Search Results (31,984)

Ambrosia trifida L. is native to North America, has been introduced into many countries in Europe and East Asia, and is also expanding its habitat in its native ranges. Ambrosia trifida grows in sunny and humid environments, such as grasslands, riverbanks, floodplains, abandoned places, and agricultural fields, as an invasive plant species. Ambrosia trifida has a strong adaptive ability to adverse conditions and shows great variation in seed germination phenology and plant morphology in response to environmental conditions. Effective natural enemies have not been found in its native or introduced ranges. The species is allelopathic and contains several allelochemicals. These characteristics may contribute to the competitive ability and invasiveness of this species. Ambrosia trifida significantly reduces species diversity and plant abundance in its infested plant communities. The species also causes significant yield loss in summer crop production, such as in maize, soybean, sunflower, and cotton production. Ambrosia trifida is capable of rapid evolution against herbicide pressure. Populations of Ambrosia trifida resistant to glyphosate, ALS-inhibiting herbicides, and PPO-inhibiting herbicides, as well as cross-resistant populations, have already appeared. An integrated weed management protocol with a more diverse combination of herbicide sites of action and other practices, such as tillage, the use of different crop species, crop rotation, smart decision tools, and innovative equipment, would be essential to mitigate herbicide-dependent weed control practices and may be one sustainable system for Ambrosia trifida management. Full article

To address the computational complexity and deployment challenges of traditional convolutional neural networks in rice disease identification, this paper proposes an efficient and lightweight model: Ghost Channel Spatial Attention ShuffleNet with Mish-ReLU Adaptive Activation Function (GCA-MiRaNet). Based on ShuffleNet V2, we effectively reduced the model’s parameter count by streamlining convolutional layers, decreasing stacking depth, and optimizing output channels. Additionally, the model incorporates the Ghost Module as a replacement for traditional 1 × 1 convolutions, further reducing computational overhead. Innovatively, we introduce a Channel Spatial Attention Mechanism (CSAM) that significantly enhances feature extraction and generalization aimed at rice disease detection. Through combining the advantages of Mish and ReLU, we designed the Mish-ReLU Adaptive Activation Function (MAAF), enhancing the model’s generalization capacity and convergence speed. Through transfer learning and ElasticNet regularization, the model’s accuracy has notably improved while effectively avoiding overfitting. Sufficient experimental results indicate that GCA-MiRaNet attains a precision of 94.76% on the rice disease dataset, with a 95.38% reduction in model parameters and a compact size of only 0.4 MB. Compared to traditional models such as ResNet50 and EfficientNet V2, GCA-MiRaNet demonstrates significant advantages in overall performance, especially on embedded devices. This model not only enables efficient and accurate real-time disease monitoring but also provides a viable solution for rice field protection drones and Internet of Things management systems, advancing the process of contemporary agricultural smart management. Full article

In the context of Industry 4.0, Artificial Intelligence (AI) methods are used to maximize the efficiency and flexibility of production processes. The adaptive management of such semantic processes can optimize energy and resource efficiency while providing high reliability, but it depends on the representation type of these models. This paper provides a literature review of current Process Modeling Languages (PMLs). Based on a suitable PML, the flexibility of production processes can be increased. Currently, a common understanding of this process flexibility in the context of adaptive workflow management is missing. Therefore, requirements derived from the business environment are presented for process flexibility. To enable the identification of suitable PLMs, requirements regarding this are also raised. Based on these, the PMLs identified in the literature review are evaluated. Thereby, based on a preselection, a detailed examination of the seven most promising languages is performed, including an example from a real smart factory. As a result, a recommendation is made for the use of BPMN, for which it is presented how it can be enriched with separate semantic information that is suitable for the use of AI planning and, thus, enables flexible control. Full article

The advancement of wearable technology has entered a new phase, leading to the creation of various wearable sensors due to the rise of technologies like IoT and AI. Wearable chemical sensors are essential components of wearable electronics and hold significant promise in monitoring health. This review reports on the recent achievements and advantages of portable smart chemical sensing for health monitoring and discusses portable chemical sensing using frictional/piezoelectric electrochemical generators, photovoltaics and thermal power accumulators. This paper also evaluates the potential of wearable chemical sensors for health monitoring. Full article

Background/Objectives: Cationic surfactants are potential antimicrobial candidates. Even so, they are the foremost irritative and incompatible group, which limits their usage. The incorporation of surfactants in biopolymer-based nanoparticles is a feasible strategy to improve their efficacy and reduce those drawbacks. Methods: Surfactants with one amino acid on the polar head (lauroyl arginine methyl ester—LAM and phenylalanine dodecyl amide—PNHC₁₂) and surfactants with two amino acids on the polar heads, arginine-phenylalanine (Lauroyl phenylalanine arginine methyl esther—C₁₂PAM and phenylalanine-arginine dodecyl amide—PANHC₁₂) were loaded to zein nanoparticles. Their antimicrobial and antibiofilm activities were evaluated. Also, the inhibitory activities of the surfactants and nanoparticles over skin-related enzymes were accessed in silico and in vitro, while their cytotoxicity was determined comparatively over immortal human keratinocytes (HaCaT) and human fibroblasts (3T3). Finally, the Vibrio fisheri luminescence reduction test was used to detect its ecotoxicity. Results: The nanoparticles were obtained successfully and exhibited good biocide activity against a wide range of pathogenic bacteria and yeasts. The surfactants were found active over the enzymes assayed: elastase > tyrosinase > collagenase > lipoxygenase, while the inhibitory activity was superior when nanoencapsulated over the enzymes tyrosinase and lipoxygenase. The surfactants and their corresponding nanoparticles presented acceptable cytotoxic levels, except for PNHC₁₂ in both forms, while their ecotoxicity was limited and acceptable. Conclusions: Accordingly, the nanoencapsulation of the arginine-phenylalanine surfactants loaded to zein nanoparticles was found to be a smart strategy to enhance the antimicrobial activity and improve their selectivity over representative skin and connective tissues cell lines. These biological properties render the arginine-phenylalanine surfactant nanoparticles as promising candidates for antimicrobial and tissue repairing applications in wound treatments. Full article

The Leaf Area Index (LAI) is a key indicator for assessing fruit tree growth and productivity, and accurate estimation using hyperspectral technology is essential for monitoring plant health. This study aimed to improve LAI estimation accuracy in apricot, jujube, and walnut trees in Xinjiang, China. Canopy hyperspectral data were processed using fractional-order differentiation (FOD) from 0 to 2.0 orders to extract spectral features. Three feature selection methods—Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA), and their combination (CARS-SPA)—were applied to identify sensitive spectral bands. Various band combinations were used to construct three-band indices (TBIs) for optimal LAI estimation. Random forest (RF) models were developed and validated for LAI prediction. The results showed that (1) the reflectance spectra of jujube and walnut trees were similar, while apricot spectra differed. (2) The correlation between fractional-order differential spectra and LAI was highest at orders 1.4 and 1.7, outperforming integer-order spectra. (3) The CARS-SPA selected a smaller set of feature bands in the 1100~2500 nm, reducing collinearity and improving spectral index construction. (4) The RF model using TBI4 demonstrated high R², low RMSE, and an RPD value > 2, indicating optimal prediction accuracy. This approach holds promise for hyperspectral LAI monitoring in fruit trees. Full article

The urban low-altitude airspace is an integral part of urban space. As low-altitude utilization activities are being performed closer to the land surface, the management of the low-altitude space has become a focus of land administration. The management of the low-altitude airspace faces challenges such as cross-departmental coordination, fuzzy airspace boundaries, and complex spatial expressions. In 2020, the concept of “3D land administration” was introduced, marking the emergence of three-dimensional geospatial regulation in land management practices. Semantic models featuring the LADM (Land Administration Domain Model) as their core are updated iteratively to promote various applications related to 3D geographic regulation, but there is still a gap in the research on low-altitude utilization. Drawing upon Chinese regulations and policies, this paper employs the LADM framework to achieve semantic descriptions and expressions for managing areas in the low-altitude airspace: (1) relevant policies governing low-altitude spaces in China are analyzed, and the boundary between low-altitude and surface management is discussed; (2) the LADM structure is utilized to establish a comprehensive model for regulating low-altitude spaces; (3) and the capability of the LADM to support 3D low-altitude modeling is demonstrated through practical use cases in Shenzhen, China. Finally, the paper provides a comprehensive overview of the avenues for improvement and prospects. Full article

Water management is a significant aspect of sustainable vegetable farming, especially in water-scarce regions. This, in addition to weed infestations, limits vegetable yields, which negatively affect food security in developing regions, particularly East Africa, where livelihoods chiefly depend on rain-fed agriculture. Vegetable farming, especially tomato cultivation, requires more water. By promoting mulching, a soil water conservation tool, we can control surface evaporation (E), which, together with irrigation, enhances effective water use and vegetable yields. The experiments for this study were conducted at the Tokyo University of Agriculture, Japan, to evaluate the influences of different irrigation conditions and poly-mulching on weed control, tomato yields, and water use efficiency. The study was conducted from May to September 2018 on a 30 m² plot in an open-ended greenhouse using drip irrigation for tomato cultivation. Three predetermined irrigation conditions of 4, 3, and 2 mm/day were applied on black poly-mulched and bare ridges. Data on soil conditions—soil temperature, as well as meteorological variables, including solar radiation and temperature—were measured using thermocouple sensors and micro-hobo weather stations, respectively, during the tomato cultivation, while yield components—growth, yield, water productivity, and sugar content—were determined after harvest. The results of a two-way ANOVA show that irrigation conditions with poly-mulching reduced the weed biomass significantly, and improved yields and water use efficiency compared to the irrigation conditions on bare ridges. The application of 4, 3, and 2 mm/day irrigation with poly-mulching significantly reduced the weed biomass by 5% compared to the same irrigation conditions on bare ridges. Similarly, 4 and 3 mm/day irrigation conditions with poly-mulching significantly increased the tomato yield by 5% compared to 2 mm/day on bare ridges. The bigger roots were concentrated and widely distributed at the shallow soil depth (0–20 cm) of the ridges with high irrigation amounts, while the small and thin roots were in deeper soil layers (30–45 cm). This study provides scientific knowledge on the application of predetermined irrigation conditions that can be (i) integrated into irrigation scheduling and (ii) adopted for regions facing water scarcity and limited or no in situ meteorological data, to improve water use efficiency for vegetable cultivation. Full article

A demand response (DR) aggregator is a specialized entity designed to collaborate with electricity producers, facilitating the exchange of energy for numerous stakeholders. This application is a pivotal development within the Russian Energy System as it transitions to a Smart Grid. Its successful operation relies on the advancement and implementation of more efficient strategies to manage emerging energy assets and structures. The holonic approach is a distributed management model used to handle systems characterized by random and dynamic changes. This paper analyzes the specific aspects of the electricity demand management mechanism in Russia, primarily aimed at reducing peak load in the energy system by engaging active consumers who are outside the wholesale market. The DR-Aggregator is considered both a cyber-physical system (CPS) with a cluster structure and a business process. The DR-Aggregator exhibits essential holonic properties, enabling the application of a holonic approach to enhance the efficiency of the DR-Aggregator mechanism. This approach will facilitate greater flexibility in managing the load schedules of individual holon consumers, bolster the reliability of power supply by aligning load schedules among holon consumers within the super-holon cluster, and improve the fault tolerance of the DR-Aggregator structure, providing greater adaptability of demand management services. Full article

Frequency-Selective Surfaces (FSSs) are structures that act as frequency-dependent electromagnetic filters, enabling innovative designs for energy-efficient building envelopes. This paper explores their potential for energy harvesting and integration into construction materials, offering insights into design strategies, performance analysis, and potential applications of FSS sin future architectural projects. A range of FSS designs are presented and systematically classified based on their performance and adaptability for building integration. This includes their use as part of traditional construction elements or as independent components of building walls. Critical issues such as the limitations, challenges, and durability of FSSs in real-world applications are also examined to provide a comprehensive view of their practical feasibility. Additionally, incorporating the electromagnetic properties of these materials into Building Information Modelling (BIM) systems is recommended. Doing so will enable architects and engineers to better utilize the novel opportunities that FSSs offer, fostering more innovative, energy-efficient building envelopes. Overall, this paper provides valuable insights into how FSSs can transform the future of sustainable architecture and energy management in buildings. Full article

Background/Objectives: Improved global data allow for a new understanding of what impact the food we produce, eat and dispose of has on the environment, human health and Nature’s resources. The overall goal is to guide decision-makers and individuals by providing in-depth knowledge about the effects of their dietary preferences on human and environmental health. Methods: The method is to investigate ways to reduce environmental degradation and to secure healthy food supplies in an urbanizing world, and to quantify the options. Results: Reviewed articles show that by eating less meat-based food and more plant-based and soilless food, as well as reducing food waste and recycling urban-disposed nutrients as fertilizers, we could reduce agriculture’s land requirement by 50% to 70% while still securing a healthy food supply. Less land under cultivation and pasture would reduce global emissions to air and water to a similar extent, and allow Nature to reclaim freed areas in order to catch more carbon and rejuvenate biodiversity. Thus, we could avoid further environmental degradation such as the current clearing of new fields needed under a business-as-usual regime. Presently, some 17 million people die each year due to poor diets, which is more than double the 7 million deaths since the onset of the COVID-19 pandemic. A return to more plant-based diets with unchanged intake of proteins but less calories, sugar, salt and fat combined with less red meat and ultra-processed food would reduce foremost non-communicable diseases by up to 20% and prolong life. The article suggests that the international focus has gradually turned to the food sector’s big contribution to climate change, biodiversity loss and harmful chemicals as well as to poor human health. It argues that this century’s rapid population growth and urbanization give urban residents a pivotal role in food’s impact on agricultural areas, which today cover half of the globe’s inhabitable areas. Their food demand, rather than the activities of farmers, fishermen or loggers, will guide remedial measures to be taken by individuals, industry and the public sector. A tool to calculate the potential environmental footprints of individual or societal measures is presented. Conclusions: Measures to make the agrifood sector more sustainable are still pending full recognition in international fora such as the UN COP Summits. Smart cities fitted with infrastructures to recycle macro- and micro-nutrients and organic matter have the potential to ameliorate human-induced impacts such as emissions to air and water bodies, crossing planetary boundaries, and polluting extraction of N (nitrogen), P (phosphorus) and K (potassium). Rapid results are within reach since dietary change and the turn-around time of nutrients in food is short compared to decades or centuries for recycled materials in cars or buildings. Full article

As the need to monitor agriculture parameters intensifies, the development of new sensor nodes for data collection is crucial. These sensor types naturally require power for operation, but conventional battery-based power solutions have certain limitations. This study investigates the potential of harnessing the natural temperature gradient between soil and air to power wireless sensor nodes deployed in environments such as agricultural areas or remote off-grid locations where the use of batteries as a power source is impractical. We evaluated existing devices that exploit similar energy sources and applied the results to develop a state-of-the-art device for extensive testing over a 12-month period. Our main objective was to precisely measure the temperature on a thermoelectric generator (TEG) (a Peltier cell, in particular) and assess the device’s energy yield. The device harvested 7852.2 J of electrical energy during the testing period. The experiment highlights the viability of using environmental temperature differences to power wireless sensor nodes in off-grid and battery-constrained applications. The results indicate significant potential for the device as a sustainable energy solution in agricultural monitoring scenarios. Full article

Imitation precious wood materials have become a research focus due to their good quality, high safety level, excellent performance, rich color, varied textures, and high utilization rates. However, their uneven dyeing, poor color stability, and lack of durability limit their further application. This study utilized a neural network model optimized with the Gray Wolf Algorithm (GWA) for color matching, using acidic dyes as raw materials and deep eutectic solvents (DESs) for modification. Functional reagents like nano tungsten trioxide (WO₃) and titanium dioxide (TiO₂) were introduced alongside polyvinyl alcohol (PVA) as a modifier. A dyeing-enhancement modification process was employed to create a poplar veneer that exhibited uniform and stable color performance with a smooth surface, mimicking that of precious wood. Computerized color matching was used to adjust the dye formulation for staining, ensuring stable colorimetric values on the veneer surface, which closely resembled natural precious wood. The average mean squared error in dye concentration prediction, after processing with the Gray Wolf Algorithm and a basic neural network algorithm, decreased from 0.13 to 0.006, ensuring repeatability and consistency in wood dyeing. Analysis and characterization using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and permeability testing revealed that nano TiO₂ and WO₃ particles were uniformly distributed within the wood cell lumens and firmly bonded. Mechanical testing on PVA-glued veneers showed that compared to untreated poplar veneers, the tensile strength of the imitation wood increased by approximately 62.5%, and the bending strength reached 809.09 MPa, significantly improving the flexibility and tensile properties of the poplar veneer. This study is the first to adopt a DES-modified dyeing-enhancement modification process to improve the dyeing performance, uniformity, durability, and structural stability of wood, showcasing its great potential in architectural decoration, high-end furniture, and artisanal crafts. Full article

Aquaculture plays an important role in the global economy. However, unscientific feeding methods often lead to problems such as feed waste and water pollution. This study aims to address this issue by accurately recognizing fish feeding behaviors to provide automatic bait casting machines with scientific feeding strategies, thereby reducing farming costs. We propose a fish feeding behavior recognition method based on semantic segmentation, which overcomes the limitations of existing methods in dealing with complex backgrounds, water splash interference, fish target overlapping, and real-time performance. In this method, we first accurately segment fish targets in the images using a semantic segmentation model. Then, these segmented images are input into our proposed fish feeding behavior recognition model. By analyzing the aggregation characteristics during the feeding process, we can identify fish feeding behaviors. Experiments show that the proposed method has excellent robustness and real-time performance, and it performs well in the case of complex water background and occlusion of fish targets. We provide the aquaculture industry with an efficient and reliable method for recognizing fish feeding behavior, offering new scientific support for intelligent aquaculture and delivering powerful solutions to improve aquaculture management and production efficiency. Although the algorithm proposed in this study has shown good performance in fish feeding behavior recognition, it requires certain lighting conditions and fish density, which may affect its adaptability in different environments. Future research could explore integrating multimodal data, such as sound information, to assist in judgment, thereby enhancing the robustness of the model and promoting the development of intelligent aquaculture. Full article

The purpose of this study is to solve the problem of low load factor and profit margin in the point-to-point transportation of international freight trains through the assembly transportation organization mode. A bi-objective location-routing optimization model is constructed to optimize problems, such as the location of the assembly center, route of freight assembly, frequency of international freight trains, and number of formations. The objectives are to minimize the total comprehensive cost and maximize the average satisfaction of the shippers. Considering the impact of blockchain technology, the proportion of customs clearance time reduction after blockchain implementation, the proportion of customs clearance fee reduction after blockchain implementation, and the cost of blockchain technology are introduced into the model. The case study is based on railroad transportation data for 2022. In this case, 43 stations in the Indo-China Peninsula are selected as origin stations, and two Chinese stations are designated terminal stations. An improved NSGA-II algorithm (ANSGAII-OD) is proposed to resolve the location-routing optimization model. This algorithm is based on opposition-based learning and its dominant strength. The case study indicates that assembly transportation is advantageous compared with direct transportation. Moreover, the comprehensive cost is reduced by 19.77%. Furthermore, blockchain technology can effectively reduce costs and improve transportation efficiency. After the implementation of blockchain technology, the comprehensive cost is reduced by 8.10%, whereas the average satisfaction of shippers is increased by 10.35%. Full article