Search Results (123)

Design and use of wearable technology have grown exponentially, particularly in consumer products and service sectors, e.g., healthcare. However, there is a lack of a comprehensive understanding of wearable technology in consumer acceptance. This systematic review utilized a PRISMA on peer-reviewed articles published between 2014 and 2024 and collected on WoS, Scopus, and ScienceDirect. A total of 38 full-text articles were systematically reviewed and analyzed using bibliometric, thematic, and descriptive analysis to understand the technical functions of digital wearable products (DWPs) in consumer acceptance. The findings revealed four key functions: (i) wearable technology, (ii) appearance and design, (iii) biomimetic innovation, and (iv) security and privacy, found in eight types of DWPs, among them smartwatches, medical robotics, fitness devices, and wearable fashions, significantly predicted the customers’ acceptance moderated by the behavioral factors. The review also identified five key outcomes: health and fitness, enjoyment, social value, biomimicry, and market growth. The review proposed a comprehensive acceptance model that combines biomimetic principles and AI-driven features into the technical functions of the technical function model (TAM) while addressing security and privacy concerns. This approach contributes to the extended definition of TAM in wearable technology, offering new pathways for biomimetic research in smart devices and robotics. Full article

Background and Clinical Significance: The retreatment of failed dental implants remains a challenging clinical scenario, particularly when complicated by peri-implantitis and as sociated bone loss. Successful management requires a comprehensive and predictable approach that addresses both hard and soft tissue deficiencies. Case Presentation: This case report illustrates a fully digital, prosthetically driven workflow for the rehabilitation of a posterior mandibular site following implant failure. A 44-year-old female patient underwent removal of a failing implant and adjacent tooth due to advanced peri-implantitis and periodontitis. After healing, a digital workflow—including intraoral scanning, cone-beam computed tomography (CBCT), and virtual planning—was employed to design and fabricate a customized CAD/CAM titanium mesh for vertical guided bone regeneration. The grafting procedure utilized a composite mixture of autogenous bone and anorganic bovine bone (A-Oss). After nine months of healing, two implants with a hydrophilic surface (SOI) were placed using a fully guided surgical protocol (OneGuide system). Subsequent soft tissue grafting and final prosthetic rehabilitation with monolithic zirconia restorations resulted in stable functional and aesthetic outcomes. Conclusions: This case highlights how the integration of modern digital technologies with advanced regenerative procedures and innovative implant surfaces can enhance the predictability and long-term success of implant retreatment in compromised posterior sites. Full article

This study aimed to enhance sustainable innovations in intelligent wood pellets by integrating smart customized furniture design with Total Quality Management (TQM) principles. Through qualitative interviews with manufacturers and the application of lean production frameworks, the research explored how sustainability-driven customization can lead to optimized resource usage, reduced environmental impact, and increased market competitiveness. While the study was exploratory and limited in sample size, it provided practical insights for green manufacturing strategies and product differentiation in circular economies. Full article

Three-dimensional (3D) printing has emerged as a transformative technology in dental splint fabrication, offering significant advancements in customization, production speed, material efficiency, and patient comfort. This comprehensive review synthesizes the current literature on the clinical use, benefits, limitations, and future directions of 3D-printed dental splints across various disciplines, including prosthodontics, orthodontics, oral surgery, and restorative dentistry. Key 3D printing technologies such as stereolithography (SLA), digital light processing (DLP), and material jetting are discussed, along with the properties of contemporary photopolymer resins used in splint fabrication. Evidence indicates that while 3D-printed splints generally meet ISO standards for flexural strength and wear resistance, their mechanical properties are often 15–30% lower than those of heat-cured PMMA in head-to-head tests (flexural strength range 50–100 MPa vs. PMMA 100–130 MPa), and study-to-study variability is high. Some reports even show significantly reduced hardness and fatigue resistance in certain resins, underscoring material-specific heterogeneity. Clinical applications reviewed include occlusal stabilization for bruxism and temporomandibular disorders, surgical wafers for orthognathic procedures, orthodontic retainers, and endodontic guides. While current limitations include material aging, post-processing complexity, and variability in long-term outcomes, ongoing innovations—such as flexible resins, multi-material printing, and AI-driven design—hold promise for broader adoption. The review concludes with evidence-based clinical recommendations and identifies critical research gaps, particularly regarding long-term durability, pediatric applications, and quality control standards. This review supports the growing role of 3D printing as an efficient and versatile tool for delivering high-quality splint therapy in modern dental practice. Full article

Modern oncology increasingly relies on personalized strategies that aim to customize medical interventions, using both tumor biology and clinical features to enhance efficacy and minimize adverse effects. In recent years, precision medicine has been implemented as part of systemic therapies; however, its integration into radiation therapy (RT) is still a work in progress. Conventional RT treatment plans are based on the Linear Quadratic (LQ) model and utilize standardized alpha and beta ratios (α/β), which ignore the high variability in terms of treatment response between and within patients. Recent advances in radiobiology, as well as general medical technologies, have also driven a shift toward more tailored approaches, including in RT. This review provides an overview of current knowledge and future perspectives for the personalization of RT, highlighting the role of tumor and patient-specific biomarkers, advanced imaging techniques, and novel therapeutic approaches. As an alternative to conventional RT modalities, hadron therapy and Flash RT are discussed as innovative approaches with the potential to improve tumor targeting while sparing normal tissues. Furthermore, the synergistic combination of RT with immunotherapy is discussed as a potential strategy to support antitumor immune responses and overcome resistance. By integrating biological insights, technological innovation, and clinical expertise, personalized radiation therapy may significantly advance the precision oncology paradigm. Full article

Understanding rockfall behavior is essential for accurately predicting hazards in both natural and engineered environments, yet prior research has predominantly focused on spherical rocks or single-impact scenarios, leaving critical gaps in highlighting the dynamics of non-spherical rocks and multiple impacts. This study addresses these shortcomings by investigating the influence of rock shape and release angle on motion, energy dissipation, and impact behavior. To achieve this, an innovative approach rooted in the Video-Assisted Rockfall Kinematics Analysis (VARKA) procedure was introduced, integrating a custom-designed apparatus, controlled experimental setups, and sophisticated data analysis techniques. Experiments utilizing a pendulum-based release system analyzed various scenarios involving different rock shapes and release angles. These tests provided comprehensive motion data for multiple impacts, including trajectories, translational and angular velocities, and the coefficient of restitution (COR). Results revealed that non-spherical rocks exhibited significantly more erratic trajectories and greater variability in COR values compared to spherical rocks. The experiments demonstrated that ellipsoidal and octahedral shapes had substantially higher variability in runout distances than spherical rocks. COR values for ellipsoidal shapes spanned a wide range, in contrast to the tighter clustering observed for spherical rocks. These findings highlight the pivotal influence of rock shape on lateral dispersion and energy dissipation, reinforcing the need for data-driven approaches to enhance and complement traditional physics-based predictive models. Full article

Background and Objectives: Complete dentures remain a primary solution for oral rehabilitation in aging and medically compromised populations. The integration of digital workflows, regenerative materials, and smart technologies is propelling prosthodontics towards a new era, transcending the limitations of traditional static prostheses. Materials and Methods: This narrative review synthesizes historical developments, current practices, and future innovations in complete denture therapy. A comprehensive review of literature from PubMed, Scopus, and Web of Science (2000–2025) was conducted, with a focus on materials science, digital design, patient-centered care, artificial intelligence (AI), and sustainable fabrication. Results: Innovations in the field include high-performance polymers, CAD–CAM systems, digital impressions, smart sensors, and bioactive liners. Recent trends in the field include the development of self-monitoring prostheses, artificial intelligence (AI)-driven design platforms, and bioprinted regenerative bases. These advances have been shown to enhance customization, durability, hygiene, and patient satisfaction. However, challenges persist in terms of accessibility, clinician training, regulatory validation, and ethical integration of digital data. Conclusions: The field of complete denture therapy is undergoing a transition toward a new paradigm of prosthetics that are personalized, intelligent, and sustainable. To ensure the integration of these technologies into standard care, ongoing interdisciplinary research, clinical validation, and equitable implementation are imperative. Full article

Non-Intrusive Load Monitoring (NILM) technology, enabled by high-precision electrical data acquisition sensors at household entry points, facilitates real-time monitoring of electricity consumption, enhancing user interaction with smart home systems and reducing electrical safety risks. However, the growing diversity of household appliances and limitations in NILM accuracy and robustness necessitate innovative solutions. Additionally, outdated public datasets fail to capture the rapid evolution of modern appliances. To address these challenges, we constructed a high-sampling-rate voltage–current dataset, measuring 15 common household appliances across diverse scenarios in a controlled laboratory environment tailored to regional grid standards (220 V/50 Hz). We propose an AI-driven NILM method that integrates power-mapped, color-coded voltage–current (V–I) trajectories with frequency-domain features to significantly improve load recognition accuracy and robustness. By leveraging deep learning frameworks, this approach enriches temporal feature representation through chromatic mapping of instantaneous power and incorporates frequency-domain spectrograms to capture dynamic load behaviors. A novel channel-wise attention mechanism optimizes multi-dimensional feature fusion, dynamically prioritizing critical information while suppressing noise. Comparative experiments on the custom dataset demonstrate superior performance, particularly in distinguishing appliances with similar load profiles, underscoring the method’s potential for advancing smart home energy management, user-centric energy feedback, and social informatics applications in complex electrical environments. Full article

The present study examines the impact of customer firms’ environmental, social, and governance (ESG) performance on suppliers’ green innovation efficiency, grounded in stakeholder theory and innovation diffusion theory. The DEA-SBM model is employed to measure green innovation efficiency and analyze transmission mechanisms through knowledge spillovers, financing constraints, and the moderating roles of executives’ green cognition and digitization. This analysis is based on panel data from 3134 customer–supplier pairs of China’s A-share listed firms from 2014 to 2023. The findings indicate that high ESG performance by customer firms has a substantial impact on suppliers’ green innovation efficiency, with a 1% increase in customer ESG score resulting in a 1.38% improvement in supplier efficiency. The phenomenon under scrutiny is hypothesized to be precipitated by knowledge spillovers and mitigated by reduced financing constraints. The hypothesis further posits that supplier firm executives’ green cognition and customer digitization will amplify the effect. A heterogeneity analysis reveals stronger effects in technology-intensive firms and regions with higher governmental environmental oversight. These findings underscore the pivotal function of ESG-driven supply chain collaboration in propelling sustainable industrialization. It is imperative that policymakers prioritize cross-regional ESG benchmarking and digital infrastructure to amplify green spillovers. Conversely, firms must integrate ESG metrics into supplier evaluation systems and foster executive training on sustainability. This research provides empirical evidence for the optimization of green innovation policies and the achievement of China’s dual carbon goals through the coordination of supply chain governance. Full article

In today’s evolving society, meaningful development cannot be fully realized without acknowledging the vital role of the electronics sector, especially as it functions within informal markets. These markets have become more than just centers of commerce; they serve as informal learning grounds where many young people acquire entrepreneurial skills, develop resilience, and find alternatives to social vices. For many, informal entrepreneurship is not just an option but a means of survival and self-empowerment. Despite their growing relevance, the link between the entrepreneurial abilities nurtured in these informal markets and actual business performance has not been adequately examined. This study, therefore, aimed to explore how informal electronics entrepreneurs in a developing economy navigate their environment, overcome challenges, and create wealth through vision, innovation, and calculated risk-taking. Anchored in institutional theory, the research employed a qualitative approach, using cluster, purposive, and simple random sampling to select participants from key informal business units. Interviews were conducted, transcribed, and analyzed using QSR NVivo 12, allowing for deep insight into the lived experiences of the entrepreneurs. Findings revealed that 78% of participants emphasized practical suggestions that aid informal business survival, such as customer-driven innovations, adaptive strategies, and avoiding confrontations with regulatory agencies. Key attributes such as foresight, adaptability, and risk management accounted for 66% of the variance in corporate success. Strategic and innovative approaches are enabling informal firms to endure and prosper, since 61% of respondents associated these competencies with organizational success. The new BSP framework, which integrates institutional and contingency theories, illustrates how informal enterprises endure by conforming to or opposing institutional pressures and adjusting to environmental changes. The results indicate that, when properly understood and supported, the informal electronics sector may develop sustainably. This study demonstrates that informal entrepreneurship is influenced by formal regulations, informal norms, and local enforcement mechanisms, therefore enhancing institutional theory and elucidating business behavior in developing nations. The Business Survival Paradigm [BSP] illustrates how informal enterprises navigate institutional obstacles to endure. It advocates for policies that integrate the official and informal sectors while fostering sustainable development. The paper advocates for ongoing market research to assist informal firms in remaining up-to-date. It implores authorities to acknowledge the innovative potential of the informal sector and to provide supportive frameworks for sustainable growth and formal transition where feasible. Full article

The management of medical waste (MW) is a critical global challenge, contributing to toxic effects on humans, environmental degradation, and economic burdens. Despite advancements, gaps remain in adopting sustainable waste disposal practices, with limited bibliometric analysis in this field. The rising volume of MW, exacerbated by global health crises, strains existing systems. This study uses bibliometric analysis of 3025 publications from 1975 to 2024, employing Web of Science data with specific Boolean operators and keywords for efficient searching algorithms. Data visualization and analysis were carried out with software such as VOSviewer version 1.6.20 and Bibliometrix 5.0.0, along with custom Python 3.12.3 thesaurus files to standardize terminology. The results reveal a significant rise in publications post-2000, particularly during the COVID-19 pandemic, with China, India, and the US as major contributors. South Korea stands out for high citation rates. Network analysis identified collaboration patterns, while trend mapping highlighted a shift toward sustainable waste management practices. The evaluation insights revealed a clear transition from incineration-based methods toward sustainable and innovative solutions such as autoclaving, plasma pyrolysis, and advanced oxidation processes, driven by environmental concerns and regulatory frameworks. This study underscores the implications of MW and the importance of analyzing publication trends over time to understand the ongoing need for development, grounded in a legislative policy framework, which is essential for advancing sustainable practices in MW management. Full article

Artificial intelligence (AI) has significant potential to transform small- and medium-sized enterprises (SMEs), yet its adoption is often hindered by challenges such as limited financial and human resources. This study addresses this issue by investigating the core AI technologies adopted by SMEs, their broad range of applications across business functions, and the strategies required for successful implementation. Through a systematic literature review of 50 studies published between 2016 and 2025, we identify prominent AI technologies, including machine learning, natural language processing, and generative AI, and their applications in enhancing efficiency, decision-making, and innovation across sales and marketing, operations and logistics, finance and other business functions. The findings emphasize the importance of workforce training, robust technological infrastructure, data-driven cultures, and strategic partnerships for SMEs. Furthermore, the review highlights methods for measuring and optimizing AI’s value, such as tracking key performance indicators and improving customer satisfaction. While acknowledging challenges like financial constraints and ethical considerations, this research provides practical guidance for SMEs to effectively leverage AI for sustainable growth and provides a foundation for future studies to explore customized AI strategies for diverse SME contexts. Full article

This research presents an advanced autonomous HVAC control system tailored for a chemical fiber factory, emphasizing the human-centric principles and collaborative potential of Industry 5.0. The system architecture employs several functional levels—actuator and sensor, process, model, critic, fault detection, and specification—to effectively monitor and predict indoor air pressure differences, which are critical for maintaining consistent product quality. Central to the system’s innovation is the integration of digital twins and physical AI, enhancing real-time monitoring and predictive capabilities. A virtual representation runs in parallel with the physical system, enabling sophisticated simulation and optimization. Development involved custom sensor kit design, embedded systems, IoT integration leveraging Node-RED for data streaming, and InfluxDB for time-series data storage. AI-driven system identification using Nonlinear Autoregressive with eXogenous inputs (NARX) neural network models significantly improved accuracy. Crucially, incorporating airflow velocity data alongside AHU output and past pressure differences boosted the NARX model’s predictive performance (R² up to 0.9648 on test data). Digital twins facilitate scenario testing and optimization, while physical AI allows the system to learn from real-time data and simulations, ensuring adaptive control and continuous improvement for enhanced operational stability in complex industrial settings. Full article

This research aims to identify patterns and root causes of aircraft downtimes by comparing various forecasting models used in the aviation industry to prevent AOG events effectively. At its heart, this study explores innovative forecasting models using time series analysis, time series modeling and binary classification to predict spare part usage, reduce downtime, and tackle the complexities of managing inventory for diverse aircraft fleets. By analyzing both data and insights shared by aviation industry experts, the research offers a practical roadmap for enhancing supply chain efficiency and reducing Mean Time Between Failures (MTBF). The thesis emphasizes how real-time data integration and hybrid forecasting approaches can transform operations, helping airlines keep spare parts available when and where they are needed most. It also shows how precise forecasting is not just about saving costs, it is about boosting customer satisfaction and staying competitive in an ever-demanding industry. In addition to data-driven insights, this research provides actionable recommendations, such as embracing predictive maintenance strategies and streamlining logistics. These steps aim to ensure smoother operations, fewer disruptions, and more reliable service for passengers and operators alike. Full article

This systematic literature review investigates the integration of artificial intelligence (AI) and the Internet of Things (IoT) in marketing, with a focus on their application in enhancing consumer engagement, personalization, and strategic decision-making. Using the Scopus database and a refined keyword search strategy, the study identified 223,671 initial records, which were narrowed down to 121 peer-reviewed academic articles after applying strict inclusion and exclusion criteria. Thematic analysis revealed that foundational technologies—such as machine learning, big data, and deep learning—dominate the field, while marketing strategy, decision systems, and customer experience emerge as central application areas. Co-citation and keyword network analyses indicate a technocentric and interdisciplinary knowledge structure, but also expose significant gaps in research related to ethics, regulation, consumer trust, and small business contexts. The review highlights opportunities for future research in underexplored areas such as sentiment analysis, sustainability, and human–AI interaction. For practitioners, the findings underscore the strategic importance of AI and IoT in driving personalized, data-driven marketing, while emphasizing the need for ethical transparency and regulatory alignment. Limitations include reliance on a single database, potential exclusion of relevant studies due to keyword constraints, and a focus on peer-reviewed journal articles only. This review addresses key gaps in the literature by offering a focused synthesis of current research and proposing directions for more balanced and responsible innovation in AI-enabled marketing. Full article