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In various domains such as energy, manufacturing, and maritime, cyber–physical systems (CPSs) have seen increased interest. Both academia and industry have focused on the cybersecurity aspects of such systems. The assessment of cyber risks in a CPS is a popular research area with many existing approaches that aim to suggest relevant methods and practices. However, few works have addressed the extensive and objective evaluation of the proposed approaches. In this paper, a standard-aligned evaluation methodology is presented and empirically conducted to evaluate a newly proposed cyber risk assessment approach for CPSs. The approach, which is called FMECA-ATT&CK is based on failure mode, effects and criticality analysis (FMECA) risk assessment process and enriched with the semantics and encoded knowledge in the Adversarial Tactics, Techniques, and Common Knowledge framework (ATT&CK). Several experts were involved in conducting two risk assessment processes, FMECA-ATT&CK and Bow-Tie, against two use cases in different application domains, particularly an autonomous passenger ship (APS) as a maritime-use case and a digital substation as an energy-use case. This allows for the evaluation of the approach based on a group of characteristics, namely, applicability, feasibility, accuracy, comprehensiveness, adaptability, scalability, and usability. The results highlight the positive utility of FMECA-ATT&CK in model-based, design-level, and component-level cyber risk assessment of CPSs with several identified directions for improvements. Moreover, the standard-aligned evaluation method and the evaluation characteristics have been demonstrated as enablers for the thorough evaluation of cyber risk assessment methods.

Faba bean is an important legume crop. The genetic diversity among faba bean genotypes is very important for the genetic improvement of target traits. A set of 128 fab bean genotypes that are originally from Egypt were used in this study to investigate the genetic diversity and population structure. The 128 genotypes were genotyped using the Single Primer Enrichment Technology (SPET) by which a set of 6759 SNP markers were generated after filtration. The SNP markers were distributed on all chromosomes with a range extending from 822 (Chr. 6) to 1872 (Chr.1). The SNP markers had wide ranges of polymorphic information content (PIC), gene diversity (GD), and minor allele frequency. The analysis of population structure divided the Egyptian faba bean population into five subpopulations. Considerable genetic distance was found among all genotypes, ranging from 0.1 to 0.4. The highly divergent genotype was highlighted in this study and the genetic distance among genotypes ranged from 0.1 and 0.6. Moreover, the structure of linkage disequilibrium was studied, and the analysis revealed a low level of LD in the Egyptian faba bean population. A slow LD decay at the genomic and chromosomal levels was observed. Interestingly, the distribution of haplotype blocks was presented in each chromosome and the number of haplotype block ranged from 65 (Chr. 4) to 156 (Chr. 1). Migration and genetic drift are the main reasons for the low LD in the Egyptian faba bean population. The results of this study shed light on the possibility of the genetic improvement of faba bean crop in Egypt and conducting genetic association analyses to identify candidate genes associated with target traits (e.g. protein content, grain yield, etc.) in this panel.

Low-power wide area network (LPWAN) technologies such as IQRF are becoming increasingly popular for a variety of Internet of Things (IoT) applications, including smart cities, industrial control, and home automation. However, LPWANs are vulnerable to cyber attacks that can disrupt the normal operation of the network or compromise sensitive information. Therefore, analyzing cybersecurity risks before deploying an LPWAN is essential, as it helps identify potential vulnerabilities and threats as well as allowing for proactive measures to be taken to secure the network and protect against potential attacks. In this paper, a security risk analysis of IQRF technology is conducted utilizing the failure mode effects analysis (FMEA) method. The results of this study indicate that the highest risk corresponds to four failure modes, namely compromised end nodes, a compromised coordinator, a compromised gateway and a compromised communication between nodes. Moreover, through this methodology, a qualitative risk evaluation is performed to identify potential security threats in the IQRF network and propose countermeasures to mitigate the risk of cyber attacks on IQRF networks.

Several disruptive attacks against companies in the maritime industry have led experts to consider the increased risk imposed by cyber threats as a major obstacle to undergoing digitization. The industry is heading toward increased automation and connectivity, leading to reduced human involvement in the different navigational functions and increased reliance on sensor data and software for more autonomous modes of operations. To meet the objectives of increased automation under the threat of cyber attacks, the different software modules that are expected to be involved in different navigational functions need to be prepared to detect such attacks utilizing suitable detection techniques. Therefore, we propose a systematic approach for analyzing the navigational NMEA messages carrying the data of the different sensors, their possible anomalies, malicious causes of such anomalies as well as the appropriate detection algorithms. The proposed approach is evaluated through two use cases, traditional Integrated Navigation System (INS) and Autonomous Passenger Ship (APS). The results reflect the utility of specification and frequency-based detection in detecting the identified anomalies with high confidence. Furthermore, the analysis is found to facilitate the communication of threats through indicating the possible impact of the identified anomalies against the navigational operations. Moreover, we have developed a testing environment that facilitates conducting the analysis. The environment includes a developed tool, NMEA-Manipulator that enables the invocation of the identified anomalies through a group of cyber attacks on sensor data. Our work paves the way for future work in the analysis of NMEA anomalies toward the development of an NMEA intrusion detection system.

Coronavirus disease-19 (COVID-19) emerged late December 2019 in the city of Wuhan, China and has since spread rapidly all over the world causing a global pandemic. While the respiratory system is the primary target of disease manifestation, COVID-19 has been shown to also affect several other organs, making it a rather complex, multi-system disease. As such, cardiovascular involvement has been a topic of discussion since the beginning of the COVID-19 pandemic, primarily due to early reports of excessive myocardial injury in these patients. Treating physicians are faced with multiple challenges in the management and early triage of patients with COVID-19, as disease severity is highly variable ranging from an asymptomatic infection to critical cases rapidly deteriorating to intensive care treatment or even fatality. Laboratory biomarkers provide important prognostic information which can guide decision making in the emergency department, especially in patients with atypical presentations. Several cardiac biomarkers, most notably high-sensitive cardiac troponin (hs-cTn) and N-terminal pro-B-type natriuretic peptide (NT-proBNP), have emerged as valuable predictors of prognosis in patients with COVID-19. The purpose of this review was to offer a concise summary on prognostic cardiac biomarkers in COVID-19 and discuss whether routine measurements of these biomarkers are warranted upon hospital admission.

Artificial intelligence (AI) has come to play a pivotal role in revolutionizing medical practices, particularly in the field of pancreatic cancer detection and management. As a leading cause of cancer-related deaths, pancreatic cancer warrants innovative approaches due to its typically advanced stage at diagnosis and dismal survival rates. Present detection methods, constrained by limitations in accuracy and efficiency, underscore the necessity for novel solutions. AI-driven methodologies present promising avenues for enhancing early detection and prognosis forecasting. Through the analysis of imaging data, biomarker profiles, and clinical information, AI algorithms excel in discerning subtle abnormalities indicative of pancreatic cancer with remarkable precision. Moreover, machine learning (ML) algorithms facilitate the amalgamation of diverse data sources to optimize patient care. However, despite its huge potential, the implementation of AI in pancreatic cancer detection faces various challenges. Issues such as the scarcity of comprehensive datasets, biases in algorithm development, and concerns regarding data privacy and security necessitate thorough scrutiny. While AI offers immense promise in transforming pancreatic cancer detection and management, ongoing research and collaborative efforts are indispensable in overcoming technical hurdles and ethical dilemmas. This review delves into the evolution of AI, its application in pancreatic cancer detection, and the challenges and ethical considerations inherent in its integration.

Most of the freshwaters worldwide are used for agriculture. Freshwater sources are expected to decline and will not suffice to support the food production needed for the growing population. Therefore, growing crops with seawater might constitute a solution. However, very little work has been done on the effect of seawater stress on wheat, an important cereal crop. The present study aimed to determine whether particular wheat genotypes provided better resistance to seawater stress. A set of 80 highly diverse spring wheat genotypes collected from different countries in Europe, Asia, Africa, North and South America was exposed to 50% seawater stress at the early growth stage. Four seeding shoot and root traits were scored for all genotypes. High genetic variations were found among all genotypes for the epicotyl length (EL), hypocotyl length (HL), number of radicles (NOR), and fresh weight (FW). Eight genotypes with high-performance scores of seedling traits were selected. The correlation analyses revealed highly significant correlations among all traits scored in this study. The strongest correlation was found between the NOR and the other seeding traits. Thus, the NOR might be an important adaptive trait for seawater tolerance. The genetic diversity among all genotypes was investigated based on genetic distance. A wide range of genetic distances among all genotypes was found. There was also a great genetic distance among the eight selected genotypes. In particular, the genetic distance between ATRI 5310 (France) and the other seven genotypes was the greatest. Such high genetic diversity might be utilized to select highly divergent genotypes for crossing in a future breeding program. The present study provides very useful information on the presence of different genetic resources in wheat for seawater tolerance.

Heat stress is one of the abiotic stresses that cause a significant reduction in barley yield. Climate change will increase the number of heatwaves, which will result in more deterioration in the agricultural sector. Therefore, understanding the physiological changes that occur in the plant to tolerate heat stress is very important. A collection of 60 Egyptian spring barley genotypes has been tested for heat stress under field conditions. To quantify the changes in yield-related traits and the grain-reserve parameters as indicators for heat tolerance, several traits were scored. The causative genes that regulate the variation of all traits of interest were identified via single-marker analysis using 16,966 single nucleotide polymorphisms (SNP). Heat stress reduced yield-related traits, while some physiological traits (chlorophyll index, soluble carbohydrates, amino acids, and proline contents) increased. The genotypes were classified into four classes, A, B, C, and D, based on a reduction in grain yield per spike (GYPS) of 10%, 20%, 30%, and 40%, respectively. The physiological aspects were extensively studied in each group. The tolerant genotypes (class A) retained high yield-related traits as well as high reserved metabolites relative to the sensitive class D. The single-marker analysis and gene annotations revealed that the most effective markers and genes resided on chromosomes 1H and 4H. One of these markers, S4_250499621, was found to be associated with increased proline content, increased chlorophyll content, and decreased reduction in grain yield per spike and thousand kernel weight. This study is a part of our extended evaluation of this collection under various abiotic stresses at different developmental stages to develop climate-resilient crops.

Background Post-extubation stridor indicates the presence of laryngeal edema. The documented occurrence of post-extubation airway blockage ranges from 4 to 37%. Aim To evaluate the effectiveness of sonar assessment of laryngeal air column width difference to predict post-extubation upper airway obstruction and its relation to cuff leak volume. Patients and methods This was an observational, descriptive cross-sectional study, conducted on 48 mechanically ventilated patients fulfilling weaning criteria at the ICU of Chest Department, Faculty of Medicine, Zagazig University, from July 2022 to March 2023. Results A statistically insignificant variance was observed among COPD, ILD, overlap (OSA-COPD), pneumonia, aspiration pneumonia, and PE regarding laryngeal air column width difference (LACWD), while a statistically significant variance was observed among COPD, ILD, overlap (OSA-COPD), pneumonia, aspiration pneumonia, and PE regarding CLV; there was no correlation among cuff leak volume and LACWD, and there were 40 patients (83.3%) who had no post-intubation upper air way obstruction and 8 patients (16.7%) had post-intubation upper airway obstruction: 4 of them (8.3%) had success weaning, and 4 (8.3%) had failed weaning; and at cutoff value 140 ml, cuff leak volume had sensitivity of 100% and specificity of 97.5% with significance for prediction of stridor, and at cutoff value 1.15 mm, LACWD had sensitivity of 87.5% and specificity of 67.5% with significance for prediction of stridor. Conclusion Cuff leak volume and ultrasound-guided LACWD effectively predict post-extubation upper airway obstruction, suggesting their integration into institutional extubation protocols.

Heat stress is one of the abiotic stresses that limit the production and productivity of barley. Understanding the genetic variation, changes in physiological processes and level of genetic diversity existing among genotypes are needed to produce new cultivars not only having a high tolerance to heat stress, but also displaying high yield. To address this challenge, a set of 60 highly homozygous, diverse barley genotypes were evaluated under normal and heat stress conditions in two seasons of 2014/2015 and 2015/2016. Seedling vigor (SV) as a morphological trait was visually scored under normal conditions. Plant height (Ph), days to flowering (DOF), 1000-kernel weight (TKW), grain yield per spike (GYPS), yield per plot (YPP) and biological yield (BY) were measured. Moreover, proline content (ProC), soluble carbohydrate content (SCC), starch content, soluble protein (SP), and amino acid (AA) content as physiological parameters were analyzed from the grains. High genetic variation was observed among genotypes for all traits scored in this study. All traits had high broad-sense heritability estimates ranging from 0.59 (SV) to 0.97 (TKW) for yield traits. Seedling vigor was significantly correlated with all yield traits under both conditions. Among all physiological traits, the increase in ProC and reduction in starch content due to heat stress had significant correlations with the reduction due to heat stress in YPP, GYPS, TKW, and BY. Furthermore, the genetic diversity based on genetic distance (GD) among genotypes was investigated using 206 highly polymorphic SSR marker alleles. The GD ranged from 0.70 to 0.98 indicating that these genotypes are highly and genetically dissimilar. The combination of analyses using molecular markers, genetic variation in yield traits, and changes in physiological traits provided useful information in identifying the tolerant genotypes which can be used to improve heat tolerance in barley through breeding.