Innovative Analysis Method Research Articles

Miscue Analysis as a Diagnostic Tool: Exploring English Reading Challenges among Chinese Middle School Students

With the increasing importance of English, Chinese middle school students face significant challenges in developing English proficiency. This study explores the English reading abilities of Chinese middle school students through the innovative method of miscue analysis, which examines the deviations from the expected response during the reading process to reveal their underlying reading strategies and shortcomings. Pioneered by Ken Goodman, miscue analysis has provided valuable insights into reading behaviors across diverse populations. This research involves 40 junior high school students from Tianjin Foreign Language School, analyzing the types and causes of miscues in their reading process. The findings indicate that these students mainly face challenges such as having a limited vocabulary, making grammatical errors and experiencing comprehension difficulties. The result emphasizes the potential of miscue analysis as a diagnostic tool in second-language education, offering educators a deeper understanding of students reading processes, guiding the development of targeted interventions to enhance the quality of second-language education. Future research should focus on expanding the sample size, developing specific scientific interventions to increase credibility, and applying miscue analysis to broader student populations and contexts to provide a comprehensive understanding of factors influencing English language learning in China.

Research Review of Power Electronic Technology in Power Grid Stability Improvement

When renewable energy are connected to the power grid through power electronic technological (such as inverters), ensuring the stability of the power grid becomes a very valuable challenge. This is because power electronic converters usually encounter some problems when converting electrical energy, such as unstable voltage, the presence of high-frequency or low-frequency harmonics, islanding effect, leakage current, etc. To solve these problems and enhance the stability of power grids when new energy is connected, improvements can be made to the components and topological structure of power electronic modules, or more precise and comprehensive control strategies can be adopted. This paper outlines the topological structures and control strategies of inverters in photovoltaic grid-connected systems, including innovative topological structures, control algorithms, and analysis methods. It summarizes recent research achievements in improving grid stability through inverters, discovering several disadvantages and raising some possible development directions of inverters in the future.

Design of an online learning scheme for quality prediction in refining and chemical units

AbstractThe refining and chemical production process is highly complex, with various uncertainties leading to fluctuations in product quality or even non‐compliance with standards. This makes real‐time monitoring of product quality essential. In recent years, with the advancement of digitalization and intelligent technologies, many quality prediction methods based on mechanistic or data‐driven modelling have emerged. However, for refining and chemical processes characterized by high dimensionality, strong coupling, and multiple working conditions, these methods may suffer from model mismatches and other limitations. Therefore, this study introduces an online learning approach for quality prediction in refining and chemical units, aiming to extract feature data across multiple working conditions from historical records without relying on process models. The method begins by efficiently extracting steady‐state data from process variables using filtering and fluctuation detection techniques. K‐means clustering and support vector data description (SVDD) are then applied to extract features at typical process points, while an innovative incremental principal component analysis (IPCA) method is employed to determine the optimal number of clusters for precise data classification. Additionally, a novel solution addresses matching issues between data sampled at different intervals, integrating process variables, working conditions, and quality indicators based on expert knowledge. A case study of a catalytic cracking distillation unit demonstrates the feasibility of the approach, highlighting its significant implications for quality prediction and online soft sensors, as well as its strong potential for future application across multiple units.

An innovative method for pinch point analysis of heat exchangers utilizing binary mixtures

An innovative method for pinch point analysis of heat exchangers utilizing binary mixtures

Инновации в изучении лингвокреативности кинодискурса

The review gives a high assessment of the monograph, whose authors present a number of original ideas and share their experience in researching linguistic creativity in film discourse. In order to conduct such a study, the famous linguist I.V. Zykova developed an innovative method of discursive-parametric analysis, which involved creating new concepts and terms. The results of the study demonstrate that creative modelling of the verbal system of film discourse is a multi-stage process whose prospects in linguistic studies are beyond doubt.

Assessment of spatiotemporal rainfall variability over the Niger Delta region, Nigeria: implications for water resource management

Adequate and reliable rainfall studies, especially those that address changes in spatial and temporal patterns, are critical for agriculture and water resource management. This study employed modified Mann‒Kendall and innovative trend analysis methods for analysing annual and seasonal trends in rainfall in Nigeria’s Niger Delta (NND). The ITA is robust for detecting hidden trends. Extreme value theory was used to obtain the rainfall extremes using Climate Research Unit (CRU) data from 1923-2022. The homogeneity tests revealed that the rainfall in the region was not homogeneous. The results of the trend analysis revealed increasing and decreasing trends in rainfall. Annual rainfall increased in the northern parts of the NND but decreased in the southern part, with rainfall in June-July-August (JJA) following a similar trend pattern. The block maxima and peaks above the threshold revealed different rainfall extremes. This study recommends improved irrigation methods and precision water application for sustainable water resource management in the region.

Spatiotemporal trend analysis of rainfall for deeper understanding of the pattern and its implications over western Amhara, Ethiopia: innovative trend analysis method

Long-term changes in rainfall are a key factor influencing climatic variability. Therefore, analyzing the spatial and temporal patterns of rainfall is essential. The examination of the spatiotemporal patterns of seasonal and yearly rainfall through creative trend analysis, however, has not been covered in previous studies. The study examined the spatiotemporal patterns of annual and seasonal rainfall in western Amhara using the Mann–Kendall test and Sen's slope estimator. Additionally, the latest innovative trend analysis method was employed to assess the clusters of low, medium, and high rainfall. Thirty chosen grid points from the Enhancing National Climate Services daily rainfall dataset were obtained from the Ethiopian Meteorology Institute, covering the period from 1991 to 2020. The result indicated that, in total, of 120 annual and seasonal time series, the ITA approach revealed 95- time series with significant trends in all annual and seasonal timeframes. However, the Mann–Kendall test and Sen’s slope estimator found significant trends in only 14-time series across all timescales at a 5% significance level. Furthermore, the ITA method demonstrated that most rainfall grid points exhibited significant clusters and upward trends during the Kiremt and annual periods; however, the Bega season experienced reduced rainfall, suggesting downward trends. As a result, the comparative results showed that the innovative trend analysis method is more reliable and effective than the Mann–Kendall test because it can identify both monotonic and non-monotonic trends that the Mann–Kendall method cannot recognize. Hence, the rainfall trends during the Kiremt and Bega seasons are increasing and decreasing, respectively. Therefore, adapting to the risks of rainfall variability requires strategies such as climate advisory services, water conservation, supplemental and deficit irrigation, and planting drought-resistant and early-maturing crops.

An enhanced deep learning approach for intelligent healthcare emotion analysis using facial expressions and feature analysis to identify pain

Abstract This study introduces an innovative deep learning method for intelligent healthcare emotion analysis, specifically targeting the recognition of pain based on facial expressions. The suggested approach combines cloud-based mobile applications, utilising separate front-end and back-end elements to optimise data processing. The main contributions consist of a Smart Automated System (SASys) that integrates statistical and deep learning methods to extract features, thereby guaranteeing both resilience and efficiency. Image preprocessing encompasses the tasks of detecting faces and normalising them, which is crucial for extracting features with high accuracy. The comparison of statistical feature representation using Histogram of Oriented Gradients and Local Binary Pattern, along with machine learning classifiers, against an enhanced deep learning-based approach with an integrated multi-tasking feature known as multi-task convolutional neural network, demonstrates encouraging outcomes that support the superiority of the convolutional neural network architecture. Statistical and deep learning-based classification scores, when combined, greatly enhance the system’s overall performance. The results of the experiments prove that the method is effective, outperforming traditional classifiers and exhibiting comparable accuracy to cutting-edge healthcare SASys.

Signal Detection at SFDA: A Comprehensive Analysis of Current Practices and Future Directions.

The Saudi Food and Drug Authority (SFDA) plays a pivotal role in safeguarding public health through robust pharmacovigilance. Since establishing its signal detection section in 2015, SFDA has implemented a comprehensive framework to monitor and evaluate drug safety through proactive data collection, advanced analytical methods, and international collaborations. To provide a comprehensive analysis of the current practices in signal detection at SFDA, assess the effectiveness of its regulatory framework, in-house priority system, active surveillance programme, and global collaborations, and to offer recommendations for future enhancements in pharmacovigilance. A scientific review was conducted using publicly available literature, official SFDA guidelines, and internal reports. The evaluation focused on the regulatory mandate, data collection methodologies, signal generation processes (including weekly scanning of diverse sources), case validation, and the integration of advanced statistical techniques. Special emphasis was given to the in-house priority algorithm, active surveillance initiatives, and collaborative efforts with international bodies. The review highlights that SFDA's robust regulatory framework and innovative in-house priority algorithm have enhanced the efficiency of signal detection. The active surveillance programme has significantly increased the capacity for real-time monitoring, while global collaborations have facilitated knowledge exchange and harmonised methodologies. However, challenges persist regarding data quality, resource allocation, and the need for continuous training and improved communication among stakeholders. SFDA's signal detection system effectively contributes to drug safety in Saudi Arabia by combining rigorous regulatory practices with innovative analytical methods and international cooperation. Addressing current challenges through advanced technologies, enhanced resource allocation, and strengthened stakeholder communication is recommended to further improve pharmacovigilance practices and ensure public health protection.

Linear State Estimation for Multi-rate NCSs With Multi-channel Observation Delays and Unknown Markov Packet Losses

Highlights Linear State Estimation for Multi-rate NCSs With Multi-channel Observation Delays and Unknown Markov Packet Losses Zhenglu Sun and Chunyan Han * School of Electrical Engineering, University of Jinan, Jinan, 250022, China * Correspondence: cyhan823@hotmail.com Received: 19 October 2023 Accepted: 27 November 2024 Published: 25 March 2025 Abstract: This paper is concerned with the linear minimum mean square error estimation (LMMSE) for the multi-rate sampling systems with multi-channel observation delays and unknown Markovian packet losses. The original system is firstly transformed into a single-rate jumping parameter system with multi-channel and delay-free observations by employing the lifting technique and introducing a set of reorganized observations and Markov chains. Then, the single-rate system is converted into a general linear system without delays by defining a new group of extended states. Based on the innovation analysis method, a liner minimum mean square error estimator is developed, and the estimator gain is obtained in terms of generalized Riccati difference equations based on a set of coupled Lyapunov equations. Therefore, the original state estimation problem is solved via the jumping parameter property. Finally, the convergence of the Riccati equation is analyzed and a stationary filter is obtained. The novelty of this paper lies in the introduction of the reorganized observations and multi-state Markov chains.

Changes in extreme rainfall indices in Wereilu district, northeastern Ethiopian highlands, using innovative trend analysis

Extreme rainfall events are changing globally due to global warming, but the direction and severity of these changes vary across regions and locations. This study aimed to assess changes in extreme rainfall indices at the local level in northeastern Ethiopia using data from 1982 to 2021. The study applied statistical and graphical innovative trend analysis (ITA) methods to examine the patterns of rainfall indices. The findings revealed a significant positive trend in all the selected extreme rainfall indices (P < 0.01), except for consecutive dry days (CDDs) at the Wereilu station. Meanwhile, at the Kabie station, total annual rainfall (RFTOT), maximum rainfall in one day (Rx1day), maximum consecutive five-day rainfall (Rx5day), heavy rainfall days (R10mm), simple daily intensity index (SDII), and consecutive wet days (CWDs) increased significantly (P < 0.01), while the number of rainy days (Rdays) and very wet days (R95p) decreased significantly (P < 0.05). The graphical ITA indicated that extreme indices exhibited varying magnitudes of change, with some showing opposing directions across different value ranges. This finding suggests that graphical analysis should be used alongside statistical monotonic trend analysis to better understand how extreme rainfall indices behave in complex ways. Overall, the results revealed significant changes in extreme rainfall indices, highlighting the need for effective adaptation measures to mitigate the effects of extreme rainfall events. This study provides crucial understandings of extreme rainfall events at the local level, offering valuable information for adaptation planning, particularly within rain-fed agricultural systems.

Design and hydrodynamic analysis of subsea development automatic placement devices

In response to the problems of low efficiency and safety hazards in traditional subsea pipeline intersection isolation release operations, this study designs a new type of automatic deployment device for subsea oil and gas pipeline intersection isolation, aimed at replacing manual and unmanned underwater vehicles (ROVs) for underwater deployment and monitoring. The device adopts an innovative framework structure design, significantly improving its resistance to water flow, and enhancing the operability and accuracy of underwater operations through a ducted propeller propulsion system. This study used computational fluid dynamics (CFD) combined with multiple reference frame (MRF) method to conduct detailed simulation analysis of the fluid dynamics characteristics of the device under different motion states, and explored its optimal working conditions. The simulation results show that during rotational motion, when the flow velocity exceeds 0.5 m/s, the resistance experienced by the device significantly increases, reaching a maximum of 7066.33 N. In addition, this study conducted an in-depth analysis of the causes of flow separation and resistance distribution under different motion states, revealing the important impact of frame style exterior design on the hydrodynamic performance and stability of the device. The research results indicate that the automatic placement device can efficiently complete the placement task of isolation materials in a water flow environment of 0.75 m/s, with a maximum driving torque of 6750 N*m, and has good engineering application prospects. The design proposed in this article not only provides a new perspective and solution for deep-sea pipeline protection technology, but also lays an important theoretical foundation for research and application in this field through innovative structural design and advanced simulation analysis methods.

Prewhitening-Aided Innovative Trend Analysis Method for Trend Detection in Hydrometeorological Time Series

Detection of trends in hydrometeorological time series is essential for understanding the complex variability of hydrometeorological data. Although different types of methods have been proposed, accurately identifying trends and their statistical significance is still challenging due to the complex characteristics of hydroclimatic data and the limitations of diverse methods. In this article, we propose a new trend detection approach, namely the prewhitening-aided innovative trend analysis (ITA). This method first corrects the significance test formula of the original ITA method, followed by a prewhitening method to eliminate serial autocorrelation and ensure independence. Results of Monte–Carlo experiments verified the superiority of the prewhitening-aided ITA method to the previous ITA methods. Moreover, serial correlations had significant impacts on the performance of diverse methods. Comparatively, the traditional ITA method kept high Type I errors and tended to overestimate the significance of trends. The four ITA methods, which were improved in previous studies, performed better than the traditional ones but could not overcome the influence of either positive or negative correlation characteristics of time series. The four prewhitening-aided ITA methods performed much better as they could effectively handle serial correlation. Among all the nine methods concerned in this study, the variance correction prewhitening-aided ITA (VCPWITA0) method performed the best. Detection of trends in precipitation data in the Qinghai-Tibet Plateau further verified the superiority of the VCPWITA0 method. The proposed method fully exploited the advantages of both ITA and prewhitening, and thus, it provided a new approach for detecting trends and has the potential for wide use in hydrological and climate sciences.

Advances in reliability analysis and risk assessment for enhanced safety

Reliability science and engineering are fundamental for guaranteeing the safe functioning of systems and products. Indeed, the risk of failure can be complex to assess and manage. Modes of failures and scenarios of accidents must be imagined and postulated, and evaluations of occurrence probabilities and consequences must be performed in the presence of uncertainties, possibly very deep. The outcomes of the evaluations inform decisions to prevent failures and undesired events and, were they to occur, mitigate and recover from their consequences. On the other hand in this world in continuous transition to meet the numerous and increasingly challenging objectives of reliability, efficiency, safety, sustainability etc, the innovations that are being developed for better-being and more benefits for all, also deepen the uncertainty related to new and unknown hazards and dangers. This calls for innovative methods of analysis, assessment and management of reliability and risk for enhanced safety. In this paper, directions of development are presented, including the use of simulation for accident scenario identification and exploration, the exploitation of monitoring data for the dynamic updating of reliability and risk assessment towards condition monitoring-based risk assessment, and the extension of the framework of risk assessment to resilience.

Combined innovative trend analysis methods for seasonal trend testing

Combined innovative trend analysis methods for seasonal trend testing

Universal Analysis Method for Metamaterial-Based Wireless Power Transfer with Arbitrary Energy Source Waveforms: Application to Triboelectric Nanogenerators.

Metamaterial-based wireless power transfer (MM-WPT) analysis has attracted substantial attention due to its great application potential. However, traditional MM-WPT analysis is constrained by frequency domain approaches which are suitable only for infinitely extended periodic signals or fixed-frequency sine waves but not suitable for complex waveforms of various energy sources. This paper presents an innovative time-domain system analysis method for MM-WPT systems tailored to evaluate energy sources with arbitrary waveforms. The foundation of the method is to use the unit impulse response. By convolving this impulse response with any type of excitation source, a temporal waveform of the voltage across the system's load can be obtained. It has demonstrated a high degree of correlation and agreement between theoretical calculations and experimental results for various input waveforms, affirming its validity, precision, and universality. Based on the framework, it is shown that triboelectric nanogenerators can efficiently self-powered transfer wireless energy through MM-WPT systems. Experiments reveal that the energy received is up to 59.6 times higher compared with that of WPT systems without metamaterials. When this system is applied in an implant, it demonstrates a remarkable energy transfer efficiency of 51% through biological tissues. These findings represent a significant breakthrough in optimizing WPT systems for compact and efficient self-powered energy applications.

Accident Factors Importance Ranking for Intelligent Energy Systems Based on a Novel Data Mining Strategy

As global energy networks expand and smart grid technology evolves rapidly, the volume of historical power accident data has increased dramatically, containing valuable risk information that is essential for building efficient public safety early warning systems. This paper introduces an innovative text analysis method, the Sparse Coefficient Optimized Weighted FP-Growth Algorithm (SCO-WFP), which is designed to optimize the processing of power accident-related textual data and more effectively uncover hidden patterns behind accidents. The method enhances the evaluation of sparse risk factors by preprocessing, clustering analysis, and calculating piecewise weights of power accident data. The SCO-WFP algorithm is then applied to extract frequent itemsets, revealing deep associations between accident severity and risk factors. Experimental results show that, compared to traditional methods, the SCO-WFP algorithm significantly improves both accuracy and execution speed. The findings demonstrate the method’s effectiveness in mining frequent itemsets from text semantics, facilitating a deeper understanding of the relationship between risk factors and accident severity.

Resilience of human gut microbiomes in autism spectrum disorder: measured using stiffness network analysis.

Autism spectrum disorder (ASD) affects an estimated 1%-2% of children worldwide, but its specific etiology remains unclear. In recent years, the gut microbiome's role in ASD pathogenesis has garnered increasing attention. However, the exact relationship between microbiota and ASD-such as which microbial species significantly impact disease onset and progression-remains unresolved, and effective methods to measure microbial interactions are still lacking. In this study, we introduce an innovative stiffness network analysis (SNA) method to quantify changes in microbial network structure and identify disease-specific microbial bacteria theoretically. The SNA method was applied to reanalyze eight ASD gut microbiome data sets, encompassing 898 ASD samples and 467 healthy control (HC) samples from 16S-rRNA sequencing data. Key findings include the following: (i) an "allies" biomarker subgroup consisting of Bacteroides plebeius, Sutterella, Lachnospira, and Prevotella copri was identified; (ii) a profile monitoring score of 0.72 for the biomarker subgroup, indicating significant relationship changes between HC and ASD states, and (iii) a P/N ratio of biomarker subgroup in ASD-associated gut bacteria that was three times higher than that of HC microbiomes. Additionally, we discuss the non-monotonic relationship alterations within microbial sub-communities in the ASD gut microbiome.IMPORTANCEIt is crucial to assess alterations in network structure in different biological states in order to promote health. The stiffness network allows for the exploration of species interactions and the measurement of resilience in complex microbial networks. The objective of this study was to develop a stiffness network analysis (SNA) method for evaluating the contribution of microbial bacteria in differentiating disease samples from healthy control samples by examining changes in network stiffness parameters. Furthermore, the SNA method was employed on both simulated and real autism spectrum disorder gut microbiome data sets to identify potential microbial biomarker subgroups, with a particular focus on the relationship alterations within microbial networks.

Innovative trend analysis of precipitation changes over Nigeria: A case study of locations across the Niger and Benue Rivers

Precipitation has become a major topic of discussion in the subject of climate change. Studies have presented correlative relationships between climate change and the existing alterations in the global water cycle, leading to more extreme precipitation events, including both droughts and floods. This has shown severe impacts on agriculture, water resources, ozone levels, and human settlements. Accordingly, this study analyzes the trends in 40 years (1981-2020) of total monthly and annual precipitation data from six selected locations in Nigeria, using both the Mann-Kendall (MK) and new Sen's innovative trend analysis (ITA) method. The stations were chosen because of their close proximity to the Niger or Benue Rivers. The data were visually analyzed using boxplots to identify seasonal patterns and outliers, and the results of the MK test show that four of the six locations detected trends for annual and wet season precipitation variations. However, this was not applicable to dry season variations. The ITA test results show that only Kainji has an increasing trend in total annual and wet season precipitation, especially for high precipitation values, whereas other locations show decreasing trends for maximum precipitation values. A comparison of the two tests suggests that, the ITA test is more sensitive in detecting trends in precipitation data for the study locations. Overall, this study provides useful information for understanding the precipitation patterns in Nigeria, and can assist in planning and implementing appropriate water resource management strategies, with regards to the major rivers in the region.

Harnessing data for impact: Transforming public health interventions through evidence-based decision-making

Data has become a cornerstone in transforming public health interventions, driving the shift towards evidence-based decision-making that ensures impactful and sustainable health outcomes. This manuscript explores the pivotal role of data in designing, implementing, and evaluating public health programs, emphasizing how robust evidence informs strategies to address pressing health challenges. With growing complexities in global health systems and disparities in resource allocation, leveraging data-driven insights has emerged as a vital approach to optimizing interventions, particularly in underserved and resource-limited populations. The manuscript begins by discussing the foundational importance of comprehensive data collection systems, highlighting the need for accuracy, consistency, and inclusivity in capturing diverse health metrics. It then examines the integration of innovative analytical methods, including machine learning and predictive analytics, to uncover trends, identify risk factors, and anticipate emerging health threats. Real-world examples illustrate how data transforms interventions, from enhancing disease surveillance to tailoring community-specific health initiatives. The application of evidence-based findings to public health decision-making is further explored, demonstrating how data empowers stakeholders to allocate resources efficiently, prioritize vulnerable populations, and evaluate program effectiveness. The manuscript also addresses challenges such as data privacy, infrastructure gaps, and disparities in access to analytical tools, offering actionable solutions to overcome these barriers. By aligning technological advancements with evidence-based approaches, this manuscript underscores the transformative potential of data in public health, paving the way for adaptive, equitable, and impactful interventions.