A Brief Review of Machine Learning Algorithms in Forest Fires Science

Due to the harm forest fires cause to the environment and the economy as they occur more frequently around the world, early fire prediction and detection are necessary. To anticipate and discover forest fires, several technologies and techniques were put forth. To forecast the likelihood of forest fires and evaluate the risk of forest fire-induced damage, artificial intelligence techniques are a crucial enabling technology. In current times, there has been a lot of interest in machine learning techniques. The machine learning methods that are used to identify and forecast forest fires are reviewed in this article. Selecting the best forecasting model is a constant gamble because each ML algorithm has advantages and disadvantages. Our main goal is to discover the research gaps and recent studies that use machine learning techniques to study forest fires. By choosing the best ML techniques based on particular forest characteristics, the current research results boost prediction power

IRS-aided UAV for Future Wireless Communications: A Survey and Research Opportunities

Both unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces (IRS) are gaining traction as transformative technologies for upcoming wireless networks. The IRS-aided UAV communication, which introduces IRSs into UAV communications, has emerged in an effort to improve the system performance while also overcoming UAV communication constraints and issues. The purpose of this paper is to provide a comprehensive overview of IRSassisted UAV communications. First, we provide five examples of how IRSs and UAVs can be combined to achieve unrivaled potential in difficult situations. The technological features of the most recent relevant researches on IRS-aided UAV communications from the perspective of the main performance criteria, i.e., energy efficiency, security, spectral efficiency, etc. Additionally, previous research studies on technology adoption as machine learning algorithms. Lastly, some promising research directions and open challenges for IRS-aided UAV communication are presented

The Significance of the Adaptive Thermal Comfort Practice over the Structure Retrofits to Sustain Indoor Thermal Comfort

Any building’s design should sustain thermal comfort for occupants and promote less energy usage during its lifetime using accurate building retrofits to convert existing buildings into low-energy buildings so that the heating and cooling loads can be minimized. Regarding the methodology adopted in this research, an energy model of an educational building located at the German Jordanian University in Jordan was constructed utilizing DesignBuilder computer software. In addition, it was calibrated utilizing real energy consumption data for a 12-month simulation of energy performance. Subsequently, a computerized evaluation of the roles of building envelope retrofits or the adaptive thermal comfort limits in the reduction of the overall building energy consumption was analyzed. The results of the study show that the current building’s external wall insulation, roof insulation, glazing, windows, and external shading devices are relatively energy-efficient but with high cost, resulting in significant financial losses, even though they achieved noticeable energy savings. For instance, equipping the building’s ventilation system with an economizer culminated in the highest financial profit, contributing to an annual energy savings of 155 MWh. On the other hand, in an occupant-centered approach, applying the adaptive thermal comfort model in wider ranges by adding 1 °C, 2 °C, and 3 °C to the existing operating temperatures would save a significant amount of energy with the least cost (while maintaining indoor thermal comfort), taking over any retrofit option. Using different adaptive thermal comfort scenarios (1 °C, 2 °C, and 3 °C) led to significant savings of around 5%, 12%, and 21%, respectively. However, using different retrofits techniques proved to be costly, with minimum energy savings compared to the adaptive approach

Kompakte Speicherung für die effiziente Verwaltung von XML-Dokumenten

XML wird mehr und mehr für Datenaustausch und -manipulation genutzt. Viele Ansätze verarbeiten XML-Daten im Hauptspeicher. Weil XML zunehmend häufig verwendet wird und die XML-Syntax zusätzlichen Speicher benötigt, können grössere XML-Dateien nicht im Hauptspeicher verarbeitet werden. Infolgedessen leiden diese Dateien unter den Begrenzungen aktueller Arbeitsspeicher.Hingegen werden objektrelationale Datenbanktechnologien wegen ihrer Ausgereiftheit und weiten Verbreitung als Alternativen zum Speichern und Verwalten von XML-Daten genannt. Die dauerhafte Speicherung von XML in seinem ursprünglichen Format vermeidet Verluste durch Umwandlung und stellt die beste Alternative dar.Daraus folgt ein steigender Bedarf an robusten, leistungsfähigen XML-Datenbanken, die XML-Daten nicht nur effizient abfragen und aktualisieren, sondern sie auch kompakt speichern können.Es gibt viele Ansätze zur Verwaltung von XML-Dokumenten. Hingegen sind zwei gängige Strategien bekannt, die eine robuste Speicherung und effiziente Suche gewährleisten.Die erste beruht auf einem Nummerierungsschema, das strukturelle Informationen aus XML-Dokumenten gewinnt. Diese Informationen werden auf eine Art gespeichert, die schnelle Identifikation zwischen den Knotenbeziehungen erlaubt. Diese Identifikation spielt eine entscheidende Rolle bei der effizienten Abfrageverarbeitung.Die zweite Strategie verkleinert XML-Dateien mittels Komprimierungstechniken. Während eine naive Darstellung von XML-Dateien eine starke Redundanz erzeugt, reduziert die Komprimierung von XML-Dateien nicht nur den benötigten Speicherplatz, sondern erhöht auch die Abfragegeschwindigkeit.Die vorliegende Arbeit präsentiert verschiedene Lösungsansätze für die effiziente Verwaltung von XML-Daten. Sie stellt Ansätze vor, die die Stärken von Kennzeichnungs- und Komprimierungs-Technologien verbinden und sowohl die Lücke zwischen diesen Technologien schließen als auch ihre Nachteile überwinden und eine bessere Leistung als bei separatem Einsatz dieser Technologien gewährleisten.Eine ausführliche experimentelle Evaluation der vorgestellten Anätze zeigt, dass sie im Vergleich mit anderen Ansätzen auf diesem Gebiet deutliche Leistungsverbesserungen bei der XML-Verarbeitung erzielen.XML is becoming widely used for data exchange and manipulation. As a consequence, an increasing number of XML documents need to be managed. There are many works that use main-memory to process XML data. Since XML usage is continuing to grow and the nature of XML is extremely verbose, large or even moderately large XML documents cannot be processed within the main memory. Consequently, these works will suffer from the limitations of current main-memory.On the other hand, because of the maturity and widespread deployment of (object) relational database technologies, they have been suggested as an alternative to store and manage XML data. However, the persistent storage of XML in its native format will avoid transformation cost and present the best alternative

Efficient Compression and Querying of XML Repositories

With the rapidly increasing popularity of XML as a data format, there is a large demand for efficient techniques in storing and querying XML documents. However XML is by nature verbose, due to repeatedly used tags that describe data. For this reason the storage requirements of XML can be excessive and lead to increased costs for data manipulation. Therefore, it seems natural to use compression techniques to increase the efficiency of storing and querying XML data. In this paper, we propose a new approach called SCQX for Storing, Compressing and Querying XML documents. This approach compresses the structure of an XML document based on exploiting repetitive consecutive tags in the structure, and then SCQX stores the compressed XML structure and the data separately in a robust storage structure that includes a set of access support structures to guarantee fast query performance. Moreover, SCQX supports querying of the compressed XML structure directly and efficiently without requiring decompression. An experimental evaluation on sets of XML data shows the effectiveness of our approach

Automatic Short Text Summarization Techniques in Social Media Platforms

The rapid expansion of social media platforms has resulted in an unprecedented surge of short text content being generated on a daily basis. Extracting valuable insights and patterns from this vast volume of textual data necessitates specialized techniques that can effectively condense information while preserving its core essence. In response to this challenge, automatic short text summarization (ASTS) techniques have emerged as a compelling solution, gaining significant importance in their development. This paper delves into the domain of summarizing short text on social media, exploring various types of short text and the associated challenges they present. It also investigates the approaches employed to generate concise and meaningful summaries. By providing a survey of the latest methods and potential avenues for future research, this paper contributes to the advancement of ASTS in the ever-evolving landscape of social media communication

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population