Search Results (382)

The enzymatic browning and oxidative deterioration of cauliflower during mechanical processing are major challenges for the fresh-cut cauliflower industry. The primary objective of this study was to investigate the impact of cutting styles on the antioxidant capacity of fresh-cut cauliflower during storage. One flower (12 × 1.8 cm) of cauliflower was designated as cutting style 1 (CS1). CS1 was cut longitudinally into strips as cutting style 2 (CS2). CS1 was also cut transversely into cubes, as cutting style 3 (CS3), and longitudinally and transversely into small cubes, as cutting style 4 (CS4). Results indicated that at the conclusion of the 72 h storage period, cutting treatments enhanced the total antioxidant capacity of fresh-cut cauliflower in the ABTS assay by 128.1%, 82.9%, 50.1%, and 38.9% for CS1, CS2, CS3, and CS4, respectively. All treatment groups except CS1 exhibited increased total antioxidant capacity in the FRAP assay. Phenolic compound accumulation increased by 106.82%, 105.24%, 270.4%, and 295.3% in CS1, CS2, CS3, and CS4, respectively. In addition, the O₂⁻· scavenging activity was enhanced; the activities of antioxidant-related enzymes, including catalase (CAT) and superoxide dismutase (SOD), were also increased. In conclusion, the extent of the effect on antioxidant capacity was as follows: CS4 > CS3 > CS2 > CS1. This study has elucidated the patterns of influence exerted by cutting methods upon the quality of fresh-cut cauliflower, thereby providing theoretical foundations and empirical data to inform the selection of appropriate cutting techniques for both commercial processing and domestic culinary applications. Full article

In recent years, large multimodal models (LMMs), such as ChatGPT 4o and DeepSeek R1—artificial intelligence systems capable of multimodal (e.g., image and text) human–computer interaction—have gained traction in industrial and civil engineering applications. Concurrently, insufficient real-world drone-view data (specifically close-distance, high-resolution imagery) for civil engineering scenarios has heightened the importance of artificially generated content (AIGC) or synthetic data as supplementary inputs. AIGC is typically produced via text-to-image generative models (e.g., Stable Diffusion, DALL-E) guided by user-defined prompts. This study leverages LMMs to interpret key parameters for drone-based image generation (e.g., color, texture, scene composition, photographic style) and applies prompt engineering to systematize these parameters. The resulting LMM-generated prompts were used to synthesize training data for a You Only Look Once version 8 segmentation model (YOLOv8-seg). To address the need for detailed crack-distribution mapping in low-altitude drone-based monitoring, the trained YOLOv8-seg model was evaluated on close-distance crack benchmark datasets. The experimental results confirm that LMM-prompted AIGC is a viable supplement for low-altitude drone crack monitoring, achieving >80% classification accuracy (images with/without cracks) at a confidence threshold of 0.5. Full article

Leaders play a crucial role in shaping employees’ safety behaviors (SBs). However, research on broader leadership styles has yielded inconsistent findings, emphasizing the need for a more tailored leadership approach, especially in high-risk industries, such as construction. Applying the social exchange theory and the positive organizational behavior framework, this study examined the impact of safety-specific transformational leadership (SSTL) on SB. This study uses a quantitative research design to collect data from employees of Turkish construction firms in Ankara and Istanbul. A cross-sectional research design was employed, with purposive sampling of data collected from 706 construction workers in Türkiye. The findings indicate that SSTL positively influences both SB and knowledge sharing, whereas knowledge sharing enhances SB. Knowledge sharing mediates the relationship between SSTL and SB. This study’s findings suggest that implementing safety-specific transformational leadership (SSTL) can significantly improve safety behavior among construction workers by promoting knowledge sharing and psychological safety. Full article

Despite China’s success in its new energy vehicle (NEV) transition, significant regional imbalances persist, raising the question of why provincial policy effectiveness is so context-dependent. To investigate this, this study develops a novel framework to measure policy “quality” and “style”, systematically quantifying 2455 provincial policy documents from 2013 to 2023. Our empirical analysis reveals that policy quality—encompassing its authoritativeness, instrument strength, and resource commitment—is a far more decisive determinant of effectiveness than sheer policy quantity. We identify three primary policy styles with distinct impacts: substantive-driving policies are crucial for stimulating market demand, whereas coordinative-programmatic policies are more effective in guiding industrial supply, revealing a significant goal-mismatch. Conversely, high-level authoritative policies can unexpectedly inhibit infrastructure development. Crucially, the study finds that provincial policies act more as “catalysts” than “creators”, their effectiveness being highly contingent on local economic, fiscal, and industrial fundamentals. The findings of this research offer direct implications for policymaking: decision-makers should shift their focus from pursuing policy quantity to enhancing policy quality and design targeted, “precision-irrigation” policy instrument portfolios tailored to the specific contexts and development objectives (e.g., promoting sales or guiding production) of different regions. Full article

Tree peony (Paeonia ostii) is widely cultivated in China as a traditional medicine and a new high-quality woody oil crop. Enhancing seed yield has become a primary breeding objective in the industrial development of oil tree peonies. Pollination and successful fertilization are essential for optimal seed yield. However, the molecular mechanisms underlying pollination and fertilization in P. ostii remain unclear. In this study, comparative transcriptomic and genetic analyses were conducted to investigate the pistils under different pollination periods of P. ostii ‘Fengdanbai’. Compared with pre-pollination, differentially expressed genes (DEGs) were screened from pistils 48 h after pollination, when most of the pollen tubes had reached the bottom of the style. Functional annotation indicated that these DEGs were involved in hormone signaling and carbohydrate metabolism pathways. Transcription factors and receptor-like kinases play a key role in pollen development, pollen tube growth, and carpel development. Key DEGs (PoUNE10 and PoLIM1) influenced pollination and fertilization and were characterized. Phylogenetic, promoter, and co-expression analyses suggest that they may affect plant pollination, fertilization, and seed yield through pathways such as hormone signaling and photosynthesis in P. ostii ‘Fengdanbai’. Our findings illustrate the molecular changes after pollination and fertilization in P. ostii ‘Fengdanbai’ and provide the molecular characterization of two key genes. These results provide insights into the molecular mechanisms underlying pollination and fertilization in tree peony and suggest potential candidate genes for molecular breeding aimed at improving seed yield in the species. Full article

Driven by the new wave of technological revolution and industrial transformation, firms are accelerating strategic change to gain new competitive advantages. Situated within a complex adaptive system, firms must adapt to highly dynamic and uncertain external environments by adjusting executive cognitive structures, reconfiguring resources and capabilities, and strengthening collaboration with industrial ecosystem elements; hence, digital strategic change is characterized by continuous evolution. Using a sample of Chinese A-share listed firms from 2015 to 2023, this study develops a “cognition–capability–strategy” pathway model grounded in upper echelons theory and dynamic capabilities theory to examine how executive cognitive styles, i.e., cognitive flexibility and cognitive complexity, drive digital strategic change via absorptive capacity and how environmental dynamism moderates these relationships. The findings show that executive cognition, as a decision node in strategic change, can dynamically adjust firms’ strategic paths by activating absorptive capacity in rapidly changing external information environments; environmental dynamism differentially affects the two cognitive styles. Heterogeneity tests further indicate that the role of executive cognition varies significantly with regional digital economy development levels, firm life cycle, and industry factor intensities. The study reveals how firms can respond to high environmental uncertainty through cognition–strategy alignment and resource capability reconfiguration in a complex adaptive system, providing theoretical references and practical insights for emerging economies to advance digital transformation and enhance competitiveness. Full article

Island settlement landscapes exhibit distinctive characteristics, and investigating their spatio–temporal differentiation features and formation mechanisms is crucial for effective landscape conservation. This study selected Qida Village, Beigang Village, and Jingsha Village in Fuzhou City, Fujian Province, China, as representative cases. It constructed an integrated evaluation framework termed “livelihood transformation–two dimensional expansion–three dimensional form” and systematically analyzed the spatio–temporal differentiation characteristics and driving mechanisms of island settlement landscapes under the context of livelihood transformation by integrating multi-source data. Research findings indicate that livelihood transformation significantly affects both the horizontal expansion and vertical evolution of settlement landscapes. Aquaculture-based villages demonstrate a high expansion rate (15.10%) and pronounced vertical differentiation (building height difference ratio of 13.30) due to industrial agglomeration. Tourism service-oriented villages, influenced by policy regulation, exhibit low architectural style heterogeneity (0.35) and a harmonized skyline. Villages experiencing significant out-migration show a high housing vacancy rate (64.70%) and reduced spatial compactness (0.13) due to population decline. The livelihood model drives landscape differentiation through the “population mobility–economic investment–land use” pathway, where capital accumulation and policy constraints emerge as key determinants of spatial form heterogeneity. This study provides a solid theoretical foundation and methodological support for the differentiated governance of island settlement landscapes. Full article

Vaccines play a crucial role in the prevention and control of chicken coccidiosis, effectively reducing economic losses in the poultry industry and significantly improving animal welfare. To ensure the production quality and immune effect of vaccines, accurate detection of chicken Coccidia oocysts in vaccine is essential. However, this task remains challenging due to the minute size of oocysts, variable spatial orientation, and morphological similarity among species. Therefore, we propose YOLO-Cocci, a chicken coccidia detection model based on YOLOv8n, designed to improve the detection accuracy of chicken coccidia oocysts in vaccine environments. Firstly, an efficient multi-scale attention (EMA) module was added to the backbone to enhance feature extraction and enable more precise focus on oocyst regions. Secondly, we developed the inception-style multi-scale fusion pyramid network (IMFPN) as an efficient neck. By integrating richer low-level features and applying convolutional kernels of varying sizes, IMFPN effectively preserves the features of small objects and enhances feature representation, thereby improving detection accuracy. Finally, we designed a lightweight feature-reconstructed and partially decoupled detection head (LFPD-Head), which enhances detection accuracy while reducing both model parameters and computational cost. The experimental results show that YOLO-Cocci achieves an mAP@0.5 of 89.6%, an increase of 6.5% over the baseline model, while reducing the number of parameters and computation by 14% and 12%, respectively. Notably, in the detection of Eimeria necatrix, mAP@0.5 increased by 14%. In order to verify the application effect of the improved detection algorithm, we developed client software that can realize automatic detection and visualize the detection results. This study will help improve the level of automated assessment of vaccine quality and thus promote the improvement of animal welfare. Full article

Miao embroidery holds significant cultural, economic, and aesthetic value. However, its transmission faces numerous challenges: a high learning threshold, a lack of interest among younger generations, and low production efficiency. These factors have created obstacles to its sustainable development. In the age of artificial intelligence (AI), generative AI is expected to improve the efficiency of pattern innovation and the adaptability of the embroidery industry. Therefore, this study proposes a Miao embroidery pattern generation and application method based on Stable Diffusion and low-rank adaptation (LoRA) fine-tuning. The process includes image preprocessing, data labeling, model training, pattern generation, and embroidery production. Combining objective indicators with subjective expert review, supplemented by feedback from local artisans, we systematically evaluated five representative Miao embroidery styles, focusing on generation quality and their social and business impact. The results demonstrate that the proposed model outperforms the original diffusion model in terms of pattern quality and style consistency, with optimal results obtained under a LoRA scale of 0.8–1.2 and diffusion steps of 20–40. Generated patterns were parameterized and successfully implemented in digital embroidery. This method uses AI technology to lower the skill threshold for embroidery training. Combined with digital embroidery machines, it reduces production costs, significantly improving productivity and increasing the income of embroiderers. This promotes broader participation in embroidery practice and supports the sustainable inheritance of Miao embroidery. It also provides a replicable technical path for the intelligent generation and sustainable design of intangible cultural heritage (ICH). Full article

Workplace health and safety issues have long plagued the construction industry. While safety efforts have traditionally focused on physical risks, increasing attention is being paid to mental health and work-related stressors, which can negatively affect both productivity and safety. In Saudi Arabia, the construction sector presents a unique context because of its highly diverse, multinational workforce. Workers of different nationalities often operate on the same job site, leading to potential communication barriers, cultural misunderstandings, and inconsistent safety practices, all of which may amplify stress and safety risks. This research aims to investigate the influence of work-related stressors on construction workers’ safety in Saudi Arabia and identify which stressors most significantly contribute to the risk of injury. A structured questionnaire was distributed to 349 construction workers across 16 job sites in Saudi Arabia. The survey measures ten key stressors identified in the literature, including job site demand, job control, job certainty, skill demand, social support, harassment and discrimination, conflict with supervisors, interpersonal conflict, and job satisfaction. Data were analyzed using logistic regression and Pearson correlation to examine relationships between stressors and self-reported injuries. The findings indicated that work-related stressors significantly predict workplace injury. While the first regression model showed a modest effect size, it was statistically significant. The second model identified job site demand and job satisfaction as the most influential predictors of injury risk. Work-related stressors, particularly high job demands and low job satisfaction, substantially increase the likelihood of injury among construction workers. These findings emphasize the importance of incorporating psychosocial risk management into construction safety practices in Saudi Arabia. Future studies should adopt longitudinal designs to explore causal relationships over time and include qualitative methods such as interviews to gain a deeper understanding. Additionally, factors such as nationality, organizational policies, and management style should be investigated to better understand their moderating effects on the stress–injury relationship. Full article

Rotating electrical machine maintenance is a core component of engineering education curricula worldwide. Within this context, vibration monitoring represents a widespread methodology for electrical rotating machinery monitoring. However, the multi-physical nature of vibration monitoring presents a complex learning scenario, including concepts from both mechanical and electrical engineering domains. This article proposes a novel knowledge-based educational experience design leveraging an integrated FEA-assisted test bench aimed at comprehensively addressing the electromechanical link between stator current and frame vibration. To this aim, a Finite Element Analysis (FEA) model is utilized to link excitation electrical signals with airgap radial forces acting in the stator. The subsequent correlation of these FEA predictions with measured frame vibrations on a physical test bench provides students with the theoretical concepts and practical tools to adequately comprehend this complex multi-physical phenomenon of wide application in real industrial scenarios. The pedagogical potential of the method also includes the development of critical thinking and problem-solving soft skills, and foundational understanding for digital twin concepts. A Delphi-style expert survey conducted with 25 specialists yielded strong support for the pedagogical robustness and relevance of the method, with mean ratings between 4.32 and 4.64 out of 5 across key dimensions. These results confirm the potential to enhance deep understanding and practical skills in vibration-based electrical machine diagnosis. Full article

Pine wood has a natural, rustic, and environmentally friendly style and is used in a large number of applications in the furniture industry. However, its soft and porous texture makes it susceptible to bacteria, mould, and other micro-organisms. Pine wood was selected as the test substrate, and tea tree essential oil@chitosan (TTO@CS) microcapsules with emulsifier concentrations of 4%, 5%, and 6% were added to the waterborne topcoat at a content of 1%–9% (in 2% intervals) to investigate their effect on the surface coating properties of pine wood. With the increase in microcapsule content, there was an overall increase in colour difference and light loss rate of pine wood surface coating, and the reflectance showed an increase and then decrease. The overall performance of the pine wood surface coatings containing 7% of 13# microcapsules was found to be excellent: the antimicrobial activity of the coatings was 62.58% for Escherichia coli and 61.29% for Staphylococcus aureus after 48 h, and the antimicrobial activity of the coatings was 40.14% for Escherichia coli and 38.89% for Staphylococcus aureus after 4 months. The colour difference in the coating was 2.37, and the light loss was 63.71%. The reflectance value was found to be 0.6860, while the hardness was determined to be 2H and the adhesion class was categorised as one. The impact resistance class was determined to be three, while the roughness was measured at 1.320 μm. The waterborne coating on the surface of pine wood was modified by microencapsulation technology with the objective of enhancing the antimicrobial properties of pine wood and expanding its scope of application. Full article

The rapid growth of live e-commerce, a sector valued at over USD 100 billion worldwide, demonstrates its transformative impact on the retail industry, especially in markets like China, where platforms such as Taobao Live and TikTok Shop have markedly altered consumer interaction. This transition is further expedited by Netflix-like entertainment marketing methods, which have demonstrated the capacity to enhance consumer retention by as much as 40%. As organizations adjust to this evolving landscape, it is essential to optimize supply chain strategies to align with these dynamic, consumer-centric environments. This paper examines the complexity of decision-making in live e-commerce supply chains, specifically regarding Netflix-inspired marketing strategies. The primary aim of this study is to design a game-theoretic framework that examines the interactions between producers and online celebrity retailers (OCRs) across different power dynamics. As live commerce integrates digital retail with immersive experiences, businesses must optimize pricing, quality, and marketing strategies in real-time. We present engagement-driven marketing as a strategic variable and incorporate consumer regret and switching costs into the demand function. To illustrate practical trade-offs in strategy, we incorporate a multi-criteria decision-making (MCDM) layer with AHP-TOPSIS, assessing profit, consumer surplus, engagement score, and channel efficiency. The experiment results indicate that Netflix-style marketing markedly increases demand and profit in retailer-led frameworks, whereas centralized tactics enhance overall channel performance. TOPSIS analysis prioritizes high-effort, high-engagement methods, whereas the Stackelberg experiment underscores the influence of power dynamics on profit distribution. This study presents an innovative integrative decision-making methodology for enhancing live-streaming commerce tactics in data-driven and consumer-focused markets. Full article

In the context of Industry 4.0, user demands for industrial robots have shifted toward diversification and experience-orientation. Effectively integrating users’ affective imagery requirements into industrial-robot form design remains a critical challenge. Traditional methods rely heavily on designers’ subjective judgments and lack objective data on user cognition. To address these limitations, this study develops a comprehensive methodology grounded in Kansei engineering that combines Extenics-based semantic analysis, eye-tracking experiments, and user imagery evaluation. First, we used web crawlers to harvest user-generated descriptors for industrial floor-cleaning robots and applied Extenics theory to quantify and filter key perceptual imagery features. Second, eye-tracking experiments captured users’ visual-attention patterns during robot observation, allowing us to identify pivotal design elements and assemble a sample repository. Finally, the semantic differential method collected users’ evaluations of these design elements, and correlation analysis mapped emotional needs onto stylistic features. Our findings reveal strong positive correlations between four core imagery preferences—“dignified,” “technological,” “agile,” and “minimalist”—and their corresponding styling elements. By integrating qualitative semantic data with quantitative eye-tracking metrics, this research provides a scientific foundation and novel insights for emotion-driven design in industrial floor-cleaning robots. Full article

Educational institutions are facing a crisis characterized by the need to address diverse learning styles and vocational aspirations, exacerbated by ongoing financial pressures. To navigate these challenges effectively, there is an urgent need to innovate educational practices and learning environments, ensuring they are adaptable and responsive to the evolving needs of students and the workforce. The adoption of the Industry 5.0 framework offers a promising solution, providing a holistic approach that emphasizes the integration of human creativity and advanced technologies to transform educational institutions into resilient, human-centric, and sustainable learning environments. In this context, this article presents a transdisciplinary methodology that integrates Asset Management (AM) with Social Innovation (SI) through Design Thinking (DT) to co-design Educational Facilities 5.0 with stakeholders. The application of the proposed approach in an AgroLab case study—a food and agricultural laboratory—demonstrates how the methodology enables the definition of an Educational Facility 5.0 and generates AM Design Knowledge to support informed decision-making in the subsequent design, implementation, and operation phases. Following DT principles—where knowledge emerges through iterative experimentation and insights from practical applications—this article also discusses the role of SI and DT in AM, the role of Large Language Models in convergent processes, and a vision for Educational Facilities 5.0. Full article