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.

Publicidade e Venda de Produtos de Tabaco em Plataformas Digitais de Delivery / Advertising and Sales of Tobacco Products on Digital Delivery Platforms / Publicidad y Venta de Productos de Tabaco en Plataformas Digitales de Delivery

Introdução: Com a pandemia do vírus Sars-CoV-2 no Brasil, a comercialização de produtos on-line cresceu significativamente por meio de plataformas digitais de delivery de produtos de diversos gêneros. Estudos mostram que as medidas de isolamento social acarretaram efeitos no comportamento e sobre a saúde dos indivíduos, incluindo o aumento do tabagismo. Objetivo: Avaliar as ocorrências e características da venda e publicidade de produtos de tabaco e de dispositivos eletrônicos para fumar, realizadas por lojas físicas, por meio de três plataformas digitais que atuam no Estado do Rio de Janeiro, entre fevereiro e junho de 2021. Método: As buscas nas plataformas simularam a jornada de compra de um consumidor por intermédio de uma rede social. Os pontos de venda foram georreferenciados a partir de centros de regiões administrativas do município ou de um ponto central nas demais cidades do Estado. Os resultados do georreferenciamento foram cruzados com indicadores sociais para caracterizar o público consumidor. Além disso, os preços de venda foram comparados com as informações da Secretaria de Receita Federal. Resultados: Os resultados indicam a prática de venda ilegal desses produtos nas plataformas digitais pesquisadas. Em regiões com padrões socioeconômicos mais altos, foi identificado um maior número de pontos de venda físicos que aderiram aos serviços das plataformas de delivery. Os valores praticados nessa modalidade foram 83% superiores aos estabelecidos pela Receita Federal. Conclusão: O estudo revela a necessidade de uma revisão das ações dos órgãos de fiscalização para o controle das novas modalidades de venda de produtos de tabaco

Introduction: With the SARS-CoV-2 virus pandemic in Brazil, the marketing of online products has grown significantly through digital delivery platforms for several products. Studies show that social isolation measures have had effects on the behavior and health of individuals, including smoking increase. Objective: Evaluate the characteristics of the sales and advertising of tobacco products and electronic smoking devices by physical stores through three digital platforms operating in the State of Rio de Janeiro between February and June 2021. Method: The searches on the platforms simulated the purchase steps of a consumer through a social network. The points of sale were georeferenced from administrative regions of the municipality of Rio de Janeiro or from a central point in other cities of the State. The georeferenced results were crossed with social indicators to characterize the consumer public. In addition, sales prices were compared with information from the Brazilian Federal Revenue. Results: The results indicate the practice of illegal sale of these products on the digital platforms investigated. In high-standard socioeconomic regions, greater number of physical points of sale that adhered to the delivery platforms services were identified. The values charged were 83% higher than those found in the Federal Revenue. Conclusion: The study highlights the necessity to review the actions of compliance bodies to control the new modalities of tobacco products sales

Introducción: Con la pandemia del virus Sars-CoV-2 en Brasil, la comercialización de productos creció significativamente a través de plataformas digitales de entrega de productos de diversos géneros. Los estudios muestran que las medidas de aislamiento social han tenido efectos en el comportamiento y la salud de las personas, incluido un aumento en el tabaquismo. Objetivo: Evaluar las ocurrencias y características de la venta y publicidad de productos de tabaco y dispositivos electrónicos para fumar realizadas por tiendas físicas a través de tres plataformas digitales que operan en el Estado de Río de Janeiro, entre febrero y junio de 2021. Método: Las búsquedas en las plataformas simularon los pasos de compra de un consumidor a través de una red social. Los puntos de venta fueron georreferenciados desde centros en regiones administrativas del municipio o desde un punto central en otras ciudades del Estado. Los resultados de la georreferenciación se cruzaron con indicadores sociales para caracterizar al público consumidor. Además, los precios de venta se compararon con información del Servicio de Ingresos Federales. Resultados: Los resultados indican la práctica de venta ilegal de estos productos en las plataformas digitales encuestadas. En las regiones con mayor nivel socioeconómico, se identificó una mayor cantidad de puntos de venta físicos que adhirieron a los servicios de plataformas de entrega. Los valores practicados en esta modalidad fueron 83% superiores a los establecidos por la Receita Federal. Conclusión: El estudio destaca la necesidad de una revisión de las acciones de los órganos de control para el control de las nuevas modalidades de venta de productos del tabaco

A data mining based clinical decision support system for survival in lung cancer.

BACKGROUND: A clinical decision support system (CDSS ) has been designed to predict the outcome (overall survival) by extracting and analyzing information from routine clinical activity as a complement to clinical guidelines in lung cancer patients. MATERIALS AND METHODS: Prospective multicenter data from 543 consecutive (2013-2017) lung cancer patients with 1167 variables were used for development of the CDSS. Data Mining analyses were based on the XGBoost and Generalized Linear Models algorithms. The predictions from guidelines and the CDSS proposed were compared. RESULTS: Overall, the highest (> 0.90) areas under the receiver-operating characteristics curve AUCs for predicting survival were obtained for small cell lung cancer patients. The AUCs for predicting survival using basic items included in the guidelines were mostly below 0.70 while those obtained using the CDSS were mostly above 0.70. The vast majority of comparisons between the guideline and CDSS AUCs were statistically significant (p < 0.05). For instance, using the guidelines, the AUC for predicting survival was 0.60 while the predictive power of the CDSS enhanced the AUC up to 0.84 (p = 0.0009). In terms of histology, there was only a statistically significant difference when comparing the AUCs of small cell lung cancer patients (0.96) and all lung cancer patients with longer (≥ 18 months) follow up (0.80; p < 0.001). CONCLUSIONS: The CDSS successfully showed potential for enhancing prediction of survival. The CDSS could assist physicians in formulating evidence-based management advice in patients with lung cancer, guiding an individualized discussion according to prognosis.

Biclustering on expression data: A review.

Biclustering has become a popular technique for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. Most of biclustering approaches use a measure or cost function that determines the quality of biclusters. In such cases, the development of both a suitable heuristics and a good measure for guiding the search are essential for discovering interesting biclusters in an expression matrix. Nevertheless, not all existing biclustering approaches base their search on evaluation measures for biclusters. There exists a diverse set of biclustering tools that follow different strategies and algorithmic concepts which guide the search towards meaningful results. In this paper we present a extensive survey of biclustering approaches, classifying them into two categories according to whether or not use evaluation metrics within the search method: biclustering algorithms based on evaluation measures and non metric-based biclustering algorithms. In both cases, they have been classified according to the type of meta-heuristics which they are based on.

Ethnic difference in risk of toxicity in prostate cancer patients treated with dynamic arc radiation therapy.

AIMS AND BACKGROUND: The objective of this study was to assess the influence of ethnicity on toxicity in patients treated with dynamic arc radiation therapy (ART) for prostate cancer (PC). METHODS: From June 2006 to May 2012, 162 cT1-T3 cN0 cM0 PC patients were treated with ART (primary diagnosis, n = 125; post-prostatectomy/brachytherapy biochemical recurrence, n = 26; adjuvant post-prostatectomy, n = 11) at 2 institutions. Forty-five patients were Latin Americans and 117 were Europeans. The dose prescribed to the prostate ranged between 68 Gy and 81 Gy. RESULTS: The median age was 69 years (range 43-87 years). The median follow-up was 18 months (range 2-74 months). Overall, only 3 patients died, none due to a cancer-related cause. Biochemical recurrence was seen in 7 patients. The rates of acute grade 2 gastrointestinal (GI) and genitourinary (GU) toxicities were 19.7% and 17%, respectively. Only 1 patient experienced acute grade 3 GI toxicity, whereas 11 patients (6.7%) experienced acute grade 3 GU toxicity. Multivariate analysis showed that undergoing whole pelvic lymph node irradiation was associated with a higher grade of acute GI toxicity (OR: 3.46; p = 0.003). In addition, older age was marginally associated with a higher grade of acute GI toxicity (OR: 2.10; p = 0.074). Finally, ethnicity was associated with acute GU toxicity: Europeans had lower-grade toxicity (OR: 0.27; p = 0.001). CONCLUSIONS: Our findings suggest an ethnic difference in GU toxicity for PC patients treated with ART. In addition, we found that ART is associated with a very low risk of severe toxicity and a low recurrence rate.

Quality measures for gene expression biclusters.

An noticeable number of biclustering approaches have been proposed proposed for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. In this context, recognizing groups of co-expressed or co-regulated genes, that is, genes which follow a similar expression pattern, is one of the main objectives. Due to the problem complexity, heuristic searches are usually used instead of exhaustive algorithms. Furthermore, most of biclustering approaches use a measure or cost function that determines the quality of biclusters. Having a suitable quality metric for bicluster is a critical aspect, not only for guiding the search, but also for establishing a comparison criteria among the results obtained by different biclustering techniques. In this paper, we analyse a large number of existing approaches to quality measures for gene expression biclusters, as well as we present a comparative study of them based on their capability to recognize different expression patterns in biclusters.

Configurable pattern-based evolutionary biclustering of gene expression data.

BACKGROUND: Biclustering algorithms for microarray data aim at discovering functionally related gene sets under different subsets of experimental conditions. Due to the problem complexity and the characteristics of microarray datasets, heuristic searches are usually used instead of exhaustive algorithms. Also, the comparison among different techniques is still a challenge. The obtained results vary in relevant features such as the number of genes or conditions, which makes it difficult to carry out a fair comparison. Moreover, existing approaches do not allow the user to specify any preferences on these properties. RESULTS: Here, we present the first biclustering algorithm in which it is possible to particularize several biclusters features in terms of different objectives. This can be done by tuning the specified features in the algorithm or also by incorporating new objectives into the search. Furthermore, our approach bases the bicluster evaluation in the use of expression patterns, being able to recognize both shifting and scaling patterns either simultaneously or not. Evolutionary computation has been chosen as the search strategy, naming thus our proposal Evo-Bexpa (Evolutionary Biclustering based in Expression Patterns). CONCLUSIONS: We have conducted experiments on both synthetic and real datasets demonstrating Evo-Bexpa abilities to obtain meaningful biclusters. Synthetic experiments have been designed in order to compare Evo-Bexpa performance with other approaches when looking for perfect patterns. Experiments with four different real datasets also confirm the proper performing of our algorithm, whose results have been biologically validated through Gene Ontology.

An effective measure for assessing the quality of biclusters.

Biclustering is becoming a popular technique for the study of gene expression data. This is mainly due to the capability of biclustering to address the data using various dimensions simultaneously, as opposed to clustering, which can use only one dimension at the time. Different heuristics have been proposed in order to discover interesting biclusters in data. Such heuristics have one common characteristic: they are guided by a measure that determines the quality of biclusters. It follows that defining such a measure is probably the most important aspect. One of the popular quality measure is the mean squared residue (MSR). However, it has been proven that MSR fails at identifying some kind of patterns. This motivates us to introduce a novel measure, called virtual error (VE), that overcomes this limitation. Results obtained by using VE confirm that it can identify interesting patterns that could not be found by MSR.