Diverse Methods Research Articles

Exploring Hornstedtia scyphifera: an extensive multimethod phytochemical investigation reveals the chemical composition and bioactive potential

Abstract Hornstedtia scyphifera (J.Koenig) Steud. represents a lesser-known member of the ginger family (Zingiberaceae) that is used in Malaysia as spice and traditional medicine. The phytochemical investigation of leaves from this species utilizing diverse analytical methods has provided comprehensive insights into its chemical profile for the first time. Headspace gas chromatography-mass spectrometry (HS-GCMS) and GCMS analyses of essential oil and nonpolar extracts verified α-pinene, camphene, p-cymene, and camphor as main volatile compounds. Metabolite profiling of the crude extract by ultra-high-performance-liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) unveiled terpenoids, flavonoids and other phenolics as major compound classes. Isolation and follow-up structure elucidation, involving 1D and 2D NMR, HRMS, UV and CD analysis, yielded two new sesquiterpenoids, (1R,5S,6S,7R,10R)-mustak-14-oic acid (1) and (1R,6S,7S,10R)-6-hydroxy-anhuienosol (2), along with 24 known compounds (seven terpenoids, seven flavonoids, ten phenolics), 21 of these never reported for H. scyphifera. Additionally, the crude extract and fractions from the purification process were screened for antibacterial and antifungal activity. This is supplemented by an extensive literature research for described bioactivities of all isolated compounds. Our results support and explain previously detected antimicrobial, antifungal and neuroprotective effects of H. scyphifera extracts and provide evidence for its potential pharmacological importance.

Assessing the Impacts of Water Diversion Project on Water Resource System Sustainability

ABSTRACTInterbasin water diversion project has been considered as an effective way to assure water resource system sustainability. In order to assess the impacts of water diversion on sustainability, we propose a framework in terms of reliability, resilience, and vulnerability. The estimated water availability from hydrological models and the projected water demand are input to a water resource allocation model. The water resource allocation model allocates the two available water sources (i.e., the local and the diverted water) in the water‐receiving areas. The differences of the allocated water resources between these two water sources are figured out to quantify the impacts of water diversion on water resource system sustainability. The water‐receiving area of Bailong River Water Diversion Project, located in Gansu, China, was selected as a case study. The results show that compared to the reference planning years, the runoff in future planning years will be reduced, while their water demands will almost increase under all scenarios. Although the current designed water diversion scheme is effective in increasing resilience, there is still potential for increasing resilience through optimizing the designed scheme. Further, the more unfavorable the water supply and demand conditions are, the larger the space for optimizing the system sustainability. This study can help understand the impacts of water diversion on water resource system sustainability in a changing environment.

Pond Water eDNA Reflects Broad Consistency with Surrounding Terrestrial Plant Ecosystems

This study evaluates the potential of using pond water eDNA to reflect the surrounding terrestrial plant communities, aiming to develop a sustainable, large-scale, and long-term monitoring method for plant diversity in forest ecosystems. Water samples were collected four times from two ponds with different vegetation types during the late spring to autumn seasons in Japan. eDNA was extracted from dissolved particles fractionated by sequential filtration through pore sizes of 200 µm, 5 µm, and 0.45 µm, followed by high-throughput amplicon sequencing targeting the plant rbcL gene. By comparing field surveys with the eDNA data, we identified 79% and 63% of plant families and genera, respectively, suggesting that pond water eDNA may reflect the surrounding terrestrial plant ecosystem. Additionally, different trends were observed in the seasonal variation of plant taxa and their composition detected in eDNA, based on particle size. This study highlights the potential of pond water eDNA to provide valuable insights into forest plant richness and seasonal dynamics, offering a novel approach for ecological monitoring.

Effects of structural design, interface optimization, and processing on the performance of lithium battery anode silicon nanomaterials

With an emphasis on the advancements in anode material research, this article offers a comprehensive summary of the major technologies used in lithium-ion batteries, a common type of electrochemical energy storage device. Because silicon has both benefits, such as low cost and high theoretical specific capacity, and significant drawbacks, including electrode pulverization and capacity loss due to volume expansion, it is the main focus of this study. This article goes into great detail on the use of silicon material nanosizing to enhance silicon anode performance, discussing how reducing particle size can mitigate the negative effects of volume changes during cycling. However, the section on compositing and structural design processes lacks sufficient depth, which limits the understanding of how these processes can further improve anode stability and efficiency. Furthermore, the article does not comprehensively cover the diverse methods utilized to enhance silicon anode performance, such as the incorporation of conductive additives or protective coatings. The challenges associated with the practical applications of silicon nanoparticles, such as scalability and cost-effectiveness, are examined in detail; nevertheless, the prospects for further research and technological advancements in these areas are not sufficiently addressed. This paper calls for more in-depth studies and innovations in compositing techniques, structural optimization, and practical solutions to realize the full potential of silicon anodes for advanced energy storage.

Exploring the Current State of Information Literacy Education: A Systematic Literature Review

Information Literacy Education is a skill taught to students of various educational levels, from elementary school to college and even postgraduate programs. This research aims to present the current state of Information Literacy Education, identify the dominant research themes, the methods of teaching and learning used, the context of learning in which the skill is taught, and recommends future research development in related fields. A systematic literature review was conducted using large electronic journal databases such as Taylor and Francis, and Science Direct, which obtained 37,633 articles. Furthermore, The PICOC and PRISMA protocols were applied to analyze 17 relevant articles and identify 14 themes in learning, 14 learning methods, 12 research methods used, and 7 scientific fields in the implementation of Information Literacy Learning from 2017 to 2021. These results provide direction for future research and contribute to enhanced information literacy curriculum development, effective integration of information literacy in specific disciplines, transitioning to online information literacy instruction, development of engaging and interactive information literacy modules, assessing learning outcomes and informing instructional strategies, collaboration and professional development in information literacy, diverse and effective teaching methods, and advancements in research methods for information literacy.

Penilaian Kinerja dan Kebutuhan Pelatihan serta Pengembangan Karyawan

This study aims to examine the methods used in employee performance appraisal, identify training and employee development needs, and determine the benefits of training and development for employees at PT. PLN (Persero) Udiklat Pandaan or UPDL Pandaan. The evaluation results show that measuring the effectiveness of programs and their impact on employee performance can enhance future training programs and ensure that employee development aligns with the company's objectives. Based on these results, PT. PLN (Persero) Udiklat Pandaan or UPDL Pandaan is committed to continuously improving the quality of human resources in the Distribution and Commerce fields through objective performance evaluations and training programs tailored to the needs of employees and the company. The implementation at PT. PLN (Persero) Udiklat Pandaan or UPDL Pandaan provides comprehensive training facilities and programs for employees. The training utilizes diverse methods, including lectures, discussions, demonstrations, case studies, games, role-playing, business simulations, e-learning, on-the-job training, off-the-job training, on-the-job development, action learning, experiential learning, and motivational learning. These programs aim to enhance employee competencies in carrying out tasks in the DMI field. This study highlights the importance of performance appraisal, training needs, and employee development to support the sustainable development of human resources.

An Enhanced Approach Using AGS Network for Skin Cancer Classification

Skin cancer accounts for over 40% of all cancer diagnoses worldwide. However, accurately diagnosing skin cancer remains challenging for dermatologists, as multiple types of skin cancer often appear visually similar. The diagnostic accuracy of dermatologists ranges between 62% and 80%. Although AI models have shown promise in assisting with skin cancer classification in various studies, obtaining the large-scale medical image datasets required for AI model training is not straightforward. To address this limitation, this study proposes the AGS network, designed to overcome the challenges of small datasets and enhance the performance of skin cancer classifiers. The AGS network integrates three key modules: Augmentation (A), GAN (G), and Segmentation (S). It was evaluated using eight deep learning classifiers—GoogLeNet, DenseNet201, ResNet50, MobileNet V3, EfficientNet B0, ViT, EfficientNet V2, and Swin Transformers—on the HAM10000 dataset. Five model configurations were also tested to assess the contribution of each module. The results showed that all eight classifiers demonstrated consistent performance improvements with the AGS network. In particular, EfficientNet V2 + AGS achieved the most significant performance gains over the baseline model, with an increase of +0.1808 in Accuracy and +0.1674 in F1-Score. Among all configurations, ResNet50+AGS achieved the best overall performance, with an Accuracy of 95.87% and an F1-Score of 95.73%. While most previous studies focused on single augmentation methods, this study demonstrates the effectiveness of combining multiple augmentation techniques within an integrated framework. The AGS network demonstrates how integrating diverse methods can improve the performance of skin cancer classification models.

Direct visualization of electric-field-stimulated ion conduction in a potassium channel.

Understanding protein function would be facilitated by direct, real-time observation of chemical kinetics in the atomic structure. The selectivity filter (SF) of the K+ channel provides an ideal model, catalyzing the dehydration and transport of K+ ions across the cell membrane through a narrow pore. We used a "pump-probe" method called electric-field-stimulated time-resolved X-ray crystallography (EFX) to initiate and observe K+ conduction in the NaK2K channel in both directions on the timescale of the transport process. We observe both known and potentially new features in the high-energy conformations visited along the conduction pathway, including the associated dynamics of protein residues that control selectivity and conduction rate. A single time series of one channel in action shows the orderly appearance of features observed in diverse homologs with diverse methods, arguing for deep conservation of the dynamics underlying the reaction coordinate in this protein family.

Cash-Out Puzzle and Long-Term Care Insurance: Welfare of the Elderly.

This study investigates the effects of diverse payment methods within long-term care insurance (LTCI) on the well-being of elderly individuals, encompassing both in-kind and cash payments. Utilizing panel data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), we leverage a generalized difference-in-differences (DID) approach and the LTCI pilots across various Chinese cities to identify the impacts of different LTCI payment methods. Our findings indicate that an in-kind LTCI policy significantly decreases the one-year mortality of older adults, with significant improvements on ADL-related care receipt as well as health status. Conversely, no significant impacts are observed under the framework of a cash LTCI policy. We term the weaker effects regarding older adults in cash LTCI policy versus in-kind LTCI policy as "the cash-out puzzle". Our findings emphasize the critical importance of developing right incentives of LTCI payment schemes thoughtfully worldwide.

Assessing Human Factors in Virtual Reality Environments for Industry 5.0: A Comprehensive Review of Factors, Metrics, Techniques, and Future Opportunities

Industry 5.0, the latest evolution in industrial processes, builds upon the principles of Industry 4.0 by emphasizing human-centric approaches and the integration of virtual reality technologies. This paradigm shift underscores the importance of collaboration between humans and advanced technologies with a focus on optimizing efficiency, safety, and worker skill development. Based on the PRISMA 2020 guidelines, this study conducts a systematic literature review, identifying 328 papers from databases. After applying inclusion and exclusion criteria, 24 papers were selected for detailed analysis. The review provides valuable insights into the diverse evaluation methods employed in the literature, and a detailed classification of 29 human factors with their associated metrics. Despite the absence of a standardized method for assessing human factors in VR experiences, this comprehensive analysis of 240 different ways of measuring factors highlights the current state of evaluating human-centered VR experiences in Industry 5.0. While the review reveals some limitations such as potential bias in study selection and heterogeneity of methods, it also identifies significant research gaps and proposes future directions. This study contributes to the establishment of a coherent structure for future research and development in human-centered design within the rapidly evolving landscape of Industry 5.0, paving the way for more effective and standardized approaches in the future.

Human in Xunzi’s Socio-Political Thought: Implications for Educational Reform in Vietnam

This study examines Xunzi’s philosophy on humanity and its relevance to contemporary educational reform in Vietnam. Xunzi, 荀子 (316– 237 BCE), a prominent philosopher of ancient China, is known for his assertion that “human nature is inherently evil.” His socio-political philosophy emphasizes the role of education in shaping human character and fostering social order. Through a materialist perspective, Xunzi proposed methods such as self-cultivation, adherence to moral principles, and active engagement in society as pathways to achieving individual and societal harmony. The study highlights the historical significance of Xunzi’s ideas in addressing modern educational challenges in Vietnam, including the identification and development of talent, the prioritization of moral education, the central role of teachers, and the implementation of diverse and dynamic teaching methods. The findings suggest that Xunzi’s philosophical insights offer valuable lessons for building an education system that aligns with the principles of equity, innovation, and moral integrity in a rapidly changing society.

Multi-Label Diabetic Retinopathy Detection Using Transfer Learning Based Convolutional Neural Network

Retinopathy is a progressive and common retinal disease that most progressive diabetics suffer from and causes blood vessels in the retina to swell and leak blood and fluid. This condition requires timely diagnosis via medical experts to prevent causing visual loss among patients. To enhance the feasibility of checking many persons, diverse deep-learning schemes have recently been developed for diabetic retinopathy detection. In this paper, retinopathy image detection system based on diverse deep learning schemes (VGG-19, DenseNet-121, and EfficientNet-B6) has been presented. The implemented deep learning schemes with multi-label classification are trained and tested using the Asia Pacific Tele Ophthalmology Society (APTOS-2019) dataset, and the two combined datasets Indian Diabetic Retinopathy Image Dataset (IDRiD) and Messidor-2. The system outcomes of classification are exhibited as sensitivity, precision, F1Score, and accuracy measurements, and the system performance is compared with recently existing related systems. The attained outcomes indicate that the implemented EfficientNetB6 network outperforms peers’ schemes and related systems via realizing supreme accuracy using balanced multi-class retinopathy datasets.

Solvent Selection in the Quality Analysis of White Tea by Nuclear Magnetic Resonance Spectroscopy

The study aimed to assess the influence of diverse solvent schemes on the qualitative analysis of white tea and to establish a more comprehensive quality evaluation. A systematic study of solvent optimization was carried out using the quality analysis of white tea as an exemplar, highlighting the critical role of selecting appropriate solvents in NMR-based sample preparation process. Six sample dissolution protocols, encompassing extraction and reconstitution solvents, were evaluated. The aqueous methanol solution proved effective as an extraction solvent, offering optimal metabolite coverage and spectral resolution. For reconstitution solvents, D2O facilitated identification of specific metabolites, such as amino acids, organic acids and carbohydrates, while DMSO-d6 exhibited better performance in detecting catechins, alkaloids and lipid resonances. Additionally, DMSO-d6 enabled better discrimination among various white tea varieties, whereas D2O system demonstrated better ability for estimating the storage duration of white tea. The characteristic metabolites were exactly those that each of the two solvents is suited to solubilize. Therefore, it can be concluded that solvents suitable for the detection of a class of metabolites are more likely to highlight differences in that type of metabolites and multi-solvent combination captures more comprehensive information, revealing the necessity of optimizing solvent protocols for different assay targets.

Validating phosphoethanolamine modification as a potential spectral marker of colistin resistance.

The antibiotic colistin is regarded as the final line of defense for treating infections caused by Gram-negative bacteria. The combination of Raman spectroscopy (RS) with diverse machine learning methods has helped unravel the complexity of various microbiology problems. This approach offers a culture-free, rapid, and objective tool for identifying antimicrobial resistance (AMR). In this study, we employed the combinatorial approach of machine learning and RS to identify a novel spectral marker associated with phosphoethanolamine modification in the lipid A moiety of colistin-resistant Gram-negative Escherichia coli. The visible spectral fingerprints of this marker have been validated using partial least squares regression and discriminant analysis. The origin of the spectral feature was confirmed through hyperspectral imaging and K-means clustering of a single bacterial cell. The chemical structure of the modified lipid A moiety was verified by employing gold standard MALDI-TOF mass spectrometry. Our findings support the futuristic applicability of this spectroscopic marker in objectively identifying colistin-sensitive and -resistant strains.

Construction of an Oxidative Stress Risk Model to Analyze the Correlation Between Liver Cancer and Tumor Immunity.

Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally. Despite advancements in immunotherapy, the prognosis for patients with HCC continues to be poor. As oxidative stress plays a significant role in the onset and progression of various diseases, including metabolism-related HCC, comprehending its mechanism in HCC is critical for effective diagnosis and treatment. This study utilized the TCGA dataset and a collection of oxidative stress genes to determine the expression of oxidative stress-related genes in HCC and their association with overall survival using diverse bioinformatics methods. A novel prognostic risk model was developed, and the TCGA cohort was divided into high-risk and low-risk groups based on each tumor sample's risk score. Levels of immune cell infiltration and the expression of immune checkpoint-related genes in different risk subgroups were analyzed to investigate the potential link between tumor immunity and oxidative stress-related features. The expression of model genes in actual samples was validated through immunohistochemistry, and their mRNA and protein expression levels were measured in cell cultures. Four oxidative stress-related genes (EZH2, ANKZF1, G6PD, and HMOX1) were identified and utilized to create a predictive risk model for HCC patient overall survival, which was subsequently validated in an independent cohort. A significant correlation was found between the expression of these prognostic genes and the infiltration of tumor immune cells. Elevated expression of EZH2, ANKZF1, G6PD, and HMOX1 was observed in both HCC tissues and cell lines. The combined assessment of EZH2, ANKZF1, G6PD, and HMOX1 gene expression can serve as a model to evaluate the risk of oxidative stress in HCC. Furthermore, there is a notable correlation between the expression of these risk model genes and tumor immunity.

Does the Administration of Intravenous Fluid Matter in the Context of the Incidence of Postoperative Complications After Radical Cystectomy?

Introduction: Intravenous fluid management is integral to perioperative care, particularly under enhanced recovery after surgery (ERAS) protocols. In radical cystectomy (RC), which carries high risks of complications and mortality, optimizing fluid management poses a significant challenge due to the absence of definitive guidelines. Aim: the purpose of this study was to investigate the effects of intravenous fluid administration on postoperative complications in patients undergoing RC. Material and methods: This study involved 288 patients who underwent laparoscopic RC and urinary diversion from 2018 to 2022. ERAS protocols were implemented for all patients. Participants were divided into four groups based on the type of urinary diversion (ureterocutaneostomy vs. ileal conduit) and the intraoperative fluid volume input (less than 1000 mL vs. more than 1000 mL). Postoperative complications were evaluated at 30 and 90 days post-surgery using the Clavien-Dindo scale. The fluid management effectiveness was measured using the absolute Vascular Bed Filling Index (aVBFI) and the adjusted Vascular Bed Filling Index (adjVFBI). Results: The UCS is associated with a lower risk of increased severity of postoperative complications. The administration of more than 1000 mL of fluids was associated with a higher risk of complications (p = 0.035). However, after adjusting for the duration of the surgery and BMI, this association did not hold statistical significance, indicating that fluid volume alone is not a direct predictor of postoperative complications. At aVBFI values between zero and eight, urinary diversion using the UCS method is associated with a lower risk of complications compared to the IC. When aVBFI equals eight, the differences in the severity of complications between the UCS and the IC are minimal. However, when aVBFI exceeds eight, the IC is associated with fewer complications during the 30 days post-operation compared to the UCS. The correlation between the adjVFBI (B = -0.27; 95% CI: -0.45 to -0.08; p = 0.005) and the severity of complications up to 30 days postoperatively is similar to that seen with the aVBFI. Similarly, the correlation of the adjVFBI with the method of urinary diversion (B = 0.24; 95% CI: 0.06 to 0.43; p = 0.011) resembles that of the aVBFI. The volume of fluids administered and the indices aVBFI and adjVFBI did not influence the occurrence of complications 90 days postoperatively. Conclusions: The volume of fluids administered is not a factor directly affecting the occurrence of complications following RC when the ERAS protocol is used. The amount of intraoperative fluid administration should be adjusted according to the intraoperative blood loss. Our findings endorse the utility of aVBFI and adjVFBI as valuable tools in guiding fluid therapy within the framework of ERAS protocols. However, further multicenter randomized trials are needed to definitively determine the best fluid therapy regimen for patients undergoing RC.

Dynamic and electrophoretic light scattering measurements on microtubules at low concentrations.

The accurate characterization of microtubules is essential for understanding their roles in various biological activities in eukaryotic cellular processes. In vitro, experimental data on these systems often need more details and information on sample preparation protocols and experimental techniques. This deficiency leads to unreproducible or contradictory outcomes. The use of diverse experimental methods and preparations yields different results of hydrodynamic and electro-mechanical properties, complicating the process of obtaining meaningful data and conclusive information. This article presents a robust and detailed protocol for performing dynamic light scattering (DLS) and electrophoretic light scattering (ELS) measurements on microtubules at low concentrations. This method ensures accurate and reproducible results on essential microtubule filament parameters such as the diffusion coefficient (D) and electrophoretic mobility (μ) from which other structures' hydrodynamics, electrical, and stability properties can be elucidated.

Exploring the Role of Artificial Intelligence in Education: Insights from Teachers’ and Students’ Perspectives in Nepal

The study explores the perspectives of teachers and students regarding the integration of artificial intelligence (AI) in higher education in Nepal. Adopting a qualitative research methodology, a survey was conducted with 200 students and 20 teachers through Google Forms from various educational backgrounds to assess their attitudes toward AI tools. The results indicate that both groups recognize AI is potential to enhance learning and teaching experiences, although their perceptions differ across specific aspects. This study explores the perspectives of teachers and students regarding the integration of artificial intelligence students generally appreciate AI’s role in improving engagement, motivation, and personalized learning but they also express concerns about privacy, collaboration, and effectiveness of these tools in promoting independent research. Teachers report benefits such as improved lesson plans, grading efficiency, and data-driven insights. However, they highlight challenges in aligning AI with their pedagogical approaches and achieving consistent student engagement. The findings underscore the transformation potential of AI in education while identifying areas for improvement, including the need for privacy safeguards and adaptability to diverse teaching methods. To address these issues, education institutions can better leverage AI’s capabilities fostering more inclusive and effective learning environments for the students.

EFFECTIVENESS OF VIDEO-BASED LEARNING VS. CONCRETE MANIPULATIVES IN THE NUMERACY SKILLS DEVELOPMENT

This study compared the effectiveness of video-based learning and concrete manipulatives on numeracy skills development in grade 11 students. Using a quasi-experimental design, two groups received instruction on addition, subtraction, multiplication, and division – one via video, the other using manipulatives. Pre- and post-tests measured numeracy skill gains. Results showed a significant advantage for the manipulative group, no significant difference. This suggests the importance of hands-on activities, the need for diverse instructional methods. Further research should explore research direction, a long-term effect for diverse learning styles.

Crops3D: a diverse 3D crop dataset for realistic perception and segmentation toward agricultural applications

Point cloud analysis is a crucial task in computer vision. Despite significant advances over the past decade, the developments in agricultural domain have faced challenges due to a scarcity of datasets. To facilitate 3D point cloud research in agriculture community, we introduce Crops3D, the diverse real-world dataset derived from authentic agricultural scenarios. Crops3D distinguishes itself through its unique properties: diversity, authenticity, and complexity. The dataset incorporates data from diverse point cloud acquisition methods, encompassing eight distinct crop types with 1,230 samples, authentically representing crops in the real-world. It stands as the pioneering dataset that comprehensively supports the three critical tasks in 3D crop phenotyping: instance segmentation of individual plants in agricultural settings, plant type perception, and plant organ segmentation. Additionally, the intricate crop structures in Crops3D exhibit higher complexity than available 3D public datasets, showcasing substantial self-occlusion and increased complexity as crops mature. We analyse diverse crop point cloud acquisition methods and evaluate multiple models’ performance with the Crops3D dataset.