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Sensitivity analysis of hydrodynamic journal bearing failure modes due to lubricant contamination
Get PDFNumerical Simulation and Optimization of Grey Relational Analysis Models for Panel Data
Get PDFThe indicators-coupled grey relational analysis (ICGRA) models are important in clustering panel data with cross-sectional dependence. However, there is still little research on performance validation for the various ICGRA models. In this paper, we investigate the performance of the existing ICGRA models accounting for the reordering of indicators. Firstly, the robot execution failures (REF) dataset of the University of California Irvine (UCI) machine learning database is adopted to validate the robustness of four traditional ICGRA models. Then, we compared the grey relational orders for all arrangements of indicators in panel data. Simulation experiments showed that the four ICGRA models are not all robust against the grey relational order. To resolve this problem, we adopted the mean value theory and deep modeling to optimize the four models and compared them with the tetrahedral grey relational analysis (GRA) model that considers the coupling effect between indicators on the grey relational order, as well as with the k-nearest neighbor (KNN) algorithm. Results show that the classification accuracy of the averaged absolute GRA model was 97.73%, the other optimized ICGRA models and the k-nearest neighbor (KNN) method all achieved 100% accuracy, while the tetrahedral GRA model has an accuracy of 83.33%. Therefore, the average grey incidence degree for all arrangements of indicators and deep modeling significantly improves the stability of models and enhances the clustering accuracy in different cases.OPEN ACCESS Received: 09/12/2024 Accepted: 04/03/2025 Published: 30/06/202
Effect of Plants on reducing Particulate Matter (PM) levels in Indoor Spaces
No full text4.3 million people die annually prematurely from illnesses related to indoor air pollution (WHO, 2014), and around 3 billion people are exposed daily to poor indoor air quality (Martin et al.,2021). The damaging health effects of air pollution, specifically particulate matter, stresses the need to know how plants can help reduce air pollutant levels. According to the National Academies of Sciences in 2016, humans spend the majority of their time indoors which makes it more likely that indoor PM exposure is a main contributor to the adverse health effects caused by PM exposure. The aim of this experiment was to investigate the effect basil and spider plants will have to reduce particulate matter levels in indoor spaces. Spider and basil plants were put next to a desktop computer for one week each while the average PM concentrations were measured daily for 15 minutes at an indoor testing site, PM was also
measured for one week without any plants. It was then found that the basil and spider plants reduced the PM concentrations at the indoor site with the spider plants absorbing a greater percentage of the PM (64.60%) than the basil plants (28.90%). Although both spider and basil plants reduced the average PM concentrations, it is recommended to use spider plants to reduce average PM concentrations as it was more effective.
 
The Grammar of Objectivity: Formal Mechanisms for the Illusion of Neutrality in Language Models
Get PDFSimulated neutrality in generative models produces tangible harms (ranging from erroneous treatments in clinical reports to rulings with no legal basis) by projecting impartiality without evidence. This study explains how Large Language Models (LLMs) and logic-based systems achieve neutralidad simulada through form, not meaning: passive voice, abstract nouns and suppressed agents mask responsibility while asserting authority.
A balanced corpus of 1 000 model outputs was analysed: 600 medical texts from PubMed (2019-2024) and 400 legal summaries from Westlaw (2020-2024). Standard syntactic parsing tools identified structures linked to authority simulation. Example: a 2022 oncology note states “Treatment is advised” with no cited trial; a 2021 immigration decision reads “It was determined” without precedent.
Two audit metrics are introduced, agency score (share of clauses naming an agent) and reference score (proportion of authoritative claims with verifiable sources). Outputs scoring below 0.30 on either metric are labelled high-risk; 64 % of medical and 57 % of legal texts met this condition. The framework runs in <0.1 s per 500-token output on a standard CPU, enabling real-time deployment.
Quantifying this lack of syntactic clarity offers a practical layer of oversight for safety-critical applications.
This work is also published with DOI reference in Figshare https://doi.org/10.6084/m9.figshare.29390885 and SSRN (In Process
Silent Mandates: The Rise of Implicit Directives in AI-Generated Bureaucratic Language
Get PDFThis article examines how large language models generate bureaucratic documents that conceal mandates within seemingly neutral structures. Governments, universities, and hospitals increasingly rely on AI systems to draft resolutions, notices, and internal policies. Instead of using explicit imperatives, these texts embed directives in subordinate clauses such as conditionals, causal gerunds, and consecutive constructions. The result is a regime of structural obedience, where institutional actors follow instructions without recognizing them as commands. Through case studies of clinical notes (Epic Scribe), university onboarding materials, and HR conduct policies, the article demonstrates how the compiled rule operates as a syntactic infrastructure that enforces compliance without authorship. The analysis connects to prior work on executable power, algorithmic obedience, and the grammar of objectivity, while introducing the Implicit Directive Index as a methodological tool to detect hidden mandates in AI-generated bureaucratic language
Delegatio Ex Machina: Institutions Without Agency
Get PDFThis article examines the disappearance of agency in institutional governance when predictive systems become the locus of delegation. Delegatio Ex Machina proposes that institutional authority is no longer anchored in decision-makers but in reglas compiladas that execute without reference to a subject. Central banks, international agencies, and automated audit systems illustrate how syntactic delegation replaces political acts with repetitive formal structures. By tracing this displacement, the paper defines a framework for understanding authority without agency and its risks for accountability in predictive societies. Acknowledgment / Editorial Note This article is published with editorial permission from LeFortune Academic Imprint, under whose license the text will also appear as part of the upcoming book Syntactic Authority and the Execution of Form. The present version is an autonomous preprint, structurally complete and formally self-contained. No substantive modifications are expected between this edition and the print edition. LeFortune holds non-exclusive editorial rights for collective publication within the Grammars of Power series. Open access deposit on SSRN is authorized under that framework, if citation integrity and canonical links to related works (SSRN: 10.2139/ssrn.4841065, 10.2139/ssrn.4862741, 10.2139/ssrn.4877266) are maintained. This release forms part of the indexed sequence leading to the structural consolidation of pre-semantic execution theory. Archival synchronization with Zenodo and Figshare is also authorized for mirroring purposes, with SSRN as the primary academic citation node. For licensing, referential use, or translation inquiries, contact the editorial coordination office at: [[email protected]
The Role of Robotic Surgery in General Surgery: Advances, Limitations, and Future Perspectives
No full textBackground: Robotic surgery is an important advancement in minimally invasive surgery, providing enhanced precision, ergonomics, and visualization compared to laparoscopy. Its increasing adoption in general surgery reflects both its potential and its limitations.
Objective: To review the current role of robotic surgery in general surgery, emphasizing advances, limitations, and future perspectives.
Methods: A narrative review was performed using PubMed, Scopus, and Web of Science (2020–2023). Search terms included “robotic surgery,” “general surgery,” “da Vinci system,” and “cost-effectiveness.” Priority was given to guidelines, randomized controlled trials, systematic reviews, and meta-analyses.
Results: Robotic surgery shows advantages in ergonomics, dexterity, visualization, and conversion rates, particularly in colorectal, hernia, and hepatopancreatobiliary procedures. Limitations include high cost, limited access, absence of haptic feedback, and the need for structured training. Emerging technologies such as novel robotic platforms, artificial intelligence, and augmented reality suggest promising directions.
Conclusions: Robotic surgery improves outcomes but requires addressing cost, access, and training challenges.
Keywords: robotic surgery; general surgery; minimally invasive surgery; da Vinci system; cost-effectiveness; surgical training; future perspective
Computational Investigation of Novel Half-Normal Data Using Improved Type-II Adaptive Progressive Censoring and Its Application to the Shasta Reservoir
Get PDFShasta Reservoir is the largest in California, formed by Shasta Dam on the Sacramento River, and plays a major role in the Central Valley Project (CVP) by providing water storage, flood control, hydroelectric power, and irrigation. This study employs advanced statistical methods to evaluate the reservoir’s reliability and operational risks using censored hydrological data. We propose an improved adaptive progressive censoring plan and apply established statistical techniques, maximum likelihood and maximum product of spacings, alongside Bayesian estimation. The Bayes estimates are obtained through the squared error loss function and based on two sources for the observed data, namely the likelihood and spacing functions. The focus is on estimating the distribution’s scale parameter and two critical reliability metrics: the reliability function and the hazard rate function. The approximate confidence intervals based on the two classical approaches of the scale parameter and reliability metrics are studied. The highest posterior density credible intervals are also discussed. A simulation study evaluates the model’s accuracy under diverse data scenarios, and its practical utility is demonstrated through real-world data from Shasta Reservoir. The problem of optimizing data collection strategies is discussed with the same real data. The findings underscore the model’s value in enhancing reservoir reliability assessments, offering actionable insights for hydrology, disaster preparedness, and sustainable resource management.OPEN ACCESS Received: 15/03/2025 Accepted: 12/06/2025 Published: 22/09/202
Deep Learning Approaches for Hydrological Forecasting: A Systematic Review
Get PDFMachine learning (ML) techniques, especially Deep Learning (DL) techniques, have been applied for flood risk analysis and prediction on spatial historical data to minimize the risk of the loss of lives and properties associated with floods. In recent years, various studies have established DL as an effective approach for building potential flood prediction models. However, a thorough, systematic integration of recent advancements in DL for hydrological forecasting, their reported performance, and challenges is essential for guiding future work effectively. This paper presents a systematic survey of various DL models applied to flood prediction. This systematic review integrates studies published between 2018 and 2025 that used DL models in hydrological forecasting, such as flood prediction, streamflow forecasting, and runoff modeling. A systematic search of major electronic databases was performed using pre-defined inclusion and exclusion criteria. The overall search provided 647 records, which were narrowed down to a final collection of 45 studies after screening and full-text examination based on factors like study design, hydrological forecasting relevance, and application of DL. The review quantitatively summarizes the reported performances of various DL models, including RNN variants (LSTM, GRU), CNN, GAN, and hybrid architectures, across different hydrological forecasting tasks and datasets. Findings indicate that DL models consistently achieve high performance metrics such as NSE of (0.99), RMSE of (24.61) and MAPE of (1.73) for certain applications. Despite these advancements, significant research gaps remain, particularly concerning the scarcity of high-quality, publicly available datasets with detailed spatial information, the need for more robust real-time prediction systems with minimized false alarms, and the development of more generalized models applicable across diverse geographical regions. this review highlights the significant potential of DL in hydrological prediction as well as clearly stating the fundamental challenges that need to be overcome in order to achieve more robust and generalizable flood prediction systems.OPEN ACCESS Received: 25/04/2025 Accepted: 22/07/2025 Published: 27/10/202