Search Results (6,801)

Efficient permanent magnets that are concomitantly economically viable are of paramount importance for allowing industrial stakeholders to maintain a growing and competitive advantage. This study provides a comprehensive overview of recent developments in the field of rare-earth-free nanocomposite permanent magnets based on hexaferrites. The basic phenomenology of exchange-spring-coupled nanocomposites, comprising hard and soft magnetic components, is thoroughly explained. The use of hexaferrites as a hard phase, serving as a viable alternative to rare-earth-based permanent magnets, is extensively discussed, taking economical, accessibility-related, and environmental aspects into consideration. State-of-the-Art architectures of hard–soft magnetic nanocomposites based on hexaferrites as the hard magnetic phase, ranging from typical nanocomposites to nanowire arrays and special core–shell-like morphologies, are explored in detail. The maximum energy product (BH)_max, representing the quality indicator for permanent magnets, is investigated by taking into consideration various degrees of freedom, such as substitutions, geometry, size, shape, preparation, and processing conditions (annealing), volume fraction of magnetic phases, and interfaces. Promising strategies to overcome the present challenges (e.g., size control, coercivity–remanence trade-off, and optimization for large-scale production) are provided within the framework of future permanent magnet design. Full article

Smart Tourism Destinations (DTIs) represent innovative places established on the basis of an infrastructure of advanced technology, whose objective is to promote accessible, sustainable development for tourist regions to enrich tourist’s experiences and inhabitants’ quality of life. Therefore, the main objective of this research is to verify whether the phenomenon indicators of existing smart tourist destinations fit within all dimensions of a smart tourist destination. For data collection, the bibliographic survey method was used in renowned databases. Data analysis comprises two approaches, bibliometric and content analysis, applied sequentially. The results of the bibliometric analysis identify three main pillars in the current perspectives on DTIs: Management and Tourism Experience, Technology, and Sustainability, in addition, it revealed a duality between approaches from the Far East and Western Europe. The content analysis culminates in the proposition of two theoretical-conceptual models for the dimensions and indicators of DTIs, encompassing elements: Information and Communication Technologies (TICs), Governance, Innovation, Accessibility, Performance, Mobility and Infrastructure, as well as Segmented Sustainability in its economic, social, and environmental dimensions. However, the analysis highlights the lack of consensus among studies, especially DTI indicators. The results of the research highlight the insufficiency of these indicators for a comprehensive assessment, notably in the environmental dimension of sustainability. Full article

Sleep disorders represent a growing global health concern with significant socio-economic impacts. GABA, a natural bioactive compound abundant in various fermented foods, especially probiotic-fermented foods, has garnered increasing attention for its potential to improve sleep quality. This review systematically elucidates the multi-pathway mechanisms by which GABA regulates sleep, focusing on (1) indirect modulation of central sleep–wake circuits via the gut–brain axis through vagal nerve, neuroendocrine, and immune pathways; (2) potential entry into the brain by leveraging the dynamic permeability of the blood–brain barrier (BBB) and transporter-mediated active transport; and (3) metabolic conversion into active substances like γ-hydroxybutyrate (GHB), which synergistically optimizes sleep architecture via multiple receptor systems and energy metabolism. Furthermore, we summarize the sleep-promoting effects of GABA-enriched foods observed in animal and clinical studies and discuss emerging applications, including high-GABA-yielding probiotics and personalized nutrition strategies for sleep intervention. This review provides a theoretical basis and innovative directions for the development of GABA-based functional foods and sleep health management. Full article

Sustainable development in rural communities faces significant challenges, such as inadequate infrastructure, poor resource management, and weak governance, especially in developing countries like Peru. This study aimed to develop a management model tailored to the local needs of smallholder communities in the Junín region, Peru, addressing social, economic, and environmental dimensions to improve quality of life. Using a descriptive mixed-method design with non-experimental and cross-sectional methods, 60 smallholder communities were evaluated based on criteria of access to information and relevance to sustainable development. Data collected through structured surveys and semi-structured interviews revealed a lack of inclusive participation, insufficient economic income, lack of financial transparency, and inadequate environmental practices. The proposed management model integrates strategies to improve community governance, foster inclusive participation, promote sustainable economic practices, and conserve the environment. It concludes that a comprehensive, flexible, and locally adapted approach, emphasizing transparency and community participation, is essential to achieving long-term sustainability in smallholder communities. Full article

As a driver of growth for the urban economy, the night-time economy plays an irreplaceable role in promoting the high-quality development of cities. However, research on the night-time economy within the context of cultural and tourism integration remains insufficient, particularly regarding its industrial and spatial characteristics and influencing factors. This study used a spatial analysis method to explore the spatial differentiation characteristics of the night-time economy, and Geodetector to explore the influencing factors of its spatial differentiation in the main urban area of Zunyi City. The results indicate that (1) night-time economic formats exhibit an overall central agglomeration pattern; (2) various formats generally show a spatial trend of “central concentration–peripheral dispersion”; (3) among the three administrative urban districts of Zunyi, Bozhou District and Huichuan District exhibit notably higher agglomeration levels of night-time economic activities, while Honghuagang District presents a relatively lower level of such agglomeration; and (4) economic, social, environmental, and transportation factors collectively shape the spatial heterogeneity of the night-time economy across the three districts, with GDP, residential density, and transportation accessibility standing out as the most influential determinants. The results are intended not only to facilitate the development of Zunyi City’s night-time economy and the prosperity of its tourism sector from the perspective of the integration of culture and tourism, but also to provide an empirical basis for the night-time economy development of this renowned historical and cultural city. Full article

Background: Obesity is a multifactorial disease with significant physical, psychological, and economic impacts on individuals and society. Workers are particularly vulnerable, as obesity is associated with reduced productivity, absenteeism, and premature mortality. Lifestyle interventions combining dietary, physical activity, and behavioural strategies have been investigated as therapeutic approaches in this population. Objective: We aimed to conduct an integrative review assessing the effectiveness of workplace-based obesity treatment models involving dietary interventions, physical activity, and behavioural change. Methods: A search was conducted in PubMed for studies published between 2006 and 2024, with no language restrictions. Eligible studies included experimental or quasi-experimental longitudinal designs involving adult workers. After screening 95 articles, 18 were evaluated in full, and 8 met all inclusion criteria. Data extraction covered study design, intervention type, comparators, outcomes, and methodological quality, assessed using the Newcastle–Ottawa Scale. Results: Half of the included studies reported no significant reduction in body mass index after 6 or 12 months, while the others showed only modest decreases. Nevertheless, all interventions demonstrated improvements in dietary habits (reduced sugar-sweetened beverage intake, increased fruit, vegetable, and fibre consumption), physical activity (increased walking, reduced sedentary behaviour), and behavioural domains (adherence to healthy routines, self-monitoring, and family or employer support). Conclusions: Lifestyle-based workplace interventions for obesity show limited long-term effectiveness in weight reduction but promote healthier lifestyle habits, cardiometabolic health, and more supportive work environments. Future research should include diverse socioeconomic settings, particularly in developing countries, and apply robust designs, longer follow-ups, and innovative strategies to enhance adherence and outcomes. Full article

Vocational training can lead to higher employment rates and improved incomes, particularly for young women in low-resource settings like Kampala’s slums. Despite these benefits, further research is needed to understand the full impact and mechanisms of vocational training on youth in low-resource environments. In 2022, a focus group project, part of a larger study, involved 60 women aged 18 to 24, recruited from three Youth Support Centers operated by the Uganda Youth Development Link (UYDEL) in Kampala. Six focus groups (about 10 women in each group) were held to explore urban stress and how vocational training might mitigate social and environmental stressors and improve mental health. Data analysis conducted using NVivo software identified five key themes: economic benefits, skill development, building confidence and self-esteem, improved social and behavioral well-being, and enhanced lifestyle and quality of life. This formative research underscores that vocational training benefits young women, highlighting outcomes such as job acquisition, financial empowerment, and skill development. Additionally, self-esteem and confidence development emphasize the training’s role in fostering mental health and agency and addressing gender inequality. These findings underscore the value of vocational training in enhancing the mental health and overall well-being of young women and suggest areas for future research for how to best optimize and scale these programs in low-resource settings. Full article

Tomato is a globally significant horticultural crop with substantial economic and nutritional value. High-precision phenotypic analysis of tomato fruit characteristics, enabled by computer vision and image-based phenotyping technologies, is essential for varietal selection and automated quality evaluation. An intelligent detection framework for phenomics analysis of tomato fruits was developed in this study, which combines image processing techniques with deep learning algorithms to automate the extraction and quantitative analysis of 12 phenotypic traits, including fruit morphology, structure, color and so on. First, a dataset of tomato fruit section images was developed using a depth camera. Second, the SegFormer model was improved by incorporating the MLLA linear attention mechanism, and a lightweight SegFormer-MLLA model for tomato fruit phenotype segmentation was proposed. Accurate segmentation of tomato fruit stem scars and locular structures was achieved, with significantly reduced computational cost by the proposed model. Finally, a Hybrid Depth Regression Model was designed to optimize the estimation of optimal depth. By fusing RGB and depth information, the framework enabled efficient detection of key phenotypic traits, including fruit longitudinal diameter, transverse diameter, mesocarp thickness, and depth and width of stem scar. Experimental results demonstrated a high correlation between the phenotypic parameters detected by the proposed model and the manually measured values, effectively validating the accuracy and feasibility of the model. Hence, we developed an equipment automatically phenotyping tomato fruits and the corresponding software system, providing reliable data support for precision tomato breeding and intelligent cultivation, as well as a reference methodology for phenotyping other fruit crops. Full article

Bovine parainfluenza virus type 3 (BPIV3) is a significant pathogen implicated in bovine respiratory disease complex (BRDC), leading to lung tissue destruction, immunosuppression, and subsequent bacterial infections in cattle, hence incurring considerable economic losses globally. Notwithstanding its importance, a limited number of commercial vaccinations are presently accessible. The fusion (F) protein and hemagglutinin-neuraminidase (HN) protein, as protective antigens of the Paramyxoviridae family, can elicit neutralizing antibodies and are regarded as optimal candidates for the creation of genetically modified vaccines. A multi-epitope-based peptide vaccine (MEBPV) was developed by immunoinformatics methodologies by choosing epitopes from the F and HN proteins characterized by high antigenicity, moderate toxicity, and limited allergenic potential. The epitopes were combined with suitable linkers and adjuvants to produce the vaccine, whose physicochemical qualities, immunological attributes, solubility, and structural stability were improved and evaluated using computational methods. Molecular docking and molecular dynamics simulations demonstrated the strong potential binding affinity and stability of the vaccination with TLR2, TLR3, and especially TLR4 receptors. Immune simulations forecasted strong humoral and cellular responses, accompanied by a significant elevation in interferon-γ (IFN-γ) production. The vaccine sequence was later cloned into the pET-28a (+) vector for possible expression in Escherichia coli. Despite in silico predictions suggesting a favorable immunogenic potential, additional in vitro and in vivo studies are necessary to confirm its protective efficacy and safety. This research establishes a solid foundation for the creation of safe and efficacious subunit vaccines targeting BPIV3 and presents novel perspectives for the formulation of vaccinations against additional viral infections. Full article

An in-depth and comprehensive evaluation of carbon emission efficiency (CEE) is essential for promoting high-quality development and achieving the “dual-carbon” goals. This study applies a super-efficiency slacks-based measure (Super-SBM) model with carbon emissions treated as an undesirable output to measure provincial CEE and the Malmquist–Luenberger (ML) index across 30 provinces and major comprehensive economic zones in China from 2010 to 2023. Efficiency trends for 2024–2025 are projected using a hybrid Autoregressive Integrated Moving Average (ARIMA)–Long Short-Term Memory (LSTM) approach. Furthermore, CEE patterns are examined at both national and regional levels, and the relationships between CEE and potential drivers are analyzed using Tobit regressions. Combining the regression outcomes with short-term forecasts, this study provides a forward-looking perspective on the evolution of CEE and its associated factors. The results indicate that (1) China’s CEE demonstrates a generally fluctuating upward trajectory, with the southern coastal and eastern coastal regions maintaining the highest efficiency levels, while other regions remain relatively lower. (2) The temporal changes in CEE across economic zones correspond to variations in technical efficiency and technological progress, with the latter contributing more prominently to overall improvement. (3) CEE shows significant associations with multiple factors: population density, economic development, technological advancement, government intervention, and environmental regulation are positively associated with efficiency, whereas urbanization tends to correlate negatively. Based on these findings, policy implications are discussed to promote differentiated pathways for enhancing CEE across China’s regions. Full article

Plant diseases have a large impact on agricultural production, leading to crop yield reduction and causing economic losses. For the development of intelligent agriculture, it is very important to identify crop diseases accurately. With the help of image recognition methods, precise prevention and control of diseases can be achieved, which significantly reduces the use of pesticides and ultimately improves crop yield and quality. Therefore, this study proposes a theoretical method that combines Attention-Guided PCA (AG-PCA) dimensionality reduction with a spatial attention mechanism. Our method is verified on the ResNet model. The AG-PCA module dynamically selects principal component features based on attention weights, which greatly preserves key disease features during dimensionality reduction. At the same time, a spatial attention mechanism is embedded in the residual blocks to enhance the representation ability of disease regions and suppress background interference. On the AppleLeaf9 dataset containing 10,211 images of 9 disease categories, the model achieved an accuracy of 93.69%, significantly outperforming the baseline methods. Experimental results indicate that it performs stably in complex backgrounds and fine-grained classification tasks, and demonstrates strong generalization ability, showing promising application potential. Full article

Moss in container seedling nurseries competes with seedlings for water and nutrients while blocking light, thereby inhibiting growth. This study aimed to address this issue by evaluating the moss control efficacy of 11 chemical compounds, including terpinyl acetate (TA), limonene, and Hinoki essential oil (HEO). The plate experiment results led to the selection of 6 substances (TA, limonene, HEO, pine leaf extract, baking soda, pelargonic acid) that stably controlled both Polytrichum commune Hedw. and Marchantia. Polymorpha L. When TA, limonene, and HEO were combined with surfactants, moss control rates increased and showed stable performance. In the container seedling experiment, TA, limonene, and HEO demonstrated high moss control effects while exhibiting low growth inhibition. When these three substances were combined with surfactants, the electrolyte leakage index (ELI) decreased, indicating minimal cell membrane damage. Additionally, TA treatment maintained stable soil physicochemical properties with no significant changes in pH or nutrient levels. Microscopic analysis of moss cells showed cell wall deformation and expansion of intercellular spaces in the three substance treatment groups. Future verification of long-term effectiveness, expansion of application targets, and assessment of economic feasibility could lead to the development of eco-friendly moss removal agents for improving container seedling quality. Full article

Natural threats are becoming increasingly frequent and difficult to anticipate, urging public authorities and stakeholders to adopt sustainable methodologies and tools capable of continuously supplying historical and real-time data on hazards and their impacts. Such tools enable the prompt activation of recovery actions, enhance the resilience of citizens and the built environment, and contribute to the achievement of the Sustainable Development Goals (SDGs). This paper presents an interoperable and multipurpose framework developed within the MULTICLIMACT project (GA n. 101123538), designed to enhance urban smartness and sustainability, and to support and improve resilience in municipal decision-making. The framework integrates heterogeneous data sources into a unified environment, covering infrastructures, buildings, and social systems. It also includes physiological monitoring, which collects physiological parameters from wearable sensors in a privacy-preserving way, and microclimate monitoring, which records indoor air quality in inhabited environments. Simulation-based analyses are applied to capture cascading effects of disruptions, while multidimensional indicators (societal, economic, operational, and health-related) are used to quantify resilience. The approach was implemented in the Italian municipality of Camerino, where hazard monitoring systems, impact assessment tools, and indoor comfort data were integrated and validated in the SCP-MULTICLIMACT platform. The proposed approach offers a replicable model for integrating environmental and health data in support of climate resilience and sustainable urban development. Full article

It is estimated that around 1.3 billion people, roughly 16% of the global population, live with some form of disability, which can be physical, auditory, visual, intellectual, or psychosocial (mental). To help this group overcome daily functional limitations and improve their ability to perform activities independently, Assistive Technologies (AT) are used. However, understanding the complex effects of these technologies on users’ lives poses challenges in measurement. This research aims to identify and systematise the impacts caused by AT within society, analysing the relationships among these impacts to offer a comprehensive understanding of their scope. A Systematic Literature Review (SLR) was carried out following the PRISMA protocol, supplemented by association rule analysis using the Apriori algorithm with Weka software. Metrics such as Support, Confidence, and Lift were used to evaluate the associations identified by the algorithm. This analysis revealed fourteen distinct types of impacts, categorised into three groups: User Quality of Life, Social and Psychosocial, and Work Environment and Productivity. The findings demonstrated consistent associations, including Autonomy → Independence, Socioeconomic Status → Social Impact, and Education → Social Impact, indicating interconnected effects of assistive devices across functional, educational, emotional, social, economic, and productivity areas. This study supports the Sustainable Development Goals by promoting the development of AT standardisation tools, guiding more inclusive public policies, and encouraging collaborative networks among stakeholders involved in AT research and development. Full article

Digital finance, representing the deep integration of finance and technology, has become a critical enabler of sustainable industrial transformation. Focusing on resource-based enterprises (RBEs)—key actors in transitioning towards sustainable practices—this study investigates how digital finance development fosters new quality productive forces (NQPFs), a core driver of high-quality, sustainable development. Utilizing panel data from Chinese A-share listed RBEs (2008–2022), we measure NQPF using the entropy method and gauge regional digital finance development with the Peking University Digital Financial Inclusion Index (DFII). Empirical analysis employing two-way fixed effects and panel threshold regression models provides robust evidence that digital finance significantly enhances NQPFs within RBEs. Crucially, mechanism analysis identifies three fundamental pathways underpinning sustainability: (1) mitigating financial constraints; (2) facilitating technological innovation and transformation; (3) strengthening green transition awareness. Furthermore, the impact of digital finance exhibits synergistic enhancement alongside increasing environmental regulation intensity and improved financial resource allocation efficiency. Heterogeneity analysis reveals that the effect is more pronounced in regions with lower marketization, within state-owned enterprises, and among RBEs in recession stages. Collectively, these findings offer significant implications for policymakers and industry practitioners aiming to strategically leverage digital finance to accelerate the sustainable transformation of resource-intensive industries, thereby contributing directly to environmentally sustainable and resilient economic development. Full article