Issues Of Interpretation Research Articles

From Submission to Love: Understanding the Motivations in Romans 13:1-10

This paper studies Rom 13:1-10, analyzing the inner connection between Paul’s exhortation to submit to governing authorities and his admonition to love one another. It addresses interpretative and exegetical issues, including possible textual interpolations and the meanings of key terms. The analysis demonstrates that Paul’s argument hinges on the incrementum (αὔξησις) of underlying rationales, moving from general and external incentives to personal and internal motivations. This study highlights that the coherence of the passage relies on understanding both its imperatives and motivations, ultimately suggesting a deeper moral imperative.

Comparative analysis of traditional and transfer learning algorithms for coal spoil classification via close-range imagery

ABSTRACT The characterisation of materials is a prerequisite for evaluating and predicting the stability of mining waste dumps. Over the past three decades, the BHP Mitsubishi Alliance Coal framework has been a cornerstone in Australian coal mines for characterising waste dump materials. However, its reliance on subjective human observations has introduced potential inaccuracies and subjectivity into the process. In response to these limitations, this study proposes an innovative approach to classify coal spoil attributes by remotely acquiring images through phones/tablets. Automated image-based classification relies on feature extraction and a substantial amount of data. Nevertheless, the inherent complexity of geological factors contributing to the formation of both rare and dominant materials leads to imbalanced data. Recognising the need for classification mechanisms to overcome these challenges in spoil classification, the study explores and compares the use of convolutional neural networks, hybrid deep learning, and traditional techniques. Among the sixteen models evaluated in this study, the ResNet18-support vector machine model emerges as a powerful tool in geotechnical characterisation. However, it is essential to address issues of interpretability and adaptability to diverse datasets. As this study evolves, the field of geotechnical characterisation of spoil can anticipate the development of more robust methods in the future.

The Existence of an Intra-Amniotic Microbiome: Assessing a Controversy

The existence of intra-amniotic and placental microbiomes during pregnancy has been the source of considerable debate, with research yielding conflicting evidence. This study evaluated investigations into the putative presence of these microbiomes in healthy pregnancies by identifying design and data interpretation issues, particularly concerning contamination in samples with low-density bacterial DNA. Positive findings from diverse populations suggest a consistent presence of microbiota in the intra-amniotic space. Negative conclusions regarding the existence of these prenatal microbiomes emphasise the impact of contamination in the analysis of samples with low-density bacterial DNA. This study concluded that there is no definitive evidence to refute the existence of intra-amniotic microbiomes in healthy pregnancies. Also, it provides suggestions for controlling potential contamination factors in future research on intra-amniotic and placental microbial populations.

Adversarial Examples on XAI-Enabled DT for Smart Healthcare Systems.

There have recently been rapid developments in smart healthcare systems, such as precision diagnosis, smart diet management, and drug discovery. These systems require the integration of the Internet of Things (IoT) for data acquisition, Digital Twins (DT) for data representation into a digital replica and Artificial Intelligence (AI) for decision-making. DT is a digital copy or replica of physical entities (e.g., patients), one of the emerging technologies that enable the advancement of smart healthcare systems. AI and Machine Learning (ML) offer great benefits to DT-based smart healthcare systems. They also pose certain risks, including security risks, and bring up issues of fairness, trustworthiness, explainability, and interpretability. One of the challenges that still make the full adaptation of AI/ML in healthcare questionable is the explainability of AI (XAI) and interpretability of ML (IML). Although the study of the explainability and interpretability of AI/ML is now a trend, there is a lack of research on the security of XAI-enabled DT for smart healthcare systems. Existing studies limit their focus to either the security of XAI or DT. This paper provides a brief overview of the research on the security of XAI-enabled DT for smart healthcare systems. It also explores potential adversarial attacks against XAI-enabled DT for smart healthcare systems. Additionally, it proposes a framework for designing XAI-enabled DT for smart healthcare systems that are secure and trusted.

Critical Review: Methodological and Interpretative Issues in COVID-19 and Acute Coronary Syndrome Study.

Critical Review: Methodological and Interpretative Issues in COVID-19 and Acute Coronary Syndrome Study.

Advancements in Technologies and Methodologies of Machine Learning in Landslide Susceptibility Research: Current Trends and Future Directions

Landslides are pervasive geological hazards that pose significant risks to human life, property, and the environment. Understanding landslide susceptibility is crucial for predicting and mitigating these disasters. This article advocates for a comprehensive review by systematically compiling and analyzing 146 relevant studies up to 2024. It assesses current progress and limitations and offers guidance for future research. This paper provides a comprehensive overview of the diverse challenges encountered by machine learning models in landslide susceptibility assessment, encompassing aspects such as model selection, the formulation of evaluation index systems, model interpretability, and spatial heterogeneity. The construction of an evaluation index system, which serves as the foundational data for the model, profoundly influences its accuracy. This study extensively investigates the selection of evaluation factors and the identification of positive and negative samples, proposing valuable methodologies. Furthermore, this paper briefly deliberates and compares classical machine learning models, offering valuable insights for model selection. Additionally, it delves into discussions concerning model interpretability and spatial heterogeneity issues. These research findings promise to enhance the precision of landslide susceptibility assessments and furnish effective strategies for risk management.

Reply to Letter to the Editor: "Critical Review: Methodological and Interpretative Issues in COVID-19 and Acute Coronary Syndrome Study".

Reply to Letter to the Editor: "Critical Review: Methodological and Interpretative Issues in COVID-19 and Acute Coronary Syndrome Study".

The patient-reported outcome measure for older people living with frailty receiving acute care (PROM-OPAC): field-testing and validation

BackgroundCurrent acute healthcare service metrics are not meaningful for older people living with frailty. Healthcare knowledge, situational security, and physical and psychosocial function are important outcomes typically not collected. The use of patient-reported outcome measures (PROMs) could support these assessments. Existing instruments are not comprehensive as they typically consider function, while older people with frailty also value enablement (self-determination and security in health and healthcare). This study field-tested and validated a PROM for older people with frailty receiving acute care (PROM-OPAC) to measure enablement.MethodsPeople aged 65+ with Clinical Frailty Scale 5–8 were recruited within seventy-two hours of an emergency attendance. Iterations of the novel instrument were administered over three stages: (1) preliminary field-testing for reliability (response distribution and internal consistency) and structure (exploratory factor analysis, EFA); (2) intermediate field-testing of an improved instrument for reliability and structure; (3) final draft validation assessing reliability, structure (confirmatory factor analysis, CFA), and construct validity based on a priori hypotheses. Feasibility was appraised throughout using data completeness and response rates and times.Results241 people participated. Three items of a preliminary seven-item measure had poor response distribution or loading and were accordingly improved. The intermediate instrument had interpretability issues and three items required further improvement. The final eight-item draft had acceptable reliability (Cronbach’s alpha: 0.71), structure (two factors for self-determination and security; RMSEA: 0.065; TLI: 0.917; CFI: 0.944), and construct validity (lower scores from respondents waiting longer and requiring admission). Feasibility was promising (response rate 39%; 98% responses complete; median completion time 11 (IQR: 12) minutes).ConclusionsAdministration of the PROM-OPAC appeared feasible and the instrument had acceptable psychometric properties. Further evaluation is required to assess generalisability.

Development of a forensic DNA research grade test material.

Advancements in forensic DNA typing technology and methods have increased sensitivity and, while beneficial, carry the weight of more challenging profile interpretation. In response, the forensic DNA community has often requested more complex reference materials to address commonly encountered measurement and interpretation issues such as complex DNA mixtures, DNA degradation, and PCR inhibition. The National Institute of Standards and Technology (NIST) released Research Grade Test Material 10235: Forensic DNA Typing Resource Samples to support the forensic DNA community. Components include three single source samples, two degraded samples, and three mixture samples. As part of the Research Grade Test Material (RGTM) process, automated methods for bottling, alternative sample tube types, and the addition of carrier RNA for stabilizing low-quantity samples were investigated. Both internal and external testing demonstrate the stability of the material over time at 4°C through qPCR testing. In the development of a data portal, users have been allowed to anonymously upload results and compare their data with NIST and others. This report describes the preparation and stability of this material.

Distance-regularized level set inversion of magnetic data

Inversion is one of the key and difficult issues in potential field data processing and interpretation, and high-precision inversion has long been a popular research topic. We develop the distance-regularized level set inversion of the magnetic data (DRLSI-M) method. A double-well function is introduced into the objective function of the level set inversion, and we take advantage of its mathematical properties to minimize the gradient of the level set function so that it reaches a minimum value at points 0 and 1. As a result, the distance-regularized term not only replaces the costly reinitialization process so that the level set function maintains a signed distance property but also stabilizes the evolution of the level set function. When solving for the minimum value of the objective function, we transform the optimization problem into an initial-boundary value problem and solve it with the finite-difference method. The distance-regularized level set inversion method and the reinitialization level set inversion method are tested using three models: a single-level-set double-inclined plate model, a double-level-set double-inclined plate model, and a multiple-level-set three-cuboid model. Through model tests, the soundness of our method is verified and compared with the reinitialization level set inversion method. Our method avoids the limitations such as low efficiency and instability caused by the reinitialization of the level set inversion method. Furthermore, we apply our method for the inversion of aeromagnetic data from the Jinchuan copper-nickel mine. The results are consistent with the known geologic information, thus validating the practicality and effectiveness of DRLSI-M. The distance-regularized level set inversion method provides a theoretical basis for deep mine exploration.

A deep learning method for beat-level risk analysis and interpretation of atrial fibrillation patients during sinus rhythm

Atrial Fibrillation (AF) is a common cardiac arrhythmia. Many AF patients experience complications such as stroke and other cardiovascular issues. Early detection of AF is crucial. The great majority of algorithms can only distinguish “AF rhythm in AF patients” from “sinus rhythm in healthy individuals” . However, AF patients do not always exhibit AF rhythm, most of the time they present with sinus rhythms, and there is also a potential risk of AF in sinus rhythms. How to detect AF from sinus rhythm is a challenge. To address this, this paper proposes a novel artificial intelligence (AI) algorithm to distinguish “sinus rhythm in AF patients” and “sinus rhythm in healthy individuals” in beat-level. We cut 1 s of sinus beats from single-lead Electrocardiogram (ECG) data and fed them to the Net1d model, a deep learning model that processes one-dimensional data, to obtain the risk probability of each beat. Besides, we have also introduced the beat-level risk interpreters, trend risk interpreters, addressing the interpretability issues of deep learning models and the difficulty in explaining AF risk trends. Additionally, the beat-level information fusion decision is presented to enhance model accuracy. The experimental results demonstrate that the average AUC for single beats used as testing data from CPSC 2021 dataset is 0.7314, with an average accuracy of 0.6606 and an F1 score of 0.6470. By employing 150 beats for information fusion decision algorithm, the average AUC can reach 0.7591, while the average accuracy and F1 score improve to 0.6887 and 0.6749. Compared to previous segment-level algorithms, we utilized beats as input, reducing data dimensionality and making the model more lightweight, facilitating deployment on portable medical devices. Furthermore, we draw new and interesting findings through average beat analysis and subgroup analysis, considering varying risk levels. Our code is publicly available at https://github.com/leijsen/ECGBeat4AFSinus.

Methodological Issues in Pollen Analysis of Pleistocene Deposits in Denisova Cave

Denisova Cave, in the northwestern Altai, is a key Paleolithic complex in North Asia. Pleistocene deposits in the cave contain lithic industries and human fossils documenting the evolution of the cultural traditions of Denisovans in the second half of the Middle and in the Upper Pleistocene. This study addresses methodological issues in paleogeographic interpretation of pollen records relating to Quaternary deposits of cave sites. We present the results of the analysis of recent and subrecent spectra of cave sediments and soil samples taken at sites of characteristic plant communities in natural zones of the Anui River valley near Denisova Cave. Findin gs from taphonomic study of pollen microremains from loose sediments in the East Chamber of the cave make it possible to obtain a correct climato-stratigraphic and climato-phytocenotic interpretation of pollen spectra from Pleistocene deposits in Denisova Cave.

SF-GPT: A training-free method to enhance capabilities for knowledge graph construction in LLMs

Knowledge graphs (KGs) are constructed by extracting knowledge triples from text and fusing knowledge, enhancing information retrieval efficiency. Current methods for knowledge triple extraction include ”Pretrain and Fine-tuning” and Large Language Models (LLMs). The former shifts effort from manual extraction to dataset annotation and suffers from performance degradation with different test and training set distributions. LLMs-based methods face errors and incompleteness in extraction. We introduce SF-GPT, a training-free method to address these issues. Firstly, we propose the Entity Extraction Filter (EEF) module to filter triple generation results, addressing evaluation and cleansing challenges. Secondly, we introduce a training-free Entity Alignment Module based on Entity Alias Generation (EAG), tackling semantic richness and interpretability issues in LLM-based knowledge fusion. Finally, our Self-Fusion Subgraph strategy uses multi-response self-fusion and a common entity list to filter triple results, reducing noise from LLMs’ multi-responses. In experiments, SF-GPT showed a 55.5% increase in recall and a 32.6% increase in F1 score on the BDNC dataset compared to the UniRel model trained on the NYT dataset and achieved a 5% improvement in F1 score compared to GPT-4+EEF baseline on the WebNLG dataset in the case of a fusion round of three. SF-GPT offers a promising way to extract knowledge from unstructured information.

Heart Sound Abnormality Detection From Multi-Institutional Collaboration: Introducing a Federated Learning Framework.

Early diagnosis of cardiovascular diseases is a crucial task in medical practice. With the application of computer audition in the healthcare field, artificial intelligence (AI) has been applied to clinical non-invasive intelligent auscultation of heart sounds to provide rapid and effective pre-screening. However, AI models generally require large amounts of data which may cause privacy issues. Unfortunately, it is difficult to collect large amounts of healthcare data from a single centre. In this study, we propose federated learning (FL) optimisation strategies for the practical application in multi-centre institutional heart sound databases. The horizontal FL is mainly employed to tackle the privacy problem by aligning the feature spaces of FL participating institutions without information leakage. In addition, techniques based on deep learning have poor interpretability due to their "black-box" property, which limits the feasibility of AI in real medical data. To this end, vertical FL is utilised to address the issues of model interpretability and data scarcity. Experimental results demonstrate that, the proposed FL framework can achieve good performance for heart sound abnormality detection by taking the personal privacy protection into account. Moreover, using the federated feature space is beneficial to balance the interpretability of the vertical FL and the privacy of the data. This work realises the potential of FL from research to clinical practice, and is expected to have extensive application in the federated smart medical system.

형사소송법상 ‘피고인이 구속된 때’에 있어서 구속 개념의 해석 및 입법론 - 대법원 2024. 5. 23. 선고 2021도6357 전원합의체 판결

Korean Criminal Procedure Act establishes “when the defendant is in custody” as one of the grounds for appointing a public defender. (Article 33, Paragraph 1(1)) The Supreme Court has traditionally viewed this as a case in which the defendant is detained and on trial in the criminal case in question. It may be reasonably concluded, therefore, that being detained in a separate case or being convicted and sentenced in another criminal case does not qualify as a reason for the appointment. However, in May 2024, the Supreme Court reconsidered its position to include cases where a warrant of arrest has been issued and executed in a separate case or where the defendant has been convicted in another criminal case and is in custody as a result of the execution of the judgment. This artice reviews the decision to reconstruct the contents of its majority opinion, separate opinion, and supplementary opinion by interpreting the legal textual meaning of the concept of restraint, interpreting the purposive meaning, and policy interpretation issues. It will then evaluate them together with related laws and theories, with a view to seeking a legislative solution to the interpretation controversy. While the existing precedents did not present the interpretation arguments in detail, the individual opinions of the judgments indicate that the arguments were based on the stable utilization of the current system and practice on public defenders and the right to counsel, as well as the efficient allocation of judicial resources. There is a difference of opinion as to whether the expansion of the right to counsel and the necessary expansion of the public defender system should be achieved by changing case law or by amending the law. Thus, the author propose the amendment of paragraph 1(1) of the Article 33, “When the criminal defendant is placed under detention”to “When the criminal defendant is placed under custody”.

Progress and opportunities of foundation models in bioinformatics.

Bioinformatics has undergone a paradigm shift in artificial intelligence (AI), particularly through foundation models (FMs), which address longstanding challenges in bioinformatics such as limited annotated data and data noise. These AI techniques have demonstrated remarkable efficacy across various downstream validation tasks, effectively representing diverse biological entities and heralding a new era in computational biology. The primary goal of this survey is to conduct a general investigation and summary of FMs in bioinformatics, tracing their evolutionary trajectory, current research landscape, and methodological frameworks. Our primary focus is on elucidating the application of FMs to specific biological problems, offering insights to guide the research community in choosing appropriate FMs for tasks like sequence analysis, structure prediction, and function annotation. Each section delves into the intricacies of the targeted challenges, contrasting the architectures and advancements of FMs with conventional methods and showcasing their utility across different biological domains. Further, this review scrutinizes the hurdles and constraints encountered by FMs in biology, including issues of data noise, model interpretability, and potential biases. This analysis provides a theoretical groundwork for understanding the circumstances under which certain FMs may exhibit suboptimal performance. Lastly, we outline prospective pathways and methodologies for the future development of FMs in biological research, facilitating ongoing innovation in the field. This comprehensive examination not only serves as an academic reference but also as a roadmap for forthcoming explorations and applications of FMs in biology.

Skinformatics: Navigating the big data landscape of dermatology.

Big data and associated approaches to analyse it are on the rise, especially in healthcare settings. This growth is also seen with unique applications in the field of dermatology. While big data offer a plethora of opportunity for improving our current understanding of disease and ability to deliver care, as with any technology innovation, the potential pitfalls should be addressed. In this piece, we highlight opportunities and challenges associated with big data in dermatology. Opportunities include large and novel data sources that may offer a wealth of information, automated detection, classification and diagnostics and improved public health monitoring. Challenges include data quality, issues of interpretability and disparities within artificial intelligence (AI) training data sets. Clinicians and researchers in the field should be aware of these developments within the field of big data to understand how best it may be used toward improving patient care and health outcomes, particularly in the field of dermatology.

Stasis Interruptus

Tanaka Atsuko’s Work (Bell) from 1955 is an important and layered work that pre-figures the genre of sound art. Part of its complexity lies in the fact that there is no “original” version, and that the artist executed several iterations over several decades, remaining true to the work’s core identity but varying its specific materiality and thereby emphasizing its situational nature. At the experiential level, the complexity of the work includes issues of agency, rupture, sonic architectonics, and transhistorical interpretation. The author responds to artist Shiraga Fujiko’s concise and insightful firsthand account of her 1955 encounter with the work as a foundation to address these and other meanings and implications of Work (Bell).

VISUAL AND ARTISTIC CHARACTERISTICS OF THE TRAGEDY "NAJMIDDIN KUBRO" (ABOUT THE TRAGEDY OF AMON MATJON)

This article focuses on the bright examples of innovation and tradition in the tragedy "Najmiddin Kubro" by Amon Matjon. It is shown through analysis that along with the work of Alisher Navoi and the influence of folklore, the original image, plot and expressions of the poet are sufficiently manifested.Historical works of Amon Matjon can provide important facts and conclusions for studying the issue of historical interpretation in dramaturgy. In it, the poet tries to maintain a balance between historical reality and creative freedom. It was impossible not to reflect the spirit, ideology, and views of the time in the poet's historical works. At the same time, attention is paid to the fact that the artist was able to place his important socio-political thoughts and, importantly, opinions that are completelycontrary to the environment of his time, in the lines that reflect the ideology of the time and are adapted to it.

Anal Cytology Interpretation Issues: Observations from the College of American Pathologists’ Nongynecologic Cytology Program

Anal Cytology Interpretation Issues: Observations from the College of American Pathologists’ Nongynecologic Cytology Program