Prediction Of Potential Risk Research Articles

Review on Harnessing Artificial Intelligence: A Paradigm Shift in Cybersecurity for a Safer Digital Future

Artificial Intelligence (AI) is transforming cybersecurity by addressing the limitations of traditional reactive systems, which often fall short against advanced and unknown threats. By leveraging machine learning, neural networks, and predictive analytics, AI-driven cybersecurity systems provide proactive and adaptive solutions to modern challenges. These systems enhance threat detection accuracy, reduce response times, and efficiently manage large-scale, complex networks, making them indispensable across industries like finance, healthcare, and e-commerce. AI's ability to process vast amounts of data enables early identification of anomalies and prediction of potential risks, ensuring robust protection against evolving cyberattacks. However, challenges such as data privacy concerns, algorithmic biases, and the risk of AI misuse must be addressed. Ethical deployment and collaboration among governments, industries, and academia are critical to overcoming these obstacles. By fostering transparency and innovation, AI can become a cornerstone of global cybersecurity, paving the way for a safer, more resilient digital future.

Trend of PFAS concentrations and prediction of potential risks in Taihu Lake of China by AQUATOX

Per- and polyfluoroalkyl substances (PFAS) are recognized as emerging environmental pollutants due to their high persistence and toxicities to humans and animals. Understanding the temporal trend of PFAS in the environment is important for their pollution control and making appropriate policies. Many studies have reported the PFAS concentrations in Taihu Lake, the third largest lake in China, while their temporal trend during the years was seldom investigated. This study summarizes the PFAS concentrations in the water, sediment and organisms in Taihu Lake from 2009 to 2020 to depict their temporal trends. Meanwhile, the ecological model of AQUATOX was applied to evaluate and predict the potential risks of PFAS from 2012 to 2030. The results showed that the total PFAS concentrations varied but without distinct increase or decrease in both water and sediment during the years, while PFAS concentrations in organisms significantly decreased. The yearly mean concentrations of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) in the water were 21.7–25.4 ng/L and 9.7–26.5 ng/L respectively, lower than the Standards for Drinking Water Quality of China and the suggested water quality criteria to protect the aquatic organisms. In sediment, PFOA and PFOS concentrations were 0.16–0.69 ng/g and 0.15–0.82 ng/g respectively, much lower than the recommended sediment quality guideline values. Based on the AQUATOX prediction, there will be no major threats caused by PFAS to the growth of biota in Taihu Lake in the near future, while the biomass of some species (e.g. carp) will be affected under the perturbation of PFAS. Both field investigation and AQUATOX simulation showed that PFOS concentrations in invertebrates and fish descend steadily, while no remarkable decrease in PFOA concentrations was expected. This study suggests a decreasing ecological risk of PFAS in Taihu Lake, while highlights the necessity of continuous monitoring of PFAS contamination.

Insights Into the Oral Microbiome and Barrett's Esophagus Early Detection: A Narrative Review.

Barrett's esophagus (BE) prevalence has increased steadily over the past several decades and continues to be the only known precursor of esophageal adenocarcinoma. The exact cause of BE is still unknown. Most evidence has linked BE to gastroesophageal reflux disease, which injures squamous esophageal mucosa and can result in the development of columnar epithelium with intestinal metaplasia. However, this relationship is inconsistent—not all patients with severe gastroesophageal reflux disease develop BE. There is increasing evidence that the host microbiome spanning the oral and esophageal environments differs in patients with and without BE. Several studies have documented the oral and esophageal microbiome's composition for BE with inconsistent findings. The scarcity and inconsistency of the literature and the dynamic phenomena of microbiota all warrant further studies to validate the findings and dissect the effects of oral microbiota, which are considered a viable proxy to represent esophageal microbiota by many researchers. This review aims to summarize the variability of the oral and esophageal microbiome in BE by using the example of Streptococcus to discuss the limitations of the current studies and suggest future directions. Further characterization of the sensitivity and specificity of the oral microbiome as a potential risk prediction or prevention marker of BE is critical, which will help develop noninvasive early detection methods for BE, esophageal adenocarcinoma, and other esophageal diseases.

Survey of Nursing Staff's Training on Early Warning Ability for Inpatients with "Three Infarcts and One Hemorrhage".

Methods A total of 787 nursing staff in a tertiary referral center in Changsha City, Hunan Province, were selected using a convenient sampling method. We used an online questionnaire designed by ourselves to survey them. The content of the questionnaire primary included basic information, related knowledge of the nursing staff on the potential risk prediction and precontrol of inpatients with “three infarcts and one hemorrhage,” relevant information on improving early warning scores, management of clinical early warning, training needs, and training methods. Results Over 50% of the nursing staff had little understanding about the risk warning knowledge of inpatients with “three infracts and one hemorrhage,” and the degree of understanding was related to education, job title, and working years. The nursing staff with higher education level or professional title or longer working experience have a better understanding of the risk warning knowledge of inpatients with “three infracts and one hemorrhage.” Conclusion The cognitive competence of nursing staff in a tertiary referral center in Changsha City, Hunan Province, on the early warning ability of inpatients with “three infarcts and one hemorrhage” needs to be improved. Medical institutions should actively train nursing staff on early warning ability for inpatients with “three infarcts and one hemorrhage” to improve the nursing staff's awareness and patients' safety and efficiency.

Analyzing the Manufacturing Supply Chain Performance for Urgent Item During COVID-19 Outbreak

As COVID-19 pandemic spreads in different regions with varying intensity, supply chains (SC) need to utilize an effective mechanism to adjust spike in both supply and demand of resources, and need techniques to detect unexpected behavior in SC at an early stage. During COVID-19 pandemic, the demand of medical supplies and essential products increases unexpectedly while the availability of recourses and raw materials decreases significantly. As such, the questions of SC and society survivability were raised. Responding to this urgent demand quickly and predicting how it will vary as the pandemic progresses is a key modeling question. In this research, we take the initiative in addressing the impact of COVID-19 disruption on manufacturing SC performance overwhelmed by the unprecedented demands of urgent items by developing a digital twin model for the manufacturing SC. In this model, we combine system dynamic simulation and artificial intelligence to dynamically monitor SC performance and predict SC reaction patterns. The simulation modeling is used to study the disruption propagation in the manufacturing SC and the efficiency of the recovery policy. Then based on this model, we develop artificial neural network models to learn from disruptions and make an online prediction of potential risks. The developed digital twin model is aimed to operate in real-time for early identification of disruptions and the respective SC reaction patterns to increase SC visibility and resilience.

Evaluation of current genetic testing reports in German-speaking countries with regard to secondary use and future electronic implementation

Modern diagnostic methods (next-generation sequencing) are one of the current hopes with regard to a personalised medicine. By applying detailed genetic analysis, it is possible to not only improve the prediction of potential risks (as, e.g., concerning hereditary breast cancer) but also the precision of therapy by targeting it to a specific genetic variant. However, there is no international standard for creating, structuring and/or transferring the results of a genetic test report. This type of test report often contains large amounts of complex information, and a standardised and consistent structure would offer potential benefits to all. These include reduced expenditure of time (due to the elimination of information-conversion steps), improved safety (due to a reduction in the occurrence of transmission errors, misunderstanding or misinterpretation of content) and improved clinical information gathering (by the respective linkage to scientific data and literature). Especially in regard to secondary use, a standardised (electronic) format would improve the suitability of these data in retrospective studies and basic research. In this study, we analysed the format and content of 96 genetic testing reports (germline and somatic) from Germany, Switzerland and Austria. Based on these results, we summarised and discussed potentially critical data that were demonstrated to be reported inconsistently, and propose a baseline structure for reporting that would also ease future electronic conversion.

Complementary analyses of concrete characteristics performed on cores taken from concrete pavements

The basic assessment of cement concrete pavement condition is performed by a visual inspection, when the surface faults which are macroscopically distinguishable are documented. Detailed data on concrete condition require further tests performed on cores. Apart from common standard tests, such as layer thickness, compressive and tensile strength, resistance against water and chemical defrosting agents, other macroscopic and microscopic analyses have become useful. Some of the most important ones include petrographic assessment of aggregates (based on the fraction and type of rocks), quantification of cracks (expressed by e.g. cracking index or damage rating index), analyses of air void characteristics in concrete (expressed by e.g. spacing factor test), indication of type and extent of expansion reactions with the help of chemical reagents that visually mark the affected concrete parts. The information can be complemented and adjusted through a suitable combination of microscopic methods. Prepared thin sections are used to determine types of aggregates including the specification of its harmfulness. In combination with concrete fragments taken at least from three height levels of the core (surface-centre-bottom), differences or conformity of concrete porousness, and level and type of mineralization of pores and cracks created by secondary phases, are determined. The identification of created phases and their correlation with applied aggregates can be used for prediction of potential risks. The paper describes additional test methods that can be performed on cores from concrete structures and that are based on macroscopic and microscopic analyses.

Machine Learning Methods for Detecting and Monitoring Extremist Information on the Internet

In this paper, we employ machine learning methods to solve the problem of countering terrorism and extremism by using information from the Internet. This problem involves retrieving electronic messages, documents, and web resources that potentially contain information of terrorist or extremist nature, identifying the structure of user groups and online communities that disseminate this information, monitoring and modeling information flows in these communities, as well as assessing threats and predicting risks based on monitoring results. We propose some original language-independent algorithms for pattern-based information retrieval, thematic modeling, and prediction of message flow characteristics, as well as assessment and prediction of potential risk coming from members of online communities by using data on the structure of relations in these communities, which makes it possible to detect potentially dangerous users even without full access to the content they distribute, e.g., through private channels and chat rooms.

Mapping Atmospheric Corrosion on Materials of Archaeological Importance in Athens

Atmospheric pollution is known to induce corrosion effects on various materials. For Greece, stone deterioration could emerge severe costs in the case of damaging cultural monuments. This work aims to investigate the corrosion process on materials of archaeological importance (marble, limestone, and sandstone) in the Greater Athens Area (GAA) by using sophisticated geoanalytical methods together with dose–response functions for selected materials, in order to derive corrosion maps for GAA in the period 2000–2009. Also, a corrosion trend analysis is performed, which can be a very helpful tool for the prediction of potential risks to monuments of cultural heritage due to atmospheric pollution. The corrosion effects on the selected materials are generally weak. Nevertheless, increasing corrosion trends are found in the eastern regions of GAA for all sheltered materials and in the northern parts of GAA for unsheltered marble. The technique is finally applied to 12 locations in GAA, which include some of the most important archaeological monuments of Athens, and provides comprehensive results for the estimation of the impact of atmospheric corrosion on the structural materials of these archaeological sites.