Search Results (177)

Accurate and non-destructive assessment of fruit firmness is critical for evaluating quality and ripeness, particularly in postharvest handling and supply chain management. This study presents the development of an image-based vibration analysis system for evaluating the firmness of kiwifruit using computer vision and machine learning. In the proposed setup, 120 kiwifruits were subjected to controlled excitation in the frequency range of 200–300 Hz using a vibration motor. A digital camera captured surface displacement over time (for 20 s), enabling the extraction of key dynamic features, namely, the damping coefficient (damping is a measure of a material’s ability to dissipate energy) and natural frequency (the first peak in the frequency spectrum), through image processing techniques. Results showed that firmer fruits exhibited higher natural frequencies and lower damping, while softer, more ripened fruits showed the opposite trend. These vibration-based features were then used as inputs to a feed-forward backpropagation neural network to predict fruit firmness. The neural network consisted of an input layer with two neurons (damping coefficient and natural frequency), a hidden layer with ten neurons, and an output layer representing firmness. The model demonstrated strong predictive performance, with a correlation coefficient (R²) of 0.9951 and a root mean square error (RMSE) of 0.0185, confirming its high accuracy. This study confirms the feasibility of using vibration-induced image data combined with machine learning for non-destructive firmness evaluation. The proposed method provides a reliable and efficient alternative to traditional firmness testing techniques and offers potential for real-time implementation in automated grading and quality control systems for kiwi and other fruit types. Full article

Optical wireless sensor networks (OWSNs) offer promising capabilities for high-speed, energy-efficient communication, particularly in mission-critical environments such as industrial automation, healthcare monitoring, and smart buildings. However, dynamic spectrum management and fault tolerance remain key challenges in ensuring reliable and timely data transmission. This paper proposes an adaptive spectrum management framework (ASMF) that addresses these challenges through a mathematically grounded and implementation-driven approach. The ASMF formulates the spectrum allocation problem as a constrained Markov decision process and leverages a dual-layer optimization strategy combining Lyapunov drift-plus-penalty for queue stability with deep reinforcement learning for adaptive long-term decision making. Additionally, ASMF integrates a hybrid fault-tolerant mechanism using LSTM-based link failure prediction and lightweight recovery logic, achieving up to 83% prediction accuracy. Experimental evaluations using real-world datasets from industrial, healthcare, and smart infrastructure scenarios demonstrate that ASMF reduces critical traffic latency by 37%, improves reliability by 42% under fault conditions, and enhances energy efficiency by 22.6% compared with state-of-the-art methods. The system also maintains a 99.94% packet delivery ratio for critical traffic and achieves 69.7% faster recovery after link failures. These results confirm the effectiveness of ASMF as a robust and scalable solution for adaptive spectrum management in dynamic, fault-prone OWSN environments. Full article

Aster spp., a key grass species for the ecological restoration of alpine degraded grasslands on the Qinghai–Tibet Plateau, often suffers from pest damage during its flowering and seed maturation stages, severely limiting the effectiveness of ecological restoration and the sustainable utilization of germplasm resources. This study focused on nine widely distributed species of Aster in the Three River Source Region of Qinghai Province, systematically investigated the structure of arthropod communities and the spatiotemporal dynamics of pests, and developed an integrated pest management (IPM) strategy. Through systematic surveys at multiple sites, a total of 109 arthropod species were identified (57 families of insects, 96 species; 7 families of spiders, 13 species). The Diptera (Tephritidae) and Hemiptera (Miridae) were identified as dominant groups. Tephritis angustipennis was determined to be the key pest, with its population density reaching a peak in mid-to-late August (p < 0.05). Based on the occurrence patterns of the pest, an IPM strategy integrating physical, chemical, and biological control methods was proposed: flower head bagging as a physical barrier significantly reduced plant damage but required balancing the risk of seed sterility. A combination lure (broad-spectrum fruit fly lure + a mixture of sugar and vinegar) showed a significant effect in attracting and killing adult flies. In chemical control, spraying a combination of insecticides (DB: 10% β-Cypermethrin aqueous emulsion (9 mL/acre) + 5% avermectin (20 mL/acre)) during the leaf expansion stage to early flowering stage achieved approximately 80% pest mortality within 24 h; additionally, supplementary spraying of 5% broflanilide (30 mL/acre) during the full flowering stage prolonged the efficacy and delayed the development of insecticide resistance. In terms of natural enemy utilization, Lycosidae and Thomisidae demonstrated significant potential for naturally regulating pest populations. Physiological mechanism studies showed that the difference in responses between plant catalase (CAT) activity and insect glutathione S-transferase (GST) activity was a key factor driving control efficacy (the cumulative explanation rate reached 94%). This IPM strategy, by integrating physical barriers, dynamic trapping, targeted spraying, and natural enemy control, significantly enhances control efficiency and ecological compatibility, providing a theoretical basis and technical paradigm for the ecological restoration of degraded alpine grasslands and the sustainable management of medicinal plants in cold regions. Full article

Resistant starch (RS) is emerging as a multifunctional dietary component and delivery platform for microbiota-accessible carbohydrates. Upon fermentation by gut microbiota, particularly in the colon, RS generates a wide spectrum of postbiotic compounds—including short-chain fatty acids (SCFAs), indoles, bile acid derivatives, and neuroactive amines such as GABA and serotonin precursors. These metabolites modulate gut–brain signaling, immune responses, and intestinal barrier integrity, which are critical pathways in the pathophysiology of irritable bowel syndrome (IBS). This review synthesizes current knowledge on RS structure, classification, and fermentation dynamics, with a special focus on RS3 due to its practical dietary relevance and strong microbiota-modulatory effects. We highlight emerging evidence from clinical studies supporting RS-mediated improvements in IBS symptoms, microbial diversity, and inflammation. Importantly, RS acts as a smart colonic delivery system by escaping enzymatic digestion in the small intestine and reaching the colon intact, where it serves as a targeted substrate for microbial fermentation into bioactive metabolites. This host–microbiota interplay underpins the development of personalized, microbiome-informed nutrition interventions tailored to specific IBS subtypes. Future directions include omics-based stratification, optimized RS formulations, and predictive algorithms for individualized responses. This review aims to clarify the mechanistic links between RS fermentation and postbiotic production, highlighting its therapeutic potential in IBS management. Full article

Target detection is a cornerstone task in hyperspectral image processing but faces significant challenges due to domain gaps. While statistical detectors like Constrained Energy Minimization (CEM) and Adaptive Cosine Estimator (ACE) are not prone to learned biases, in practice they still suffer from mismatches between the reference target spectrum and the spectral characteristics of the target in the test scene. We propose Test-time Adaptive Spectrum Refinement (TASR), a novel framework addressing this problem. TASR operates in an interpretable, lightweight, data-efficient manner, requiring only a single labeled source image of the target material. At test time, TASR dynamically refines the target spectrum to better align with the spectral properties of the test scene. This adaptive refinement enables detectors to effectively handle data with spectral variations, bridging the gap between the source and test spectra. To validate TASR, we conduct extensive experiments on established benchmarks and introduce a new dataset—ShadySunnyDiffuse (SSD)—which explicitly tests detector robustness to naturally occurring illumination changes. We further demonstrate the method’s versatility by applying it to camouflage detection and show compatibility with multiple statistical detectors. Our results establish TASR as a state-of-the-art approach in domain-adaptive hyperspectral target detection and target spectrum management. Full article

Efficient and intelligent spectrum access is crucial for meeting the diverse Quality of Service (QoS) demands of Vehicular Internet of Things (V-IoT) systems in next-generation cellular networks. This work proposes a novel reinforcement learning (RL)-based priority-aware spectrum management (RL-PASM) framework, a centralized self-learning priority-aware spectrum management framework operating through Roadside Units (RSUs). RL-PASM dynamically allocates spectrum resources across three traffic classes: high-priority (HP), low-priority (LP), and best-effort (BE), utilizing reinforcement learning (RL). This work compares four RL algorithms: Q-Learning, Double Q-Learning, Deep Q-Network (DQN), and Actor-Critic (AC) methods. The environment is modeled as a discrete-time Markov Decision Process (MDP), and a context-sensitive reward function guides fairness-preserving decisions for access, preemption, coexistence, and hand-off. Extensive simulations conducted under realistic vehicular load conditions evaluate the performance across key metrics, including throughput, delay, energy efficiency, fairness, blocking, and interruption probability. Unlike prior approaches, RL-PASM introduces a unified multi-objective reward formulation and centralized RSU-based control to support adaptive priority-aware access for dynamic vehicular environments. Simulation results confirm that RL-PASM balances throughput, latency, fairness, and energy efficiency, demonstrating its suitability for scalable and resource-constrained deployments. The results also demonstrate that DQN achieves the highest average throughput, followed by vanilla QL. DQL and AC maintain fairness at high levels and low average interruption probability. QL demonstrates the lowest average delay and the highest energy efficiency, making it a suitable candidate for edge-constrained vehicular deployments. Selecting the appropriate RL method, RL-PASM offers a robust and adaptable solution for scalable, intelligent, and priority-aware spectrum access in vehicular communication infrastructures. Full article

Modulation recognition, as one of the key technologies in the field of wireless communications, holds significant importance in applications such as spectrum resource management, interference suppression, and cognitive radio. While deep learning has substantially improved the performance of Automatic Modulation Recognition (AMR), it heavily relies on large amounts of labeled data. Given the high annotation costs and privacy concerns, researching semi-supervised AMR methods that leverage readily available unlabeled data for training is of great significance. This study constructs a semi-supervised AMR method based on dual-student. Specifically, we first adopt a dual-branch co-training architecture to fully exploit unlabeled data and effectively learn deep feature representations. Then, we develop a dynamic stability evaluation module using strong and weak augmentation strategies to improve the accuracy of generated pseudo-labels. Finally, based on the dual-student semi-supervised framework and pseudo-label stability evaluation, we propose a stability-guided consistency regularization constraint method and conduct semi-supervised AMR model training. The experimental results demonstrate that the proposed DualBranch-AMR method significantly outperforms traditional supervised baseline approaches on benchmark datasets. With only 5% labeled data, it achieves a recognition accuracy of 55.84%, reaching over 90% of the performance of fully supervised training. This validates the superiority of the proposed method under semi-supervised conditions. Full article

Kyasanur Forest disease virus (KFDV), a tick-borne Orthoflavivirus endemic to the Indian subcontinent, is a public health threat due to its recurrent outbreaks and expanding geographic range. This review provides a comprehensive overview of KFDV, encompassing its epidemiological trends, transmission dynamics, and ecological determinants that influence its spread. We delve into the current understanding of KFDV pathogenesis, highlighting key viral and host factors that drive infection and disease progression. Despite the absence of targeted antiviral therapies, recent advances have spurred the development of candidate therapeutics, including broad-spectrum antivirals and immunomodulators. We also discuss progress in vaccine development, with an emphasis on the limitations of the existing formalin-inactivated vaccine and the promise of next-generation platforms. Furthermore, we explore recent innovations in diagnostics, including molecular and serological tools, that aim to improve early detection and surveillance. A multidisciplinary approach integrating virology, immunology, ecology, and public health is essential for the effective management and eventual control of KFDV outbreaks. Full article

The pervasive scale of networks brought about by smart city applications has created infeasible energy footprints and necessitates the inclusion of sensing sustained operations with minimal human intervention. Consequently, integrated sensing and communication (ISAC) is emerging as a key technology for 6G systems. ISAC systems realize dual functions using shared spectrum, which complicates interference management. This motivates the development of advanced signal processing and multiplexing techniques. In this context, extremely large antenna arrays (ELAAs) have emerged as a promising solution. ELAAs offer substantial gains in spatial resolution, enabling precise beamforming and higher multiplexing gains by operating in the near-field (NF) region. Despite these advantages, the use of ELAAs increases energy consumption and exacerbates carbon emissions. To address this, NF multiple-input multiple-output (NF-MIMO) systems must incorporate sustainable architectures and scalable solutions. This paper provides a comprehensive review of the various methodologies utilized in the design of energy-efficient NF-MIMO-based ISAC systems. It introduces the foundational principles of the latest research while identifying the strengths and limitations of green NF-MIMO-based ISAC systems. Furthermore, this work provides an in-depth analysis of the open challenges associated with these systems. Finally, it offers a detailed overview of emerging opportunities for sustainable designs, encompassing backscatter communication, dynamic spectrum access, fluid antenna systems, reconfigurable intelligent surfaces, and energy harvesting technologies. Full article

Musculoskeletal (MSK) disorders remain a major global cause of disability, with diagnostic complexity arising from their heterogeneous presentation and multifactorial pathophysiology. Recent advances across imaging modalities, molecular biomarkers, artificial intelligence applications, and point-of-care technologies are fundamentally reshaping musculoskeletal diagnostics. This review offers a novel synthesis by unifying recent innovations across multiple diagnostic imaging modalities, such as CT, MRI, and ultrasound, with emerging biochemical, genetic, and digital technologies. While existing reviews typically focus on advances within a single modality or for specific MSK conditions, this paper integrates a broad spectrum of developments to highlight how use of multimodal diagnostic strategies in combination can improve disease detection, stratification, and clinical decision-making in real-world settings. Technological developments in imaging, including photon-counting detector computed tomography, quantitative magnetic resonance imaging, and four-dimensional computed tomography, have enhanced the ability to visualize structural and dynamic musculoskeletal abnormalities with greater precision. Molecular imaging and biochemical markers such as CTX-II (C-terminal cross-linked telopeptides of type II collagen) and PINP (procollagen type I N-propeptide) provide early, objective indicators of tissue degeneration and bone turnover, while genetic and epigenetic profiling can elucidate individual patterns of susceptibility. Point-of-care ultrasound and portable diagnostic devices have expanded real-time imaging and functional assessment capabilities across diverse clinical settings. Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. Wearable sensors and mobile health technologies extend continuous monitoring beyond traditional healthcare environments, generating real-world data critical for dynamic disease management. However, standardization of diagnostic protocols, rigorous validation of novel methodologies, and thoughtful integration of multimodal data remain essential for translating technological advances into improved patient outcomes. Despite these advances, several key limitations constrain widespread clinical adoption. Imaging modalities lack standardized acquisition protocols and reference values, making cross-site comparison and clinical interpretation difficult. AI-driven diagnostic tools often suffer from limited external validation and transparency (“black-box” models), impacting clinicians’ trust and hindering regulatory approval. Molecular markers like CTX-II and PINP, though promising, show variability due to diurnal fluctuations and comorbid conditions, complicating their use in routine monitoring. Integration of multimodal data, especially across imaging, omics, and wearable devices, remains technically and logistically complex, requiring robust data infrastructure and informatics expertise not yet widely available in MSK clinical practice. Furthermore, reimbursement models have not caught up with many of these innovations, limiting access in resource-constrained healthcare settings. As these fields converge, musculoskeletal diagnostics methods are poised to evolve into a more precise, personalized, and patient-centered discipline, driving meaningful improvements in musculoskeletal health worldwide. Full article

This study numerically examines laminar natural convection within a square cavity that has a horizontally attached adiabatic fin on its heated vertical wall. The analysis employed the finite element method to investigate how fin position, length, thickness, and thermal conductivity affect heat transfer behavior over a broad spectrum of Rayleigh numbers (Ra = 10 to 10⁶) and Prandtl numbers (Pr = 0.1 to 10). The findings indicate that the geometric configuration and the properties of the fluid largely influence the thermal disturbances caused by the fin. At lower Ra values, conduction is the primary mechanism, resulting in minimal impact from the fin. However, as Ra rises, convection becomes increasingly significant, with the fin positioned at mid-height (Y_fin = 0.5), significantly improving thermal mixing and flow symmetry, especially for high-Pr fluids. Extending the fin complicates vortex dynamics, whereas thickening the fin improves conductive heat transfer, thereby enhancing convection to the fluid. A new fluid-focused metric, the normalized Nusselt ratio (NNR), is introduced to evaluate the true thermal contribution of fin geometry beyond area-based scaling. It exhibits a non-monotonic response to geometric changes, with peak enhancement observed at high Ra and Pr. The findings provide practical guidance for designing passive thermal management systems in sealed enclosures, such as electronics housings, battery modules, and solar thermal collectors, where active cooling is infeasible. This study offers a scalable reference for optimizing natural convection performance in laminar regimes by characterizing the interplay between buoyancy, fluid properties, and fin geometry. Full article

This review paper examines the integration of artificial intelligence (AI) in wireless communication, focusing on cognitive radio (CR), spectrum sensing, and dynamic spectrum access (DSA). As the demand for spectrum continues to rise with the expansion of mobile users and connected devices, cognitive radio networks (CRNs), leveraging AI-driven spectrum sensing and dynamic access, provide a promising solution to improve spectrum utilization. The paper reviews various deep learning (DL)-based spectrum-sensing methods, highlighting their advantages and challenges. It also explores the use of multi-agent reinforcement learning (MARL) for distributed DSA networks, where agents autonomously optimize power allocation (PA) to minimize interference and enhance quality of service. Additionally, the paper discusses the role of machine learning (ML) in predicting spectrum requirements, which is crucial for efficient frequency management in the fifth generation (5G) networks and beyond. Case studies show how ML can help self-optimize networks, reducing energy consumption while improving performance. The review also introduces the potential of generative AI (GenAI) for demand-planning and network optimization, enhancing spectrum efficiency and energy conservation in wireless networks (WNs). Finally, the paper highlights future research directions, including improving AI-driven network resilience, refining predictive models, and addressing ethical considerations. Overall, AI is poised to transform wireless communication, offering innovative solutions for spectrum management (SM), security, and network performance. Full article

Multiband spectrum sensing in a cooperative environment is a novel solution for efficient spectrum resource management under the cognitive radio networks (CRNs) paradigm. This paper presents a distinctive framework where a central entity collects power spectral density data from multiple geographically distributed secondary users and applies the Gramian angular field (GAF) summation method to transform the time-series data into image representations. A major contribution of this work is the integration of these GAF images with a convolutional neural network (CNN), enabling precise and real-time detection of primary user activity and spectrum occupancy. The proposed approach achieves 99.6% accuracy in determining spectrum occupancy, significantly outperforming traditional sensing techniques. The main contributions of this study are (i) the introduction of GAF-based image representations for cooperative spectrum sensing in CRNs; (ii) the development of a CNN-based classification framework for enhanced spectrum occupancy detection; and (iii) the demonstration of superior detection performance in dynamic, real-time environments. Full article

Spiders play a crucial role as predators in terrestrial ecosystems, particularly in controlling insect populations. Tetragnatha keyserlingi Simon (Araneae: Tetragnathidae) is a dominant species in rice field ecosystems, where it builds webs amidst rice clusters to capture prey. Despite its known predation on major rice pests like rice planthoppers, comprehensive field reports on its prey composition are scarce. Herein, we performed a field investigation to explore the population dynamic relationships between T. keyserlingi and major rice pests. Additionally, we employed DNA metabarcoding to analyze the prey spectrum of this spider from both the spider’s opisthosoma and its web. The results showed that the population dynamics of T. keyserlingi and Nilaparvata lugens (Stål) displayed synchrony. Dietary DNA metabarcoding analysis revealed that, compared with the opisthosoma, DNA extracted from spider webs exhibited a higher abundance of prey reads and yielded a higher diversity of identified prey species. Phytophagous pests were the dominant prey group identified in both sample types. In web samples, the most abundant prey reads were from Chironomidae, followed by Delphacidae, Ceratopogonidae, Aleyrodidae, Muscidae, Coenagrionidae, and other prey families. Notably, Delphacidae constituted the predominant prey reads identified from the spider’s opisthosoma, and the corresponding positive rate for Delphacidae was 86.7%. These results indicate that the web of T. keyserlingi can capture a diverse range of prey in rice fields. Among the prey captured by the spider web, rice planthoppers appear to be a primary dietary component of T. keyserlingi, emphasizing its potential as a biocontrol agent for rice planthoppers in integrated pest management strategies. Leveraging spider web DNA metabarcoding enhances our understanding of T. keyserlingi’s prey capture ability, as the residual prey DNA in webs provides critical insights into the foraging dynamics and ecological interactions of web-building spiders. Full article

Indoor farming presents a sustainable response to urbanization and climate change, yet optimizing light use efficiency (LUE) remains vital for maximizing crop yield and minimizing energy use. This study introduces an IoT-based framework for adaptive light management in controlled environments, using lettuce (Lactuca sativa L.) as a model crop due to its rapid growth and sensitivity to light spectra. The system integrates advanced LED lighting, real-time sensors, and cloud-based analytics to enhance light distribution and automate adjustments based on growth stages. The key findings indicate a 20% increase in energy efficiency and a 15% improvement in lettuce growth compared to traditional static models. Novel metrics—Light Use Efficiency at Growth stage Canopy Level (LUEP) and Lamp Level (LUEL)—were developed to assess system performance comprehensively. Simulations identified optimal growth conditions, including a light intensity of 350–400 µmol/m²/s and photoperiods of 16–17 h/day. Spectral optimization showed that a balanced blue-red light mix benefits vegetative growth, while higher red content supports flowering. The framework’s feedback control ensures rapid (<2 s) and accurate (>97%) adjustments to environmental deviations, maintaining ideal conditions throughout growth stages. Comparative analysis confirms the adaptive system’s superiority over static models in responding to dynamic environmental conditions and improving performance metrics like LUEP and LUEL. Practical recommendations include stage-specific guidelines for light spectrum, intensity, and duration to enhance both energy efficiency and crop productivity. While tailored to lettuce, the modular system design allows for adaptation to a variety of leafy greens and other crops with species-specific calibration. This research demonstrates the potential of IoT-driven adaptive lighting systems to advance precision agriculture in indoor environments, offering scalable, energy-efficient solutions for sustainable food production. Full article