Search Results (12,410)

The hospitality sector is undergoing a rapid digital change due to smart technology and artificial intelligence. This presents both possibilities and problems for the development of sustainable innovation. Yet, little is known about how leaders’ technological competencies affect employees’ capacity to engage in environmentally responsible innovation. This study addresses this gap by examining how leaders’ competencies in smart technology, artificial intelligence, robotics, and algorithms (STARA) shape employees’ green innovative behavior in hotels. Anchored in person–job fit theory and cognitive appraisal theory, we propose that when employees perceive a strong alignment between their skills and the technological demands introduced by STARA, they are more likely to appraise such technologies as opportunities (challenge appraisals) rather than threats (hindrance appraisals). These appraisals, in turn, mediate the link between leadership and green innovation. Convenience sampling was used to gather data from staff members at five-star, ecologically certified hotels in Sharm El-Sheikh, Egypt. According to structural equation modeling using SmartPLS, employees’ green innovation behaviors are improved by leaders’ STARA abilities. Crucially, staff members who viewed STARA technologies as challenges (i.e., chances for learning and development) converted leadership skills into more robust green innovation results. Conversely, employees who perceived these technologies as obstacles, such as burdens or threats, diminished this beneficial effect and decreased their desire to participate in green innovation. These findings highlight that the way employees cognitively evaluate technological change determines whether leadership efforts foster or obstruct sustainable innovation in hotels. Full article

Background/Objective: Predicting pharmacological response in cancer remains a key challenge in precision oncology due to intertumoral heterogeneity and the complexity of drug–gene interactions. While machine learning models using multi-omics data have shown promise in predicting pharmacological response, selecting the features with the highest predictive power critically affects model performance and biological interpretability. This study aims to compare computational and biologically informed gene selection strategies for predicting drug response in cancer cell lines and to propose a feature selection strategy that optimizes performance. Methods: Using gene expression and drug response data, we trained models on both data-driven and biologically informed gene sets based on the drug target pathways to predict IC₅₀ values for seven anticancer drugs. Several feature selection methods were tested on gene expression profiles of cancer cell lines, including Recursive Feature Elimination (RFE) with Support Vector Regression (SVR) against gene sets derived from drug-specific pathways in KEGG and CTD databases. The predictability was comparatively analyzed using both AUC and IC₅₀ values and further assessed on proteomics data. Results: RFE with SVR outperformed other computational methods, while pathway-based gene sets showed lower performance compared to data-driven methods. The integration of computational and biologically informed gene sets consistently improved prediction accuracy across several anticancer drugs, while the predictive value of the corresponding proteomic features was significantly lower compared with the mRNA profiles. Conclusions: Integrating biological knowledge into feature selection enhances both the accuracy and interpretability of drug response prediction models. Integrative approaches offer a more robust and generalizable framework with potential applications in biomarker discovery, drug repurposing, and personalized treatment strategies. Full article

A personalized framework for smart home automation is introduced, utilizing machine learning to predict user activities and allow for the context-aware control of living spaces. Predicting user activities, such as ‘Watch_TV’, ‘Sleep’, ‘Work_On_Computer’, and ‘Cook_Dinner’, is essential for improving occupant comfort, optimizing energy consumption, and offering proactive support in smart home settings. The Edge Light Human Activity Recognition Predictor, or EL-HARP, is the main prediction model used in this framework to predict user behavior. The system combines open-source software for real-time sensing, facial recognition, and appliance control with affordable hardware, including the Raspberry Pi 5, ESP32-CAM, Tuya smart switches, NFC (Near Field Communication), and ultrasonic sensors. In order to predict daily user activities, three gradient-boosting models—XGBoost, CatBoost, and LightGBM (Gradient Boosting Models)—are trained for each household using engineered features and past behaviour patterns. Using extended temporal features, LightGBM in particular achieves strong predictive performance within EL-HARP. The framework is optimized for edge deployment with efficient training, regularization, and class imbalance handling. A fully functional prototype demonstrates real-time performance and adaptability to individual behavior patterns. This work contributes a scalable, privacy-preserving, and user-centric approach to intelligent home automation. Full article

In order to optimize the preparation process of microlens arrays, improve preparation efficiency, and reduce preparation costs, 248 nm KrF excimer laser direct writing is combined with a motion mask to prepare microlens arrays on PMMA substrates. Firstly, a specific exposure mask based on the contour characteristics of the microlens unit was designed, and the preparation principle was analyzed. Using COMSOL Multiphysics 6.3 simulation software, a microlens preparation model was built to intuitively describe the process of preparing microlenses by the motion mask method. Secondly, a preparation system was built, and the laser processing technology was optimized. Finally, microlens arrays were prepared based on the optimized process, and an optical microscope and white-light interferometer were used to observe their morphology. The experimental results show that this method can effectively prepare cylindrical and circular microlens arrays. The width of the cylindrical microlens array unit exceeded 90 μm, the height was 7.08 μm, and the roughness was 0.09 μm. The diameter of the circular microlens array unit was φ100 μm, the height was 4 μm, and the curvature radius was 230 μm. The geometric dimensions of the mask can be adjusted to obtain microlens units of the desired size, achieving personalized preparation of microlens arrays. The excimer laser motion mask method can prepare various types of microlens arrays, and the array units have a high consistency and high surface quality, which helps to improve the efficiency, flexibility, stability, and specificity of microlens array preparation. Full article

This research focuses on developing innovative nanoparticle-based drug delivery systems to enhance therapeutic efficacy while minimizing adverse effects. We engineered biocompatible polymeric nanoparticles capable of encapsulating various therapeutic agents, demonstrating improved stability, prolonged circulation times, and preferential accumulation in target tissues. Surface functionalization with targeting ligands achieved unprecedented specificity in drug delivery. Our nanoparticle formulations exhibited superior tumor accumulation in preclinical cancer models, enhancing therapeutic efficacy and reducing systemic toxicity. Additionally, we developed stimuli-responsive nanoparticles for precise spatiotemporal control over drug release. These advanced delivery systems promise to improve patient outcomes and advance personalized nanomedicine. Full article

Professional certification of engineers plays a crucial role in verifying competencies and ensuring the safety and quality of engineering outputs. However, most existing certification systems assign fixed validity periods (e.g., 3–5 years) without considering individual engineer characteristics or the intensity of technological progress in specific fields. This study examines the key factors influencing the optimal validity period of engineering certifications and proposes it as a measurable indicator to support safety in engineering practice. A new model is introduced that integrates expert judgment, fuzzy set theory, and bibliometric analysis of Q1/Q2 Scopus-indexed publications. The model incorporates three main factors: competence level, professional experience, and the technological intensity of the discipline. A case study from the engineering certification system of Kazakhstan demonstrates the model’s practical applicability. Certification bodies, policymakers, and engineering organizations can use these findings to establish more flexible certification validity periods, thereby ensuring timely reassessment of competencies and reducing safety risks. For example, for mechanical engineers, the optimal validity period is 3 years rather than the statutory 5 years; in other words, the model recommends a 40% reduction in certification validity. This reduction reflects the combined effects of competency level, professional experience, and technology intensity on certification renewal schedules. Overall, the proposed factorial approach supports a more personalized and safety-oriented certification process and offers insights into improving national qualification systems. Full article

We propose an edge-enhanced federated learning framework for real-time itinerary optimization in elderly oriented adventure tourism, addressing the critical need for adaptive scheduling that balances activity intensity with health constraints. The system integrates lightweight convolutional neural networks with a priority-based scheduling algorithm, processing participant profiles and real-time biometric data through a decentralized computation model to enable dynamic adjustments. A modified Hungarian algorithm incorporates physical exertion scores, temporal proximity weights, and health risk factors, then optimizes activity assignments while respecting physiological recovery requirements. The federated learning architecture operates across distributed edge nodes, preserving data privacy through localized model training and periodic global aggregation. Furthermore, the framework interfaces with transportation systems and medical monitoring infrastructure, automatically triggering itinerary modifications when vital sign anomalies exceed adaptive thresholds. Implemented on NVIDIA Jetson AGX Orin modules, the system achieves 300 ms end-to-end latency for real-time schedule updates, meeting stringent safety requirements for elderly participants. The proposed method demonstrates significant improvements over conventional itinerary planners through its edge computing efficiency and personalized adaptation capabilities, particularly in handling the latency-sensitive demands of intensive tourism scenarios. Experimental results show robust performance across diverse participant profiles and activity types, confirming the system’s practical viability for real-world deployment in elderly adventure tourism operations. Full article

Mobile commerce has transformed the retail landscape, yet the determinants of impulse buying behavior in this environment remain understudied, particularly in emerging markets. This research investigates the factors influencing impulse buying in mobile commerce in Chile using the Stimulus–Organism–Response framework. A quantitative cross-sectional study collected data from 451 mobile shoppers via an online survey. Structural equation modeling with PLS-SEM revealed that eight of the thirteen hypothesized relationships were significant. Mobile application factors (visual appeal and portability) positively influenced hedonic and utilitarian values. Among personal factors, economic well-being, family influence, and credit card use directly impacted impulse buying, while time availability did not. Hedonic value strongly influenced impulse buying behavior, but utilitarian value showed no significant effect. Contrary to expectations, the COVID-19 pandemic negatively impacted impulse buying. These findings extend theoretical understanding of mobile impulse buying determinants and provide practical insights for mobile commerce developers and marketers to enhance their platforms and strategies. Full article

This research introduces a hybrid framework that integrates stochastic modeling and machine learning for predicting postprandial glucose levels in individuals with Type 1 Diabetes (T1D). The primary aim is to enhance the accuracy of glucose predictions by merging a biophysical Glucose–Insulin–Meal (GIM) model with advanced machine learning techniques. This framework is tailored to utilize the Kaggle BRIST1D dataset, which comprises real-world data from continuous glucose monitoring (CGM), insulin administration, and meal intake records. The methodology employs the GIM model as a physiological prior to generate simulated glucose and insulin trajectories, which are then utilized as input features for the machine learning (ML) component. For this component, the study leverages the Light Gradient Boosting Machine (LightGBM) due to its efficiency and strong performance with tabular data, while Long Short-Term Memory (LSTM) networks are applied to capture temporal dependencies. Additionally, Bayesian regression is integrated to assess prediction uncertainty. A key advancement of this research is the transition from a deterministic GIM formulation to a stochastic differential equation (SDE) framework, which allows the model to represent the probabilistic range of physiological responses and improves uncertainty management when working with real-world data. The findings reveal that this hybrid methodology enhances both the precision and applicability of glucose predictions by integrating the physiological insights of Glucose Interaction Models (GIM) with the flexibility of data-driven machine learning techniques to accommodate real-world variability. This innovative framework facilitates the creation of robust, transparent, and personalized decision-support systems aimed at improving diabetes management. Full article

Background/Objectives: Cancer remains a leading global cause of death, with breast, lung, colorectal, and prostate cancers being among the most prevalent. The integration of Artificial Intelligence (AI) into cancer imaging research offers opportunities for earlier diagnosis and personalized treatment. However, the effectiveness of AI models depends critically on the quality, standardization, and fairness of the input data. The EU-funded INCISIVE project aimed to create a federated, pan-European repository of imaging and clinical data for cancer cases, with a key objective to develop a robust framework for pre-validating data prior to its use in AI development. Methods: We propose a data validation framework to assess clinical (meta)data and imaging data across five dimensions: completeness, validity, consistency, integrity, and fairness. The framework includes procedures for deduplication, annotation verification, DICOM metadata analysis, and anonymization compliance. Results: The pre-validation process identified key data quality issues, such as missing clinical information, inconsistent formatting, and subgroup imbalances, while also demonstrating the added value of structured data entry and standardized protocols. Conclusions: This structured framework addresses common challenges in curating large-scale, multimodal medical data. By applying this approach, the INCISIVE project ensures data quality, interoperability, and equity, providing a transferable model for future health data repositories supporting AI research in oncology. Full article

Background/Objectives: A high-performing blood biomarker (BBM) test for Alzheimer’s disease (AD) represents an accurate, accessible, and scalable tool to aid healthcare professionals (HCPs) evaluating patients presenting with signs or symptoms of mild cognitive impairment (MCI) or dementia. However, implementation of AD blood tests into clinical practice has not been extensively evaluated. The objective of this study was to assess the implementation of the multi-analyte PrecivityAD2™ blood test (C2N Diagnostics, LLC, St. Louis, MO, USA) into the clinical workflow of memory care clinics. Methods: A total of 8 HCPs (neurologists, geriatricians, geriatric psychiatrists) who served as site directors from 8 outpatient sites that evaluated 203 cognitively symptomatic patients were included in this sub-study of the real-world QUIP II Study (NCT06025877). Implementation of this blood test was assessed through surveying these HCPs using published frameworks including the Technology Acceptance Model, net promoter score, and forced choice preference questions. These assessments were analyzed using Wilcoxon signed-rank test, Fisher’s Exact test, and Wilcoxon signed-rank test, respectively. Results: HCPs reported acceptance scores that averaged 9.6 out of 10 (p < 0.0001, effect size 0.840): the test’s contribution to clinical decision-making as well as the ease of understanding test results received the highest ratings. The net promoter score was 75 (p < 0.0001), exceeding the typical benchmark of 30 reported as good levels of satisfaction in healthcare settings. The APS2 results and individual blood analyte results were rated with similar preference around their roles in HCP clinical decision-making. Conclusions: The results indicate early evidence of user acceptance and recognition by HCPs that this AD blood test can personalize the clinical care pathway for evaluating cognitively symptomatic patients. Full article

This narrative review synthesizes PubMed- and Scopus-indexed studies from 2020 to 2025, including preclinical animal models, prospective cohort studies, and level I and II randomized trials, to compare two leading biologic augmentation strategies: platelet-rich plasma (PRP) and bone marrow aspirate concentrate (BMAC). The review examines underlying mechanisms of action, delivery techniques, imaging biomarkers of graft maturation, patient-reported and functional outcomes, safety profiles, cost-effectiveness, and regulatory frameworks. PRP provides early anti-inflammatory and proangiogenic signaling, while BMAC delivers a concentrated population of mesenchymal stem cells and growth factors to the tendon–bone interface. Both modalities consistently enhance MRI-defined graft maturation, yet evidence of long-term functional or biomechanical superiority remains inconclusive. Emerging therapies such as peptide hydrogels, adipose-derived stem cells, and exosome delivery offer promising avenues for future research. Standardized protocols and large multicenter trials are needed to clarify comparative efficacy and inform personalized rehabilitation strategies. Full article

Circulating tumor cells (CTCs) are crucial biomarkers for lung cancer metastasis and recurrence, garnering significant clinical attention. Despite this, efficient and cost-effective detection methods remain scarce. Consequently, there is an urgent demand for the development of highly sensitive CTC detection technologies to enhance lung cancer diagnosis and treatment. This study utilized microspheres and A549 cells to model CTCs, assessing the impact of acoustic field forces on cell viability and proliferation and confirming capture efficiency. Subsequently, CTCs from the peripheral blood of patients with lung cancer were captured and identified using fluorescence in situ hybridization, and the results were compared to the immunomagnetic bead method to evaluate the differences between the techniques. Finally, epidermal growth factor receptor (EGFR) mutation analysis was conducted on CTC-positive samples. The findings showed that acoustic microfluidic technology effectively captures microspheres, A549 cells, and CTCs without compromising cell viability or proliferation. Moreover, EGFR mutation analysis successfully identified mutation types in four samples, establishing a basis for personalized targeted therapy. In conclusion, acoustic microfluidic technology preserves cell viability while efficiently capturing CTCs. When integrated with EGFR mutation analysis, it provides robust support for the precise diagnosis and treatment of lung cancer as well as personalized drug therapy. Full article

The COVID-19 pandemic caused the world to pause temporarily on an almost planetary scale. The creation and diffusion of knowledge about environmental planning and public health are now almost taken for granted. However, such processes were rather different in pre-pandemic times. It took a substantial dose of labor and resources to generate the information needed to produce useful and usable knowledge, and especially to make it available to others in a timely and effective way. As automobility has come to occupy center stage in the lives of an increasing number of suburbanized dwellers, it has taken multiple energy and public health crises, bold leadership, and the real threat of climate change to create the conditions needed to bolster sustainable Non-Motorized Transportation (NMT) as a complement to cleaner and more convenient mass transit options in cities. How does knowledge about sustainable NMT get created? How are sustainable NMT innovations diffused? How can technological and societal transitions to more sustainable realities be nurtured and augmented? This article utilizes a longitudinal and integrated knowledge creation and diffusion model with a Participatory Planning Process to analyze the adoption of measures aimed at reducing the negative consequences of too much automobility and encouraging higher levels of walking, cycling, and mass transportation. The research methods comprised autoethnographic, qualitative, and policy evaluation techniques. The study makes use of the means and ends matrix to discuss cases from five distinct realms: personal, academic, institutional, volunteering NGO, and private sector. The key findings and lessons learned promote scenarios of managed degrowth and sustainable urban transitions. Full article

Objectives: The process of social rehabilitation involves several stages, from assessing an individual’s condition and determining their potential for rehabilitation to implementing a personalized plan with continuous monitoring of progress. Advances in information technology, including artificial intelligence, enable the use of software-assisted solutions for objective assessments and personalized rehabilitation strategies. The research aims to present interconnected semantic models that represent expandable knowledge in the field of rehabilitation, as well as an integrated framework and methodology for constructing virtual assistants and personalized decision support systems based on these models. Materials and Methods: The knowledge and data accumulated in these areas require special tools for their representation, access, and use. To develop a set of models that form the basis of decision support systems in rehabilitation, it is necessary to (1) analyze the domain, identify concepts and group them by type, and establish a set of resources that should contain knowledge for intellectual support; (2) create a set of semantic models to represent knowledge for the rehabilitation of patients. The ontological approach, combined with the cloud cover of the IACPaaS platform, has been proposed. Results: This paper presents a suite of semantic models and a methodology for implementing decision support systems capable of expanding rehabilitation knowledge through updated regulatory frameworks and empirical data. Conclusions: The potential advantage of such systems is the combination of the most relevant knowledge with a high degree of personalization in rehabilitation planning. Full article