Search Results (480)

Background/Objectives: Cleft lip and/or palate are common craniofacial anomalies whose surgical repair is classified as clean-contaminated and may be complicated by surgical site infection or palatal fistula. Despite widespread perioperative antibiotic use, there are no standardized, evidence-based recommendations, and rising antimicrobial resistance underlines the need for rational prescribing. This systematic scoping review aimed to map current evidence on prophylactic antibiotic therapy and related perioperative measures in cleft surgery. Methods: A scoping review was conducted using the Arksey and O’Malley framework and reported in line with PRISMA 2020. PubMed, Mendeley and Google Scholar were searched (January 2015–10 February 2025) for English-language retrospective studies, clinical trials, survey studies and systematic reviews concerning prophylactic antibiotics, bone grafting procedures, mouthwash use or oral microbiota in patients undergoing cleft lip and/or palate surgery. Six reviewers independently screened records; two experienced clinicians extracted data on study characteristics, antimicrobial regimens and infectious or microbiological outcomes. Given heterogeneity and the scoping aim, no formal risk-of-bias assessment or meta-analysis was performed. Results: A total of 40 studies met the inclusion criteria, including 21 original research articles. Considerable variation in antibiotic choice, timing and duration was observed, with no clear superiority of any regimen. Single-dose perioperative prophylaxis appeared non-inferior to prolonged courses in several settings. Oral microbiota studies highlighted colonization by resistant and opportunistic pathogens in cleft patients. Conclusions: Current evidence supports individualized, often short-course perioperative antibiotic strategies rather than routine prolonged therapy. High-quality randomized and microbiological studies are required to develop standardized, resistance-conscious guidelines. Full article

Firms globally are transforming digitally to enhance performance through building differentiated organizational capabilities within their digital ecosystem to maximize value. Drawing from the dynamic capability theory, this study aims to investigate the sources of sustainable competitive advantage, based on data from the UAE, by examining the impact of strategic orientations on firms’ survival through integrated strategic capabilities, adaptive marketing capability, and market ambidexterity. The choice of the UAE was based on two rational reasons. First, the adoption of new technologies is excelling in the UAE’s competitive environment especially AI, cloud, and data solutions across services industries, e.g., ICT, Telecom, Aviation, etc. Second, the government drives the digital economy to enhance the country’s positioning globally. Following a quantitative approach with a sample size of 185 service firms operating in the UAE, the study identifies how strategic orientations enable service firms’ long-term survival. Moreover, it assesses the moderating role of digital transformation between strategic orientations and sustainable competitive advantage through integrated strategic capabilities. Thus, it provides a better understanding of the dynamic capabilities of firms transforming digitally. The study revealed that strategic orientations positively enable the development of integrated strategic capabilities. The latter mediate significantly between strategic orientations and sustainable competitive advantage. It confirms that digital transformation is strengthening the relationship between strategic orientations and sustainable competitive advantage through the integrated strategic capabilities. The study contributes to evolving new forms of integrated strategic capabilities as sources for sustainable competitive advantage. It confirms the adaptive marketing capability and market ambidexterity integration and thus enriches the dynamic capability theory and ambidexterity theory body of knowledge. Full article

Cholangiocarcinoma (CCA) is a highly aggressive cancer of the biliary tract, distinguished by significant intratumoral heterogeneity (ITH), which contributes to therapy resistance and unfavorable clinical outcomes. Traditional genome profiling has revealed recurring driver changes in CCA; yet, genomic data alone fails to elucidate functional pathway activation, adaptive signaling, and the diverse treatment responses reported among tumor locations and disease subtypes. This review analyses the use of integrated sequencing technologies, proteogenomics, and phosphoproteomics to systematically characterize intratumoral heterogeneity in cholangiocarcinoma and convert molecular diversity into therapeutically applicable discoveries. We present evidence that the combination of genomic sequencing and mass spectrometry–based proteomics facilitates the direct correlation of genetic mutations with protein expression, post-translational modifications, and signaling system activity. Phosphoproteomic profiling specifically offers functional insights into kinase-driven networks that dictate tumor aggressiveness, therapeutic susceptibility, and adaptive resistance mechanisms, which cannot be anticipated only from DNA-level analysis. We propose that integrating proteogenomic and phosphoproteomic analyses into diagnostic and therapeutic assessments can enhance molecular classification, reveal subtype- and region-specific therapeutic dependencies, and guide rational combination treatment strategies, based on recent extensive proteogenomic studies and functional proteomic investigations in CCA. Pathway-level analysis of intratumoral heterogeneity provides a framework for selecting targeted medicines, predicting resistance, and informing personalized treatment strategies in CCA. The combination of sequencing, proteogenomics, and phosphoproteomics is essential for advancing precision oncology in cholangiocarcinoma. The implementation of this multi-layered analytical approach may better patient classification, refine therapy choices, and eventually improve clinical outcomes for individuals with this particular heterogeneous cancer. Full article

Controlled-release systems must translate material design choices into predictable pharmacokinetic (PK) profiles, yet purely mechanistic or purely data-driven models often underperform when tuning complex polymer networks. Here, we develop tunable poly(vinyl formal) membranes (PVFMs) for diclofenac delivery and integrate classical kinetic analysis with a Generalized Regression Neural Network (GRNN) to connect formulation variables to release behavior and PK-relevant targets. PVFMs were synthesized across a gradient of crosslink densities by varying HCl content; diclofenac release was quantified under standardized conditions with geometry and dosing rigorously controlled (thickness, effective area, surface-area-to-volume ratio, and areal drug loading are reported to ensure reproducibility). Release profiles were fitted to Korsmeyer–Peppas, zero-order, first-order, Higuchi, and hyperbolic tangent models, while a GRNN was trained on material descriptors and time to predict cumulative release and flux, including out-of-sample conditions. Increasing crosslink density monotonically reduced swelling, areal release rate, and overall release efficiency (strong linear trends; r ≈ 0.99) and shifted transport from anomalous to Super Case II at the highest crosslinking. Classical models captured regime transitions but did not sustain high accuracy across the full design space; in contrast, the GRNN delivered superior predictive performance and generalized to conditions absent from training, enabling accurate interpolation/extrapolation of release trajectories. Beyond prior work, we provide a material-to-PK design map in which crosslinking, porosity/tortuosity, and hydrophobicity act as explicit “knobs” to shape burst, flux, and near-zero-order behavior, and we introduce a hybrid framework where mechanistic models guide interpretation while GRNN supplies robust, data-driven prediction for formulation selection. This integrated PVFM–GRNN approach supports rational design and quality control of controlled-release devices for diclofenac and is extendable to other therapeutics given appropriate descriptors and training data. Full article

The rapid rise in cryptocurrency presents both opportunities and challenges for retail investors due to its volatility and technological complexity. Research on investment decisions has primarily focused on behavioural finance, often overlooking how learning and literacy shape investor actions. This study addresses this gap by examining how herding behaviour, financial literacy, and digital literacy impact cryptocurrency investment decisions. Grounded in Social Learning Theory and supported by UTAUT to operationalise digital literacy, this study examines how herding behaviour, financial literacy, and digital literacy shape cryptocurrency investment decisions. We analyse survey data from 138 Indonesian retail investors through PLS-SEM. Key findings show that financial literacy (β = 0.443, t = 5.041) and digital literacy (β = 0.495, t = 4.246) are primary determinants of investment decisions, while herding behaviour (β = 0.016, t = 0.628) does not directly influence them but does so indirectly by enhancing investor literacy. This demonstrates that social observation and learning can convert herd-driven impulses into rational choices when mediated by literacy. By extending Social Learning Theory into digital investment contexts, this study provides insights for investors and policymakers seeking to enhance financial and digital literacy. Full article

This study investigates why Generation Z in Surabaya remains reluctant to live in vertical housing despite strong urbanization pressures and policy promotion. Using an explanatory sequential mixed-methods approach with 340 respondents aged 18–27, the research identifies five key factors influencing preferences: physical environment, psychological-social concerns, social status and stress, economic considerations, and cultural accommodation. Factor analysis explains 45.1% of total variance, while structural equation modeling reveals that physical environment preferences play a central mediating role. Economic factors affect psychological-social concerns both directly and indirectly, and cultural accommodation strongly shapes social status perceptions but does not directly influence physical preferences. Qualitative analysis of 411 statements shows consistently negative psychological themes, predominantly negative economic sentiments, and more balanced views of physical attributes. Subgroup analysis reveals stronger economic effects among early-career professionals than students. The findings challenge purely rational housing choice models, demonstrating that cultural and economic factors shape psychological acceptance through indirect pathways. Recommendations include culturally sensitive designs (flexible guest rooms, communal gathering spaces), innovative ownership schemes (rent-to-own, cooperative models), and reframed marketing emphasizing lifestyle enablement rather than amenity features. Full article

Evaluating the human–machine interface (HMI) of service robots remains challenging due to the complex integration of perceptual aesthetics and functional rationality. To address this, we propose a hybrid multidimensional HMI evaluation method that quantifies three key dimensions—layout aesthetics, color aesthetics, and functional layout rationality—by integrating visual cognition theory and axiomatic design (AD). The framework operationalizes five layout principles (balance, proportion, unity, regularity, density) and a four-component color model (color difference, distribution, harmony, and personality), complemented by a biologically grounded metric—visual perceptual intensity (VPI)—derived from cone cell response theory. Subjective weights from expert judgments (via analytic hierarchy process, AHP) and objective weights from the entropy weighting method (EWM) are fused within an AD-based information axiom framework to enable balanced, data-driven assessment. Applied to five candidate HMIs for a medical service robot (N = 15 participants), the method identified the design scheme x₃ as optimal when the balancing coefficient α ≥ 0.5 (reflecting greater emphasis on subjective judgment), whereas design scheme x₂ was preferred when α < 0.5 (prioritizing objective data). Given the modest sample size, correlation analysis revealed moderate-to-large—though not reaching conventional significance—between evaluation indicator scores and eye-tracking behavior: unity correlated with total fixation duration (Pearson_r = 0.682), and color harmony with first fixation duration (Pearson_r = 0.788), suggesting alignment between design attributes and visual attention patterns. These preliminary findings suggest that key design attributes may influence visual attention patterns, supporting the framework’s potential to link aesthetic and visual choices to measurable perceptual outcomes. Full article

Passenger heterogeneity in loyalty fundamentally influences their choice behaviors, and is pivotal to railway differentiated pricing. Thus, travelers are categorized into loyal passengers and non-loyal passengers. According to the generalized cost minimization, we identify a train priority sequence reflecting consistent preferences of loyal passengers and establish a train selection probability model based on stochastic preferences of non-loyal passengers. Then, a hybrid choice model resulting from the distinct decision-making processes of these two passenger categories is formulated. A nonlinear pricing optimization model in the scenario of multiple train categories with multiple trains is established. An improved Particle Swarm Optimization algorithm based on the Sampling Fitness Strategy (SFS-PSO) is proposed to improve the solution accuracy. The SFS-PSO enhances the search diversity for the personal historical best positions and global best position without expanding the size of the particle swarm as much as possible. The case analysis demonstrates that the proposed pricing optimization approach can increase the expected revenue by 1.7%, validating the rationality of considering the hybrid choice behavior of passenger loyalty heterogeneity for the railway pricing optimization problem. Meanwhile, the case results highlighted the significant impact of the proportion of loyal passengers on revenue improvement. Full article

This study presents a tripartite analytical framework for the (1+1)-dimensional nonlinear Klein–Fock–Gordon equation, a key model for spinless particles in relativistic quantum mechanics. The investigation begins with a Painlevé analysis showing that the equation is completely integrable via the Painlevé test by using Maple. Subsequently, classical Lie symmetry analysis is employed to derive the infinitesimal generators of the equation. A Lagrangian formulation is constructed for these generators, from which similarity variables are systematically obtained. This framework enables a complete similarity reduction, transforming the complex nonlinear partial differential equation into a more tractable ordinary differential equation. To solve this reduced ordinary differential equation and to obtain a spectrum of soliton solutions, we implement the new generalized exponential differential rational function method. This advanced technique utilizes a rational trial function based on the $ith$ derivatives of exponentials, generating a diverse spectrum of closed-form soliton solutions through strategic choices of arbitrary constants. The novelty of this approach provides a unified framework for handling higher-order nonlinearities, yielding solutions such as multi-peakons and lump solitons, which are vividly characterized using Mathematica-generated 3D, 2D, and contour plots. These findings provide significant insights into nonlinear wave dynamics with potential applications in quantum field theory, nonlinear optics, plasma physics, etc. Full article

Although viral outbreaks are increasing, vaccination rates are decreasing. Our aim was to explain this baffling behavior that seems to contradict rational self-interest, and, thus, be beyond the purview of rational choice theories. We integrated fuzzy-trace theory and major theoretical alternatives and applied them to influenza, testing theoretical predictions in two samples: young adults (who are major viral vectors), N = 722, and community members, N = 185. Controlling for prior knowledge and other psychosocial factors that influence vaccination, explained variance jumped significantly when key predictors from fuzzy-trace theory were added, reaching 62% and 80% for vaccination intentions and 37% and 59% for behavior for each sample, respectively. Single items assessing global gist perceptions of risks and benefits achieved remarkable levels of diagnosticity. Key predictors were intuitive in that they were gisty, imprecise, and non-analytical. In contrast, rational system 2 measures—numeracy and cognitive reflection—were not predictive. These results provide new insights into why individuals vaccinate or not and new avenues for interventions to improve shared clinical decision-making. Full article

Understanding why households choose particular urban neighborhoods requires bridging traditional rational-choice explanations with emerging evidence on cognitive, social, and informational influences. This study investigates how territorial intelligence (TI)—defined as the availability and use of spatial data, planning information, and participatory knowledge platforms—interacts with behavioral factors to shape residential relocation decisions. Employing an explanatory sequential mixed-methods design, we surveyed 356 recent movers in Casablanca, Morocco, and conducted 20 follow-up semi-structured interviews. Quantitative analysis shows that each additional consulted data source increased the odds of selecting a central, transit-rich location by 45 %, while prior awareness of development plans raised those odds by 60 %, controlling for income, tenure, affordability, dwelling attributes, and socio-demographics. Data use also predicted higher post-move satisfaction, particularly when individual housing preferences aligned with chosen locations. Qualitative findings reveal that residents view territorial data as a tool for “future-proofing” but also experience information overload, leading some to revert to heuristics or social advice. The interplay of rational cost–benefit logic, bounded cognitive processing, and TI-mediated knowledge underscores the need for planning strategies that combine economic fundamentals with behaviorally informed data provision. By integrating micro-level decision evidence with the territorial intelligence framework, the study offers practical guidance for urban planners aiming to nudge residential choices toward more sustainable, policy-consistent outcomes. Full article

Reliable estimates of soil stiffness and energy dissipation are essential for dynamic-response design. This study benchmarks machine learning models for predicting shear modulus (G) and damping ratio (D) using 2738 resonant-column measurements. After data quality control and F-test feature screening, five model families—decision trees and ensembles, support-vector machines, Gaussian-process regression, neural networks, and linear baselines—were trained under uniform 10-fold cross-validation and evaluated with R², RMSE, MAE, and MSE, while recording training time to reflect practical constraints. Results show that model choice materially affects performance. For G, a bagged ensemble of trees delivered the best accuracy (R² = 0.9827) with short training times; single trees provided transparent, fast screening models. For D, tree-based ensembles again performed strongly (R² up to 0.8565), while a rational-quadratic Gaussian-process model offered competitive accuracy (R² ≈ 0.81) together with prediction intervals that support risk-aware design. Feature influence aligned with soil mechanics: G was most sensitive to effective confining pressure (σ′0), initial void ratio (e0), and density (ρ); D was governed mainly by overconsolidation ratio (OCR), depth (z), σ′0, and plasticity, with notable interactions among stress, strain amplitude (γ), and moisture state. The findings provide practice-oriented guidance: use bagged trees for routine predictions of G and D, and add Gaussian-process regression when uncertainty quantification is required. The approach complements laboratory testing and supports safer, more economical dynamic-response design. Full article

Background: Non-small cell lung cancer (NSCLC) is the most common form of lung cancer and a leading cause of cancer-related death. Despite therapeutic advances, long-term survival in stage IV disease is uncommon. Tumor analyses that combine genomic and functional platforms may provide the opportunity to monitor clonal dynamics and guide therapy selection. Case Presentation: We report a 67-year-old woman with metastatic poorly differentiated lung adenocarcinoma, who achieved four durable remissions and survived nearly 12 years. Serial studies using ex vivo analysis of programmed cell death (EVA/PCD) functional-profiling-guided therapeutic choices were correlated with next-generation sequencing (NGS). Molecular events included the emergence of a BRAF V600E mutation responsive to dabrafenib plus trametinib and the acquisition of an EGFR exon 19 deletion responsive to Osimertinib. EVA/PCD identified activity for targeted agents and revealed synergy for vinorelbine plus Osimertinib not predicted by genomic profiling, which provided additional response. Discussion: This case highlights clonal evolution in NSCLC and illustrates how serial tissue analyses correlating phenotypic and genomic events can offer therapeutic interventions to provide long-term survival. Conclusions: The integration of functional and genomic profiling may improve personalized treatment in NSCLC by interrogating tumor heterogeneity and clonal evolution to inform rational therapeutic selection. Full article

A strategic fulcrum for leading high-quality economic development and shaping the nation’s future. Core competitiveness lies in how governments can effectively stimulate consumer demand for green consumption and motivate enterprises to pursue green technology innovation through the development of precise and efficient innovative regulation models. In this paper, a tripartite evolutionary game model is constructed based on evolutionary game theory, encompassing the government, enterprises, and consumers. We analyze the strategic interactions and evolutionary path among these three entities under conditions of bounded rationality and information asymmetry. The research reveals the following: (1) the government can effectively guide enterprises towards genuine green innovation through enhanced rewards for substantive innovation and increased penalties for strategic innovation; (2) consumer purchasing decisions are significantly shaped by economic benefits, perceived social value, and government subsidies, with their market choices forming a critical external supervisory force; and (3) government regulatory strategies are dynamically adjusted in response to market integrity levels and social welfare, with a tendency to implement innovative regulation when “greenwashing” risk is elevated. In conclusion, simulation analysis is conducted using MATLAB 2018a, and governance recommendations are offered based on three dimensions: precise government regulation, enhanced corporate responsibility, and enhanced consumer capabilities. These recommendations offer both a theoretical basis and a practical path for establishing an integrated green innovation governance system based on incentive constraint empowerment. Full article

Metaverse safety training (MST) is popular in highway construction projects (HCPs). While researchers have statically examined the influence of MST, one of the essential gaps is that the interaction among stakeholders on how to improve MST effect is neglected. This paper adopts a game theory approach to illustrate the dynamics among stakeholders, namely, contractors, subcontractors, and construction crews, regarding MST within the framework of HCPs. A tripartite evolutionary game model is developed to analyze the interaction among contractors, subcontractors, and construction crews. The evolutionary stability of the stakeholders’ strategies and the equilibrium point were elucidated by solving the proposed model. A numerical simulation was conducted to validate the rationality of the results. The results show that the choice of behavioral strategies and their evolutionary paths for each stakeholder are closely related to the behavioral strategies of other stakeholders in the game, with significant differences in effects on each other’s initial strategies. The incentive mechanism must match the incentive measures provided to subcontractors and construction crews, ensuring a stable MST. The reward and penalty system implemented by contractors heightens the awareness of subcontractors and construction crews partly. This model provides practical recommendations to enhance training interactions, optimize strategies, increase security awareness, and streamline resource allocation. Full article