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Integration with rural tourism is an important way to achieve the sustainable development of ecological farms. Existing literature on the integration of agriculture and tourism lacks discussion from the microscopic farm level, making it difficult to capture the complex mechanisms of the integration of ecological farms and rural tourism. This paper attempts to address this problem by exploring the driving factors of the integration of ecological farms and rural tourism. The research aim of this paper is to construct a theoretical framework for driving the integration of ecological farms and rural tourism. We first conducted research on farms in four ecological agriculture demonstration zones: Ziquejie in Loudi, Hunan Province; Heshi in Shilin, Yunnan Province; Rongjiang in Dali, Yunnan Province; and Youxiqiao Village in Hunan Province. We interviewed 64 stakeholders in ecotourism and used grounded theory methods to construct a model and propose hypotheses. On this basis, a measurement scale was designed, and data was collected from 1041 Chinese ecological farms (ecological farm operators) using a structured questionnaire. The partial least squares structural equation model (PLS-SEM) was used to model and analyze the data to test the constructed model. The study found that higher market demand, regional economic level, intrinsic development needs, intrinsic resource endowments, technical support, and resource integration can promote the integration of ecological farms and rural tourism. Market demand and intrinsic development needs constitute the generative force of agritourism integration, while resource integration and intrinsic resource endowments constitute the development force of agritourism integration, and technical support and the regional economic level constitute the supporting force of agritourism integration. Full article

(This article belongs to the Section Agricultural Economics, Policies and Rural Management)

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14 pages, 3238 KiB

Open AccessArticle

Integrated Transcriptomics and Metabolomics Reveal Key Genes and Metabolic Pathway in Flower and Fruit Color Formation of Cerasus humilis (Bge.) Sok

by Shuai Zhang, Tianyuan Li, Shan Liu, Xinliang Qi, Yu Yang, Jiancheng Zhang, Luting Jia, Pengfei Wang and Xiaopeng Mu

Plants 2025, 14(7), 1103; https://doi.org/10.3390/plants14071103 (registering DOI) - 2 Apr 2025

Abstract

Anthocyanins play a pivotal role in determining the color diversity in the flowers and fruits of Cerasus humilis (Bge.) Sok. This study performed a metabolomic analysis of the flowers and fruits of two varieties differing in pigmentation phenotypes (‘Jinou 1’ and ‘Nongda 5’), and the results indicated that the cyanidin, pelargonidin, paeonidin, and delphinidin were the main substances serving as the primary pigments contributing to their striking chromatic divergence between two varieties. Transcriptome profiling revealed that several key structural genes (ChCHS1, ChDFR, ChF3H, and ChF3’H) in the anthocyanin biosynthesis pathway exhibited significantly elevated expression levels in ’Jinou 1’ compared to ’Nongda 5’. Further metabolomic and transcriptomic correlation analyses identified that ChMYB9 and ChMYB12 exhibited strong positive associations with anthocyanin pathway metabolites in both floral and fruit tissues. Notably, ChMYB9 displayed the strongest correlation with the metabolite profiles, suggesting it may serve as a core regulatory component of the anthocyanin biosynthesis. This research provides new insights into the regulatory mechanisms of anthocyanin biosynthesis in C. humilis. Full article

(This article belongs to the Section Plant Physiology and Metabolism)

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24 pages, 5650 KiB

Open AccessArticle

A Bi-Level Capacity Optimization Method for Hybrid Energy Storage Systems Combining the IBWO and MVMD Algorithms

by Qiaoqiao Xing, Shidong Li, Da Qiu, Yang Long, Qinyi Liao, Xiangjin Yin, Yunxiang Li and Kai Qian

Energies 2025, 18(7), 1777; https://doi.org/10.3390/en18071777 (registering DOI) - 2 Apr 2025

Abstract

With the swift evolution of renewable energy technologies, the design and optimization of microgrids have emerged as vital components for fostering energy transition and promoting sustainable development. This study presents a bi-level capacity optimization model for microgrids, integrating wind–solar generation with hybrid electric–hydrogen energy storage systems to simultaneously enhance economic efficiency and system stability. The outer layer minimizes the annual total cost through the application of an Improved Beluga Whale Optimization (IBWO) algorithm, which is enhanced by strategies including the reverse elitism strategy, horizontal and vertical crossover operations, and a whirlwind scavenging strategy to improve performance. The inner layer builds on the optimized results from the outer layer, employing a Multivariable Variational Mode Decomposition (MVMD) algorithm to regulate the power output of the energy storage system. By integrating electric–hydrogen hybrid storage technology, the inner layer effectively mitigates power fluctuations. Furthermore, this study designs a modal decomposition-based charging and discharging scheduling strategy to ensures the system’s continuous and stable operation. Simulations performed on MATLAB 2018b and CPLEX 12.8 platforms indicate that the proposed dual-layer model decreases annual total expenses by 27.5% compared to a single-layer model while keeping grid-connected power variations within 10% of the installed capacity. This research provides innovative perspectives on microgrid optimization design and offers substantial technical support for ensuring stability and economic efficiency in intricate operational settings. Full article

(This article belongs to the Section A1: Smart Grids and Microgrids)

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8 pages, 1016 KiB

Open AccessStudy Protocol

Efficacy of Segmental Muscle Vibration on Pain Modulation in Patients with Primary Cervical Dystonia Treated with Botulinum Type-A Toxin: A Protocol for a Randomized Controlled Trial

by Riccardo Buraschi, Paolo Pedersini, Giacomo Redegalli, Rosa Pullara, Joel Pollet, Marina Rossi, Massimiliano Gobbo, Sara Gueli and Maurizio Falso

NeuroSci 2025, 6(2), 30; https://doi.org/10.3390/neurosci6020030 (registering DOI) - 2 Apr 2025

Abstract

Primary cervical dystonia (PCD), or spasmodic torticollis, is a focal dystonia characterized by involuntary and often painful muscle contractions, leading to abnormal cervical movements and postures. While botulinum toxin injections are the first-line treatment, additional therapies, such as segmental muscle vibration (SMV), remain underexplored. SMV, a non-invasive neuromodulation technique, may enhance motor cortex excitability and promote neuroplasticity, offering potential benefits in PCD management. This single-center triple-blinded randomized controlled trial evaluates SMV’s efficacy in reducing dystonic pain and improving quality of life in PCD patients undergoing standardized rehabilitation after botulinum toxin treatment. Participants with a pain level of ≥3 on the Numerical Rating Scale will be randomized into two groups. The experimental group will receive 80 Hz SMV during a 10-session rehabilitation program, while the control group will undergo sham SMV. Both groups will follow identical physiotherapy and occupational therapy protocols. The primary outcomes include changes in pain intensity and function, assessed at baseline, mid-treatment, and post-treatment using validated scales. The secondary outcomes will evaluate quality of life and patient satisfaction. This study hypothesizes that SMV will significantly reduce dystonic pain and enhance quality of life, supporting its integration into multidisciplinary rehabilitation for dystonic disorders. Trial registration number: NCT06748846. Full article

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10 pages, 864 KiB

Open AccessReview

Role of Artificial Intelligence in Thyroid Cancer Diagnosis

by Alessio Cece, Massimo Agresti, Nadia De Falco, Pasquale Sperlongano, Giancarlo Moccia, Pasquale Luongo, Francesco Miele, Alfredo Allaria, Francesco Torelli, Paola Bassi, Antonella Sciarra, Stefano Avenia, Paola Della Monica, Federica Colapietra, Marina Di Domenico, Ludovico Docimo and Domenico Parmeggiani

J. Clin. Med. 2025, 14(7), 2422; https://doi.org/10.3390/jcm14072422 (registering DOI) - 2 Apr 2025

Abstract

The progress of artificial intelligence (AI), particularly its core algorithms—machine learning (ML) and deep learning (DL)—has been significant in the medical field, impacting both scientific research and clinical practice. These algorithms are now capable of analyzing ultrasound images, processing them, and providing outcomes, such as determining the benignity or malignancy of thyroid nodules. This integration into ultrasound machines is referred to as computer-aided diagnosis (CAD). The use of such software extends beyond ultrasound to include cytopathological and molecular assessments, enhancing the estimation of malignancy risk. AI’s considerable potential in cancer diagnosis and prevention is evident. This article provides an overview of AI models based on ML and DL algorithms used in thyroid diagnostics. Recent studies demonstrate their effectiveness and diagnostic role in ultrasound, pathology, and molecular fields. Notable advancements include content-based image retrieval (CBIR), enhanced saliency CBIR (SE-CBIR), Restore-Generative Adversarial Networks (GANs), and Vision Transformers (ViTs). These new algorithms show remarkable results, indicating their potential as diagnostic and prognostic tools for thyroid pathology. The future trend points to these AI systems becoming the preferred choice for thyroid diagnostics. Full article

(This article belongs to the Section Oncology)

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19 pages, 1670 KiB

Open AccessReview

Bioelectric Membrane Potential and Breast Cancer: Advances in Neuroreceptor Pharmacology for Targeted Therapeutic Strategies

by Chitaranjan Mahapatra, Jineetkumar Gawad, Chandrakant Bonde and Mahesh B. Palkar

Receptors 2025, 4(2), 9; https://doi.org/10.3390/receptors4020009 (registering DOI) - 2 Apr 2025

Abstract

Bioelectric membrane potentials regulate cellular growth, differentiation, and movement. Disruptions in bioelectric signaling are strongly linked to cancer development, as abnormal membrane potentials and ion channel activity can drive tumor progression. In breast cancer, ion channel dysfunction and neuroreceptor-related pathways play significant roles in the cell cycle, epithelial–mesenchymal transition, angiogenesis, inflammation, the tumor microenvironment, and tumor progression. Neuroreceptors are critical not only in initiating and advancing cancer but also in conferring resistance to treatments. Neuroreceptors also play a key role, with dopamine receptor D2 activation reducing breast tumor growth by 40% in preclinical models, while serotonin signaling has been shown to promote epithelial–mesenchymal transition (EMT), increasing invasiveness. Advances in understanding these biological mechanisms could lead to more cost-effective and less invasive therapeutic strategies to treat tumors. This review explores the expanding evidence connecting bioelectric activity to breast cancer, focusing on neuroreceptor pharmacology as a transformative therapeutic approach. Examining the modulation of bioelectricity through neuroreceptor pharmacology to influence breast cancer progression and integrating these insights into therapeutic development offers a promising path for addressing treatment challenges and improving precision in managing aggressive cancer subtypes. Full article

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22 pages, 3983 KiB

Open AccessArticle

Transforming Education in the AI Era: A Technology–Organization–Environment Framework Inquiry into Public Discourse

by Jinqiao Zhou and Hongfeng Zhang

Appl. Sci. 2025, 15(7), 3886; https://doi.org/10.3390/app15073886 (registering DOI) - 2 Apr 2025

Abstract

The advent of generative artificial intelligence (GAI) technologies has significantly influenced the educational landscape. However, public perceptions and the underlying emotions toward artificial intelligence-generated content (AIGC) applications in education remain complex issues. To address this issue, this study employs LDA network public opinion topic mining and SnowNLP sentiment analysis to comprehensively analyze over 40,000 comments collected from multiple social media platforms in China. Through a detailed analysis of the data, this study examines the distribution of positive and negative emotions and identifies six topics. The study further utilizes visual tools such as word clouds and heatmaps to present the research findings. The results indicate that the emotional polarity across all topics is characterized by a predominance of positive emotions over negative ones. Moreover, an analysis of the keywords across the six topics reveals that each has its own emphasis, yet there are overlaps between them. Therefore, this study, through quantitative methods, also reflects the complex interconnections among the elements within the educational ecosystem. Additionally, this study integrates the six identified topics with the Technology–Organization–Environment (TOE) framework to explore the broad impact of AIGC on education from the perspectives of technology, organization, and environment. This research provides a novel perspective on the emotional attitudes and key concerns of the Chinese public regarding the use of AIGC in education. Full article

(This article belongs to the Special Issue Social Media Meets AI and Data Science)

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8 pages, 2079 KiB

Open AccessProceeding Paper

Video Surveillance and Augmented Reality in Maritime Safety

by Igor Vujović, Mario Miličević and Joško Šoda

Eng. Proc. 2025, 87(1), 32; https://doi.org/10.3390/engproc2025087032 (registering DOI) - 2 Apr 2025

Abstract

Recently, augmented reality and machine learning have become integral parts of many developed systems. In the maritime domain, it is particularly interesting to develop a concept that combines augmented reality with the visualization of collision risks, using machine learning for motion prediction as its foundation. Hence, this research aims to propose a system that visualizes the risk in an augmented reality application. The paper presents a distance estimation method that mainly uses a single stationary camera placed at the harbor entrance. The machine learning component involves training the YOLO algorithm on the Split Port Ship Classification Dataset. This distance estimation is an input for the speed estimation algorithm. Speed is a key parameter for the prediction of collision risk. Preliminary experiments were conducted to provide proof of concept for further research, and the description of a case study is included in this paper. Full article

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26 pages, 10537 KiB

Open AccessArticle

SAMNet++: A Segment Anything Model for Supervised 3D Point Cloud Semantic Segmentation

by Mohsen Shahraki, Ahmed Elamin and Ahmed El-Rabbany

Remote Sens. 2025, 17(7), 1256; https://doi.org/10.3390/rs17071256 (registering DOI) - 2 Apr 2025

Abstract

Segmentation of 3D point clouds is essential for applications such as environmental monitoring and autonomous navigation, where making accurate distinctions between different classes from high-resolution 3D datasets is critical. Segmenting 3D point clouds often requires a trade-off between preserving spatial information and achieving computational efficiency. In this paper, we present SAMNet++, a hybrid 3D segmentation model that integrates segment anything model (SAM) and adopted PointNet++ in a sequential two-stage pipeline. Firstly, SAM performs an initial unsupervised segmentation, which is then refined using adopted PointNet++ to improve the accuracy. The key innovations of SAMNet++ include its hybrid architecture, which combines SAM’s generalization with PointNet++’s local feature extraction, and a feature refinement strategy that enhances precision while reducing computational overhead. Additionally, SAMNet++ minimizes the reliance on extensive supervised training, while maintaining high accuracy. The proposed model is tested on three urban datasets, which are collected by an unmanned aerial vehicle (UAV). The proposed SAMNet++ model demonstrates high segmentation performance, achieving accuracy, precision, recall, and F1-score values above 0.97 across all classes on our experimental datasets. Furthermore, its mean intersection over union (mIoU) of 86.93% on a public benchmark dataset signifies a more balanced and precise segmentation across all classes, surpassing previous state-of-the-art methods. In addition to its improved accuracy, SAMNet++ showcases remarkable computational efficiency, requiring almost half the processing time of standard PointNet++ and nearly one-sixteenth of the time needed by the original PointNet algorithm. Full article

(This article belongs to the Special Issue 3D Scene Reconstruction, Modeling and Analysis Using Remote Sensing)

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22 pages, 3121 KiB

Open AccessArticle

The Impact of Digital Cognitive Stimulation Therapy Combined with Online Hearing Training on Quality of Life in Dementia Patients

by Gregor Hohenberg, Jan Ehlers and Thomas Ostermann

J. Dement. Alzheimer's Dis. 2025, 2(2), 8; https://doi.org/10.3390/jdad2020008 (registering DOI) - 2 Apr 2025

Abstract

Background: Dementia patients often experience a decline in both their cognitive and sensory functions, particularly hearing, which significantly impacts their quality of life. This study evaluates the effectiveness of a combined Digital Cognitive Stimulation Therapy (DKST) and online hearing training intervention in enhancing the quality of life of individuals with dementia. Methods: Twenty-three patients participated in a six-month program integrating cognitive exercises and hearing rehabilitation, facilitated by trained co-therapists. Quality of life was assessed using the Quality of Life (QoL) Questionnaire, while the Mini-Mental State Examination (MMSE) was employed to categorize participants based on their cognitive status. Results: The results revealed significant improvements in the overall quality of life. Conclusions: This study demonstrates that combining DKST with hearing training effectively addresses sensory and cognitive challenges, supporting improved quality of life and highlighting the potential of digital interventions in dementia care. Full article

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13 pages, 2054 KiB

Open AccessArticle

Effects of Telehealth-Supervised Respiratory Exercise Training on Respiratory Function, Fatigue, Quality of Life, and Functional Capacity of Patients with Multiple Sclerosis

by Şeyda Öznur Ayçiçek, Abdulkadir Tunç and Cahit Bağcı

Medicina 2025, 61(4), 651; https://doi.org/10.3390/medicina61040651 (registering DOI) - 2 Apr 2025

Abstract

Background and Objectives: Telerehabilitation (TR) offers an innovative approach to overcome accessibility challenges in managing multiple sclerosis (MS). This exploratory study evaluated the efficacy of integrating respiratory exercises into TR programs for improving respiratory function, fatigue, and quality of life. Materials and Methods: A randomized controlled trial involving 48 MS patients randomized into TR and control groups was conducted. Both groups performed respiratory exercises over eight weeks. Pulmonary function, fatigue severity (FSS), quality of life (MSQOL-54), and functional capacity (6MWT) were assessed before and after the intervention. Results: Both groups demonstrated significant within-group improvements in FEV1 (L), PEF (L), FEF%25–75 (L), FSS, MSQOL-54 physical and mental subscales, and the 6MWT distance (p < 0.05). The TR group exhibited unique improvements in FEV1 (%) and slightly greater reductions in fatigue, although the intergroup differences were not statistically significant. Conclusions: Telerehabilitation incorporating respiratory exercises effectively enhances the respiratory function, fatigue, and quality of life of MS patients, suggesting a viable alternative to conventional rehabilitation. Future studies should focus on advanced-stage MS, long-term sustainability, and technological integration to optimize the potential of TR. Full article

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21 pages, 11116 KiB

Open AccessArticle

Dual-Faced Role of GDF6 in Cancer: Mechanistic Insights into Its Context-Dependent Regulation of Metastasis and Immune Evasion Across Human Malignancies

by Qi Zhu, Jianshu Wei and Weidong Han

Curr. Issues Mol. Biol. 2025, 47(4), 249; https://doi.org/10.3390/cimb47040249 (registering DOI) - 2 Apr 2025

Abstract

Growth differentiation factor 6 (GDF6), a member of the TGF-β superfamily, plays multifaceted roles in tumorigenesis, yet its molecular mechanisms and cancer-type-specific regulatory networks remain poorly defined. This study investigates GDF6’s context-dependent functions through pan-cancer multi-omics integration and functional validation. Transcriptomic data from TCGA (33 cancers, n = 10,535) and GTEx were analyzed to assess GDF6 dysregulation. Co-expression networks, pathway enrichment (KEGG/GO), and epigenetic interactions (m6A, m5C, m1A) were explored. Functional assays included siRNA knockdown, wound healing, and validation in immunotherapy cohorts. GDF6 exhibited bidirectional expression patterns, with downregulation in 23 cancers (e.g., GBM, BRCA) and upregulation in 7 malignancies (e.g., KIRC, PAAD). Mechanistically, GDF6 activated the PI3K-Akt/VEGF pathways, thereby promoting angiogenesis and metastasis. It modulated epigenetic regulation through interactions with m6A readers and erasers. Additionally, GDF6 reshaped the immune microenvironment by recruiting myeloid-derived suppressor cells (MDSCs) and cancer-associated fibroblasts. Notably, GDF6’s dual role extended to immunotherapy: it suppressed anti-PD1 efficacy but enhanced anti-PD-L1 sensitivity, linked to differential MHC-II and hypoxia-response regulation. This study deciphers GDF6’s context-dependent molecular networks, revealing its dual roles in metastasis and immune evasion. These findings highlight GDF6 as a central node in TGF-β-mediated oncogenic signaling and a potential therapeutic target for precision intervention. Full article

(This article belongs to the Section Molecular Medicine)

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11 pages, 235 KiB

Open AccessCommunication

Talking Resilience: Embedded Natural Language Cyber-Organizations by Design

by Andrea Tomassi, Andrea Falegnami and Elpidio Romano

Systems 2025, 13(4), 247; https://doi.org/10.3390/systems13040247 (registering DOI) - 2 Apr 2025

Abstract

This communication examines the interplay between linguistic mediation and knowledge conversion in cyber-sociotechnical systems (CSTSs) via the WAx framework, which outlines various work representations and eight key conversion activities. Grounded in enactivist principles, we argue that language is a dynamic mechanism that shapes, and is shaped by, human–machine interactions, enhancing system resilience and adaptability. By integrating the concepts of simplexity, complixity, and complexity compression, we illustrate how complex cognitive and operational processes can be selectively condensed into efficient outcomes. A case study of a chatbot-based customer support system demonstrates how the phases of socialization, introspection, externalization, combination, internalization, conceptualization, reification, and influence collaboratively drive the evolution of resilient CSTS designs. Our findings indicate that natural language serves as a bridging tool for effective sense-making, adaptive coordination, and continuous learning, offering novel insights into designing technologically advanced, socially grounded, and evolving sociotechnical systems. Full article

(This article belongs to the Special Issue Cognitive and Practical Perspectives on Resilience, Organization and Entangled Systems)

18 pages, 3958 KiB

Open AccessArticle

AI-Driven UAV Surveillance for Agricultural Fire Safety

by Akmalbek Abdusalomov, Sabina Umirzakova, Komil Tashev, Nodir Egamberdiev, Guzalxon Belalova, Azizjon Meliboev, Ibragim Atadjanov, Zavqiddin Temirov and Young Im Cho

Fire 2025, 8(4), 142; https://doi.org/10.3390/fire8040142 (registering DOI) - 2 Apr 2025

Abstract

The increasing frequency and severity of agricultural fires pose significant threats to food security, economic stability, and environmental sustainability. Traditional fire-detection methods, relying on satellite imagery and ground-based sensors, often suffer from delayed response times and high false-positive rates, limiting their effectiveness in mitigating fire-related damages. In this study, we propose an advanced deep learning-based fire-detection framework that integrates the Single-Shot MultiBox Detector (SSD) with the computationally efficient MobileNetV2 architecture. This integration enhances real-time fire- and smoke-detection capabilities while maintaining a lightweight and deployable model suitable for Unmanned Aerial Vehicle (UAV)-based agricultural monitoring. The proposed model was trained and evaluated on a custom dataset comprising diverse fire scenarios, including various environmental conditions and fire intensities. Comprehensive experiments and comparative analyses against state-of-the-art object-detection models, such as You Only Look Once (YOLO), Faster Region-based Convolutional Neural Network (Faster R-CNN), and SSD-based variants, demonstrated the superior performance of our model. The results indicate that our approach achieves a mean Average Precision (mAP) of 97.7%, significantly surpassing conventional models while maintaining a detection speed of 45 frames per second (fps) and requiring only 5.0 GFLOPs of computational power. These characteristics make it particularly suitable for deployment in edge-computing environments, such as UAVs and remote agricultural monitoring systems. Full article

(This article belongs to the Special Issue Integrating AI and Remote Sensing for Monitoring and Mapping Fire Impacts on Agroforestry and Wildlife Systems)

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28 pages, 2270 KiB

Open AccessReview

Bacterial Cellulose for Scalable and Sustainable Bio-Gels in the Circular Economy

by Giovanni Venturelli, Federica Villa, Mariagioia Petraretti, Giuseppe Guagliano, Marinella Levi and Paola Petrini

Gels 2025, 11(4), 262; https://doi.org/10.3390/gels11040262 (registering DOI) - 2 Apr 2025

Abstract

Microbial-derived materials are emerging for applications in biomedicine, sensors, food, cosmetics, construction, and fashion. They offer considerable structural properties and process reproducibility compared to other bio-based materials. However, challenges related to efficient and sustainable large-scale production of microbial-derived materials must be addressed to exploit their potential fully. This review analyzes the synergistic contribution of circular, sustainable, and biotechnological approaches to enhance bacterial cellulose (BC) production and fine-tune its physico-chemical properties. BC was chosen as an ideal example due to its mechanical strength and chemical stability, making it promising for industrial applications. The review discusses upcycling strategies that utilize waste for microbial fermentation, simultaneously boosting BC production. Additionally, biotechnology techniques are identified as key to enhance BC yield and tailor its physico-chemical properties. Among the different areas where cellulose-based materials are employed, BC shows promise for mitigating the environmental impact of the garment industry. The review emphasizes that integrating circular and biotechnological approaches could significantly improve large-scale production and enhance the tunability of BC properties. Additionally, these approaches may simultaneously provide environmental benefits, depending on their future progresses. Future advancements should prioritize circular fermentation and biotechnological techniques to expand the potential of BC for sustainable industrial applications. Full article

(This article belongs to the Special Issue Gel Materials for Green Applications)

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