GFDA: Route Discovery Algorithms for On-demand Mobile Ad Hoc Routing Protocols

AbstractRoute discovery in manymobile ad hoc protocols is based on flooding. However, flooding suffers from high overhead, which can increase contention and communication delays. In this paper,we propose twonewroute discovery algorithms that are aimed towards reducing these delays. Both algorithms are suitable for use with ad hoc protocols where nodes periodically broadcast Hello Messages. Using the GloMoSim simulator, the proposed algorithms were evaluated and compared to existing methods. The simulation results show that the proposed approach can reduce routing overhead, number of broken links, average delay, and the number of dropped packets. Small improvements in message delivery ratios are also observed

Green finance and sustainable development in Europe

This study provides a comprehensive analysis of whether financial development impacts environmental degradation, over time. It highlights how financial development, institutional frameworks, and foreign investment dictate the extent of green development. The sample includes 40 countries in Europe and data is collected on a large set of variables, for the years from 1990 to 2019. Financial development is measured through domestic credit to the private sector, bank credit to the private sector and foreign direct investment (FDI). Environmental degradation is measured through energy use, CO2 emissions, greenhouse emissions and natural resource depletion. The model controls for income levels, institutional quality, technology, education, population, and urbanization. Regression analysis is conducted to analyze the data. The results suggest that financial development has a negative relationship with four different measures of environmental degradation, while FDI and institutional quality appear to worsen the environmental measures. Recommendations for policy makers include development of green finance policies and strong institutions, to lower environmental degradation in the long run

Adversity Quotient Dan Stres Akademik Mahasiswa Yang Menyusun Skripsi

Penelitian ini di dasari oleh adanya stres akademik yang dialami oleh mahasiswa yang sedang menyusun skripsi. Penelitian ini memiliki tujuan untuk mengetahui apakah ada keterkaitan antara adversity quotient dengan stres akademik pada mahasiswa yang sedang menyusun skripsi pada Fakultas Psikologi dan Ilmu Pendidikan Universitas Muhammadiyah Sidoarjo. Penelitian ini menggunakan jenis penelitian kuantitatif korelasional. Adapun populasinya 650 mahasiswa FPIP yang sedang menyusun skripsi dengan memakai        tabel krejcie dan mendapatkan sampel sebanyak 242 mahasiswa yang diambil memakai teknik simple random sampling. Analisis penelitian ini menggunakan JASP vers 0.15 Hasil analisis data penelitian ini menunjukkan bahwa nilai koefisien korelasi (rxy) sebesar signifikasi = -0.436 yang berarti bahwa terdapat hubungan negatif yang signifikan antara adversity quotient dengan stres akademik pada mahasiswa yang sedang menyusun skripsi di Fakultas Psikologi dan Ilmu Pendidikan Universitas Muhammadiyah Sidoarjo

Automated conflict resolution in collaborative data sharing systems using community feedbacks

a b s t r a c t In collaborative data sharing systems, groups of users usually work on disparate schemas and database instances, and agree to share the related data among them (periodically). Each group can extend, curate, and revise its own database instance in a disconnected mode. At some later point, the group can publish its updates to other groups and get updates of other ones (if any). The reconciliation operation in the CDSS engine is responsible for propagating updates and handling any data disagreements between the different groups. If a conflict is found, any involved updates are rejected temporally and marked as deferred. Deferred updates are not accepted by the reconciliation operation until a user resolves the conflict manually. In this paper, we propose an automated conflict resolution approach that depends on community feedbacks, to handle the conflicts that may arise in collaborative data sharing communities, with potentially disparate schemas and data instances. The experiment results show that extending the CDSS by our proposed approach can resolve such conflicts in an accurate and efficient manner

Genetic diversity and relationship assessment among mulberry (Morus spp) genotypes by simple sequence repeat (SSR) marker profile

Mulberry (Morus L.) is essential for sericulture industry as the primary source of food for silkworm Bombyx mori L. In India, long tradition of practising sericulture includes the use of a large number of indigenous cultivars. Since knowledge on genetic divergence of these cultivars/varieties is imperative for conservation and gainful utilization, simple sequence repeat (SSR) profiling was employed to assess genetic relatedness among 17 mulberry genotypes maintained in the Germplasm Bank of Temperate Sericulture Institute, SKUAST Kashmir, Mirgund. Six SSR primers were utilised which generates 17 alleles among the genotypes. The polymorphism information content (PIC) value varied from 0.260 (MulSTR3) to 0.623 (MulSTR4), with an average of 0.438 per locus. The highest similarity value of 0.92 was observed between Lemoncina and Kanva-2, as compared to the lowest similarity coefficient of 0.15 was between SKM-48 and Chinese white. Clustering of the genotypes was done with unweight pair group method using arithmetic average (UPGMA) which generates five clusters. Cluster-2 contained maximum (six) genotypes.Keywords: Clustering, genetic relatedness, mulberry, SSRAfrican Journal of Biotechnology Vol. 12(21), pp. 3181-318

Knowledge, attitudes, and practices among nurses in Pakistan towards diabetic foot

Introduction: Diabetic foot ulcers are a pressing complication of diabetes mellitus. Wound care requires a significant proportion of healthcare resources. It is imperative, therefore, for healthcare professionals to possess sound knowledge of the disease along with a positive attitude to ensure better clinical practice. Our literature search revealed a scarcity of data pertaining to diabetic foot ulcers. Therefore, this study aims to evaluate the knowledge and attitudes of nurses regarding diabetic foot care. Methods: A cross-sectional study design was employed, a pre-validated and pre-tested questionnaire was used to collect data from a sample size of 250 nurses working at two tertiary care hospitals in Karachi, Pakistan. The study was conducted over a period of three months (January to March 2018) and included all nurses who possessed at least one year of clinical experience in diabetic ulcer care. The statistical software employed was SPSS version 19 (IBM Corp., Armonk, NY, US). Non-parametric tests and descriptive statistics were used for data analysis and statistical significance was assumed at a p-value of less than 0.5. Results: Only 54% of the nurses in our study possessed adequate knowledge of diabetic foot ulcers. The mean score of knowledge was 74.9 (±9.5). Macdonald’s standard criteria for learning outcomes was used to gauge the knowledge levels of our study population. Nurses performed best in the domain of ulcer care with 65.3% of the participants possessing good knowledge of the topic. The overall attitude of nurses towards patients with diabetic ulcers was positive. Conclusion: This study highlights important gaps in nurses’ knowledge and sheds light on the lack of evidence-based practice. Poor knowledge can compromise healthcare standards, even with the presence of positive attitudes. Hence, a comprehensive revision of nursing curricula across local tertiary hospitals for allowing nurses to update their knowledge is warrante

Toward Adaptive Trust Calibration for Level 2 Driving Automation

Properly calibrated human trust is essential for successful interaction between humans and automation. However, while human trust calibration can be improved by increased automation transparency, too much transparency can overwhelm human workload. To address this tradeoff, we present a probabilistic framework using a partially observable Markov decision process (POMDP) for modeling the coupled trust-workload dynamics of human behavior in an action-automation context. We specifically consider hands-off Level 2 driving automation in a city environment involving multiple intersections where the human chooses whether or not to rely on the automation. We consider automation reliability, automation transparency, and scene complexity, along with human reliance and eye-gaze behavior, to model the dynamics of human trust and workload. We demonstrate that our model framework can appropriately vary automation transparency based on real-time human trust and workload belief estimates to achieve trust calibration.Comment: 10 pages, 8 figure

Quantitative pedigree analysis and mitochondrial DNA sequence variants in adults with cyclic vomiting syndrome

Abstract Background Children with cyclic vomiting syndrome (CVS) have a high degree of maternal inheritance of functional gastrointestinal and neurological disorders. CVS in children is also associated with an increased prevalence of mitochondrial DNA single-nucleotide polymorphisms (mtDNA SNPs) 16519 T and 3010A. Preliminary data suggests that age of onset of symptoms (pediatric vs. adult) may be a determinant of the presence of such mtDNA SNP’s. We sought to examine the degree of maternal inheritance pattern of functional disorders and the prevalence of mtDNA SNP’s16519T and 3010A in adults with CVS and correlate this with age of onset of disease. Methods A Quantitative Pedigree Analysis (QPA) was performed in 195 of a total of 216 patients and all were genotyped using Restriction Fragment Length Polymorphism (RFLP) or sequencing. Results Adults with CVS had a higher degree of probable maternal inheritance (PMI) of functional disorders than controls (12% vs. 1%, p < 0.001). However, the prevalence of mitochondrial SNP’s 16519 T, 3010A and the AT genotype were similar in Haplogroup H CVS patients compared to historical controls. There was no correlation between age of onset of disease and prevalence of these mtDNA SNP’s. Conclusions A subset of adults with CVS has a significantly higher degree of maternal inheritance pattern of functional disorders than controls. There was no association with mtDNA SNP’s 16519 T and 3010A as seen in children and future studies sequencing the entire mitochondrial and nuclear genome to identify potential causes for this maternal inheritance pattern in adults are warranted

Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes

Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of Machine Learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. ML has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management