Search Results (3,342)

Harnessing nature’s ingenuity with microorganisms for industrial production is an attractive solution to today’s climate concerns. Nature’s innate diversity allows the production of many value-added chemicals and can be expanded on through genetic engineering. Although the use of microbial cell factories (MCFs) has been extremely successful at lab scale, the numbers of successful bioprocesses remain limited. High cell densities and long cultivation times lead to reductions in productivity over the course of the cultivation through the effects of genetic and expression instability of the strain. This instability leads to population diversification. In this review, we explore the roots of genetic instability in microorganisms, focusing on prokaryotic bioprocesses, and how organisms cope with this instability. We spotlight single-cell detection methods capable of monitoring populations within the bioprocess both in- and on-line. We also examine different approaches to minimizing population diversification, both through strain development and bioprocess engineering. With this review, we highlight the fact that population-averaged metrics overlook the single-cell stresses driving genetic and functional instability, leading to an overestimation of microbial bioprocess robustness. High-throughput single-cell monitoring in industry-like conditions remains essential to identify and select truly stable microbial cell factories and bioprocesses. Full article

Adaptive filtering remains a cornerstone of modern signal processing but faces fundamental challenges when confronted with rapidly changing or nonlinear environments. This work investigates the integration of deep learning into adaptive-filter architectures to enhance tracking capability and robustness in non-stationary conditions. After reviewing and analyzing classical algorithms—LMS, NLMS, RLS, and a variable step-size LMS (VSS-LMS)—their theoretical stability and mean-square error behavior are formalized under a slow-variation system model. Comprehensive simulations using drifting autoregressive (AR(2)) processes, piecewise-stationary FIR systems, and time-varying sinusoidal signals confirm the classical trade-off between performance and complexity: RLS achieves the lowest steady-state error, at a quadratic cost, whereas LMS remains computationally efficient with slower adaptation. A stabilized VSS-LMS algorithm is proposed to balance these extremes; the results show that it maintains numerical stability under abrupt parameter jumps while attaining steady-state MSEs that are comparable to RLS (approximately 3 × 10⁻²) and superior robustness to noise. These findings are validated by theoretical tracking-error bounds that are derived for bounded parameter drift. Building on this foundation, a deep-learning-driven adaptive filter is introduced, where the update rule is parameterized by a neural function, $U_{\theta }$, that generalizes the classical gradient descent. This approach offers a pathway toward adaptive filters that are capable of self-tuning and context-aware learning, aligning with emerging trends in AI-augmented system architectures and next-generation computing. Future work will focus on online learning and FPGA/ASIC implementations for real-time deployment. Full article

A universal approach to managing a person’s digital identity may be the single most important advancement to the Internet since its inception, promising the seamless flow of information, averting cybercrime, eliminating login credentials, and restoring privacy and trust through greater control of one’s identity online. However, this advancement brings significant risks, especially regarding personal privacy. It demands the meticulous development of digital identity infrastructure that balances robust data security measures with ethical handling of sensitive information, thereby safeguarding against misuse and unauthorised access. Currently, a consolidated vision for digital identity implementation remains unresolved, and aligning the different stakeholders’ motives and expectations is a challenging task. This article reviews and analyses the perspectives and expectations of four key stakeholder groups—government, business, academia, and consumers—regarding a digital identity ecosystem, aiming to increase trust in an eventual design framework. Using an online survey stratified across government, business, academia, and consumers, we identify areas of alignment and divergence regarding privacy, trust, usability, and governance expectations. We then encode these stakeholder expectations into a layered conceptual structure and illustrate its use as metadata for context-layered retrieval-augmented generation (RAG) in digital identity scenarios. Full article

With the rapid development of the Internet, online public opinion monitoring has emerged as a crucial task in the information era. Multimodal sentiment analysis, through the integration of multiple modalities such as text, images, and audio, combined with technologies including natural language processing and computer vision, offers novel technical means for online public opinion monitoring. Nevertheless, current research still faces many challenges, such as the scarcity of high-quality datasets, limited model generalization ability, and difficulties with cross-modal feature fusion. This paper reviews the current research progress of multimodal sentiment analysis in online public opinion monitoring, including its development history, key technologies, and application scenarios. Existing problems are analyzed and future research directions are discussed. In particular, we emphasize a fusion-architecture-centric comparison under online public opinion monitoring, and discuss cross-lingual differences that affect multimodal alignment and evaluation. Full article

In struggling against invasive species ravaging riverscape ecosystems, gaps in dispersal pathway knowledge and fragmented approaches across scales have long stalled effective riparian management worldwide. To reduce these limitations and enhance invasion management strategies, selecting appropriate alien species as models for in-depth pathway analysis is essential. Alternanthera philoxeroides (Mart.) Griseb. (alligator weed) emerges as an exemplary model species, boasting an invasion record of around 120 years spanning five continents worldwide, supported by genetic evidence of repeated introductions. In addition, the clonal reproduction of A. philoxeroides supports swift establishment, while its amphibious versatility allows occupation of varied riparian environments, with spread driven by natural water-mediated dispersal (hydrochory) and human-related vectors at multiple scales. Thus, leveraging A. philoxeroides, this review proposes a comprehensive multi-scale framework, which integrates monitoring with remote sensing, environmental DNA, Internet of Things, and crowdsourcing for real-time detection. Also, the framework can further integrate, e.g., MaxEnt (Maximum Entropy Model) for climatic suitability and mechanistic simulations of hydrodynamics and human-mediated dispersal to forecast invasion risks. Furthermore, decision-support systems developed from the framework can optimize controls like herbicides and biocontrol, managing uncertainties adaptively. At the global scale, the dispersal paradigm can employ AI-driven knowledge graphs for genetic attribution, multilayer networks, and causal inference to trace pathways and identify disruptions. Based on the premise that our multi-scale framework can bridge invasion ecology with riverscape management using A. philoxeroides as a model, we contend that the implementation of the proposed framework tackles core challenges, such as sampling biases, shifting environmental dynamics, eco–evolutionary interactions using stratified sampling, and adaptive online algorithms. This methodology is purposed to offer scalable tools for other aquatic invasives, evolving management from reactive measures to proactive, network-based approaches that effectively interrupt dispersal routes. Full article

Background: Malaria remains a major public health concern around the world. Microscopic blood smear examination continues to be the gold standard for diagnosis; however, it requires high technical skills and expertise, limiting diagnostic accuracy in resource-poor settings. Artificial intelligence (AI) has emerged as a promising tool to support malaria detection. This systematic review provides an overview of the diagnostic performance of AI-based systems for malaria diagnosis in a clinical setting. Methods: This study followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and involved articles within the last 10 years that were collected from PubMed, ScienceDirect, Cochrane, EBSCO, and Wiley Online Library. Original articles that reported AI diagnostic accuracy with external validation were involved. The quality of each study was evaluated using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Results: Ten studies with 6754 patients were analyzed. Pooled results of sensitivity [87.7% (95% CI: 78.2–93.4)] and specificity [91.4% (95% CI: 77.3–97.1)] revealed how much the AI agrees with each method when that method is used as a gold standard. Additionally, AI achieved a sensitivity of 87.7% and a specificity of 91.4% compared to microscopy examination and a sensitivity of 90.7% and a specificity of 88.3% compared to polymerase chain reaction (PCR). Conclusions: AI-based systems improve malaria diagnosis by providing high accuracy, automation, and lower costs. Showing performance comparable to reference methods such as microscopy and PCR, AI is a promising complementary tool for malaria control. Full article

Background/Objectives: Postoperative Delirium (POD) is a frequent complication in older patients undergoing elective surgery. Although multicomponent interventions are effective, deficits in interdisciplinary communication and intersectoral collaboration persist. This study developed and evaluated the “Geriatric Delirium Pass (GeDePa)”, a paper-based tool to systematically document risk factors for POD across care settings. Methods: A multi-method design was applied, comprising (i) a structured literature review, (ii) semi-structured expert interviews, and (iii) a standardized online survey utilizing the RAND/UCLA Appropriateness Method (RAM). A total of 21 healthcare professionals (general practitioners, geriatricians, anaesthetists, surgeons, and nurses) were recruited from Salzburg, Austria, and South Tyrol, Italy (2023–2024). Results: Healthcare professionals confirmed the GeDePa’s practical applicability for early POD risk detection across care settings. The expert rating using the RAM Disagreement Index (DI) method deemed all 45 risk factors as sufficiently relevant and, with the exemption of two risk factors (alcohol use, intraoperative complications), feasible. A detailed analysis provided a more differentiated picture, with full consensus reached for only 18 items. Several factors with uncertain consensus (e.g., cognitive impairment and polypharmacy) were retained based on strong evidence in the literature. Others were excluded despite high ratings if they were considered redundant or impractical (e.g., detailed intraoperative complications). In total, 38 of the 45 risk factors were retained. Conclusions: The GeDePa is a feasible and relevant tool for structured delirium risk assessment and enhancing interdisciplinary communication between primary and hospital care. The finalized German and Italian versions are now available and will undergo further testing and implementation in clinical practice. Full article

The food supplement market has expanded rapidly in recent years, driven by demand for health, wellness, and healthy ageing; yet, the integrity of associated Health Claims (HC) remains uncertain. In the European Union (EU), food supplements are regulated under Directive 2002/46/EC, while HC use is governed by Regulation (EC) No. 1924/2006 (NHCR), which requires scientific substantiation evaluated by the European Food Safety Authority and subsequent authorisation by the European Commission/Member States. Despite this framework, concerns persist regarding unauthorised or non-compliant HC. This study examined the prevalence and compliance of HC on food supplement labels in the Republic of Ireland, comparing products sold in-store and online. A total of 192 food supplements were randomly selected across multiple categories, with HC compliance assessed against the EU Register of Nutrition and Health Claims and mandatory labelling requirements. In total, 2604 HC were identified, with multivitamins and botanicals as the most common categories reviewed. Although most HC referred to vitamins D and C and focused on immune function, only 80.7% of in-store claims and 75.6% of online claims were authorised, and only around one-third used the prescribed wording. Compliance was notably lower for botanicals, reflecting regulatory ambiguities around their use. These findings highlight persistent challenges in enforcing the NHCR, particularly for online sales and botanicals, and underscore the need for greater regulatory clarity and consumer protection. Full article

The growing global environmental crisis calls for fundamental transformations in production and consumption systems, but the understanding of how circular economy strategies translate into quantifiable environmental benefits remains fragmented across sectors and geographies. The objective of this study is to synthesize current scientific knowledge on the circular economy as an environmental mitigation strategy, identifying conceptual convergences, methodological patterns, geographic distributions, and critical knowledge gaps. A systematic review combined with a bibliometric analysis of 62 peer-reviewed articles published between 2018 and 2024, retrieved from Scopus, Web of Science, ScienceDirect, Springer Link and Wiley Online Library, was conducted following the PRISMA 2020 guidelines. The results reveal a marked methodological convergence around life cycle assessment, with Europe dominating the scientific output (58% of the corpus). Four complementary conceptual frameworks emerged, emphasizing closed-loop material flows, environmental performance, integration of economic sustainability and business model innovation. The thematic analysis identified bioenergy and waste valorization as the most mature implementation pathways, constituting 23% of the research emphasis. However, critical gaps remain: geographic concentration limits the transferability of knowledge to diverse socioeconomic contexts; social, cultural and behavioral dimensions remain underexplored (12% of publications); and environmental justice considerations receive negligible attention. Crucially, the evidence reveals nonlinear relationships between circularity metrics and environmental outcomes, calling into question automatic benefits assumptions. This review contributes to an integrative synthesis that advances theoretical understanding of circularity-environment relationships while providing evidence-based guidance for researchers, practitioners, and policy makers involved in transitions to the circular economy. Full article

Objectives. To integrate clinical and economic evidence on the main non-pharmacological interventions aimed to reduce the burden of caregivers of people with dementia, with specific attention to stated preference measures (SPM), Willingness-to-Pay (WTP) and Willingness-to-Accept (WTA), alongside other cost-effectiveness indicators (ICER, QALY). Methods. A systematic review was conducted on randomized and quasi-experimental evaluations, economic models, and preference studies concerning psychoeducational/coping interventions, activity-centered/occupational programs (TAP), technological solutions and tele-support, and goal-oriented cognitive rehabilitation (CR). For each study, the following indexes were extracted: design, sample size, psychological outcomes (anxiety/depression, burden, engagement), utility per QALY, costs per perspective (the health–social and the broader societal perspectives), ICER, WTP/WTA, and sensitivity results. Results. Psychoeducational programs and CR show consistent benefits on distress, anxiety/depression, and caregiver quality of life; TAP reduces caregiver burden and patient behavioral problems, with favorable signs of cost–effectiveness; results on the effects of technologies are heterogeneous, but online modules with telephone support improve psychological morbidity. QALY improvement is generally modest, but the probability of cost-effectiveness remains high when costs do not differ significantly from treatment as usual, or when, from a societal perspective, the unpaid caregiving time of the caregiver is valued. Preference studies indicate positive WTP for additional hours of home care, health–social integration, and facilitated groups; evidence on WTA is scarcer and methodologically variable. Conclusions. Short, structured interventions with a human support component offer good value-for-money; the adoption of societal perspectives and the systematic use of WTP/WTA can better capture the value perceived by caregivers. Heterogeneity issues persist. Full article

The rapid technological development caused by industrial revolutions (Industry 4.0 and 5.0) puts a lot of pressure on the education system that regulates initial and continuous human resource development. The present study undertakes a scoping review of the policy papers of WEF, OECD, and UNESCO to understand the future challenges faced by education. The online databases of these international organizations were used to identify the English versions of the education policy reports published between 2020 and 2025 using the keywords “skills policy”, “closing skills gap”, “future skills”, “drivers of change”, “trends transforming education”, and “future education”. After screening and performing a thematic analysis, we identified fifteen publications that met the inclusion criteria. Choosing a systematic-narrative hybrid strategy, we conducted a systemic scoping review using the PRISMA-ScR guidelines. We found that the analyzed WEF and OECD policy reports contribute the most to understanding global skills policy and global trends driving changes in education. Our review has also revealed that the WEF-developed Global Skills Taxonomy and Taxonomy Adoption Toolkit contribute to further skills policy improvement and its practical implementation in bridging the skills gap. Full article

This paper introduces Saudi Dialects Cyber Violence Detection (SD-CVD) corpus, a large-scale, class-balanced Saudi-dialect corpus for fine-grained cyber violence detection on online platforms. The dataset contains 88,687 Saudi Arabic tweets annotated using a three-level hierarchical scheme that assigns each tweet to one of 11 mutually exclusive classes, covering benign sentiment (positive, neutral, negative), cyberbullying, and seven hate-speech subtypes (incitement to violence, gender, national, social class, tribal, religious, and regional discrimination). To mitigate the class imbalance common in Arabic cyber violence datasets, data augmentation was applied to achieve a near-uniform class distribution. Annotation quality was ensured through multi-stage review, yielding excellent inter-annotator agreement (Fleiss’ κ > 0.89). We evaluate three modeling paradigms: traditional machine learning with TF–IDF and n-gram features (SVM, logistic regression, random forest), deep learning models trained on fixed sentence embeddings (LSTM, RNN, MLP, CNN), and fine-tuned transformer models (AraBERTv02-Twitter, CAMeLBERT-MSA). Experimental results show that transformers perform best, with AraBERTv02-Twitter achieving the highest weighted F1-score (0.882) followed by CAMeLBERT-MSA (0.869). Among non-transformer baselines, SVM is most competitive (0.853), while CNN performs worst (0.561). Overall, SD-CVD provides a high-quality benchmark and strong baselines to support future research on robust and interpretable Arabic cyber-violence detection. Full article

With the acceleration of modernization, historical residences are facing increasingly prominent conflicts between cultural inheritance and contemporary visitor experiences. However, existing research on the revitalization of architectural heritage predominantly focuses on spatial functional replacement and value assessment, with insufficient attention paid to user-perceived issues and the transformation of architectural features into specific design practices. To address these gaps, this study takes the Ganxi Former Residence as an example and proposes an innovative pathway that integrates online review data, architectural meme theory, eye-tracking experiments, shape grammar, and design application, aiming to explore the contemporary transformation of architectural heritage in a user-demand-oriented manner. Based on 2845 valid online reviews, the study identified an imperfect signage system as the primary existing problem of the Ganxi Former Residence. Subsequently, comprehensive architectural meme maps encompassing architectural form memes, spatial memes, and cognitive memes were constructed based on architectural meme theory; high-visual-attention architectural factors were objectively screened through eye-tracking experiments; and these factors were innovatively evolved using shape grammar and applied to signage board design. Evaluation results indicate that the design proposal yielded positive effects in wayfinding clarity, aesthetic appeal, cultural fit, and overall satisfaction. This study not only accomplishes the cross-media transformation of traditional architecture from its physical form to visual signage boards but also provides a replicable and verifiable methodological paradigm for the creative transformation and innovative development of other architectural cultural heritage sites worldwide. Full article

Recommendation systems are popular information systems that help consumers manage information overload. Whilst personality has been recognised as an important factor influencing consumers’ choice, it has not yet been fully exploited in recommendation systems. This study proposes a restaurant recommendation approach that integrates customer personality traits, opinions and preferences, extracted either directly from online review platforms or derived from electronic word of mouth (eWOM) text using information extraction techniques. The proposed method leverages the concept of venue personality grounded in personality–brand congruence theory, which posits that customers are more satisfied with brands whose personalities align with their own. A novel model is introduced that combines fine-tuned BERT embeddings with linguistic features to infer users’ personality traits from the text of their reviews. Customers’ preferences are identified using a custom named-entity recogniser, while their opinions are extracted through structural topic modelling. The overall framework integrates neural collaborative filtering (NCF) features with both directly observed and derived information from eWOM to train an extreme gradient boosting (XGBoost) regression model. The proposed approach is compared to baseline collaborative filtering methods and state-of-the-art neural network techniques commonly used in industry. Results across multiple performance metrics demonstrate that incorporating personality, preferences and opinions significantly improves recommendation performance. Full article

The rapid digitalization of financial services in the European Union (EU) has not only enhanced convenience and inclusion but also increased exposure to sophisticated online financial fraud. Digital financial literacy (DFL) is widely promoted as a key tool for empowering consumers and reducing fraud victimization. However, the empirical and conceptual landscape linking DFL to fraud reduction within the specific sociolegal context of the EU remains fragmented. This study uses bibliometric analysis to map the research area, define major themes within the field, and determine the role of DFL in reducing online financial fraud in the EU. Peer-reviewed journal articles were targeted to ensure academic rigor, with a publication window of 2010–2025 reflecting key fintech and regulatory developments. After adhering to PRISMA principles, 87 peer-reviewed publications were chosen out of a total of 568 records identified through OpenAlex and Web of Science, coauthorship, keyword co-occurrence, citation, temporal, and density representations were analyzed using VOSviewer. Findings indicate an increasingly diffuse research field with new clusters concentrating on macroeconomic policy, business technology, social psychology, and interdisciplinary foundations. Results demonstrate that successful implementation of DFL interventions combines behavioral insights, technological protection, and non-discriminatory policy considerations. The study concludes by identifying major gaps in research and providing a path forward for future evidence-based policy efforts toward enhancing consumer protection in the EU digital financial market. Full article