Explore the vast range of titles available.

Terrorism adversely impacts the investment decisions of multinational enterprises. This research proposes the iterative outlier analysis heuristic capable of utilizing a univariate attribute to extract high impact attacks (HIA) from a comprehensive and actively maintained global terrorism database (GTD). The two different univariate attributes used for HIA extraction were nkill (pure GTD univariate attribute) and global terrorism impact score (GTI-IS) (a derived composite attribute). Calendar year extraction of HIAs returns local point outliers, whereas taking whole dataset at once provides global point outliers. The nkill-based extraction resulted in 5 and 14 times more exclusive HIAs than GTI-IS in local and global point analysis, thereby establishing its superiority. Further, correspondence analysis using extracted HIAs demonstrated the affinity towards explosives in the Middle East & North Africa region, with Military and Business as preferred targets. Also, HIAs facilitated the geospatial visualization of terrorism hotbeds. Eliciting location-specific relationships from HIAs on weapon type and target type can assist in formulating better counterterrorism strategies. This study scrutinized the averaging out evaluation methodology of GTI ranking based on GTI-IS score. By equalizing terrorist attacks of distinct outcomes using an average score can induce bias among business decision-makers interested in a particular nation.

Terrorist attacks aim to maximize human fatalities and related damages to instill fear within the community. Such attacks are considered High Impact Attacks (HIAs) and orchestrating them requires considerable organizational setup and resources. This study extends the implementation of the Iterative Outlier Analysis (IOA) heuristics developed earlier to each region reported in the Global Terrorism Database (GTD). It reinforces and generalizes the finding that the \"nkill\" attribute resulted in richer sets of HIAs in regions where terrorism is prolific compared to the composite measure Global Terrorism Index-Impact Score (GTI-IS). HIA dataset of each region facilitates the identification and ranking of the Most Active Organizations (MAOs). Moreover, this study proposes a consistency and intensity code (CIC) to classify terrorist organizations capable of HIAs using four color categories. K-Means validate the number of clusters. The frequency of MAOs rank follows distinct probability distribution in each CIC category. Finally, this research identified the most virulent consistent and intense terrorist organizations (CITO) capable of perpetrating attacks in multiple regions. Regional counterterrorism policymakers can use such a classification method. A non-parametric hypothesis test confirmed that the contribution of ideologies varies significantly by region.

Terrorism perpetrated in any country by either internal or external actors jeopardizes the country’s security, economic growth, societal peace, and harmony. Hence, accurate modelling of terrorism has become a necessary component of the national security mission of most nations. This research extracted and analyzed high impact attacks (HIAs) perpetrated by terrorists in India and its neighboring countries since 1970 using the Global Terrorism Database (GTD). We evaluated the extraction efficacy of the Global Terrorism Index Impact Score (GTI-IS) against the GTD measure “nkill” using the iterative outlier analysis (IOA) heuristic. The heuristic identified 6117 common HIAs using nkill or GTI-IS attributes. GTI-IS extracted 1718 exclusive HIAs that nkill missed, while nkill extracted 2233 exclusive HIAs. We further classified the extracted HIAs into lethal and non-lethal attacks. Next, we conducted a rigorous spatiotemporal exploratory analysis of countries that reported the most HIAs. Though Afghanistan, India, and Sri Lanka exhibited global spatial autocorrelation, Pakistan did not. Ripley’s G function suggested the recurrence of lethal attacks near other similar events. This analysis showed that lethal and non-lethal attacks in those countries follow different statistical distributions, which can aid in focused counterterrorism tactics.

Terrorism perpetrated by both internal and external actors destabilizes the economy, hampers social harmony, and jeopardizes any nation’s internal security. While various forms of terror attacks occur globally, most terrorist organizations focus on high impact attacks (HIA) that maximize human fatalities and associated damages to precipitate fear within the community. Realistic modeling of terrorism is essential to identify critical trends and patterns to devise appropriate countermeasures. global terrorism database (GTD) is the most comprehensive terrorism-related database available for research purposes. Comparing terrorist attacks require specific benchmarks. A specific attribute from the GTD (nkill) and a derived composite parameter from GTD (global terrorism index (GTI)) are compared to establish their efficacy in successfully segregating HIAs from other terrorist attacks. This research focuses on the Middle East and North Africa (MENA) region and Europe. A sequential filtering heuristic based on local point outlier analysis utilizes both measures to extract HIAs of the MENA region. The proposed heuristic using nkill reports 1,055 unique HIAs in comparison with GTI and 522 vice versa. A simple geospatial analysis of these HIAs indicates the most fertile regions for terror organizations. Terrorism proliferation plots are developed to visually identify the migration timelines of major terrorist organizations that are capable of HIAs from the MENA region to Europe. The study concludes that virulent terrorist groups of the MENA region like Al-Qaida in the Arabian Peninsula (AQAP), Islamic State of Iraq and the Levant (ISIL), Muslim Extremists (ME), etc. migrated to Europe successfully.

Hydrogen embrittlement is a common, dangerous, and poorly understood cause of failure in many metal alloys. In practice, it is observed that different types of damage to industrial components have been tied to the presence and localization of hydrogen in metals. Many efforts have been made at understanding the effects of hydrogen on materials, resulting in an abundance of theoretical models and papers. However, a fully developed and practically-applicable predictive physical model still does not exist industrially for predicting and preventing hydrogen embrittlement. The connection of microstructure-based behaviors of materials and effects on the macroscopic measurable characteristics (stress levels, hardness, strength, and impact toughness) is of the utmost importance to achieve a unified model for hydrogen embrittlement. This paper gives an overview of the application of a model for structural integrity analysis of boiler tubes made of plain carbon steel exposed during operation to a local corrosion process and multiple hydrogen assisted degradation processes: hydrogen embrittlement and high-temperature hydrogen attack. The model is based on the correlation of mechanical properties to scanning electron microscopy fractography analysis of fracture surfaces in the presence of simultaneously active hydrogen embrittlement micro-mechanisms. The proposed model is practical for use as a predictive maintenance in power plants, as it is based on the use of standard macro-mechanical tests.

During the last decade, the incidence and mortality rates of ST-elevation myocardial infarction (STEMI) has been steadily increasing in young women but not in men. Environmental variables that contribute to cardiovascular events in women remain ill-defined. A total of 2199 consecutive patients presenting with acute ST-elevation myocardial infarction (STEMI, 25.8% women, mean age 62.6±12.4 years) were admitted at the Montreal Heart Institute between June 2010 and December 2014. Snow fall exceeding 2cm/day was identified as a positive predictor for STEMI admission rates in the overall population (RR 1.28, 95% CI 1.07-1.48, p = 0.005), with a significant effect being seen in men (RR 1.30, 95% CI 1.06-1.53, p = 0.01) but not in women (p = NS). An age-specific analysis revealed a significant increase in hospital admission rates for STEMI in younger women ≤55 years, (n = 104) during days with higher outside temperature (p = 0.004 vs men ≤55 years) and longer daylight hours (p = 0.0009 vs men ≤55 years). Accordingly, summer season, increased outside temperature and sunshine hours were identified as strong positive predictors for STEMI occurrence in women ≤55 years (RR 1.66, 95% CI 1.1-2.5, p = 0.012, RR 1.70, 95% CI 1.2-2.5, p = 0.007, and RR 1.67, 95% CI 1.2-2.5, p = 0.011, respectively), while an opposite trend was observed in men ≤55 years (RR for outside temperature 0.8, 95% CI 0.73-0.95, p = 0.01). The impact of environmental variables on STEMI is age- and sex-dependent. Higher temperature may play an important role in triggering such acute events in young women.

The hypothesis that not only diagnosed diabetes (DM), but also milder dysglycemia may affect the development of atherosclerosis still requires further study. In our population-based study, we aimed to evaluate the impact of prediabetic state on preclinical atherosclerosis and whether it may affect the cardiovascular risk (CVR) in the general population. The analysis was a part of the Bialystok PLUS cohort study and represented a random sample of Bialystok (Poland) residents aged 20-79 years at the time of sampling (July 2017-January 2023). The cross-sectional analysis included 1431 participants of a population-based study (mean age 46.82 years). Comprehensive biochemical assessments were performed. An Oral Glucose Tolerance Test (OGTT) was performed on fasting patients who did not report having a DM. The population with prediabetes, based on HbA1c and OGTT, accounted for more than half of the study participants (n = 797, 55.7%). Atherosclerotic plaques in the carotid arteries were significantly more common in individuals with prediabetes considering all CVR categories. Prediabetes was associated with the occurrence of more advanced preclinical atherosclerosis, especially in the low to moderate CVR category. Serum glucose concentration after 1h and HbA1c proved to be statistically significant indicators of the presence of atherosclerotic plaques in ultrasound (respectively, AUC = 0.73 and 0.72). In multivariate logistic regression, prediabetes was independently associated with significantly increased risk of preclinical atherosclerosis (OR = 1.56, 95% CI 1.09-2.24), along with CVR categories, pulse wave velocity and central blood pressure augmentation index. Prediabetes is associated with the occurrence and progression of the preclinical atherosclerosis. Importantly, many of those patients are in the low to moderate cardiovascular risk category, hence may have a severely underestimated risk. Inclusion of prediabetes into CVR assessment may improve risk stratification. An early identification of dysglycemic population is necessary to effectively implement the cardiovascular and metabolic prevention measures.

Business Impact Analysis (BIA) evaluates how cyberattacks affect essential business processes and IT assets. Traditionally conducted through manual interviews by consultants, this approach is often inefficient and prone to errors and omissions. In this paper, we present an automated methodology leveraging process mining to assess the impact of cybersecurity incidents on business processes. This methodology extracts event logs from information systems to construct business dependency graphs, quantify impact propagation across them, and integrate cybersecurity risk inputs from security officers. Tested on procurement workflows for an international transportation company, and compared with established baselines as well as the insight and knowledge of the company itself, our methodology proved to be effective at identifying risks stemming from a cybersecurity incident without significant labor, as well as uncovering high-risk paths that weren’t yet identified, resulting in actionable insights. This is an extended and revised version of this methodology, evaluated with an extensive case study encompassing a company’s BIA, historical data and expert opinion, first presented in Raptaki (IEEE Access 12: 194322–194339, 2024).

Background It is widely known that the incidence rate and short-term mortality of acute myocardial infarctions (AMIs) are generally higher during the winter months. The goal of this study was to determine how the temperature of the environment influences fatal acute myocardial infarctions in Xuzhou. Methods This observational study used the daily meteorological data and the data on the cause of death from acute myocardial infarction in Xuzhou from January 1, 2018, to December 31, 2020. After controlling meteorological variables and pollutants, the distributed nonlinear lag model (DLNM) was used to estimate the correlation between temperature and lethal AMI. Results A total of 27,712 patients with fatal AMI were enrolled. 82.4% were over the age of 65, and 50.9% were men. Relative to the reference temperature (15 ℃), the 30-day cumulative RRs of the extremely cold temperature (− 2 ℃) for the general population, women, and people aged 65 years and above were 4.66 (95% CI: 1.76, 12.30), 15.29 (95% CI: 3.94, 59.36), and 7.13 (95% CI: 2.50, 20.35), respectively. The 30-day cumulative RRs of the cold temperature (2 ℃) for the general population, women, and people aged 65 years and above were 2.55 (1.37, 4.75), 12.78 (2.24, 5.36), and 3.15 (1.61, 6.16), respectively. No statistically significant association was observed between high temperatures and the risk of fatal AMI. The influence of the cold effect (1st and 10th) was at its peak on that day, and the entire cold effect persisted for 30 days. Temperature extremes had an effect on the lag patterns of distinct age and gender stratifications. Conclusion According to this study, the risk of fatal AMI increases significantly in cold weather but not in hot weather. Women above the age of 65 are particularly sensitive to severe weather events. The influence of frigid weather on public health should also be considered.

Backgrounds. Serum total bilirubin (STB) is recently more regarded as an antioxidant with vascular protective effects. However, we noticed that elevated STB appeared in unstable angina pectoris (UAP) patients with diffused coronary lesions. We aimed to explore STB’s roles in UAP patients, which have not been reported by articles. Methods and Results. 1120 UAP patients were retrospectively screened, and 296 patients were finally enrolled. They were grouped by Canadian Cardiovascular Society (CCS) angina grades. The synergy between PCI with TAXUS stent and cardiac surgery score (SYNTAX score) and corrected thrombolysis in myocardial infarction flow count (CTFC) were adopted to profile coronary features. The results showed that STB, mean platelet volume (MPV), hs-CRP, fasting blood glucose (FBG), red blood cell width (RDW), and CTFC elevated significantly in the CCS high-risk group. STB (B=0.59, 95% CI: 0.39-0.74, P<0.01) and MPV (B=0.86, 95% CI: 0.42-1.31, P<0.01) could indicate SYNTAX score changes for these patients. STB (≥21.7 μmol/L) could even indicate a coronary slow flow condition (AUC: 0.88, 95% CI: 0.84-0.93, P<0.01). Moreover, UAP patients with elevated STB had a lower event-free survival rate by the Kaplan-Meier curve. STB ≥21.7 μmol/L could reflect a poor coronary flow status and indicate 1-year poor outcomes for these patients (HR: 2.01, 95% CI: 1.06-3.84, P<0.01). Conclusion. Elevated STB in UAP patients has a close relationship with changes in SYNTAX score. STB (over 21.7 μmol/L) could even indicate a coronary slow flow condition and poor outcomes for the UAP patients.