This article examines how ethnographic methodology and literary theory can advance research engines and artificial intelligence systems beyond the reductive computational approaches that dominate contemporary AI development. Drawing on recent Stanford research revealing fundamental gaps in large language models’ ability to distinguish factual knowledge from belief, I argue that contemporary AI systems enact what I term “abducted semantics”—appropriating the inferential logic of human meaning-making while systematically attenuating the culturally embedded, phenomenologically grounded capacities that generate authentic understanding. Through close analysis of Clifford Geertz’s thick description, Charles Sanders Peirce’s triadic semiotics, and canonical literary works—Miguel de Cervantes’ <i>Don Quixote</i> and Gabriel García Márquez’s <i>One Hundred Years of Solitude</i>—I demonstrate that human understanding operates through complex semiotic processes irreducible to pattern-matching and statistical prediction. The article proposes concrete interventions to transform research engines from tools of semantic extraction into technologies that preserve and enhance interpretive richness, arguing that ethnographic and literary methodologies offer essential correctives to the epistemological impoverishment inherent in current AI architectures.
Background:
Digital health can flatten traditional medical knowledge into reductionist codes. Ayurveda locates valid knowledge (Pramāṇa) at the intersection of experience, environment, and awareness – dimensions not exhausted by quantitative computation.
Aim and Objective:
The aim of the study was to present Structured Distributed Introspection (SDI) as an epistemic framework that operationalizes a perception–reflection–reintegration loop for semantic coherence and to embed it within Collaborative Medicine and Science (CoMS), a translational methodology (Reformulation → Modeling → Localization) that preserves Ayurvedic meaning while enabling computation.
Materials and Methods:
Conceptual synthesis integrating classical Ayurvedic epistemology with embodied/active-inference theories, anchored to current Ayush/World Health Organization (WHO) standards (ICD-11 TM2, NAMASTE, Ayush Grid). An SDI validation summary is provided from dual-protocol studies across 10 Large Language Models (LLMs; n = 400 responses), quantifying cross-frame consistency.
Results:
SDI formalizes introspection as structural feedback; within CoMS, it supports (a) semantically faithful, computable Prakṛti/Vikṛti assessment; (b) prodrome-aware saṃprāpti modeling; (c) adaptive learning mirroring introspective practice; and (d) methodical guardrails for Artificial Intelligence (AI) use in Ayurveda.
Limitations:
SDI’s engineering/architectural phase is a prospective research program; empirical validation beyond LLM self-description requires multicenter studies and clinician-rated endpoints.
Conclusion:
SDI (epistemic core) within CoMS (translational core) provides a rigorous, concept-first pathway for digitizing Ayurveda that enables interoperability without erasing identity, aligning practice with WHO principles for responsible, culturally grounded AI.
Given the significant impact of Artificial Intelligence (AI) technology on corporate energy management and the lack of research in this area, this paper employs text mining techniques to objectively assess the relative level of AI adoption among Chinese listed companies. Using econometric modelling methods, we verify these hypotheses and investigate both the direct and indirect effects of AI on corporate carbon emission intensity. Our research finds that the carbon emission intensity of Chinese enterprises significantly decreased in the early stage, then stabilized, and has notably decreased again in recent years. The average level of AI among listed Chinese enterprises shows an overall upward trend, but the growth rate has slowed down. The level of AI in private enterprises is significantly higher than that in other types of enterprises, while the level of AI in state-owned enterprises is relatively lower. The level of AI in enterprises has a significant negative impact on carbon emission intensity, presenting an “S”-shaped relationship, characterized by initial emission reduction, mid-term rebound, and subsequent emission reduction. AI technology reduces the level of carbon emissions in enterprises by enhancing their green development standards and promoting technological innovation. There are significant differences in the impact of AI levels on carbon emission intensity across different types and regions of enterprises. The empirical conclusions remain robust after addressing endogeneity issues or variable substitution. This study provides important insights for corporate energy transitions and sustainable development, as well as for the formulation of government energy policies.
Accurate estimation of regional rice yields is crucial for food security and efficient agricultural management. In this regard, the use of Unmanned Aerial Vehicles (UAVs) that have revolutionized crop monitoring by providing high-resolution images for precision agriculture, is beneficial. This study explores the potential of Segment Anything Model (SAM) for detecting rice seedlings, focusing on determining the optimal approach and prompt for this task. We examined three SAM scenarios: automatic mask generation, bounding box prompt, and point prompt. Our evaluation criteria included processing time, visual interpretation, and accuracy indexes. The results demonstrated the effectiveness of SAM in rice seedling detection, highlighting the importance of selecting the appropriate prompt for specific agricultural applications. Our findings reveal that the point prompt method emerges as the preferred choice for rice seedling detection, offering superior accuracy and reliability. Specifically, it achieved mIoU and mDice scores of 94.57 % and 0.97, respectively. While the bounding box approach showed promise, despite slightly lower precision, it may still be suitable depending on application-specific requirements. Conversely, the automatic mask generation scenario proved unsuitable for this task due to its low accuracy and inability to effectively detect rice seedlings. The outcomes of this study serve as a baseline for evaluating SAM prompts, guiding future improvements and refinements to enhance its performance in real-world agricultural applications.
Dental implantation has revolutionized the traditional prosthetic approach to tooth loss and is regarded as the optimal solution for replacing missing teeth and restoring oral function and aesthetics. With the swift development of artificial intelligence (AI) technology and its profound integration with oral implantology, a growing number of studies have begun to explore the application of AI in the field of oral implantology, covering multiple aspects such as auxiliary diagnosis, treatment planning and surgical robotics. This review systematically introduces the multi-dimensional progress of AI in the digital and intelligent transformation of oral implantology in recent years and provides an outlook on its potential and research value.
This paper discusses how integrating renewable energy, AI, and IoT becomes important in promoting climate-smart agriculture. Due to the changing climate, rise in energy costs, and ensuring food security, agriculture faces unprecedented challenges; therefore, development toward innovative technologies is emerging for its sustainability and efficiency. This review synthesizes existing literature systematically to identify how AI and IoT could optimize resource management, increase productivity, and reduce greenhouse gas emissions within an agricultural context. Key findings pointed to the importance of managing resources sustainably, the scalability of technologies, and, finally, policy interventions to ensure technology adoption. The paper further outlines trends in the global adoption of renewable energy and smart agriculture solutions, indicating areas of commonality and difference and emphasizing the need for focused policies and capacity-building initiatives that will help, particularly in the developing world, the benefits of such innovations. Eventually, this research covers some gaps in understanding how AI, IoT, and renewable energy could jointly contribute to driving towards a greener and more resilient agriculture sector.
Steven Bradley Graefe MD, Noel A. Jeansonne MD, Andrew Meister MD
et al.
Category: Trauma Introduction/Purpose: Before presenting for an orthopaedic clinical evaluation, patients have access to numerous resources on common orthopaedic injuries, their management, and related procedures. Recently, artificial intelligence (AI)-driven chatbots have provided a platform for patient engagement - encompassing various topics ranging from basic skills modules to detailed literature reviews. ChatGPT (OpenAI), a recently developed AI-based chat model, is one application that has rapidly grown in popularity and has garnered worldwide media attention. Utilizing common language communication, this technology allows patients to engage with an interface that supplies convincing, human-like responses. Given the likelihood that patients may turn to this technology for orthopaedic and preoperative education, we sought to determine whether ChatGPT could effectively answer frequently asked questions regarding ankle fractures. Methods: Frequently asked questions (FAQs) pertaining to ankle fractures were identified through an online search engine (Google). A compilation of commonly encountered questions regarding ankle fractures was generated, followed by a comprehensive review of all identified questions. A final set of twelve questions deemed pertinent and frequently encountered in clinical settings was determined by the authors (N.J.& B.G.). These twelve questions regarding ankle fractures were posed to the chatbot during a conversation thread on January 14th, 2024, without follow-up questions or any repeat queries. Each response was analyzed for accuracy utilizing an evidence-based approach. Three authors (P.J., K.P., & A.M.) board-certified in orthopaedic surgery independently rated each question response from ChatGPT in a blinded, sequential fashion. Ratings were designated as 1. “excellent response not requiring clarification,” 2. “satisfactory requiring minimal clarification,” 3. “satisfactory requiring moderate clarification,” or 4. “unsatisfactory requiring substantial clarification.” Results: None of ChatGPT’s responses received an “unsatisfactory” rating from the authors. Just under half (5/12) of the responses required “minimal clarification,” with 4 “not requiring clarification.” 3 responses were reported to require “moderate clarification.” Although several responses required nuanced clarification, the chatbot’s responses were reported to be generally unbiased and evidence-based. More complex queries were likely to receive ratings “requiring minimal clarification” or “requiring moderate clarification,” including those regarding clinical decision-making, indications for preoperative workup and surgery, benefits and drawbacks, and complications. All participants would consider using ChatGPT to improve their patient education materials and expressed willingness to use ChatGPT to help create future patient education material. Conclusion: The ChatGPT AI chatbot may have the potential to provide evidence-based responses to questions commonly asked by patients regarding ankle fractures. ChatGPT may provide a unique and valuable clinical tool for patient education and establishing a basic understanding of ankle fractures before consultation with an orthopaedic surgeon.
Carlos M. Travieso-González, Fidel Cabrera-Quintero, Alejandro Piñán-Roescher
et al.
The increasing penetration of solar energy into the grid has led to management difficulties that require high accuracy forecasting systems. New techniques and approaches are emerging worldwide every year to improve the accuracy of solar power forecasting models and reduce uncertainty in predictions. This article aims to evaluate and compare various solar power forecasting methods based on their characteristics and performance using imagery. To achieve this goal, this article presents an updated analysis of diverse research, which is classified in terms of the technologies and methodologies applied. This analysis distinguishes studies that use ground-based sensor measurements, satellite data processing, or all-sky camera images, as well as statistical regression approaches, artificial intelligence, numerical models, image processing, or a combination of these technologies and methods. Key findings include the superior accuracy of hybrid models that integrate multiple data sources and methodologies, and the promising potential of all-sky camera systems for very short-term forecasting due to their ability to capture rapid changes in cloud cover. Additionally, the evaluation of different error metrics highlights the importance of selecting appropriate benchmarks, such as the smart persistence model, to enhance forecast reliability. This review underscores the need for continued innovation and integration of advanced technologies to meet the challenges of solar energy forecasting.
Software and information systems have become a core competency for every business in this connected world. Any enhancement in software delivery and operations will tremendously impact businesses and society. Sustainable software development is one of the key focus areas for software organizations. The application of intelligent automation leveraging artificial intelligence and cloud computing to deliver continuous value from software is in its nascent stage across the industry and is evolving rapidly. The advent of agile methodologies with DevOps has increased software quality and accelerated its delivery. Numerous software organizations have adopted DevOps to develop and operate their software systems and improve efficiency. Software organizations try to implement DevOps activities by taking advantage of various expert services. The adoption of DevOps by software organizations is beset with multiple challenges. These issues can be overcome by understanding and structurally addressing the pain points. This paper presents the preliminary analysis of the interviews with the relevant stakeholders. Ground truths were established and applied to evaluate various machine learning algorithms to compare their accuracy and test our hypothesis. This study aims to help researchers and practitioners understand the adoption of DevOps and the contexts in which the DevOps practices are viable. The experimental results will show that machine learning can predict an organization's readiness to adopt DevOps.
Elaheh Foroudi Sefat, Mohammad Mehdi Ahmadi, Kourosh Qaderi
et al.
AbstractIntroduction: Accurate forecasting of runoff and flooding to avoid human and financial losses is one of the most challenging tasks in hydrological studies of a given locale. Therefore, researchers have paid more attention to the development of accurate flood forecasting models, including the use of artificial intelligence methods.Methods: In this investigation, the efficiency of 3 models, ANN, GMDH and ARIMA, has been investigated in order to simulate the flood of a part of Halil river basin in Kerman province. ANN model is a non-linear modeling method that improves its performance over time. The GMDH composed code is an artificial intelligence model with exploratory self-organizing features, at the conclusion of which a complex system with optimal performance is formed. Composed ARIMA code builds a model to describe the structure of the data and then predict the time series. The input data to the above models included discharge, precipitation, temperature, wind and monthly humidity, and the simulated runoff values were compared with the observed values.Findings: In order to evaluate the accuracy of the models in this research, statistical indices were used and the results showed that the ANN model (RMSE=0.042, MSD=0.001, MAE=0.027) had the possibility to estimate the runoff with higher accuracy compared to the GMDH model (RMSE=0.068, MSD=0.005, MAE=0.056) and the ARIMA time series (RMSE=0.096, MSD=0.009, MAE=0.063) in the studied basin. The mean error in runoff estimation with ANN model has been reduced by 38.23% and 56.25%, respectively, compared to the values estimated with GMDH and ARIMA models. According to the results obtained in this study, the artificial neural network model has been able to show a better performance than the other two models in predicting the outputs due to its suitable structural ability to find the nonlinear relationship between the input and output data.
Accidents caused by operators failing to wear safety gloves are a frequent problem at electric power operation sites, and the inefficiency of manual supervision and the lack of effective supervision methods result in frequent electricity safety accidents. To address the issue of low accuracy in glove detection with small-scale glove datasets. This article proposes a real-time glove detection algorithm using video surveillance to address these issues. The approach employs transfer learning and an attention mechanism to enhance detection average precision. The key ideas of our algorithm are as follows: (1) introducing the Combine Attention Partial Network (CAPN) based on convolutional neural networks, which can accurately recognize whether gloves are being worn, (2) combining channel attention and spatial attention modules to improve CAPN’s ability to extract deeper feature information and recognition accuracy, and (3) using transfer learning to transfer human hand features in different states to gloves to enhance the small sample dataset of gloves. Experimental results show that the proposed network structure achieves high performance in terms of detection average precision. The average precision of glove detection reached 96.59%, demonstrating the efficacy of CAPN.
Juan A. SÁNCHEZ-MARGALLO, Francisco M. SÁNCHEZ-MARGALLO
Advances in sensors, internet of things and artificial intelligence are allowing wearable technology to constantly evolve, making it possible to have increasingly compact and versatile devices with clinically relevant and promising functionalities in the field of surgery. In this sense, wearable technology has been used in various fields of clinical and preclinical application such as the evaluation of the surgeon's ergonomic conditions, the interaction with the patient or the quality of the intervention, as well as surgical planning and assistance during the intervention. In this work we will present different types of wearable technologies for their application in the validation of surgical devices in minimally invasive surgery, and their application in assisting the surgical process. Within these technologies we will show electrodermal activity and electrocardiography devices to monitor the surgeon’s physiological state, and electromyography and motion analysis systems to study his/her ergonomics during the surgical practice. Apart from these systems, the introduction of extended reality technology (virtual, augmented, and mixed reality) has fostered the emergence of new immersive and interactive tools to assist in the planning of complex surgical procedures, surgical support and telementoring. As we can see, the application of wearable technology has a high impact on the validation of surgical systems in minimally invasive surgery, including laparoscopic surgery, microsurgery, and surgical robotics, as well as in the assistance of the surgical process, with the consequent benefit in the quality of patient care.
Computer applications to medicine. Medical informatics
Babakhouya Ayoub, Naji Abdelwahab, Daaif Abdelaziz
et al.
Agriculture plays a crucial role in our existence by supplying food, raw materials, and employment opportunities. In Morocco, it serves as the backbone of the economy, employing 40% of the workforce and contributing approximately 13% to the country's GDP [1]. IoT (Internet of things) and Artificial Intelligence (AI), as well as other advanced computing technologies, have long been used in the agri-food industry. The primary focus of this paper is to assess the diverse utilization of Artificial Intelligence in agriculture, specifically in tasks like irrigation, weeding, and spraying. These applications employ sensors and integrated systems in robots and drones, effectively reducing water and chemical usage, preserving soil fertility, optimizing labor, and enhancing productivity and quality. The research identifies the most common AI strategies used in the industry. Furthermore, we conducted an analysis of significant trends and provided researchers and practitioners with valuable insights for future research endeavors in addition to challenges hindering AgriTech applications in Moroccan farms.
Neuroproteomics, an emerging field at the intersection of neuroscience and proteomics, has garnered significant attention in the context of neurotrauma research. Neuroproteomics involves the quantitative and qualitative analysis of nervous system components, essential for understanding the dynamic events involved in the vast areas of neuroscience, including, but not limited to, neuropsychiatric disorders, neurodegenerative disorders, mental illness, traumatic brain injury, chronic traumatic encephalopathy, and other neurodegenerative diseases. With advancements in mass spectrometry coupled with bioinformatics and systems biology, neuroproteomics has led to the development of innovative techniques such as microproteomics, single-cell proteomics, and imaging mass spectrometry, which have significantly impacted neuronal biomarker research. By analyzing the complex protein interactions and alterations that occur in the injured brain, neuroproteomics provides valuable insights into the pathophysiological mechanisms underlying neurotrauma. This review explores how such insights can be harnessed to advance personalized medicine (PM) approaches, tailoring treatments based on individual patient profiles. Additionally, we highlight the potential future prospects of neuroproteomics, such as identifying novel biomarkers and developing targeted therapies by employing artificial intelligence (AI) and machine learning (ML). By shedding light on neurotrauma’s current state and future directions, this review aims to stimulate further research and collaboration in this promising and transformative field.