and region-specific models tailored to Iraq's energy landscape. The research contributes valuable insights into the dynamics of electricity supply and demand in Iraq and offers performance evaluations for better energy planning and management, ultimately promoting sustainable development and improving the quality of life for the Iraqi population.

A symbolic Neanderthal accumulation of large herbivore crania.

This work examines the possible behaviour of Neanderthal groups at the Cueva Des-Cubierta (central Spain) via the analysis of the latter's archaeological assemblage. Alongside evidence of Mousterian lithic industry, Level 3 of the cave infill was found to contain an assemblage of mammalian bone remains dominated by the crania of large ungulates, some associated with small hearths. The scarcity of post-cranial elements, teeth, mandibles and maxillae, along with evidence of anthropogenic modification of the crania (cut and percussion marks), indicates that the carcasses of the corresponding animals were initially processed outside the cave, and the crania were later brought inside. A second round of processing then took place, possibly related to the removal of the brain. The continued presence of crania throughout Level 3 indicates that this behaviour was recurrent during this level's formation. This behaviour seems to have no subsistence-related purpose but to be more symbolic in its intent.

Determination of Mammalian Deoxyribonucleic Acid in Commercial Canine Treats and Supplements.

Feeding an elimination diet exclusively is currently the only accurate diagnostic test for an adverse food reaction in dogs and cats. However, owner compliance has been identified as a challenge, and the inability to limit exposure to other items (including treats and supplements) is a remarkable reason for failure. The objective of the current study was to evaluate the presence of declared and undeclared mammalian deoxyribonucleic acid (DNA) in commercially available canine treats and supplements using polymerase chain reaction methodology. Eight treat products and 20 supplement products were analyzed for the DNA of 10 mammalian species (bison, cat, cow, dog, goat, horse, mouse, rat, pig, and sheep). The results showed that 88% (7/8) of treats and 40% (8/20) of supplements were found to contain at least one source of undeclared mammalian DNA. Undeclared pig and cow DNA were the most frequently identified, and there were only two instances of negative results for declared species. Because of the frequent finding of undeclared mammalian DNA in the assessed products, avoiding using treats and supplements during elimination trials is recommended.

Deep Learning Techniques to Improve the Performance of Olive Oil Classification.

The olive oil assessment involves the use of a standardized sensory analysis according to the "panel test" method. However, there is an important interest to design novel strategies based on the use of Gas Chromatography (GC) coupled to mass spectrometry (MS), or ion mobility spectrometry (IMS) together with a chemometric data treatment for olive oil classification. It is an essential task in an attempt to get the most robust model over time and, both to avoid fraud in the price and to know whether it is suitable for consumption or not. The aim of this paper is to combine chemical techniques and Deep Learning approaches to automatically classify olive oil samples from two different harvests in their three corresponding classes: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). Our Deep Learning model is built with 701 samples, which were obtained from two olive oil campaigns (2014-2015 and 2015-2016). The data from the two harvests are built from the selection of specific olive oil markers from the whole spectral fingerprint obtained with GC-IMS method. In order to obtain the best results we have configured the parameters of our model according to the nature of the data. The results obtained show that a deep learning approach applied to data obtained from chemical instrumental techniques is a good method when classifying oil samples in their corresponding categories, with higher success rates than those obtained in previous works.