Search Results (10,311)

This study analyzed the planting characteristics and spatial patterns of public-type private gardens in urban areas. Five gardens in Daejeon and Ulsan were surveyed using quadrats to record tree locations and sizes and were digitized for layout mapping. Planting and analysis units were defined, and spatial patterns were examined using degree centrality. The gardens were classified into one site under mixed artificial–natural management and four sites under artificial management with commercial linkage. The mixed site featured both canopy and shrub layers, with spontaneous vegetation surrounding Pinus thunbergii, Pinus densiflora, and Prunus yedoensis. The commercial sites included either canopy-only or canopy-shrub structures. Lagerstroemia indica, P. densiflora, and Euonymus japonicus. were predominant in the temperate central region, while P. densiflora and Diospyros kaki. dominated in the southern region. This study identified the potential of public-type private gardens as planting models and their capacity to contribute to urban environmental improvement. Full article

Current scientific and technological developments indicate that the need for dendrimers and nanomaterials should be taken into account in aspects such as the detection and remediation of chemical, biological, radiological and nuclear (CBRN) contamination. To evaluate the benefits of dendrimers in CBRN contamination, different characterization methods, toxicological evaluation, and recyclability must be used. The aim of this article is to systematize knowledge about selected nanomaterials and dendrimers as well as chemical, biological, radiological and nuclear (CBRN) hazards in accordance with the principles of green chemistry, engineering, technology and environmental safety. So far, many review articles on dendrimers and nanomaterials have focused on biomedical applications or environmental remediation. In this article, we discuss this topic in more detail, especially in relation to the integration of dendrimers with artificial intelligence and remote sensing technologies. We highlight interdisciplinary synergies—artificial intelligence for smarter design and remote sensing for deployment—that could bridge the gap between nanoscale innovation and real CBRN countermeasures. Full article

Edible and medicinal fungi are a general term for large fungi with both edible and medicinal values. As a unique wild edible and medicinal fungus in the Qinghai-Tibet Plateau, the ‘Four Medical Classics’ of the Tang Dynasty has recorded Floccularia luteovirens effects of external application and internal administration on swelling, cold disease, and neck stiffness. At present, it has not been artificially domesticated and has significant development potential. The mushroom is rich in nutrients. The crude protein content of 100 g dried product is 33~39% (up to 38.71 g, about 2.2 times that of Flammulina velutipes). It contains 19 amino acids (including 8 essential amino acids for the human body; tryptophan accounts for 21.55~22.63%). It is also rich in minerals such as selenium, zinc (0.09 g/kg), and iron (0.3 g/kg) and vitamins B1 (0.10 mg), B2 (1.10 mg), C (4.50 mg), and E (6.20 mg). Among the functional active substances, polysaccharides (containing 20.1% β-glucan and 5.7% mannan-oligosaccharide) had antioxidant and immunomodulatory effects, which could alleviate the weight loss of diabetic rats. The IC50 of DPPH free radical scavenging rate of phenolics (ferulic acid, etc.; total phenolic content of 4.21 ± 0.06 mg/g) was 43.85 μg/mL; there was also adenosine, volatile oil, and other components. Pharmacologically, the DPPH free radical scavenging rate of the extract was 65 ± 0.46%, the tumor inhibition rate of the polysaccharide on the tumor-bearing mice was 42.48%, the gastrodin was biocatalyzed (conversion rate 85.2%), and the extracellular polysaccharide could inhibit the color change in shrimp to achieve preservation. This paper reviews its related research progress and provides a reference for its development in the fields of healthy food and biomedicine. Full article

Antibody–drug conjugates (ADCs) have revolutionized breast cancer (BC) therapy by combining targeted antibody specificity with potent cytotoxic payloads, thereby enhancing efficacy while minimizing systemic toxicity. This review highlights significant innovations driving ADC development alongside persistent challenges. Recent advancements include novel antibody–drug conjugate (ADC) designs targeting diverse antigens, such as HER2, HER3, and CD276, demonstrating potent anti-tumor activity and improved strategies for drug delivery. For instance, dual-payload ADCs and those leveraging extracellular vesicles offer new dimensions in precision oncology. The integration of ADCs into sequential therapy, such as sacituzumab govitecan with TOP1/PARP inhibitors, further underscores their synergistic potential. Despite these innovations, critical challenges remain, including tumor heterogeneity and acquired drug resistance, which often involve complex molecular alterations. Moreover, optimizing ADC components, including linker chemistry and payload characteristics, is essential for ensuring stability and minimizing off-target toxicity. The burgeoning role of artificial intelligence and machine learning is pivotal in accelerating the design of ADCs, target identification, and personalized patient stratification. This review aims to comprehensively explore the cutting-edge innovations and inherent challenges in ADC development for BC, providing a holistic perspective on their current impact and future trajectory. Full article

This paper examines artificial intelligence (AI) bias in power systems applications through systematic analysis of three critical use cases: load forecasting, predictive maintenance, and ontology matching for system interoperability. While AI solutions show great potential for addressing complex power system challenges, they face adoption barriers due to biases that compromise fairness, reliability, and operational performance. Our investigation demonstrates how different bias types—including data representation, algorithmic, and sampling biases—manifest in power systems contexts, directly affecting grid efficiency, resource allocation, and socioeconomic equity across the electrical power and energy domain. For each use case, we provide quantitative evidence of bias impact and propose targeted mitigation strategies that emphasize data diversity, ensemble methods, explainable AI techniques, and fairness-aware algorithms. By establishing a comprehensive taxonomy of bias types relevant to power systems and developing practical mitigation frameworks, this work bridges the critical gap between abstract bias concepts and real-world power system applications. The resulting framework provides a structured approach for developing equitable, robust AI systems that align with power systems’ operational requirements while accelerating the responsible adoption of AI in safety-critical infrastructure. Full article

Determining sow (Sus scrofa domesticus Erxleben) estrus status requires considerable labor investment, and continuous real-time monitoring is impractical. Workers typically identify estrus at scheduled intervals and determine artificial insemination timing based on experience. However, experience-based methods are subjective, vary with operator expertise, and impede standardized management in large-scale farms. This study employs cameras and deep learning to detect sows and analyze postural changes, enabling estrus detection and optimal insemination timing prediction. Experimental results indicate that the proposed method achieved an accuracy of 70% (42/60), where the recommended insemination timing differed by less than 24 h from human decisions. This approach facilitates data-driven estrus detection and insemination scheduling, potentially reducing labor intensity and improving reproductive outcomes, particularly beneficial for labor-intensive and large-scale swine production systems. Full article

Orchard mobile robots (OMR) represent a critical research focus in the field of modern intelligent agricultural equipment, offering the potential to significantly enhance operational efficiency through the integration of path planning and tracking control navigation methods. However, the inherent complexity of orchard environments presents substantial challenges for robotic systems. Researchers have extensively investigated the robustness of various path planning and tracking control techniques for OMR in complex scenes, aiming to improve the robots’ security, stability, efficiency, and adaptability. This paper provides a comprehensive review of the state-of-the-art path planning and tracking control strategies for OMR in such environments. First, it discusses the advances in both global and local path planning methods designed for OMR navigating through complex orchard scenes. Second, it examines tracking control approaches in the context of different motion models, with an emphasis on the application characteristics and current trends in various scene types. Finally, the paper highlights the technical challenges faced by OMR in autonomous tasks within these complex environments and emphasizes the need for further research into navigation technologies that integrate artificial intelligence with end-to-end control systems. This fusion is identified as a promising direction for achieving efficient autonomous operations in orchard environments. Full article

This paper aims to explore the impact of marine ranching construction on water quality and fishery resources in the surrounding marine areas. Utilizing in situ water quality and fishery resource data collected before and after the establishment of marine ranching, the study analyzes changes in water quality parameters from both temporal and spatial perspectives. A quantitative evaluation of the water quality data is conducted using several models to assess the accuracy of different evaluation methods. By integrating the SHAP algorithm with physical significance, the study examines the differences between optically sensitive and non-optically sensitive water quality parameters during the machine learning evaluation process. Finally, based on the inverted water quality data, the potential impact range and resource output following the deployment of artificial reefs are investigated. The results indicate that in the marine area near Wailingding Island, Zhuhai, the deployment of artificial reefs with a volume of 38,048 cubic meters led to an increase in fishery resources by 318 kg/km² in spring and 660 kg/km² in autumn. Additionally, deployment had varying degrees of impact on the concentrations of chlorophyll a (Chla), dissolved oxygen (DO), chemical oxygen demand (COD), and phosphate (PO4-P) in the surface water within an approximate range of 10 km. This study provides a valuable reference for calculating input–output ratios, as well as for the management and evaluation of marine ranching. Full article

Dementia is a progressive condition that affects cognitive and functional abilities. Psycho-motor agitation represents a frequent and challenging manifestation in People Living with Dementia (PLwD). This behavior contributes to heightened distress and increased risk of harm for patients, while posing a significant burden for caregivers, who must navigate the complexities of managing unpredictable and potentially harmful agitation episodes. Accurately predicting and promptly responding to agitation events is thus critical for enhancing the safety and well-being of PLwD. Leveraging artificial intelligence, tools can be used to monitor behavioral patterns and alert healthcare providers about potential agitation to facilitate timely and effective interventions. Despite the link between poor sleep quality and the likelihood of agitation, there remains a gap in utilizing sleep parameters for predictive analytics in this domain. This study explores the potential of integrating sleep and associated physiological data to predict the risk of agitation in dementia patients the next day, leveraging the Technology Integrated Health Management (TIHM) dataset. Our analysis reveals that the LightGBM model, enhanced with combined feature sets, delivers superior performance, achieving a weighted F1 score of 93.6% compared to standard baseline models. The findings underscore the value of incorporating sleep data into automated models and advocate for continued efforts to develop long-term agitation prediction methods. Full article

Breast cancer faces significant therapeutic challenges, particularly for triple-negative breast cancer (TNBC), due to limited targeted therapies and drug resistance. Drug repositioning leverages existing safety and pharmacokinetic data to expedite the identification of new indications with cost-effective benefits compared to de novo drug discovery. In this critical narrative review, we examine recent advances in computational repositioning strategies for breast cancer, focusing on network-based methods, computer-aided drug design, artificial intelligence and machine learning, transcriptomic signature matching, and multi-omics integration. We highlight key case studies that have progressed to preclinical validation or clinical evaluation. We assess comparative performance metrics, experimental validation outcomes, and real-world success rates. We also present critical methodological challenges, including data heterogeneity, bias in real-world data, and the need for study reproducibility. Our review emphasizes the importance of window-of-opportunity trials and the need for standardized data sharing and reproducible pipelines. These insights highlight the groundbreaking potential of in silico repositioning in addressing unmet needs in breast cancer therapy. Full article

Landscape Character Assessments (LCAs) support planning decisions by offering structured descriptions of landscape character. However, producing these texts is often resource-intensive and shaped by subjective judgement. This study explores whether Generative Artificial Intelligence (GenAI), specifically ChatGPT, can support the drafting of LCA descriptions using a structured, prompt-based framework. Applied to Harku Municipality in Estonia, the method integrates spatial input, reference material, and standardised prompts to generate consistent descriptions of landscape character areas (LCAs) and facilitate scenario building. The results show that ChatGPT outputs align with core LCA components and maintain internal coherence, although variations in terminology and ecological specificity require expert review. A stakeholder role play using ChatGPT highlighted its potential for enhancing early-stage planning, education, and participatory dialogue. The limitations include the reliance on prompt quality, static inputs, and the absence of real-time community validation. Recommendations include piloting AI-assisted workflows in education and practice, adopting prompt protocols, and prioritising human oversight, both experts and stakeholders, to ensure contextual relevance and build trust. This research proposes a practical framework for embedding GenAI into planning processes while preserving the social and interpretive dimensions central to landscape governance. Full article

Three-dimensional (3D) bioprinting has emerged as a versatile platform in regenerative medicine, capable of replicating the structural and functional intricacies of the central and peripheral nervous systems (CNS and PNS). Beyond structural repair, it enables the construction of engineered tissues that closely recapitulate neural microenvironments. This review provides a comprehensive and critical synthesis of current bioprinting strategies for neural tissue engineering, with particular emphasis on comparing natural, synthetic, and hybrid polymer-based bioinks from mechanistic and translational perspectives. Distinctively, it highlights gradient-based modulation of Schwann cell behavior and axonal pathfinding using mechanically and chemically patterned constructs. Special attention is given to printing modalities such as extrusion, inkjet, and electrohydrodynamic jet printing, examining their respective capacities for controlling spatial organization and microenvironmental cues. Representative applications include brain development models, neurodegenerative disease platforms, and glioblastoma scaffolds with integrated functional properties. Furthermore, this review identifies key translational barriers—including host tissue integration and bioink standardization—and explores emerging directions such as artificial intelligence-guided biofabrication and organ-on-chip integration, to enhance the fidelity and therapeutic potential of neural bioprinted constructs. Full article

Prototype-Based Networks (PBNs) are inherently interpretable architectures that facilitate understanding of model outputs by analyzing the activation of specific neurons—referred to as prototypes—during the forward pass. The learned prototypes serve as transformations of the input space into a latent representation that more effectively encapsulates the main characteristics shared across data samples, thereby enhancing classification performance. Crucially, these prototypes can be decoded and projected back into the original input space, providing direct interpretability of the features learned by the network. While this characteristic marks a meaningful advancement toward the realization of fully interpretable artificial intelligence systems, our findings reveal that prototype representations can be deliberately or inadvertently manipulated without compromising the superficial appearance of explainability. In this study, we conduct a series of empirical investigations that demonstrate this phenomenon, framing it as a structural paradox potentially intrinsic to the architecture or its design, which may represent a significant robustness challenge for explainable AI methodologies. Full article

The consumption of edible flowers is gaining global popularity due to their culinary appeal, vibrant colors, and health-promoting compounds. Traditional production methods—including wild collection, open-field cultivation, and greenhouse systems—offer limited control over environmental factors, often resulting in inconsistent yield, quality, and safety. To address these limitations, plant factories with artificial lighting (PFALs) have emerged as a promising technology for producing high-quality edible flowers year-round in controlled environments. This review explores the evolution of edible flower cultivation, from conventional methods to PFALs, and highlights key environmental factors—light, temperature, and nutrient management—that influence growth, flowering, and phytochemical profiles. Special attention is given to how light intensity, spectrum, and photoperiod affect morphogenesis and metabolite accumulation, and how nutrient solution composition, particularly nitrogen form and EC levels, modulates flowering and plant health. While recent studies have demonstrated the potential of PFALs in cultivating species such as calendula, nasturtium, and marigold, research remains limited for many commercially relevant species. The review identifies current challenges, such as high operational costs and knowledge gaps in species-specific protocols, and outlines future research directions aimed at improving efficiency, optimizing quality, and expanding market viability. PFALs offer a transformative opportunity for the edible flower industry by integrating precision agriculture with consumer demand for safe, functional, and visually appealing food products. Full article

This study analyses the potential of artificial neural networks (ANN) in accurately predicting potato yields based on 11 parameters characterising the soil environment. Accurate yield forecasting is crucial for optimising potato production, especially in the context of potato processing. Due to the significant impact of soil properties on yield, there is a need for comprehensive predictive models that take these factors into account. The field studies (2021–2024) included an analysis of soil parameters determining potato tuber yield. The developed ANN model was highly accurate, as evidenced by the following indicators: R² = 0.8227, RMSE = 4.19 t∙ha⁻¹, MAE = 3.35 t∙ha⁻¹, MAPE = 7.34%. Global sensitivity analysis showed that cation exchange capacity (CEC), base saturation percentage (V), and sum of exchangeable bases (S) are key parameters influencing tuber yield. The results indicate that neural networks are effective in modelling complex relationships between soil parameters and potato yield, and that soil properties play a fundamental role in increasing yields and improving potato quality. The approach used may contribute to optimizing the nutrient content of potato tubers intended for French fry production. Future studies should incorporate climate data and micronutrients to enhance the accuracy of predictive models, potentially leading to a 10–15% improvement in yield predictions. Full article