Search Results (13,382)

The strategic development of the low-altitude economy requires efficient urban logistics solutions. The existing Unmanned Aerial Vehicle (UAV) truck delivery system faces severe challenges in dealing with dynamic weather constraints and multi-agent coordination. This article proposes a neural network-based optimisation framework that integrates constrained K-means clustering and a three-stage neural architecture. In this work, a mathematical model for heterogeneous vehicle constraints considering time windows and UAV energy consumption is developed, and it is validated through reference to the Solomon benchmark’s arithmetic examples. Experimental results show that the Truck–Drone Cooperative Traveling Salesman Problem (TDCTSP) model reduces the cost by 21.3% and the delivery time variance by 18.7% compared to the truck-only solution (Truck Traveling Salesman Problem (TTSP)). Improved neural network (INN) algorithms are also superior to the traditional genetic algorithm (GA) and Adaptive Large Neighborhood Search (ALNS) methods in terms of the quality of computed solutions. This research provides an adaptive solution for intelligent low-altitude logistics, which provides a theoretical basis and practical tools for the development of urban air traffic under environmental uncertainty. Full article

This work aimed to design a strategy for providing a collection service of urban solid waste in communities without it, using a case study in Sombrerete, Zacatecas, Mexico. The service is provided to the municipal seat and 17 of the 173 communities, resulting in a collection coverage of 10%. Information provided by the Cleaning Department of Sombrerete was collected and analyzed on the number of collection vehicles, communities served, and final waste disposal sites. Communities without urban solid waste collection and disposal services were identified. The strategy was designed to increase the collection coverage using geographic information systems, vehicle routing problem tools, and territory sectorization. Waste collection routes were developed for 11 sectors without service, and final waste disposal sites were evaluated based on environmental protection criteria of the Mexican Official Standard. The technical and economic feasibility of the strategy were analyzed. The results obtained were the design of the collection routes strategy to increase the coverage to 100% in Sombrerete. The designed strategy was feasible since it did not require the purchase of waste collection vehicles and hiring more staff. Approximately MXN 1000 (≈USD 54, EUR 47) in economic benefits were achieved weekly. Full article

Air pollution is one of the major environmental challenges worldwide. Settleable particulate matter (SPM), related to this environmental problem, contains metals capable of producing negative effects on human health (e.g., cardiovascular and respiratory illness). For this study, continuous monitoring was carried in the urban city of Loja (Ecuador), where 10 points were distributed based on different land uses. Samples were collected on a monthly basis using a passive method, by means of samplers built based on the 502 Method. The gravimetric method was then used in the laboratory to determine the concentration of SPM. The inductively coupled plasma–optical emission spectroscopy (ICP-OES) technique was used to identify the presence of metals as such as Copper (Cu), Lead (Pb), Cobalt (Co), Cadmium (Cd), Chromium (Cr), Silver (Ag), Arsenic (As), and Mercury (Hg) in SPM. The results obtained showed that SPM and As differed significantly between land uses, but most metals showed significant differences in relation to temporal changes. Although 90% of the sampling points show SPM concentrations within the limits established by environmental regulations, some of the points exceed the World Health Organization (WHO) limit of 0.5 mg/cm². Finally, the temporal changes in more metals were clearly observed, probably because of increased combustion processes (vehicular traffic), with a higher percentage of metals clearly observed during the April and August months. Furthermore, the highest levels of vegetation burning in Loja province, including the surroundings of the city of Loja, occurred in August. This analysis provides essential data to guide environmental monitoring and air quality management strategies, aiming to reduce health risks from long-term exposure to metal-enriched particulate matter. Full article

The fixation time of alleles is a fundamental concept in population genetics, traditionally studied using the Wright–Fisher model and classical coalescent theory. However, these models often assume homogeneous environments and equal reproductive success among individuals, limiting their applicability to real-world populations where environmental heterogeneity plays a significant role. In this paper, we introduce a new forward-time model for estimating fixation time that incorporates environmental heterogeneity through the use of fractional calculus. By introducing a fractional parameter α, we capture the effects of heterogeneous environments on offspring production. To solve the resulting fractional differential equations, we develop a novel spectral method based on Eta-based functions, which are well-suited for approximating solutions to complex, high-variation systems. The proposed method reduces the problem to an optimization framework via the operational matrix of fractional derivatives. We demonstrate the effectiveness and accuracy of this approach through numerical examples and show that it consistently captures fixation dynamics across various scenarios. This work offers a robust and flexible framework for modeling evolutionary processes in heterogeneous environments. Full article

Background: The fetal period is critical for neurodevelopment, with maternal diet emerging as a key environmental factor influencing long-term child health. This study investigated the associations between maternal dietary patterns during pregnancy and neurocognitive and behavioral outcomes in 4-year-old children, with a particular focus on sex-related differences. Methods: We used data from the Piccolipiù Italian birth cohort, including 2006 mother/child pairs. Maternal dietary intake during pregnancy was assessed via a questionnaire and categorized into distinct patterns using Principal Component Analysis (PCA). Child neurodevelopment was evaluated at age 4 using the Wechsler Preschool and Primary Scale of Intelligence (WPPSI) and the Child Behavior Checklist (CBCL 1.5–5). Linear and logistic regression models were employed, adjusting for potential confounders and stratifying by child sex. Results: Two major maternal dietary patterns were identified: “Processed and high-fat foods” and “Fresh foods and fish”. Higher maternal adherence to the “Processed and high-fat foods” pattern was associated with increased externalizing behaviors in offspring (β = 0.88; 95%CI 0.28–1.49; p = 0.004). In males, this pattern was associated with an increased clinical risk of Attention Deficit Hyperactivity Disorder (ADHD) (OR (Odds Ratio) = 1.13; 95%CI: 1.02–1.26; p = 0.021). Conclusions: Our findings indicate that maternal consumption of a diet rich in processed and high-fat foods during pregnancy is associated with increased behavioral problems in children, with sex-specific vulnerabilities: slightly higher externalizing behaviors in girls and an increased risk of ADHD in boys. These results underscore the importance of promoting healthy maternal dietary patterns during pregnancy as a targeted early prevention strategy for supporting child neurodevelopment. Full article

Smart-beehive technologies represent a paradigm shift in beekeeping, transitioning from traditional, reactive methods toward proactive, data-driven management. This systematic literature review investigates the current landscape of intelligent systems applied to beehives, focusing on the integration of IoT-based monitoring, sensor modalities, machine learning techniques, and their applications in precision apiculture. The review adheres to PRISMA guidelines and analyzes 135 peer-reviewed publications identified through searches of Web of Science, IEEE Xplore, and Scopus between 1990 and 2025. It addresses key research questions related to the role of intelligent systems in early problem detection, hive condition monitoring, and predictive intervention. Common sensor types include environmental, acoustic, visual, and structural modalities, each supporting diverse functional goals such as health assessment, behavior analysis, and forecasting. A notable trend toward deep learning, computer vision, and multimodal sensor fusion is evident, particularly in applications involving disease detection and colony behavior modeling. Furthermore, the review highlights a growing corpus of publicly available datasets critical for the training and evaluation of machine learning models. Despite the promising developments, challenges remain in system integration, dataset standardization, and large-scale deployment. This review offers a comprehensive foundation for the advancement of smart apiculture technologies, aiming to improve colony health, productivity, and resilience in increasingly complex environmental conditions. Full article

Winter concrete construction is a pivotal engineering issue that needs to be addressed due to the failure of cementitious materials to hydrate under severely low temperatures. To solve the problem, the electric-induced heating curing (EIH) method was presented to prepare cement mortar (CF-CM) at an environmental temperature of −20 °C. The influence of some key parameters, including carbon fiber (CF) content (0–0.9 vol%), preparation methods, and EIH curing regimes (constant power vs. constant voltage; frequency: 30–70 Hz), on the performance of CF-CM were examined. Furthermore, the curing temperature of EIH-cured specimens were simulated based on COMSOL Multiphysics software. The results demonstrated that the electrical percolation threshold of CFs inside the specimen was 0.6 vol%. EIH curing achieved 1-day early strength equivalent to 2 days of standard curing, and increasing CF content showed little influence on the mechanical properties of CF-CM specimens. Moreover, constant-power EIH maintained stable curing temperatures (>50 °C), outperforming unstable constant voltage curing. Applied frequency (30–70 Hz) exhibited negligible impact on compressive strength, validating standard 50 Hz AC for practical application. Furthermore, the optimal EIH power density identified based on COMSOL Multiphysics software was 667 W/m², successfully maintaining specimen temperatures between 60 °C and 70 °C to enable rapid strength development under sub-zero conditions, laying a foundation for the use of COMSOL in the guidance of EIH curing regime design. This work provides a scientifically grounded and applicable solution for winter concrete construction. Full article

This article discusses unattributable small-scale marine oil spills, particularly focusing on their environmental and socio-economic impacts in Musandam, Oman. There is a research gap in the literature on unattributable small-scale marine oil spills that reflects the lack of attention paid to these minor yet frequent spills, whose perpetrators invariably escape detection and accountability. The research method combines a literature review with extensive fieldwork, including community mapping, key informant interviews, and focus group discussions, to understand the extent, causes, and challenges of untraceable spills. The findings reveal significant ecological damage, economic losses for local fishers and tourism, and systemic issues of untraceability, limited enforcement, and inadequate compensation mechanisms. The article recommends establishing a regional compensation scheme, deploying advanced detection technologies, improving spill reporting, and fostering regional cooperation to enhance spill traceability, upgrade remediation techniques, and obtain redress for affected communities. These recommendations aim to inform policy actions that mitigate environmental risks and uphold environmental justice in the Arabian Gulf region. Full article

Drilling fluid plays a critical role in drilling engineering. With the deepening implementation of clean production concepts and increasingly stringent environmental regulations, the treatment standards for drilling wastewater at operational sites have been significantly elevated. In response to the characteristics of high oil content, high COD, high chromaticity, high ammonia nitrogen, and total phosphorus content in drilling, the use of fine bubbles to improve gas utilization efficiency and mass transfer effect, combined with ozone gas, is aimed at degrading difficult-to-degrade high-molecular-weight organic compounds, aiming to solve the problems of high viscosity and high oil content in drilling fluids returned from offshore platforms. Indoor simulation experiments have shown that by using ozone fine bubble technology to treat drilling fluids, the viscosity reduction rate can reach over 29%, and the oil removal rate can reach 40%. Ozone fine bubble technology has significant viscosity reduction and oil removal effects on high viscosity drilling fluids. Full article

New energy vehicles (NEVs) have gained increasing favor among NEV consumers due to their dual advantages of “low cost” and “environmental friendliness.” In recent years, the share of NEVs in the global automotive market has been steadily rising. For instance, in the Chinese market, the sales of new energy vehicles in 2024 increased by 35.5% year-on-year, accounting for 70.5% of global NEV sales. However, as the diversity of NEV brands and models expands, selecting the most suitable model from a vast amount of information has become the primary challenge for NEV consumers. Although online service platforms offer extensive user reviews and rating data, the uncertainty, inconsistent quality, and sheer volume of this information pose significant challenges to decision-making for NEV consumers. Against this backdrop, leveraging the strengths of the quasi OWA (QOWA) operator in information aggregation and interval basic uncertain linguistic information (IBULI) information aggregation and two-dimensional information representation of “information + quality”, this study proposes a large-scale group data aggregation method for decision support based on the IBULIQOWA operator. This approach aims to assist consumers of new energy vehicles in making informed decisions from the perspective of information quality. Firstly, the quasi ordered weighted averaging (QOWA) operator on the unit interval is extended to the closed interval $\left[0,\tau \right]$, and the extended basic uncertain information quasi ordered weighted averaging (EBUIQOWA) operator is defined. Secondly, in order to aggregate groups of IBULI, based on the EBUIQOWA operator, the basic uncertain linguistic information QOWA (BULIQOWA) operator and the IBULIQOWA operator are proposed, and the monotonicity and degeneracy of the proposed operators are discussed. Finally, for the problem of product decision making in online service platforms, considering the credibility of information, a product decision-making method based on the IBULIQOWA operator is proposed, and its effectiveness and applicability are verified through a case study of NEV product decision making in a car online service platform, providing a reference for decision support in product ranking of online service platforms. Full article

Noise interference and multipath effects in complex marine environments seriously constrain the performance of hydroacoustic positioning systems. Traditional millisecond-level signal application and processing methods are widely used in existing research; however, it is difficult to meet the requirements of centimeter-level positioning accuracy in marine engineering. To address this problem, this study proposes a hydroacoustic positioning method based on a short baseline system for the cooperative reception of multi-channel signals. The method adopts ultra-short pulse signals with microsecond pulse width, and significantly improves the system signal-to-noise ratio and anti-interference capability through multi-channel signal alignment and coherent superposition techniques; meanwhile, a joint energy gradient-phase detection algorithm is designed, which solves the instability problem of the traditional cross-correlation algorithm in the detection of ultra-short pulse signals through the identification of signal stability intervals and accurate phase estimation. Simulation verification shows that the 8-hydrophone × 4-channel configuration can achieve 36.06% signal-to-noise gain under harsh environmental conditions (−10 dB), and the performance of the joint energy gradient-phase detection algorithm is improved by about 19.1% compared with the traditional method in an integrated manner. Marine tests further validate the engineering practicability of the method, with an average SNR gain of 2.27 dB achieved for multi-channel signal reception, and the TDOA estimation stability of the new algorithm is up to 32.0% higher than that of the conventional method, which highlights the significant advantages of the proposed method in complex marine environments. The results show that the proposed method can effectively mitigate the noise interference and multipath effects in complex marine environments, significantly improve the accuracy and stability of hydroacoustic positioning, and provide reliable technical support for centimeter-level accuracy applications in marine engineering. Full article

Nitrogen is an essential nutrient for the growth and development of maize (Zea mays L.), and soil nitrogen deficiency is an important factor limiting maize yield. Although excessive application of nitrogen fertilizer can increase yield, it can also cause environmental problems. Therefore, screening low-nitrogen-tolerant (LNT) germplasm resources and analyzing their genetic mechanisms are of great significance for the development of efficient and environmentally friendly agriculture. In this study, 201 maize inbred lines were used as materials. Two levels of low nitrogen (LN) (0.05 mmol/L, N1) and normal nitrogen (4 mmol/L, N2) were set up. Phenotypic indicators such as seedling length, root length and biomass were measured, and they were classified into LNT type (18 samples), nitrogen-sensitive (NS) type (27 samples) and intermediate type (156 samples). A total of 47 significant SNP loci were detected through a genome-wide association study (GWAS), and 36 candidate genes were predicted. Transcriptome sequencing (RNA-seq) analysis revealed that the differentially expressed genes (753 upregulated and 620 downregulated) in LNT materials under low nitrogen stress (LNS) were significantly fewer than those in NS materials (2436 upregulated and 2228 downregulated). Further analysis using WGCNA identified a total of eight co-expression modules. Among them, the red module was significantly correlated with root length and underground fresh weight under LN conditions (r = 0.75), and three key genes for stress response (Zm00001d005264, Zm00001d053931, Zm00001d044292) were screened out. Combined with GWAS, RNA-seq and qRT-PCR verification, eight candidate genes closely related to LNT at the seedling stage of maize were finally determined, involving biological processes such as stress response, nitrogen metabolism and substance formation. This study initially revealed the molecular mechanism of maize tolerance to LN through multi-omics analysis, providing a theoretical basis and genetic resources for breeding new nitrogen-efficient maize varieties. Full article

In many scientific and engineering fields, such as hydrogeology, petroleum engineering, geotechnical research, and developing renewable energy solutions, fluid flow modeling in porous media is essential. In these areas, optimizing extraction techniques, forecasting environmental effects, and guaranteeing structural safety all depend on an understanding of the behavior of single-phase flows—fluids passing through connected pore spaces in rocks or soils. Darcy’s law, which results in an elliptic partial differential equation controlling the pressure field, is usually the mathematical basis for such modeling. Analytical solutions to these partial differential equations are seldom accessible due to the complexity and variability in natural porous formations, which makes the employment of numerical techniques necessary. To approximate subsurface flow solutions, traditional methods like the finite difference method, two-point flux approximation, and multi-point flux approximation have been employed extensively. Accuracy, stability, and computing economy are trade-offs for each, though. Deep learning techniques, in particular convolutional neural networks, physics-informed neural networks, and neural operators such as the Fourier neural operator, have become strong substitutes or enhancers of conventional solvers in recent years. These models have the potential to generalize across various permeability configurations and greatly speed up simulations. The purpose of this study is to examine and contrast the mentioned deep learning and numerical approaches to the problem of pressure distribution in single-phase Darcy flow, considering a 2D domain with mixed boundary conditions, localized sources, and sinks, and both homogeneous and heterogeneous permeability fields. The result of this study shows that the two-point flux approximation method is one of the best regarding computational speed and accuracy and the Fourier neural operator has potential to speed up more accurate methods like multi-point flux approximation. Different permeability field types only impacted each methods’ accuracy while computational time remained unchanged. This work aims to illustrate the advantages and disadvantages of each method and support the continuous development of effective solutions for porous medium flow problems by assessing solution accuracy and computing performance over a range of permeability situations. Full article

To address the issues of link mismatch and channel impairment in wireless optical communication across atmospheric-oceanic media, this paper proposes a two-hop relay transmission architecture based on the multiple-input multiple-output (MIMO)-enhanced multi-level hybrid multiplexing. The system implements decode-and-forward operations via maritime buoy/ship relays, achieving physical layer isolation between atmospheric and oceanic channels. The transmitter employs coherent orthogonal frequency division multiplexing technology with quadrature amplitude modulation to achieve frequency division multiplexing of baseband signals, combines with orthogonal polarization modulation to generate polarization-multiplexed signal beams, and finally realizes multi-dimensional signal transmission through MIMO spatial diversity. To cope with cross-medium environmental interference, a composite channel model is established, which includes atmospheric turbulence (Gamma–Gamma model), rain attenuation, and oceanic chlorophyll absorption and scattering effects. Simulation results show that the multi-level hybrid multiplexing method can significantly improve the data transmission rate of the system. Since the system adopts three channels of polarization-state data, the data transmission rate is increased by 200%; the two-hop relay method can effectively improve the communication performance of cross-medium optical communication and fundamentally solve the problem of light transmission in cross-medium planes; the use of MIMO technology has a compensating effect on the impacts of both atmospheric and marine environments, and as the number of light beams increases, the system performance can be further improved. This research provides technical implementation schemes and reference data for the design of high-capacity optical communication systems across air-sea media. Full article

Navigation line extraction is vital for visual navigation with agricultural machinery. The current methods primarily utilize plant canopy detection frames to extract feature points for navigation line fitting. However, this approach is highly susceptible to environmental changes, causing position instability and reduced extraction accuracy. To address this problem, this study aims to develop a robust navigation line extraction method that overcomes canopy-based feature instability. We propose extracting feature points from root detection frames for navigation line fitting. Compared to canopy points, root feature point positions remain more stable under natural interference and less prone to fluctuations. A dataset of corn crop row images under multiple growth environments was collected. Based on YOLOv8n (You Only Look Once version 8, nano model), we proposed the RS-LineNet lightweight model and introduced a root subordination relationship filtering algorithm to further improve detection precision. Compared with the YOLOv8n model, RS-LineNet achieves 4.2% higher precision, 16.2% improved recall, and an 11.8% increase in mean average precision (mAP₅₀), while reducing the model weight and parameters to 32% and 23% of the original. Navigation lines extracted under different environments exhibit an 0.8° average angular error, which is 3.1° lower than canopy-based methods. On Jetson TX2, the frame rate exceeds 12 FPS, meeting practical application requirements. Full article