Abstract: Background: Emotional aspects of early dental experiences have long-lasting effects. This study aimed to assess parents’ childhood dental memories and their impact on current attitudes toward dental treatment and to evaluate the perceived usefulness of educational material focused on psychological management of children’s dental visits. Methods: An educational booklet was developed and distributed to parents, who were encouraged to read it and complete a short questionnaire. Responses were analysed using IBM SPSS Statistics 25. Results: In the first month, 142 parents (88% mothers) participated. Negative childhood dental experiences were reported by 44.4% (more frequent among mothers, p

Abstract: This study describes the development and mechanistic analysis of a coaxial jet antisolvent process for the continuous production of nanocarriers. A single experimental platform was used to generate both curcumin-based nanoparticles and nanoliposomes, enabling direct comparison of how mixing regime and formulation variables influence product characteristics. Fluid-dynamic behavior was first characterized using tracer and micromixing experiments, revealing a strong dependence of mixing time and composition gradients on flow conditions. Nanoparticles and liposomes obtained under optimized conditions exhibited submicron sizes and controlled polydispersity. To rationalize these observations, a preliminary computational framework was implemented, combining Reynolds-averaged computational fluid dynamics with a population balance formulation solved by the method of moments. The model provided spatially resolved insight into solvent exchange, supersaturation development, and nucleation–growth dynamics, offering qualitative agreement with experimental trends. Although simplified, the modeling approach establishes the basis for future extensions toward full population-balance distribution simulations capable of predicting complete particle size distributions. Overall, the coaxial jet mixer emerges as a versatile and informative tool for continuous nanocarrier production and for advancing a rational, model-assisted design of pharmaceutical nano-systems.

Abstract: Contemporary organizations function as complex networks, yet leadership cognition remains dominated by linear metaphors that assume sequential causality and hierarchical control. This article introduces Graph Thinking as a multi-dimensional leadership capability comprising cognitive, analytical, and behavioral components that enable leaders to perceive, analyze, and deliberately shape organizational network structures. We position Graph Thinking at the intersection of systems thinking, social network analysis, and ecosystem strategy, arguing that it synthesizes these traditions while extending them to address the specific challenges of artificial intelligence deployment. Drawing on network science and strategic management theory, we develop a multi-level framework specifying how Graph Thinking manifests at individual, organizational, and ecosystem levels, with explicit attention to network dynamics and temporal evolution. Through illustrative thought experiments spanning diverse organizational contexts, we demonstrate how network properties function as diagnostic instruments for strategic decision-making. We argue that AI integration creates conditions that may reward explicit network mapping, while acknowledging this relationship is contingent and politically contested. The article contributes to strategic management literature by specifying measurement approaches for future empirical research, addressing power dynamics inherent in network legibility efforts, and providing actionable developmental frameworks. We conclude with boundary conditions, limitations, and directions for empirical validation.

Abstract: Hyperspectral image (HSI) analysis plays a central role in remote sensing tasks requiring fine-grained material discrimination, vegetation health assessment, and post-disturbance monitoring. Yet, the high dimensionality and strong spectral redundancy in HSIs often reduce the efficiency and reliability of machine learning models. These challenges are especially important in wildfire science and prescribed-fire monitoring, where spectral responses vary due to burn severity, char deposition, canopy structure, and early vegetation recovery. Benchmark datasets such as Indian Pines and Pavia University provide controlled environments for algorithm evaluation, but real-world post-fire forest conditions pose additional complexity. This study presents a unified and comprehensive evaluation of four band-selection strategies: Principal Component Analysis (PCA), Spatial–Spectral Edge Preservation (SSEP), Spectral-Redundancy Penalized Attention (SRPA), and a Deep Reinforcement Learning (DRL)–based selector. These strategies are combined with classical machine learning and deep learning classifiers: Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and 3D Convolutional Neural Networks (3D-CNN). The full pipeline includes exploratory data analysis, preprocessing, patch-based spatial–spectral modeling, consistent train–validation protocols, and multi-dataset evaluation across Indian Pines, Pavia University, and a new custom VNIR hyperspectral dataset collected after prescribed burns at the Lubrecht Experimental Forest in Montana, USA. By systematically comparing statistical, edge-aware, attention-guided, and reinforcement-learning-based band-selection strategies, this work identifies compact yet informative spectral subsets that enhance classification performance while reducing computational cost. Importantly, the inclusion of the Montana prescribed-burn dataset provides a unique real-world testbed for understanding band-selection behavior in fire-affected forest environments. Overall, this study contributes a generalizable and extensible framework for HSI dimensionality reduction and classification, laying the groundwork for future applications in wildfire assessment, vegetation recovery monitoring, and remote sensing.

Abstract: Traditional search engines primarily rely on keyword matching and ranking algorithms, which often fail to capture users’ implicit intents and contextual needs. This paper presents an LLM-based search framework that integrates user memory and behavioral modeling to enable proactive, context-aware retrieval. By continuously analyzing user interaction patterns such as past queries, click behavior, and temporal preferences the system builds dynamic user profiles that guide the generation of adaptive query embeddings. This approach allows the model to infer what users intend to search, rather than what they type, resulting in faster response times and significantly higher relevance in returned results. Experimental evaluations demonstrate that the proposed LLM-memory framework reduces query latency by 21.8% and improves top-1 precision by 15.6% compared to traditional retrieval systems. The study highlights the potential of user memory augmented LLMs to reshape search paradigms, bridging the gap between explicit queries and latent human intentions.

Abstract: This study examines structural changes in regulatory risk disclosure using a semantic modeling framework that integrates sentence embeddings, multivariate anomaly detection, and explainable artificial intelligence. Prior research typically relies on dictionary-based word frequencies, tone indicators, or topic proportions to quantify risk disclosure. While these measures capture disclosure intensity, they do not directly assess whether the internal semantic organization of risk narratives has shifted relative to historical patterns. We propose a structural semantic deviation framework that represents each company-year disclosure using thematic shares and embedding-based dispersion statistics, and evaluates deviations from a historical baseline through unsupervised anomaly detection. Using Item 1A Risk Factors from Wells Fargo and JPMorgan Chase surrounding the 2016 regulatory shock, we demonstrate that traditional lexical metrics fail to isolate structural breaks, whereas embedding-based semantic trajectories reveal substantial narrative reconfiguration. Isolation-based modeling provides stable and discriminative anomaly scores, and SHAP decomposition identifies semantic distance, litigation emphasis, and disclosure contraction as key drivers of deviation in 2025 out-of-sample disclosures. The results suggest that structural semantic modeling captures risk narrative transformation beyond word accumulation, offering an interpretable and scalable framework for regulatory risk assessment.

Abstract: This paper explores the cross-domain application of AI-driven personalization in structured search scenarios that combine intent understanding with spatial and categorical constraints across dining, lodging, and leisure experiences. By integrating LLM-based coordination with reinforcement learning and user memory modules, the system continuously learns from users’ long-term preferences and interaction history to support complex, context-rich needs. Experimental evaluations show that memory-enhanced personalization improved result helpfulness by 17.25% and increased transactional referrals by 4.16% in lodging-related searches, while also achieving measurable satisfaction gains in dining and leisure domains. The study demonstrates that crossdomain LLM personalization frameworks with user memory can effectively capture evolving user intents within local categorical contexts, enhance contextual reasoning, and advance the design of adaptive information service systems in the digital economy

Abstract: This review paper examines innovative urban design strategies for sustainable noise management through a structured analysis framed by ten guiding questions. It begins with an overview of conventional noise assessment technologies and progresses to advanced mitigation approaches. Core principles of sustainable urban design are explored, alongside evaluations of urban and transportation planning, traffic-reduction measures, green infrastructure, and resilient architectural strategies. Material innovations and modern noise-control technologies are presented as complementary solutions. Community-based methods, including citizen science and participatory planning, are highlighted for fostering inclusive governance. The discussion concludes by addressing key challenges and future directions, underscoring interdisciplinary collaboration to transform urban noise pollution into opportunities for healthier, more livable cities.

Abstract: Predicting pharmaceutical product quality from manufacturing process parameters is a central objective of Quality by Design and Process Analytical Technology frameworks. This study presents a systematic machine learning analysis of 1,005 tablet compression batches characterized by 27 process parameters and six critical quality attributes including drug release, residual solvent, and total impurities. Nine regression and seven classification algorithms were evaluated with randomized hyperparameter optimization and five-fold cross-validation. Tree-based ensemble methods, particularly Extra Trees, consistently outperformed linear approaches across all quality targets. Total impurities achieved the highest predictive accuracy with a test R² of 0.8855, driven primarily by formulation-specific categorical identifiers, while drug release targets yielded moderate R² values of 0.40 to 0.47, reflecting the inherently complex process-dissolution relationships. Classification of weekend batch production using Logistic Regression yielded an AUC of 0.9215 and cross-validated accuracy of 0.9493, confirming that production schedule characteristics are reliably encoded in process signatures. Feature importance analysis identified tablet fill weight and compaction force as the dominant drivers of dissolution performance. A dedicated Streamlit web application, PharmaQAI, was developed to integrate data exploration, supervised model training, residual diagnostics, and interactive contour-based response surface visualization into a single accessible platform, supporting proactive data-driven decision making in pharmaceutical manufacturing without requiring programming expertise.

Abstract: This study uses the data of G20 countries from 2000 to 2022 as samples to examine the impact of geopolitical risk on the green economic efficiency. We use the Super-SBM model to measure the green economic efficiency of each country. Our results show that the rise of geopolitical risk significantly reduces the green economic efficiency of G20 members reflecting the negative impact of external uncertainty on international climate cooperation and environmental performance. The heterogeneity analysis finds that geopolitical risks are significantly negatively correlated with the green economic efficiency of countries in developed economies, but the effect is not significant in emerging economies that indicating that the impact mechanism of countries with different institutional soundness and development levels is different. The mechanism analysis reveals that geopolitical risks will aggravate the country's concerns about energy security, promote the increase in fossil fuel consumption, and then inhibit the improvement of the country's green economic efficiency; and geopolitical risks also cause exchange rate fluctuations to lead to increasing trade costs and restricting green investment. In addition, foreign direct investment alleviates the negative impact of geopolitical risks by promoting technology spillovers and capital inflows. Overall, this study provides a new perspective for coping with climate change; and provides suggestions for policy makers and relevant scholars in the field of environmental economics.

Abstract: Healthcare-associated infections (HAIs) remain a major contributor to morbidity and mortality in intensive care units (ICUs), particularly in low- and middle-income countries (LMICs), where surveillance data from rural tertiary centres are limited. This study determined the prevalence, risk factors, and mortality impact of HAIs among patients admitted for more than 48 hours to adult and paediatric ICUs at Nelson Mandela Academic Hospital between January 2024 and December 2025. A retrospective cross-sectional design was used, and HAIs were defined using standard ≥48-hour post-admission criteria. Logistic regression analysis identified independent predictors of HAI occurrence. Among 266 patients (median age 6.5 years), the prevalence of HAIs was 28.95%. Ventilator-associated pneumonia was the most frequent infection, followed by central line-associated bloodstream infection and catheter-associated urinary tract infection. Prolonged ICU stay (>8 days) independently predicted HAI (adjusted odds ratio 4.51; p<0.001). HAIs were significantly associated with increased mortality compared with non-HAI patients (46.8% vs. 19.0%; p<0.001), with infants disproportionately affected. These findings demonstrate a substantial HAI burden and associated mortality in this rural ICU setting, underscoring the need for strengthened infection prevention bundles and early risk-stratification strategies in resource-limited environments.

Abstract: In multi-turn conversational AI, individually innocuous personally identifiable information (PII) fragments disclosed across successive turns can accumulate into a re-identification risk that no single utterance reveals on its own. Existing PII detectors operate on isolated utterances and therefore cannot track this cross-turn evidence build-up. We propose a stateful middleware guardrail whose core design principle is speaker-attributed entity isolation: every extracted PII fragment is classified by its originating conversational participant (first-person USER vs. incidentally mentioned third parties), and evidence is accumulated in entity-isolated subgraphs that structurally prevent cross-entity contamination. A three-tier extraction pipeline (Tier-0 deterministic regex; Tier-1 Presidio/spaCy NER with zero-shot NER independent verification; Tier-2 independent zero-shot NER; plus rule-based post-processing) refines noisy NER candidates, and an evidence-gated Commit Gate writes only corroborated cues to entity state, firing a re-identification onset signal t_pred at the earliest turn where combination-based onset rules grounded in the re-identification uniqueness literature are satisfied. On a 184-record template-synthetic evaluation corpus, the system achieves OW@5= 70.7% with MAE= 2.442 turns, reducing naïve accumulation MAE by 56% (BL2 MAE= 5.522). We confirm structural robustness on a 300-record mutation stress set and sanity-check RULE_B generalization on the ABCD external corpus (OW@0= 97.1%, MAE= 0.011). The pipeline requires no modification to the underlying conversational model and serves as a drop-in runtime guardrail for existing dialogue systems.

Abstract: Based on a previously validated head-to-tail dimer vaccine model, we designed a dimeric form of the Newcastle disease virus (NDV) HN protein expressed in rice endosperm (designated as Osr2HN) and initially characterized its molecular expression profile. Previous immunization studies in chickens demonstrated that two doses (0.5 μg) or a single dose (5 μg) of Osr2HN provided complete protection against viral challenge. To facilitate its commercialization, two transgenic rice lines (HN-1 and HN-2) were propagated for three generations to systematically evaluate their molecular characteristics, genetic stability and environmental safety. Insertion site analysis, combined with PCR, qRT-PCR and western blotting, confirmed that the exogenous HN gene was stably integrated into the nuclear genome without sequence variations. The transgenic lines exhibited germination rates, growth cycles and 12 agronomic traits comparable to those of the wild-type TP309, with the exception of increased grain chalkiness in HN-2. No horizontal transfer of the HN gene to weed species was detected, and pollen viability remained unchanged. Field-based biodiversity analysis revealed no adverse effects of the HN gene on pest or weed communities. Collectively, these findings from comprehensive molecular analyses and field evaluations confirm the genetic stability, agronomic performance, and environmental safety of Osr2HN-transgenic rice, providing essential data to support its commercialization as a plant-derived vaccine platform.

Abstract: Functional information measures how rare functional configurations are. Wong and colleagues argue that selection should drive a law of increasing functional information. This is often read as a claim that complexity must increase. We give a cleaner interpretation, which is that survivors tend to be the systems that did not overcommit. We model a system as a policy π, meaning a bundle of commitments expressed in a finite embodied vocabulary. New selection pressures arrive as a set of future requirements drawn from the unobserved outcome set U. A currently viable policy leaves an unobserved buffer B_π ⊆ U of outcomes it still permits. Under a maximally ignorant novelty model, the survival probability of π is exactly 2^|B_π|−|U|. Under any exchangeable novelty prior, survival remains monotone in |B_π|. So persistence favours weaker constraints on function, where weakness counts the compatible completions left open. We define degree of function as survival probability and functional information as Hazen and Szostak rarity among currently viable policies. Conditioning on persistence reweights the population toward larger buffers, hence higher functional information. This yields a formal version of Wong’s law under explicit assumptions. In fully enumerated toy worlds, weakness maximisation improves mean log survival probability by 1.674 bits relative to random choice. Weakness and simplicity are not the same thing. Weakness helps a system persist under novelty, because it keeps more futures compatible. Simplicity can help a system persist because there is less to break. That obviates the need for repair. Complexity requires self-repair to persist, increasing weakness. Life is persistent complexity. In between complex life and simple nonlife is the void of the unviable; complexity which is not alive.

Abstract: This study investigates the influence of circular-economy transparency (CET) on responsible purchase intention (RPI) within the electronics market, elucidating the mediating role of perceived green authenticity (PGA) and the boundary condition of greenwashing skepticism (GWS). We used PLS-SEM (SmartPLS 4) with bootstrapping to test direct effects, mediation, moderation, and moderated mediation on a cross-sectional online survey of 400 adult electronics customers in Saudi Arabia. The results indicate that CET positively predicts PGA and RPI, with PGA significantly enhancing RPI. This suggests that perceptions of authenticity convey a significant aspect of transparency's impact on responsible intentions. Nonetheless, GWS considerably diminishes the CET→PGA and PGA→RPI relationships and lessens the potency of the indirect CET→PGA→RPI pathway, indicating that skeptical consumers more rigorously disregard cues of transparency and authenticity. The model provides a strong description of the observed variance in both PGA and RPI, justifying its explanatory and predictive value. These results suggest that electronics brands and policymakers would do well to complement transparency programs with measurable, decision-relevant information disclosures and trust-enhancing procedures (e.g., traceability and third-party validation) in order to minimize distrust and enable responsible purchasing.

Abstract: Background: Childhood obesity has become a major global health concern and is increasingly recognized as a factor influencing skeletal development. Emerging evidence suggests that excess adiposity may alter craniofacial growth patterns, potentially affecting orthodontic diagnosis, treatment planning, and airway function. However, the extent to which obesity influences craniofacial morphology in growing individuals remains incompletely understood. Objective: To evaluate the relationship between body mass index (BMI) and craniofacial morphology in children and adolescents using selected sagittal and linear craniofacial parameters, and to determine the independent effects of age and sex on these associations. Methods: This cross-sectional orthodontic study included 130 healthy subjects aged 19 or younger. Anthropometric measurements were recorded, and BMI was calculated to classify participants into normal weight, overweight, and obese groups. Standardized lateral cephalometric radiographs were obtained, and twenty-one skeletal and soft-tissue parameters were analyzed. Statistical evaluation included tests of normality, one-way ANOVA, and post-hoc comparisons to assess differences according to BMI, sex, and age groups. Results: Obesity was significantly associated with increased sagittal skeletal dimensions. Mandibular body length, mandibular unit length, SNB angle, and maxillary unit length demonstrated progressive increases across BMI categories (p < 0.05). In contrast, vertical growth parameters showed no significant differences. Soft-tissue analysis revealed reduced facial convexity and lower facial height ratios in obese subjects. Age was strongly associated with increases in linear jaw dimensions, whereas sex differences were limited primarily to skeletal size rather than morphological relationships. Conclusions: Childhood obesity is associated with enhanced sagittal craniofacial growth, particularly involving mandibular structures, while vertical skeletal patterns remain largely unaffected. These findings highlight the importance of incorporating BMI assessment into orthodontic evaluation and suggest that obesity may influence growth timing, facial morphology, and airway-related risk factors.

Abstract: The Global Navigation Satellite System (GNSS) has emerged as a backbone of modern civilisation, industry, and society. Degradations and disruptions of the GNSS Positioning, Navigation, and Timing (PNT) service performance are caused by natural and adversarial sources. The ionospheric effects form the principal single class of the GNSS PNT performance degradation causes. Traditional GNSS ionospheric correction models appear unable to resolve the problem for their global nature, and the intrinsic lack of agility and flexibility. Here we contribute to the case with the proposal of concept and methodology for tailored GNSS ionospheric correction model development in support of GNSS resilience development, based on: (i) a massive dataset of long-term (annual) GNSS-derived total electron content TEC observations, as target variable (ii) a massive dataset of geomagnetic field density components, as predictors, and (iii) utilisation of statistical/machine learning predictive model development methods. The proposed approach emerges as a component of the previously introduced architecture-agnostic Ambient-Aware Application-Aligned (AA2) GNSS PNT concept, introducing the GNSS positioning environment situation awareness. Proposed concept and methodology is successfully demonstrated in the case of tailored GNSS ionospheric correction model development using the R environment for statistical computing in the case-scenario of mid-latitude single-frequency commercial-grade GNSS rover.

Abstract: Amid U.S. housing and labor shortages, Appalachia needs solutions that strengthen communities. This study examines how establishing an industrialized off-site construction (IOC) ecosystem can address regional housing, workforce, and construction challenges. From March–June 2024, we conducted seven participatory design workshops across Appalachia (n=129). Using a standardized prompt sequence (status quo, opportunities, IOC solutions), affinity clustering, and PICK chart prioritization, participants identified needs, capacities, and gaps, then ranked actions to advance IOC. Validity was tested through independent re-clustering with a shared codebook; inter-rater agreement was substantial (weighted κ=0.80). Five cross-cutting levers emerged: Education & Training; Policy & Regulation; Marketing & Awareness; Financing & Funding; and Technology & Innovation. Marketing & Awareness were consistently viewed as high-impact and easier to implement near term; Education & Training were high-impact but resource-intensive; Policy and Financing were impactful yet harder to shift; Technology & Innovation should be introduced incrementally to fit tradition-bound industry and regional norms. The resulting roadmap emphasizes near-term pilots, targeted talent pipelines, permitting/code alignment, and fit-for-purpose capital. The main contribution is a globally reproducible participatory protocol with transparent prompts, a shared codebook, independent re-clustering, and reliability metrics that enable replication and benchmarking across regions.

Abstract: Background: Autism spectrum disorder (ASD) affects approximately 1–2% of children worldwide, yet its etiology remains incompletely understood. Emerging evidence suggests that offspring of parents with autoimmune diseases show elevated autism prevalence. Notably, children of parents with psoriasis (OR 1.59), type 1 diabetes (OR 1.49–2.36), and rheumatoid arthritis (OR 1.51) demonstrate particularly strong associations. Hypothesis: I propose that autism is fundamentally an immune-metabolic disorder characterized by TNF-α–mediated mitochondrial dysfunction leading to cerebral energy deficiency. This energy deficit impairs three critical processes:(1) synaptic pruning during neurodevelopment,(2) real-time social cognition including gaze processing and emotion recognition, and(3) protein synthesis of critical synaptic scaffolding molecules. The primary mechanism involves TNF-α pathway dysregulation—through genetic inheritance from parents with autoimmune diseases such as psoriasis, type 1 diabetes, and rheumatoid arthritis, and/or through direct fetal exposure to elevated maternal TNF-α during pregnancy. I further propose that the well-documented “firstborn effect” in autism reflects maternal immune maladaptation during primigravid pregnancies. Additionally, for cases without parental autoimmune history, I propose a speculative secondary mechanism: mitonuclear immune conflict, wherein paternal immune genes may partially recognize maternal mitochondria as non-self, generating endogenous TNF-α.

Abstract: Background and Objectives: Anticholinergic burden (ACB) cumulatively leads to adverse outcomes and increased mortality. Taiwan's NHIRD prevalence studies indicate ~60% of elderly patients (≥65) annually receive anticholinergic medications. In those with polypharmacy, 60–80% were exposed to anticholinergic medications, which was linked to higher risks of pneumonia, myocardial infarction, and stroke. This study aims to survey physicians’ knowledge and attitudes on Anticholinergic Burden (ACB) in Taiwan, directly addressing a recognized gap in clinical practice. Materials and Methods: This nationwide, anonymized online 3-month survey (July–September 2025), used the KAP framework. Physicians from 92 hospitals across Taiwan were invited to participate via e-mail. The survey covered demographics, ACB knowledge, attitudes, practices, and feedback, utilizing branching logic. Data was analyzed and presented descriptively. Of 62 respondents, only 23% of physicians were aware of the term 'Anticholinergic Burden' (ACB). Knowledge of specific common medication ACB scores was low (average 1.86/10), and respondents showed significant uncertainty regarding the risks associated with high ACB. Despite 53% of physicians (33/62) rating ACB assessment as "Important/Very Important," this rarely translated into practice: most never calculated scores, and only one did so routinely. Fifty-six participants' overwhelming demand for increased ACB education emphasizes the critical need for training to close both the identified knowledge and knowledge-practice gaps. Conclusions: Physician’s knowledge of medicines with anticholinergic property is relatively low in Taiwan despite the awareness of its importance. There is a clear knowledge and knowledge-practice gap which can be addressed through targeted educational activities.