Search Results (339)

Background/Objectives: Pterygoid implants are increasingly used to provide distal support in full-arch maxillary rehabilitation, improving biomechanical stability and reducing posterior cantilevers. Despite high reported survival rates, their placement is technically demanding because of the complex anatomy of the pterygomaxillary region and the proximity of critical neurovascular structures. Implant displacement into adjacent deep anatomical spaces is a rare but potentially serious complication. The aim of this report is to describe the management of sequential intraoperative complications involving pterygoid implants—initial migration into the maxillary sinus followed by displacement into the pterygopalatine fossa—and to analyse the technical factors that contributed to them. Case Description: A 68-year-old woman underwent full-arch maxillary rehabilitation with eight implants, including bilateral pterygoid implants. Both pterygoid implants showed progressively decreasing primary stability and migrated into the maxillary sinus. During a salvage attempt, a newly inserted pterygoid implant deviated and was displaced beyond the pyramidal process into the pterygopalatine fossa together with the insertion driver. Cone-beam computed tomography (CBCT) localized the implant—lying medial to the lateral pterygoid plate, anterior to the pterygoid process—and guided a targeted retrieval based on careful blunt dissection, performed in an outpatient setting without general anesthesia or endoscopic assistance. A correctly positioned pterygoid implant was then placed. The anterior implants (insertion torque > 60 N·cm) were immediately loaded; at four months the right pterygoid implant was uncovered, and the left implant retained in the sinus was retrieved and replaced using a trans-sinus technique with elevation of the Schneiderian membrane. Conclusions: This case highlights the importance of accurate, consistent anatomical localization, complete osteotomy preparation, appropriate implant selection, and controlled, torque-monitored insertion during pterygoid implant placement. It also demonstrates that rare displacement of an implant into a deep anatomical space can be safely managed in an outpatient dental setting by experienced clinicians using a structured, image-guided approach. Full article

Against a backdrop of global climate fluctuations and intensifying human activities, wetlands are undergoing severe degradation. Understanding the mechanisms that govern wetland evolution is essential for the sustainable development of wetland ecosystems. However, wetland evolution is highly heterogeneous across space and time, and existing studies have generally paid insufficient attention to nonlinear effects and interactions among driving mechanisms, limiting a comprehensive understanding of its intrinsic processes. We integrated the Light Gradient Boosting Machine model with SHapley Additive exPlanations to explain the nonlinear driving mechanisms of spatiotemporal wetland evolution across the Sanjiang Plain using multitemporal remote sensing data from 1990 to 2023. Wetland dynamics exhibited a stage-dependent pattern characterized by substantial natural-wetland loss in the early period, followed by partial marsh-wetland recovery and continued artificial-wetland expansion. Artificial wetlands expanded continuously, whereas marsh wetlands declined markedly before 2005 and showed partial recovery thereafter. From 1990 to 2005, wetland evolution was mainly controlled by topographic and climatic factors. From 2005 to 2023, the influence of socioeconomic development and proximity to the road network on wetland change intensified. Nonlinear interactions among driving factors shifted from synergistic promotion by natural factors in the early stage to inhibitory effects among natural, socioeconomic, and locational factors in the later stage. This stage-specific change in driving mechanisms was crucial to the shift in dominant drivers of wetland evolution. By characterizing single-factor nonlinear effects and multifactor interactions, this study reveals the stage-specific driving mechanisms of wetland evolution in the Sanjiang Plain and provides scientific support for regional wetland management and remote sensing monitoring. Full article

Recent studies on users of urban parks call for the need for more insight into the importance of green spaces as catalysts of more inclusive spaces. This paper contributes to the ongoing debate by examining urban parks as age-inclusive infrastructures in a post-socialist context. Three parks in the City of Timișoara, Romania, are selected as a comparative case study. Our analysis combines systematic field observations and GIS-based mapping of park accessibility and facilities with 42 semi-structured interviews with young, mid-aged and older visitors to the three contrasting parks: a renovated historic central park, a peripheral forest-like park and a small neighbourhood park embedded in a communist-era housing estate. The findings suggest that inclusiveness for all generational categories, as well as attachment for neighbourhood parks, are important drivers for urban parks users. Inclusiveness is driven not only by amenities, but also by park-specific attachment, as well as the quality of maintenance and lighting. These factors shape perceived equity and convenience. We advocate for a differentiated management model tailored to each park, balancing conservation-oriented quiet zones with flexible, event-capable areas. This model prioritises lighting, seating ergonomics and safety measures as core components of age-inclusive planning. Our findings support the need for different management of urban green spaces that capitalises on the social and spatial specificities of each park. The comparison further shows that the smallest and least equipped park generates the strongest attachment and the most regular use across all generations: proximity and continuity of maintenance matter more than surface area or scale of investment. Full article

Understanding the ecological drivers shaping animal foraging and interactions remains a central question in ecology. Among the most studied systems in this field are the ant–plant interactions, although disentangling the complexity of factors acting in different contexts remains a relevant question. This study investigated how habitat structure, liquid food rewards, and interspecific competition interact to modulate the foraging patterns of the abundant ant Camponotus termitarius on the rosette-shaped plant Eryngium chamissonis in the Brazilian Pampa, where ant–plant interactions are still poorly studied. We monitored 115 plants across three sampling events, measuring ant foraging, trophobiont abundance, vegetation density, plant size, and local nest distributions, and analyzed the relationships using Piecewise Structural Equation Modeling (pSEM). The pSEM revealed that surrounding vegetation density negatively affected C. termitarius nest density, nest extensions, and hemipteran trophobionts. Conversely, denser vegetation and larger plants favored the aggressive competitor Camponotus rufipes. While trophobiont presence and proximal nesting infrastructure directly facilitated C. termitarius activity, hostplant inflorescences promoted the construction of nest extensions on plants. We conclude that C. termitarius foraging is regulated by a multidimensional network where microhabitat complexity mediates spatial niche partitioning and competitive dynamics between sympatric ants. Full article

Augmented reality head-up display (AR-HUD) navigation is becoming a practical road–vehicle interface in intelligent transportation systems. However, it remains unclear how AR-HUD navigation symbols reshape lateral fixation transitions and attention distribution across maneuver phases. This eye-tracking study divided fixation sequences into nine maneuver scenarios and analyzed them using first-order Markov transition probabilities, the time-normalized area under the curve (AUC) of horizontal fixation position, and fixation-to-arrow-tip distance. Exploratory maneuver-level analyses suggested that AR-HUD cues attenuated, but did not eliminate, center bias, and redirected fixation toward the target direction. A participant-mean sensitivity analysis (n = 20) retained AUC differences after false discovery rate (FDR) correction in the pre-lane-change-left, pre-lane-change-right, and pre-turn-left scenarios, whereas several maneuver-phase effects were less robust after aggregation. The converging proximity and lateral-distribution patterns suggest guided spatial occupancy as a provisional description of attentional influence extending from the rendered arrow toward the maneuver-relevant region it implies. The findings concern driver attention and interface behavior; traffic-flow and environmental outcomes were outside the scope of the present measurements. These findings provide empirical guidance for optimizing the timing and spatial placement of in-vehicle AR cues during maneuver preparation, while their safety benefits require confirmation in interactive and naturalistic driving. Because the retained sample comprised young licensed drivers aged 19–25 years and the task involved passive video viewing, these implications should not be generalized to older drivers, the wider licensed-driver population, or closed-loop vehicle control without age-diverse interactive validation. Full article

Rapid urbanization, increasing demand variability, and digitalisation of urban infrastructure require intelligent decision-support systems that can improve sustainability, resilience, and operational efficiency in smart city operations. Artificial intelligence (AI) and data-driven optimization offer strong potential for adaptive urban services, including dynamic pricing, demand response, resource allocation, and energy-aware management. However, many reinforcement learning applications still focus mainly on short-term performance while giving limited attention to transparency, fairness, stability, and accountability. This study proposes a trustworthy reinforcement learning framework for AI-driven urban decision-making, using sustainable dynamic pricing and resource optimization as mechanisms for adaptive and responsible decision-making. A custom reinforcement learning environment was developed using historical e-commerce transactional data as a methodological proxy to simulate interactions among demand, resource or inventory availability, service categories, price elasticity, and changing market conditions. Three reinforcement learning algorithms, namely Deep Q-Network, Proximal Policy Optimization, and Advantage Actor–Critic, were evaluated under comparable experimental conditions. Performance was assessed using profitability, decision stability, fairness-oriented pricing behavior, decision consistency, and interpretability. To improve transparency, trajectory-based policy audits and SHapley Additive exPlanations were applied to identify the main factors influencing pricing decisions. The results show that the Deep Q-Network agent achieved the most balanced performance, increasing total profit by 12.58% while recording no unethical price increases under low-demand conditions. Explainability analysis showed that stock or resource levels, demand shifts, and price elasticity were the strongest positive drivers of pricing actions, whereas inventory hoarding and unfavorable price increases reduced decision quality. The findings indicate that reinforcement learning can support sustainable and resilient urban decision-making when optimization objectives are combined with trustworthy AI principles. The proposed framework provides a practical basis for accountable AI-based decision-support systems in smart city operations, including demand-responsive services, resource optimization, sustainable dynamic pricing, and energy-aware management. Full article

Soy protein, apple fiber, and corn starch are widely used functional ingredients, yet their combined effects on food matrix properties remain poorly characterized. A D-optimal mixture design (16 runs) was used to evaluate how these three components, individually and in binary and ternary combinations, determine the proximate composition, CIELab color parameters, and techno-functional properties (water and oil absorption and solubility, pH, apparent density, emulsifying and foaming capacity, least gelation concentration, and foam stability over 120 min) of model blends. Response surface models were statistically significant for 19 of the 21 responses evaluated (R² = 0.70–1.00, p ≤ 0.05); water and oil absorption capacity did not reach significance (R² = 0.66–0.72, p > 0.05). Formulation was the main driver of the system’s behavior. Starch increased moisture, carbohydrate content, and lightness; protein determined ash, protein content, and water solubility; and fiber dominated crude fiber, lipid content, and red–yellow chromaticity, producing the greatest total color difference relative to pure starch. Unlike composition and color, which followed largely additive, single-ingredient-driven trends, interfacial functionality was governed by strong binary interactions: a synergistic protein–fiber interaction dominated emulsifying capacity (positive) and foaming capacity (strongly negative, almost completely suppressing foam formation even at protein levels comparable to the pure-protein vertex), while a synergistic protein–starch interaction enhanced foam stability. Water and oil absorption capacities varied within narrow, statistically non-significant ranges, indicating structural rather than compositional control. These findings show that mixture design and response surface methodology can quantitatively predict and tune the physicochemical, color, and functional performance of protein–fiber–starch blends, providing a practical framework for designing plant-based functional ingredients with targeted nutritional and technological profiles. Full article

Background: Teacher coaching is a central driver of implementation science, yet no recent review has centered the active components of coaching and consultation, rather than social-emotional learning (SEL) programs themselves, as its primary object of synthesis. (“Basic education” is used throughout this manuscript as the English rendering of the Latin American usage of the term, corresponding to ISCED 2011 levels 0–2: early childhood, primary, and lower-secondary education; it does not include upper-secondary or tertiary education.) This mixed-methods systematic review with thematic synthesis examined how coaching and consultation models with explicit active ingredients (modeling, guided practice, reflection, goal setting, and performance feedback) affect implementation fidelity, dosage, and quality, and students’ socioemotional, behavioral, and academic outcomes. Methods: Following PRISMA 2020 and a prospectively registered protocol (OSF M3846), we searched Scopus, Web of Science, and ERIC, supplemented by snowballing and grey literature searches. Of 341 screened records, 36 studies (2010–2026) met eligibility criteria and underwent data extraction and MMAT quality appraisal by independent reviewers (κ ≥ 0.84). A convergent segregated mixed-methods synthesis integrated qualitative thematic analysis with narrative quantitative synthesis. Results: Four interdependent macro-themes emerged: the systemic and individual conditions enabling coaching; its active technical-relational architecture; fidelity–adaptation tensions when scaling programs across cultures; and a translational cascade whereby high-fidelity coaching is associated with stronger proximal teacher practice and classroom climate, with academic gains emerging later and inconsistently. Conclusions: Coaching appears most effective when active technical components combine with a non-evaluative consultative alliance under supportive school conditions. We propose adaptive fidelity as a framework reconciling technical rigor with cultural responsiveness, relevant for scaling coaching to lower-resource, culturally diverse contexts, including the Global South. Full article

Metabolic changes are often described as drivers of plant development even when the evidence establishes only association or a permissive requirement. This review separates biological role from causal status and evaluates claims across seven dimensions: association, necessity, localization, transport, rescue and mediation, sufficiency, and quantitative-threshold testing. We apply a when–where–how framework to four cases. Perturbations of T6P, SnRK1, and TOR support T6P-dependent kinase regulation as a mediator of lateral-root development and are consistent with a context-dependent candidate gate, but its window and threshold remain unresolved. Circadian regulation of AHA3 and SUC2 by CCA1 provides the most complete chain, combining cell-specific perturbation, transport, and rescue. The OsARF18–OsARF2–OsSUT1 pathway supports sucrose transport as a mediator of rice fertility, although receiving-cell necessity and quantitative restoration remain unresolved. H⁺-ATPase perturbations identify apoplastic pH as a proximal mediator of Arabidopsis hypocotyl and cotton-fiber elongation, with context-dependent optima rather than a shared threshold. Nutrient and redox regulation, specialized metabolism, stress, and senescence provide boundary comparisons. Causal confidence depends on claim-matched evidence for responding cells and developmental windows and, where relevant, transport routes, mechanisms, and whole-plant trade-offs. The framework can guide AI-assisted breeding by prioritizing genotype-, stage-, and tissue-specific interventions for prospective perturbation and rescue. Full article

Accommodation establishments constitute a core component of tourism infrastructure, and their location choices directly affect water resource utilization, land pressure, and the spatial equilibrium of tourism development—issues that are particularly acute in vast arid regions. Yet the spatial organization of accommodation supply across extensive drylands characterized by fragmented oasis distribution, and the reasons why standard and non-standard accommodation follow divergent location logics, remain poorly understood. This study addresses three questions: (1) How are nine accommodation categories, differentiated by type and quality, distributed across Xinjiang? (2) Do directional spatial associations exist among categories that are consistent with hierarchical, path-dependent development? (3) Which factors drive these patterns, and do their effects exhibit the nonlinearity and threshold behavior predicted by location theory? Drawing on 12,073 accommodation establishments from the Ctrip platform, we construct a staged analytical framework in which each technique answers a specific question: the nearest-neighbor index and standard deviational ellipse characterize global patterns; kernel density estimation and OPTICS clustering identify local agglomerations; directional local co-location quotients measure asymmetric spatial associations; and XGBoost–SHAP isolates nonlinear drivers and threshold effects. Results reveal a highly concentrated “single-core, multi-center” structure anchored by Urumqi, Yining, and Kashgar, with rapid expansion toward the Ili Valley, Kashgar, and Altay since 2019. Standard accommodation tracks urban centrality and transport nodes, while non-standard accommodation tracks tourism resource endowments, consistent with location-theoretic expectations. Directional co-location analysis reveals hierarchical spatial associations among categories, and driving factors exhibit pronounced nonlinear threshold effects. From a sustainability perspective, the identified thresholds—elevation (1360 m), water-body proximity, and distance to rural tourism demonstration sites (3 km)—constitute quantifiable, spatially explicit sustainability indicators that can be incorporated into planning tools to monitor and steer accommodation development away from ecologically sensitive zones. Global Moran’s I diagnostics of model residuals (reduction of 83–99.7%) suggest that these findings are unlikely to be artifacts of spatial autocorrelation; this diagnostic, however, complements rather than replaces spatially blocked validation. The study contributes category-differentiated, spatially directed evidence for policies balancing tourism expansion against water security and ecosystem integrity, serving sustainable tourism development in arid-region destinations. Full article

Background: Pediatric craniopharyngiomas are rare, histologically differentiated epithelial tumors arising in the sellar–suprasellar region. Despite their low-grade classification, these tumors are associated with significant long-term morbidity because of their proximity to critical neurovascular and hypothalamic structures. Management remains challenging and requires a careful balance between durable tumor control and the preservation of neurological, endocrine, and neurocognitive functions. Methods: This narrative review summarizes the current evidence concerning the epidemiology, molecular pathogenesis, clinical presentation, diagnostic evaluation, and treatment strategies for pediatric craniopharyngiomas. Emphasis is placed on anatomical and hypothalamic involvement-based classifications, evolving surgical philosophies, and the role of multimodal management. Results: Advances in molecular characterization have resulted in the delineation of two distinct subtypes—adamantinomatous and papillary craniopharyngiomas—with different biological drivers and therapeutic implications. Surgical management has evolved from historically aggressive gross total resection to risk-adapted, anatomy-driven approaches that prioritize hypothalamic preservation. In selected patients, subtotal resection combined with modern radiotherapy provides tumor control comparable to that of radical surgery, with reduced morbidity. Endoscopic endonasal and microsurgical transcranial approaches play complementary roles, with approach selection guided by tumor anatomy, hypothalamic involvement, and surgical expertise. Emerging targeted therapies and intracystic treatments further expand therapeutic options. Conclusions: The contemporary management of pediatric craniopharyngiomas emphasizes individualized, multidisciplinary strategies focused on functional preservation rather than anatomical radicality alone. Integration of molecular insights, advanced imaging, and long-term outcome data is essential to further improve survival and quality of life in this vulnerable population. Full article

Autonomous vehicles (AVs) will operate alongside human-driven vehicles for an extended transition period, during which aggressive human driving may become a major source of risk. This study proposes an integrated safety-control framework that combines real-world-data-driven behavior modeling with deep reinforcement learning to design longitudinal AV control strategies for mixed traffic. Aggressive, general, and defensive driving patterns are calibrated from the CitySim dataset, and dynamic aggressiveness is incorporated into an improved car-following model. A proximal policy optimization algorithm with a Kullback–Leibler penalty is then used to learn multi-objective strategies balancing safety, efficiency, comfort, and fuel economy in freeway and signalized-intersection scenarios. The results show that the behavior-aware strategies exhibit different strengths across traffic environments. On the freeway, the defensive-threshold strategy maintains a larger time headway, reduces positive acceleration, and lowers system-level fuel consumption, whereas the default, aggressive, and general strategies preserve higher traffic efficiency. At the intersection, signal control narrows the differences among strategies and limits the influence of longitudinal threshold settings on most evaluated indicators. These findings provide a quantitative basis for selecting behavior-aware control thresholds and designing robust AV strategies for mixed-autonomy traffic containing aggressive human drivers. Full article

Coal mining landscapes require restoration strategies that account not only for where ecological risk is concentrated but also for how risk constrains landscape connectivity. However, landscape ecological risk assessment is still commonly used as a zoning tool, with weak links to resistance surface parameterization and node-level restoration. Using the Shenmu coal mining area in northern China as a case study, we developed a risk-informed ecological network framework based on multi-source spatial data from 1995 to 2020. The framework combined landscape ecological risk assessment, GeoDetector-based driver analysis, ecological source screening, resistance surface construction, minimum cumulative resistance modeling, a gravity model, and circuit theory-based node diagnosis. Landscape dominance showed the highest explanatory power within the tested factor set (q = 0.06083), followed by land use type, water body proximity, and landscape fragmentation, while most factor interactions showed bivariate or nonlinear enhancement. Risk zoning delineated ecological conservation (467.62 km²), enhancement (1434.86 km²), and restoration areas (2566.49 km²). The framework identified 10 ecological sources; 18 potential corridors with a total length of 213.18 km; and 89 key nodes, including 52 pinch points, 4 barrier points, and 33 fracture points. The main contribution of this framework lies not in combining established ecological network tools, but in transferring ecological risk information into resistance surface parameterization and linking different types of critical nodes to differentiated restoration priorities. These outputs should be interpreted as model-based structural and potential functional connectivity priorities, rather than as direct evidence of realized species movement. Full article

Traditional MBSE faces challenges in verifying the dynamic performance of autonomous systems due to a semantic gap between static architecture and executable algorithms. To address this limitation, this study proposes a system-of-systems (SoS) modeling methodology that connects the Unified Architecture Framework (UAF) with multi-agent reinforcement learning. A Drivers–Challenges–Opportunities–Goals (DCOG) framework maps strategic intent to operational capabilities, which are formalized in the UAF. Driven by the UAF Domain Metamodel (DMM), a pipeline transforms behavioral and resource specifications into a Markov Decision Process (MDP). An Automatic Curriculum Learning Multi-Agent Proximal Policy Optimization (AutoCL-MAPPO) algorithm then resolves the MDP. The autonomous fleet achieves a 73% mission success rate, outperforming the standard MAPPO baseline (65%). These performance metrics are averaged over 1000 independent test episodes to ensure statistical significance, with baseline algorithms evaluated under identical environmental conditions. Using Systems Modeling Language (SysML) as a verification carrier, activity simulations confirm that the generated decision sequences conform to UAF structural logic. Metric constraint deviation analysis provides empirical feedback for iterative design refinement. This methodology establishes a verifiable digital thread, closing the loop between architecture modeling and learned behavior for autonomous SoS and Human–AI Teaming. Full article

Background/Objectives: Pituitary neuroendocrine tumors (PitNETs) frequently exhibit invasive behaviors that complicate clinical treatment. While cyclin-dependent kinase 5 (CDK5) and lymphokine-activated killer T-cell-originated protein kinase (PBK, also known as PDZ-binding kinase) are implicated in tumor progression, their reciprocal regulatory mechanism remains unclear. This study aims to elucidate the CDK5-PBK interaction in PitNETs and identify potential therapeutic agents targeting this pathway. Methods: We utilized proximity labeling and phospho-specific assays to characterize the CDK5 and PBK interaction in PitNET cell lines. Immunohistochemical analysis was performed on patient tumor tissues to evaluate clinical relevance. Artificial intelligence (AI)-based virtual screening was employed to discover dual-target inhibitors. The therapeutic efficacy of the identified compound, proguanil hydrochloride, was subsequently evaluated using in vitro functional assays, alongside in vivo xenograft animal models. Results: We identified a mutual phosphorylation loop between CDK5 (at S159) and PBK (at T9) that activates insulin signaling, thereby promoting cellular proliferation and invasion in PitNETs. Patient tumor analysis revealed that the co-expression of phosphorylated CDK5 (S159) and PBK (T9) significantly correlates with tumor invasiveness (p < 0.001). Through AI screening, proguanil hydrochloride was identified as a candidate dual-target inhibitor. In vitro assays confirmed that it effectively reduces tumor cell growth, while in vivo xenograft studies validated its capacity to inhibit tumor progression. Conclusions: The CDK5-PBK mutual phosphorylation axis serves as a key driver of invasiveness in PitNETs. Proguanil hydrochloride represents a promising candidate dual-target therapeutic agent capable of disrupting this pathway to suppress tumor growth. Full article