Search Results (142,034)

This study investigates the dynamic changes in wheat canopy spectral characteristics across seven critical growth stages (Tillering, Pre-Jointing, Jointing, Post-Jointing, Booting, Flowering, and Ripening) using UAV-based multispectral remote sensing. By analyzing four key spectral bands—green (G), red (R), red-edge (RE), and near-infrared (NIR)—and their combinations, we identify spectral features that reflect changes in canopy activity, health, and structure. Results show that the green band is highly sensitive to chlorophyll activity and low canopy coverage during the Tillering stage, while the NIR band captures structural complexity and canopy density during the Jointing and Booting stages. The combination of G and NIR bands reveals increased canopy density and spectral concentration during the Booting stage, while the RE band effectively detects plant senescence and reduced spectral uniformity during the ripening stage. Time-series analysis of spectral data across growth stages improves the accuracy of growth stage identification, with dynamic spectral changes offering insights into growth inflection points. Spatially, the study demonstrates the potential for identifying field-level anomalies, such as water stress or disease, providing actionable data for targeted interventions. This comprehensive spatio-temporal monitoring framework improves crop management and offers a cost-effective, precise solution for disease prediction, yield forecasting, and resource optimization. The study paves the way for integrating UAV remote sensing into precision agriculture practices, with future research focusing on hyperspectral data integration to enhance monitoring models. Full article

Crop yields are influenced by various factors, including seed quality and environmental conditions. Detecting seed vigor is a critical task for seed researchers, as it plays a vital role in seed quality assessment. Traditionally, this evaluation was performed manually, which is time-consuming and labor-intensive. To address this limitation, this study integrates the ConvUpDownModule (a customized convolutional module), C2f-DSConv(C2f module with Integrated Dynamic Snake Convolution), and T-SPPF (the SPPF module integrated with the transformer multi-head attention mechanism) into the VT-YOLOv8-Seg network (the improved YOLOv8-Seg Network), an enhancement of the YOLOv8-Seg architecture. The ConvUpDownModule reduces the computational complexity and model parameters. The C2f-DSConv leverages flexible convolutional kernels to enhance the accuracy of maize germ edge segmentation. The T-SPPF integrates global information to improve multi-scale segmentation performance. The proposed model is designed for detecting and segmenting maize seeds and germs, facilitating seed germination detection and germination speed computation. In detection tasks, the VT-YOLOv8-Seg model achieved 97.3% accuracy, 97.9% recall, and 98.5% mAP50, while in segmentation tasks, it demonstrated 97.2% accuracy, 97.7% recall, and 98.2% mAP50. Comparative experiments with Mask R-CNN, YOLOv5-Seg, and YOLOv7-Seg further validated the superior performance of our model in both detection and segmentation. Additionally, the impact of seed aging on maize seed growth and development was investigated through artificial aging studies. Key metrics such as germination rate and germ growth speed, both closely associated with germination vigor, were analyzed, demonstrating the effectiveness of the proposed approach for seed vigor assessment. Full article

Video super-resolution aims to generate high-resolution video sequences with realistic details from existing low-resolution video sequences. However, most existing video super-resolution models require substantial computational power and are not suitable for resource-constrained devices such as smartphones and tablets. In this paper, we propose a lightweight video super-resolution (LightVSR) model that employs a novel feature aggregation module to enhance video quality by efficiently reconstructing high-resolution frames from compressed low-resolution inputs. LightVSR integrates several novel mechanisms, including head-tail convolution, cross-layer shortcut connections, and multi-input attention, to enhance computational efficiency while guaranteeing video super-resolution performance. Extensive experiments show that LightVSR achieves a frame rate of 28.57 FPS and a PSNR of 39.25 dB on the UDM10 dataset and 36.91 dB on the Vimeo-90k dataset, validating its efficiency and effectiveness. Full article

Nicotinamide (NAM), the amide form of vitamin B3, is a precursor to essential cofactors nicotinamide adenine dinucleotide (NAD⁺) and NADPH. NAD⁺ is integral to numerous cellular processes, including metabolism regulation, ATP production, mitochondrial respiration, reactive oxygen species (ROS) management, DNA repair, cellular senescence, and aging. NAM supplementation has demonstrated efficacy in restoring cellular energy, repairing DNA damage, and inhibiting inflammation by suppressing pro-inflammatory cytokines release. Due to its natural presence in a variety of foods and its excellent safety profile—even at high doses of up to 3 g/day—NAM is extensively used in the chemoprevention of non-melanoma skin cancers and the treatment of dermatological conditions such as blistering diseases, atopic dermatitis, rosacea, and acne vulgaris. Recently, its anti-aging properties have elevated NAM’s prominence in skincare formulations. Beyond DNA repair and energy replenishment, NAM significantly impacts oxidative stress reduction, cell cycle regulation, and apoptosis modulation. Despite these multifaceted benefits, the comprehensive molecular mechanisms underlying NAM’s actions remain not fully elucidated. This review consolidates recent research to shed light on these mechanisms, emphasizing the critical role of NAM in cellular health and its therapeutic potential. By enhancing our understanding, this work underscores the importance of continued exploration into NAM’s applications, aiming to inform future clinical practices and skincare innovations. Full article

Background: Hereditary hemoglobin disorders are the most common globally distributed monogenic red cell diseases. The rights of women with thalassemia or sickle cell disease (SCD) to motherhood need to be protected by creating a roadmap to guide her, and her family network, along all the phases of the event. In fact, pregnancy in these vulnerable patients requires special attention and guidelines from the counseling stage (giving information about the special requirement and risks posed by their pregnancy with respect to the general population) the pre-conception stage, the early and mid-late pregnancy stage, to labor and lactation. The biocomplexity of these diseases requires a multidisciplinary team synergizing with gynecologists and obstetricians. In addition, the presence of a multicultural scenario requires healthcare workers to overcome stereotypes and adopt appropriate anthropological tools that might help them integrate the different cultural models of disease and motherhood. Methods: The Management Committee of the Society for Thalassemia and Hemoglobinopathies (SITE) selected and brought together a multidisciplinary and multiprofessional group made up of experts in hemoglobinopathies and experts in anthropology, flanked along with by experts with methodological and organizational expertise in order to create recommendations based on the integration of available scientific evidence together with expert opinion. Results: The panelists critically analyzed the literature, combining in a single document practices developed over several years of managing young women with hemoglobinopathies in a sensitive phase of their lives. Conclusions: This good practice document is the result of a collegial effort by Italian experts on hemoglobinopathies who are members of SITE. (SITE). Full article

Computer vision seeks to mimic the human visual system and plays an essential role in artificial intelligence. It is based on different signal reprocessing techniques; therefore, developing efficient techniques becomes essential to achieving fast and reliable processing. Various signal preprocessing operations have been used for computer vision, including smoothing techniques, signal analyzing, resizing, sharpening, and enhancement, to reduce reluctant falsifications, segmentation, and image feature improvement. For example, to reduce the noise in a disturbed signal, smoothing kernels can be effectively used. This is achievedby convolving the distributed signal with smoothing kernels. In addition, orthogonal moments (OMs) are a crucial technique in signal preprocessing, serving as key descriptors for signal analysis and recognition. OMs are obtained by the projection of orthogonal polynomials (OPs) onto the signal domain. However, when dealing with 3D signals, the traditional approach of convolving kernels with the signal and computing OMs beforehand significantly increases the computational cost of computer vision algorithms. To address this issue, this paper develops a novel mathematical model to embed the kernel directly into the OPs functions, seamlessly integrating these two processes into a more efficient and accurate approach. The proposed model allows the computation of OMs for smoothed versions of 3D signals directly, thereby reducing computational overhead. Extensive experiments conducted on 3D objects demonstrate that the proposed method outperforms traditional approaches across various metrics. The average recognition accuracy improves to 83.85% when the polynomial order is increased to 10. Experimental results show that the proposed method exhibits higher accuracy and lower computational costs compared to the benchmark methods in various conditions for a wide range of parameter values. Full article

We study inverse problems of identification of lower-order coefficients in a second-order parabolic equation. The coefficients are sought in the form of a finite series segment with unknown coefficients, depending on time. The linear case is also considered. Overdetermination conditions are the integrals over the boundary of a solution’s domain with weights. We focus on existence and uniqueness theorems and stability estimates for solutions to these inverse problems. An operator equation to which the problem is reduced is studied with the use of the contraction mapping principle. A solution belongs to some Sobolev space and has all generalized derivatives occurring into the equation summable to some power. The method of the proof is constructive, and it can be used for developing new numerical algorithms for solving the problem. Full article

Compact high-power sub-nanosecond laser pulse sources with a wavelength of 940 nm are developed and studied. A design for laser pulse sources based on a vertical stack is proposed, which includes a semiconductor laser chip and a current switch chip. To create a compact high-speed current switch, a three-electrode heterothyristor is developed. It is found that the use of heterothyristor-based current switches allows the creation of a low-loss pump current circuit, generating short current pulses and operating the semiconductor laser in gain-switching mode. For the semiconductor laser chip, an asymmetric semiconductor heterostructure with a quantum-well active region is designed. The design of the emitting aperture of the laser chip is optimized to improve the operating characteristics of the laser beam when generating sub-ns optical pulses. It is shown that the transition to a monolithic emitting aperture design reduces the laser pulse turn-on spatial inhomogeneity, which is 90 ps over the entire range of optical powers studied. It is also demonstrated that by increasing the emitting aperture width to 400 μm, laser pulses with a peak power of 39.5 W and a pulse width at full width at half maximum (FWHM) of 120 ps can be generated. Full article

A growing body of research has explored how technology can enhance the development of pragmatic competence in a second language (L2). This systematic review synthesizes 37 empirical studies published between 2015 and 2024, focusing on various technological applications such as computer-mediated communication (CMC), interactive automated dialogues, virtual environments, and digital games. The analysis highlights that these tools promote pragmatic development by providing authentic or semi-authentic interaction, contextualized learning, and personalized practices. Meanwhile, the review also uncovers key challenges from both technological constraints and individual dimensions. Based on the findings, this review suggests several directions for future research. Further studies should adopt longitudinal, multimodal, and socially situated approaches, explore emerging generative AI technologies, and examine the interaction between individual learner differences and technological affordances to increase understanding of this evolving field. Full article

This study examines the role and contribution of the Sustainable Development Goals (SDGs) as a transformative framework in higher education (HE), focusing on the University of Split (UOS), Croatia. By applying a qualitative longitudinal methodology, it examines how UOS has engaged with the SDG agenda while overcoming the challenges of institutional disintegration, resource scarcity, and cultural change. Data from strategy documents, action plans, and interviews with university leaders reveal a significant evolution in UOS’s strategic alignment with the SDGs, from initial compliance to a more integrated and systemic approach. This study applies Steele and Rickards’ framework of institutional engagement and innovation culture and positions the UOS journey as a pathway to a transformative scenario. The findings highlight key drivers such as strategic planning and policymaking, as well as barriers such as fragmented governance and cultural resistance that shape the UOS journey. This research contributes to the understanding of SDG implementation at universities in former transition countries and provides insights into the use of the SDG framework to drive systemic change in higher education. Full article

Cutting tools are executive components in metal processing, and tool wear directly affects the quality of the workpiece and processing efficiency; monitoring the change in its state is crucial to avoid accidents and ensure the safety of workers. The traditional monitoring model cannot compress a large amount of cutting data effectively, failing to obtain reliable feature data, and there are some defects in generalization ability and monitoring accuracy. For this purpose, this article takes milling cutters as the research object, and it integrates signals from force sensors, vibration sensors, and acoustic emission sensors, combining the advantages of the denoising autoencoder (DAE) model in data compression and the high monitoring accuracy of the support vector regression (SVR) model, to establish a tool wear monitoring model based on DAE–SVR. The results show that compared with traditional DAE and SVR models in multiple datasets, the maximum improvement in monitoring performance (MAE) is 43.58%. Full article

Repetitive lifting tasks in occupational settings often result in shoulder injuries, impacting both health and productivity. Accurately assessing the biomechanical risk of these tasks remains a significant challenge in occupational ergonomics, particularly within manufacturing environments. Traditional assessment methods frequently rely on subjective reports and limited observations, which can introduce bias and yield incomplete evaluations. This study addresses these limitations by generating and utilizing a comprehensive dataset containing detailed time-series electromyography (EMG) data from 25 participants. Using high-precision wearable sensors, EMG data were collected from eight muscles as participants performed repetitive lifting tasks. For each task, the lifting index was calculated using the revised National Institute for Occupational Safety and Health (NIOSH) lifting equation (RNLE). Participants completed cycles of both low-risk and high-risk repetitive lifting tasks within a four-minute period, allowing for the assessment of muscle performance under realistic working conditions. This extensive dataset, comprising over 7 million data points sampled at approximately 1259 Hz, was leveraged to develop deep learning models to classify lifting risk. To provide actionable insights for practical occupational ergonomics and risk assessments, statistical features were extracted from the raw EMG data. Three deep learning models, Convolutional Neural Networks (CNNs), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM), were employed to analyze the data and predict the occupational lifting risk level. The CNN model achieved the highest performance, with a precision of 98.92% and a recall of 98.57%, proving its effectiveness for real-time risk assessments. These findings underscore the importance of aligning model architectures with data characteristics to optimize risk management. By integrating wearable EMG sensors with deep learning models, this study enables precise, real-time, and dynamic risk assessments, significantly enhancing workplace safety protocols. This approach has the potential to improve safety planning and reduce the incidence and severity of work-related musculoskeletal disorders, ultimately promoting better health and safety outcomes across various occupational settings. Full article

This paper evaluates the technical feasibility of Distributed Ledger Technology (DLT) within the healthcare ecosystem, with a focus on the use of Corda DLT to enhance governance and performance in a decentralized ecosystem, ensuring data integrity, security, and trustworthiness. Key attributes examined include the guarantee of data integrity, ensuring that transmitted data remain unaltered; authenticity through the implementation of digital signatures and certificates; confidentiality achieved via secure peer-to-peer communication accessible only to authorized parties; and traceability and auditing mechanisms that enable tracking of information changes and accountability. To validate these features, a Corda Distributed Application (CorDapp) was developed to manage the core logic of the healthcare ecosystem. The CorDapp was deployed across nodes and executed within the Corda network. Its performance was assessed using metrics such as throughput, latency, CPU usage, and memory consumption in both local and cloud network environments. Results demonstrate the feasibility of using Corda DLT technology in healthcare, effectively addressing critical requirements such as integrity, authenticity, confidentiality, traceability, and auditing while maintaining satisfactory performance across diverse deployment scenarios. Full article

Epistemology and technology have been working in synergy throughout history. This relationship has culminated in large language models (LLMs). LLMs are rapidly becoming integral parts of our daily lives through smartphones and personal computers, and we are coming to accept the functionality of LLMs as a given. As LLMs become more entrenched in societal functioning, questions have begun to emerge: Are LLMs capable of real understanding? What is knowledge in LLMs? Can knowledge exist independently of a conscious observer? While these questions cannot be answered definitively, we can argue that modern LLMs are more than mere symbol-manipulators and that LLMs in deep neural networks should be considered capable of a form of knowledge, though it may not qualify as justified true belief (JTB) in the traditional definition. This deep neural network design may have endowed LLMs with the capacity for internal representations, basic reasoning, and the performance of seemingly cognitive tasks, possible only through a compressive but generative form of representation that can be best termed as knowledge. In addition, the non-symbolic nature of LLMs renders them incompatible with the criticism posed by Searle’s “Chinese room” argument. These insights encourage us to revisit fundamental questions of epistemology in the age of LLMs, which we believe can advance the field. Full article

This research paper explores the development of AI-optimized lattice structures for biomechanics scaffold design, aiming to enhance bone implant functionality by utilizing advanced human–AI systems. The primary objective is to create scaffold structures that mimic the mechanical properties of natural bone and improve bioactivity and biocompatibility, adapting to patient-specific needs. We employed polylactic acid (PLA), calcium hydroxyapatite (cHAP), and reduced graphene oxide (rGO) as base materials, leveraging their synergistic properties. The scaffolds were intricately designed using nTopology software (nTop 5.12) and fabricated via 3D printing techniques, optimizing for biomechanical load-bearing and cellular integration. The study’s findings highlight a notable enhancement in the mechanical properties of the scaffolds, with the Gyroid lattice design demonstrating a 20% higher energy-absorption capacity than traditional designs. Thermal and chemical analysis revealed a 15% increase in the thermal stability of the composites, enhancing their resilience under physiological conditions. However, the research identified minor inconsistencies in filament diameter during 3D printing, which could affect scaffold uniformity. These findings underscore the potential of integrating AI-driven design with advanced material composites in revolutionizing orthopedic implant technologies. Full article