Search Results (11,951)

Tomato (Solanum lycopersicum L.), a member of the Solanaceae family, originates from South America and is currently cultivated worldwide. In tropical regions, it grows as a perennial plant, whereas in temperate and subtropical climates, it is cultivated as an annual because of its sensitivity to frost. Tomato fruits are an important source of bioactive compounds, including carotenoids, polyphenols, minerals, and vitamins. Their widespread consumption and status as one of the most commonly consumed horticultural crops worldwide make tomatoes an important dietary source of these compounds. Tomatoes are commonly consumed fresh, but they are also an important raw material for the food industry. The main tomato products include juices, concentrates, purées, and sauces. The chemical composition and concentration of bioactive compounds in tomato fruits depend on several factors, including cultivar, stage of ripeness, environmental conditions, and cultivation and processing technologies. Numerous studies indicate that compounds present in tomatoes exhibit antioxidant properties and have been associated with potential health-promoting effects. Among these, carotenoids, particularly lycopene, play a key role. This review summarizes current knowledge on the nutritional value, composition, and functional properties of tomatoes. It also addresses the antinutritional aspects of tomato compounds, as well as the influence of agrotechnical, environmental, and technological factors on the content of bioactive compounds. Furthermore, this review may support the design of future research by critically analyzing existing studies and highlighting inconsistencies and knowledge gaps. Full article

While existing curricula often focus on theoretical aspects of sustainability, they frequently fail to equip students with practical design skills required by the green industry. To address this disconnect, this study seeks to answer: How can a structured pedagogical framework effectively enhance students’ ability to translate abstract sustainability principles into concrete technical solutions? This study introduces a comprehensive CDIO-based framework reform for Embedded Intelligent Systems education, weaving sustainability throughout every phase. We put forward a “Sustainable CDIO Capability Model” that charts a progressive pathway—starting from basic resource awareness and advancing through to sophisticated sustainable system innovation. Our four-dimensional teaching strategy brings this model to life: first, project-based learning driven by real sustainability challenges; second, a hybrid ecosystem blending online resources, hands-on practice, and immersion in green industry contexts; third, hierarchical team-based pedagogy backed by personalized support mechanisms; and fourth, a multi-dimensional assessment system that weights energy efficiency, resource stewardship, and social value creation alongside conventional metrics. We implemented this approach with Intelligent Science and Technology majors at Wuhan Institute of Technology. The results show the model effectively bridges the persistent gap between dry technical content and the practical demands of green industry. Students made substantial gains not merely in core engineering capabilities—system architecture, hardware-software co-development—but crucially in sustainable design awareness and their capacity to untangle complex sustainability challenges. This work offers a readily transferable framework for embedding Education for Sustainable Development (ESD) into engineering curricula worldwide. It provides practitioners with a concrete, tested model for cultivating the next generation of engineers who naturally think and act with sustainability in mind. Full article

Recently, mycotoxin prediction has mainly relied on meteorological data and crop physiology. The contribution of soil characteristics as additional environmental variables remains largely unexplored. A systematic literature search was carried out to analyze the latest research (from 2020 to 2025) on the relationship between soil properties (temperature, water content, pH, and electrical conductivity), fungal communities (particularly Aspergillus and Fusarium), and different crops (mainly peanut, wheat, and maize). Measurement methodologies were analyzed, with a focus on the use of in-field soil sensors in correlation studies and predictive models. Disease incidence and mycotoxin occurrence were related to stressful soil conditions, such as different pH levels, wetness or drought, and temperatures above 25 °C. Other external variables (crop and field management) must also be considered. Laboratory equipment was primarily used in correlation studies, with limited in-field sensor implementation. Although recent predictive models included soil properties as effective inputs, they mostly relied on satellite data. However, real-time conditions and fluctuations, which can be captured by in-field soil sensors, are essential for training new functional models. To monitor soil properties, IoT technologies must be considered, but their implementation is still not sufficient to collect widespread data. Therefore, groundwork is needed to fill this gap with high-quality soil data for future in-field experimentation. Full article

Social media platforms and user-generated content (UGC) have become central to how travelers discover and evaluate cultural destinations, yet lesser-known second-tier heritage sites remain substantially underrepresented in digital tourism research. This study investigates how Chinese tourists perceive and engage with the intangible cultural heritage (ICH) of Lamphun, Thailand, through UGC collected from three major Chinese social media platforms (WeChat, Douyin, and Rednote) spanning the period from 2019 to 2023. A total of 642 relevant posts were analyzed using a mixed-methods analytical framework comprising SnowNLP-based Chinese-language sentiment analysis, rule-based tourism intention classification, and TF-IDF-driven K-means thematic clustering. Results indicate an overall predominance of positive sentiment, with sentiment score emerging as the strongest predictor of tourism intention. Thematic clustering revealed three distinct experiential dimensions, with culinary heritage and contemporary local lifestyle and cafe exploration generating the highest sentiment distribution and within-cluster tourism intention rate. These findings demonstrate the analytical value of integrated UGC data mining for underrepresented ICH destinations and offer empirical insights to support data-driven destination marketing strategies and destination management organization (DMO) decision-making for the promotion of secondary cultural heritage destinations. Full article

Background: Lychee fruits are sweet and juicy, yet mitochondrial genomic data for this species remains scarce, limiting in-depth studies of its genetic and evolutionary characteristics. To address this gap, in this study, the abortive-seeded cultivar ‘Xianjinfeng’ (XJF) and the large-seeded cultivar ‘Xinqiumili’ (XQML) were selected for analysis. Using third-generation sequencing technology, we sequenced, assembled, and annotated their mitochondrial genomes, and compared their structural characteristics and evolutionary relationships. Results: Assembly revealed mitochondrial genome sizes of 579,270 bp for XJF and 579,261 bp for XQML, both with 45.41% GC content. The mitogenomes contain 396 repetitive sequences, including 47 tandem repeats and 165 dispersed repeats, with SSR loci primarily 10–14 bp in length. Each genome encoded 62 genes, comprising 22 tRNAs, 3 rRNAs, and 35 protein-coding genes. Further analysis revealed 15 homologous sequences originating from chloroplasts in both mitochondrial genomes, totaling 12,194 bp (2.11% of the mitochondrial genome). These included 9 tRNA genes, 4 rRNA genes, and partial protein-coding sequences. Additionally, 184 simple sequence repeats (SSRs) were identified in both cultivars, whereas 564 and 563 potential RNA editing sites were predicted by computational tools in XJF and XQML, respectively, indicating subtle genetic differences between the cultivars. This study also analyzed codon usage preferences, nucleotide diversity, and chloroplast-to-mitochondria gene transfer events. Collinearity and comparative genomics results indicate that lychee is closely related to Nephelium lappaceum L. and Xanthoceras sorbifolium Bunge within the Sapindaceae family. Conclusions: In this study, two high-quality lychee mitochondrial genomes were successfully assembled and annotated, enriching the mitochondrial genome resources of Sapindaceae plants and laying a foundation for future lychee phylogenetic and evolutionary studies of closely related species. Full article

The rapid expansion of wind and solar generation has significantly increased the share of variable renewable energy in power systems worldwide, introducing new operational challenges. Among these, the simultaneous growth of renewable energy curtailment and persistent blackout risk reveals structural limitations in energy planning and system flexibility. This study conducts a Systematic Literature Review (SLR) following the PRISMA protocol to examine how the scientific literature has addressed the relationship between curtailment, energy security, and optimization strategies in high-renewable power systems. A total of 53 Q1-indexed articles published between 2021 and 2025 were analyzed using bibliometric and qualitative content analysis techniques. The results indicate that curtailment should not be interpreted solely as an operational inefficiency but rather as a potential flexibility asset when integrated with energy storage systems, power-to-X technologies, demand-side management, and stochastic optimization frameworks. The findings also highlight a shift from deterministic planning approaches toward robust and distributionally aware models capable of managing renewable uncertainty. Despite significant advances, geographic imbalances in case studies and limited integration between regulatory mechanisms and technical optimization remain key research gaps. This review contributes by synthesizing mitigation strategies into a structured flexibility framework and by outlining research directions for enhancing reliability in renewable-dominated systems. Full article

In recent years, the wine industry has searched for alternatives to reduce the use of sulfites, with ultra-high-pressure homogenization (UHPH) emerging as a promising technology. The objective of this study was to evaluate the combined effect of UHPH and non-Saccharomyces yeasts (Lachancea thermotolerans and Metschnikowia pulcherrima) on wine quality. To this end, fermentations were carried out using control Verdejo must, must treated with UHPH, and must treated with 50 mg/L SO₂, using pure cultures and co-inoculations. Enological parameters, volatile compounds, colour, redox potential, and sensory profile were analyzed. The results showed that the co-inoculation of L. thermotolerans and M. pulcherrima reduced the final ethanol content by 0.5% (v/v) and increased lactic acid production, resulting in a decrease in pH of 0.3 units; however, L. thermotolerans in monoculture failed to implant properly in UHPH-treated must. In addition, wines from UHPH-treated musts exhibited 20% lower redox potential, suggesting that treatment with UHPH was effective in inactivating oxidative enzymes, as did those fermented with M. pulcherrima. Regarding volatile compounds, UHPH wines showed a 30% reduction in higher alcohols and carbonyl compounds, resulting in a characteristic aromatic profile that was positively evaluated in sensory analysis. In conclusion, the combination of UHPH and non-Saccharomyces yeasts represents an effective strategy for improving wine quality and reducing the use of sulfites. Full article

Despite growing interest in Virtual Labs (VLs), limited research examines the factors influencing educators’ willingness to adopt them through the lens of inquiry-based learning (IBL). This exploratory pilot study evaluates educators’ interaction with the VHEalthLab VLs toolkit, examining their perceptions on usability, pedagogical value, IBL support, and intention to use. The study combines IBL, as a pedagogical lens, with the Technology Acceptance Model (TAM) within a Triple Bottom Line sustainability framework aligned with Sustainable Development Goals 4 and 10. Using an exploratory cross-sectional design with an embedded qualitative component, data were collected from seventy Biology educators across four European countries (Cyprus, Greece, Romania, Spain) through two structured questionnaires. Data analysis included descriptive statistics, Pearson correlations, directed content analysis, and joint display integration. Findings indicate that adoption intention was associated primarily with pedagogical rather than technological factors; IBL alignment showed the strongest association with intention to implement VLs (r = 0.63, p < 0.001), while perceived usefulness was most strongly associated with pedagogical materials (r = 0.65, p < 0.001). Assessment and inclusion functioned as quality criteria rather than factors associated with adoption intention. Educators consistently endorsed VLs as complements to physical laboratories, with their perceptions suggesting potential environmental, economic, and social sustainability implications within a blended model. Full article

The current research investigates the extraction efficiency of an emerging green technology, pressurized liquid extraction (PLE), compared to traditional stirring extraction (STE) in order to recover higher antioxidant capacity from three plant species of the Asteraceae family, namely Solidago virgaurea, Tussilago farfara, and Helichrysum stoechas. The optimal PLE conditions were achieved through a combined response surface methodology (RSM) approach. The resulting optimized PLE parameters (40% ethanol, 160 °C, 25 min, 1700 psi) were experimentally verified and directly contrasted with STE (40% ethanol, 80 °C, 60 min, 500 rpm). Despite having the same solvent polarity, the two methods showed significant variations in mass transfer kinetics and heat intensity. Across all species, PLE significantly boosted the ascorbic acid antioxidant capacity (p < 0.05), thereby showing enhanced recovery of compounds that contribute to the overall antioxidant capacity. STE generated noticeably increased total polyphenolic content and DPPH radical scavenging activity (p < 0.05), indicating that some phenolic subclasses might be susceptible to PLE at higher temperatures. Values for ferric-reducing antioxidant power were largely similar among approaches. Overall, PLE was shown to be highly effective in maximizing the total antioxidant capacity in shorter extraction times, while STE can better preserve specific polyphenolic fractions, as demonstrated through analysis of the optimal extracts by HPLC-DAD. The integration of experimental validation with chemometric modeling supports the reliability and practical applicability of the optimized PLE protocol. Full article

Rapid and non-destructive detection of nitrogen content in greenhouse cucumber leaves is essential for precision fertilization, yet traditional chemical methods are destructive and time-consuming, and existing spectral technologies suffer from high cost and poor field adaptability. This study aims to propose a high-precision detection scheme for cucumber leaf nitrogen content based on a lightweight model, suitable for complex scenarios. A total of 698 cucumber leaf images covering three growth stages were collected to build a segmentation dataset. Four categories and eight types of deep learning segmentation models were optimized and compared, and the optimal one was selected to extract leaf regions. Nine color features were extracted and combined with Kjeldahl-measured nitrogen content to construct and optimize three machine learning models, forming a deep learning segmentation–color feature extraction–machine learning prediction process. The results showed that YOLOv11n achieved the best segmentation accuracy, with an IoU of 0.9212 and AP of 0.9998 for high-resolution images. The optimized XGBoost had the highest prediction accuracy, with an MAE of 0.469, MSE of 0.461, and RMSE of 0.679, which are 10.15%, 8.71%, and 4.36% lower than Support Vector Regression with Radial Basis Function kernel (SVR_RBF) respectively, and its predicted nitrogen content aligned well with true values. The proposed scheme integrating YOLOv11n and XGBoost offers a lightweight technical solution for nitrogen nutrition diagnosis and precise fertilization of greenhouse cucumbers. Full article

The energy transition constitutes not only a technological and regulatory challenge but also a communicative and cultural one, in which corporate narratives play a decisive role in shaping social understanding, legitimacy, and trust. This study examines how major energy companies operating in Spain construct the narrative of the energy transition through their corporate discourse and evaluates the extent to which these narratives integrate pedagogical and relational dimensions oriented toward society. Using a qualitative content analysis approach supported by lexical frequency analysis as a heuristic tool, the study analyzes the CEO or Chair letters published in sustainability reports by four energy companies—Iberdrola, Endesa, Naturgy, and Holaluz—over a five-year period (2020–2024), comprising a total of 20 reports, from which 18 CEO/Chair letters were extracted and treated as a single analytical unit. Two reports (Iberdrola and Naturgy, 2024) adopted the ESRS/CSRD format directly, eliminating the traditional chairperson’s letter. To triangulate and contextualize the documentary analysis, a two-round Delphi study was conducted with 11 independent experts. The findings reveal a predominantly technical and self-referential discourse focused on corporate strategy, performance, and regulatory compliance, with a limited presence of explanatory or citizen-oriented narratives. Despite increasing terminological convergence driven by regulatory standardization, the analysis reveals persistent divergence in narrative framing, with the challenger company articulating purpose-driven and citizen-empowerment frames largely absent from incumbent discourse. The Delphi results reinforce these findings, emphasizing the need to strengthen pedagogical clarity, accessibility, and relational orientation in energy communication. On this basis, the study proposes a relational model of energy communication that highlights narrative mediation, social intelligibility, and stakeholder-oriented discourse as key factors for enhancing legitimacy and trust in the context of the energy transition. The analysis further identifies a structural tension between regulatory standardization and narrative capacity, exemplified by the elimination of the CEO letter in one company’s 2024 report following ESRS adoption. Full article

Background: Artificial intelligence (AI) adoption is increasingly reported as a priority for organizations, yet they face a growing, fragmented body of evidence concerning the factors that influence successful AI integration. Method: To identify the relevant factors for organizational AI adoption, we conducted a systematic literature review (SLR) following PRISMA guidelines, which yielded 37 quantitative empirical studies. From these studies we extracted 1229 paper-item instances, of which 810 were retained after applying structured exclusion criteria to develop a domain model relevant to organizational AI adoption. The model’s content validity was assessed and supported through expert feedback using the Content Validity Index (CVI) methodology. Results: We organized 24 subclusters into nine main clusters across the three dimensions Technology (Enablers, Usability, Trust), Organization (Leadership, People, Process), and Environment (Market, Regulatory, Partner). Our analysis suggests that workforce skills, perceived intelligence, and resources are among the most frequently studied and positively associated antecedents of AI adoption, and that constructs related to AI explainability and control (human-in-the-loop oversight) have received little research attention and remain underrepresented despite growing regulatory requirements such as the EU AI Act. Conclusions: The resulting domain model provides an empirically grounded classification of organizational AI adoption factors and can serve as a foundation for future measurement instruments. Full article

Bipolar vessel sealing systems are widely used in surgery, yet their effectiveness varies depending on the histological composition of the target vessel. In particular, the influence of elastin on seal stability is not well understood. Porcine carotid arteries, which show a pronounced proximal–distal elastin gradient, provide an ideal model for systematic analysis. In this study, fresh porcine carotid arteries were divided into three segments based on vessel diameter (<5 mm, 5–7 mm, >7 mm). Histological EvG staining was used to quantify elastin and collagen content. All vessels (n = 8 per group) were sealed using a bipolar marSeal^® 5 plus device, followed by burst pressure testing and peel force measurements. Elastin content increased significantly from peripheral to central segments (9% → 25% → 42%; p < 0.001), while collagen content remained constant (22 ± 2%). In parallel, seal stability decreased markedly: burst pressures dropped from 723 mmHg to 240 mmHg and to 31.5 mmHg (p < 0.001). Peel forces showed the same trend (1.75 ± 0.07 N → 0.65 ± 0.03 N → 0.26 ± 0.11 N; p < 0.001). Wall thickness showed no proportional relationship to seal quality. Interestingly, the sealing performance of bipolar systems seems to be greatly influenced by the histological structure of the vessel wall. A high elastin content—rising from 9% to 42% along the carotid artery—was associated with a reduction in burst pressure and peel strength. These findings highlight the need to consider tissue composition when selecting sealing methods and support the development of adaptive energy delivery technologies. Full article

The growing prevalence of chronic degenerative diseases has increased interest in nutritional strategies based on natural bioactive compounds such as polyphenols. This study aimed to develop a polyphenol-fortified tomato juice using extracts from unripe green tomatoes and to evaluate its physicochemical, antioxidant, sensory, and storage properties. Polyphenolic extracts obtained from tomato by-products were characterized using spectrophotometric and HPLC analyses and incorporated into tomato juice, which was then pasteurized and stored for six months. Total polyphenol content increased from 40.97 to 82.45 mg GAE/100 g, decreasing to 71.44 mg after storage; HPLC confirmed higher levels of key phenolic compounds in fortified juice. DPPH antioxidant activity increased in fortified juice compared to control, since pasteurization had limited effects but decreased after storage, with a moderate reduction in bioactivity. Colorimetric and sensory analyses showed changes in color, aroma, and sweetness after storage, potentially affecting consumer acceptance, although overall composition remained largely stable. Overall, results demonstrate the feasibility of producing a polyphenol-enriched tomato juice from agro-industrial by-products with improved antioxidant properties and acceptable technological stability. These findings support the valorization of tomato processing waste and suggest potential applications in functional food development, human health promotion, and the sustainability of agri-food systems’ overall approach. Full article

Platform algorithms are widely used to personalize content and organize users’ everyday social media experiences. Yet they may also become objects of resistance when algorithmic recommendations are perceived as intrusive, repetitive, or difficult to escape. Drawing on the critical theory of technology, this study develops a parallel mediation model to explain why users resist algorithm-driven social media platforms. Focusing on algorithmic power and algorithmic technicality as two perceived characteristics of platform algorithms, the model examines whether these perceptions are associated with algorithmic resistance through fairness evaluations and negative emotions. Based on survey data from users of Chinese algorithm-driven social media platforms, the results show that both algorithmic power and algorithmic technicality are associated with stronger algorithmic resistance through lower fairness evaluations and stronger negative emotions. These findings suggest that algorithmic resistance is not merely a response to inaccurate or opaque recommendations, but also reflects users’ reactions to algorithms experienced as systems of platform control and data-driven inference. By identifying fairness evaluations and negative emotions as parallel cognitive and affective pathways, this study shifts attention from algorithmic acceptance to algorithmic resistance and provides a more critical understanding of user agency in human–algorithm relations. Full article