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Biometrics Systems Challenges in a Post-COVID-19 Pandemic World: A review
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One of the most serious health disasters in recent memory is the COVID-19 epidemic. Several restriction rules have been forced to reduce the virus spreading. Masks that are properly fitted can help prevent the virus from spreading from the person wearing the mask to others. Masks alone will not protect against COVID-19; they must be used in conjunction with physical separation and avoidance of direct contact. The fast spread of this disease, as well as the growing usage of prevention methods, underscore the critical need for a shift in biometrics-based authentication schemes. Biometrics systems are affected differently depending on whether are used as one of the preventive techniques based on COVID-19 pandemic rules. This study provides an overview of biometrics systems and approaches in the post-COVID-19 pandemic. The fundamental COVID-19 prevention rules are first reviewed. The relationships between each regulation and the biometrics that may be impacted are then thoroughly investigated. Recommendations for future trends of feasible approaches are provided to assist researchers in advance and enhance the performance of the biometric system for the post-COVID-19 pandemic environment.

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Publication Date
Sun Jan 01 2017
Journal Name
Journal Of Sensors
Sequential Monte Carlo Localization Methods in Mobile Wireless Sensor Networks: A Review
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The advancement of digital technology has increased the deployment of wireless sensor networks (WSNs) in our daily life. However, locating sensor nodes is a challenging task in WSNs. Sensing data without an accurate location is worthless, especially in critical applications. The pioneering technique in range-free localization schemes is a sequential Monte Carlo (SMC) method, which utilizes network connectivity to estimate sensor location without additional hardware. This study presents a comprehensive survey of state-of-the-art SMC localization schemes. We present the schemes as a thematic taxonomy of localization operation in SMC. Moreover, the critical characteristics of each existing scheme are analyzed to identify its advantages

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Publication Date
Mon Feb 01 2021
Journal Name
Journal Of Engineering
The Synergic Effects of Mineral Admixtures in Ternary Blended Cement: A Review
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In the last decades, using mineral admixture in concrete became very necessary to improve concrete properties and reduce CO2 emissions associated with the cement production process. Subsequently, more sustainable concrete can be obtained.  Ternary blended cement containing two different types of mineral admixture can achieve ambitious steps in this trend. In this research, the synergic effects of mineral admixtures in ternary blended cement and its effects on concrete fresh properties, strength, durability, and efficiency factors of mineral admixture in ternary blended cement, were reviewed. The main conclusion reached after reviewing many literature pieces is that the concrete with ternary blended cement

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Publication Date
Fri Mar 04 2022
Journal Name
Journal Of Water Resources And Geosciences
A Review Study on Gypseous Soils Stabilized with Different Additives in Iraq
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Publication Date
Thu May 30 2024
Journal Name
Iraqi Journal Of Science
A Review Study on Forgery and Tamper Detection Techniques in Digital Images
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Digital tampering identification, which detects picture modification, is a significant area of image analysis studies. This area has grown with time with exceptional precision employing machine learning and deep learning-based strategies during the last five years. Synthesis and reinforcement-based learning techniques must now evolve to keep with the research. However, before doing any experimentation, a scientist must first comprehend the current state of the art in that domain. Diverse paths, associated outcomes, and analysis lay the groundwork for successful experimentation and superior results. Before starting with experiments, universal image forensics approaches must be thoroughly researched. As a result, this review of variou

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Publication Date
Mon Aug 16 2021
Journal Name
TÜrkÇe SÖzlÜkte Tdk Yer Alan ArapÇa Kelİmeler Üzerİne Bİr Anlam Bİlİmİ İncelemesİ
A SEMANTICS REVIEW ON THE ARABIC WORDS IN THE TURKISH DICTIONARY (TDK)
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Publication Date
Tue Dec 01 2015
Journal Name
Solar Energy
A review of studies on using nanofluids in flat-plate solar collectors
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Continuous escalation of the cost of generating energy is preceded by the fact of scary depletion of the energy reserve of the fossil fuels and pollution of the environment as developed and developing countries burn these fuels. To meet the challenge of the impending energy crisis, renewable energy has been growing rapidly in the last decade. Among the renewable energy sources, solar energy is the most extensively available energy, has the least effect on the environment, and is very efficient in terms of energy conversion. Thus, solar energy has become one of the preferred sources of renewable energy. Flat-plate solar collectors are one of the extensively-used and well-known types of solar collectors. However, the effectiveness of the coll

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Publication Date
Tue Aug 26 2025
Journal Name
Microbial Science Archives
Listeria monocytogens: A review of its characteristics, pathogenicity and prevalence in Iraq
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Listeria monocytogenes represents a critical foodborne pathogen causing listeriosis, a severe infection with mortality rates of 20- 30%. This comprehensive review integrates cutting-edge research from 2015-2024 with Iraqi epidemiological data to address significant knowledge gaps in regional surveillance and global comparative analysis. Recent discoveries include five novel Listeria species in 2021, revolutionary whole genome sequencing (WGS) surveillance systems, and advanced understanding of RNA-mediated regulation. Iraqi prevalence data reveals concerning patterns with rates ranging from 3.5% to 93.8% across different sample types, substantially higher than global averages. Critically, Iraqi isolates demonstrate alarming antibiotic resis

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Publication Date
Fri Jul 24 2020
Journal Name
Al-kindy College Medical Journal
Axillary Artery Injuries Surgical experience review in a single vascular trauma center
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Background: Injuries to blood vessels are among the most dramatic challenges facing trauma surgeons because repair is often urgent, the surgeon has to decide between management options (open or endovascular), and gaining control and reconstructing a major arterial injury can be technically demanding .
Objective:,To analyze the cause of injury, surgical approach, outcome and complications of axillary artery injuries.
Methods A descriptive cross-sectional study on fifty patients at Ibn-Alnafees hospital in Baghdad from January 2005 to December 2010
Results Males were more commonly affected than female with ratio of 6.1:1. Most injuries were caused by bullet and shell (84%), followed by stab wounds (10%) and blunt trauma (6%). Pati

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Publication Date
Wed Oct 25 2023
Journal Name
Urban Planning And Construction
A Review on Urban Planning and Its Role in Managing Flood Risks
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This review delves deep into the intricate relationship between urban planning and flood risk management, tracing its historical trajectory and the evolution of methodologies over time. Traditionally, urban centers prioritized defensive measures, like dikes and levees, with an emphasis on immediate solutions over long-term resilience. These practices, though effective in the short term, often overlooked broader environmental implications and the necessity for holistic planning. However, as urban areas burgeoned and climate change introduced new challenges, there has been a marked shift in approach. Modern urban planning now emphasizes integrated blue-green infrastructure, aiming to harmonize human habitation with water cycles. Resil

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Publication Date
Fri Apr 14 2023
Journal Name
Journal Of Big Data
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
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Abstract<p>Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for</p> ... Show More
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