Analysis System Research Articles

Growth, yield and anatomy of alpinia calcarata (<em>Heen Araththa</em>) as affected by irrigation frequency

Alpinia calcarata, often known as Heen Araththa, is a precious medicinal plant owing to its chemical properties in herbal treatments. The growth, yield and anatomical features related to the synthesis and accumulation of secondary metabolites of this plant are highly affected by drought stress. Therefore, this work investigated the influence of irrigation frequency on growth, yield and selected anatomical features of A. calcarata. Five levels of irrigation frequencies including daily watering (T1), 7-day (T2), 14-day (T3), 21-day (T4) and 28-day (T5) intervals were investigated in a protected plant house under controlled environmental conditions. A. calcarata rhizomes with a similar number of sprouting buds planted in common potting media (topsoil: sand: compost- 1:1:1) were arranged in Completely Randomized Design in triplicates. After three weeks of establishment, five distinct irrigation frequencies were implemented, with pots being watered daily up until that point. Growth and yield parameters (shoot height (cm), number of roots, number of shoots, number of leaves per shoot, leaf area (mm2), rhizome dry weight (g)) and the anatomical parameters (root epidermis thickness (μm), rhizome epidermis thickness (μm) and oil cell density (μm2)) were collected 10 weeks after establishment. Data were analyzed using Analysis of Variance (ANOVA) employing the SAS analysis system. Results revealed that all selected growth, yield and anatomical parameters of A. calcarata were significantly (p<0.05) affected by irrigation frequency. Significantly (p<0.05) the highest mean shoot height (20.8cm), mean number of roots (11.3), mean number of shoots (6.8), mean rhizome dry weight (9.3g) and significantly (p<0.05) higher mean leaf area (24.9mm2) and mean number of leaves per shoot (4) of A. calcarata weregiven by 14-day irrigation frequency. It was also observed that the mean root epidermis and rhizome thickness of A. calcarata gradually increased in response to escalating drought stress. In contrast, the highest oil cell density was observed under the lowest drought stress. Based on the results, it can be concluded that the most promising irrigation frequency for higher growth and yield of A. calcarata is watering with 14-day intervals. Daily watering appeared to be the most suitable treatment to enhance oil cell density associated with secondary metabolite production of A. calcarata.

Analysis of interrelationships of human errors using linguistic decision-making trial and evaluation laboratory with consensus reaching process

Analyzing human errors' interrelationships is one of the most important assignments for human reliability improvement in sociotechnical systems. Human factor analysis and classification system (HFACS) is effective in human error analysis due to its taxonomy and systematical perspective. It reveals interrelationships among human errors emerging in a multi-hierarchy of systems. However, the conventional HFACS method is incapable of quantifying their interrelationship. Especially, due to the nature of human errors, their objective data is limited. Experts' opinions are important resources to facilitate human error analysis. However, limited improved HFACS considers experts' consensus on interrelationships analysis results, especially in linguistic environments. Accordingly, this paper aims to address HFACS-based interrelationships analysis problems utilizing linguistic decision-making trial and evaluation laboratory (DEMATEL) with consensus reaching process (CRP). First, probabilistic linguistic terms are utilized to represent experts' opinions on human errors' interrelationships. Second, CRP is introduced to derive consensual opinions on human errors' interrelationships, shifting the focus to identifying human errors with low consensus levels rather than experts. Then, a hybrid weighting method is introduced to determine the weight of experts' opinions in the information fusion phase, which reflects inherent uncertainty and inter-recognition of experts’ opinions. Furthermore, DEMATEL is introduced to model direct and indirect interrelationships among human errors. Finally, a case study of a drug administration process is conducted to validate the efficiency of the proposed method. The case study indicates that neglect of safety culture development and limited financial and human resources are the top two human errors, with importance degree 0.148 and 0.107.

Laban Effort in Empty-Handed Interactions of Hindustani Dhrupad Vocal Improvisation

Effort, commonly understood as the power of an action toward an intended goal, is acknowledged as an important aspect of music expressivity. Previous studies in Hindustani Dhrupad vocal improvisation, particularly those focusing on manual interactions with imaginary objects, have revealed the intricate connection between effort and various movement and melodic variables. The study employed manual annotations by participants who visually inspected and assessed the amount of effort that such interactions were perceived to require. However, since effort is inherently perceptual and subjective and the way that an observer makes assessments on effort levels remains a non-transparent process, the paper seeks to examine the applicability of the Laban Movement Analysis (LMA) system in this task. For this, it relies on a multi-way analysis of variance (ANOVA) to infer manually annotated (numerical) effort levels from Laban’s (categorical) Effort Factors, namely Weight, Flow, Time, and Space, for two Dhrupad performances. The results suggest that apart from the Space factor, which was excluded for reasons delineated, a good part of effort’s variance can be explained through the remaining three statistically significant Effort Factors, leading to the rejection of the null hypothesis that they are unrelated. By ascertaining this relationship, effort-related melodic aspects in Dhrupad improvisation can be predicted using the three Laban Effort Factors.

Network alignment in multiplex social networks using the information diffusion dynamics

Modern social networking applications (SNAs) typically rely on independent data management systems, leading to fragmented user identities and the creation of data silos. This fragmentation impedes the development of unified user profiles and limits the effectiveness of cross-platform behavioural analysis and personalized recommendations. As the demand for integrated services grows, the need for a unified user identity across platforms becomes increasingly critical. However, existing identity integration methods face significant challenges, including data isolation, privacy risks, and a lack of universal standards. To address these issues, we propose a framework that utilizes node dynamics time-series data for network alignment in multiplex social networks. Our approach employs the UIU diffusion model to simulate information diffusion dynamics across multiple platforms and uses the Expectation-Maximization (EM) algorithm for network alignment. Crucially, our method relies solely on publicly available user information from different platforms, avoiding the need for access to private user data, thereby enhancing security. Experimental results demonstrate the model’s efficacy, achieving F1 scores of 100% for interlayer links and over 90% for intralayer links on real-world datasets. These findings highlight the model’s potential for applications in social influence analysis, community detection, and recommendation systems.

Intelligent visual analysis of accident behavior and mechanism inherent in ship collision accident data

The analysis of ship collision accident data plays a crucial role in ensuring maritime transportation safety. This paper proposes an intelligent visual analysis-based method focusing on the co-occurrence patterns, collision risk, similarity analysis, and classification of ship collision data. Based on data from 1573 maritime accidents that occurred globally between 2010 and 2023, this paper first designs co-occurrence patterns to identify regularities between accident attributes. Second, a fusion model combining fuzzy comprehensive evaluation and logistic regression is developed to calculate ship collision risk, and the model is applied to reconstruct and visually analyze the collision process. Third, the similarity between two collision accidents is analyzed based on Sentence-BERT algorithm, and a heterogeneous graph neural network model is constructed for accident classification. Finally, all the analysis contents are integrated into the visual analysis system of the ship collision accident data to illustrate the accident behavior and mechanism inherent in ship collision accident data. The rich content within the visualization system provides trainee crew members with a more comprehensive understanding of accidents and serves as a reference for maritime management agencies to analyze similar and related accidents, derive lessons learned, identify high-risk areas, and formulate targeted risk management strategies.

Innovative Applications and Perspectives of Surface-Enhanced Raman Spectroscopy Technology in Biomedicine.

Surface-enhanced Raman spectroscopy (SERS) has become a revolutionary technique in the biomedical field, providing unparalleled sensitivity for the detection and characterization of biological samples. In this review, recent SERS innovations are comprehensively discussed, including advanced substrate materials, different SERS detection strategies, and multimodal approaches that combine SERS with other biotechnologies. Among them, the role of SERS in the accurate diagnosis of tumors is highlighted, which has promoted accurate molecular analysis and real-time monitoring of treatment effects. In addition, the growing potential of SERS in the treatment of chronic diseases such as cardiovascular disease, diabetes, and neurodegenerative diseases is discussed. Moreover, the integration with microfluidic chip systems for precise single-cell analysis is presented. To give a forward-looking view, the key challenges faced by SERS technology are also proposed, and possible solutions to overcome these obstacles are provided.

Retraction Note: Fuzzy assisted fog and cloud computing with MIoT system for performance analysis of health surveillance system

Retraction Note: Fuzzy assisted fog and cloud computing with MIoT system for performance analysis of health surveillance system

A Conversation Analysis-Complex Dynamics Systems Theory (CA-CDST) Approach for Analyzing Longitudinal Development in L2 Pragmatics

The compatibility of Conversation Analysis (CA) and Complex Dynamics Systems Theory Approach (CDST) in analyzing second language (L2) development has long been discussed (e.g., Larsen-Freeman & Cameron, 2008a). Nevertheless, how CA and CDST are compatible and how the method can be applied to data to analyze L2 development has been rarely demonstrated, with a few exceptions (e.g., Siegel & Seedhouse, 2024). This paper builds upon these prior discussions, explaining the compatibility of CA and CDST and a demonstration of the Conversation Analysis-Complex Dynamics Systems Theory (CA-CDST) approach to holistically analyze L2 pragmatic development. The CA-CDST approach is exemplified through the analysis of the use of code-switching in L2 interaction. The analysis demonstrates that unmarked code-switching in L2 interaction is not an omnipresent interactional phenomenon. Rather, it is learned; it is an interactional resource that is co-constructed through the sequential process of multiple encounters, adaptation, and accommodation to both the interlocutor and the interactional setting. Moreover, the analysis demonstrates the adaptable nature of CA and how the CA-CDST can elucidate the non-linear nature of L2 pragmatic development.

A Micro Total Analysis System (μTAS) for the In Situ, Real-Time Tracking of Produced Water Discharges through Detection of PAHs and Other Aromatic Compounds.

Here, we present a novel micro Total Analysis System (μTAS) for the measurement of poly cyclic aromatic hydrocarbon (PAH) and other aromatic hydrocarbons (AHs) in water at ng/L levels in situ and in real time (IMiRO). The μTAS is based on in-line membrane extraction followed by detection of extracted aromatic substances with fluorescence. An offshore field demonstration of the method was conducted close to produced water (PW) discharged in the North Sea. PW was monitored with the IMiRO μTAS and compared to results from a simultaneously conducted independent tracer release experiment, where fluorescein was added to the PW as a tracer. The μTAS monitoring and fluorescein tracer experiment showed similar ability to track the dispersion of the PW plume in space, depth, and time. Moreover, the method detected the sum of phenanthrenes and the sum of heavier PAHs with limits of detection down to 6 ng/L, with a response time of 6 min. The novel μTAS system opens up for in situ real-time discharge monitoring of both permitted and accidental oil or PW releases from oil platforms as well as other sources. Such monitoring can also be used to test and verify dispersion models used for environmental risk assessment.

Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective study

BackgroundMethods for accurately predicting the prognosis of patients with recurrent hepatolithiasis (RH) after biliary surgery are lacking. This study aimed to develop a model that dynamically predicts the risk of hepatolithiasis recurrence using a machine-learning (ML) approach based on multiple clinical high-order correlation data.Materials and methodsData from patients with RH who underwent surgery at five centres between January 2015 and December 2020 were collected and divided into training and testing sets. Nine predictive models, which we named the Correlation Analysis and Recurrence Evaluation System (CARES), were developed and compared using machine learning (ML) methods to predict the patients’ dynamic recurrence risk within 5 post-operative years. We adopted a k-fold cross validation with k = 10 and tested model performance on a separate testing set. The area under the receiver operating characteristic curve was used to evaluate the performance of the models, and the significance and direction of each predictive variable were interpreted and justified based on Shapley Additive Explanations.ResultsModels based on ML methods outperformed those based on traditional regression analysis in predicting the recurrent risk of patients with RH, with Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) showing the best performance, both yielding an AUC (Area Under the receiver operating characteristic Curve) of∼0.9 or higher at predictions. These models were proved to have even better performance on testing sets than in a 10-fold cross validation, indicating that the model was not overfitted. The SHAP method revealed that immediate stone clearance, final stone clearance, number of previous surgeries, and preoperative CA19-9 index were the most important predictors of recurrence after reoperation in RH patients. An online version of the CARES model was implemented.ConclusionThe CARES model was firstly developed based on ML methods and further encapsulated into an online version for predicting the recurrence of patients with RH after hepatectomy, which can guide clinical decision-making and personalised postoperative surveillance.

Vortex-Mixing Microfluidic Fabrication of Micafungin-Loaded Magnetite–Salicylic Acid–Silica Nanocomposite with Sustained-Release Capacity

Iron oxide nanoparticles were synthesized using a vortex microfluidic system and subsequently functionalized with a primary shell of salicylic acid, recognized for its ability to increase the stability and biocompatibility of coated materials. In the second stage, the vortex platform was placed in a magnetic field to facilitate the growth and development of a porous silica shell. The selected drug for this study was micafungin, an antifungal agent well regarded for its effectiveness in combating fungal infections and identified as a priority compound by the World Health Organization (WHO). The resulting nanocomposite system was characterized using various techniques, including Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), transmission electron microscopy (TEM), dynamic light scattering (DLS), Brunauer–Emmett–Teller (BET) analysis, UV-Vis spectroscopy, and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). The synthesis method produced nanoparticles with dimensions of 5–7 nm, highlighting the advantages of the chosen approach. A desorption profile was established using a continuous-flow, UV-Vis analysis system, indicating that the bioactive compound was released slowly; after two hours, approximately 50% of the loaded micafungin was detected in the release medium. Furthermore, the results obtained from the FT-ICR MS analysis provided molecular-level confirmation, thereby supporting the release mechanism of micafungin from the nanosystem.

Evaluating the effect of semen storage and dilution rate on boar sperm quality

ABSTRACT This study analyzed the effect of boar semen storage and dilution rate on sperm motility, swimming patterns and kinematics. It used 35 sexually mature, healthy boars, with an average age of 19.0 ± 10.34 months and known fertility. Samples were separated into four treatments according to dilution ratio (1:1, 1:4, 1:8 and 1:16; v:v) with Androstar Plus® and refrigerated at 17°C in the dark for 96 h. Subsamples were analyzed at 12, 24, 28, 72 and 96 h using the ISAS®v1 Computer-Assisted Semen Analysis System (CASA). Total and progressive motility decreased (P < 0.05) as semen storage time increased. The percentage of morphological abnormalities increased after 48 h of semen storage. Samples diluted 1:1 (v:v) had higher values of total motility and faster spermatozoa compared to other dilution rates. It was concluded that sperm motility, swimming patterns and plasma membrane integrity showed a significant downward pattern as semen storage time and dilution rate increased.

Contact-Free Online Monitoring of Bioreactor Cell Cultures with Magnetic Resonance Relaxometry.

Frequent, low-latency measurements of bioreactor culture growth are critical for achieving maximum culture efficiency and productivity. Typical cell density and viability measurements are made by manually removing a sample from the culture, but this approach is both slow and unsuitable for small culture volumes, which cannot support frequent destructive sampling. In this work, automated magnetic resonance relaxometry measurements of a sealed bioreactor system are used to estimate the cell density and provide qualitative information about the culture in near real-time. The system detects variations in cell density in minutes, enabling rapid intervention that would be impossible with the once-daily measurements taken by a traditional sampling-based culture analysis system.

A IMPORTÂNCIA DO PRÉ-NATAL NA ATENÇÃO PRIMÁRIA PARA A REDUÇÃO DE COMPLICAÇÕES OBSTÉTRICAS PARA GESTANTES: REVISÃO INTEGRATIVA

INTRODUCTION: Prenatal care is the complete health monitoring of pregnant women health through consultations. High maternal mortality hates in Brazil underscore the importance of prenatal care in primary health services to reduce pregnancy-related complications. Qualified monitoring is essential for early detection of problems, ensuring the health of both mother and baby, with nurses playing a central role in consultations and preventive strategies for pregnant women. OBJECTIVE: To explore the importance of prenatal care in primary health, highlighting its fundamental role in preventing obstetric complications in pregnant women. METHODOLOGY: This study is an integrative review guided by six steps of the review process. Articles were selected from databases, including Medical Literature Analysis and Retrieval System Online (MEDLINE), LILACS, Scientific Electronic Library (SciELO), and PubMed, following the established inclusion and exclusion criteria. RESULTS: Initially, 108 articles were identified, of which three were duplicates, 84 were excluded, and 14 were included in the study. DISCUSSION: The central role of primary care in gestational follow-up is discussed, emphasizing that the quality of care directly has a direct impact on both, maternal and fetal health. Failures in basic healthcare units that compromise the effectiveness of care are highlighted. Challenges in conducting essential diagnostic tests, such as rapid tests, are mentioned, alongside the need for preventive strategies to manage common obstetric complications. CONCLUSION: Effective prenatal follow-up enable early detection of adverse conditions, preventing negative outcomes for both mother and baby. This underscores the importance of nursing professionals and the Family Health Strategy (ESF) in providing comprehensive care, ensuring quality in prenatal services and ongoing education.

Intelligent Support for Low Literacy Adults: The European Portuguese iRead4Skills Corpus

This paper presents the Portuguese dataset of the iRead4Skills project (Dataset 1: corpora by complexity level for FR, PT, and SP – v.2.0), a representative sample of written European Portuguese for automatic complexity assessment that addresses a gap in existing resources for Portuguese. The corpus was created within the framework of the iRead4Skills project, which encompasses Portuguese, French, and Spanish. The project aims to develop an intelligent system to evaluate text complexity while recommending appropriate reading materials to native adult learners with low literacy skills. The corpus compilation involved a manual selection of text samples across various textual genres and document types, covering a wide range of existing written materials and focusing on the reading needs and reading habits of the target audience—low literacy adults enrolled in vocational education and training centres or adult learning (AL) centres. The collected texts were categorised into the three distinct levels of complexity targeted and defined by the project: very easy, easy, and plain levels. Texts of higher complexity were also included, resulting in the creation of four distinct sub-corpora. The resulting Portuguese dataset consists of 2,186 texts and 942,818 tokens and serves as the foundational source for training and testing the project’s complexity analysis systems. This paper presents a comprehensive overview of the compilation process of the corpus, encompassing its methodological design and the challenges faced. Although some existing Portuguese corpora were used for complexity studies and tool development, these primarily consist of texts classified according to CERF levels and retrieved from didactic materials designed for L2 teaching/learning or texts produced by L2 learners. The corpus presented in this paper introduces a new resource that addresses a significant gap in materials needed to inform and support studies and applications related to text complexity. The resulting dataset provides a novel and important language resource for European Portuguese, with several applications including research on linguistic complexity, development of automatic text complexity and readability assessment systems, and educational purposes.

A comparison of kinematic and kinetic characteristics of intrinsic flexor muscles of the foot exercises: Effect on the medial longitudinal arch of the foot

Background Standing toe curl (STC) has an immediate effect on muscle activity, but the kinematics of the exercise and its effect on the medial longitudinal arch of the foot are unknown. Objective The purpose of this study was to examine the differences in kinematic and kinetic changes during toe flexion while performing towel curl (TC) in a standing position and STC. Methods Ten healthy adults with normal feet (18 limbs) participated in this study. Using a three-dimensional motion analysis system, we calculated the toe flexion angle, medial longitudinal arch (MLA) angle, MLA height, MLA length, ankle joint angle, center of foot pressure, and center position of the hip joint during toe flexion using TC and STC exercises. Results The TC exercise resulted in a decrease in the MLA due to the backward shift in the center of pressure and ankle dorsiflexion. On the other hand, STC did not demonstrate any significant difference in the position of the center of pressure or ankle joint angle, allowing the toes to flex while maintaining a standing posture and resulting in an increase in the MLA. Conclusions STC was suggested to be effective in maintaining MLA for weight support.

Image Analysis Characterizes Phenotypic Variation in the Growth of Mushroom-Forming Fungus Schizophyllum commune.

Schizophyllum commune, a common wood-decay mushroom known for its extremely high genetic variation and as a rare cause of human respiratory diseases, could be a promising model fungus contributing to both biology and medicine. To better understand its phenotypic variation, we developed an image analysis system that quantifies morphological and physiological traits of mycelial colonies in Petri dishes. This study evaluated growth of six wild and one clinical isolates of Japanese S. commune, subjected to different temperatures and glucose concentrations, including a condition mimicking the human respiratory environment. Our analysis revealed that combinations of two growth indices, area and whiteness, and profiling by clustering algorithms highlighted strain-specific responses. For example, the clinical isolate was the whitest under the respiratory-like condition. We also found that the growth rate was strongly determined by glucose concentration, while the effects of temperature on growth varied among the strains, suggesting that while glucose preference is common in this species, responses to temperature differ between strains. This system showed sufficient sensitivity to detect variation in mycelial growth. Our study provides a key to unraveling morphological and physiological traits behind the high polymorphisms in S. commune, including the ability to colonize the human respiratory tract.

The Interconnectedness Of Cognitive And Formative Skills In Elementary Students With Specific Reading Disabilities: Insights For Early Education

The current research aims to investigate the development of formative abilities in elementary school-aged children with specific reading disabilities and its relationship to reading efficiency. In this context, the analysis depicted poor performance in design tasks exhibited by SRD students compared to regular readers and GLD students. The researchers attribute these results to cognitive failures within the concerned mechanisms, including orientation, sequence, and organization of the Visual Analysis System. The findings indicate that these deficiencies affect not only visual representation but also reading and writing. More importantly, there were solid correlations between design abilities, perceptual-motor skills, and memory, pointing to the interrelationship between those skills. As a result, the findings recommend highly targeted interventions at an early stage of education and suggest the need to incorporate creative design-based activities into reading skill enhancement and support for students with special needs.

BERT with an Augmented Cross-attention Decoder (BERT-ACD) for Binary and Fine-grained Multiband Sentiment Detection

In contemporary times, research in sentiment analysis has taken deeper steps into a finer and more granular analysis, transcending beyond the traditional binary or ternary classification of sentiment/opinion into positive, negative, or neutral. With the increasing complexity and challenging nature of such tasks, large language models inspired by transformer architecture are frequently deployed to address such challenges. Despite recorded improvements, challenges in identifying different levels, strengths or bands of sentiment intensity and the aspect for which the sentiment is expressed remain unresolved. In this article, we propose a banded sentiment analysis system for categorizing texts into 7 meaningful and relatable bands of sentiment for modern applications. It is also capable of performing aspect-based sentiment analysis in the same pipeline. The system architecture is inspired by the transformer language model with a BERT-based encoder and a newly proposed cross-attention, non-autoregressive decoder with augmented inputs. The decoder receives an n-gram-based augmented input sequence embedding that is specifically extracted from the original input, which comprises a list of the subjects, descriptive phrases, and modification phrases that underscore cases of amplification or undertone in a sentence. Rule-based tree parsing was proposed for use with dependency parsing for the extraction of these augmented inputs for the cross-attention decoder. Extensive experiments were conducted under different architecture setups and conditions with popular sentiment analysis datasets (Amazon reviews 2023, IMDB Movies review, SST-5 and SST-2 datasets) to verify the efficacy of the system. Extended labeling was also performed on the SST-5 dataset to generate 7 sentiment classes with the help of GPT4 and Bard. Experiments validate the efficacy of the proposed models.

Enhancing Engine Cylinder Heat Dissipation Capacity Through Direct Optimization (DO) Techniques

Internal combustion (IC) engines are used widely as the primary power source for automobiles of all types, cars, motorcycles, and trucks. Because of the high combustion temperatures involved in the operation, the excess heat is removed by means of extended fins that increase the surface area for adequate cooling. Significant improvement in the heat dissipation characteristics of the engine cylinder can be achieved by optimizing the design of these fins. The aim of this study is to evaluate the thermal performance of engine cylinder fins using an analytical system of finite element analysis (ANSYS FEA) software, using a direct optimization (DO) approach to identify optimal fin design. Analysis shows that fin length and width play critical roles in improving cooling efficiency, lowering the maximum temperature within the cylinder to 549.46 K and enhancing total heat flux to 7225.31 W/m2, which is a 25.87% increase from the generic design, capable of heating removal of 5740.22 W/m2. The current fin design is effective but could be improved in heat dissipation, mainly at fin tips. To optimize thermal performance while minimizing material costs, a balanced fin dimension is recommended. Alternative materials, transient heating analysis, and experimental verification may be examined in the future to achieve a total understanding of fin geometry and behavior under real operating conditions. These insights lay a foundation to accelerate cooling systems development in the automotive, aerospace, and heavy equipment industries, where efficient heat transfer is key for performance and long-term durability.