Search Results (521)

The environmental and social crises affecting global agri-food systems highlight the need for a profound transformation of production models and their territorial relations. In this context, agroecology, understood as science, practice, and movement, has emerged as a paradigm capable of integrating ecological sustainability, social equity, and community participation. Within this framework, the work carried out by CREA in the AgrEcoMed project (new agroecological approach for soil fertility and biodiversity restoration to improve economic and social resilience of Mediterranean farming systems), funded by the PRIMA programme, investigates agroecology as a social and political process of territorial regeneration. This process is grounded in co-design with local stakeholders, collective learning, and the construction of multi-actor networks for agroecology in the Mediterranean. The Manifesto functions as a tool for participatory governance and value convergence, aiming to consolidate a shared vision for the Mediterranean agroecological transition. The article examines, through an analysis of the existing literature, the role of agroecological networks and empirically examines the function of the collective co-creation of the Manifesto as a tool for social innovation. The methodology is based on a participatory action-research approach that used local focus groups, World Café, and thematic analysis to identify the needs of the companies involved. The results highlight the formation of a multi-actor network currently comprising around 90 members and confirm the effectiveness of the Manifesto as a boundary object for horizontal governance. This demonstrates how sustainability can emerge from dialogue, cooperation, and the co-production of knowledge among local actors. Full article

Genome-wide association studies (GWAS) are essential for identifying genomic regions associated with agronomic traits, but Linear Mixed Model (LMM)-based GWAS face challenges in capturing complex gene interactions. This study explores the potential of machine learning (ML) methodologies to enhance marker identification and association modeling in plant breeding. Unlike LMM-based GWAS, ML approaches do not require prior assumptions about marker–phenotype relationships, enabling the detection of epistatic effects and non-linear interactions. The research sought to assess and contrast approaches utilizing ML (Decision Tree—DT; Bagging—BA; Random Forest—RF; Boosting—BO; and Multivariate Adaptive Regression Splines—MARS) and LMM-based GWAS. A simulated F₂ population comprising 1000 individuals was analyzed using 4010 SNP markers and ten traits modeled with epistatic interactions. The simulation included quantitative trait loci (QTL) counts varying between 8 and 240, with heritability levels set at 0.5 and 0.8. These characteristics simulate traits of candidate crops that represent a diverse range of agronomic species, including major cereal crops (e.g., maize and wheat) as well as leguminous crops (e.g., soybean), such as yield, with moderate heritability and a high number of QTLs, and plant height, with high heritability and an average number of QTLs, among others. To validate the simulation findings, the methodologies were further applied to a real Coffea arabica population (n = 195) to identify genomic regions associated with yield, a complex polygenic trait. Results demonstrated a fundamental trade-off between sensitivity and precision. Specifically, for the most complex trait evaluated (240 QTLs under epistatic control), Ensemble methods (Bagging and Random Forest) maintained a Detection Power (DP) exceeding 90%, significantly outperforming state-of-the-art GWAS methods (FarmCPU), which dropped to approximately 30%, and traditional Linear Mixed Models, which failed to detect signals (0%). However, this sensitivity resulted in lower precision for ensembles. In contrast, MARS (Degree 1) and BLINK achieved exceptional Specificity (>99%) and Precision (>90%), effectively minimizing false positives. The real data analysis corroborated these trends: while standard GWAS models failed to detect significant associations, the ML framework successfully prioritized consensus genomic regions harboring functional candidates, such as SWEET sugar transporters and NAC transcription factors. In conclusion, ML Ensembles are recommended for broad exploratory screening to recover missing heritability, while MARS and BLINK are the most effective methods for precise candidate gene validation. Full article

Coffea, a plant species of significant agricultural value used in coffee production, is a key commodity that supports the livelihoods of millions of people worldwide. However, coffee cultivation faces substantial threats from various pathogens, including Pseudomonas coronafaciens pv. garcae (Pcg), the causative agent of bacterial blight. This pathogen compromises coffee plant health, leading to reduced yields and plant death and impacting farmers and large-scale producers. Understanding the mechanisms underlying resistance to Pcg in the leaves of the resistant IAC 2211-6 Coffea arabica accession is crucial for developing effective control strategies. This study aimed to identify candidate biomarkers of resistance by comparing the leaf metabolome of (i) the resistant IAC 2211-6 and the susceptible IAC 125 RN Coffea arabica accessions and (ii) Pcg-infected and uninfected leaves. Untargeted metabolomics revealed distinct metabolic profiles between accessions. Flavonoids were more abundant in susceptible leaves. In contrast, resistant leaves showed increased levels of pipecolic acid ethyl ester, a structural derivative of a key systemic acquired resistance signal, and spiropreussione B, a compound associated with fungal endophytes. These findings highlight candidates potentially linked to resistance and suggest that systemic signaling and beneficial microbial interactions may contribute to resilience. Full article

To address the challenging requirements of citrus detection in complex orchard environments, this paper proposes ACDNet (Adaptive Citrus Detection Network), a novel deep learning framework specifically designed for automated citrus harvesting. The proposed method introduces three key innovations: (1) Citrus-Adaptive Feature Extraction (CAFE) module that combines fruit-aware partial convolution with illumination-adaptive attention mechanisms to enhance feature representation with improved efficiency; (2) Dynamic Multi-Scale Sampling (DMS) operator that adaptively focuses sampling points on fruit regions while suppressing background interference through content-aware offset generation; and (3) Fruit-Shape Aware IoU (FSA-IoU) loss function that incorporates citrus morphological priors and occlusion patterns to improve localization accuracy. Extensive experiments on our newly constructed CitrusSet dataset, which comprises 2887 images capturing diverse lighting conditions, occlusion levels, and fruit overlapping scenarios, demonstrate that ACDNet achieves superior performance with mAP@0.5 of 97.5%, precision of 92.1%, and recall of 92.8%, while maintaining real-time inference at 55.6 FPS. Compared to the baseline YOLOv8n model, ACDNet achieves improvements of 1.7%, 3.4%, and 3.6% in mAP@0.5, precision, and recall, respectively, while reducing model parameters by 11% (to 2.67 M) and computational cost by 20% (to 6.5 G FLOPs), making it highly suitable for deployment in resource-constrained robotic harvesting systems. However, the current study is primarily validated on citrus fruits, and future work will focus on extending ACDNet to other spherical fruits and exploring its generalization under extreme weather conditions. Full article

Weed competition represents a primary constraint in sesame production, causing substantial yield losses typically ranging from 18 to 68% under inadequate control measures. Precise crop–weed discrimination remains challenging due to morphological similarities, complex field conditions, and vegetation overlapping. To address these issues, we developed Context-Aware Feature-Enhanced Detection Transformer (CAFE-DETR), an enhanced Real-Time Detection Transformer (RT-DETR) architecture optimized for sesame–weed identification. First, the C2f with a Unified Attention-Gating (C2f-UAG) module integrates unified head attention with convolutional gating mechanisms to enhance morphological discrimination capabilities. Second, the Hierarchical Context-Adaptive Fusion Network (HCAF-Net) incorporates hierarchical context extraction and spatial–channel enhancement to achieve multi-scale feature representation. Furthermore, the Polarized Linear Spatial Multi-scale Fusion Network (PLSM-Encoder) reduces computational complexity from O(N²) to O(N) through polarized linear attention while maintaining global semantic modeling. Additionally, the Focaler-MPDIoU loss function improves localization accuracy through point distance constraints and adaptive sample focusing. Experimental results on the sesame–weed dataset demonstrate that CAFE-DETR achieves 90.0% precision, 89.5% mAP₅₀, and 59.5% mAP_50–95, representing improvements of 13.07%, 4.92%, and 2.06% above the baseline RT-DETR, respectively, while reducing computational cost by 23.73% (43.4 GFLOPs) and parameter count by 10.55% (17.8 M). These results suggest that CAFE-DETR is a viable alternative for implementation in intelligent spraying systems and precision agriculture platforms. Notably, this study lacks external validation, cross-dataset testing, and field trials, which limits the generalizability of the model to diverse real-world agricultural scenarios. Full article

Adaptive reuse of heritage buildings is a vital strategy for balancing cultural preservation with modern functionality needs. This study provides a post-adaptive reuse evaluation of Dar Al Saraya in Madaba, Jordan, a significant Ottoman-era landmark, to examine how adaptive reuse strategies influence interior environments and heritage value. The analysis employs Zhang and Zhang’s evaluation framework focusing on existing fabric, special character, and policy and value, operationalized through 15 factors. A qualitative methodology was adopted, integrating site observations, photographic documentation, and semi-structured interviews with heritage experts, municipal representatives, residents, visitors, and site staff. Fieldwork was conducted in two phases (November 2024 and October 2025) to capture evolving conditions and perceptions. Findings indicate that challenges in spatial reconstruction were few and well addressed, but gaps in adaptation and reuse function strategies created significant issues. These included a lack of coordinated policies and the failure of municipal authorities and property owners to sustain the building’s reuse and involve the local community in reuse decisions. Despite various initiatives, from a museum, hotel, cultural center and gallery to its recent adaptation into a café, these efforts lacked sustainability and inclusive strategic planning. Consequently, the café has faced difficulties since opening, leaving its future uncertain. These findings highlight the importance of post-adaptive reuse evaluation and of integrating policy, planning, and community participation into adaptive reuse strategies to promote sustainable, community-centred conservation. Full article

Social health is increasingly recognized as a key domain in dementia research, yet interventions explicitly addressing it remain scarce. This study presents the co-creation of an empowering and meaningful social health intervention for people living with dementia. An evidence-based intervention scaffolding was enriched with the lived experiences of people living with dementia through a seven-step co-creation process, in which they held sole decision-making authority in selecting intervention topics using an adapted World Café method, shared responsibility for designing session content, and joint responsibility for implementation. Twenty-nine residents living with dementia in three long-term care facilities co-created and implemented twelve group sessions based on their “heart topics,” emphasizing personal strengths, reciprocity, and shared experiences. Each session integrated peer-led, co-creative, and sensory elements and was collaboratively prepared and implemented together with at least one peer host. The co-creation process effectively captured the lived experiences of people living with dementia and resulted in an intervention with the potential to foster and deepen social relationships in long-term care. This study calls on researchers and practitioners to take bolder steps toward empowering people living with dementia to assume active, visible, and meaningful roles in intervention development and implementation. Full article

Most polymeric plastics used as food packaging are obtained from petroleum or made with non-biodegradable synthetic molecules, which slowly degrade and leach into the environment, resulting in the accumulation of microplastics along the trophic chains. To mitigate these impacts, biodegradable packaging derived from agro-industrial biomass residues has emerged as a promising alternative. In this study, bio-based methylcellulose films reinforced with cellulose nanocrystals (CNCs) extracted from low-quality coffee beans were developed and fully characterized. The extracted CNCs presented a needle-like morphology, with an average height of 7.27 nm and a length of 221.34 nm, with 65.75% crystallinity, were stable at pH 7–8, and presented thermogravimetric mass loss of 8.0%. Methylcellulose films containing 0.6% w/w of CNC were produced by casting and characterized in terms of thermal, mechanical, and optical properties. Notably, the incorporation of CNCs resulted in significantly more flexible and less rigid films, as evidenced by the higher elongation at break (57.90%) and lower Young’s modulus (0.0015 GPa) compared to neat methylcellulose film. The tensile strength was not affected (p > 0.05). Additionally, the MCNC 0.6% films effectively blocked UV light in the 200–300 nm range without compromising transparency. Altogether, these findings underscore the MCNC 0.6% film as a flexible, biodegradable packaging material suitable for food industry application. Full article

Servicescape, the physical and social environment of a service setting, is a critical strategic tool for creating competitive advantage. While its influence on customer loyalty and electronic word-of-mouth (e-WOM) is established, the underlying psychological mechanisms remain inadequately specified. This study addresses this gap by proposing and testing a dual-mediation model grounded in an integrated Stimulus–Organism–Response (S-O-R) framework, with cognitive evaluations informed by Expectancy-Disconfirmation Theory (EDT), distinguishing between affective (service experience) and cognitive (customer satisfaction) pathways. Data were collected from 420 patrons of nature-themed cafés in Nakhon Ratchasima, Thailand, and analyzed using structural equation modeling (SEM-PLS). The results confirm that servicescape significantly enhances both service experience (β = 0.805, p < 0.001) and customer satisfaction (β = 0.816, p < 0.001). However, its effects on customer loyalty and e-WOM are fully mediated through these parallel pathways. Customer satisfaction demonstrated a stronger influence on loyalty than service experience, while both were significant drivers of e-WOM. The findings suggest theoretical contributions by delineating the distinct affective and cognitive processes through which the service environment translates into digital advocacy and loyalty. For managers, this study suggests a strategic framework for allocating resources to foster both shareable experiences and satisfaction-driven loyalty. Full article

Online reviews on platforms such as Google Maps strongly influence consumer decisions. However, aggregated ratings mask nuanced opinions about specific aspects such as food, drinks, service, lounge, and price. This study presents a multi-aspect sentiment analysis framework for Arabic café reviews. Specifically, we combine machine learning (Linear SVC, Naïve Bayes, Logistic Regression, Decision Tree, Random Forest) and a Convolutional Neural Network (CNN) to perform aspect identification and sentiment classification. A rigorous preprocessing and feature-engineering with TF-IDF and n-gram was implemented and statistically validated through bootstrap confidence intervals and Friedman–Nemenyi significance tests. Experimental results demonstrate that Linear SVC with optimized TF-IDF tri-grams achieved a macro-F1 of 0.89 for aspect identification and 0.71 for sentiment classification. Meanwhile, the CNN model yielded a comparable F1 of 0.89 for aspect identification and a higher 0.76 for sentiment classification. The findings highlight that effective feature representation and model selection can substantially improve Arabic opinion mining. The proposed framework provides a reliable foundation for analyzing Arabic user feedback on location-based platforms and supports more interpretable and data-driven business insights. These insights are essential to enhance personalized recommendations and business intelligence in the hospitality sector. Full article

The present study explores the role of food as a symbolic, material, and relational device in identity construction and community processes. This study draws on a qualitative case study of a community-based social restaurant located in a mid-sized city in central-northern Italy. The initiative’s objective is to promote the social and labor inclusion of migrant women through training and experiential programs. The research, conducted over a period of nine months from October 2024 to June 2025, was based on a participatory qualitative design, which integrated semi-structured interviews, ecological maps, photointervention, world café, and affective cartography, involving 35 participants including operators, trainees, local community members, and politicians. The results demonstrate the multifaceted role of food practices at the restaurant, which serve to strengthen internal relationships, regulate community life, construct intercultural narratives, and establish spaces of recognition and agency for the women involved. Moreover, the restaurant has been shown to have the capacity to influence the broader social representations of migration in the urban context, thereby promoting processes of cohesion and belonging. It is evident that food-related activities manifest as quotidian micro-political practices, which have the capacity to subvert stereotypes, recognize frequently unseen abilities, and generate new forms of inclusive citizenship. The present study underscores the transformative capacity of initiatives that employ food practices as innovative instruments for fostering empowerment; well-being; and social participation; through the third element of food. The limitations and future prospects of the present situation are discussed; with particular reference to the need to ensure continuity and institutional sustainability for similar experiences. Full article

As a vital carrier of human intangible culture, dance plays an important role in cultural transmission through digital generation. However, existing dance generation methods rely heavily on high-precision motion capture and manually annotated datasets, and they fail to effectively model the culturally distinctive movements of Chinese ethnic folk dance, resulting in semantic distortion and cross-modal mismatch. Building on the Chinese traditional ethnic Helou Dance, this paper proposes a culture-aware Chinese ethnic folk dance generation framework, CAFE-Dance, which dispenses with manual annotation and automatically generates dance sequences that achieve high cultural fidelity, precise music synchronization, and natural, fluent motion. To address the high cost and poor scalability of cultural annotation, we introduce a Zero-Manual-Label Cultural Data Construction Module (ZDCM) that performs self-supervised cultural learning from raw dance videos, using cross-modal semantic alignment and a knowledge-base-guided automatic annotation mechanism to construct a high-quality dataset of Chinese ethnic folk dance covering 108 classes of curated cultural attributes without any frame-level manual labels. To address the difficulty of modeling cultural semantics and the weak interpretability, we propose a Culture-Aware Attention Mechanism (CAAM) that incorporates cultural gating and co-attention to adaptively enhance culturally key movements. To address the challenge of aligning the music–motion–culture tri-modalities, we propose a Tri-Modal Alignment Network (TMA-Net) that achieves dynamic coupling and temporal synchronization of tri-modal semantics under weak supervision. Experimental results show that our framework improves Beat Alignment and Cultural Accuracy by 4.0–5.0 percentage points and over 30 percentage points, respectively, compared with the strongest baseline (Music2Dance), and it reveals an intrinsic coupling between cultural embedding density and motion stability. The code and the curated Helouwu dataset are publicly available. Full article

This study focuses on the interplay between digital marketing and the F&B industry in Greece during the health crisis of COVID-19 in shaping consumer choices. The theoretical section discusses the most popular digital marketing methods and their importance, particularly during a crisis that confined most citizens to their homes and forced F&B stores to find new ways to attract customers. From an empirical perspective, a survey was conducted utilizing a structured questionnaire, involving 70 consumers in Athens, Greece. Participants expressed their views through closed-ended questions on the criteria for selecting F&B stores, the positive and negative aspects of digital marketing, and their preferences in general, as well as specifically for restaurants, bars, and cafés. The findings highlight that consumer confidence is a key priority (as the most important criterion for selecting F&B stores is the quality of the products) and that digital transformation of the F&B industry is essential as it can bolster resilience and drive growth in the F&B sector amid ongoing challenges. Full article

Background: This study reports on a community engagement knowledge-translation world café hosted in British Columbia, built on the research project “Enhancing cancer navigation for newly diagnosed, treated and post-treatment of people living with breast cancer in interior region”. The aim was to co-create a knowledge translation initiative with community stakeholders to enhance interdisciplinary approaches to psychosocial oncology. Methods: This study drew on implementation science and the consolidated framework for implementation research, which emphasize the importance of creating partnerships between researchers and engaging people for whom the research is meant to be of use—knowledge users and service users. Guided world café and purposeful sampling were used to engage a diverse range of stakeholders. Eighty stakeholders participated in this study from April 2023 to April 2024. Thematic analysis was conducted through familiarization, coding, theme development, review, definition, and reporting. Results: Eleven key themes emerged, including compassionate connection, time as a healing gift, empowering health literacy, informed compassion, holistic support ecosystem, empowering patient navigators, shared decision-making, empowering partnerships, digital–physical synergy, person-centered transformation, and accountability and collaboration. Conclusions: The key findings highlighted the need for continuous professional development for primary care providers, integrating patient-reported outcomes in electronic health records, leveraging digital health tools, and establishing community-engaged psychosocial oncology hubs to enhance care in rural communities. Recommendation: Recommendations include ongoing professional learning, embedding patient voices and lived experiences into care planning through digital tools, and empowering rural and diverse communities through inclusive and accessible cancer models of care. Full article

This paper explores the café–gym symbiosis mode in Xi’an and its key influencing factors. Taking 63 sub-districts in the seven main urban districts of Xi’an as an example, based on the Dianping.com data of 753 cafés and 335 gyms and survey data from 492 questionnaires, this paper uses methods such as the symbiotic degree, symbiotic coefficient, and binary logistic regression model. On the basis of evaluating the symbiotic model between cafés and fitness centers, it explores the key factors influencing the symbiotic model of cafés and fitness centers. The results showed that cafés and gyms in Xi’an have a variety of characteristics, including agglomeration, correlation, complementarity, and combination, laying the foundation for a symbiosis between them. Among the subject symbiosis modes in Xi’an, point symbiosis was the main symbiotic organization mode. Simultaneously, the proportion of the point symbiosis mode was higher in the urban–rural transitional area than in other areas (traditional inner-city areas, mature built-up areas, emerging expansion areas). An asymmetric reciprocal symbiosis mode dominated the symbiotic behavior mode of entertainment industry objects in Xi’an. In terms of the total weekly entertainment consumer and the additional entertainment consumer dimensions, in the asymmetric reciprocal symbiosis mode, the proportion of cafés having a large impact on gyms was the highest: 60.00% and 62.86%, respectively. However, from the composite index dimension, in the asymmetric reciprocal symbiosis mode, the proportion of gyms having a large impact on cafés was the highest: 39.13%. From the symbiotic interface, the physical space within urban residential areas, office areas, commercial areas, and other main material spaces was the important basic support force for the symbiotic development of urban culture and the entertainment industry. The influence of the symbiosis mode of the culture and entertainment industry has stability. From the perspective of the symbiotic environment, cultural and creative elements, government policies, and consumer spending on entertainment foster the formation of an asymmetrical mutualistic symbiosis model between cafés and gyms. Conversely, factors such as marketization, globalization, and demographic factors inhibit its development. These findings offer valuable insights for urban planners and businesses, which help optimize the layout of the urban entertainment industry. Full article