Search Results (1,257)

In the era of smart agriculture, accurate identification of plant species is critical for effective crop management, biodiversity monitoring, and the sustainable use of medicinal resources. However, existing deep learning approaches often underperform when applied to fine-grained plant classification tasks due to the lack of domain-specific, high-quality datasets and the limited representational capacity of traditional architectures. This study addresses these challenges by introducing a novel, well-curated leaf image dataset consisting of 39 classes of medicinal and aromatic plants collected from the Aromatic and Medicinal Plant Research Station in Odakkali, Kerala, India. To overcome performance bottlenecks observed with a baseline Convolutional Neural Network (CNN) that achieved only 44.94% accuracy, we progressively enhanced model performance through a series of architectural innovations. These included the use of a pre-trained VGG16 network, data augmentation techniques, and fine-tuning of deeper convolutional layers, followed by the integration of Squeeze-and-Excitation (SE) attention blocks. Ultimately, we propose a hybrid deep learning architecture that combines VGG16 with Batch Normalization, Gated Recurrent Units (GRUs), Transformer modules, and Dilated Convolutions. This final model achieved a peak validation accuracy of 95.24%, significantly outperforming several baseline models, such as custom CNN (44.94%), VGG-19 (59.49%), VGG-16 before augmentation (71.52%), Xception (85.44%), Inception v3 (87.97%), VGG-16 after data augumentation (89.24%), VGG-16 after fine-tuning (90.51%), MobileNetV2 (93.67), and VGG16 with SE block (94.94%). These results demonstrate superior capability in capturing both local textures and global morphological features. The proposed solution not only advances the state of the art in plant classification but also contributes a valuable dataset to the research community. Its real-world applicability spans field-based plant identification, biodiversity conservation, and precision agriculture, offering a scalable tool for automated plant recognition in complex ecological and agricultural environments. Full article

We present a model that integrates the mapping of the phenology and climatic suitability for the spotted lanternfly (SLF), Lycorma delicatula (White, 1845) (Hemiptera: Fulgoridae), to provide guidance on when and where to conduct surveillance and management of this highly invasive pest. The model was designed for use in the Degree-Day, Establishment Risk, and Phenological Event Maps (DDRP) platform, which is an open-source decision support tool to help to detect, monitor, and manage invasive threats. We validated the model using presence records and phenological observations derived from monitoring studies and the iNaturalist database. The model performed well, with more than >99.9% of the presence records included in the potential distribution for North America, a large proportion of the iNaturalist observations correctly predicted, and a low error rate for dates of the first appearance of adults. Cold and heat stresses were insufficient to exclude the SLF from most areas of the conterminous United States (CONUS), but an inability for the pest to complete its life cycle in cold areas may hinder establishment. The appearance of adults occurred several months earlier in warmer regions of North America and Europe, which suggests that host plants in these areas may experience stronger feeding pressure. The near-real-time forecasts produced by the model are available at USPest.org and the USA National Phenology Network to support decision making for the CONUS. Forecasts of egg hatch and the appearance of adults are particularly relevant for surveillance to prevent new establishments and for managing existing populations. Full article

The American Cancer Society estimates that over 152,000 new cases of colorectal cancer (CRC) were diagnosed in 2024, with more than 105,000 cases affecting the colon and 46,000 involving the rectum. CRC remains the second leading cause of cancer-related deaths in the United States, with an estimated 53,010 deaths in 2024. In the era of precision medicine, which incorporates molecular and environmental information into clinical decision-making, identifying patients harboring a deficiency in Deoxyribonucleic acid (DNA) repair allowed for targeted immunotherapies and significantly reduced CRC-related mortality. A significant advancement in this domain is the application of liquid biopsy, which has emerged as a promising tool for prognostication, guiding therapy, and monitoring treatment response in CRC. This review aims to comprehensively explore the role of liquid biopsy in colorectal malignancies, describing its practical applications, prognostic significance, and potential to revolutionize CRC management in the future. At the end, we also aim to show a schematic representation of showing integration of Circulating Tumor (Ct) DNA in routine clinical management of CRC. The highlight of this article is the structured and evidence-based schematic framework and its integration into future practice. The schematic pathway is designed to optimize ctDNA utilization across various stages of colorectal cancer management. Full article

In this study, the tool axis inclination angle with respect to the surface normal is used to compare the wear of spherical and toroidal milling cutters. Five different surface types were machined in this investigation using three toroidal and three spherical milling cutters at inclination angles of 15°, 35°, and 55°. Each cutter processed 45 surfaces, nine of each type. The MM1-200 microscope was used to monitor tool wear under a microscope after the surfaces had been machined. A comparison diagram and microscopic pictures of tool wear in relation to the wear surface are presented in the paper. Additionally, it contrasts the tool edge’s state at the conclusion of machining with its state following five years of storage, during which time the tool was subjected to environmental influences. Full article

Myelodysplastic syndromes/neoplasms (MDS) represent a biologically and clinically diverse group of myeloid malignancies marked by cytopenias, morphological dysplasia, and an inherent risk of progression to acute myeloid leukemia. Over the past two decades, the field has made significant advances in characterizing the molecular landscape of MDS, leading to refined classification systems to reflect the underlying genetic and biological diversity. In 2025, the treatment of MDS is increasingly individualized, guided by integrated clinical, cytogenetic, and molecular risk stratification tools. For lower-risk MDS, the treatment paradigm has evolved beyond erythropoiesis-stimulating agents (ESAs) with the introduction of novel effective agents such as luspatercept and imetelstat, as well as shortened schedules of hypomethylating agents (HMAs). For higher-risk disease, monotherapy with HMAs continue to be the standard of care as combination therapies of HMAs with novel agents have, to date, failed to redefine treatment paradigms. The recognition of precursor states like clonal hematopoiesis of indeterminate potential (CHIP) and the increasing use of molecular monitoring will hopefully enable earlier intervention/prevention strategies. This review provides a comprehensive overview of the current treatment approach for MDS, highlighting new classifications, prognostic tools, evolving therapeutic options, and ongoing challenges. We discuss evidence-based recommendations, treatment sequencing, and emerging clinical trials, with a focus on translating biological insights into improved outcomes for patients with MDS. Full article

With the ongoing advancement of industrialization and rapid urbanization, the emission of volatile organic compounds (VOCs) has increased significantly. As key precursors of PM_2.5 and ozone formation, VOCs pose a growing threat to the health of ecosystems. Due to their complex and dynamic transformation processes across air, water, and soil media, the ecological risks associated with VOCs have attracted increasing attention from both the scientific community and policy-makers. This study systematically reviews the core literature on the ecological impacts of VOCs published between 2005 and 2024, based on data from the Web of Science and Google Scholar databases. Utilizing three bibliometric tools (CiteSpace, VOSviewer, and Bibliometrix), we conducted a comprehensive visual analysis, constructing knowledge maps from multiple perspectives, including research trends, international collaboration, keyword evolution, and author–institution co-occurrence networks. The results reveal a rapid growth in the ecological impact of VOCs (EIVOCs), with an average annual increase exceeding 11% since 2013. Key research themes include source apportionment of air pollutants, ecotoxicological effects, biological response mechanisms, and health risk assessment. China, the United States, and Germany have emerged as leading contributors in this field, with China showing a remarkable surge in research activity in recent years. Keyword co-occurrence and burst analyses highlight “air pollution”, “exposure”, “health”, and “source apportionment” as major research hotspots. However, challenges remain in areas such as ecosystem functional responses, the integration of multimedia pollution pathways, and interdisciplinary coordination mechanisms. There is an urgent need to enhance monitoring technology integration, develop robust ecological risk assessment frameworks, and improve predictive modeling capabilities under climate change scenarios. This study provides scientific insights and theoretical support for the development of future environmental protection policies and comprehensive VOCs management strategies. Full article

The ongoing decline in global biodiversity constitutes a critical challenge for environmental science, necessitating the prompt development of effective monitoring frameworks and conservation protocols to safeguard the structure and function of natural ecosystems. Recent progress in ecoacoustic monitoring, supported by advances in artificial intelligence, might finally offer scalable tools for systematic biodiversity assessment. In this study, we evaluate the performance of BirdNET, a state-of-the-art deep learning model for avian sound recognition, in the context of selected bird species characteristic of the Italian Alpine region. To this end, we assemble a comprehensive, manually annotated audio dataset targeting key regional species, and we investigate a variety of strategies for model adaptation, including fine-tuning with data augmentation techniques to enhance recognition under challenging recording conditions. As a baseline, we also develop and evaluate a simple Convolutional Neural Network (CNN) trained exclusively on our domain-specific dataset. Our findings indicate that BirdNET performance can be greatly improved by fine-tuning the pre-trained network with data collected within the specific regional soundscape, outperforming both the original BirdNET and the baseline CNN by a significant margin. These findings underscore the importance of environmental adaptation and data variability for the development of automated ecoacoustic monitoring devices while highlighting the potential of deep learning methods in supporting conservation efforts and informing soundscape management in protected areas. Full article

Nature-based solutions (NbS) are increasingly recognized as strategic alternatives and complements to grey infrastructure for addressing water-related challenges in the context of climate change, urbanization, and biodiversity decline. This article presents a critical, theory-informed review of the state of NbS implementation in European water management, drawing on a structured synthesis of empirical evidence from regional case studies and policy frameworks. The analysis found that while NbS are effective in reducing surface runoff, mitigating floods, and improving water quality under low- to moderate-intensity events, their performance remains uncertain under extreme climate scenarios. Key gaps identified include the lack of long-term monitoring data, limited assessment of NbS under future climate conditions, and weak integration into mainstream planning and financing systems. Existing evaluation frameworks are critiqued for treating NbS as static interventions, overlooking their ecological dynamics and temporal variability. In response, a dynamic, climate-resilient assessment model is proposed—grounded in systems thinking, backcasting, and participatory scenario planning—to evaluate NbS adaptively. Emerging innovations, such as hybrid green–grey infrastructure, adaptive governance models, and novel financing mechanisms, are highlighted as key enablers for scaling NbS. The article contributes to the scientific literature by bridging theoretical and empirical insights, offering region-specific findings and recommendations based on a comparative analysis across diverse European contexts. These findings provide conceptual and methodological tools to better design, evaluate, and scale NbS for transformative, equitable, and climate-resilient water governance. Full article

Background/Objective: Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique with growing relevance in psychiatry. Its ability to measure cortical hemodynamics positions it as a potential tool for monitoring neurofunctional changes related to treatment. However, the specific features and level of consistency of its use in clinical psychiatric settings remain unclear. A scoping review was conducted under PRISMA-ScR guidelines to systematically map how fNIRS has been used in monitoring treatment response among individuals with psychiatric disorders. Methods: Forty-seven studies published between 2009 and 2025 were included based on predefined eligibility criteria. Data was extracted on publication trends, research design, sample characteristics, fNIRS paradigms, signal acquisition, preprocessing methods, and integration of clinical outcomes. Reported limitations and conflicts of interest were also analyzed. Results: The number of publications increased sharply after 2020, predominantly from Asia. Most studies used experimental designs, with 31.9% employing randomized controlled trials. Adults were the primary focus (93.6%), with verbal fluency tasks and DLPFC-targeted paradigms most common. Over half of the studies used high-density (>32-channel) systems. However, only 44.7% reported motion correction procedures, and 53.2% did not report activation direction. Clinical outcome linkage was explicitly stated in only 12.8% of studies. Conclusions: Despite growing clinical interest, with fNIRS showing promise as a non-invasive neuroimaging tool for monitoring psychiatric treatment response, the current evidence base is limited by methodological variability and inconsistent outcome integration. There is a rising need for the adoption of standardized protocols for both design and reporting. Future research should also include longitudinal studies and multimodal approaches to enhance validity and clinical relevance. Full article

Three-dimensional (3D) concrete printing (3DCP) has gained significant attention over the last decade due to its many claimed benefits. The absence of effective real-time quality control mechanisms, however, can lead to inconsistencies in extrusion, compromising the integrity of 3D-printed structures. Although the importance of quality control in 3DCP is broadly acknowledged, research lacks systematic methods. This research investigates the feasibility of using ultrasonic pulse velocity (UPV) as a practical, in situ, real-time monitoring tool for 3DCP. Two different groups of binders were investigated: limestone calcined clay (LC³) and zeolite-based mixes in binary and ternary blends. Filaments of 200 mm were extruded every 5 min, and UPV, pocket hand vane, flow table, and viscometer tests were performed to measure pulse velocity, shear strength, relative deformation, yield stress, and plastic viscosity, respectively, in the fresh state. Once the filaments presented printing defects (e.g., filament tearing, filament width reduction), the tests were concluded, and the open time was recorded. Isothermal calorimetry tests were conducted to obtain the initial heat release and reactivity of the supplementary cementitious materials (SCMs). Results showed a strong correlation (R² = 0.93) between UPV and initial heat release, indicating that early hydration (ettringite formation) influenced UPV and determined printability across different mixes. No correlation was observed between the other tests and hydration kinetics. UPV demonstrated potential as a real-time monitoring tool, provided the mix-specific pulse velocity is established beforehand. Further research is needed to evaluate UPV performance during active printing when there is an active flow through the printer. Full article

Accurate SOH prediction provides a reliable reference for lithium-ion battery maintenance. However, novel algorithms are still needed because few studies have considered the correlations between monitored parameters in Euclidean space and non-Euclidean space at different time points. To address this challenge, a novel gated-temporal network assisted by improved grasshopper optimization (IGOA-GGNN-TCN) is developed. In this model, features obtained from lithium-ion batteries are used to construct graph data based on cosine similarity. On this basis, the GGNN-TCN is employed to obtain the potential correlations between monitored parameters in Euclidean and non-Euclidean spaces. Furthermore, IGOA is introduced to overcome the issue of hyperparameter optimization for GGNN-TCN, improving the convergence speed and the local optimal problem. Competitive results on the Oxford dataset indicate that the SOH prediction performance of proposed IGOA-GGNN-TCN surpasses conventional methods, such as convolutional neural networks (CNNs) and gate recurrent unit (GRUs), achieving an R² value greater than 0.99. The experimental results demonstrate that the proposed IGOA-GGNN-TCN framework offers a novel and effective approach for state-of-health (SOH) estimation in lithium-ion batteries. By integrating improved grasshopper optimization (IGOA) with hybrid graph-temporal modeling, the method achieves superior prediction accuracy compared to conventional techniques, providing a promising tool for battery management systems in real-world applications. Full article

Forest drought monitoring tools are crucial for managing tree water stress and enhancing ecosystem resilience. The Forest Drought Response Index (ForDRI) was developed to monitor drought conditions in forested areas across the contiguous United States (CONUS), integrating vegetation health, climate data, groundwater, and soil moisture content. This study evaluated ForDRI using Pearson correlations with the Bowen Ratio (BR) at 24 AmeriFlux sites and Spearman correlations with the Tree-Ring Growth Index (TRSGI) at 135 sites, along with feedback from 58 stakeholders. CONUS was divided into four forest subgroups: (1) the West/Pacific Northwest, (2) Rocky Mountains/Southwest, (3) East/Northeast, and (4) South/Central/Southeast Forest regions. Strong positive ForDRI-TRSGI correlations (ρ > 0.7, p < 0.05) were observed in the western regions, where drought significantly impacts growth, while moderate alignment with BR (R = 0.35–0.65, p < 0.05) was noted. In contrast, correlations in Eastern and Southern forests were weak to moderate (ρ = 0.4–0.6 for TRSGI and R = 0.1–0.3 for BR). Stakeholders’ feedback indicated that ForDRI realistically maps historical drought years and recent trends, though suggestions for improvements, including trend maps and enhanced visualizations, were made. ForDRI is a valuable complementary tool for monitoring forest droughts and informing management decisions. Full article

This study aimed to monitor the operating state of an induction motor, a type of electromagnetic motor, using a highly sensitive magnetic sensor, which could be applied for anomaly detection in the future. Monitoring the health of electromagnetic motors is very important to minimize losses due to failures. Detecting anomalies using the changes compared with the initial state is a possible solution, but there are issues such as a lack of training data for machine learning and the need to install multiple sensors. Therefore, an attempt was made to acquire the various operating states of a motor from magnetic signals using a single magnetic sensor capable of non-contact measurement. The relationships between the magnetic flux density from the motor and the other motor conditions were investigated. As a result, the magnetic spectrum was found to contain information on the rotor rotation frequency, torque, and output power. Therefore, the magnetic sensor can be applied to monitor a motor’s operating conditions, making it a useful tool for advanced data analysis. Full article

The human voice is an invaluable tool for communication, carrying information about a speaker’s emotional state and cognitive health. Recent research highlights the potential of acoustic biomarkers to detect early signs of mental health and neurodegenerative conditions. Despite their promise, vocal biomarkers remain underutilized in clinical settings, with limited standardized protocols for assessment. This Perspective article argues for the integration of acoustic biomarkers into digital health solutions to improve the detection and monitoring of cognitive impairment and emotional disturbances. Advances in speech analysis and machine learning have demonstrated the feasibility of using voice features such as pitch, jitter, shimmer, and speech rate to assess these conditions. Moreover, we propose that singing, particularly simple melodic structures, could be an effective and accessible means of gathering vocal biomarkers, offering additional insights into cognitive and emotional states. Given its potential to engage multiple neural networks, singing could function as an assessment tool and an intervention strategy for individuals with cognitive decline. We highlight the necessity of further research to establish robust, reproducible methodologies for analyzing vocal biomarkers and standardizing voice-based diagnostic approaches. By integrating vocal analysis into routine health assessments, clinicians and researchers could significantly advance early detection and personalized interventions for cognitive and emotional disorders. Full article

Cracks and seepage in dam structures pose a serious risk to their safety, yet traditional inspection methods often fall short when it comes to detecting shallow or early-stage fractures. This study proposes a new approach that uses spectral response analysis to quickly identify signs of seepage in concrete dams. Researchers developed a three-layer model—representing the concrete, a seepage zone, and water—to better understand how cracks affect the way electrical signals behave, thereby inverting the state of the dam based on how electrical signals behave in actual engineering measurements. Through computer simulations and lab experiments, the team explored how changes in the resistivity and thickness of the seepage layer, along with the resistivity of surrounding water, influence key indicators like impedance and signal angle. The results show that the “spectrum-based method” can effectively detect seepage in concrete cracks of dams, and the measurement method of the “spectral quadrupole method” based on the “spectrum-based method” is highly sensitive to these variations, making it a promising tool for spotting early seepage. Field tests backed up the lab findings, confirming that this method is significantly better than traditional techniques at detecting cracks less than a meter deep and identifying early signs of water intrusion. It could provide dam inspectors with a more reliable way to monitor structural health and prevent potential failures. Full article