Search Results (24,130)

Background/Objectives: Clinically node-negative (cN0) neck management in cT1–T2 oral cavity squamous cell carcinoma continues to be a subject of controversy. The eighth edition AJCC has incorporated depth of invasion (DOI) as a significant factor in staging and consideration for possible neck dissection. Establishment of accurate DOI thresholds and their clinical relevance is crucial to maximize oncological outcomes with reduced unnecessary morbidity. Methods: A comprehensive analysis of clinical research assessing elective neck dissection (END) techniques in oral cavity cancers classified by DOI in cT1–T2N0 patients was carried out. The included studies reported occult nodal metastasis rates, overall survival, disease-specific survival, disease-free survival, and regional control. Results: With hazard ratios favoring END for overall survival (HR 0.64; 95% CI 0.45–0.92) and disease-free survival (HR 0.45; 95% CI 0.34–0.59), elective neck dissection provided advantages in both survival and regional control. In a national registry, DOI ≥ 5 mm independently raised the risk of nodal failure (HR 2.099; 95% CI 1.346–3.271), while END enhanced neck control in comparison to observation (HR 1.749; 95% CI 1.141–2.680). With ROC-derived cut-offs like 4.59 mm producing positive predictive values for nodal metastasis up to 41.7%, diagnostic thresholds clustered around 4 mm. Conclusions: Under DOI guidance, elective neck dissection consistently showed oncologic benefit, with practical thresholds convergent around 4 mm for sites in the mixed oral cavity and 3 mm for high-risk subsites. The synthesized results confirmed that DOI is the primary determinant of END when combined with histopathologic and subsite-specific risk factors. Full article

Nanobubbles (NBs) (<1 µm) exhibit unique physicochemical properties and high stability, which have potential applications in biological systems, including microbial metabolism enhancement, fibroblast proliferation, and tumor inhibition. However, the influence of different gaseous components on cell viability remains unclear. This study examined the effects of nitrogen, oxygen, and hydrogen NBs on human lung fibroblast cell (MRC-5) viability. NBs were generated in a Dulbecco’s modified Eagle’s medium using a gas–liquid mixing method and characterized by a nanoparticle tracking analysis. After 48 h of culture, cell viability increased 1.34-, 1.30-, and 1.29-fold for nitrogen, oxygen, and hydrogen NBs, respectively, with only nitrogen NBs showing a significant increase (p < 0.05). Flow cytometry with carboxyfluorescein succinimidyl ester analysis showed no difference in proliferation among groups, indicating that enhanced viability might result from gas-specific effects rather than direct stimulation of cell division. These findings highlight the potential biomedical applications of nanobubbles and their constituent gases. Full article

Background/Objectives: We conducted this study to develop a generic amiodarone tablet pharmaceutically equivalent to the reference drug. This development is crucial for securing a stable supply chain for this orphan drug, which currently faces domestic market instability. Amiodarone, a national essential medicine, often experiences unstable supply due to its limited profitability. Methods: To secure this stable supply chain, we employed a factorial design, utilizing a Quality by Design (QbD) approach, to create the most suitable formulation. Initially, we observed a limitation where the formulation exhibited a flowability of 25% based on the Carr’s Index, which exceeded the target of 20%. To address this challenge, we incorporated lactose monohydrate during the pre-mixing stage rather than the post-mixing stage. Subsequently, we identified the binder content and the amount of granulation solvent as Critical Material Attributes (CMAs), and we performed a Design of Experiments (DoE). Result: Based on these investigations, we determined that the optimal prescription utilizes 5.71% povidone K25 and 40 mg/T of purified water. The final formulation successfully achieved an excellent flowability of 15.8%. Furthermore, this formulation showed a dissolution and bioequivalence PK profile equivalent to the reference drug in pH 1.2, 4.0, and 6.8 buffer solutions, each containing 1% Tween 80. Conclusion: Ultimately, the developed formulation is anticipated to establish a stable domestic supply chain and concurrently reduce national healthcare costs. These research findings also establish the groundwork for future continuous manufacturing implementation. Full article

Electrical Resistivity Tomography (ERT) has proven to be a highly sensitive geophysical method for characterizing the dynamics of seawater intrusion. This study uses tank experiments to simulate seawater intrusion, utilizing electrical resistivity tomography to monitor real-time changes in groundwater resistivity during the intrusion process. The objective is to quantitatively reveal the development and evolution mechanisms of seawater intrusion wedges in sandy aquifers, thereby establishing a real-time resistivity monitoring method for groundwater distribution and migration characteristics. This study improves resistivity imaging data processing methods, enhancing both efficiency and accuracy. The refined cross-hole ERT technique is applicable not only to meter-scale indoor experiments; its optimized forward and inverse algorithms can also be directly transferred to regional-scale field monitoring. Experimental results show that the average resistivity in the study area continuously decreases from 57 Ω·m in the initial freshwater state to 1.1 Ω·m at the intrusion stabilization point. Areas with resistivity values below 20 Ω·m corresponded exactly to the brine intrusion zone. The evolution of the freshwater-saltwater interface unfolded in three stages: Initially, the density difference (0.025 g/cm³) dominated, with the saltwater intrusion depth at the aquifer base reaching 0.45 m, significantly exceeding the 0.04 m penetration at the upper section. During the intermediate stage, the interface morphology differentiated into an “upper triangular, lower arc-shaped” configuration. The bottom intrusion distance increased to 1.65 m, and the thickness of the brackish-freshwater mixing zone expanded from 0.1 m to 0.3 m. In the final stage, the interface stabilized and began intruding toward the surface, establishing a new hydrodynamic equilibrium. In addition, the migration rate of saline water at the aquifer base gradually decreased from 6.25 × 10⁻⁴ cm/s initially to 1.16 × 10⁻⁵ cm/s at steady state. These results reflect the dynamic coupling process between seepage and dispersion and demonstrate that this method enables effective real-time monitoring of seawater intrusion development and conditions, as well as early warning capabilities. Full article

Accurate localization is a persistent challenge for Mixed Reality (MR) applications in the construction industry, where reliable alignment between digital building models and physical environments is critical. Commercial MR devices such as the Microsoft HoloLens rely on Visual-Inertial Simultaneous Localization and Mapping (VISLAM) for pose estimation, but accumulated drift over extended trajectories and visually ambiguous indoor spaces often reduces localization accuracy. This paper presents a complementary localization refinement methodology that integrates HoloLens spatial tracking with image style transfer and geometry-based pose estimation for Building Information Modeling (BIM)-aligned MR visualization. Image style transfer is used to reduce appearance discrepancies between real-world images and synthetic BIM renderings, improving feature correspondence for geometric alignment. Pose refinement is then applied using feature matching and Perspective-n-Point (PnP) estimation to mitigate accumulated drift when sufficient visual evidence is available. The method is evaluated using 1408 image pairs captured along an indoor trajectory, demonstrating improved BIM alignment, significantly reducing accumulated drift to 1–2 pixels. The proposed approach supports more reliable MR visualization for construction-related tasks such as inspection, coordination, and spatial decision-making. Full article

Background: Cardiovascular disease (CVD) is the leading cause of mortality worldwide, creating demand for continuous, unobtrusive monitoring solutions. This clinical validation evaluates the accuracy of remote photoplethysmography (rPPG), a contactless method using camera video, for measuring pulse rate (PR) in patients with CVD. Methods: We enrolled 50 adults with confirmed CVD at a clinical trial center. In a 6 min rested session, synchronized facial video (under controlled lighting), electrocardiogram (ECG), and photoplethysmography (PPG) signals were recorded. PR was derived from 25 s video segments using rPPG-enabled software and compared to ECG-derived PR via regression and Bland–Altman analysis. Results: Data from 47 participants (n = 817 samples) were analyzed. rPPG-derived PR showed strong agreement with ECG, with a mean absolute error of 1.061 bpm, root-mean-squared error of 2.845 bpm, and Pearson correlation of 0.962. Mixed-effects regression analyses (after 2% outlier removal, n = 782) indicated minimal influence from demographic, environmental, or CVD factors on accuracy. PPG-ECG discrepancies reflected inherent methodological differences. Conclusion: The rPPG method provides accurate, contactless PR monitoring in CVD patients, supporting its potential for remote patient monitoring and early deterioration detection. Future work will validate rPPG for irregular rhythms, additional vital signs, and diverse cohorts to strengthen clinical robustness for cardiometabolic risk assessment. Full article

This study examines the role of artificial intelligence (AI) as a mediating tool in values training, based on university students’ reflections on their own values and those represented in literary characters. The research, developed at the Catholic University of Murcia (Spain) and University Collegue Cork (Ireland) integrated the humanistic approach of literature with the pedagogical potential of AI. An exploratory–descriptive mixed-methods design was applied with 126 students of Education and Philology. The instruments included the Hall–Tonna questionnaire, a 12-item Likert scale, and open-ended questions, analyzed using descriptive statistics, mean comparison, and thematic content analysis. The results reflect a preference for values such as justice, perseverance, and empathy, with cultural differences: in Spain, solidarity and community spirit stood out; and in Ireland, integrity and individual responsibility stood out. A total of 78% positively rated AI mediation for its capacity to stimulate critical reflection and ethical debate, although risks linked to technological dependence and cultural bias were noted. It is concluded that the synergy between literature and AI enhances ethical and civic education, provided it is implemented from an ethical and humanizing perspective. Full article

Objectives: This study aims to evaluate whether changes in perfusion index (PI) after the first deflation of the blood pressure cuff during remote ischemic conditioning (RIC) are associated with passive leg raising (PLR)-induced changes in stroke volume. In addition, we compared PI changes after cuff deflation during RIC between critically ill patients and healthy controls. Methods: This prospective, single-center study was conducted in a mixed ICU at a tertiary teaching hospital. Patients aged >18 years admitted to the ICU, monitored using calibrated pulse contour analysis, and scheduled for a PLR test as decided by the attending physicians were included. The PI was measured after blood pressure cuff deflations during RIC (3 cycles of brachial cuff inflation to 200 mmHg for 5 min, followed by instantaneous deflation to 0 mmHg for another 5 min) in the supine position after PLR. Preload responsiveness was defined as a ≥10% increase in the stroke volume index (SVI) during PLR. Data were compared with a healthy control group. Results: Thirty-three patients were included (median age 62; 45% in shock; 55% mechanically ventilated). When comparing critically ill patients with healthy volunteers, the maximum PI change (dPImax) and the time to reach it were higher in critically ill patients after the first and second cuff deflations (p < 0.05). However, after the third deflation, the difference was no longer significant. Following the first deflation, dPImax was significantly correlated with SVI changes during PLR (r = 0.63, p < 0.001). After the cuff was first deflated, we detected a PI cutoff with a positive SVI response (≥10%) during PLR, with a sensitivity of 64% and a specificity of 94% (area under the receiver operating characteristic curve 0.752; 95% CI, 0.564–0.940; p = 0.008). Conclusions: The maximum change in perfusion index following brachial blood pressure cuff deflation after five minutes of inflation may serve as a promising noninvasive bedside indicator of preload responsiveness in critically ill patients. Additionally, the observed normalization of PI kinetics during RIC suggests possible acute modulation of vascular reactivity, though further research is needed to confirm an association between PI changes and endothelial function. Full article

This paper proposes the Fuzzy Difference Equation Matrix Model (FDEMM), a novel predictive control algorithm designed for nonlinear multivariable systems. Standard Dynamic Matrix Control (DMC) often struggles with computational load and nonlinearities. FDEMM addresses this by integrating the Difference Equation Matrix Model (DEMM) with a generalized Takagi-Sugeno (T-S) fuzzy framework, utilizing a parameter-weighting scheme to handle overlapping membership functions. The method is validated on two distinct nonlinear systems: a binary distillation column and a delayed thermal mixing tank. Results demonstrate FDEMM’s ability to control complex systems achieving the desired output even in the presence of disturbances and noise. The proposed strategy offers a computationally efficient alternative for real-time control of complex nonlinear processes. Full article

Objective: The current study assessed outcomes of a continuing professional education program aimed at managing job-related stress to assist employees with recognizing and managing burnout and enhancing both productivity and overall well-being. Study Design: This study outlines the implementation of a needs assessment survey and the development of a non-credit training course for working professionals that addressed risks of burnout, suicidality, and self-care strategies to support mental health in the workplace. Methods: The sample for the current study consisted of 398 predominantly mid- to senior-level professionals. Participants were divided into two cohorts. The first cohort completed a structured needs assessment survey between June 2023 and July 2023 and provided ideas for curriculum development. The second cohort participated in synchronous, instructor-led virtual training sessions and completed pre- and post-training questionnaires between January 2024 and June 2024. A mixed-method content analysis was conducted to identify recurring themes and their frequency in course questionnaires. Results: Findings suggest that the training successfully expanded participants’ understanding of signs of burnout and of new approaches to improve well-being in the workplace including forming friendships, engaging in mindfulness activities, and taking time off for a mental health day. Conclusions: Future research should explore the long-term impacts of such interventions and compare delivery methods, including virtual and in-person formats, to determine the most effective approaches for promoting mental well-being at work. Full article

Text-to-music generation aims to automatically produce audio content with semantic consistency and coherent musical structure based on natural language descriptions. However, existing methods still face challenges in terms of style diversity, rhythmic consistency, and long-term structural modeling. To address these issues, we propose a novel text-to-music generation model, termed MusicDiffusionNet (MDN), which integrates diffusion models with the WaveNet architecture to jointly model musical semantics and temporal structure in a continuous latent space. By decoupling high-level semantic conditioning from low-level audio generation, MDN enhances its ability to model long-range musical structure while improving semantic alignment between text and generated music with stable generation behavior. Building upon this framework, we further design two complementary mixing strategies to improve generation quality and structural coherence. Adaptive Style Mixing (ASM) performs weighted interpolation among stylistically similar music samples in the style embedding space, incorporating key and harmonic compatibility constraints to expand the style distribution while avoiding dissonance. Multi-scale Temporal Mixing (MTM) adopts beat-aware temporal decomposition, mixing, and reorganization across multiple time scales, thereby enhancing the modeling of both local and global temporal variations while preserving rhythmic periodicity and musical groove. Both strategies are integrated into the diffusion process as conditional augmentation mechanisms, contributing to improved learning stability and representational capacity under limited data conditions. Experimental results on the Audiostock dataset demonstrate that MDN and its mixing strategies achieve consistent improvements across multiple objective metrics, including generation quality, style diversity, and rhythmic coherence, validating the effectiveness of the proposed approach for text-to-music generation. Full article

Under the rapid development of live commerce, impulse buying has become a core consumption phenomenon, yet its psychological triggering pathways across different consumer groups remain to be fully elucidated. Drawing on the S–O–R framework, this study conceptualizes live-stream interactivity, novelty, and streamer attractiveness as external “stimuli,” and positions immersive experience as the core “organism” mechanism, thereby constructing and testing an integrated “stimulus–experience–response (impulse buying intention)” model. Using a mixed-method approach that combines structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), the results show that all three live-stream features significantly enhance impulse buying intention, primarily by strengthening immersive experience, with immersion exerting a significant partial mediating effect. Moreover, consumers’ loneliness significantly amplifies the indirect effect of live-stream features on impulse buying via immersive experience. The fsQCA further uncovers multiple equivalent pathways leading to high impulse buying intention, including a strong-experience pattern centered on “streamer attractiveness + immersive experience,” as well as a social compensation pattern centered on “high interactivity + high loneliness.” This study provides a testable theoretical framework, actionable operational strategies, and sustainable ethical guidance for live commerce, offering a pathway for the industry to achieve a “high experience × high conversion × high well-being” triple-win outcome. Full article

Background/Objectives: Inter-platform variability in beam characteristics and low-dose exposure may arise from differences in linear accelerator head design, multileaf collimator geometry, and dose calculation algorithms. This study aimed to evaluate system-level dosimetric differences between two widely used linear accelerator platforms under clinically commissioned conditions. Methods: A comparative dosimetric analysis was performed between Elekta Synergy and Varian TrueBeam linear accelerators. Beam data were acquired using a SunSCAN™ 3D water phantom, and patient-specific quality assurance was conducted with the Octavius^® 4D system. Treatment plans were generated for left-sided breast, prostate, and head and neck cases using clinically commissioned treatment planning systems. Beam flatness, symmetry, penumbra width, low-dose exposure, conformity, homogeneity, and organ-at-risk dose metrics were evaluated. Results: Platform-dependent differences were observed in penumbra behavior and out-of-field dose, primarily attributable to intrinsic linac head design and collimation characteristics. These differences propagated into clinical plans, with greater variability observed for breast and head and neck cases, while prostate plans showed higher consistency between platforms. Algorithm-dependent trends were noted for conformity and homogeneity indices; however, all plans met institutional clinical acceptance criteria during quality assurance. Stricter gamma evaluation criteria revealed systematic but limited inter-platform deviations. Conclusions: Elekta Synergy and Varian TrueBeam demonstrated clinically acceptable dosimetric performance, with modest platform-dependent differences. While target coverage and overall plan quality were comparable, these variations were primarily observed in peripheral dose regions and may be relevant for platform-specific planning optimization and quality assurance. This supports the importance of comprehensive commissioning and QA procedures in both mixed- and single-platform clinical settings, particularly for highly modulated techniques. Full article

Petrochemical hybrid renewable energy systems (PHRESs), integrating renewable and fossil energy sources, have garnered more and more attention for sustainable manufacturing. However, achieving concurrent optimization of energy supply reliability and carbon mitigation in these complex systems remains a critical challenge. This study proposes an innovative bilateral optimization framework coupling supply-side energy management with demand-side flexibility. On the supply side, a scenario-based two-stage stochastic programming method synergizes with energy storage systems to address renewable energy intermittency, considering a time-of-day tariff from the grid. On the utilization side, heat energy-based and shaft work-based energy interchangeability are introduced and leveraged to enable both qualitative and quantitative flexibility in process unit requirements and thus obtain energy consumption relaxation models for relaxing the design boundaries of PHRESs. These dual strategies are then coupled in a two-stage mixed-integer programming model framework for the optimal design of PHRESs. Applied to a large-scale refinery incorporating carbon taxation and dynamic electricity price, the proposed methodology demonstrates superior performance through five comparative cases. Compared to the Base Case, the Optimal Case using the proposed method can reduce the total annual cost by 14.82%, and stochastic programming reveals over a 40% probability of carbon mitigation in the uncertain space. Full article

Learning Management Systems (LMSs) remain largely static and administrative, often failing to support personalization and inclusive access to learning resources. This paper presents AI for All, a practical approach to building an adaptive, accessible, and inclusive learning experience within a mainstream LMS, demonstrated through the PREPARE project (Personalized Education Framework for AI-Enabled Adaptive and AR-Enhanced Learning) implemented in Moodle. PREPARE operationalizes an end-to-end generative AI pipeline that transforms a single authoritative PDF textbook into multimodal learning assets, including chapter summaries, structured notes and slide decks, formative quiz items, video mini-lectures with captions, podcast-style audio, and chapter-level augmented reality (AR) activities. In parallel, the system maintains a hybrid learner model by combining an initial FSLSM/ILS questionnaire with continuous behavior-based profiling derived from Moodle logs. Learner profiles drive non-prescriptive personalization through resource prioritization and recommendations, while preserving learner agency and access to all modalities. We describe the system architecture, Moodle integration mechanisms, and adaptation logic, and report an ongoing mixed-methods evaluation focusing on engagement, interaction diversity, perceived usefulness, and accessibility benefits. The system-level validation and deployment readiness suggest that AI-augmented LMS workflows can reduce instructor authoring effort while improving flexibility and inclusivity, provided that human-in-the-loop validation and privacy-aware analytics are embedded from the outset. Full article