Search Results (2,457)

Fermentation has been a crucial process in the preparation of foods and beverages for consumption, especially for the purpose of adding value to nutrients and bioactive compounds; however, conventional approaches have certain drawbacks such as not being able to fulfill the requirements of the ever-increasing global population as well as the sustainability goals. This review aims to evaluate how the application of advanced fermentation techniques can transform the food production system to be more effective, nutritious, and environmentally friendly. The techniques discussed include metabolic engineering, synthetic biology, AI-driven fermentation, quorum sensing regulation, and high-pressure processing, with an emphasis on their ability to enhance microbial activity with a view to enhancing product output. Authentic, wide-coverage scientific research search engines were used such as Google Scholar, Research Gate, Science Direct, PubMed, and Frontiers. The literature search was carried out for reports, articles, as well as papers in peer-reviewed journals from 2010 to 2024. A statistical analysis with a graphical representation of publication trends on the main topics was conducted using PubMed data from 2010 to 2024. In this present review, 112 references were used to investigate novel fermentation technologies that fortify the end food products with nutritional and functional value. Images that illustrate the processes involved in novel fermentation technologies were designed using Adobe Photoshop. The findings indicate that, although there are issues regarding costs, the scalability of the process, and the acceptability of the products by the consumers, the technologies provide a way of developing healthy foods and products produced using sustainable systems. This paper thus calls for more research and development as well as for the establishment of a legal frameworks to allow for the integration of these technologies into the food production system and make the food industry future-proof. Full article

This scoping review examines the affective–psychological and social characteristics of undergraduate engineering students in South Korea, identifying key research trends, thematic focuses, and gaps in the literature. A total of 95 peer-reviewed articles published between 2000 and 2024 were analyzed based on publication year, journal outlet, research topics, and related variables. The literature search was conducted using major databases, including RISS, KCI, and DBpia. The findings highlight self-efficacy—particularly domain-specific self-efficacy—as a core construct linked to academic achievement, persistence, and career development. Social competencies such as communication, teamwork, and convergence ability are also emphasized; however, limited attention has been paid to emotional resilience, burnout, and ethical responsibility. Despite their growing importance in the artificial intelligence-driven era, gender differences, digital literacy, and global competencies remain underexplored. These findings underscore the need for learner-centered, evidence-based instructional strategies, as well as more longitudinal, comparative, and intervention-focused studies. This review offers foundational insights for designing inclusive, future-oriented educational programs tailored to the diverse needs of South Korean undergraduate engineering students. Full article

The aim of this study was to analyse the relationship between sustainability and customer lifetime value (CLV) through the mediation–moderation effect of search engine optimization (SEO) metrics of websites. We obtained a data sample of 296 European sustainable firms from both industrial and technological industries. Based on the theory of source credibility, the firm’s official website, where SEO techniques are applied, is more credible regarding its sustainability activities than other sources such as social media, paid advertising, etc. As a result, we show that sustainability is a precursor of financial performance over time in sustainable firms, represented by CLV. Furthermore, we found that the value of the moderating variable, website traffic, alters the indirect effects produced by the mediating variable called website relevance (domain authority), thereby demonstrating a moderated mediation effect. The contribution of this research to the body of literature is twofold. First, it deepens the understanding of how sustainability predicts marketing outcomes based on both digital and customer metrics over time. Second, we rely on recent literature on prediction-oriented modelling (PLS-SEM) to support that it is not suitable for estimation by reflective measurement models due to the woozle effect. Full article

Spaceflight and microgravity (μg) environments induce numerous cardiovascular changes that affect cardiac structure and function, and understanding these effects is essential for astronaut health and tissue engineering in space. This review compiles and analyzes over 30 years of research on the impact of real and simulated μg on cardiomyocytes. A comprehensive literature search was conducted across five databases, and 62 eligible studies involving cardiac cells under μg or spaceflight conditions were compiled and analyzed. Despite the great heterogeneity in terms of cardiac model, microgravity platform, and exposure duration, multiple studies consistently reported alterations in Ca²⁺ handling, metabolism, contractility, and gene expression. Three-dimensional human-induced pluripotent stem cell-derived cardiomyocyte (HiPSC-CM) models generally showed enhanced tissue maturation and proliferation parameters, suggesting potential therapeutic benefits, while 2D models mostly exhibited stress-related dysfunction. In vivo simulated microgravity studies, such as the hindlimb unloading (HU) model, show structural and functional cardiac remodeling, and real μg studies confirmed various effects seen under the HU model in multiple rodent species. Thus, μg exposure consistently induces cardiac changes at the cellular and molecular level, while model choice, microgravity platform, and exposure duration critically influence the outcomes. Full article

This paper presents a hybrid computational architecture for efficient and robust digital transmission inspired by helical genetic structures. The proposed system integrates advanced modulation schemes, such as multi-pulse-position modulation (MPPM), high-order quadrature amplitude modulation (QAM), and chirp spread spectrum (CSS), along with Reed–Solomon error correction and quantum-assisted search, to optimize performance in noisy and non-line-of-sight (NLOS) optical environments, including VLC channels modeled with log-normal fading. Through mathematical modeling and simulation, we demonstrate that the number of helical transmissions required for genome-scale data can be drastically reduced—up to 95% when using parallel strands and high-order modulation. The trade-off between redundancy, spectral efficiency, and error resilience is quantified across several configurations. Furthermore, we compare classical genetic algorithms and Grover’s quantum search algorithm, highlighting the potential of quantum computing in accelerating decision-making and data encoding. These results contribute to the field of operations research and supply chain communication by offering a scalable, energy-efficient framework for data transmission in distributed systems, such as logistics networks, smart sensing platforms, and industrial monitoring systems. The proposed architecture aligns with the goals of advanced computational modeling and optimization in engineering and operations management. Full article

To address the inadequate balance between exploration and exploitation of existing algorithms in complex solution spaces, this paper proposes a novel mathematical metaheuristic optimization algorithm—the Perpendicular Bisector Optimization Algorithm (PBOA). Inspired by the geometric symmetry of perpendicular bisectors (the endpoints of a line segment are symmetric about them), the algorithm designs differentiated convergence strategies. In the exploration phase, a slow convergence strategy is adopted (deliberately steering particles away from the optimal region defined by the perpendicular bisector) to expand the search space; in the exploitation phase, fast convergence refines the search process and improves accuracy. It selects 4 particles to construct line segments and perpendicular bisectors with the current particle, enhancing global exploration capability. The experimental results on 27 benchmark functions, compared with 15 state-of-the-art algorithms, show that the PBOA outperforms others in accuracy, stability, and efficiency. When applied to 5 engineering design problems, its fitness values are significantly lower. For H-type motion platforms, the PBOA-optimized platform not only achieves high unidirectional motion accuracy, but also the average synchronization error of the two Y-direction motion mechanisms reaches ±2.6 × 10⁻⁵ mm, with stable anti-interference performance. Full article

High-precision assembly plays a central role in aerospace, defense, and precision instrumentation, where errors in bolt preload or tightening sequences can directly degrade product reliability and lead to costly rework. Traditional finite element analysis (FEA) offers accuracy but is too computationally expensive for iterative or real-time optimization. Surrogate models are a promising alternative, yet conventional machine learning methods often neglect the sequential and constraint-aware nature of multi-bolt assembly. To overcome these limitations, this paper introduces an integrated framework that combines an Attention-based Bidirectional Long Short-Term Memory (AB-BiLSTM) surrogate with a stratified version of the Harris Hawks Optimizer (SEG-HHO). The AB-BiLSTM captures temporal dependencies in preload evolution while providing interpretability through attention–weight visualization, linking model focus to physical assembly dynamics. SEG-HHO employs an encoding–decoding mechanism to embed engineering constraints, enabling efficient search in complex and constrained design spaces. Validation on a gyroscope assembly task demonstrates that the framework achieves high predictive accuracy (Mean Absolute Error of 3.59 × 10⁻⁵), reduces optimization cost by orders of magnitude compared with FEA, and reveals physically meaningful patterns in bolt interactions. These results indicate a scalable and interpretable solution for precision assembly optimization. Full article

In the search for lightweight and sustainable engineering approaches, enhancing the surface wear resistance of structural materials, such as 6061-T6 aluminum alloy, has become increasingly important. This study investigates the effect of coverage rates on the tribological properties of shot-peened 6061-T6 alloy, aiming to improve its usage in industries where weight reduction and durability are important, such as aerospace, automotive, railway, and renewable energy systems. A shot peening process was applied at four different coverage rates of 100%, 200%, 500%, and 1500% for comprehensive evaluation. A series of experimental analyses were conducted, including microhardness tests, ball-on-plate wear tests, residual stress measurements, and surface roughness evaluations. Furthermore, microstructural analysis was performed to investigate subsurface deformation, and scanning electron microscopy (SEM) was carried out to identify the wear mechanisms of the worn surfaces in detail. The results demonstrated a clear trend of gradual improvement in wear resistance with increasing shot peen coverage. The sample treated at a 1500% coverage rate exhibited 1.34 times higher hardness and 19 times higher wear resistance compared to the untreated sample. This study highlights that shot peening is an effective and feasible surface engineering method for enhancing the wear performance of 6061-T6 alloy. The findings offer valuable contributions for the development of lightweight and wear-resistant components considering sustainable material design. Full article

Ant Colony Optimization (ACO) has been widely used in engineering implementation due to its simplicity and effectiveness. However, it often faces challenges such as slow convergence, susceptibility to local optima, and generating paths with excessive turning points. To address these limitations, this paper introduces a Novel Multi-Strategy Enhanced Ant Colony Optimization algorithm (NMS-EACO) for mobile robot path planning under nonholonomic constraints. NMS-EACO integrates five key strategies: an A*-guided heuristic function, an adaptive enhanced pheromone update rule, a state transition probability under nonholonomic constraints, a smoothing factor embedded in the state transition probability, and a global path smoothing technique. Comprehensive simulation experiments are conducted across six distinct map types, with comparisons made against six existing algorithms through extensive trials.Results demonstrate that NMS-EACO significantly improves convergence speed, enhances global search capability, and reduces path irregularities. These results validate the robustness and efficiency of the proposed multi-strategy method for nonholonomic mobile robot navigation. Full article

The pipe-jacking inertial guidance method is a key technology to solve the guidance problems of complex pipe-jacking projects, such as long distances and curves. However, since its guidance information is obtained by gyroscope integration. System errors will accumulate over time and affect the guidance accuracy. To address the above issues, this study proposes an intelligent online system error correction scheme based on single-axis rotation and data backtracking. The method enhances system observability by actively exciting the sensor states and introducing data reuse technology. Then, a Bayesian optimization algorithm is incorporated to construct a multi-objective function. The algorithm autonomously searches for the optimal values of three key control parameters, thereby constructing an optimal correction strategy. The results show that the inclination accuracy improving by 99.36%. The tool face accuracy improving by 94.05%. The azimuth accuracy improving by 94.42% improvement. By comparing different correction schemes, the proposed method shows better performance in estimating gyro bias. In summary, the proposed method uses single-axis rotation and data backtracking, and can correct system errors in inertial navigation effectively. It has better value for engineering and provides a technical foundation for high-accuracy navigation in tunnel, pipe-jacking, and other complex tasks with low-cost inertial systems. Full article

This study investigates how students of English Language and Literature Studies and those of Translation at the University of Maribor, Slovenia, perceive and engage with digital tools in academic and language learning contexts. Although students report high levels of confidence in their digital skills and express positive attitudes towards educational technologies, the survey results reveal a significant gap between perceived competence and actual usage. The study identifies the underutilization of institutional tools, limited awareness of resources available, and a reliance on general-purpose search engines rather than academic platforms. These findings highlight the need for improved digital literacy training, structured onboarding, and integration of digital tools into discipline-specific curricula. By focusing on a student population specializing in linguistics and translation in a Central and Eastern European context, this research contributes a localized perspective to broader discussions on digital transformation in higher education. The study offers applicable recommendations for enhancing institutional strategies and supporting students in becoming competent and critical users of educational technology. Full article

The advancement of photocatalytic materials is critical for addressing environmental challenges such as water remediation, where efficient, robust, and reusable systems are in high demand. In this search, the development of hierarchically organized photocatalytic configurations with spatial control over active sites can significantly enhance performance. With this in mind, we present here a novel biomimetic approach for the patterned growth of TiO₂-ZnO photocatalytic heterostructures using solid-binding peptides (SBPs) as molecular linkers. Specifically, using bi-functional SBPs with selective affinity for both oxides, we achieve site-specific, molecularly guided deposition of TiO₂ nanoparticles onto pre-patterned ZnO-coated substrates. Leveraging the specific recognition capabilities and strong binding affinities of the engineered SBPs, the proposed biomimetic methodology allows for the fabrication of well-organized hybrid nanostructures under sustainable conditions. Photocatalytic degradation assays employing methyl orange as a model contaminant indicate that the patterned architecture enhances both the accessibility of the active photocatalytic sites and the recoverability of the material. This reusability is a critical parameter for the practical deployment of photocatalytic systems in water purification technologies. The obtained results underscore the potential of SBP-mediated molecular recognition as a versatile tool for green nanofabrication of functional materials with advanced architectural and catalytic properties. Full article

In the rapidly evolving landscape of digital marketing and electronic commerce, short-form content—particularly on platforms like Twitter (now X)—has become pivotal for real-time branding, community engagement, and product promotion. The rise of Non-Fungible Tokens (NFTs) and Web3 ecosystems further underscores the need for domain-specific, engagement-oriented social media content. However, automating the generation of such content while balancing linguistic quality, semantic relevance, and audience engagement remains a substantial challenge. To address this, we propose RL-TweetGen, a socio-technical framework that integrates instruction-tuned large language models (LLMs) with reinforcement learning (RL) to generate concise, impactful, and engagement-optimized tweets. The framework incorporates a structured pipeline comprising domain-specific data curation, semantic classification, and intent-aware prompt engineering, and leverages Parameter-Efficient Fine-Tuning (PEFT) with LoRA for scalable model adaptation. We fine-tuned and evaluated three LLMs—LLaMA-3.1-8B, Mistral-7B Instruct, and DeepSeek 7B Chat—guided by a hybrid reward function that blends XGBoost-predicted engagement scores with expert-in-the-loop feedback. To enhance lexical diversity and contextual alignment, we implemented advanced decoding strategies, including Tailored Beam Search, Enhanced Top-p Sampling, and Contextual Temperature Scaling. A case study focused on NFT-related tweet generation demonstrated the practical effectiveness of RL-TweetGen. Experimental results showed that Mistral-7B achieved the highest lexical fluency (BLEU: 0.2285), LLaMA-3.1 exhibited superior semantic precision (BERT-F1: 0.8155), while DeepSeek 7B provided balanced performance. Overall, RL-TweetGen presents a scalable and adaptive solution for marketers, content strategists, and Web3 platforms seeking to automate and optimize social media engagement. The framework advances the role of generative AI in digital commerce by aligning content generation with platform dynamics, user preferences, and marketing goals. Full article

Accurate identification of the endometrial region is critical for the early detection of endometrial lesions. However, current detection models still face two major challenges when processing endometrial imaging data: (1) In complex and noisy environments, recognition accuracy remains limited, partly due to the insufficient exploitation of color information within the images; (2) Traditional Two-dimensional PCA-based (2DPCA-based) feature selection methods have limited capacity to capture and represent key characteristics of the endometrial region. To address these challenges, this paper proposes a novel algorithm named Feature-Level Image Fusion and Improved Swarm Intelligence Optimization Algorithm (FLFSI), which integrates a learning guided binary particle swarm optimization (BPSO) strategy with an image feature selection and reconstruction framework to enhance the detection of endometrial regions in clinical ultrasound images. Specifically, FLFSI contributes to improving feature selection accuracy and image reconstruction quality, thereby enhancing the overall performance of region recognition tasks. First, we enhance endometrial image representation by incorporating feature engineering techniques that combine structural and color information, thereby improving reconstruction quality and emphasizing critical regional features. Second, the BPSO algorithm is introduced into the feature selection stage, improving the accuracy of feature selection and its global search ability while effectively reducing the impact of redundant features. Furthermore, we refined the BPSO design to accelerate convergence and enhance optimization efficiency during the selection process. The proposed FLFSI algorithm can be integrated into mainstream detection models such as YOLO11 and YOLOv12. When applied to YOLO11, FLFSI achieves 96.6% Box mAP and 87.8% Mask mAP. With YOLOv12, it further improves the Mask mAP to 88.8%, demonstrating excellent cross-model adaptability and robust detection performance. Extensive experimental results validate the effectiveness and broad applicability of FLFSI in enhancing endometrial region detection for clinical ultrasound image analysis. Full article

Background/Objectives: This scoping review aims to map existing AI applications in dental education, in student learning, assessment, and diagnostic training, identifying key limitations and challenges. Methods: Following the Arksey and O’Malley framework and PRISMA-ScR guidelines, six databases were searched in March 2025 using combinations of the following search words: “dental education,” “artificial intelligence,” “machine learning,” and “student assessment”. Inclusion was limited to English-language empirical studies focused on dental student education. Of 547 identified studies, 17 met the inclusion criteria. They were categorized into four domains: (1) Preclinical Training, (2) AI in Clinical, Diagnostic Training, and Radiographic Interpretation, (3) AI as an Assessment Tool and Feedback System, and (4) AI in Content Generation for Dental Education. Results: AI has positively influenced various domains, enhancing procedural accuracy, diagnostic confidence, assessment efficiency, and content delivery. However, it struggles to assess nuanced competencies like dexterity and clinical judgment. The challenges faced include disparate definitions of AI, ethical and privacy concerns, model variability, and a deficiency of dental leadership in AI development. At present, most tools are engineered by computer scientists and may not align effectively with the priorities of dental education. Conclusions: AI holds significant potential to enhance dental education outcomes. However, to guarantee its relevance and reliability, it requires standard frameworks, ethical oversight, and clinician-led development. Future research should concentrate on implementing real-time AI-driven feedback systems during preclinical training and advocate for more precise definitions to support consistent AI application and evaluation in dental education. Full article