Benchmarking of Recommendation Trust Computation for Trust/Trustworthiness Estimation in HDNs

In the recent years, Heterogeneous Distributed Networks (HDNs) is a predominant technology implemented to enable various application in different fields like transportation, medicine, war zone, etc. Due to its arbitrary self-organizing nature and temporary topologies in the spatial-temporal region, distributed systems are vulnerable with a few security issues and demands high security countermeasures. Unlike other static networks, the unique characteristics of HDNs demands cutting edge security policies. Numerous cryptographic techniques have been proposed by different researchers to address the security issues in HDNs. These techniques utilize too many resources, resulting in higher network overheads. This being classified under light weight security scheme, the Trust Management System (TMS) tends to be one of the most promising technology, featured with more efficiency in terms of availability, scalability and simplicity. It advocates both the node level validation and data level verification enhancing trust between the attributes. Further, it thwarts a wide range of security attacks by incorporating various statistical techniques and integrated security services. In this paper, we present a literature survey on different TMS that highlights reliable techniques adapted across the entire HDNs. We then comprehensively study the existing distributed trust computations and benchmark them in accordance to their effectiveness. Further, performance analysis is applied on the existing computation techniques and the benchmarked outcome delivered by Recommendation Trust Computations (RTC) is discussed. A Receiver Operating Characteristics (ROC) curve illustrates better accuracy for Recommendation Trust Computations (RTC), in comparison with Direct Trust Computations (DTC) and Hybrid Trust Computations (HTC). Finally, we propose the future directions for research and highlight reliable techniques to build an efficient TMS in HDNs

Investigating the Potential of Ridesharing to Reduce Vehicle Emissions

As urban populations grow, cities need new strategies to maintain a good standard of living while enhancing services and infrastructure development. A key area for improving city operations and spatial layout is the transportation of people and goods. While conventional transportation systems (i.e., fossil fuel based) are struggling to serve mobility needs for growing populations, they also represent serious environmental threats. Alternative-fuel vehicles can reduce emissions that contribute to local air pollution and greenhouse gases as mobility needs grow. However, even if alternative-powered vehicles were widely employed, road congestion would still increase. This paper investigates ridesharing as a mobility option to reduce emissions (carbon, particulates and ozone) while accommodating growing transportation needs and reducing overall congestion. The potential of ridesharing to reduce carbon emissions from personal vehicles in Changsha, China, is examined by reviewing mobility patterns of approximately 8,900 privately-owned vehicles over two months. Big data analytics identify ridesharing potential among these drivers by grouping vehicles by their trajectory similarity. The approach includes five steps: data preprocessing, trip recognition, feature vector creation, similarity measurement and clustering. Potential reductions in vehicle emissions through ridesharing among a specific group of drivers are calculated and discussed. While the quantitative results of this analysis are specific to the population of Changsha, they provide useful insights for the potential of ridesharing to reduce vehicle emissions and the congestion expected to grow with mobility needs. Within the study area, ridesharing has the potential to reduce total kilometers driven by about 24% assuming a maximum distance between trips less than 10 kilometers, and schedule time less than 60 minutes. For a more conservative maximum trip distance of 2 kilometers and passenger schedule time of less than 40 minutes, the reductions in traveled kilometers could translate to the equivalent of approximately 4.0 tons CO2 emission reductions daily

Investigating the Potential of Ridesharing to Reduce Vehicle Emissions

As urban populations grow, cities need new strategies to maintain a good standard of living while enhancing services and infrastructure development. A key area for improving city operations and spatial layout is the transportation of people and goods. While conventional transportation systems (i.e., fossil fuel based) are struggling to serve mobility needs for growing populations, they also represent serious environmental threats. Alternative-fuel vehicles can reduce emissions that contribute to local air pollution and greenhouse gases as mobility needs grow. However, even if alternative-powered vehicles were widely employed, road congestion would still increase. This paper investigates ridesharing as a mobility option to reduce emissions (carbon, particulates and ozone) while accommodating growing transportation needs and reducing overall congestion. The potential of ridesharing to reduce carbon emissions from personal vehicles in Changsha, China, is examined by reviewing mobility patterns of approximately 8,900 privately-owned vehicles over two months. Big data analytics identify ridesharing potential among these drivers by grouping vehicles by their trajectory similarity. The approach includes five steps: data preprocessing, trip recognition, feature vector creation, similarity measurement and clustering. Potential reductions in vehicle emissions through ridesharing among a specific group of drivers are calculated and discussed. While the quantitative results of this analysis are specific to the population of Changsha, they provide useful insights for the potential of ridesharing to reduce vehicle emissions and the congestion expected to grow with mobility needs. Within the study area, ridesharing has the potential to reduce total kilometers driven by about 24% assuming a maximum distance between trips less than 10 kilometers, and schedule time less than 60 minutes. For a more conservative maximum trip distance of 2 kilometers and passenger schedule time of less than 40 minutes, the reductions in traveled kilometers could translate to the equivalent of approximately 4.0 tons CO2 emission reductions daily

Transition to adult care of young people with congenital heart disease: Impact of a service on knowledge and self-care skills, and correlates of a successful transition

Aims Less than one-third of adolescents with congenital heart disease (CHD) successfully transition to adult care, missing out on education of their cardiac condition, and risking loss to follow-up. We assessed the efficacy of our transition clinic on patient education and empowerment and identified correlates of successful transition. Methods and results Overall, 592 patients were seen at least once in our transition service between 2015 and 2022 (age 15.2 ± 1.8 years, 47.5% female). Most adolescents (53%) had moderate CHD, followed by simple (27.9%) and severe (19.1%) CHD. Learning disability (LD) was present in 18.9% and physical disability (PD) in 4.7%. In patients without LD, knowledge of their cardiac condition improved significantly from the first to the second visit (naming their condition: from 20 to 52.3%, P 0.05). Treatment adherence and management involvement, self-reported anxiety, and dental care awareness did not change over time. Successful transition (attendance of ≥ 2 clinics) was achieved in 49.3%. Younger age at the first visit, simpler CHD, and absence of PD were associated with successful transition. Conclusion A transition service positively impacts on patient education and empowerment in most CHD adolescents transitioning to adult care. Strategies to promote a tailored support for patients with LD should be sought, and earlier engagement should be encouraged to minimize follow-up losses

Advanced heart failure in adult congenital heart disease: the role of renal dysfunction in management and outcomes

Aims Previous studies in adult congenital heart disease (CHD) have demonstrated a link between renal dysfunction and mortality. However, the prognostic significance of renal dysfunction in CHD and decompensated heart failure (HF) remains unclear. We sought to assess the association between renal dysfunction and outcomes in adults with CHD presenting to our centre with acute HF between 2010 and 2021. Methods and results This retrospective analysis focused on the association between renal dysfunction, pre-existing and on admission, and outcomes during and after the index hospitalization. Chronic kidney disease (CKD) was defined as an estimated glomerular filtration rate <60 mL/min/1.73 m2. Cox regression analysis was used to identify the predictors of death post-discharge. In total, 176 HF admissions were included (mean age 47.7 ± 14.5 years, 43.2% females). One-half of patients had a CHD of great complexity, 22.2% had a systemic right ventricle, and 18.8% had Eisenmenger syndrome. Chronic kidney disease was present in one-quarter of patients. The median length of intravenous diuretic therapy was 7 (4–12) days, with a maximum dose of 120 (80–160) mg furosemide equivalents/day, and 15.3% required inotropic support. The in-hospital mortality rate was 4.5%. The 1- and 5-year survival rates free of transplant or ventricular assist device (VAD) post-discharge were 75.4% [95% confidence interval (CI): 69.2–82.3%] and 43.3% (95% CI: 36–52%), respectively. On multivariable Cox analysis, CKD was the strongest predictor of mortality or transplantation/VAD. Highly complex CHD and inpatient requirement of inotropes also remained predictive of an adverse outcome. Conclusion Adult patients with CHD admitted with acute HF are a high-risk cohort. CKD is common and triples the risk of death/transplantation/VAD. An expert multidisciplinary approach is essential for optimizing outcomes

Karyotypic Determinants of Chromosome Instability in Aneuploid Budding Yeast

Recent studies in cancer cells and budding yeast demonstrated that aneuploidy, the state of having abnormal chromosome numbers, correlates with elevated chromosome instability (CIN), i.e. the propensity of gaining and losing chromosomes at a high frequency. Here we have investigated ploidy- and chromosome-specific determinants underlying aneuploidy-induced CIN by observing karyotype dynamics in fully isogenic aneuploid yeast strains with ploidies between 1N and 2N obtained through a random meiotic process. The aneuploid strains exhibited various levels of whole-chromosome instability (i.e. chromosome gains and losses). CIN correlates with cellular ploidy in an unexpected way: cells with a chromosomal content close to the haploid state are significantly more stable than cells displaying an apparent ploidy between 1.5 and 2N. We propose that the capacity for accurate chromosome segregation by the mitotic system does not scale continuously with an increasing number of chromosomes, but may occur via discrete steps each time a full set of chromosomes is added to the genome. On top of such general ploidy-related effect, CIN is also associated with the presence of specific aneuploid chromosomes as well as dosage imbalance between specific chromosome pairs. Our findings potentially help reconcile the divide between gene-centric versus genome-centric theories in cancer evolution

A Feature Extraction Method Based on Information Theory for Fault Diagnosis of Reciprocating Machinery

This paper proposes a feature extraction method based on information theory for fault diagnosis of reciprocating machinery. A method to obtain symptom parameter waves is defined in the time domain using the vibration signals, and an information wave is presented based on information theory, using the symptom parameter waves. A new way to determine the difference spectrum of envelope information waves is also derived, by which the feature spectrum can be extracted clearly and machine faults can be effectively differentiated. This paper also compares the proposed method with the conventional Hilbert-transform-based envelope detection and with a wavelet analysis technique. Practical examples of diagnosis for a rolling element bearing used in a diesel engine are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race, inner-race, and roller defects, can be effectively identified by the proposed method, while these bearing faults are difficult to detect using either of the other techniques it was compared to

The Significance of Cell-related Challenges in the Clinical Application of Tissue Engineering.

Tissue engineering is increasingly being recognized as a new approach that could alleviate the burden of tissue damage currently managed with transplants or synthetic devices. Making this novel approach available in the future for patients who would potentially benefit is largely dependent on understanding and addressing all those factors that impede the translation of this technology to the clinic. Cell-associated factors in particular raise many challenges, including those related to cell sources, up- and downstream techniques, preservation, and the creation of in vitro microenvironments that enable cells to grow and function as far as possible as they would in vivo. This paper highlights the main confounding issues associated with cells in tissue engineering and how these issues may hinder the advancement of therapeutic tissue engineering. This article is protected by copyright. All rights reserved