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Increasing use of left ventricular assist devices (LVADs) has been accompanied by rising incidence of gastrointestinal bleeding (GIB). Objectives of this study were to determine the yearly incidence of GIB in LVAD recipients, compare outcomes of continuous-flow (CF) and pulsatile-flow LVAD eras, and investigate for risk factors. The Healthcare Cost and Utilization Project–Nationwide Inpatient Sample database from 2005 to 2010 was analyzed. Primary outcome of interest was incidence of GIB in LVAD recipients. Multivariate logistic regression model was used to examine independent associations of GIB with risk factors and outcomes. An estimated 8,879 LVAD index admissions and 8,722 readmissions in LVAD recipients over 6 years were analyzed. The yearly incidence of GIB after LVAD implantation increased from 5% in 2005 to 10% in 2010. On multivariate regression analysis, the odds of GIB was 3.24 times greater (95% confidence interval 1.53 to 6.89) in the era of CF LVADs than in the era of pulsatile-flow LVADs. Compared to their younger counterparts, in LVAD recipients aged >65 years, the adjusted odds of GIB was 20.5 times greater (95% confidence interval 2.24 to 188). GIB did not significantly increase the inhospital mortality but increased the inpatient length of stay. In conclusion, the incidence of GIB in LVAD recipients has increased since the use of CF LVADs has increased, leading to greater inpatient lengths of stay and hospital charges. Older recipients of CF LVADs appear to be at a greater risk of GIB.

Physical systems such as, electrical power system, aircraft, chemical plants, urban traffic networks, digital communication networks, economic systems and control system can be described mathematically, which is complex and large in dimension. In most of the practical situations higher order model is obtained from theoretical considerations. The higher order model possess so many problems in the analysis and design. So, it is usually recommended to reduce the order of the model retaining the dominant behaviour of the original system. Order reduction techniques help to decrease the computational complexity, reduce the hardware complexity and better understand the large scale system. In this paper, an order reduction technique such as pade approximation and mixed method of routh approximation is used. In mixed technique, the routh approximation method is used for determining the denominator coefficients of the reduced model and the numerator coefficients are calculated by the pade method. The response of reduced order model obtained is compared on the basis of unit step response also time domain and frequency domain characteristics is calculated and compared with previously obtain model.

Rice, a staple meal for about half of the world's population, is critical to global food security, especially in Asia. However, diseases have a severe impact on rice production, resulting in significant yield losses or outright crop failure. Traditional techniques of identifying rice diseases are time-consuming, labor-intensive, and rely heavily on specialist knowledge. As a result, a rapid, cost-effective, and automated method for detecting rice illnesses is critical for modernizing agricultural techniques and ensuring sustainable food production. This paper presents a novel hybrid deep-learning and machine-learning framework for automatically identifying rice plant diseases from leaf photos. We extracted deep features from rice leaf images using pre-trained CNN models-MobileNetV2, DarkNet19, and ResNet18. These features are then classified using machine learning classifiers with various kernel functions, which apply a strong 10-fold cross-validation technique to assure model reliability. Using a medium Gaussian kernel of the SVM classifier, the proposed system achieved a classification accuracy of 98.61%, specificity of 98.85%, and sensitivity of 97.25%. The framework is computationally efficient and scalable, allowing for greater dataset testing. The proposed technique provides a dependable and efficient solution for accurate identification of rice leaf diseases, reducing farmers' reliance on manual inspection and supporting timely intervention.

Rice, a staple meal for about half of the world’s population, is critical to global food security, especially in Asia. However, diseases have a severe impact on rice production, resulting in significant yield losses or outright crop failure. Traditional techniques of identifying rice diseases are time-consuming, labor-intensive, and rely heavily on specialist knowledge. As a result, a rapid, cost-effective, and automated method for detecting rice illnesses is critical for modernizing agricultural techniques and ensuring sustainable food production. This paper presents a novel hybrid deep-learning and machine-learning framework for automatically identifying rice plant diseases from leaf photos. We extracted deep features from rice leaf images using pre-trained CNN models—MobileNetV2, DarkNet19, and ResNet18. These features are then classified using machine learning classifiers with various kernel functions, which apply a strong 10-fold cross-validation technique to assure model reliability. Using a medium Gaussian kernel of the SVM classifier, the proposed system achieved a classification accuracy of 98.61%, specificity of 98.85%, and sensitivity of 97.25%. The framework is computationally efficient and scalable, allowing for greater dataset testing. The proposed technique provides a dependable and efficient solution for accurate identification of rice leaf diseases, reducing farmers’ reliance on manual inspection and supporting timely intervention.

Despite the success of guided wave ultrasonic inspection for internal defect detection in steel pipes, its application on polyethylene (PE) pipe remains relatively unexplored. The growth of internal cracks in PE pipe severely affects its pressure-holding capacity, hence the early detection of internal cracks is crucial for effective pipeline maintenance strategies. This study extends the scope of guided wave-based ultrasonic testing to detect the growth of internal cracks in a natural gas distribution PE pipe. Laboratory experiments and a finite element model were planned to study the wave-crack interaction at different stages of axially oriented internal crack growth with a piezoceramic transducer-based setup arranged in a pitch-catch configuration. Mode dispersion analysis supplemented with preliminary experiments was performed to isolate the optimal inspection frequency, leading to the selection of the T(0,1) mode at 50-kHz for the investigation. A transmission index based on the energy of the T(0,1) mode was developed to trace the extent of simulated crack growth. The findings revealed an inverse linear correlation between the transmission index and the crack depth for crack growth beyond 20% crack depth.

Understanding the variability of surface water in the context of climate change is crucial for sustainable water management in semi-arid regions. This study utilizes a multi-index remote sensing approach via Google Earth Engine (GEE) to analyse the spatiotemporal dynamics of surface water bodies in Telangana, India, from 2000 to 2023. Five remote sensing-based water indices (NDWI, MNDWI, NDPI, MBWI, and AWEInsh) were evaluated using Otsu and simple-thresholding techniques to optimize thresholding and estimate changes in surface water area. Among these indices, AWEInsh demonstrated the highest visual accuracy, particularly in detecting ephemeral water bodies, while MNDWI and NDPI were identified as the most effective overall. To assess the applicability of ensemble and fusion of water indexes, a weighted average fusion index (Weighted Composite Water Index or WCWI) was developed combining all the indices used in this study, where the overall accuracy (OA) of each index was used for weightage selection. This OA-based weighting strategy ensures that indices with higher reliability contribute more significantly, while still leveraging the complementary strengths of lower-performing indices. . Correlation analysis further revealed a strong agreement among most indices and their fusion, emphasizing the reliability of multi-index fusion for regional water body mapping. Temporal trends reveal significant seasonal and inter-annual variability in surface water extent, closely associated with Indian Summer Monsoon rainfall patterns. Notably, drought years linked to El Niño events (e.g., 2004, 2014, 2015) showed sharp declines in surface water area, whereas high-rainfall years (e.g., 2005, 2013, 2016) corresponded to an increase in surface water coverage. Despite moderate rainfall, a declining trend in observed area after 2016 suggests anthropogenic influences such as land-use change and reduced catchment efficiency. This approach enhances classification accuracy and aids in water resource planning, drought preparedness, and agricultural decision-making. The findings underscore the value of remote sensing in shaping climate adaptation strategies in water-stressed, semi-arid regions.

This study compares countries with common law with countries with civil law systems and investigates the possibility of predicting legal systems using artificial neural networks (ANNs). The OLS model, ANOVA, ANN, and Tensor Flow are used in the research to analyze the data. The goal is to find out how board characteristics and country legislative frameworks affect how European corporations disclose their social performance. The performance of a hidden layer with five nodes is best, according to the ANN model. The model's accuracy throughout testing and validation is 0.750. The confusion matrix shows that, of the four observations in the test set, three were correctly categorized as \"Civil law\" and one was incorrectly categorized as \"Common law.\" To evaluate the model's efficacy, evaluation metrics are computed. The model's accuracy is 0.750, which represents a prediction success rate of 75%. For the \"Civil law\" class, the recall (true positive rate) is 1.0, indicating that all \"Civil law\" cases are correctly identified. Metrics for the \"Common law\" class, however, are not available due to the scant amount of data that is available. The prevalence of countries with common law and civil law systems is compared in the ANOVA analysis. As shown by the computed F-value of 0.482, there is less variance inside each legal system than there is between the two. There is no statistically significant difference in frequency between the two legal systems, according to the p-value of 0.495. Overall, the research's conclusions imply that social performance disclosure between countries with common law and civil law systems differs only slightly. The neural network model's network weights provide insight into the importance of different features in prediction.

The fragile X mental retardation ( FMR1 ) gene contains an expansion-prone CGG repeat within its 5′ UTR. Alleles with 55–200 repeats are known as premutation (PM) alleles and confer risk for one or more of the FMR1 premutation (PM) disorders that include Fragile X-associated Tremor/Ataxia Syndrome (FXTAS), Fragile X-associated Primary Ovarian Insufficiency (FXPOI), and Fragile X-Associated Neuropsychiatric Disorders (FXAND). PM alleles expand on intergenerational transmission, with the children of PM mothers being at risk of inheriting alleles with > 200 CGG repeats (full mutation FM) alleles) and thus developing Fragile X Syndrome (FXS). PM alleles can be somatically unstable. This can lead to individuals being mosaic for multiple size alleles. Here, we describe a detailed evaluation of somatic mosaicism in a large cohort of female PM carriers and show that 94% display some evidence of somatic instability with the presence of a series of expanded alleles that differ from the next allele by a single repeat unit. Using two different metrics for instability that we have developed, we show that, as with intergenerational instability, there is a direct relationship between the extent of somatic expansion and the number of CGG repeats in the originally inherited allele and an inverse relationship with the number of AGG interruptions. Expansions are progressive as evidenced by a positive correlation with age and by examination of blood samples from the same individual taken at different time points. Our data also suggests the existence of other genetic or environmental factors that affect the extent of somatic expansion. Importantly, the analysis of candidate single nucleotide polymorphisms (SNPs) suggests that two DNA repair factors, FAN1 and MSH3 , may be modifiers of somatic expansion risk in the PM population as observed in other repeat expansion disorders.

Progression of diabetic retinopathy resists arrest even after institution of intensive glycemic control, suggesting a “metabolic memory” phenomenon, but the mechanism responsible for this phenomenon is still elusive. Gene expression and biological processes can also be regulated by long noncoding RNAs (LncRNAs), the RNAs with >200 nucleotides and no open reading frame for translation, and several LncRNAs are aberrantly expressed in diabetes. Our aim was to identify retinal LncRNAs that fail to reverse after termination of hyperglycemia. Microarray analysis was performed on retinal RNA from streptozotocin‐induced diabetic rats in poor glycemic control for 8 months, followed by in good glycemic control (blood glucose >400 mg/dL), or for 4 months, with four additional months of good glycemic control (blood glucose <150 mg/dL). Differentially expressed LncRNAs and mRNAs were identified through Volcano filtering, and their functions were predicted using gene ontology and pathway enrichment analyses. Compared with age‐matched normal rats, rats in continuous poor glycemic control had >1479 differentially expressed LncRNAs (710 downregulated, 769 upregulated), and among those, 511 common LncRNAs had similar expression in Diab and Rev groups (139 downregulated, 372 upregulated). Gene Ontology/pathway analysis identified limited LncRNAs in biological processes, but analysis based on biological processes/molecular function revealed >350 genes with similar expression in Diab and Rev groups; these genes were mainly associated with stress response, cell death, mitochondrial damage and cytokine production. Thus, identifying retinal LncRNAs and their gene targets that do not benefit from termination of hyperglycemia have potential to serve as therapeutic targets to slow down the progression of diabetic retinopathy. Highlights Hyperglycemia results in aberrant expression of several LncRNAs (RNAs with >200 nucleotides), leading to altered expression of many genes and metabolic abnormalities implicated in the development of diabetic retinopathy. However, reversal of hyperglycemia by normoglycemia does not provide any benefit to these aberrantly expressed LncRNAs, and the retinopathy continues to progress.

•SIJ pain is more likely to occur with PT compared to FT S2AI screw fixation.•FT S2AI screws are associated with better SIJ outcomes compared to PT screws.•S2AI screw instrumentation may not be sufficient to achieve fusion. Sacroiliac joint (SIJ) arthropathy is an increasingly recognized problem in adult spinal deformity patients undergoing long construct surgery. S2-alar-iliac (S2AI) screw instrumentation is thought to reduce morbidity from pelvic fixation in these patients. The goal of this study is to assess the overall incidence of SIJ arthropathy in patients with long constructs to the pelvis as well as compare SIJ outcomes of partially threaded (PT) versus fully threaded (FT) S2AI screws. Data of eligible patients were collected from a prospectively maintained database with retrospective review of electronic records at an academic institution between 2016 and 2019. 65 consecutive patients who underwent S2AI screw instrumentation (40 in PT group, 25 in FT group) were enrolled. The rate of postoperative SIJ pain was higher in the PT (52.5 %) compared to FT (32 %) group. There was a significantly shorter time-to-pain development in the PT compared to FT group (11.8 versus 20.1 months, respectively). Of those who developed SIJ pain in the PT group, the pain worsened in 80.9 % versus only 25 % of those in the FT group despite conservative treatment. Cox regression found the PT group more likely to develop SIJ pain at any point during follow-up compared to the FT group (Hazard Ratio = 7.308). SIJ fusion was not detected on imaging of any patient during follow-up. FT S2AI screws are associated with better SIJ outcomes compared to PT screws. However, our data suggest that S2AI screw instrumentation is not sufficient to achieve fusion or prevent development of SIJ pain. Concurrent SIJ fusion may be necessary in patients with long constructs to prevent SIJ arthropathy.