Search Results (2,557)

Generative artificial intelligence (AI) is increasingly used to support decision-making in manufacturing quality assurance (QA), but its adoption raises concerns regarding governance, traceability, and auditability. This paper proposes a proactive framework that integrates generative AI into quality management and auditing while preserving standards alignment and human oversight. The framework structures quality activities across supplier, in-process, and post-market domains and across three hierarchical levels—product, process, and operation—to link quality outcomes with documentary evidence requirements. A proof-of-concept (PoC) study in electronics manufacturing focused on New Product Introduction (NPI) planning and compared two parallel workflows: an expert QA team and a generative AI-assisted chatbot workflow. Within a fixed time window, both workflows produced an aligned Process Failure Mode and Effects Analysis (PFMEA), Control Plan, supplier Production Part Approval Process (PPAP) request package, and internal audit evidence pack. Three independent experts evaluated the integrated deliverable package using five indices covering documentation quality and audit readiness, detection and containment logic, process capability and stability, governance and provenance safeguards, and execution (time) efficiency. Compared with the expert package, the generative AI–assisted workflow produced more traceable, governance-rich documentation (ownership, versioning, clause-to-evidence links) and reduced manual audit-evidence consolidation, supporting quality planning and change-control readiness. Full article

Azemiops feae (Fea’s viper) is a phylogenetically distinctive Asian viper with poorly defined medical significance, and human envenomations remain rarely reported in the English-language literature. We describe a new case of A. feae envenoming from Chongqing, China, and present a scoping review of published clinical case reports and case series to better characterize its epidemiology, clinical manifestations, and management. A 53-year-old male developed marked local pain and swelling following a bite to the hand, accompanied by transient neurotoxic symptoms, as well as mild hypofibrinogenemia. Treatment with a single vial of Gloydius brevicaudus monovalent antivenom was followed by clinical improvement and full recovery. Review of the literature identified nine previously published studies from China and one captive case from Europe. Envenoming typically occurred during agricultural activities, most commonly affected the lower extremities, and was characterized by prominent local effects with occasional mild neurotoxic features and inconsistent, generally mild coagulation abnormalities. Antivenom use was highly variable, involving multiple heterologous monovalent antivenoms, and outcomes were uniformly favourable regardless of antivenom administration. Collectively, available evidence indicates that A. feae envenoming is usually self-limited, with predominantly local effects and infrequent, mild systemic involvement. However, the absence of species-specific antivenom and the heterogeneity of current treatment practices highlight the need for systematic venom characterization and functional antivenom efficacy studies to inform evidence-based clinical management. Full article

Over the past two decades, Citizen Science (CS) has experienced rapid growth, driven by technological advancements and the rise of digital platforms. This work examines the necessity for standardization in Citizen Science data management and discusses how existing data standards can enhance the impact of citizen-generated data. CS standardization ensures data quality, comparability, reusability, and interoperability, making data suitable for contributing to the Marine Strategy Framework Directive (MSFD) and the United Nations Sustainable Development Goals (SDGs). This paper examined 130 Citizen Science publications and found that most collected data referred to the MSFD Descriptor 1 (Biodiversity—44.96%) and Descriptor 10 (Marine Litter—20.93%), followed by the alien species distribution (D2—11.63%), hydrography (D7—6.20%), eutrophication (D5—6.20%), and marine pollution (D8—3.10%). Analysis of 108 publications on SDG alignment revealed that the majority (35.58%) focused on reducing marine pollution. This paper reviews the best practices for effective Citizen Science data management, including standards for data structures, content, values, and exchange. Based on this review, Darwin Core, Ecological Metadata Language (EML), and the OGC SensorThings API appear to be the most suitable standards for MSFD-relevant CS data. Therefore, policymakers could enable the formal integration of standardized CS datasets into MSFD monitoring workflows. Full article

Background/Objectives: The optimal management of anterior cruciate ligament (ACL) rupture remains debated, especially regarding long-term outcomes after early ACL reconstruction (ACLR) versus rehabilitation-first with optional delayed ACLR. The interpretation of randomized evidence is complicated by frequent treatment crossover. This review synthesized evidence from randomized controlled trial (RCT) cohorts comparing surgical versus rehabilitation-first management strategies across available follow-up durations. Methods: A structured review based on a systematic literature search and narrative synthesis was conducted, with study identification and reporting guided by PRISMA 2020. MEDLINE (via PubMed) and Google Scholar were searched in February 2026 for English-language human RCTs (2000–2026) comparing early ACLR plus rehabilitation with rehabilitation-first management allowing delayed ACLR for persistent instability. A linked-report PubMed search using the KANON trial registration number (ISRCTN84752559) was additionally performed to identify cohort-derived follow-up publications. Reports were grouped by underlying RCT cohort. Data were extracted on crossover, follow-up, and clinical outcomes. Risk of bias for primary RCT reports was assessed with Cochrane RoB 2. Results: Twenty-seven reports representing three RCT cohorts (KANON, COMPARE, ACL SNNAP) were included; six index reports were prioritized for synthesis. In acute ACL rupture (KANON, COMPARE), early ACLR did not show a consistent long-term superiority in patient-reported outcomes versus rehabilitation-first with optional delayed ACLR, although COMPARE reported a statistically significant 2-year subjective functional difference favoring early ACLR; early ACLR more consistently improved mechanical stability and reduced instability episodes. Crossover from rehabilitation to delayed ACLR was common. In non-acute ACL injury with persistent symptomatic instability (ACL SNNAP), surgery-first improved 18-month patient-reported outcomes. Meniscal procedure rates and osteoarthritis-related outcomes did not consistently favor early ACLR. Conclusions: In acute ACL rupture, rehabilitation-first with timely access to delayed ACLR appears to provide long-term patient-reported outcomes comparable to an early ACLR strategy in many patients, while early ACLR more consistently improves knee stability. In non-acute symptomatic ACL deficiency, a surgery-first strategy appears more effective in the mid-term. These randomized trials should be interpreted as comparisons of management strategies rather than of “pure” operative versus nonoperative treatment approaches. Full article

South African irrigation schemes face critical challenges of water scarcity, infrastructure deterioration, and limited monitoring capacity, threatening agricultural productivity and food security. This scoping review systematically analyses 59 peer-reviewed publications (2000–2025) on sensor-based and acoustic irrigation monitoring technologies in South Africa, using transformer-based natural language processing (Sentence-BERT embeddings), unsupervised Machine Learning (UMAP dimensionality reduction, HDBSCAN clustering), and geospatial mapping applied to literature retrieved from Web of Science and Scopus. Results show that water quality monitoring (42.4% of studies) and remote sensing (25.4%) dominate the national research landscape, while soil moisture sensing and modelling remain comparatively limited. Notably, no peer-reviewed studies applying acoustic monitoring technologies to irrigation were identified, representing a critical gap despite proven international applications for leak detection (95–98% accuracy), widespread infrastructure aging (over 50% of schemes exceeding 30 years), and reported water losses of 30–60% in poorly managed systems. Reported experimental water savings range from 15% to 30%, yet applications remain largely confined to pilot-scale implementations concentrated within a limited number of Water Management Areas. Persistent adoption barriers include infrastructure unreliability, financial inaccessibility, limited digital literacy, and weak institutional coordination. The review recommends: (i) expanding research coverage across underrepresented regions and Water Management Areas; (ii) strengthening extension support and technical training to enable broader adoption; and (iii) integrating low-cost sensor networks with predictive, data-driven irrigation advisory systems. These priorities aim to support scalable, context-sensitive irrigation modernisation under increasing water scarcity pressures. Full article

This study investigates emotional patterns in state government disaster guideline documents using keyword-level emotion analysis and TF–IDF based topic modeling, framing disaster policy communication as an emotional–cognitive dual structure, drawing from Situational Crisis Communication Theory. The findings demonstrate a strong negative relationship between fear and neutrality, indicating a functional separation between risk awareness and administrative clarity. Nine topics were identified and organized into clusters centered on operational support, administrative structures, and policy frameworks, while content related to hazards and recovery emerged as a distinct semantic category based on cosine similarity analysis. In the integrated analysis of sentiment and topics, neutral language predominates, reflecting the cognitive dimension of government guidelines, with fear and sadness appearing as secondary but systematically patterned emotions. Fear concentrates in topics addressing hazardous conditions and risk-related content. Emotionally neutral language has traditionally been privileged in public administration, but the findings highlight disaster policy communication shaped by governance objectives that privilege specific emotional orientations aligned with coordination, participation, and risk management. State disaster guidelines function not only as technical instructions but also as structured communicative instruments that operate along a dual cognitive–emotional model, shaping public attention and response. Full article

Urban squares remain underrepresented in conservation-oriented literature compared with parks, street trees and green infrastructure. This scoping review uses CS-derived categories as an analytical lens to examine how the literature on urban squares frames conservation targets, direct threats, contributing factors and conservation actions. Following PRISMA-ScR, we searched Scopus and Web of Science for English-language peer-reviewed articles (2014–2024). After screening, 69 studies were included. Full texts were coded into CS-derived components and synthesised through frequency distributions, a cross-case conceptual synthesis, and an exploratory clustering of recurrent CF-DT-CT configurations. The reviewed literature is strongly centred on human-centred outcomes, particularly health, air quality and water quality, while biodiversity-related targets remain comparatively underrepresented. The most frequently investigated direct threats are pollution-related and linked to natural system management and modification, whereas other pressures are addressed less consistently. Contributing factors are dominated by meteorological conditions and vegetation coverage and composition. Reported conservation actions emphasise monitoring technologies, regulatory policy and green infrastructure, while others receive limited attention. Together, these analytical steps help make recurrent pathways and underrepresented dimensions more explicit, providing a more transparent evidence base for context-sensitive urban planning and nature-based solutions. Full article

Application Tracking Systems (ATSs) have evolved significantly since their inception in 1996, transitioning from simple resumérepositories to AI-driven tools with advanced capabilities. While these developments have improved recruitment efficiency, they have also raised important ethical, organizational, and human-rights-related concerns. Bias in machine learning (ML) training data, opaque decision criteria, and excessive reliance on automated judgment may contribute to unfair treatment, reduced transparency, and limited human oversight in hiring processes. This study addresses these challenges by proposing a human-centered approach to ATS-supported recruitment based on a set of Humanization Services. Using a Design Science Research approach, three main artifacts were developed: a Job Requirements Validation Module, a Bias Trigger Removal Module, and a blockchain-supported dual-authorization mechanism for vacancy approval, which requires digital signatures from qualified professionals to approve job postings, ensuring that there are humans that assume responsibility. These components are intended to improve job posting quality, reduce bias-conducive information in applicant data, and strengthen accountability in recruitment workflows. The evaluation provides initial empirical support for the operational feasibility of the proposed approach under the tested conditions. The study therefore contributes a practical and theoretically grounded step toward more transparent, accountable, and human-centered AI-supported recruitment. Full article

Teachers’ readiness in bilingual early childhood education is increasingly recognized as a multidimensional construct shaped by both professional and language-related factors. However, existing research has typically examined these factors separately, with limited evidence on how they combine across teacher groups, particularly in minority-language contexts. This study examined teachers’ readiness to deliver state-language instruction to dual language learners (DLLs) in Hungarian-medium kindergartens in Slovakia. A total of 313 kindergarten teachers participated in the study. Data were collected through a survey assessing multiple dimensions of readiness. Principal component analysis and confirmatory factor analysis supported a six-factor model comprising professional preparation, teacher competencies, challenge management, instructional aids use, professional needs, and Slovak language use outside kindergarten. Latent profile analysis identified three readiness profiles (low, moderate, and high), reflecting differences in overall preparedness. Background characteristics, particularly age, teaching experience, and language-related factors, were significantly associated with higher readiness. Teachers who used Slovak more frequently in everyday contexts showed higher readiness. Mediation analysis indicated that language proficiency and preferred language use did not mediate the relationship between teaching experience and teachers’ readiness, but functioned as independent predictors. These findings highlight the joint importance of professional and language-related factors in shaping teachers’ readiness and offer implications for teacher education and policy in bilingual early childhood settings. Full article

Background/Objectives: Acute carotid tandem lesions (TLs), defined by concurrent cervical internal carotid artery (ICA) stenosis or occlusion and intracranial large vessel occlusion, occur in 10–20% of patients undergoing mechanical thrombectomy (MT) for acute ischemic stroke (AIS). Optimal periprocedural antiplatelet management during emergent carotid artery stenting (eCAS) remains uncertain, particularly regarding the balance between preventing stent thrombosis and avoiding hemorrhagic complications. Methods: A narrative review was conducted using PubMed and Scopus (until 6 March 2026) to identify English-language studies evaluating antiplatelet therapies during eCAS for TLs. We included seven real-world studies and registry analyses. Data on study design, patient characteristics, procedural strategies, angiographic results, functional outcomes, and safety metrics were extracted. Results: No randomized controlled trials (RCTs) were identified. The available evidence is derived exclusively from observational studies. Across these cohorts, glycoprotein IIb/IIIa inhibitors (GPIs), particularly tirofiban, were generally associated with lower rates of in-stent thrombosis and higher reperfusion success, with symptomatic intracranial hemorrhage (sICH) rates that appeared comparable to or lower than those reported with acetylsalicylic acid (ASA). Cangrelor, an intravenous (IV) P2Y₁₂ inhibitor, was associated with improved stent patency and increased likelihood of complete reperfusion, although reported effects on clinical outcomes were inconsistent when compared with GPIs or ASA. Aside from abciximab, potent IV antiplatelet agents did not consistently show an increased sICH signal. Oral dual antiplatelet therapy was also associated with improved technical outcomes without a clear excess in bleeding complications. Conclusions: Current real-world observational data suggest that rapid-acting IV antiplatelet agents—particularly GPIs and, increasingly, cangrelor—may represent feasible periprocedural options during eCAS for TLs, with potential benefits for technical success and no consistent evidence of increased hemorrhagic risk. However, interpretation is limited by study heterogeneity and non-randomized designs. The absence of RCTs highlights the need for prospective comparative studies and standardized periprocedural antiplatelet protocols. Full article

The rapid growth of e-commerce has highlighted the critical need for efficient customer review sentiment analysis, yet natural language complexities like sarcasm and mixed sentiments remain challenging. To address these ambiguities, this study proposes a novel sentiment analysis architecture. The methodology integrates a bidirectional Long Short-Term Memory (Bi-LSTM) network with a Luong Attention mechanism. The Bi-LSTM component models the sequential and bidirectional context of the text, while the Luong Attention mechanism isolates and emphasizes the most significant parts of the reviews for precise sentiment detection. The proposed hybrid model demonstrates exceptional performance compared to traditional methods, achieving an accuracy of 96.67%, a precision of 96.83%, and a recall of 96.67%, alongside relatively low overfitting. Ultimately, the findings confirm that this architecture effectively manages ambiguous language and is highly capable of large-scale, real-time sentiment analysis, offering robust analytical tools for shaping e-commerce marketing strategies. Full article

Cancer has become a major global health threat due to its high incidence and mortality. However, the development of anti-cancer drugs is limited by high costs, long cycles, and low success rates, slowing the progress of new treatments. As a method that simulates human cognitive functions, artificial intelligence (AI) has greatly improved the efficiency of drug development. Machine learning is a core part of AI and supports applications such as natural language processing and computer vision. This paper reviews recent advances in AI for optimizing anti-cancer drug discovery, development, and medication therapy management. First, we highlight the applications of AI in target identification, druggability assessment, drug screening, and repurposing. Second, we detail how AI optimizes drug combination therapy and clinical trial design. Finally, we describe the role of AI in treatment management, including nanoparticle delivery systems, personalized dosing, and adaptive therapy. AI greatly streamlines anti-cancer drug development and provides new directions for precision cancer therapy. Full article

Background/Objectives: Overcrowding in emergency departments (EDs) remains a critical challenge in modern healthcare systems, driven in part by patient uncertainty regarding symptom urgency and a lack of accessible medical guidance. Recent advances in artificial intelligence, particularly large language models (LLMs), present a novel opportunity to support patient navigation and relieve pressure on ED infrastructures. Methods: A total of 238 unique patient questions were identified through a structured web search. Following deduplication and thematic clustering, 15 representative questions were selected. Each question was submitted to the three LLMs—ChatGPT (v3.5), DeepSeek, and Gemini—using a standardized prompt. Responses were assessed by clinical experts (N = 8) who were blinded to the model source. Reviewers selected the best overall response per question, as well as the individual responses of the three LLMs for each respective question. Results: ChatGPT was selected as the best-performing model in 60% of cases, with DeepSeek and Gemini selected in 23% and 17%, respectively. ChatGPT responses also achieved the highest proportion of “excellent” quality ratings and the lowest proportion of “unsatisfactory” outputs. Across all models, clarity was the most positively rated domain (79% agreement), followed by empathy (72%), length/detail appropriateness (71%), and completeness (65%). Over two-thirds of raters expressed willingness to integrate LLM-based tools into clinical practice for patient education and pre-triage counseling. Conclusions: Large language models demonstrate promising capabilities in responding to emergency care-related patient queries. Their ability to deliver medically sound and communicatively effective answers positions them as potential digital adjuncts in the management of low-acuity ED presentations and prehospital triage. Full article

This paper presents a literature review regarding the automatic extraction of meaningful information regarding suppliers’ ESG and sustainability compliance from textual sources. Assessing suppliers’ ESG compliance has become a key challenge for procurement managers. Given the large number of suppliers and required data points, traditional approaches such as questionnaires and audits are inefficient, ineffective and difficult to scale. To solve this problem, we investigate whether the required information can be automatically harvested from suppliers’ textual sources. Our structured literature review identified 82 papers on which we performed a descriptive analysis, finding a rich and flourishing body of literature produced by a heterogeneous scientific community. We further reduced our sample to 73 full-text articles that supported a more in-depth content-based analysis. We investigated which data sources can be used in particular, which technologies can be leveraged, and which types of outputs can be generated. Even though they could provide much of the required information, corporate websites are rarely utilized as data sources, partly due to the limited adoption of large language models (LLMs). LLMs are less diffused than traditional Natural Language Processing (NLP) techniques due to their recent introduction and some gaps that still limit their performance. This represents both a constraint and an opportunity for future research. Full article

While the quantum emergence of spacetime is becoming a major research topic in physics, the quantum emergence of intelligence has not been widely researched in quantum information science (QIS). Following causal-logical quantum gravity theory, bipolar entropy vs. entropy and negative entropy (or negentropy) are reviewed and distinguished for quantum emergence/submergence of quantum agent (QA) and quantum intelligence (QI) in algebraic terms. This work refers to QA as an entangled bipolar string/superstring in bipolar dynamic equilibrium (BDE) and QI being centered on logically definable causality in regularity, mind-light-matter unity, and brain-universe similarity. ER = EPR is extended to ER ≥≥ EPR for the mathematical scalability of bipolar strings and their collective entanglement. The extension leads to a number of conjectures, testable predictions, and theorems. The term “equilibraton” is proposed as a type of EPR or bipolar generic string to serve as an entropic stitch to collectively hold the universe together as a quantum entanglement in BDE with ubiquitous, regulated local emergence and submergence of QA&QI. Equilibraton leads to the concept of bipolar entropy square—a complete entropic solution to the background issue in quantum gravity. With complete background independence, energy/information conservational bipolar entropy, energy/information invariance, bipolar entropy non-additivity, and equilibrium-based plateau concavity are introduced. The nature of the one-dimensional arrow of time is conjectured. As a unification of order and disorder for equilibrium-based regulation, bipolar entropy bridges QA&QI to agentic AI, where quantum-bio-economics can be viewed as a topological intervention of a natural dynamic equilibrium in a social or natural world. Use cases are reviewed to illustrate the practical and theoretical aspects of bipolar entropy in business management, quantum-bio-economics, quantum cryptography, physics, and biology. Eddington–Einstein’s comments on entropy are revisited. It is expected that bipolar entropy will bring quantum emergence/submergence to agentic AI&QI for entangled machine thinking and imagination as a naturally scalable and testable foundation of real-world quantum gravity, quantum information science (QIS), quantum cognition, and quantum biology (QCQB) to enhance Large Language AI Models (LLMs) and machine intelligence. Full article