Search Results (3,150)

Conventional grain monitoring systems often rely on isolated data points (e.g., point-based temperature measurements), limiting holistic condition assessment. This study proposes a novel Mechanism and Data Driven (MDD) framework that integrates physical mechanisms with real-time sensor data. The framework quantitatively analyzes solar radiation and external air temperature effects on silo boundaries and introduces a novel interpolation-optimized model parameter initialization technique to enable comprehensive grain condition perception. Rigorous multidimensional validation confirms the method’s accuracy: The novel initialization technique achieved high precision, demonstrating only 1.89% error in Day-2 low-temperature zone predictions (27.02 m² measured vs. 26.52 m² simulated). Temperature fields were accurately reconstructed (≤0.5 °C deviation in YOZ planes), capturing spatiotemporal dynamics with ≤0.45 m² maximum low-temperature zone deviation. Cloud map comparisons showed superior simulation fidelity (SSIM > 0.97). Further analysis revealed a 22.97% reduction in total low-temperature zone area (XOZ plane), with Zone 1 (near south exterior wall) declining 27.64%, Zone 2 (center) 25.30%, and Zone 3 20.35%. For dynamic evolution patterns, high-temperature zones exhibit low moisture (<14%), while low-temperature zones retain elevated moisture (>14%). A strong positive correlation between temperature and relative humidity fields; temperature homogenization drives humidity uniformity. The framework enables holistic monitoring, providing actionable insights for smart ventilation control, condensation risk warnings, and mold prevention. It establishes a robust foundation for intelligent grain storage management, ultimately reducing post-harvest losses. Full article

Accurate real-time detection of blueberry maturity is vital for automated harvesting. However, existing methods often fail under occlusion, variable lighting, and dense fruit distribution, leading to reduced accuracy and efficiency. To address these challenges, we designed a lightweight deep learning framework that integrates improved feature extraction, attention-based fusion, and progressive transfer learning to enhance robustness and adaptability To overcome these challenges, we propose BMDNet-YOLO, a lightweight model based on an enhanced YOLOv8n. The backbone incorporates a FasterPW module with parallel convolution and point-wise weighting to improve feature extraction efficiency and robustness. A coordinate attention (CA) mechanism in the neck enhances spatial-channel feature selection, while adaptive weighted concatenation ensures efficient multi-scale fusion. The detection head employs a heterogeneous lightweight structure combining group and depthwise separable convolutions to minimize parameter redundancy and boost inference speed. Additionally, a three-stage transfer learning framework (source-domain pretraining, cross-domain adaptation, and target-domain fine-tuning) improves generalization. Experiments on 8,250 field-collected and augmented images show BMDNet-YOLO achieves 95.6% mAP@0.5, 98.27% precision, and 94.36% recall, surpassing existing baselines. This work offers a robust solution for deploying automated blueberry harvesting systems. Full article

Environmental vibrations may affect the functional use of engineering structures and even lead to disastrous consequences. Vibration suppression and energy harvesting based on Nonlinear Energy Sink (NES) and the piezoelectric effect have gained significant attention in recent years. The harvested electrical energy can supply power to the structural health monitoring sensor device. In this work, the electromechanical-coupled governing equations of the primary structure coupled with the series-connected 2-degree-of-freedom NES (2-DOF NES) integrated by a piezoelectric energy harvester are derived. The absorption and dissipation performances of the system under varying transient excitation intensities are investigated. Additionally, the targeted energy transfer mechanism between the primary structure and the two NESs oscillators is investigated using the wavelet analysis. The reduced slow flow of the dynamical system is explored through the complex-variable averaging method, and the primary factors for triggering the target energy transfer phenomenon are revealed. Furthermore, a comparison is made between the vibration suppression performance of the single-degree-of-freedom NES (S-DOF NES) system and the 2-DOF NES system as a function of external excitation velocity. The results indicate that the vibration suppression performance of the first-level NES (NES1) oscillator is first stimulated. As the external excitation intensity gradually increases, the vibration suppression performance of the second-level NES (NES2) oscillator is also triggered. The 1:1:1, high-frequency, and low-frequency transient resonance captures are observed between the primary structure and NES1 and NES2 oscillators over a wide frequency range. The 2-DOF NES demonstrates superior efficiency in suppressing vibrations of the primary structure and exhibits enhanced robustness to varying external excitation intensities. This provides a new strategy for structural vibration suppression and online power supply for health monitoring devices. Full article

Adverse climatic dynamics in recent years have intensified the need for resilient and multifunctional agricultural systems that integrate productivity, ecological sustainability, and socio-economic viability. This study evaluates the harvesting performance of three cropping systems: intercropping of cardoon and safflower (IT) and monocultures of cardoon (DC) and safflower (DS). Field trials were conducted during three following growing seasons to assess key harvesting parameters, including working speed, effective field capacity, harvesting costs, biomass yield, seed yield, seed losses, and seed moisture content. DC demonstrated the better performance, with a working speed of 6.35 ha h⁻¹ and a field capacity of 2.56 ha h⁻¹, also resulting in the lowest harvesting cost (EUR 70.24 ha⁻¹). In contrast, IT exhibited the lowest performance and the highest cost (EUR 98.61 ha⁻¹). DS achieved the highest effective seed yield (1.394 Mg ha⁻¹), while IT produced the greatest biomass (22.96 Mg ha⁻¹). Seed losses were lowest in DS (0.020 Mg ha⁻¹) and highest in IT (0.425 Mg ha⁻¹). Moisture content ranged from 5.82% in DC to 9.40% in DS. These findings highlighted the trade-offs between productivity, efficiency, and system complexity, offering valuable insights into the comparative performance and sustainability of innovative cropping systems under changing climatic conditions. Full article

The harvesting of dragon fruit remains challenging due to uneven clamping forces, high fruit damage rates, and low redundancy in conventional end-effectors. To address these issues, we developed a novel embracing end-effector with a Variable-Span Arch (VSA) structure. The VSA design enables adaptive clamping force distribution and effective torsional fruit separation, significantly reducing static pressure damage. Theoretical modeling, mechanical testing, and field experiments were conducted to evaluate its performance. Results show that the proposed end-effector achieves a 95% harvesting success rate, with an average picking time of 15 s per fruit, and can output a maximum torque of 18 kgf·cm, which is sufficient for dragon fruit detachment. These findings demonstrate that the VSA-based embracing end-effector offers a low-damage, efficient, and robust solution for dragon fruit harvesting, providing practical guidance for robotic applications in tropical fruit production. Full article

Due to its benefits and efficiency, mechanized sugarcane harvest is a common practice in Brazil; however, continuous traffic of agricultural machinery leads to soil compaction at the end of each harvest cycle. Hence, this study evaluated whether machine traffic affects soil physical and hydraulic properties, root growth, and crop productivity in sugarcane areas during different harvest cycles. Four treatments were performed consisting of an area planted with different stages (years) of sugarcane crop: T1 = after the first harvest—plant cane (area 1); T2 = after the second harvest—first ratoon cane (area 2); T3 = after the third harvest—second ratoon cane (area 3); T4 = after fourth harvest—third ratoon cane (area 4). Five sampling sites were considered in each area, constituting five replicates collected from four layers. Two collection positions were considered: wheel track (WT) and planting row (PR). Soil physical properties, root system, productivity, and biometric characteristics of the sugarcane crop were evaluated at depths of 0.00–0.05 m, 0.05–0.10 m, 0.10–0.20 m, and 0.20–0.40 m. Traffic during the sugarcane crop growth cycles affected soil physical and hydraulic properties, showing sensitivity to the effects of the different treatments, producing variations in root growth and crop productivity. Plant cane cycle showed lower soil penetration resistance, bulk density, microporosity, higher saturated soil hydraulic conductivity, and macroporosity when compared with the other cycles studied. In the 0.10–0.20 m layer, all treatments produced higher soil penetration resistance and density, and lower saturated soil hydraulic conductivity. Dry biomass, volume, and root area were higher for the plant cane cycle in the 0.00–0.05 m and 0.05–0.10 m layers compared with the other crop cycles. Root dry biomass is directly related to crop productivity in layers up to 0.40 m deep. Sugarcane productivity was affected along the crop cycles, with higher productivity observed in the plant cane and first ratoon cane cycles compared with the second and third ratoon cane cycles. Full article

This paper studies joint data collection and wireless power transfer in a UAV-assisted IoT network. A rotary-wing UAV follows a fly–hover–communicate cycle. At each hover, it simultaneously receives uplink data in full-duplex mode while delivering radio-frequency energy to nearby devices. Using a realistic propulsion-power model and a nonlinear energy-harvesting model, we formulate trajectory and hover control as a multi-objective optimization problem that maximizes the aggregate data rate and total harvested energy while minimizing the UAV’s energy consumption over the mission. To enable flexible trade-offs among these objectives under time-varying conditions, we propose a dynamic, state-adaptive weighting mechanism that generates environment-conditioned weights online, which is integrated into an enhanced deep deterministic policy gradient (DDPG) framework. The resulting dynamic-weight MODDPG (DW-MODDPG) policy adaptively adjusts the UAV’s trajectory and hover strategy in response to real-time variations in data demand and energy status. Simulation results demonstrate that DW-MODDPG achieves superior overall performance and a more favorable balance among the three objectives. Compared with the fixed-weight baseline, our algorithm increases total harvested energy by up to 13.8% and the sum data rate by up to 5.4% while maintaining comparable or even lower UAV energy consumption. Full article

The accurate assessment of Chinese bayberry (Myrica rubra) maturity is critical for intelligent harvesting. This study proposes a novel cascaded framework combining instance segmentation and multi-feature regression for accurate maturity detection. First, a lightweight SOLOv2-Light network is employed to segment each fruit individually, which significantly reduces computational costs with only a marginal drop in accuracy. Then, a multi-feature extraction network is developed to fuse deep semantic, color (LAB space), and multi-scale texture features, enhanced by a channel attention mechanism for adaptive weighting. The maturity ground truth is defined using the a*/b* ratio measured by a colorimeter, which correlates strongly with anthocyanin accumulation and visual ripeness. Experimental results demonstrated that the proposed method achieves a mask mAP of 0.788 on the instance segmentation task, outperforming Mask R-CNN and YOLACT. For maturity prediction, a mean absolute error of 3.946% is attained, which is a significant improvement over the baseline. When the data are discretized into three maturity categories, the overall accuracy reaches 95.51%, surpassing YOLOX-s and Faster R-CNN by a considerable margin while reducing processing time by approximately 46%. The modular design facilitates easy adaptation to new varieties. This research provides a robust and efficient solution for in-field bayberry maturity detection, offering substantial value for the development of automated harvesting systems. Full article

Bismuth vanadate (BiVO₄) has attracted significant attention as a photoanode material for photoelectrochemical (PEC) water splitting due to its suitable bandgap (~2.4 eV), strong visible light absorption, chemical stability, and cost-effectiveness. Despite these advantages, its practical application remains constrained by intrinsic limitations, including poor charge carrier mobility, short diffusion length, and sluggish oxygen evolution reaction (OER) kinetics. This review critically summarizes recent advancements aimed at enhancing BiVO₄ PEC performance, encompassing synthesis strategies, defect engineering, heterojunction formation, cocatalyst integration, light-harvesting optimization, and stability improvements. Key fabrication methods—such as solution-based, vapor-phase, and electrochemical approaches—along with targeted modifications, including metal/nonmetal doping, surface passivation, and incorporation of electron transport layers, are discussed. Emphasis is placed on strategies to improve light absorption, charge separation efficiency (η_sep), and charge transfer efficiency (η_trans) through bandgap engineering, optical structure design, and catalytic interface optimization. Approaches to enhance stability via protective overlayers and electrolyte tuning are also reviewed, alongside emerging applications of BiVO₄ in tandem PEC systems and selective solar-driven production of value-added chemicals, such as H₂O₂. Finally, critical challenges, including the scale-up of electrode fabrication and the elucidation of fundamental reaction mechanisms, are highlighted, providing perspectives for bridging the gap between laboratory performance and practical implementation. Full article

The pea aphid, Acyrthosiphon pisum, possesses horizontally acquired fungal carotenoid biosynthesis genes, enabling de novo production of carotenoids. Although carotenoids are known to contribute to photo-protection and coloration, their potential role in energy metabolism and population fitness under thermal stress is still unclear. This study investigated the interactive effects of temperature and light intensity on energy homeostasis and life-history traits in A. pisum. Using controlled environmental regimes, we demonstrate that light intensity significantly influenced the ATP content, development, and reproductive output of A. pisum at 12 °C, but not at 22 °C. Under cold stress (12 °C), high light intensity (5000 lux) increased ATP content by 240%, shortened the pre-reproductive period by 46%, extended reproductive duration by 62%, and enhanced the net reproductive rate (R₀) and intrinsic rate of increase (rₘ) compared to low light intensity (200 lux). These effects were abolished at the optimal temperature (22 °C), indicating a temperature-gated, light-dependent mechanism. Demographic analyses revealed that carotenoid-associated solar energy harvesting significantly improves fitness under cold conditions, likely compensating for metabolic depression. Our findings reveal a novel ecological adaptation in aphids, where horizontally transferred genes may enable light-driven energy supplementation during thermal stress. This study provides new insights into the physiological mechanisms underlying insect resilience to climate variability and highlights the importance of light as a key environmental factor in shaping life-history strategies in temperate agroecosystems. Full article

Bamboo (subfamily Bambusoideae, Poaceae) ranks among the fastest-growing plants on Earth, achieving up to 1 m day⁻¹, significantly faster than other fast growing woody plant such as Eucalyptus (up to 0.6 m day⁻¹) and Populus (up to 0.5 m day⁻¹). Native to Asia, South America and Africa, and cultivated on approximately 37 million ha worldwide, bamboo delivers multifaceted environmental, social, and economic benefits. Historically central to construction, handicrafts, paper and cuisine, bamboo has evolved into a high-value cash crop and green innovation platform. Its rapid renewability allows multiple harvests of young shoots in fast-growing species such as Phyllostachys edulis and Dendrocalamus asper. Its high tensile strength, flexibility, and ecological adaptability make it suitable for applications in bioenergy (bioethanol, biogas, biochar), advanced materials (engineered composites, textiles, activated carbon), and biotechnology (fermentable sugars, prebiotics, biochemicals). Bamboo shoots and leaves provide essential nutrients, antioxidants and bioactive compounds with documented health and pharmaceutical potential. With a global market value exceeding USD 41 billion, bamboo demand continues to grow in response to the call for sustainable materials. Ecologically, bamboo sequesters up to 259 t C ha⁻¹, stabilizes soil, enhances agroforestry systems and enables phytoremediation of degraded lands. Nonetheless, challenges persist, including species- and age-dependent mechanical variability; vulnerability to decay and pests; flammability; lack of standardized harvesting and engineering codes; and environmental impacts of certain processing methods. This review traces bamboo’s trajectory from a traditional resource to a strategic bioresource aligned with Industry 5.0, underscores its role in low-emission, circular bioeconomies and identifies pathways for optimized cultivation, green processing technologies and integration into carbon-credit frameworks. By addressing these challenges through innovation and policy support, bamboo can underpin resilient, human-centric economies and drive sustainable development. Full article

The efficient degradation of antibiotics in pharmaceutical wastewater remains a critical challenge against environmental contaminants. Conventional photocatalysts face potential limitations such as narrow visible-light absorption, rapid carrier recombination, and reliance on precious metal cocatalysts. This review investigates the coordination structure of MXene as a cocatalyst to synergistically enhance photocatalytic antibiotic degradation efficiency and the coordination structure modification mechanisms. MXene’s tunable bandgap (0.92–1.75 eV), exceptional conductivity (100–20,000 S/cm), and abundant surface terminations (-O, -OH, -F) enable the construction of Schottky or Z-scheme heterojunctions with semiconductors (Cu₂O, TiO₂, g-C₃N₄), achieving 50–70% efficiency improvement compared to pristine semiconductors. The “electron sponge” effect of MXene suppresses electron-hole recombination by 3–5 times, while its surface functional groups dynamically optimize pollutant adsorption. Notably, MXene’s localized surface plasmon resonance extends light harvesting from visible (400–800 nm) to near-infrared regions (800–2000 nm), tripling photon utilization efficiency. Theoretical simulations demonstrate that d-orbital electronic configurations and terminal groups cooperatively regulate catalytic active sites at atomic scales. The MXene composites demonstrate remarkable environmental stability, maintaining over 90% degradation efficiency of antibiotic under high salinity (2 M NaCl) and broad pH range (4–10). Future research should prioritize green synthesis protocols and mechanistic investigations of interfacial dynamics in multicomponent wastewater systems to facilitate engineering applications. This work provides fundamental insights into designing MXene-based photocatalysts for sustainable water purification. Full article

Nature-Based Solutions (NBS) represent an alternative for achieving environmental and resilience goals in diverse global contexts with varying needs. As such, NBS can be understood as processes involving actions that promote circular economy (CE) strategies within their function. Therefore, this research aims to conduct a systematic literature review to identify and analyze the main NBS applied and explore how they are associated with CE strategies. This study performs a systematic literature review of NBS and their relationship with the CE using the PRISMA methodology, analyzing a total of 32 articles retrieved from the SCOPUS database. The main NBS include constructed wetlands, green infrastructure, and soil restoration and enrichment solutions. Constructed wetlands are linked to strategies such as recycling and reuse due to their role in treating urban and domestic wastewater for reuse, thereby increasing water availability. Green infrastructure is associated with strategies like redesign and reduction, as it involves the use of lower-impact materials and designs for rainwater harvesting and thermal comfort improvement. Soil enrichment and remediation solutions are connected to reuse and recycling strategies, as most derive from organic waste composting or microorganisms. NBS and CE strategies highlight how these solutions not only provide direct environmental benefits but also, when analyzed from a sustainability perspective, can offer social and economic benefits. Furthermore, understanding their relationship will facilitate their integration into regulations for transitioning toward circularity in industries and cities. The contribution of this article lies in synthesizing and systematizing the evidence on how NBS operationalizes CE strategies, identifying the main mechanisms and gaps, and proposing a conceptual model that can guide future research and policy design. Full article

With the development of embedded electronic devices, energy consumption has become a significant design issue in modern systems-on-a-chip. Conventional SRAMs cannot maintain data after powering turned off, limiting their use in applications such as battery-powered smartphone devices that require non-volatility and no leakage current. RRAM devices are recently used extensively in applications such as self-charging wireless sensor networks and storage elements, owing to their intrinsic non-volatility and multi-bit capabilities, making them a potential candidate for mitigating the von Neumann bottleneck. We propose a new RRAM-based hybrid memristor model incorporated with a permanent magnet. The proposed design (1T2R) was simulated in Cadence Virtuoso with a 1.5 V power supply, and the finite-element approach was adopted to simulate magnetization. This model can retain the data after the power is off and provides fast power on/off transitions. It is possible to charge a smartphone battery without an external power source by utilizing a portable charger that uses magnetic induction to convert mechanical energy into electrical energy. In an embedded smartphone self-charging device this addresses eco-friendly concerns and lowers environmental effects. It would lead to the development of magnetic field-assisted embedded portable electronic devices and open the door to new types of energy harvesting for RRAM devices. Our proposed design and simulation results reveal that, under usual conditions, the magnet-based device provide a high voltage to charge a smartphone battery. Full article

Background/Objectives: There is clear evidence for the higher prevalence of varicose veins (VVs) among women. In this regard, the research on sex differences affecting this condition is very important for sex-specific health care. We aimed to assess how male or female sex may contribute to the changes to gene expression profiles in the vein wall during varicose transformation. Methods: Paired varicose vein (VV) and non-varicose vein (NV) segments were harvested from patients with VVs after venous surgery. Processed RNAs from those samples were subjected to gene expression analysis by reverse transcription quantitative polymerase chain reaction (RT-qPCR) followed by further data analysis. Multiple linear regression (MLR) analysis was performed to identify and characterize relationships among multiple factors (relative mRNA levels of a gene in NV or VV or their ratio, as dependent variables) and sex (independent variable, used individually or in combination with other patient’s characteristics). For sex-specific gene regulation analysis, all potential binding sites for sex hormone receptors were identified in each gene’s regulatory region sequence. Results: Using the independent method and a replicative patient sample set, we validated our previous data on 23 genes’ differential expression in VVs and obtained insights on their sex-specific regulation. Sex (as an individual independent variable or in combination with other parameters—patient characteristics such as Age, BMI, CEAP class, Height, VVD manifestation and duration) was a moderate predictor (0.40 < R < 0.59; p (R) < 0.05) for the STK38L expression in VVs (with its higher mRNA level in NVs and VVs of women compared to men); sex was a strong predictor (0.6 < R < 0.79; p (R) < 0.05) for the TIMP1 expression in VVs (with its lower mRNA level in VVs of women compared to men); sex was a moderate predictor (0.40 < R < 0.59; p (R) < 0.05) for the EBF1 expression in NVs (with its lower mRNA level in NVs of women compared to men). Conclusions: Confirmed differential expression of the studied genes in VVs indicates their plausible participation in vein wall remodeling. Sex-specific expression in veins for the subset of those genes suggests their hormonal regulation as well as other mechanisms involved in VV pathogenesis. This work enriches our understanding of sex features for the development of VVs and may provide the foundation for future investigations and beneficial treatment options. Full article