Search Results (870)

This study established an HPLC-MS/MS method to quantify the enantiomers of spirotetramat in tubifex. To assess the accuracy and precision of the approach, recovery tests were conducted for insecticide. For all enantiomers, the limits of detection were 0.003 mg/kg. The quantization limits were 0.01 mg/kg. Spirotetramat enantiomers recovery rates in tubifex were found to be between 81 and 114%, with relative standard deviations being less than 7%. The half-lives of spirotetramat enantiomers in tubifex were 3.81–10.58 d, respectively. The 22.4% spirotetramat suspension was sprayed on tubifex three times at a low dosage (high dosage advised). After 14 days after harvesting, the terminal residues of spirotetramat enantiomers in the tubifex were less than 0.03 mg/kg. The findings offer a quantitative foundation for setting China’s maximum residue limits as well as a recommendation for the safe and responsible usage of spirotetramat enantiomers in tubifex. Full article

Chemical soil properties contribute to the resilience of soil ecosystems. Healthy soils with optimal nutrient levels, balanced pH and good organic matter content are better able to withstand environmental stresses, such as drought, disease or pests. When comparing the chemical soil properties of temperate grassland and arable land, several differences can be observed due to differences in soil cover and management. Grasslands typically sequester more carbon, limit nitrogen leaching, and have lower nitrous oxide emissions and losses of phosphorus due to less soil disturbance and a more closed nutrient cycle. In contrast, arable land has higher nutrient losses through harvest, leaching, gaseous emissions and erosion due to regular tillage, frequent bare phases, and sequesters less carbon, typically due to higher mineralisation rates and lower nutrient returns. Monitoring and managing chemical soil properties, appropriate nutrient management, addition of organic matter such as organic fertilisers, inclusion of grassland phases and catch crops in crop rotations, incorporation of crop residues into the topsoil after harvest and further sustainable agricultural practices are essential to promote soil health. By optimising chemical soil properties, farmers and land managers can improve productivity, conserve natural resources and support the long-term sustainability of the soil ecosystem. Full article

The end-to-end efficiency of radio-frequency (RF)-powered wireless communication networks (WPCNs) in post-disaster underground mine environments can be enhanced through adaptive beamforming. The primary challenges in such scenarios include (i) identifying the most energy-constrained nodes, i.e., nodes with the lowest residual energy to prevent the loss of tracking and localization functionality; (ii) avoiding reliance on the computationally intensive channel state information (CSI) acquisition process; and (iii) ensuring long-range RF wireless power transfer (LoRa-RFWPT). To address these issues, this paper introduces an adaptive and safety-aware deep reinforcement learning (DRL) framework for energy beamforming in LoRa-enabled underground disaster networks. Specifically, we develop a Safe Adaptive Deep Q-Network (SADQN) that incorporates residual energy awareness to enhance energy harvesting under mobility, while also formulating a SADQN approach with dual-variable updates to mitigate constraint violations associated with fairness, minimum energy thresholds, duty cycle, and uplink utilization. A mathematical model is proposed to capture the dynamics of post-disaster underground mine environments, and the problem is formulated as a constrained Markov decision process (CMDP). To address the inherent NP hardness of this constrained reinforcement learning (CRL) formulation, we employ a Lagrangian relaxation technique to reduce complexity and derive near-optimal solutions. Comprehensive simulation results demonstrate that SADQN significantly outperforms all baseline algorithms: increasing cumulative harvested energy by approximately 11% versus DQN, 15% versus Safe-DQN, and 40% versus PSO, and achieving substantial gains over random beamforming and non-beamforming approaches. The proposed SADQN framework maintains fairness indices above 0.90, converges 27% faster than Safe-DQN and 43% faster than standard DQN in terms of episodes, and demonstrates superior stability, with 33% lower performance variance than Safe-DQN and 66% lower than DQN after convergence, making it particularly suitable for safety-critical underground mining disaster scenarios where reliable energy delivery and operational stability are paramount. Full article

Proteins are generally characterized by three-dimensional structures that are well suited for their specific function. It is much less accepted that a particular flexibility or plasticity of a protein is essential for performing its function. The latter plasticity encompasses the stochastic motions of small protein sidechains on the picosecond timescale that serve as “lubricating grease”, allowing slower functionally relevant conformational changes. Some remarkable examples of potential correlations between protein dynamics and function were observed for pigment–protein complexes in photosynthesis. For example, electron transfer and protein plasticity are concurrently suppressed in Photosystem II upon decreases in temperature or hydration, thus suggesting a prominent functional role of protein dynamics. An unusual dynamics–function correlation was observed for the major light-harvesting complex II, where the dynamics of charged protein residues affect the pigment absorption frequencies in photosynthetic light-harvesting. Generally, proteins exhibit a wide variety of motions on multiple time and length scales. However, there is an approach to characterize the plasticity of a protein as a single effective force constant that permits a straightforward comparison between different protein systems. Within this review, we determine the latter effective force constant for three photosynthetic proteins in different functional and organizational states. The force constant values determined appear to be rather different for each protein and are consistent with the requirements imposed by the various functions. These findings highlight the individual character of a protein’s flexibility and the role(s) it is playing for the specific function. Full article

Biological N₂ fixation (BNF) and ratoon rice growth are biological processes that mediate N and C cycling in rice paddy ecosystems, but their contributions to paddy soil fertility have rarely been evaluated in a quantitative and unified manner. In this study, we analyzed the contribution of BNF and ratoon rice growth to soil N fertility at six rice paddy sites in four prefectures of Japan, combining 2-year field monitoring and simulation using the DNDC-Rice biogeochemistry model. Across the sites and years, ratoon rice was found to accumulate up to 30 kg N ha⁻¹ without fertilization and irrigation after main rice harvest. BNF was not measured but estimated to be 33–63 kg N ha⁻¹ yr⁻¹ at the six sites, by applying a newly built BNF model after calibration against a literature dataset. Based on the simulations using DNDC-Rice under typical local management strategies, we estimated the following contributions of BNF and ratoon rice to soil N fertility, with variations based on the climate, soil properties, and management, as follows: (a) BNF and ratoon rice contributed 4–33% and 3–23% of the N supply from soil during the main rice season, respectively. (b) While BNF contributed 3–29% of the main rice N uptake, that from ratoon rice was much lower (6% or less), presumably because the decomposition of ratoon rice residue induced N immobilization during the main rice season. (c) Although the major part of N gain by BNF was being lost via denitrification and N leaching, BNF was contributing up to 6.6% of the organic N pool at the 0–30 cm soil layer. Ratoon rice was working to save N loss by reducing N leaching, consequently contributing up to 3.3% of the soil N pool. These findings provide quantitative insights into what roles BNF and ratoon rice play in paddy soil fertility. Full article

Background/Objectives: Ash made from juniper trees and added to cornmeal-based dishes may have provided calcium (Ca) to traditional Indigenous diets. Few studies have quantified the mineral content of juniper ash, including its Ca content. The objective of this study was to determine whether juniper ash could serve as a safe source of non-dairy Ca in an intervention study. Methods: Branches from two varieties of Juniper (Rocky Mountain Juniper, or Juniperus scopulorum and Eastern Red Cedar, or Juniperus virginiana) were harvested and burned to ash in a laboratory setting. Juniper ash from the southwestern U.S. available for retail purchase was used for comparison. All samples were tested for content of 10 nutritive elements (Ca, copper, iron, potassium, magnesium, manganese, sodium, phosphorus, selenium, and zinc) and 20 potentially toxic elements (silver, aluminum, arsenic, barium, beryllium, cadmium, cobalt, chromium, mercury, lithium, molybdenum, nickel, lead, antimony, tin, strontium, thallium, uranium, and vanadium) as well as n = 576 pesticide residues. Results: All samples contained both nutritive and potentially toxic elements. Each teaspoon of ash contained an average of 445 ± 141 mg Ca. However, the samples also contained lead in amounts ranging from 1.09 ppm to 15 ppm. Conclusions: Information on the nutritive and potentially toxic elemental content of juniper ash and how it may interact within a food matrix is insufficient to determine its safety as a Ca source. Further investigation is needed on the bioavailability of calcium oxide and its interaction with other dietary components to clarify the potential role of juniper ash in contemporary food patterns. Full article

Cabbage stubble left in fields after harvest forms a mechanically complex stubble–soil composite that hinders subsequent tillage and crop establishment. Although the Discrete Element Method (DEM) is widely used to model soil-root systems, calibrated contact parameters for taproot-dominated vegetables like cabbage remain unreported. This study addresses this gap by calibrating a novel DEM framework that couples the JKR model and the Bonding V2 model to represent adhesion and mechanical interlocking at the stubble–soil interface. Soil intrinsic properties and contact parameters were determined through triaxial tests and angle-of-repose experiments. Physical pullout tests on ‘Zhonggan 21’ cabbage stubble yielded a mean peak force of 165.5 N, used as the calibration target. A three-stage strategy—factor screening, steepest ascent, and Box–Behnken design (BBD)—identified optimal interfacial parameters: shear stiffness per unit area = 4.40 × 10⁸ N·m⁻³, normal strength = 6.26 × 10⁴ Pa, and shear strength = 6.38 × 10⁴ Pa. Simulation predicted a peak pullout force of 162.0 N, showing only a 2.1% deviation from experiments and accurately replicating the force-time trend. This work establishes the first validated DEM framework for cabbage stubble–soil interaction, enabling reliable virtual prototyping of residue management implements and supporting low-resistance, energy-efficient tillage tool development for vegetable production. Full article

Accurate modeling of stem taper is essential for forest management decisions, including the definition of cutting cycles, the feasibility of annual harvesting, assortment classification, size and volume estimation, and ensuring sustainable production continuity. This study modeled the stem taper of Araucaria angustifolia (Bertol.) Kuntze stands in southern Brazil using Kozak’s variable-exponent model fitted with nonlinear mixed-effects techniques. Both fixed- and mixed-effects models showed high predictive performance, regardless of calibration. An unstructured (UN) covariance structure was required to reduce autocorrelation. The mixed-effects model improved predictive accuracy by up to 22%, achieved R² values above 0.99 with RMSE < 0.74 cm, and significantly reduced residual autocorrelation in diameter estimates. The most effective calibration of random effects was achieved using diameter measurements taken at heights between 0.3 and 6.3 m above ground (approximately between 1.3% and 28.3% of the total height, considering the tallest tree as a reference). This research improves the accuracy of volume estimation and the definition of timber assortments for A. angustifolia, thereby supporting forest management decision-making in southern Brazil. Full article

The winemaking industry represents one of the most important sectors of the Mediterranean agrifood economy, generating large amounts of solid residues, especially grape pomace. The study aimed to evaluate during two consecutive harvest years the influence of the production system (conventional vs. organic) and cultivar on the mineral, chemical, and antioxidant composition, as well as the colorimetric properties, of grape pomaces obtained from four Vitis vinifera L. cultivars in Alentejo-Portugal. The results showed that mineral composition was significantly affected by both production system and cultivar, with organic grape pomace showing higher K and Mn contents, whereas Ca and Cu showed consistently higher content under conventional. Protein content tended to increase under organic production, while fiber and fat were overall higher in conventional, particularly in the first year. Sugars displayed strong cultivar specificity, with Arinto showing the highest concentrations (30 to 40%), and considering all cultivars, total phenolic content ranged between 4000 ando 9000 mg GAE/100 g, while antioxidant capacity varied among cultivars and years. Colorimetric parameters were essentially influenced by cultivar and harvest year rather than production system. The PCA revealed that PC1 (44.06%) represented a gradient associated with mineral and antioxidant composition, while PC2 (21.26%) reflected minor variation in color and sugars, and the hierarchical clustering distinguished Syrah and Alicante Bouschet as the cultivars most responsive to production system, whereas Aragonez and Arinto exhibited greater compositional stability across years. Overall, the findings indicate that both cultivar and management practices (organic and conventional) influence the compositional profile of grape pomace, with organic showing a tendency to enhance K, Mn, protein, and antioxidant parameters, whereas conventional practices favored higher levels of Ca, Cu, and fiber. The results provide valuable insights for the valorization of grape pomace and the development of sustainable viticultural strategies in Mediterranean environments. Full article

Aquatic environments have been a critical source of nutrition for millennia, with wild fisheries supplying protein and nutrients to populations worldwide. A notable shift has occurred in recent decades with the expansion of aquaculture, now representing a fast-growing sector in food production. Aquaculture plays a key role in mitigating the depletion of wild fish stocks and addressing issues related to overfishing. Despite its potential benefits, the sustainability of both wild and farmed aquatic food systems is challenged by anthropogenic pollution. Contaminants from agricultural runoff, industrial discharges, and domestic effluents enter freshwater systems and eventually reach marine environments, where they may be transported globally through ocean currents. Maintaining water quality is paramount to food safety, environmental integrity, and long-term food security. In addition to conventional seafood products such as fish and shellfish, foods such as those derived from microalgae are gaining attention in Western markets for their high nutritional value and potential functional properties. These organisms have been consumed in Asia for generations and are now being explored as sustainable foods and ingredients as an alternative source of protein. Contaminants in aquatic food products include residues of agrochemicals, persistent organic pollutants (POPs) such as dioxins, polychlorinated biphenyls (PCBs), and per- and polyfluoroalkyl substances (PFASs), as well as brominated flame retardants and heavy metals. Public and scientific attention has intensified around plastic pollution, particularly microplastics and nanoplastics, which are increasingly detected in aquatic organisms and are the subject of ongoing toxicological and ecological risk assessments. While the presence of these hazards necessitates robust risk assessment and regulatory oversight, it is important to balance these concerns against the health benefits of aquatic foods, which are rich in omega-3 fatty acids, high-quality proteins, vitamins, and trace elements. Furthermore, beyond direct human health implications, the environmental impact of pollutant sources must be addressed through integrated management approaches to ensure the long-term sustainability of aquatic ecosystems and the food systems they support. This review covers regulatory frameworks, risk assessments, and management issues relating to aquatic environments, including the impact of climate change. It aims to serve as a comprehensive resource for researchers, policymakers, food businesses who harvest food from aquatic systems and other stakeholders. Full article

This study addresses the lack of transparent methods for calculating the energy requirements of pulsed electric field (PEF) pretreatments in biogas research. Two detailed approaches are proposed and evaluated to quantify the energy consumed during the pretreatment of lignocellulosic harvest residues (corn, soybean, and sunflower) using a low-frequency electric field. The first approach is based on previously measured capacitor parameters, including resistance (R_s, R_p), inductance (L_s), capacitance (C_p), and loss factor (D), which were interpolated to 50 Hz from measurements performed over the frequency range of 100 Hz to 10 kHz. The second approach relies on direct measurements of the effective voltage and current waveforms across the capacitor, followed by calculation of the power factor (cos φ). Both methods enable reliable estimation of energy consumption and differ primarily in the type of input data required: Method 1 is based on capacitor characteristics determined before and after pretreatment, while Method 2 uses real-time treatment data. Despite these differences, the two approaches yielded highly consistent results, confirming their robustness and applicability. The calculated energy values were subsequently incorporated into a net energy balance by comparing the energy consumed during pretreatment with the methane energy output from anaerobic digestion. For all three investigated lignocellulosic substrates, PEF pretreatment resulted in a positive energy balance under the applied process conditions. Full article

To address the issue of low depth estimation accuracy in complex goji berry orchards, this paper proposes a method for identifying and locating goji berries that combines the YOLO-VitBiS object detection network with stereo vision technology. Based on the YOLO11n backbone network, the C3K2 module in the backbone is first improved using the AdditiveBlock module to enhance its detail-capturing capability in complex environments. The AdditiveBlock introduces lightweight long-range interactions via residual additive operations, thereby strengthening global context modeling without significantly increasing computation. Subsequently, a weighted bidirectional feature pyramid network is introduced into the Neck to enable more flexible and efficient feature fusion. Finally, a lightweight shared detail-enhanced detection head is proposed to further reduce the network’s computational complexity and parameter count. The enhanced model is integrated with binocular stereo vision technology, employing the CREStereo depth estimation algorithm for disparity calculation during binocular stereo matching to derive the three-dimensional spatial coordinates of the goji berry target. This approach enables efficient and precise positioning. Experimental results demonstrate that the YOLO-VitBiS model achieves a detection accuracy of $96.6\%$, with a model size of $4.3\mathrm{MB}$ and only $1.856\mathrm{M}$ parameters. Compared to the traditional SGBM method and other deep learning approaches such as UniMatch, the CREStereo algorithm generates superior depth maps under complex conditions. Within a distance range of 400 mm to 1000 mm, the average relative error between the estimated and actual depth measurements is $2.42\%$, meeting the detection and ranging accuracy requirements for field operations and providing reliable recognition and localization support for subsequent goji berry harvesting robots. Full article

The use of wireless sensor networks (WSNs) enables multidimensional and high-precision forest environment monitoring around the clock. However, the limited energy supply of sensor nodes using solely batteries is insufficient to support long-term data collection. Furthermore, since the complex terrain, dense vegetation, and variable weather in forests present unique challenges, relying on a single energy source is insufficient to ensure a stable energy supply for sensor nodes. Combining multiple energy sources is a promising way which has not been well studied. In this paper, to effectively utilize multiple energy sources, we propose a novel dynamic clustering routing protocol which considers the inherent diversity and intermittency of energy sources of the WSN in the forest. First, to address the inconsistency in residual energy caused by uneven energy harvesting among sensor nodes, a cluster head selection weight function is developed, and a dynamic weight-based cluster head election algorithm is proposed. This mechanism effectively prevents low-energy nodes from being selected as cluster heads, thereby maximizing the utilization of harvested energy. Second, a Q-learning-based adaptive hybrid transmission scheme is introduced, integrating both single-hop and multi-hop communication. The scheme dynamically optimizes intra-cluster transmission paths based on the current network state, reducing energy consumption during data transmission. The simulation results show that the proposed routing algorithm significantly outperforms existing methods in total network energy consumption, network lifetime, and energy balance. These advantages make it particularly suitable for forest environments characterized by strong fluctuations in harvested energy. In summary, this work provides an energy-efficient and adaptive routing solution suitable for forest environments with fluctuating energy availability. Full article

Residues in the grain tank of seed-production wheat harvesters often cause varietal admixture, challenging seed purity maintenance above 99%. To address this, an intelligent cleaning system was developed for automatic residue recognition and removal. The system utilizes an RGB-D camera and an embedded AI unit paired with an improved lightweight object detection model. This model, enhanced for feature extraction and compressed via LAMP, was successfully deployed on a Jetson Nano, achieving 92.5% detection accuracy and 13.37 FPS for real-time 3D localization of impurities. A D–H kinematic model was established for the 4-DOF cleaning manipulator. By integrating the PSO and FWA models, the motion trajectory was optimized for time-optimality, reducing movement time from 9 s to 5.96 s. Furthermore, a gas–solid coupled simulation verified the separation capability of the cyclone-type dust extraction unit, which prevents motor damage and centralizes residue collection. Field tests confirmed the system’s comprehensive functionality, achieving an average cleaning rate of 92.6%. The proposed system successfully enables autonomous residue cleanup, effectively minimizing the risk of variety mixing and significantly improving the harvest purity and operational reliability of seed-production wheat. It presents a novel technological path for efficient seed production under the paradigm of smart agriculture. Full article

Widespread antibiotic residues in aquatic environments pose escalating threats to ecological stability and human health, highlighting the urgent demand for effective remediation strategies. In recent years, photocatalytic technology based on advanced nanomaterials has emerged as a sustainable and efficient strategy for antibiotic degradation, enabling the effective utilization of solar energy for environmental remediation. This review provides an in-depth discussion of six representative categories of photocatalytic nanomaterials that have demonstrated remarkable performance in antibiotic degradation, including metal oxide-based systems with defect engineering and hollow architectures, bismuth-based semiconductors with narrow band gaps and heterojunction designs, silver-based plasmonic composites with enhanced light harvesting, metal–organic frameworks (MOFs) featuring tunable porosity and hybrid interfaces, carbon-based materials such as g-C₃N₄ and biochar that facilitate charge transfer and adsorption, and emerging MXene–semiconductor hybrids exhibiting exceptional conductivity and interfacial activity. The photocatalytic performance of these nanomaterials is compared in terms of degradation efficiency, recyclability, and visible-light response to evaluate their suitability for antibiotic degradation. Beyond parent compound removal, we emphasize transformation products, mineralization, and post-treatment toxicity evolution as critical metrics for assessing true detoxification and environmental risk. In addition, the incorporation of artificial intelligence into photocatalyst design, mechanistic modeling, and process optimization is highlighted as a promising direction for accelerating material innovation and advancing toward scalable, safe, and sustainable photocatalytic applications. Full article