Search Results (176)

Global food production must reduce environmental impact while meeting rising demand for dietary protein. Plant-based meats aim to preserve the sensory and cultural role of animal meat while lowering greenhouse gas emissions, land use, and health risks. Advances in protein structure and flavor chemistry have improved product quality, yet consumers continue to prioritize taste and texture over sustainability, and systematic large-scale consumer surveys are scarce. It remains unclear how plant-based products rank against animal benchmarks and which product attributes most strongly influence overall liking. Here we show, in a large-scale blinded in-person sensory evaluation across 14 product categories, 2684 consumers, more than 11,000 product evaluations and 800,000 data points, that plant-based products still trail animal benchmarks at the category average level but approach parity in selected formats. Plant-based unbreaded chicken filets, chicken nuggets, and burgers achieved mean overall liking scores of 5.1, 4.9, and 5.2, differing from the animal benchmarks by only $\Delta$ = 0.1, 0.2, and 0.3 points on a seven-point scale. For unbreaded chicken filets and burgers, 48% and 47% of the participants rated the plant-based product the same as or better than the animal benchmark. Categories with higher sensory parity captured 5–14% market share compared with less than 1% for low-parity categories. Penalty analysis identified savoriness, aftertaste, juiciness, and tenderness as the strongest determinants of liking. These findings show that sensory parity is technically achievable but not yet consistent across product types. By publicly sharing all the sensory, preference, and market-linked data, we establish an open benchmark for alternative protein performance to democratize research and accelerate principled data-driven innovation. Full article

Background: Vehicle routing optimization traditionally requires specialized software and technical expertise, limiting accessibility for small-to-medium enterprises. This study investigates whether generative AI (Claude 3.5 Sonnet via Claude.ai) can provide competitive vehicle routing solutions compared to traditional optimization methods while eliminating technical barriers. Methods: Fifty independent optimization trials were conducted across four methods—Claude.ai (generative AI), VRP Spreadsheet (Linear Programming), Routific (commercial heuristic), and genetic algorithm (evolutionary metaheuristic)—applied to a real-world case study of AED maintenance routing across 80 service locations in Chiang Rai, Thailand. Performance was evaluated across solution quality, ease of use, setup time, and implementation constraints. Results: The Genetic Algorithm achieved the best performance (908.34 km, −27.9% vs. manual routing), followed by Claude.ai best trial (941.64 km, −25.3%), VRP Spreadsheet (949.26 km, −24.7%), and Routific (964.36 km, −23.5%). Notably, Claude.ai’s best trial outperformed deterministic VRP Spreadsheet while requiring only 12 min setup versus 15 min. Probabilistic methods (Claude.ai, Genetic Algorithm) exhibited acceptable variability (CV: 2.24–2.28%), which was substantially lower than typical operational uncertainties. Conclusions: Generative AI provides accessible, competitive vehicle routing optimization, achieving 25%+ improvements with minimal technical expertise, democratizing advanced logistics planning for resource-constrained organizations. Full article

Public participation is a central component of democratic decision-making processes, particularly in planning and approval procedures. However, increasing data complexity and the growing number of submitted objections significantly raise the effort required for their review and processing. Against this background, this study developed an AI-supported objection management system that uses a large language model (LLM) to automatically pre-sort objections by topic and generate response suggestions based on historical objection texts from previous infrastructure projects. The aim is to increase efficiency in the processing workflow while maintaining consistent response quality without replacing human decision-making. The prototype development is preceded by a literature review to identify key user requirements and derive relevant use cases. Subsequently, four expert workshops with representatives from German road and rail infrastructure administrations at the state and federal level were conducted to evaluate the prototype. The results indicate significant efficiency potential, particularly through automated thematic pre-sorting of objections. However, topic structures must be adapted to the specific procedure. AI currently mainly serves as supportive pre-processing and requires human review (“human-in-the-loop”). Transparent labeling of AI use is also necessary to ensure traceability and acceptance. The findings will be incorporated into the ongoing development of the prototype within the BIM4People research project funded by the German Federal Ministry of Transport (BMV), with the aim of further improving the system’s functionality and exploring additional applications. Full article

The burden of disability is rising rapidly in Africa, where a severe shortage of rehabilitation professionals and limited infrastructure create a major treatment gap. Immersive virtual reality and serious games have shown promise for upper limb rehabilitation, but current extended reality (XR) solutions lack personalization, cultural adaptability, real-time feedback, and scalability. This perspective paper proposes a conceptual AI-enhanced XR framework tailored to African low- and middle-income countries. We identify how generative AI, large language models, multiagent systems, and explainable AI can address specific rehabilitation barriers. The framework integrates these four pillars into a three-layer architecture covering content creation, interaction, and decision support. We analyze implementation considerations specific to African contexts—infrastructure, capacity building, cultural adaptation, ethics, and financing—and outline a detailed research agenda with near, medium, and longer term priorities. Realizing this vision requires co-design with African communities, investment in local capacity, adaptation to infrastructure constraints, and development of ethical frameworks. AI-enhanced XR has the potential to democratize access to quality rehabilitation across Africa, but this potential must be validated through rigorous, context-sensitive research. Full article

Secular changes in the physical growth of children in the 20th century have been examined largely between cohorts of boys or men, with fewer studies examining changes among girls/women or both sexes. Sex-specific growth trajectories and differential cultural treatment of the sexes can affect how girls and boys respond to changes in the ontogenetic environment. This study examined secular change in height (cm) and weight (kg) in affluent Portuguese school children from three periods over the 20th century: an early (1913–1916), middle (1929–1943), and late (1992–2012) period. Anthropometric data was taken from medical records and archives of two boarding schools located in or near Lisbon: the Colégio Militar for boys and the Instituto de Odivelas for girls. Height and weight data were collated from over 1349 children (over 825 boys and 524 girls), aged to 10 to 17 years. Height and weight were plotted against age for the three periods to assess secular changes and sex differences in the secular trend. Results indicate a similar pattern of secular change across boys and girls, wherein children measured in the late period demonstrated an increase in height and weight, with the greatest increase occurring between the middle and late periods. The increase in height and weight can be attributed to changes to the socioeconomic environment in Portugal after the 1960s, but particularly after the democratic transition of 1974. This includes population-wide improvements in living standards, sanitation, decreased disease load, access to medical care and improved quantity and quality of nutrition. Cultural-based preferential treatment of boys may have taken place, as boys increased more in relative and absolute height and weight. Full article

Marine turtle and cetacean strandings along the Italian coastline represent critical ecological events that require systematic documentation, yet historical data have suffered from fragmentation and poor accessibility across heterogeneous archives. GeoCetus addresses this gap by providing a unified national framework for the centralized collection, management, and open visualization of these data. The platform’s architecture integrates a spatially enabled database with a modern RESTful API, utilizing automated workflows to push data to a public GitHub.com repository. This system unifies historical and contemporary datasets, comprising over 4700 georeferenced records dating back to 1999, while ensuring data quality through structured validation, qualified contributors and reverse geocoding. The results demonstrate a significant improvement in data interoperability and democratization, with the dataset expanding by an average of 150–300 new records annually under a CC-BY-SA license. By adhering to FAIR Data Principles, GeoCetus offers the necessary infrastructure to support real-time operational responses and reproducible ecological analyses. We conclude that this standardized, machine-readable approach is essential for evidence-based national conservation strategies and effective environmental monitoring. Full article

Public participation in planning, though a foundational democratic principle, faces persistent implementation challenges across diverse planning systems. This paper examines participatory planning practice in Ireland and Serbia—two countries representing distinct planning traditions (discretionary and conformance-based, respectively) yet confronting shared structural pressures. Through comparative analysis of four local land use planning instruments (the Development Plan and Local Area Plan in Ireland; the Municipal Spatial Plan and General Regulation Plan in Serbia), the study investigates how institutional design and legislative frameworks shape the depth and quality of participatory practice. Methodologically, the research triangulates statutory regulations, public hearing documentation, and non-statutory participation records across two planning scales (county/municipal and local/sub-municipal). A four-dimensional analytical framework—informing, consultation, collaboration, and monitoring—guides the systematic comparison of participatory mechanisms across the selected cases. Findings reveal that, while both systems remain predominantly at the informing and consultation levels, critical differences emerge in how participation is structured and documented in institutional practice. Ireland’s discretionary system enables multi-channel information dissemination, feedback-oriented consultation, and non-statutory collaborative experimentation beyond legal minimums. Serbia’s conformance-based system confines participation largely to statutory procedures, with objection-based consultation and limited collaborative mechanisms, though distinctive features, such as the public hearing session, provide direct opportunities for deliberation absent in the Irish context. The study contributes to European comparative planning scholarship by demonstrating that participatory depth is shaped less by the formal existence of legal provisions than by the interplay between institutional design, procedural arrangements, transparency, and responsiveness. Full article

Massive Open Online Courses (MOOCs) serve as catalysts for sustainable education by democratizing access to lifelong learning. While this potentially positions them as a key driver of the United Nations Sustainable Development Goal 4 (SDG 4), their long-term impact depends heavily on the implementation of inclusive design and ethical governance. This study evaluates the social sustainability of the AKADEMA platform—defined through equity of access, institutional trust, and long-term learner retention—using Badrul Khan’s e-learning framework. Employing a multi-layered mixed-methods design, the study triangulates subjective user perceptions—gathered via quantitative surveys (N = 209; a convenience sample of 6140 contacted users) and qualitative insights (n = 122)—with objective structural evidence from a technical accessibility audit. Although the results indicate high satisfaction with pedagogical quality, the findings reveal specific structural nuances regarding platform inclusivity and user diversity. Specifically, data triangulation highlights a notable ‘privacy awareness gap’—where working professionals demonstrate higher sensitivity regarding data governance than learners—alongside structural barriers hindering ‘Universal Design’ for learners with disabilities. Consequently, to strengthen the sustainability of open education models, future strategies should emphasize digital equity and institutional trust, ensuring that technical environments align with the promise of inclusive quality education. Full article

The volatility of oil prices has a considerable impact on the economies of oil-exporting countries, making it critical to understand how price variations affect labour markets and unemployment. This study investigates the distinct role of labour market institutions in moderating the effects of oil price volatility on unemployment. Using the Cross-Sectionally Augmented Autoregressive Distributed Lag Model (CS-ARDL) on a panel dataset of nine African oil-exporting countries from 1994 to 2024, the study establishes a strong negative link between oil price changes and unemployment. Furthermore, the results show that real GDP growth leads to a reduction in unemployment in the long run, while the labour market institutional index has a negative impact on unemployment. Interacting the oil price with the labour market institutional index causes a further reduction in unemployment. These results suggest that good labour market institutions and macroeconomic stability are essential for reducing unemployment. While increases in oil prices directly stimulate a reduction in unemployment in African oil-exporting countries, this impact is reinforced by the presence of good labour market institutions in an economy. Therefore, the results suggest that countries with strong labour market institutions are more resilient in reducing the negative impact of oil price volatility on employment. As such, policymakers must prioritise labour market institutional reforms to enhance countries’ capacity to absorb oil price shocks and reduce unemployment during periods of oil prosperity and shield against employment declines when oil prices drop. Furthermore, the creation of oil stabilisation funds in these countries may serve a similar purpose. Contribution/originality: Against a background of inconclusive empirical evidence in the literature and a dearth of research on African countries, this study investigates the role of labour market institutions (LMIs) in the oil price–unemployment nexus in African oil-exporting countries. While highly dependent on oil revenue, these countries record persistent structural unemployment. Therefore, the study provides critical evidence to guide the formulation of policies necessary to deal with external shocks and facilitate structural shifts required for employment growth. Existing studies consider general institutional variables such as democratic accountability and the rule of law and do not assess the effect of labour market institutions. The current study fills in this gap by assessing the distinct role of labour market institutions that are specifically designed to regulate only work-related activities, such as quality of labour regulations, adequacy of social protection and unemployment benefits. Furthermore, this study employed the cross-sectionally augmented autoregressive distributed lag (CS-ARDL) for econometric estimations. Compared to previous studies, this is a more appropriate method that accounts for unobserved common factors such as oil price shocks affecting all oil-exporting countries simultaneously. Full article

The Democratic Republic of the Congo faces a high tuberculosis (TB) burden. In 2022, 61% of an estimated 402,000 TB cases were reported (World Health Organization Global tuberculosis report). To enhance case detection, the national TB program (NTP) introduced a program quality and efficiency approach (PQE), integrating systematic TB screening into outpatient departments (OPDs). Observational data of the PQE on the TB care cascade (from screening to treatment) across 70 sites in Kinshasa that initiated PQE during the first quarter of 2023 are presented. Data were collected monthly and validated during supervision visits, and disaggregated by sex, healthcare facility type (public, private, or faith-based), facility level (primary or secondary), and OPD within each facility. In 2024, 639,464 individuals were consulted in various OPDs in the participating facilities, 57% of which were female. The median number needed to screen (NNS) was 22.1, with an interquartile range of [9.5–104.3]. There was a significantly lower NNS observed in general practice and human immunodeficiency virus departments. Throughout the TB care cascade, women were less likely than men to be screened, tested, or treated. These findings, to be interpreted within the context of Kinshasa pilot facilities, provide insights to the NTP for developing PQE implementation research aimed at understanding the reasons for these discrepancies and informing NTP scale-up at the national level. Full article

This study investigates how integrating the 4PADAFE instructional design methodology with generative artificial intelligence (GAI) tools helps develop innovative, pedagogically sound digital learning environments in higher education. To meet the demand for scalable and flexible instructional models, 4PADAFE offers a seven-phase, iterative framework that connects pedagogical goals with the creative use of AI-powered tools. Using a qualitative exploratory approach, 20 Systems Engineering students applied the methodology to collaboratively create a four-week Massive Open Online Course (MOOC) titled “Generative Artificial Intelligence Tools for University Teaching.” They utilized ChatGPT, DALL·E, and Gamma to produce educational materials without direct input from subject-matter experts. Data collection included semi-structured interviews, non-participant observation, and analysis of student-created artifacts. The findings revealed increased learner autonomy, creativity, and digital skills, along with more efficient instructional design processes supported by prompt engineering and real-time feedback. The structured 4PADAFE framework helped participants align AI-generated content with specific learning outcomes while maintaining ethical safeguards. This study concludes that, with proper guidance and a systematic framework, students with technical backgrounds can serve as effective instructional designers, demonstrating the potential of combining structured methodologies and GAI to democratize high-quality course development in digital higher education. Full article

This article examines how political journalism can distinguish itself in a fast-paced information environment by providing in-depth contextualization and thereby contribute to the functioning of democratic societies in a digitalized world. Focusing on the ‘Causa Brosius-Gersdorf’—a highly polarized controversy surrounding judicial appointments to Germany’s Federal Constitutional Court in July 2025—we ask the following questions: to what extent can German-language podcasts offer in-depth analysis, and which types of contextualization can be observed across different podcast formats? The study is based on a qualitative content analysis of 39 episodes from 15 popular podcasts drawn from the German Spotify Top 200. Drawing on a theoretically grounded analytical framework comprising the categories ‘topics’, ‘dimensions of context’, and ‘relational levels’, we identify distinct types of contextualization. We analyze the distribution of these types using distant reading and interpret salient patterns through close reading. Our results show that the podcasts analyzed offer an in-depth contextualization of the issue, with a focus on political and societal evaluation. In doing so, they provide their audiences with orientation and therefore enable them to form their own well-founded opinions. Since we conducted our analysis at the level of individual statements rather than at the level of the news items themselves, our study advances research on quality in journalism, highlights the role of podcasts in digital transformation, and addresses the democratic value of contextualizing political communication. Full article

This study assessed the technical capacity and specific support needs of 28 small, medium, and community cassava processing centers across the Ruzizi Plain, Kinshasa, and Kongo Central Provinces of the Democratic Republic of Congo. A rapid appraisal methodology involving physical visits and direct interviews with proprietors and operators was conducted between March and May 2023. Data collection focused on product types, machinery, production capacity, operational status, challenges, and quality management. The study revealed significant technical and infrastructural deficiencies. Key challenges include reliance on inefficient sun-drying, inadequate infrastructure, lack of basic utilities, obsolete machinery, poor local capacity for machine repair, minimal adherence to Good Manufacturing Practices, and inadequate product quality testing, all leading to inconsistent product quality. The study highlights urgent need for investments in efficient drying facilities, equipment upgrades, and capacity building in quality control and business management. By differentiating technical assistance needs based on enterprise scale and product type, this study provides evidence-based recommendations essential for tailoring effective and sustainable intervention strategies to transform the DRC’s cassava processing sector and enhance food security. Full article

Disinformation has become established as a strategic tool in political communication, with the capacity to erode public trust and undermine democratic quality. In an information environment increasingly mediated by artificial intelligence, it is essential to understand how the media articulates disinformation discursively. This study analyses, using a mixed design of quantitative and qualitative content analysis, 178 articles published in the five main Spanish digital newspapers (El País, El Mundo, La Vanguardia, El Español and Eldiario.es), comparing the treatment of two cases of alleged political corruption. The results show significant differences in volume, journalistic genre, tone, framing, and use of disinformation terminology, confirming that the media do not act as neutral transmitters but rather as discursive actors that use disinformation lexicon for the purposes of attack, defence, or ideological legitimisation. There is also a predominance of emotional tones and rhetorical strategies that favour polarisation. Full article

The occurrence of filter bubbles and echo chambers in social media recommendation systems poses a significant threat to information diversity and democratic discourse. Although graph neural networks (GNNs) achieve leading accuracy in user recommendation, their optimization for engagement metrics inadvertently reinforces homophily, creating isolated information ecosystems. This research developed community-aware two-stage diversification with GNNs (CATD-GNN), a method that leverages the inherent community structure of social networks to promote diversity without sacrificing recommendation quality. CATD-GNN integrates community detection with GNN learning through a two-stage diversification process. The proposed method employs the Louvain method to identify community structures as pseudo-categories, then applies submodular neighbor selection and community-based loss reweighting during GNN training (Stage 1), followed by coverage and redundancy-aware reranking (Stage 2). Twitter data capturing Black Lives Matter discourse and Reddit political discussion networks were used to evaluate the method. CATD-GNN achieves improvements in diversity metrics while maintaining competitive accuracy. The two-stage architecture demonstrates a synergistic effect: the combination of diversity-aware training and coverage-based reranking produces greater improvements than either component alone. The proposed method successfully identifies and recommends users from different communities while preserving recommendation relevance. Full article