Efficient Analysis Research Articles

Cost analysis of three different analyzers to select most effective, efficient and appropriate analyzer to perform urinary investigations in a tertiary care center

Cost analysis of three different analyzers to select most effective, efficient and appropriate analyzer to perform urinary investigations in a tertiary care center

Computation Offloading in MEC-Enabled IoV Networks: Average Energy Efficiency Analysis and Learning-Based Maximization

Computation Offloading in MEC-Enabled IoV Networks: Average Energy Efficiency Analysis and Learning-Based Maximization

Oscillation-specific nodal differences in Parkinson's disease patients with anxiety.

Parkinson's disease (PD) is a common neurodegenerative disorder that is predominantly known for its motor symptoms but is also accompanied by non-motor symptoms, including anxiety. The underlying neurobiological substrates and brain network changes associated with comorbid anxiety in PD require further exploration. An analysis of oscillation-specific nodal properties in patients with and without anxiety was conducted using resting-state functional magnetic resonance imaging (rs-fMRI) and graph theory. We used a band-pass filtering approach to differentiate oscillatory frequency bands for subsequent functional connectivity (FC) and graph analyses. The study included 68 non-anxiety PD (naPD) patients, 62 anxiety PD (aPD) patients, and 64 healthy controls (NC). Analyses of nodal betweenness centrality (BC), degree centrality (DC), and efficiency were conducted across multiple frequency bands. The findings indicated no significant differences in BC among naPD, aPD, and NC within the 0.01-0.08 Hz frequency range. However, we observed a specific reduction in BC at narrower frequency ranges in aPD patients, as well as differing patterns of change in DC and efficiency, which are believed to reflect the neurophysiological bases of anxiety symptoms in PD. Differential oscillation-specific nodal characteristics have been identified in PD patients with anxiety, suggesting potential dysregulations in brain network dynamics. These findings emphasize the complexity of brain network alterations in anxiety-associated PD and identify oscillatory frequencies as potential biomarkers. The study highlights the importance of considering oscillatory frequency bands in the analysis of brain network changes.

Efficiency Analysis of Regenerative Brake System Using Flywheel Energy Storage Technology in Electric Vehicles

Efficiency Analysis of Regenerative Brake System Using Flywheel Energy Storage Technology in Electric Vehicles

Quantitative measurement of cell-surface displayed proteins based on split-GFP assembly

BackgroundMicrobial cell surface display technology allows immobilizing proteins on the cell surface by fusing them to anchoring motifs, thereby endowing the cells with diverse functionalities. However, the assessment of successful protein display and the quantification of displayed proteins remain challenging. The green fluorescent protein (GFP) can be split into two non-fluorescent fragments, while they spontaneously assemble and emit fluorescence when brought together through complementation. Based on split-GFP assembly, we aim to: (1) confirm the success display of passenger proteins, (2) quantify the number of passenger proteins displayed on individual cells.ResultsIn this study, we propose two innovative methods based on split-green fluorescent protein (split-GFP), named GFP1-10/GFP11 and GFP1-9/GFP10-11 assembly, for the purpose of confirming successful display and quantifying the number of proteins displayed on individual cells. We evaluated the display efficiency of SUMO and ubiquitin using different anchor proteins to demonstrate the feasibility of the two split-GFP assembly systems. To measure the display efficiency of functional proteins, laccase expression was measured using the split-GFP assembly system by co-displaying GFP11 or GFP10-11 tags, respectively.ConclusionsOur study provides two split-GFP based methods that enable qualitative and quantitative analyses of individual cell display efficiency with a simple workflow, thus facilitating further comprehensive investigations into microbial cell surface display technology. Both split-GFP assembly systems offer a one-step procedure with minimal cost, simplifying the fluorescence analysis of surface-displaying cells.

A Sustainable Development Study on Innovation Factor Allocation Efficiency and Spatial Correlation Based on Regions along the Belt and Road in China

The level of development of the innovation factor plays a crucial role in supporting the high-quality sustainable development of China’s economy. In order to advance the sustainable development of regional innovation factor allocation efficiency along the Belt and Road this study introduces the super-efficient slacks-based measure (SBM)-data envelopment analysis (DEA)-Malmquist model for static and dynamic analyses of innovation factor allocation efficiency in 17 provinces along the Belt and Road from 2012 to 2021. This study used the Moran index model to analyze spatial correlation. The results show the following: (1) The overall innovation factor allocation efficiency along the Belt and Road is not high, and there are obvious differences among different regions. The eastern region’s efficiency is the highest compared to other regions. (2) According to the efficiency decomposition results, pure technical efficiency (PTE) is the main reason for the low innovation factor allocation efficiency. (3) Through the Malmquist index and decomposition index, it was found that pure technical efficiency (PECH) and scale efficiency (SECH) are key factors in improving technical efficiency (TECH). (4) The analysis of spatial correlation revealed a strong spatial agglomeration feature among the provinces along the Belt and Road. Innovation factor allocation efficiency is mainly manifested in the third quadrant. Finally, drawing on the results of the analysis, suggestions and policies are put forward to improve innovation factor allocation efficiency in the regions along the Belt and Road. This study is of great significance for promoting the sustainable development of the regional innovation level along the Belt and Road in China.

Efficient Synthesis and Structural Analysis of Chiral 4,4'-Biazulene.

4,4'-Biazulene is a potentially attractive key component of an axially chiral biaryl compound, however, its structure and properties have not been clarified owing to the lack of its efficient synthesis. We report a breakthrough in the reliable synthesis of 4,4'-biazulene, which is achieved by the access to azulen-4-ylboronic acid pinacol ester and 4-iodoazulene as novel key synthetic intermediates for the Suzuki-Miyaura cross-coupling reaction. The X-ray crystallographic analysis of 4,4'-biazulene confirmed its axial chirality. The enantiomers of 4,4'-biazulene were successfully resolved by HPLC on the chiral stationary phase column. The kinetic experiments and DFT calculations indicate that the racemization energy barrier of 4,4'-biazulene is comparable to that of 1,1'-binaphthyl.

Multimodal Contrastive Transformer for Explainable Recommendation

Explanations play an essential role in helping users evaluate results from recommender systems. Various natural language generation methods have been proposed to generate explanations for the recommendation. However, they usually suffer from two problems. First, since user-provided review text contains noisy data, the generated explanations may be irrelevant to the recommended items. Second, as lacking some supervision signals, most of the generated sentences are similar, which cannot meet the diversity and personalized needs of users. To tackle these problems, we propose a multimodal contrastive transformer (MMCT) model for an explainable recommendation, which incorporates multimodal information into the learning process, including sentiment features, item features, item images, and refined user reviews. Meanwhile, we propose a dynamic fusion mechanism during the decoding stage, which generates supervision signals to guide the explanation generation. Additionally, we develop a contrastive objective to generate diverse explainable texts. Comprehensive experiments on two real-world datasets show that the proposed model outperforms comparable explainable recommendation baselines in terms of explanation performance and recommendation performance. Efficiency analysis and robustness analysis verify the advantages of the proposed model. While ablation analysis establishes the relative contributions of the respective components and various modalities, the case study shows the working of our model from an intuitive sense.

Spatially dependent node regularity in meshless approximation of partial differential equations

In this paper, we address a way to reduce the total computational cost of meshless approximation by reducing the required stencil size through spatially varying computational node regularity. Rather than covering the entire domain with scattered nodes, only regions with geometric details are covered with scattered nodes, while the rest of the domain is discretized with regular nodes. A simpler approximation can be used in regions covered by regular nodes, effectively reducing the required stencil size and computational cost compared to the approximation on scattered nodes where a set of polyharmonic splines is added to ensure convergent behaviour.This paper is an extended version of conference paper entitled “Spatially-varying meshless approximation method for enhanced computational efficiency” (Jančič et al., 2023) presented at “International Conference on Computational Science (ICCS) 2023”. The paper is extended with discussion on development and implementation of a hybrid regular-scattered node positioning algorithm (HyNP). The performance of the proposed HyNP algorithm is analysed in terms of separation distance and maximal empty sphere radius. Furthermore, it is demonstrated that HyNP nodes can be used for solving problems from fluid flow and linear elasticity, both in 2D and 3D, using meshless methods.The extension also provides additional analyses of computational efficiency and accuracy of the numerical solution obtained on the spatially-variable regularity of discretization nodes. In particular, different levels of refinement aggressiveness and scattered layer widths are considered to exploit the computational efficiency gains offered by such solution procedure.

An advanced ensemble load balancing approach for fog computing applications

Fog computing has emerged as a viable concept for expanding the capabilities of cloud computing to the periphery of the network allowing for efficient data processing and analysis from internet of things (IoT) devices. Load balancing is essential in fog computing because it ensures optimal resource utilization and performance among distributed fog nodes. This paper proposed an ensemble-based load-balancing approach for fog computing environments. An advanced ensemble load balancing approach (AELBA) uses real-time monitoring and analysis of fog node metrics, such as resource utilization, network congestion, and service response times, to facilitate effective load distribution. Based on the ensemble's collective decision-making, these metrics are fed into a centralized load-balancing controller, which dynamically adjusts the load distribution across fog nodes. Performance of the proposed ensemble load-balancing approach is evaluated and compared it to traditional load-balancing techniques in fog using extensive simulation experiments. The results demonstrate that our ensemble-based approach outperforms individual load-balancing algorithms regarding response time, resource utilization, and scalability. It adapts to dynamic fog environments, providing efficient load balancing even under varying workload conditions.

InVADo: Interactive Visual Analysis of Molecular Docking Data.

Molecular docking is a key technique in various fields like structural biology, medicinal chemistry, and biotechnology. It is widely used for virtual screening during drug discovery, computer-assisted drug design, and protein engineering. A general molecular docking process consists of the target and ligand selection, their preparation, and the docking process itself, followed by the evaluation of the results. However, the most commonly used docking software provides no or very basic evaluation possibilities. Scripting and external molecular viewers are often used, which are not designed for an efficient analysis of docking results. Therefore, we developed InVADo, a comprehensive interactive visual analysis tool for large docking data. It consists of multiple linked 2D and 3D views. It filters and spatially clusters the data, and enriches it with post-docking analysis results of protein-ligand interactions and functional groups, to enable well-founded decision-making. In an exemplary case study, domain experts confirmed that InVADo facilitates and accelerates the analysis workflow. They rated it as a convenient, comprehensive, and feature-rich tool, especially useful for virtual screening.

The role of the People’s Advocate (Ombudsman) in Albania: The fulfillment of its duties in defending the rights of the citizens and the need for a resizing

The constant change in state territories and therefore even in human rights, it was necessary to guarantee (in this case) the rights of minorities, which were compromised due to the historical and political circumstances. For this reason the Convention on Human Rights and the Convention on the Rights of Minorities have continuously become more protective and completed. European countries and not only have been willing to respect and guarantee every right that is connected to the fact of being a “human being”. Not only in this aspect, but more or less remaining on the same line Europe has also been caring for the institutionalization of certain rights, which should be guaranteed to an individual in its relationship with the state administration. The constitutions of European countries (as well as the latest ones, the countries of Eastern Europe and Western Balkan) installed the institution of the Ombudsman (People’s Advocate). This article aims to present a comparative analysis of the real efficiency of this very important institution in Albania and Balkan. Its legal vacuum, juridical power and its need for resizing.

A survey on brain MRI segmentation

Neurological disorders pose a significant challenge in medical diagnostics, requiring accurate and efficient analysis of brain Magnetic Resonance Imaging (MRI) scans. This study introduces a novel approach for enhanced neurological diagnosis through the application of deep learning techniques to automate the segmentation of brain structures in MRI images. The proposed method leverages the power of convolutional neural networks (CNNs) to extract intricate patterns and features from complex neuro imaging data. The research involves the development and training of a deep learning model capable of accurately delineating key anatomical regions, such as the cortex, hippocampus, and ventricles. The model is trained on a large data set of annotated MRI scans, optimizing its performance through rigorous validation processes. The utilization of deep learning enables the algorithm to learn and generalize from diverse imaging data, improving its adaptability to variations in patient demographics and scanner characteristics. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a diverse set of MRI datasets, encompassing various neurological conditions. Quantitative metrics, including Dice coefficient and Hausdorff distance, are employed to assess the segmentation accuracy compared to ground truth annotations. Additionally, the clinical relevance of the automated segmentation is validated through collaboration with neurologists and radiologists. The results demonstrate that the deep learning- enabled segmentation method consistently outperforms traditional image processing techniques, providing more accurate and reliable segmentation results. The proposed approach not only streamlines the diagnostic process but also has the potential to uncover subtle abnormalities that may be overlooked by manual inspection. In conclusion, the integration of deep learning into the segmentation of brain MRI scans presents a promising avenue for enhancing neurological diagnosis. The automated and precise delineation of brain structures contributes to the efficiency and accuracy of diagnostic workflows, ultimately improving patient outcomes and facilitating timely interventions in the realm of neurological disorders.

Advanced simulation-based predictive modelling for solar irradiance sensor farms

ABSTRACT The need for accurate solar power forecasting is critical for grid stability as solar energy becomes more prevalent. This paper presents a new framework called Cloud-based Analysis and Integration for Data Efficiency (CAIDE) for real-time monitoring and forecasting of solar irradiance in sensor farms. CAIDE can handle multiple sensor farms, enhance predictive models in real-time, and is built on Model Based Systems Engineering (MBSE) and Internet of Things (IoT) technologies. It can correct its forecasts, ensuring they stay current, and operates on various architectures, ensuring scalability. Tested on multiple sensor farms, CAIDE proved to be scalable and improved the initial accuracy of solar power production forecasts in real-time. This framework is significant for solar plant deployment and the advancement of renewable energy.

Genomic analyses of nitrogen utilization efficiency, its indicator trait blood urea nitrogen and the relationship to classical growth performance and feed efficiency traits in a Landrace × Piétrain crossbred population.

Improving the nutrient efficiency in pork production is required to reduce the resource competition between human food and animal feed regarding diet components edible for humans and to minimize emissions relevant to climate or the environment. Thereby, protein utilization efficiency and its equivalent nitrogen utilization efficiency (NUE) play a major role. Breeding for more nitrogen (N) efficient pigs bears a promising strategy to improve such traits, however, directly phenotyping NUE based on N balance data is neither cost-efficient nor straightforward and not applicable for routine evaluations. Blood urea nitrogen (BUN) levels in the pig are suitable to predict the NUE and, therefore, might be an indicator trait for NUE because BUN is a relatively easy-to-measure trait. This study investigated the suitability of NUE as a selection trait in future breeding programs. The relationships to classical growth performance and feed efficiency traits were analysed as well as the relationship to BUN to infer the role of BUN as an indicator trait to improve NUE via breeding. The analyzes were based on a Landrace F1 cross population consisting of 502 individuals who descended from 20 Piétrain sires. All animals were genotyped for 48,525 SNPs. They were phenotyped in two different fattening phases, i.e., FP1 and FP2, during the experiment. Uni- and bivariate analyses were run to estimate variance components and to determine the genetic correlation between different traits or between the same trait measured at different time points. Moderate heritabilities were estimated for all traits, whereby the heritability for NUE was h2 = 0.293 in FP1 and h2 = 0.163 in FP2 and BUN had the by far highest heritability (h2 = 0.415 in FP1 and h2 = 0.460 in FP2). The significant genetic correlation between NUE and BUN showed the potential of BUN to be considered an indicator trait for NUE. This was particularly pronounced when NUE was measured in FP1 (genetic correlations and between NUE and BUN measured in FP1 and FP2, respectively). The genetic correlations of NUE and BUN with important production traits suggest selecting pigs with high growth rates and low BUN levels to breed more efficient pigs in future breeding programs.

Physical characterization and bioavailability assessment of 5-fluorouracil-based nanostructured lipid carrier (NLC): In vitro drug release, Hemolysis, and permeability modulation.

5-Fluorouracil (5-FU) is an anticancer agent belonging to BCS Class III that exhibits poor release characteristics and low retention in the biological system. The main objective of this investigation was to develop a drug delivery system, i.e., Nanostructure Lipid Carriers (NLCs) loaded with 5-FU to prolong its biological retention through 5-FU-loaded NLCs (5-FUNLC) were designed to manipulate physicochemical characteristics and assessment of in vitroandin vivoperformance. The developed NLCs underwent comprehensive characterization, including assessments for particle size, zeta potential, morphological evaluation, and FT-IR spectroscopy. Additionally, specific evaluations were conducted for 5-FUNLCs, encompassing analyses for encapsulation efficiency of the drug, release characteristics in PBS at pH 6.8, and stability study. The lipophilic character of 5-FUNLC was confirmed through the measurement of the partition coefficient (log P). 5-FUNLCs were observed as spherical-shaped particles with a mean size of 300 ± 25nm. The encapsulation efficiency was determined to be 89%, indicating effective drug loading within the NLCs. Furthermore, these NLCs exhibited a sustained release nature lasting up to 3-4h, indicating their potential for controlled drug release over time. Lipid components were biocompatible with the 5-FU to determine thermal transition temperature and show good stability for 30days. Additionally,an in vitrohemolysis study that confirmed the system did not cause any destruction to the RBCs during intravenous administration. The drug's gut permeability was assessed utilizing the optimized 5-FUNLC (F2) in comparison to 5-FU through the intestine or gut sac model (in the apical to basolateral direction, A → B). The permeability coefficient was measured as 4.91 × 10-5cm/h with a significant difference. Additionally, the antioxidant potential of the NLCs was demonstrated through the DPPH method. The NLCs' performance was further assessed throughin vivopharmacokinetic studies on Wistar Rats, resulting in a 1.5-fold enhancement in their activity compared to free 5-FU. These NLCs offer improved drug solubility and sustained release, which collectively contribute to enhanced therapeutic outcomes and modulate bioavailability. The study concludes by highlighting the potential of 5-FUNLC as an innovative and efficient drug delivery system. The findings suggest that further preclinical investigations are warranted, indicating a promising avenue for the development of more effective and well-tolerated treatments for cancer.

Application of Ensemble Learning and Feature Selection Models in Energy Expenditure Estimation of Fitness Tracking Devices

The use of fitness tracking devices to monitor energy expenditure (EE) is becoming increasingly popular among consumers. However, the accuracy of EE estimation still needs to be improved due to the lack of accurate data filtering and more efficient data analysis models. To address this issue, we designed an energy expenditure estimation (EEE) model based on smart tracking devices. This paper proposes an algorithm for data reconstruction called PC-DF-RFECV: firstly, Pearson correlation analysis (PC) is used for initial data screening, then Feature Derivation (DF) is applied to reconstruct new features, and finally, the Recursive Feature Elimination algorithm based on cross-validation (RFECV) is employed to select the new features. Furthermore, the EEE model adopts a weighted average (Wei-Voting) strategy to enhance the robustness of the model by integrating the predictions of multiple base learners (XGBoost, LightGBM, Random Forest). During algorithm optimization, a Bayesian hyperparameter optimization technique is employed to fine-tune the model's hyperparameters. Empirical findings indicate that the EEE model, when applied to the dataset collected from motion tracking devices, attains an score of 0.9985, of 1.033, and of 0.865. These results demonstrate superior performance compared to current state-of-the-art research and conventional algorithms including random forests, gradient boosting, and bagging.

Development and Analysis of GIS Regional Political Profiles in West Java Utilizing the Spatial Overlay Join Method

This research aims to build Geographic Information System (GIS) of Political Region Profiles in West Java using the Overlay Spatial Join method. The study addresses the political complexity in Indonesia, particularly in West Java, which serves as a significant political base. The success of political campaigns requires a profound understanding of voter preferences, demographic dynamics, and local issues. Limited access to accurate location data poses a serious obstacle to the effective design of campaign strategies. This research responds to these challenges by designing a GIS that integrates political and spatial data. The research focuses on West Java, where regional diversity poses unique challenges. Through the Overlay Spatial Join method, the research aims to provide in-depth insights into political region profiles, serving as a foundation for intelligent political campaign strategies. The benefits of the research include an understanding of regional characteristics, performance comparisons of political parties, and in-depth analysis for campaign resource efficiency. The system integrates data from various sources, provides spatial visualization, and enables the analysis of regional profiles for the identification of deep political information. The research's contribution is not only academic but also practical in enhancing the effectiveness of democracy and general elections in West Java.

Abstract 6207: Exploring colorectal cancer with ARIA: Biomedical insights

Abstract Gaining the promised benefits from the population-scale datasets, both clinical and genomic, is often hindered by the inability to easily access, explore and manipulate such complex data - a difficulty that ARIA was designed to overcome. In the field of biomedicine, organizations harness extensive datasets, gaining profound insights into patients and participants, thereby unleashing possibilities for accelerating research, especially in the realms of drug development and precision medicine. To facilitate navigation through the intricate landscape of biomedical data, Velsera has innovated ARIA to catalyse efficient and insightful analysis, with a keen emphasis on user-friendliness to ensure accessibility across a diverse professional audience. To demonstrate the practical application of ARIA, we explore genomic and clinical datasets to identify key differences in colorectal cancer progression. CRC, the third most common cancer globally, has unique characteristics in origination and progression from benign to pathogenic. Here we investigate differences between patients with polyps that do and do not progress to CRC, including key environmental factors such as smoking history, inactive lifestyle and obesity in conjunction with their known clinical and genetic risk factors. This exploration into the abilities of ARIA demonstrate the ability to analyse complex datasets combining both clinical and genomics data at a large scale. For the purpose of this demonstration, a Synthea Dataset, encompassing synthetic clinical and genomic data for 122,011 patients across various age groups, was utilized. This dataset was generated by Velsera specifically for testing ARIA functionalities. In conclusion, ARIA empowers efficient analysis of complex biomedical datasets, providing a comprehensive understanding of disease mechanisms and facilitating valuable insights. Citation Format: Nevena Nikolic, Sarah Kleinstein, Jelena Radenkovic, Milan Kovacevic. Exploring colorectal cancer with ARIA: Biomedical insights [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6207.

Computational Pulsatile Flow and Efficiency Analysis of Biocompatible Microfluidic Artificial Lungs for Different Fiber Configurations.

Average-sized microfluidic artificial lungs consisting of rows and columns of fiber bundles with different column to row aspect ratios (AR) are numerically analyzed for flow characteristics, maximum gas transfer performance, minimum pressure drop, and proper wall shear stress (WSS) values in terms of biocompatibility. The flow is fully laminar and assumed to be incompressible and Newtonian. The transport analysis is performed using a combined convection-diffusion model, and the numerical simulations are carried out with the finite element method. The inlet volumetric flow is modeled as a sinusoidal wave function to simulate the cardiac cycle and its effect on the device performance. The model is first validated with experimental studies in steady-state condition and compared with existing correlations for transient conditions. Then, the validated model is used for a parametric study in both steady and pulsatile flow conditions. The results show that increasing the aspect ratio in fiber configuration leads to converging gas transfer, higher pressure drop, and higher WSS. While determining the optimum configuration, the acceptable shear stress levels play a decisive role to ensure biocompatibility. Also, it is observed that the steady analysis underestimates the gas transfer for higher aspect ratios.