Efficient Analysis Research Articles

Efficient Extraction and Analysis of Nanoplastics by Ionic Liquid-Assisted Cloud-Point Extraction Coupled with Electromagnetic Heating Pyrolysis Mass Spectrometry.

Nanoplastics have attracted much attention due to their potential hazards. However, analysis of nanoplastics remains challenging. In this study, ionic liquid-assisted cloud-point extraction (IL-assisted CPE) was developed to enrich nanoplastics in the aqueous environment and further coupled with electromagnetic heating pyrolysis mass spectrometry. The use of trace ILs improves the extraction efficiency of CPE for nanoplastics. The effects of ILs (types, contents), nanoplastic properties (type, size), and environmental factors (aging time, humic acid content) were systematically investigated to evaluate the applicability. The limits of detection of poly(methyl methacrylate) (PMMA) and polystyrene (PS) were determined to be 1.78 and 2.67 μg/L, respectively. Real environmental samples including lake water, rainwater, and influent and effluent from wastewater treatment plant were analyzed with good accuracy (79.58-116.87%) and satisfactory precision (RSD ≤ 11.99%). A possible mechanism for ILs being absorbed into the ordered surfactant micellar and generating larger micelles to synergically enclose hydrophobic nanoplastics was proposed. This work provides a simple and efficient approach to the extraction and analysis of nanoplastics in aqueous environments.

Virtual laboratory enabled constitutive modelling of dual phase steels

Accurate and efficient characterization and modelling of deformation responses are critically essential in the development of advanced metal forming processes. This work presents a virtual laboratory enabled constitutive framework for modelling complex deformation responses of dual phase (DP) steels under complex stress states, based on multi-phase full-field crystal plasticity (CP) and advanced phenomenological modelling. In the CP based virtual testing method, kinematic hardening and degradation of elastic modulus are modelled in particular to improve the capability for describing evolving microstructure induced mechanical responses of DP steels. The workflow of this framework is completely built and numerically implemented, including (i) representative volume element (RVE) generation based on microstructure characterization, (ii) identification of multiphase CP model parameters, (iii) prediction of elastic modulus degradation, (iv) prediction of yield stresses and plastic potentials in uniaxial tension and biaxial tension with various stress ratios, and (v) prediction of stress–strain curves in reverse tension compression. Using DP780 and DP980 as case materials, the corresponding physical experiments are conducted to verify the accuracy of the virtual tests, showing a good agreement between the virtual and experimental approaches. Both experimental and virtual tests are used to calibrate advanced phenomenological constitutive models that include non-linear elasticity, anisotropic yielding, and kinematic hardening. The calibrated models are implemented into the finite element (FE) codes to predict complex deformation and springback behaviours of DP780 and DP980 sheets in U-bending processes. In comparison with forming experiments of U-channel parts, the virtually calibrated models are validated and show good performance in predicting deformation and springback behaviours, providing a high capability for process analysis. The findings support that the virtual laboratory enabled modelling approach could be a substitute for extensive, expensive, and hard-to-access physically mechanical experiments required in the model calibration for a more effective and efficient analysis of metal forming processes.

New Trends in E-learning and New Approaches to the Development of E-learning Educational Materials

E-learning represents an area that is dynamically developing and closely correlated with society's technological progress. Since its inception, we have witnessed many changes in e-learning development and the creation of electronic educational materials. With the growing development of modern technologies, E-learning is becoming a key element in education. This article aims to identify and describe the main trends in e-learning and the creation of electronic educational materials. As part of our survey, we identified the main trends shaping E-learning. These trends include various aspects, including the personalisation of education, the interactivity of courses, the use of artificial intelligence and data analysis for better efficiency, the development of virtual reality and augmented reality in the educational environment, the inclusion of various multimedia elements, and many others. We expect these trends to have a significant impact on the way educational materials are created and delivered. They open the door to new possibilities and innovations in the E-learning field, which promise a more effective and adaptable education for everyone.

Panel Data Analysis of Subjective Well-Being in European Countries in the Years 2013–2022

Panel Data Analysis of Subjective Well-Being in European Countries in the Years 2013–2022

Behavior analyses of chromium into LiZnNbO4 host through optical spectroscopy and X-ray diffraction

A LiZnNbO4 sample was produced through the solid-state reaction method, according to which, chromium was introduced in the sample as impurity. X-ray diffraction (XRD), luminescence, excitation, emission decay time and emission quantum efficiency analyses were carried out. Structural analysis based on the sample's XRD data showed LiZnNbO4 compound belonging to the P4122 space group. Luminescence measurements evidenced broad emission band centered at 800 nm, which was associated with spin-allowed electronic transition from the excited state 4T2(4F) to the fundamental state 4A2(4F). Emission decay time reached 7.8 μs under 460 nm excitation and 0.43 quantum efficiency. Excitation spectrum showed bands associated with of Cr3+ ions transitions4A2(4F)→ 4T1(4F) and 4A2(4F)→ 4T2(4F), at 460 nm and 644 nm, respectively in octahedral symmetry. Crystal field parameter Dq = 1553 cm−1 and Racah B = 621 cm−1 were found by using transition energies from both the excitation spectrum and Tanabe-Sugano matrices.

Evolution and trends of the metaverse in business and management: A bibliometric analysis

AbstractThe Metaverse is an immersive digital environment that enables users, through avatars, to create worlds, objects, and interact with each other for various purposes. Furthermore, its significance in Business and Management is increasingly evident. The objective of this research is to examine the evolution, trends, and research approaches related to the Metaverse in the field of Business and Management. This study presents a comprehensive contextualization and bibliometric analysis of the existing literature in this area. In the contextualization phase, publications per year, citations received per year, most cited publications, and languages used in publications were examined. These analyses provided an overview of the temporal evolution of research, influential publications, and language diversity in the field of Business and Management. Subsequently, a bibliometric analysis was conducted using Co-citation, Bibliographic coupling, and Co-occurrence of author keywords. These analyses helped identify relationships between scientific works, thematic connections, and collaborations among researchers in the studied field. The use of the Web of Science database ensured the quality and comprehensiveness of the collected data, while the Vosviewer tool facilitated efficient visualization and analysis of the results. The findings of this study provide a detailed understanding of the research landscape in Business and Management, revealing key areas of focus, significant contributions, and existing collaboration networks. These insights can be valuable for researchers, professionals, and regulators in the field of Business and Management.

Extensive analysis of monosaccharide diversity in fermented soybean paste using LC-MS/MS

Doenjang, a widely consumed fermented soybean paste in Korea, contains abundant carbohydrates. However, the composition of various monosaccharides present in fermented foods has not been thoroughly investigated. Thus, we used a rapid and accurate liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to simultaneously analyze monosaccharides in the soybean paste. The method exhibited excellent linearity, sensitivity, and accuracy as confirmed by spiking experiments. In the soybean paste, we identified 11 monosaccharides, with galactose, glucose, and arabinose being predominant, alongside galacturonic acid, N-acetylglucosamine, and glucuronic acid. Analyzing 8 modified and 9 traditional soybean paste samples from different regions, we found a significant correlation between manufacturing method and monosaccharide profiles, with traditional soybean paste exhibiting greater monosaccharide diversity. This method is adaptable for rapid and efficient analysis of monosaccharides in various fermented foods.

Study on the Development Efficiency of Leisure Agriculture in Suburban Towns: An Empirical Analysis Based on the Case of Xinkou Town, Tianjin City

The leisure agriculture in suburban towns provides important space for citizens to carry out leisure activities. This paper takes Xinkou town in Tianjin city as an example to analyze the development efficiency of leisure agriculture in suburban towns. On the premise of establishing the indicators for evaluating the development efficiency of leisure agriculture in Xinkou town, this paper first uses the data envelopment analysis (DEA) to measure the input and output indicators of leisure agriculture in 18 natural villages in Xinkou town, and calculates the comprehensive technical efficiency, target values, redundancy and returns to scale of leisure agriculture, and then evaluates the development efficiency of leisure agriculture in Xinkou town. Furthermore, by using the high/low clustering tool under the ArcGIS spatial statistics module, this paper conducts a high/low clustering analysis of the development efficiency of leisure agriculture in 18 natural villages of Xinkou town, and figures out the reasons for the high/low clustering of natural villages in Xinkou town spatially. After the research and analysis, the following conclusions are drawn: From the input–output perspective, except for Zhengzhuangzi village, Diliubu village, Yuejiakai village, and Dangcheng village, the development efficiency of leisure agriculture in other villages is relatively low due to their poor resource foundations, or deficiencies in management experience and transportation location. The development of leisure agriculture in Xinkou town is unbalanced, showing a pattern of polarized clusters: Diliubu village and Shuigaozhuang village form the most obvious high-value cluster, while Xiaoshawo village, Bijia village and Xuanjiayuan village form the most obvious low-value cluster, where the southeast corner of Xinkou town registers the lowest development efficiency value of leisure agriculture because it is far away from the town center and the main transportation arteries, and falls short of high-quality pastoral scenery. For the future development of leisure agriculture in Xinkou town, it is supposed to optimize the land use policy, provide active support to the competitive industrial and commercial capital, non-governmental capital, rural collective economic organizations, farmers’ cooperatives, and family farms, strengthen the in-depth integration of industries, improve the support system for new-type leisure agriculture, and build leisure agriculture brands with local features.

Topical co-delivery of tacrolimus and calcipotriol loaded nanostructured lipid carrier: a potential and synergistic approach in the management of psoriasis

The focus of this study was to develop a Vitamin-D3 (Calcipotriol)-based Tacrolimus (TAC)-loaded nanostructured lipid carrier (NLC) for topical application to treat psoriasis. The sonication technique followed by melt emulsification was used to formulate TAC-NLC, in which Precirol ATO-5, Calcipotriol, and Solutol HS-15 were used as excipients. Based on the analysis of particle size, PDI, and entrapment efficiency of various TAC-NLCs using Box-Behnken Design, an optimized NLC was selected. The selected nanocarrier was further transformed into the gel and then characterized for in vitro release, in vitro and ex vivo permeation, and dermatokinetic, including in vitro characterization studies. The optimized TAC-NLC had a particle size of 231 ± 1.52 nm with a narrow PDI of 0.40 ± 0.001 along with high entrapment efficiency (84.04 ± 1.36%) and drug loading (12.61 ± 0.20%)”. The in vitro release showed sustained release throughout 12 h. Moreover, dissimilarity (f1:52) and the similarity factor (f2:37) showed a lack of any similarity in the evaluation of drug release from TAC-NLC formulation and Conventional formulation. The time requisite to release 50% (T50), 30% (T30), and 20% (T20) of the drug is also calculated. The flux and permeability coefficients were found to be 11.59 µg/cm2/h and 0.0386 cm/h. A dermatokinetic study of TAC-NLC indicated a remarkable rise in AUC0-12h and CSkin max compared to conventional formulation (p < 0.005). Further, the vitro studies indicated higher cell viability (85%-80%) on melanoma cells than aqueous suspension of the drug (60–70%). The study indicated that the topical application of TAC-NLC nanogel was found potentially useful for the management of psoriasis.

Hybrid uncertainty propagation based on multi-fidelity surrogate model

There always exist multiple uncertainties including random uncertainty, interval uncertainty, and fuzzy uncertainty in engineering structures. In the presence of hybrid uncertainties, the hybrid uncertainty propagation analysis can be a challenging problem, which suffers from the computational burden of double-loop procedure when numerical simulation techniques are employed. In this work, a novel method for efficient hybrid uncertainty propagation analysis with the three types of uncertainties is proposed. Generally, multi-fidelity surrogate models, such as Co-Kriging, can greatly improve the computational efficiency by leveraging information from a low-fidelity model to build a high-fidelity approximate model. However, the traditional multi-fidelity surrogate model methods always calculate the hybrid uncertainty propagation result by combining with several numerical simulation techniques. This process can introduce post-processing errors unless unlimited number of samples are used, which is impossible in engineering application. In order to address this issue, the analytical solutions of the output mean and output variance are derived based on the Co-Kriging, and the resulting mean and variance are both random variables. Moreover, a new adaptive framework is established to strengthen the estimation accuracy of the hybrid uncertainty propagation result, by combining the augmented expected improvement function and the derived mean random variable. Several applications are introduced to demonstrate the effectiveness of the proposed method for solving hybrid uncertainty propagation problems.

POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems

POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems

On the Efficient Electromagnetic Scattering Analysis From Targets With Electrically Small Surface Discontinuities

On the Efficient Electromagnetic Scattering Analysis From Targets With Electrically Small Surface Discontinuities

A Survey on Efficient Analysis of Soft Tissues Tumors using Machine Learning

Soft tissue tumors (STT) represent a significant medical challenge, requiring accurate and efficient analysis for timely diagnosis and treatment. This survey explores the application of machine learning techniques in the analysis of soft tissue tumors, focusing on enhancing efficiency in detection and classification. The review encompasses various approaches, including traditional image processing methods and the more recent advancements in machine learning. One of the key contributions of this survey is the proposal of a method employing Convolutional Neural Networks (CNN) for the analysis of soft tissue tumors. CNNs have demonstrated remarkable success in image-related tasks, making them particularly suitable for medical image analysis. The research particularly aims in distinguishing Melanoma from normal skin tissue. To enhance the efficiency of research on the analysis of soft tissue tumors, the proposed study provides a comprehensive review of relevant literature, focusing on the application of machine learning in the identification of soft tissue tumors. This review includes an evaluation of the merits and demerits of each system. Furthermore, the study introduces a suggested model capable of providing accurate classification for soft tissue tumors.

Clinical Efficacy and Nursing Efficiency Analysis of Jiuwei Zhusheng Decoction in Promoting Cervical Ripening in the Perinatal Midwifery Holistic Care

To evaluate the clinical efficacy of Jiuwei Zhusheng Decoction (JWZSD) and perinatal midwifery holistic nursing care in promoting cervical ripening, we conducted a randomized controlled trial involving 80 primiparous and full-term pregnant women with indications for labor induction. The participants were divided into a control group (n = 40) receiving intravenous oxytocin for labor induction and a study group (n = 40) receiving oral administration of JWZSD. Additionally, 30 female Wistar pregnant rats were randomly assigned to the blank group, oxytocin group, and traditional Chinese medicine (TCM) group to compare relevant indicators. In the clinical study, both groups showed significant increases in Bishop scores after treatment. However, the study group demonstrated significantly greater improvements compared to the control group. Moreover, the study group exhibited higher rates of clinical effectiveness and successful vaginal delivery than the control group. In the animal experiment, both the oxytocin and TCM groups displayed disorganized and relaxed cervical connective tissue with evident interstitial edema when compared to the blank group. Furthermore, rats in the TCM group exhibited significantly elevated serum levels of interleukin-8 (IL-8), matrix metalloproteinase-9 (MMP-9), prostaglandin E2 (PGE2), and cyclooxygenase-2 (COX-2) compared to those in the oxytocin group. The combination of JWZSD and perinatal midwifery holistic nursing care for labor induction can effectively enhance cervical ripening and reduce the incidence of cesarean section, potentially through the regulation of IL-8, MMP-9, COX-2, and PGE2 expression. However, further investigations are required to elucidate the underlying mechanisms.

Various Blowing-Suction Schemes for Manipulating Turbulent Boundary Layers

ABSTRACT The methodology of efficiency analysis of turbulent flow modification by making permeable sections in the streamlined wing surface with the aim to reduce aerodynamic drag is the principal subject of the presented research. The numerical analysis of the effect of laterally and longitudinally located permeable sections on boundary-layer properties showed the following flow features: (1) the most effective place for permeable surface is an upwind side of the wing; (2) multi-sectional blowing can simply be organised as non-uniform (especially in the case of laterally arranged permeable sections) that brings additional flexibility to change the blowing intensity depending on flight mode and, first of all, on angle of attack; and (3) arrays of longitudinal permeable sections allow to intensify turbulent vortical structures exchange in the lateral direction and improve flow stability to stall. Moreover, due to creating the regular anisotropy of the boundary layer in the lateral direction, this modified blowing technique can potentially have some synergistic properties, which can give the additional benefit. All these effects are too delicate and their experimental study cannot be performed with the use of directed measurements of aerodynamic forces. The comparison of the obtained flow properties with the corresponding experimental data demonstrates an appropriate level of agreement.

Comparative Network Efficiency Analysis of the Airlines in Turkey After Deregulation

Deregulation has significantly developed the civil air transport industry in an ever-globalizing world. Even though deregulation has caused significant structural transformations in airline companies, the effect of deregulation effect on the production, marketing efficiency, and competitiveness of airline carriers worldwide, especially in Turkey, has not been fully revealed yet. Therefore, this study aims to analyze the efficiency of Turkish air carriers after the deregulation process in Turkish civil aviation by dividing the efficiency into production and market efficiency. Production and marketing efficiencies of airlines were estimated using the window network data envelopment analysis methodology. Efficiency analysis results showed production efficiency at 0.887, marketing efficiency at 0.764, and system efficiency at 0.796. Results also indicate that low-cost airlines have a higher production efficiency score (0.918) than full-service airlines (0.825). In comparison, the marketing efficiency of full-service airlines (0.879) is higher than that of low-cost carriers (0.708). The study determined that the system efficiency does not change according to the business model. The system efficiency score of the full-service provider airlines with a larger market share is higher and more balanced. The close and dynamic monitoring of the air transport market and the continuation of operations under a business model incorporating an appropriate marketing mix will increase the marketing efficiency of the airlines.

Evaluation and prediction of carbon emission from logistics at city scale for low-carbon development strategy.

Low-carbon is a part of China's efforts to pursue the national strategy of "carbon peaking and carbon neutrality." Meanwhile, the path of low-carbon transformation of logistics has become a topic of global concern. This study constructs a technical framework of logistics carbon emissions (LCE), which is composed of carbon emission evaluation, carbon emission prediction and low-carbon strategy. All 13 prefecture-level cities in Jiangsu, China, are the application objects in empirical research. Then, the influence analysis of the LCE efficiency based on the panel Tobit model and the evolution of LCE under different scenarios are explored. The results show that: (ⅰ) during the study period (2013-2020), the LCE in Jiangsu showed an overall upward trend, with Xuzhou, Suzhou and Nanjing being the cities with the highest carbon emissions; (ⅱ) the static efficiency of LCE in Jiangsu is at a medium level, with fluctuations in Suzhou, Changzhou, Zhenjiang, Nantong, and Suqian caused by the technical change index; (ⅲ) economic level, industrial structure, fixed asset utilization rate, and ecological environment in Jiangsu are significantly positively correlated with LCE efficiency, while education popularization and energy intensity are negative; (ⅳ) LCE in Jiangsu has been drastically reduced in the low-carbon scenario compared to the baseline scenario. On the above basis, this study proposes suggestions for the low-carbon development strategies of logistics in Jiangsu.

Predicting Student Placement in College Using Machine Learning

Abstract: The research on student placement prediction in higher education has been a focal point, addressing challenges related to balanced accuracy and generalization across diverse datasets. Prior studies grappled with issues in feature representation and interpretability. This study aims to overcome these challenges by introducing a comprehensive machine learning framework for student placement prediction, leveraging advanced techniques in exploratory data analysis, preprocessing, and model evaluation. Drawing on previous research experiences, our proposed work targets specific issues related to feature engineering, categorical variable representation, and result interpretability. The methodology employs key libraries, including NumPy, pandas, Matplotlib, Seaborn, Plotly, scikit-learn, WordCloud, and DateTime, for efficient data manipulation, visualization, and analysis. Ensemble learning techniques, such as Random Forest and XGBoost, along with traditional algorithms like Decision Trees and K-Nearest Neighbors, contribute to enhancing predictive accuracy and model robustness. To fine-tune the models, a Randomized Search for Hyperparameters is implemented for the XGBoost classifier, optimizing parameters like learning rate, maximum depth, minimum child weight, gamma, and colsample by tree. This approach effectively addresses overfitting and underfitting issues, maximizing overall model performance. The accuracy percentages achieved through our models represent significant advancements. For instance, the Decision Tree model achieves an accuracy of 87.74%, the Random Forest model achieves 87.60%, the XGBoost model achieves 87.60%, and the K-Nearest Neighbors model achieves 85.18%. These results underscore the effectiveness of our approach in achieving high accuracy while maintaining interpretability.

Feature Selection Techniques for Enhancing Credit Card Fraud Detection Performance: A Hybrid Metaheuristic Approach Using Nature-Inspired Algorithms

Abstract: The surge in credit card usage has led to a parallel increase in fraudulent transactions, necessitating advanced detection systems. Traditional methods encounter challenges with high-dimensional and imbalanced datasets. This paper proposes a comprehensive approach integrating metaheuristic algorithms and deep learning techniques to enhance fraud detection accuracy. The first contribution, the Rock Hyrax Swarm Optimization Feature Selection (RHSOFS) algorithm, draws inspiration from the collective behaviors of rock hyrax swarms to effectively select relevant features from high-dimensional datasets. RHSOFS identifies an optimal subset of features critical for fraud detection through supervised machine learning. Complementing this, the second contribution leverages a hybrid deep learning model. Beginning by organizing transactional data and constructing a Logical Graph of Behavior Profile (LGBP) to abstract transaction details, the Modified Butterfly Optimization Algorithm (MBOA) selects important features from the dataset. The hybrid model, integrating Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), establishes rational connections between transactional characteristics, enhancing detection performance. To evaluate the approach, comparative efficiency analyses against existing methods, including Differential Evolutionary Feature Selection (DEFS), Genetic Algorithm Feature Selection (GAFS), Particle Swarm Optimization Feature Selection (PSOFS), and Ant Colony Optimization Feature Selection (ACOFS), are conducted. Results demonstrate the superiority of the approach in terms of both reliability and recognition rate, validated through rigorous statistical testing. The proposed hybrid approach signifies a significant advancement in credit card fraud detection systems, offering enhanced accuracy and efficiency in combating fraudulent activities amidst the growing complexity of transactional datasets.

Understanding energy performance in drinking water treatment plants using the efficiency analysis tree approach

Water treatment processes are known to consume substantial amounts of energy, making it crucial to understand their efficiency, drivers, and potential energy savings. In this study, we apply Efficiency Analysis Tree (EAT), which combines machine learning and linear programming techniques to assess the energy performance of 146 Chilean drinking water treatment plants (DWTPs) for 2020. Additionally, we utilize bootstrap regression techniques to examine the influence of operating characteristics on energy efficiency. The results indicate that the evaluated DWTPs exhibited poor energy performance, with an average energy efficiency score of 0.197. The estimated potential energy savings were found to be 0.005 kWh/m3. Several factors, such as the age of the facility, source of raw water, and treatment technology, were identified as significant drivers of energy efficiency in DWTPs. The insights gained from our study can be valuable for policymakers in making informed decisions regarding the adoption of practices that promote efficient and sustainable energy use within the water cycle.