Analysis Of Features Research Articles

Cultivating a culture of learning: the role of leadership in fostering lifelong development

Purpose Leadership is key to building a culture of continuous learning within organizations. This study aims to explore the pivotal role of leadership in creating a culture of constant learning within organizations by bibliometric and content analysis. It also introduces propositions and frameworks that emphasize the importance of fostering a growth mindset, encouraging knowledge sharing, promoting learning agility and leveraging technology to support lifelong development. Design/methodology/approach Using data from the Web of Science Core Collection, the author performed a complete analysis of publication features, collaboration networks and keywords in the field using VOSviewer software. Furthermore, drawing on social cognitive theory, this paper also presents propositions that integrate key concepts and strategies for fostering a culture of learning. Findings The data shows a large increase in publications on leadership and organizational learning, particularly since 2000, indicating an increasing interest and importance in this field. The author proposes leaders who demonstrate a commitment to continuous improvement and invest in learning resources empower their teams to embrace new challenges and explore innovative solutions. By fostering a culture of learning, organizations can enhance employee engagement, foster creativity and innovation and adapt more effectively to changing market dynamics. Originality/value This study offers a unique perspective on the role of leadership in driving learning and development initiatives. By implementing the principles, organizations can create a competitive advantage by cultivating a workforce that is agile, resilient and equipped to thrive in an ever-changing world.

Теоретичні засади формування корпоративної соціальної відповідальності в агробізнесі

The research paper aims to substantiate the theoretical foundations of corporate social responsibility, determine its place and role in modern economic science, and analyze the features of corporate social responsibility in agribusiness. In the article, we used the abstract-logical method to identify the theoretical foundations of the development of corporate social responsibility in agribusiness. Additionally, we researched the evolution of management practice and various approaches to interpreting the concept of “corporate social responsibility” using a monographic approach. We also used the method of analysis and synthesis to identify the key characteristics of corporate social responsibility and to distinguish the levels of corporate social responsibility in agribusiness. The article explores the development of “corporate social responsibility”. It defines a system of critical concepts, introductory provisions, and approaches that form the framework of corporate social responsibility. The concept of “corporate social responsibility” is theoretically interpreted and is commonly viewed as one of the elements of sustainable development. The article also discusses the specificities of using different approaches to interpret the concept of “corporate social responsibility” in management practice — elements of scientific novelty. A thorough analysis of the main characteristic features of corporate social responsibility was carried out, and the levels of corporate social responsibility in agribusiness were determined. The essential elements of internal and external corporate social responsibility in agrarian business were identified. Research has established that the most acceptable approach is that corporate social responsibility in agribusiness is considered the responsibility of an enterprise in the agrarian sector to its employees and other stakeholders (society, consumers, state authorities, public organizations, etc.), with which the organization interacts in the process own activity. The author has developed a mechanism for implementing the concept of corporate social responsibility in agribusiness. Practical relevance. Implementation of a mechanism for the implementation of social responsibility in agribusiness developed by the author in the activities of enterprises of the agrarian sector of the economy of Ukraine (based on the systematization of internal and external CSR with a division into reactive and strategic), which will contribute to the mitigation of already existing problems and negative consequences of the company’s activities and the successful implementation of the strategy CSR regarding the formation of a positive image of agrarian formations. Keywords: social responsibility, corporate social responsibility, corporation, the concept of implementation of state policy in the sphere of promoting the development of socially responsible business in Ukraine, agribusiness, and the mechanism of implementation of the concept of CSR in agribusiness.

UQCRB and LBH are correlated with Gleason score progression in prostate cancer: Spatial transcriptomics and experimental validation

Prostate cancer (PCa) is a multifocal disease characterized by genomic and phenotypic heterogeneity within a single gland. In this study, Visium spatial transcriptomics (ST) analysis was applied to PCa tissues with different histological structures to infer the molecular events involved in Gleason score (GS) progression. The spots in tissue sections were classified into various groups using Principal Component Analysis (PCA) and Louvain clustering analysis based on transcriptome data. Anotation of the spots according to GS revealed notable similarities between transcriptomic profiles and histologically identifiable structures. The accuracy of macroscopic GS determination was bioinformatically verified through malignancy-related feature analysis, specifically inferred copy number variation (inferCNV), as well as developmental trajectory analyses, such as diffusion pseudotime (DPT) and partition-based graph abstraction (PAGA). Genes related to GS progression were identified from the differentially expressed genes (DEGs) through pairwise comparisons of groups along a GS gradient. The proteins encoded by the representative oncogenes UQCRB and LBH were found to be highly expressed in advanced-stage PCa tissues. Knockdown of their mRNAs significantly suppressed PCa cell proliferation and invasion. These findings were validated using The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset, as well as through histological and cytological experiments. The results presented here establish a foundation for ST-based evaluation of GS progression and provide valuable insights into the GS progression-related genes UQCRB and LBH.

Key Technologies in Developing Chip-Scale Hot Atomic Devices for Precision Quantum Metrology.

Chip-scale devices harnessing the interaction between hot atomic ensembles and light are pushing the boundaries of precision measurement techniques into unprecedented territory. These advancements enable the realization of super-sensitive, miniaturized sensing instruments for measuring various physical parameters. The evolution of this field is propelled by a suite of sophisticated components, including miniaturized single-mode lasers, microfabricated alkali atom vapor cells, compact coil systems, scaled-down heating systems, and the application of cutting-edge micro-electro-mechanical system (MEMS) technologies. This review delves into the essential technologies needed to develop chip-scale hot atomic devices for quantum metrology, providing a comparative analysis of each technology's features. Concluding with a forward-looking perspective, this review discusses the future potential of chip-scale hot atomic devices and the critical technologies that will drive their advancement.

Phlorotannin Alleviates Liver Injury by Regulating Redox Balance, Apoptosis, and Ferroptosis of Broilers under Heat Stress.

Heat stress (HS) poses a great challenge to the poultry industry by inducing oxidative damage to the liver, endangering the health and production of broilers. As an important type of seaweed polyphenols, phlorotannin has been shown to have antioxidant properties. The present study evaluated the protective effects of dietary phlorotannin on HS-induced liver injury in broilers based on oxidative damage parameters. A total of 108 twenty-one days old male Arbor Acres plus (AA+) broilers were randomly divided into three groups: TN group (thermoneutral, 24 ± 1 °C, fed with basal diet), HS group (HS, 33 ± 1 °C for 8 h/day, fed with basal diet), and HS + phlorotannin group (HS + 600 mg/kg phlorotannin). Each group has six replicate cages with six birds per cage. The feeding experiment lasted 21 days. At the termination of the feeding experiment (42 days old), samples were collected for analysis of morphological and biochemical features. The results showed that HS decreased the liver index, serum albumin (ALB) content, hepatic antioxidant enzymes activities of catalase (CAT), total superoxide dismutase (T-SOD), glutathione S-transferase (GST), and glutathione peroxidase (GSH-Px) (p < 0.05), while increasing the hepatic histopathology score, apoptosis rate, and malondialdehyde (MDA) content (p < 0.05) in 42-day-old broilers. Compared with the HS group, dietary phlorotannin improved the activities of antioxidant enzymes (GST and GSH-Px) but decreased the histopathology score and apoptosis rate in the liver (p < 0.05). Moreover, HS down-regulated hepatic mRNA expression of CAT1, NQO1, HO-1, and SLC7A11 (p < 0.05), while up-regulated hepatic mRNA expression of Keap1, MafG, IκBα, NF-κB P65, IFN-γ, TFR1, ACSL4, Bax, and Caspase-9 (p < 0.05). Compared with HS group, dietary phlorotannin up-regulated hepatic mRNA expression of Nrf2, CAT1, MafF, GSTT1, NQO1, HO-1, GCLC, GPX1, TNF-α, Fpn1, and SLC7A11 (p < 0.05), while down-regulated hepatic mRNA expression of IκBα, Bax, Caspase-9, and TFR1 (p < 0.05). In conclusion, dietary supplementation of 600 mg/kg phlorotannin could alleviate HS-induced liver injury via regulating oxidative status, apoptosis, and ferroptosis in broilers; these roles of phlorotannin might be associated with the regulation of the Nrf2 signaling pathway.

Mathematics teachers’ multiple perspectives on adaptive tasks: task evaluation and selection as core practices for teaching quality

The selection of tasks based on the evaluation of task features can be considered a core practice of teaching and a relevant component of teaching quality. This is typically part of teachers’ preparation for their classroom teaching, which prompts the following question: What are the characteristics of the tasks that teachers use when selecting tasks for differentiated teaching? To answer this question, we analyzed systematic differences in the focus of 78 in-service high school and lower secondary school teachers during the evaluation of task features. The teachers had to select eight tasks about the practice of fractions with respect to their differentiation potential—operationalizing their adaptive teaching competence from a mathematics educational perspective. To analyze the differences, we performed a cluster analysis of the task features that the teachers drew upon. Three groups of teachers could be identified with variations in their focus on directly or indirectly relevant, domain-specific or domain-general task features. Taking into account such variations may explain differences in teaching quality and student outcomes and may be relevant when designing teacher professional development programs.

Automated analysis of scattering-based light sheet microscopy images of anal squamous intraepithelial lesions.

We developed an algorithm for automatically analyzing scattering-based light sheet microscopy (sLSM) images of anal squamous intraepithelial lesions. We developed a method for automatically segmenting sLSM images for nuclei and calculating seven features: nuclear intensity, intensity slope as a function of depth, nuclear-to-nuclear distance, nuclear-to-cytoplasm ratio, cell density, nuclear area, and proportion of pixels corresponding to nuclei. 187 images from 80 anal biopsies were used for feature analysis and classifier development. The automated nuclear segmentation method provided reliable performance with the precision of 0.97 and recall of 0.91 when compared with the manual segmentation. Among the seven features, six showed statistically significant differences between high-grade squamous intraepithelial lesion (HSIL) and non-HSIL (non-dysplastic or low-grade squamous intraepithelial lesion, LSIL). A classifier using linear support vector machine (SVM) achieved promising performance in diagnosing HSIL versus non-HSIL: sensitivity of 90%, specificity of 70%, and area under the curve (AUC) of 0.89 for per-image diagnosis, and sensitivity of 90%, specificity of 80%, and AUC of 0.92 for per-biopsy diagnosis.

A step length estimation method based on frequency domain feature analysis and gait recognition for pedestrian dead reckoning

Purpose Step length is a key factor for pedestrian dead reckoning (PDR), which affects positioning accuracy and reliability. Traditional methods are difficult to handle step length estimation of dynamic gait, which have larger error and are not adapted to real walking. This paper aims to propose a step length estimation method based on frequency domain feature analysis and gait recognition for PDR, which considers the effects of real-time gait. Design/methodology/approach The new step length estimation method transformed the acceleration of pedestrians from time domain to frequency domain, and gait characteristics of pedestrians were obtained and matched with different walking speeds. Findings Many experiments are conducted and compared with Weinberg and Kim models, and the results show that the average errors of the new method were improved by about 2 meters to 5 meters. It also shows that the proposed method has strong stability and device robustness and meets the accuracy requirements of positioning. Originality/value A sliding window strategy used in fast Fourier transform is proposed to implement frequency domain analysis of the acceleration, and a fast adaptive gait recognition mechanism is proposed to identify gait of pedestrians.

Jazz Trio Database: Automated Annotation of Jazz Piano Trio Recordings Processed Using Audio Source Separation

Recent advances in automatic music transcription have facilitated the creation of large databases of symbolic transcriptions of improvised music forms including jazz, where traditional notated scores are not normally available. In conjunction with music source separation models that enable audio to be “demixed” into separate signals for multiple instrument classes, these algorithms can also be applied to generate annotations for every musician in a performance. This can enable the analysis of interesting performer-level and ensemble-level features that have often been difficult to explore. To this end, we introduce Jazz Trio Database (JTD), a dataset of 44.5 h of jazz piano solos accompanied by bass and drums, with automatically generated annotations for each performer. These annotations consist of onset, beat, and downbeat timestamps, alongside MIDI for the piano soloist. Suitable recordings, broadly representative of the “straight-ahead” jazz style, were identified by scraping user-based listening and discographic data; source separation models were applied to isolate audio for each performer in the trio; annotations were generated by applying appropriate algorithms to both the separated and the mixed audio sources. Onset annotations generated by the pipeline achieved a mean F-measure of 0.94 when compared with ground truth annotations. We conduct several analyses of JTD, including with relation to swing and inter-performer synchronization. We anticipate that JTD will be useful in a variety of music information–retrieval tasks, including artist identification and expressive performance modeling. We have made JTD, including the annotations and associated source code, available at https://github.com/HuwCheston/Jazz-Trio-Database

Leveraging large language models through natural language processing to provide interpretable machine learning predictions of mental deterioration in real time

AbstractBased on official estimates, 50 million people worldwide are affected by dementia, and this number increases by 10 million new patients every year. Without a cure, clinical prognostication and early intervention represent the most effective ways to delay its progression. To this end, artificial intelligence and computational linguistics can be exploited for natural language analysis, personalized assessment, monitoring, and treatment. However, traditional approaches need more semantic knowledge management and explicability capabilities. Moreover, using large language models (llms) for cognitive decline diagnosis is still scarce, even though these models represent the most advanced way for clinical–patient communication using intelligent systems. Consequently, we leverage an llm using the latest natural language processing (nlp) techniques in a chatbot solution to provide interpretable machine learning prediction of cognitive decline in real-time. Linguistic-conceptual features are exploited for appropriate natural language analysis. Through explainability, we aim to fight potential biases of the models and improve their potential to help clinical workers in their diagnosis decisions. More in detail, the proposed pipeline is composed of (i) data extraction employing nlp-based prompt engineering; (ii) stream-based data processing including feature engineering, analysis, and selection; (iii) real-time classification; and (iv) the explainability dashboard to provide visual and natural language descriptions of the prediction outcome. Classification results exceed 80% in all evaluation metrics, with a recall value for the mental deterioration class about 85%. To sum up, we contribute with an affordable, flexible, non-invasive, personalized diagnostic system to this work.

Comparative analysis of features and classification techniques in breast cancer detection for Biglycan biomarker images.

Breast cancer (BC) is considered the world's most prevalent cancer. Early diagnosis of BC enables patients to receive better care and treatment, hence lowering patient mortality rates. Breast lesion identification and classification are challenging even for experienced radiologists due to the complexity of breast tissue and variations in lesion presentations. This work aims to investigate appropriate features and classification techniques for accurate breast cancer detection in 336 Biglycan biomarker images. The Biglycan biomarker images were retrieved from the Mendeley Data website (Repository name: Biglycan breast cancer dataset). Five features were extracted and compared based on shape characteristics (i.e., Harris Points and Minimum Eigenvalue (MinEigen) Points), frequency domain characteristics (i.e., The Two-dimensional Fourier Transform and the Wavelet Transform), and statistical characteristics (i.e., histogram). Six different commonly used classification algorithms were used; i.e., K-nearest neighbours (k-NN), Naïve Bayes (NB), Pseudo-Linear Discriminate Analysis (pl-DA), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The histogram of greyscale images showed the best performance for the k-NN (97.6%), SVM (95.8%), and RF (95.3%) classifiers. Additionally, among the five features, the greyscale histogram feature achieved the best accuracy in all classifiers with a maximum accuracy of 97.6%, while the wavelet feature provided a promising accuracy in most classifiers (up to 94.6%). Machine learning demonstrates high accuracy in estimating cancer and such technology can assist doctors in the analysis of routine medical images and biopsy samples to improve early diagnosis and risk stratification.

AlphaFold-SFA: Accelerated sampling of cryptic pocket opening, protein-ligand binding and allostery by AlphaFold, slow feature analysis and metadynamics.

Sampling rare events in proteins is crucial for comprehending complex phenomena like cryptic pocket opening, where transient structural changes expose new binding sites. Understanding these rare events also sheds light on protein-ligand binding and allosteric communications, where distant site interactions influence protein function. Traditional unbiased molecular dynamics simulations often fail to sample such rare events, as the free energy barrier between metastable states is large relative to the thermal energy. This renders these events inaccessible on the timescales typically simulated by unbiased molecular dynamics, limiting our understanding of these critical processes. In this paper, we proposed a novel unsupervised learning approach termed as slow feature analysis (SFA) which aims to extract slowly varying features from high-dimensional temporal data. SFA trained on small unbiased molecular dynamics simulations launched from AlphaFold generated conformational ensembles manages to capture rare events governing cryptic pocket opening, protein-ligand binding, and allosteric communications in a kinase. Metadynamics simulations using SFA as collective variables manage to sample 'deep' cryptic pocket opening within a few hundreds of nanoseconds which was beyond the reach of microsecond long unbiased molecular dynamics simulations. SFA augmented metadynamics also managed to capture conformational plasticity of protein upon ligand binding/unbinding and provided novel insights into allosteric communication in receptor-interacting protein kinase 2 (RIPK2) which dictates protein-protein interaction. Taken together, our results show how SFA acts as a dimensionality reduction tool which bridges the gap between AlphaFold, molecular dynamics simulation and metadynamics in context of capturing rare events in biomolecules, extending the scope of structure-based drug discovery in the era of AlphaFold.

Intelligent prediction of incipient fault in vinyl chloride production process based on deep learning

With the development of industrial information technology, deep learning (DL) has been successfully applied in chemical process fault detection. However, the features of incipient faults could not be more evident at the initial stage, which makes it difficult for deep learning to fully mine the feature probability information, resulting in poor performance of incipient fault monitoring. This paper proposes an intelligent predictive model based on mechanistic modeling to predict incipient faults and determine optimal response times for vinyl chloride production (VCP) processes. Firstly, a dynamic simulation of the VCP production process is performed to obtain datasets of early faults. Secondly, data dimensionality reduction is performed by cleverly combining spearman ranking correlation coefficient (SRCC) and slow feature analysis (SFA). Then, a long short-term memory network (LSTM) with attention mechanism (AM) is built to predict the future trends of key variables. Finally, the optimal response time for different types of incipient faults is determined by comparing the effectiveness of various control schemes. Compared with traditional methods, the R2 of the proposed prediction model corresponding to different step sizes can reach 99.7%, 99.5%, 99.2%, and 98.7%. In addition, the response times for single and parallel faults are 2 and 2.5 h, which helps to control and eliminate potential incidents in advance.

GBSR: Graph-based suspiciousness refinement for improving fault localization

Fault Localization (FL) is an important and time-consuming phase of software debugging. The essence of FL lies in the process of calculating the suspiciousness of different program entities (e.g., statements) and generating a ranking list to guide developers in their code inspection. Nonetheless, a prevalent challenge within existing FL methodologies is the propensity for program entities with analogous execution information to receive a similar suspiciousness. This phenomenon can lead to confusion among developers, thereby reducing the effectiveness of debugging significantly. To alleviate this issue, we introduce fine-grained contextual information (such as partial code structural, coverage, and features from mutation analysis) to enrich the characteristics of program entities. Graphical structures are proposed to organize such information, where the passed and failed tests are constructed separately with the consideration of their differential impacts. In order to support the analysis of multidimensional features and the representation of large-scale programs, the PageRank algorithm is adopted to compute each program entity’s weight. Rather than altering the fundamental FL process, we leverage these computed weights to refine the suspiciousness produced by various FL techniques, thereby providing developers with a more precise and actionable ranking of potential fault locations. The proposed strategy Graph-Based Suspiciousness Refinement (GBSR) is evaluated on 243 real-world faulty programs from the Defects4J. The results demonstrate that GBSR can improve the accuracy of various FL techniques. Specifically, for the refinement with traditional SBFL and MBFL techniques, the number of faults localized by the first position of the ranking list (Top-1) is increased by 189% and 68%, respectively. Furthermore, GBSR can also boost the state-of-the-art learning-based FL technique Grace by achieving a 2.8% performance improvement in Top-1.

Analyzing Wav2Vec 1.0 Embeddings for Cross-Database Parkinson's Disease Detection and Speech Features Extraction.

Advancements in deep learning speech representations have facilitated the effective use of extensive unlabeled speech datasets for Parkinson's disease (PD) modeling with minimal annotated data. This study employs the non-fine-tuned wav2vec 1.0 architecture to develop machine learning models for PD speech diagnosis tasks, such as cross-database classification and regression to predict demographic and articulation characteristics. The primary aim is to analyze overlapping components within the embeddings on both classification and regression tasks, investigating whether latent speech representations in PD are shared across models, particularly for related tasks. Firstly, evaluation using three multi-language PD datasets showed that wav2vec accurately detected PD based on speech, outperforming feature extraction using mel-frequency cepstral coefficients in the proposed cross-database classification scenarios. In cross-database scenarios using Italian and English-read texts, wav2vec demonstrated performance comparable to intra-dataset evaluations. We also compared our cross-database findings against those of other related studies. Secondly, wav2vec proved effective in regression, modeling various quantitative speech characteristics related to articulation and aging. Ultimately, subsequent analysis of important features examined the presence of significant overlaps between classification and regression models. The feature importance experiments discovered shared features across trained models, with increased sharing for related tasks, further suggesting that wav2vec contributes to improved generalizability. The study proposes wav2vec embeddings as a next promising step toward a speech-based universal model to assist in the evaluation of PD.

Automated peripheral nerve segmentation for MR-neurography

BackgroundMagnetic resonance neurography (MRN) is increasingly used as a diagnostic tool for peripheral neuropathies. Quantitative measures enhance MRN interpretation but require nerve segmentation which is time-consuming and error-prone and has not become clinical routine. In this study, we applied neural networks for the automated segmentation of peripheral nerves.MethodsA neural segmentation network was trained to segment the sciatic nerve and its proximal branches on the MRN scans of the right and left upper leg of 35 healthy individuals, resulting in 70 training examples, via 5-fold cross-validation (CV). The model performance was evaluated on an independent test set of one-sided MRN scans of 60 healthy individuals.ResultsMean Dice similarity coefficient (DSC) in CV was 0.892 (95% confidence interval [CI]: 0.888–0.897) with a mean Jaccard index (JI) of 0.806 (95% CI: 0.799–0.814) and mean Hausdorff distance (HD) of 2.146 (95% CI: 2.184–2.208). For the independent test set, DSC and JI were lower while HD was higher, with a mean DSC of 0.789 (95% CI: 0.760–0.815), mean JI of 0.672 (95% CI: 0.642–0.699), and mean HD of 2.118 (95% CI: 2.047–2.190).ConclusionThe deep learning-based segmentation model showed a good performance for the task of nerve segmentation. Future work will focus on extending training data and including individuals with peripheral neuropathies in training to enable advanced peripheral nerve disease characterization.Relevance statementThe results will serve as a baseline to build upon while developing an automated quantitative MRN feature analysis framework for application in routine reading of MRN examinations.Key PointsQuantitative measures enhance MRN interpretation, requiring complex and challenging nerve segmentation.We present a deep learning-based segmentation model with good performance.Our results may serve as a baseline for clinical automated quantitative MRN segmentation.Graphical

Protocol for Cerebellar Stimulation for Aphasia Rehabilitation (CeSAR): A randomized, double-blind, sham-controlled trial.

In this randomized, double-blind, sham-controlled trial of Cerebellar Stimulation for Aphasia Rehabilitation (CeSAR), we will determine the effectiveness of cathodal tDCS (transcranial direct current stimulation) to the right cerebellum for the treatment of chronic aphasia (>6 months post stroke). We will test the hypothesis that cerebellar tDCS in combination with an evidenced-based anomia treatment (semantic feature analysis, SFA) will be associated with greater improvement in naming untrained pictures (as measured by the change in Philadelphia Picture Naming Test), 1-week post-treatment, compared to sham plus SFA. We will also evaluate the effects of cerebellar tDCS on naming trained items as well as the effects on functional communication, content, efficiency, and word-retrieval of picture description, and quality of life. Finally, we will identify imaging and linguistic biomarkers to determine the characteristics of stroke patients that benefit from cerebellar tDCS and SFA treatment. We expect to enroll 60 participants over five years. Participants will receive 15, 25-minute sessions of cerebellar tDCS (3-5 sessions per week) or sham tDCS combined with 1 hour of SFA treatment. Participants will be evaluated prior to the start of treatment, one-week post-treatment, 1-, 3-, and 6-months post-treatment on primary and secondary outcome variables. The long-term aim of this study is to provide the basis for a Phase III randomized controlled trial of cerebellar tDCS vs sham with concurrent language therapy for treatment of chronic aphasia. Trial registration: The trial is registered with ClinicalTrials.gov NCT05093673.

Narrative value theory: an educational biography as an iterative theory-of-self

ABSTRACT Personal narratives can be seen as iterative theories-of-valued-selves: thick, deliberate outlines traced over and selectively enhancing and obscuring the finer details of life as lived. Through telling such stories we represent and orient ourselves towards certain potential actions while turning away from others. Biographical narration is in this way a mobilisation of our pasts to identify valued futures. This article draws together narrative theory, identity psychology and educational philosophy to submit a tentative theory of educational narratives as iterative theories of personal value. The author’s autoethnographic account of completing a postgraduate course online throughout the COVID pandemic is then used as the basis for an inductive analysis of narrative features and their felt and represented significance. This process highlights the ways in which the author’s personal narrative can be read as a theory-of-valued-self, identifying key moments of interaction which characterise the narrator’s valued identity and open causal pathways to future valued action. Such practices may have potential in application with so-called ‘lifelong learners’, adults who continually engage with education throughout life particularly in response to major professional and personal disruption which can otherwise result in a lack of continuity and identity coherence.

Construction of TCM Institution Image: A Corpus-Based Comparative Critical Discourse Study of Chinese and Western Mainstream Media Reports

This study conducts a corpus-assisted comparative analysis of the media images of Traditional Chinese Medicine (TCM) institutions in Chinese and Western media contexts. Applying Fairclough’s three-dimensional critical discourse analysis (CDA) model alongside the TCM Social Image Evaluation Index System, this study explores media image construction and ideological factors shaping the perception of TCM institutions globally through an analysis of linguistic features in keywords, evaluative collocates and modal verbs. It’s found that Chinese media present TCM institutions in a positive light, depicting them as multifaceted organizations with a rich cultural legacy and robust capabilities in medical and scientific research. They showcase TCM institutions as cooperative, promising in integration with modern healthcare, efficient, rapid, flexible, innovative, and imbued with humanitarian values especially during the COVID-19 epidemic. Western media, however, adopt a more cautious stance. They tend to frame TCM institutions as providers of alternative medicine and temporary solutions, with a focus on health preservation, leisure and overall well-being. Concerns have also been raised about the potential illicit activities associated with some massage schools. These disparities in presenting TCM institutions reflect cultural, political and audience demand differences between China and the West. The results of this study contribute to a deeper understanding of the media portrayal of TCM institutions, provide insights into the challenges facing the global recognition and acceptance of TCM institutions, and suggest strategies for Chinese media, TCM practitioners, and policymakers to enhance their international image.

Logging Identification Method for Reservoir Facies in Fractured-Vuggy Dolomite Reservoirs Based on AI: A Case Study of Ediacaran Dengying Formation, Sichuan Basin, China

As an important target for deep to ultra-deep carbonate oil and gas exploration, Fractured-Vuggy dolomite reservoirs have strong heterogeneity. Accurate characterization of reservoir facies is crucial for their exploration and exploitation. Three methods, including the unsupervised intelligent clustering method of improved Fuzzy C-means clustering Algorithm Based on Density Sensitive Distance and Fuzzy Partrition (FCM-DSDFP), the fusion method of Principal Components Analysis (PCA) dimensionality reduction and noise reduction, and the principle of clustering feature analysis are applied to identify reservoir facies based on logging data. Based on the PCA method, the logging response characteristics of the reservoir facies are excavated, and the fusion characterization data of dimensionality reduction and noise reduction are extracted. The FCM-DSDFP unsupervised intelligent clustering method, a model that approximates the subsurface conditions is established, and the reliability of the model is tested according to the elbow rule and silhouette coefficient. Combining drilling core observation, Fractured-Vuggy type, partially cemented Fractured-Vuggy type, Pore-Vuggy type, Pore Type I, Pore Type II, Tight Type I, and Tight Type II are divided in the Dengying Formation 4th Member. Fractured-Vuggy type, partially cemented Fractured-Vuggy type, Pore-Vuggy Type I, Pore-Vuggy Type II, Pore Type I, Pore Type II, and Tight Type are divided in the Dengying Formation 2nd Member, respectively. Two methods were applied to verify the reservoir facies identification results based on intelligent algorithms. The first method is to compare the identification results with the reservoir facies types identified by core observations (Well PT103 and PS13). The second method is to verify the recognition results of intelligent algorithms by utilizing the relationship between reservoir facies types and bitumen. The test results show that the accuracy of the reservoir level identification is higher than 0.9, and the applicability is better than the commonly used algorithms such as FCM and K-means.