Exogenous Cushing's syndrome (CS) is most commonly caused by therapeutic glucocorticoids (GC), but rare and atypical sources can mimic endogenous GC. This narrative review provides a concise overview of these uncommon causes and their diagnostic implications. A PubMed search was performed using the keywords "Cushing syndrome," "factitious Cushing syndrome," "herbal medicine", "supplements" and "endocrine disruption" on March 9th, 2026. Additional articles were identified through manual screening of reference lists. No restrictions were applied regarding language or study design, and the most relevant publications were selected. Three main categories of rare exogenous CS were identified. First, herbal or traditional remedies adulterated with undeclared GC. Second, compounds with GC-like activity, which activate the glucocorticoid receptor and suppress the hypothalamic-pituitary-adrenal axis. Third, factitious CS due to covert GC self-administration, often associated with psychiatric or caregiver-fabricated conditions. Diagnosis can be challenging; advanced analytical methods, including liquid chromatography-tandem mass spectrometry (LC-MS/MS) and liquid chromatography-high resolution mass spectrometry (LC-HRMS), are essential to detect synthetic or undeclared GC and differentiate these cases from endogenous hypercortisolism. Rare exogenous causes of CS represent important diagnostic challenges. Careful evaluation of patient history, combined with the use of LC-MS/MS or LC-HRMS, is crucial to identify these conditions, prevent misdiagnosis, and avoid unnecessary diagnostic or surgical interventions.

The rapid development of information and communication technology has brought various innovations in the field of education, including the utilization of Augmented Reality (AR) for learning. This research aims to develop an Augmented Reality-based application for introducing the culture of West Sumatra, which can be used as an interactive learning medium. The method used in the development of this application employs Unified Modeling Language (UML) for system design. This research utilizes Marker-Based AR technology. The application is developed using Unity 3D and displays 3D objects that represent elements of West Sumatra's culture. In this study, the application was tested using the User Acceptance Test (UAT) method, yielding a result of 93%. This indicates that the application is helpful in understanding the culture of West Sumatra and is effective as a learning media tool.

Physicians increasingly deliver health information and medical support through online medical teams (OMTs) in telemedicine platforms, where patients interact with multiple physicians within a single consultation episode. Yet limited research has systematically examined how physician collaboration influences team effectiveness in OMTs. This study investigates the impact of physician collaboration on team effectiveness using a panel dataset of 1,181 OMTs from 2017 to 2023 on a leading Chinese telemedicine platform. We employ a difference-in-differences design and large language models to identify the effects of physician collaboration and uncover the underlying mechanisms. Our results show that physician collaboration reduces team effectiveness. To unpack the underlying mechanisms, we further examine two potential effects: the cognitive-load effect and the free-riding effect. The empirical results reveal that the cognitive-load effect significantly decreases team effectiveness, whereas there is no empirical evidence to support the free-riding effect. Additionally, heterogeneity analyses indicate that the negative effect of collaboration is mitigated when more experienced physicians are involved. These results provide theoretical insights into team performance in telemedicine contexts and advance health communication research. The findings also offer practical guidance for platform designers to strengthen communication management in team-based telemedicine platforms.

The dynamics of sponge city parks: Integrated stormwater management and urban parks in shanghai

Scientific production on induced abortion in Peru: A scoping review of studies from 1965-2024.

ABSTRACT Hue Imperial Architecture, the core of the UNESCO-listed Complex of Hue Monuments (inscribed in 1993), provides a rich foundation for architectural and heritage scholarship. This study integrates classical Chinese historical sources, Nguyen-dynasty drawings, and extensive fieldwork across key sites such as the citadel, imperial city, mausoleums, pagodas, and shrines. Through doctrinal analysis, site investigation, typological classification, and diagrammatic methods, the research identifies five major building categories and twenty-two architectural patterns. Findings show that the architectural system is structured by three fundamental parameters – floor-plan configuration, truss type, and façade form – which together define a coherent design language. By adjusting these parameters, builders systematically generated building categories and pattern variations. This modular design logic ensured balanced relationships between aesthetic expression, structural order, and architectural scale. The study further demonstrates that Nguyen-dynasty methods of shaping, patterning, and modularization enabled efficient large-scale construction while preserving visual harmony, structural stability, and functional flexibility. Although rooted in a premodern craft tradition, these techniques remain highly relevant – and in some respects remarkably advanced – when compared with contemporary practice. The research contributes to architectural history and heritage conservation while offering practical guidance for the preservation and interpretation of the Monuments.

Coal mining causes significant environmental degradation, transforming land from a stable ecosystem into a disturbed area with complex ecological and chemical challenges. This study aims to evaluate the feasibility of post-coal mining reclaimed land as an area for livestock feed and animal husbandry production, focusing on the main research question: Does the land meet specific criteria such as heavy metal thresholds (e.g., Pb <0.3 mg/kg, Cd <0.1 mg/kg according to FAO/WHO), microbial community recovery (enzyme activity >30% increase), and nutrient availability (N, P, K >1% total)? The systematic literature review method involved searching 94 empirical articles from Google Scholar (years 2016-2025), selected based on criteria: empirical research design, English or Indonesian language, complete text, and relevance to the theme of mine land reclamation. Data analysis used a thematic approach to identify main themes related to reclaimed land conditions. The results show that 80% of studies report heavy metal pollution with Pb concentrations ranging from 5-50 mg/kg, Cd 0.5-5 mg/kg, and Hg 0.1-2 mg/kg, which pose a risk of bioaccumulation in feed plants; post-reclamation soil quality improvement trends reach 20-50% for soil organic carbon (SOC) and 30-60% for microbial enzyme activity, although the main constraints are slow microbial community recovery and chronic nutrient deficiency. In conclusion, reclaimed land is conditionally suitable for livestock feed and animal husbandry production if integrated restoration strategies are implemented to address microbial contamination, heavy metals, and nutrients, as well as continuous monitoring to ensure feed safety and animal health.

This paper presents an approach to designing a locally deployed smart home control system as an educational platform based on the integration of Artificial Intelligence (AI), Natural Language Processing (NLP), and Internet of Things (IoT) technologies. The relevance of the study is driven by the widespread reliance of modern smart home solutions on cloud-based platforms, which results in increased response latency, limited offline functionality, higher security risks, and insufficient support for less common natural languages, including Ukrainian, as well as the growing need for practical educational platforms that enable students to gain hands-on experience with AI-driven automation systems and intelligent interfaces. The primary objective is to improve the reliability, responsiveness, and privacy of smart home systems while developing a versatile educational tool for teaching modern technologies in intelligent systems design and natural language processing. The proposed system is built on the open-source Home Assistant platform and the MQTT messaging protocol, enabling low-latency message delivery, reduced network overhead, and stable operation in environments with limited internet connectivity. Special attention is given to system security through MQTT broker configuration with authentication mechanisms and encrypted credentials. User interaction is enhanced through an AI-driven NLP module implemented in Python using the spaCy library and the Pymorphy3 morphological analyzer. The module performs text normalization, tokenization, lemmatization, and morphological analysis of Ukrainian-language commands, allowing accurate extraction of user intent and target devices. A Telegram bot provides a cross-platform user interface without additional client-side software. The modular architecture allows students to modify individual components, experiment with NLP models, and develop custom extensions, making the platform suitable for laboratory work and research activities. Experimental validation demonstrates reduced command execution latency of 150–200 milliseconds compared to cloud-dependent solutions (500–800 milliseconds). The NLP module achieved accuracy rates of 94% і for single-device commands, 91% і for multi-device commands, and 87% і for complex conditional commands. The results indicate that the system represents both a viable alternative to proprietary cloud-based platforms and an effective educational platform for developing professional competencies in AI-driven automation technologies.

Abstract Search-and-Rescue (SaR) missions often expose first responders to extreme risks, particularly when operating in hazardous or unstable environments. Recent advances have made it possible to deploy swarms of Unmanned Aerial Vehicles (UAVs) to assist in these operations, offering enhanced situational awareness, faster coverage, and reduced human exposure. Yet, the dynamic and unpredictable nature of SaR environments poses persistent challenges in coordinating drone teams, ensuring communication, and maintaining adaptability in the face of unexpected changes. Command & Control (C2) is a long-established military concept concerned with how entities and resources are organised, coordinated, and directed to achieve strategic objectives. In recent years, C2 has found renewed relevance in highly distributed and information-rich domains, such as disaster relief, humanitarian response, and large-scale coordination systems. Its core principles, namely structured communication, adaptive organization, and mission-driven alignment, make it an appealing framework for managing drones fleets in SaR missions. In this paper, we extend our previous work by providing a refined and formalised representation of C2 for drone-based SaR operations. First, we revisit and clarify NATO’s concepts of C2 Approaches and C2 Agility, grounding them in the operational realities of autonomous drone coordination. Second, we introduce a formal specification of C2 Systems, expressed through both a Domain-Specific Modeling Language (DSML) for system design, and a Structural Operational Semantics (SOS) that formally defines their dynamic behaviour. This dual representation enables systematic reasoning about the correctness and adaptability of C2-driven drone coordination. Third, we report on a simulation environment that operationalises our metamodel, allowing the design, execution, and observation of C2 System behaviours under evolving mission conditions. Altough preliminary, our paper aims at pushing forward formal foundations of C2 for (semi-)autonomous drone systems, and provide a way toward designing fleets capable of adaptive, reliable, and verifiable behaviour.

This study investigates how exhibition labels in contemporary art museums contribute to the construction and reinforcement of cultural hierarchies through both language and spatial design. While museums are often perceived as neutral repositories of knowledge, they are in fact sites where power operates subtly, shaping visitors’ perceptions of cultural value. Drawing on Foucault’s concept of knowledge/power and Lefebvre’s theory of the production of space, this research focuses on the geographical dimension of cultural hierarchies, specifically the divide between Western art and non-Western art. The analysis is based on qualitative case studies of selected contemporary art museums, combining textual analysis of exhibition labels with spatial mapping of display layouts. Findings reveal that label phrasing, narrative framing, and spatial positioning often privilege artistic traditions from Western art, while relegating works from non-Western art to marginal positions in both language and space. The study also identifies emerging curatorial strategies that decentralize authority and promote more inclusive, decolonial exhibition practices.

Color is a powerful visual element that transcends linguistic boundaries and communicates meaning across cultures. This study investigates color as a universal language in supporting Inclusive Design (ID), which aims to create products usable by individuals regardless of background or ability. The research examines how color influences perception, emotion, and cognition across different cultural contexts. A total of 547 design professionals from diverse disciplines were selected using stratified random sampling. Data were collected through a structured questionnaire and analyzed using SPSS software. The findings indicate that applying consistent color principles enhances cross-cultural usability and improves accessibility in design outcomes. Results also reveal that certain colors evoke similar psychological responses across countries, reinforcing their universal communicative potential. The study’s innovation lies in conceptualizing color as a shared design language that bridges cultural differences. By emphasizing strategic color decisions, this research provides practical insights for designers to create more inclusive, accessible, and culturally responsive products.

This qualitative study investigates the challenges teachers face when teaching English as a foreign language (EFL) to deaf (in this article, deaf (lower case) refers to the audiological condition of hearing loss, whereas Deaf (capitalized) is used to denote individuals who identify as members of the Deaf community and share a common sign language and distinct cultural values) and hard-of-hearing (DHH) students in German schools for the Deaf. The study is situated within a structural–theoretical professionalization framework, which focuses on the relationship between institutional conditions, teacher education structures, and professional action. Semi-structured interviews were conducted with 16 teachers of DHH students and the data were examined using qualitative content analysis. The findings reveal five central areas of challenge: (1) heterogeneity of the student body; (2) limited time (for preparing and adapting materials); (3) restricted subject-matter and sign-language competence, including missing links between EFL didactics and Deaf education in teacher training; (4) uncertainties surrounding the language design of EFL instruction, particularly the role of American Sign Language (ASL), German Sign Language (DGS), and written English; and (5) the lack of consistent, accessible exam formats and standards. Teachers report substantial insecurity due to the absence of coherent concepts, policy frameworks, and specialized training pathways, which fosters divergent classroom practices and tensions within teaching staff. The results highlight an urgent need for systematic integration of Deaf education, sign language training, and EFL pedagogy in teacher education, as well as for evidence-based guidelines on language classroom practice and assessment for DHH learners.

Mycobacterium riyadhense is an emerging nontuberculous mycobacterium associated with infections in immunocompetent and immunocompromised patients. To identify the epidemiology, risk factors, presentations, and antimicrobial susceptibility of M. riyadhense, we conducted a systematic review on PubMed, Web of Science, and Google Scholar adhering to PRISMA guidelines. We included publications during 2009-2025, with no limitation on language or study design. Cases of uncertain infection or identification were excluded. Review protocol was pre-registere in PROSPERO with protocol number; CRD420250653025. We identified 22 studies reporting on 69 cases of M. riyadhense. Patient median age was 35.5 and 80% were male. Common presentations included pneumonia (n=52), lymphadenitis (n=13), osteomyelitis (n=8), or disseminated infection (n=8). Susceptibility to Rifampin and Ethambutol was observed in all tested isolates. Resistance to Isoniazid was detected in 5/8 isolates. This review identified a significant variability in the diagnosis and treatment of M. riyadhense. This underscores the urgent need for standardized diagnostic protocols and targeted treatment strategies to effectively manage infections. https://www.crd.york.ac.uk/prospero/, identifier CRD420250653025.

ABSTRACT The quick development of financial technology (FinTech) necessitates robust backend infrastructures in addition to extremely responsive, secure, and visually appealing user interfaces. This study offers a thorough technical analysis of a contemporary Financial Operating System (Financial OS) constructed with Tailwind CSS, TypeScript, and React. The system uses sophisticated UI concepts, such as glass morphism and smooth transitions, and is inspired by the CRED design language. The program uses Supabase (PostgreSQL) for safe, scalable data storage and React Router v7's Data Mode for stateful, complicated routing. The deployment of a Service Abstraction Layer (SAL) that guarantees offline resilience with AES-GCM encrypted local storage fallbacks is a fundamental contribution. During network throttling, experimental results show a 66% improvement in Time to Interactive (TTI) and 100% uptime for read operations. KEYWORDS: Row Level Security (RLS), PostgreSQL, Offline-First, Architecture Service Abstraction Layer (SAL), React Router v7, and FinTech Frameworks.

This study focuses on the costume culture depicted in Tang Dynasty Dunhuang murals, exploring its digital translation and integration into contemporary fashion design. It aims to address the research question of how ancient mural art can merge with digital fashion design. A combination of iconographic analysis, practice-based research, and 3D digital reconstruction was employed to systematically examine and reinterpret the form, color, and structure of Dunhuang mural costumes. The findings reveal that these costumes feature flowing lines, balanced silhouettes, and vivid color palettes, reflecting the Tang Dynasty’s aesthetic ideals of grandeur, vitality, and harmony with nature. Through digital reconstruction and creative experimentation, the study successfully transforms traditional Dunhuang costume elements into digital fashion works that embody both cultural depth and modern aesthetics. The innovation of this study lies in introducing a digital fashion design perspective to systematically analyze the aesthetic structure of Dunhuang mural costumes, establishing a translation pathway from traditional imagery to digital fashion, and demonstrating that the fusion of traditional aesthetics and digital technologies not only broadens the expressive language of contemporary fashion design but also contributes to the preservation, inheritance, and global dissemination of cultural heritage.

Implementing a declarative query language for high level machine learning application design

Cellular metabolic pathway reconstruction is an essential yet challenging goal in synthetic biology. We outline a conceptual framework integrating retrosynthetic planning with biological constraints to enhance biological feasibility. We believe the approach spans individual- and systems-level modeling, enabling large language model-driven understanding, design, evaluation, and optimization of metabolic networks.

Understanding how landscape composition relates to aesthetic perception remains a challenge in landscape design research. While semantic segmentation methods have achieved strong performance in scene understanding, they are rarely used to analyze design structure and aesthetic interpretation in garden landscapes. This study introduces a computational framework that integrates semantic segmentation with compositional design descriptors and social aesthetic indicators for the analysis of garden design language. A dataset containing more than 2,000 garden images was constructed with pixel-level semantic annotations and associated aesthetic labels. Based on the segmentation outputs, a set of quantitative descriptors was derived to represent spatial composition of landscape elements, including spatial distribution, proportional composition, and element adjacency relationships. These compositional descriptors were further combined with social aesthetic indicators obtained from public surveys and textual analysis to explore relationships between landscape structure and perceived aesthetic qualities. Experimental results show that the proposed framework achieves competitive performance in semantic segmentation while providing interpretable representations of landscape design composition. The analysis further reveals consistent associations between certain compositional patterns and aesthetic perception. The proposed approach demonstrates the potential of integrating semantic scene understanding with design-aware feature representation for data-driven analysis of landscape aesthetics and garden design language.

Diagnostic reasoning, the cognitive process of interpreting clinical information to arrive at a diagnosis, is a critical competency in medical education, and has been taught using authentic methods such as standardized patients and peer role-playing that simulate real clinical encounters. Although effective, these approaches require substantial time, space, and cost. To address the limitations of traditional approaches, AI-powered virtual patient chatbots were developed between April 21-28, 2025, with a chatbot builder that incorporates natural language processing. The design was guided by two core cognitive models of diagnostic reasoning-the dual process model and the memory model. The chatbots simulated authentic physician-patient interactions across three clinical scenarios: fatigue, stomachache, and memory loss. Students can perform differential diagnoses based on various clinical data and received immediate feedback on diagnostic accuracy. The patient chatbots were evaluated through surveys and interviews with faculty (May 2025) and students (November 2025). Findings consistently indicated that the chatbots functioned as an effective (4.50/5) and usable tool (4.46/5) for diagnostic reasoning practice. Participants perceived that the chatbots supported 'core cognitive processes of diagnostic reasoning' by prompting active hypothetico-deductive reasoning, while repeated exposure to different cases was viewed as facilitating pattern recognition. Faculty highlighted the chatbots' cost-efficiency and scalability within curricula, while students emphasized authentic "from-zero" clinical reasoning and the value of reviewing dialogue logs for reflection. Overall, the results demonstrate that the chatbots can provide a feasible and educationally meaningful environment for practicing diagnostic reasoning in medical education. Future work should refine AI-powered virtual patient chatbots by incorporating tiered case complexity, patient-centered language, and equity-oriented design. Effective use will require systematic instructional design and learner preparation for emotionally challenging scenarios. Larger empirical studies using both outcome and interaction data are needed to evaluate educational impact and guide integration into clinical training.

This study investigates the effects of task interactivity (monologic versus dialogic tasks) and willingness to communicate (WTC) on the speech fluency of 88 Hong Kong undergraduate English as a second language learners. Participants, categorised into high- and low-WTC groups based on a WTC questionnaire, completed either a monologic or a dialogic collaborative “spot-the-picture-difference” task. Fluency was assessed through speech rate, frequency of filled pauses and unfilled pauses at difference locations, and repairs. Results indicated that learners with high WTC demonstrated significantly faster speech rates and produced fewer repairs than those with low WTC. Monologic tasks elicited significantly more mid-clause pauses and slightly more filled pauses compared to dialogic tasks. Crucially, a significant interaction effect revealed that the disparity in speech rate between high- and low-WTC learners was more pronounced during monologic tasks. This suggests that the detrimental effect of lower WTC on speed fluency is particularly marked when learners engage in independent speaking, while dialogic tasks may offer a more supportive environment for learners with lower WTC to maintain a fluent speech rate. These findings highlight the complex interplay among affective factors, task design and second language oral performance, with implications for language pedagogy and assessment.