Search Results (1,692)

With the growth of the global population and the increasing scarcity of arable land, traditional agricultural production is confronted with multiple challenges, such as efficiency improvement, precision operation, and sustainable development. The progressive advancement of artificial intelligence (AI) technology has created a transformative opportunity for the intelligent upgrade of agricultural equipment. This article systematically presents recent progress in computer vision, machine learning (ML), and intelligent sensing. The key innovations are highlighted in areas such as object detection and recognition (e.g., a K-nearest neighbor (KNN) achieved 98% accuracy in distinguishing vibration signals across operation stages); autonomous navigation and path planning (e.g., a deep reinforcement learning (DRL)-optimized task planner for multi-arm harvesting robots reduced execution time by 10.7%); state perception (e.g., a multilayer perceptron (MLP) yielded 96.9% accuracy in plug seedling health classification); and precision control (e.g., an intelligent multi-module coordinated control system achieved a transplanting efficiency of 5000 plants/h). The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. This integration is catalyzing the transition toward intelligent, automated, and sustainable agricultural systems. Nevertheless, intelligent agricultural equipment still faces technical challenges regarding data sample acquisition, adaptation to complex field environments, and the coordination between algorithms and hardware. Looking ahead, the convergence of digital twin (DT) technology, edge computing, and big data-driven collaborative optimization is expected to become the core of next-generation intelligent agricultural systems. These technologies have the potential to overcome current limitations in perception and decision-making, ultimately enabling intelligent management and autonomous decision-making across the entire agricultural production chain. This article aims to provide a comprehensive foundation for advancing agricultural modernization and supporting green, sustainable development. Full article

Critical Internet of Things (IoT) data in Fifth Generation Vehicular Ad Hoc Networks (5G VANETs) demands Ultra-Reliable Low-Latency Communication (URLLC) to support mission-critical vehicular applications such as autonomous driving and collision avoidance. Achieving the stringent Quality of Service (QoS) requirements for these applications remains a significant challenge. This paper proposes a novel framework integrating Software-Defined Networking (SDN) and Network Functions Virtualisation (NFV) as embedded functionalities in connected vehicles. A lightweight SDN Controller model, implemented via vehicle on-board computing resources, optimised QoS for communications between connected vehicles and the Next-Generation Node B (gNB), achieving a consistent packet delivery rate of 100%, compared to 81–96% for existing solutions leveraging SDN. Furthermore, a Software-Defined Wide-Area Network (SD-WAN) model deployed at the gNB enabled the efficient management of data, network, identity, and server access. Performance evaluations indicate that SDN and NFV are reliable and scalable technologies for virtualised and distributed 5G VANET infrastructures. Our SDN-based in-vehicle traffic classification model for dynamic resource allocation achieved 100% accuracy, outperforming existing Artificial Intelligence (AI)-based methods with 88–99% accuracy. In addition, a significant increase of 187% in flow rates over time highlights the framework’s decreasing latency, adaptability, and scalability in supporting URLLC class guarantees for critical vehicular services. Full article

Nanotechnologies bring a rapid paradigm shift in hard and soft bone tissue regeneration (BTR) through unprecedented control over the nanoscale structures and chemistry of biocompatible materials to regenerate the intricate architecture and functional adaptability of bone. This review focuses on the transformative analyses and prospects of current and next-generation nanomaterials in designing bioactive bone scaffolds, emphasizing hierarchical architecture, mechanical resilience, and regenerative precision. Mainly, this review elucidated the innovative findings, new capabilities, unmet challenges, and possible future opportunities associated with biocompatible inorganic ceramics (e.g., phosphates, metallic oxides) and the United States Food and Drug Administration (USFDA) approved synthetic polymers, including their nanoscale structures. Furthermore, this review demonstrates the newly available approaches for achieving customized standard porosity, mechanical strengths, and accelerated bioactivity to construct an optimized nanomaterial-oriented scaffold. Numerous strategies including three-dimensional bioprinting, electro-spinning techniques and meticulous nanomaterials (NMs) fabrication are well established to achieve radical scientific precision in BTR engineering. The contemporary research is unceasingly decoding the pathways for spatial and temporal release of osteoinductive agents to enhance targeted therapy and prompt healing processes. Additionally, successful material design and integration of an osteoinductive and osteoconductive agents with the blend of contemporary technologies will bring radical success in this field. Furthermore, machine learning (ML) and artificial intelligence (AI) can further decode the current complexities of material design for BTR, notwithstanding the fact that these methods call for an in-depth understanding of bone composition, relationships and impacts on biochemical processes, distribution of stem cells on the matrix, and functionalization strategies of NMs for better scaffold development. Overall, this review integrated important technological progress with ethical considerations, aiming for a future where nanotechnology-facilitated bone regeneration is boosted by enhanced functionality, safety, inclusivity, and long-term environmental responsibility. Therefore, the assimilation of a specialized research design, while upholding ethical standards, will elucidate the challenge and questions we are presently encountering. Full article

Background: Mesenchymal stem cells (MSCs) are a promising tool in regenerative medicine due to their ability to secrete paracrine factors that modulate tissue repair. Extracellular vesicles (EVs) released by MSCs contain bioactive molecules (e.g., mRNAs, miRNAs, proteins) and play a key role in intercellular communication. Methods: This study compared the transcriptomic profiles (mRNA and miRNA) of equine MSCs derived from adipose tissue (AT-MSCs), bone marrow (BM-MSCs), and ovarian fibroblasts (as a differentiated control). Additionally, miRNAs present in EVs secreted by these cells were characterized using next-generation sequencing. Results: All cell types met ISCT criteria for MSCs, including CD90 expression, lack of MHC II, trilineage differentiation, and adherence. EVs were isolated using ultracentrifugation and validated with nanoparticle tracking analysis and flow cytometry (CD63, CD81). Differential expression analysis revealed distinct mRNA and miRNA profiles across cell types and their secreted EVs, correlating with tissue origin. BM-MSCs showed unique regulation of genes linked to early development and osteogenesis. EVs contained diverse RNA species, including miRNA, mRNA, lncRNA, rRNA, and others. In total, 227 and 256 mature miRNAs were detected in BM-MSCs and AT-MSCs, respectively, including two novel miRNAs per MSC type. Fibroblasts expressed 209 mature miRNAs, including one novel miRNA also found in MSCs. Compared to fibroblasts, 60 and 92 differentially expressed miRNAs were identified in AT-MSCs and BM-MSCs, respectively. Conclusions: The results indicate that MSC tissue origin influences both transcriptomic profiles and EV miRNA content, which may help to interpret their therapeutic potential. Identifying key mRNAs and miRNAs could aid in future optimizing of MSC-based therapies in horses. Full article

In most commercial lithium-ion batteries, graphite remains the primary anode material. However, its theoretical specific capacity is only 372 mAh∙g⁻¹, which falls short of meeting the demands of high-performance electronic devices. Silicon anodes, despite boasting an ultra-high theoretical specific capacity of 4200 mAh∙g⁻¹, suffer from significant volume expansion (>300%) during cycling, leading to severe capacity fade and limiting their commercial viability. Currently, silicon-based polymer-derived ceramics have emerged as a highly promising next-generation anode material for lithium-ion batteries, thanks to their unique nano-cluster structure, tunable composition, and low volume expansion characteristics. The maximum capacity of the ceramics can exceed 1000 mAh∙g⁻¹, and their unique synthesis routes enable customization to align with diverse electrochemical application requirements. In this paper, we present the progress of silicon oxycarbide (SiOC), silicon carbonitride (SiCN), silicon boron carbonitride (SiBCN) and silicon oxycarbonitride (SiOCN) in the field of LIBs, including their synthesis, structural characteristics and electrochemical properties, etc. The mechanisms of lithium-ion storage in the Si-based anode materials are summarized as well, including the key role of free carbon in these materials. Full article

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. Full article

Invasive fungal infections (IFIs) represent a growing global health threat, particularly for immunocompromised populations, with mortality exceeding 1.5 million deaths annually. Despite their clinical and economic burden—costing billions in healthcare expenditures—fungal infections remain underprioritized in public health agendas. This review examines the current landscape of antifungal therapy, focusing on advances, challenges, and future directions. Key drug classes (polyenes, azoles, echinocandins, and novel agents) are analyzed for their mechanisms of action, pharmacokinetics, and clinical applications, alongside emerging resistance patterns in pathogens like Candida auris and azole-resistant Aspergillus fumigatus. The rise of resistance, driven by agricultural fungicide use and nosocomial transmission, underscores the need for innovative antifungals, rapid diagnostics, and stewardship programs. Promising developments include next-generation echinocandins (e.g., rezafungin), triterpenoids (ibrexafungerp), and orotomides (olorofim), which target resistant strains and offer improved safety profiles. The review also highlights the critical role of “One Health” strategies to mitigate environmental and clinical resistance. Future success hinges on multidisciplinary collaboration, enhanced surveillance, and accelerated drug development to address unmet needs in antifungal therapy. Full article

The detection of trace chemicals at low and ultra-low concentrations is critical for applications in environmental monitoring, medical diagnostics, food safety and other fields. Conventional detection techniques often lack the required sensitivity, specificity, or cost-effectiveness, making real-time, in situ analysis challenging. Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical tool, offering improved sensitivity through the enhancement of Raman scattering by plasmonic nanostructures. While noble metals such as Ag and Au are currently the reference choices for SERS substrates, fabrication methods should balance enhancement efficiency, reproducibility and scalability. In this study, we propose a novel approach for SERS substrate fabrication using reactive Aerosol Jet Printing (r-AJP) as an innovative additive manufacturing technique. The r-AJP process enables in-flight Ag seed reduction and nucleation of Ag nanoparticles (NPs) by mixing silver nitrate and ascorbic acid aerosols before deposition, as suggested by computational fluid dynamics (CFD) simulations. The resulting coatings were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses, revealing the formation of nanoporous crystalline Ag agglomerates partially covered by residual matter. The as-prepared SERS substrates exhibited remarkable SERS activity, demonstrating a high enhancement factor (10⁶) for rhodamine (R6G) detection. Our findings highlight the potential of r-AJP as a scalable and cost-effective fabrication strategy for next-generation SERS sensors, paving the way for the development of a new additive manufacturing tool for noble metal material deposition. Full article

Background: The candidate therapeutic peptide TnP demonstrates broad, system-level regulatory capacity, revealed through integrated network analysis from transcriptomic data in zebrafish. Our study primarily identifies TnP as a multifaceted modulator of drug metabolism, wound healing, proteolytic activity, and pigmentation pathways. Results: Transcriptomic profiling of TnP-treated larvae following tail fin amputation revealed 558 differentially expressed genes (DEGs), categorized into four functional networks: (1) drug-metabolizing enzymes (cyp3a65, cyp1a) and transporters (SLC/ABC families), where TnP alters xenobiotic processing through Phase I/II modulation; (2) cellular trafficking and immune regulation, with upregulated myosin genes (myhb/mylz3) enhancing wound repair and tlr5-cdc42 signaling fine-tuning inflammation; (3) proteolytic cascades (c6ast4, prss1) coupled to autophagy (ulk1a, atg2a) and metabolic rewiring (g6pca.1-tg axis); and (4) melanogenesis-circadian networks (pmela/dct-fbxl3l) linked to ubiquitin-mediated protein turnover. Key findings highlight TnP’s unique coordination of rapid (protease activation) and sustained (metabolic adaptation) responses, enabled by short network path lengths (1.6–2.1 edges). Hub genes, such as nr1i2 (pxr), ppara, and bcl6aa/b, mediate crosstalk between these systems, while potential risks—including muscle hypercontractility (myhb overexpression) or cardiovascular effects (ace2-ppp3ccb)—underscore the need for targeted delivery. The zebrafish model validated TnP-conserved mechanisms with human relevance, particularly in drug metabolism and tissue repair. TnP’s ability to synchronize extracellular matrix remodeling, immune resolution, and metabolic homeostasis supports its development for the treatment of fibrosis, metabolic disorders, and inflammatory conditions. Conclusions: Future work should focus on optimizing tissue-specific delivery and assessing genetic variability to advance clinical translation. This system-level analysis positions TnP as a model example for next-generation multi-pathway therapeutics. Full article

This research focuses on designing polymer membranes as biocompatible materials using home-built electrospinning equipment, offering alternative solutions for tissue regeneration applications. This technological development supports cell growth on biomaterial substrates, including hepatocellular carcinoma (Hep-G2) cells. This work researches the compatibility of polymer membranes (fiber mats) made of polyvinylidene difluoride (PVDF) for possible use in cellular engineering. A standard culture medium was employed to support the proliferation of Hep-G2 cells under controlled conditions (37 °C, 4.8% CO₂, and 100% relative humidity). Subsequently, after the incubation period, electrochemical impedance spectroscopy (EIS) assays were conducted in a physiological environment to characterize the electrical cellular response, providing insights into the biocompatibility of the material. Scanning electron microscopy (SEM) was employed to evaluate cell adhesion, morphology, and growth on the PVDF polymer membranes. The results suggest that PVDF polymer membranes can be successfully produced through electrospinning technology, resulting in the formation of a dipole structure, including the possible presence of a polar β-phase, contributing to piezoelectric activity. EIS measurements, based on R_ct and C_dl values, are indicators of ion charge transfer and strong electrical interactions at the membrane interface. These findings suggest a favorable environment for cell proliferation, thereby enhancing cellular interactions at the fiber interface within the electrolyte. SEM observations displayed a consistent distribution of fibers with a distinctive spherical agglomeration on the entire PVDF surface. Finally, integrating piezoelectric properties into cell culture systems provides new opportunities for investigating the influence of electrical interactions on cellular behavior through electrochemical techniques. Based on the experimental results, this electrospun polymer demonstrates great potential as a promising candidate for next-generation biomaterials, with a probable application in tissue regeneration. Full article

Pathologies of the heart (e.g., ischemic disease, valve fibrosis and calcification, progressive myocardial fibrosis, heart failure, and arrhythmogenic disorders) stem from the irreversible deterioration of cardiac tissues, leading to severe clinical consequences. The limited regenerative capacity of the adult myocardium and the architectural complexity of the heart present major challenges for tissue engineering. However, recent advances in biomaterials and biofabrication techniques have opened new avenues for recreating functional cardiac tissues. Particularly relevant in this context is the integration of biomimetic design principles, such as structural anisotropy, mechanical and electrical responsiveness, and tissue-specific composition, into 3D bioprinting platforms. This review aims to provide a comprehensive overview of current approaches in cardiac bioprinting, with a focus on how structural and functional biomimicry can be achieved using advanced hydrogels, bioprinting techniques, and post-fabrication stimulation. By critically evaluating materials, methods, and applications such as patches, vasculature, valves, and chamber models, we define the state of the art and highlight opportunities for developing next-generation bioengineered cardiac constructs. Full article

The increasing demand for plant-based foods has led to significant growth in the availability, at a retail level, of plant-based cheese analogue products. This study presents the first comprehensive benchmarking of commercially available plant-based cheese analogue (PBCA) products in the Irish market, comparing them against conventional cheddar and processed dairy cheeses. A total of 16 cheese products were selected from Irish retail outlets, comprising five block-style plant-based analogues, seven slice-style analogues, two cheddar samples, and two processed cheese samples. Results showed that plant-based cheese analogues had significantly lower protein content (0.1–1.7 g/100 g) than cheddar (25 g/100 g) and processed cheese (12.9–18.2 g/100 g) and lacked a continuous protein matrix, being instead stabilized largely by solid fats, starch, and hydrocolloids. While cheddar showed the highest hardness, some plant-based cheeses achieved comparable hardness using texturizing agents but still demonstrated lower tan δ_max values, indicating inferior melting behaviour. Thermograms of differential scanning calorimetry presented a consistent single peak at ~20 °C across most vegan-based variants, unlike the dual-phase melting transitions observed in dairy cheeses. Sensory analysis further highlighted strong negative associations between PBCAs and consumer-relevant attributes such as flavour, texture, and overall acceptability. By integrating structural, functional, and sensory findings, this study identifies key formulation and performance deficits across cheese formats and provides direction for targeted improvements in next-generation PBCA product development. Full article

The glucagon-like peptide-1 receptor (GLP-1R), which belongs to the class B1 G protein-coupled receptor (GPCR) family, is an important target for treatment of metabolic disorders, including type 2 diabetes and obesity. The growing interest in GLP-1R-based therapies is driven by the development of various functional agonists as well as the huge commercial market. Thus, understanding the structural details of ligand-induced signaling are important for developing improved GLP-1R drugs. Here, we investigated the conformational dynamics of the receptor in complex with a selection of prototypical functional agonists, including CHU-128 (small molecule-biased), danuglipron (small molecule balanced), and Peptide 19 (peptide balanced), which exhibit unique, distinct binding modes and induced helix packing. Furthermore, our all-atom molecular dynamics (MD) simulations revealed atomic feature how different those ligands led to signaling pathway preference. Our findings offer valuable insights into the mechanistic principle of GLP-1R activation, which are helpful for the rational design of next-generation GLP-1R drug molecules. Full article

Electrochemical biosensors are revolutionizing food testing by addressing critical limitations of conventional strategies that suffer from cost, complexity, and field-deployment challenges. Emerging fluorescence and Raman techniques, while promising, face intrinsic drawbacks like photobleaching and matrix interference in opaque or heterogeneous samples. In contrast, electrochemical biosensors leverage electrical signals to bypass optical constraints, enabling rapid, cost-effective, and pretreatment-free analysis of turbid food matrices. This review highlights their operational mechanisms, emphasizing nano-enhanced signal amplification (e.g., Au nanoparticles and graphene) and biorecognition elements (antibodies, aptamers, and molecularly imprinted polymers) for ultrasensitive assay of contaminants, additives, and adulterants. By integrating portability, scalability, and real-time capabilities, electrochemical biosensors align with global food safety regulations and sustainability goals. Challenges in standardization, multiplexed analysis, and long-term stability are discussed, alongside future directions toward AI-driven analytics, biodegradable sensors, and blockchain-enabled traceability, ultimately fostering precision-driven, next-generation food safety and quality testing. Full article

In industrial wireless networks beyond 5G and toward 6G, combining uplink non-orthogonal multiple access (NOMA) with the Kalman filter (KF) effectively reduces interruption risks and transmission delays in remote state estimation. However, the complexity of wireless environments and concurrent multi-sensor transmissions introduce significant interference and latency, impairing the KF’s ability to continuously obtain reliable observations. Meanwhile, existing remote state estimation systems typically rely on oversimplified wireless communication models, unable to adequately handle the dynamics and interference in realistic network scenarios. To address these limitations, this paper formulates a novel dynamic wireless resource allocation problem as a mixed-integer nonlinear programming (MINLP) model. By jointly optimizing sensor grouping and power allocation—considering sensor available power and outage probability constraints—the proposed scheme minimizes both estimation outage and transmission delay. Simulation results demonstrate that, compared to conventional approaches, our method significantly improves transmission reliability and KF estimation performance, thus providing robust technical support for remote state estimation in next-generation industrial wireless networks. Full article