Search Results (15,205)

Global trends in health, climate, and population growth drive the demand for high-nutrient plants like spinach, which thrive under controlled conditions with minimal resources. Despite technological advances in agriculture, current systems often rely on traditional methods and need robust computational models for precise plant growth forecasting. Optimizing vegetable growth using advanced agricultural and computational techniques, addressing challenges in food security, and obtaining efficient resource utilization within urban agriculture systems are open problems for humanity. Considering the above, this paper presents an enclosed agriculture system for growth and modeling spinach of the Viroflay (Spinacia oleracea L.) species. It encompasses a methodology combining data science, machine learning, and mathematical modeling. The growth system was built using LED lighting, automated irrigation, temperature control with fans, and sensors to monitor environmental variables. Data were collected over 60 days, recording temperature, humidity, substrate moisture, and light spectra information. The experimental results demonstrate the effectiveness of polynomial regression models in predicting spinach growth patterns. The best-fitting polynomial models for leaf length achieved a minimum Mean Squared Error (MSE) of 0.158, while the highest MSE observed was 1.2153, highlighting variability across different leaf pairs. Leaf width models exhibited improved predictability, with MSE values ranging from 0.0741 to 0.822. Similarly, leaf stem length models showed high accuracy, with the lowest MSE recorded at 0.0312 and the highest at 0.3907. Full article

The increasing global reliance on water resources has necessitated improvements in turfgrass irrigation efficiency. This study aimed to compare measured field data with predicted data on irrigation water distribution in turfgrass rootzones to verify and enhance the accuracy of the HYDRUS-2D simulation model. Data were collected under controlled greenhouse conditions across unvegetated plots with two- and three-layered rootzone construction methods, each receiving 10 mm of water (intensity of 10 mm h⁻¹) via subsurface drip irrigation (SDI) or a sprinkler (SPR). The water content was monitored at various depths and time intervals. The hydraulic soil parameters required for the simulation model were determined through laboratory analysis. The HYDRUS-2D model was used for testing the sensitivity of various soil hydraulic parameters and subsequently for model calibration. Sensitivity analysis revealed that soil hydraulic property shape factor (n) was most sensitive, followed by factor θ_s^w (water content at saturation for the wetting water retention curve). The model calibration based on shape factors n and α_w either in Layer 1 for SPR variants or in both upper layers for SDI variants yielded the highest improvement in model efficiency values (NSEs). The calibrated models exhibited good overall performance, achieving NSEs up to 0.81 for the SDI variants and 0.75 for the SPR variants. The results of the irrigation management evaluation showed that, under SPR, dividing the irrigation amount of 10 mm into multiple smaller applications resulted in a higher soil storage of irrigation water (SOIL_S) and lower drainage flux (DFLU) compared to single large applications. Furthermore, the model data under the hybrid irrigation approach (HYBRID-IA) utilizing SPR and SDI indicated, after 48 h of observation, the following order in SOIL_S (mm of water storage in the topmost 50 cm of soil): HYBRID-IA3 (3.61 mm) > SDI-IA4 (2.53 mm) > SPR-IA3 (0.38 mm). HYDRUS-2D shows promise as an effective tool for optimizing irrigation management in turfgrass rootzones, although further refinement may be necessary for specific rootzone/irrigation combinations. This modeling approach has the potential to optimize irrigation management, improving water-use efficiency, sustainability, and ecosystem services in urban turfgrass management. Full article

This study discusses the issue of train rescheduling in high-speed railways (HSR) when unexpected interruptions occur. These interruptions can lead to delays, cancellations, and disruptions to passenger travel. An optimization model for train rescheduling under uncertain-duration interruptions is proposed. The model aims to minimize both the decline in passenger service quality and the total operating cost, thereby achieving sustainable rescheduling. Then, a hybrid optimization algorithm combining rolling horizon optimization with a deep-learning-embedded NSGA-II algorithm is introduced to solve this multi-objective problem. This hybrid algorithm combines the advantages of each single algorithm, significantly improving computational efficiency and solution quality, particularly in large-scale scenarios. Furthermore, a case study on the Beijing–Shanghai high-speed railway shows the effectiveness of the model and algorithm. The optimization rates are 16.27% for service quality and 15.58% for operational costs in the small-scale experiment. Compared to other single algorithms or algorithm combinations, the hybrid algorithm enhances computational efficiency by 26.21%, 15.73%, and 25.13%. Comparative analysis shows that the hybrid algorithm outperforms traditional methods in both optimization quality and computational efficiency, contributing to enhanced overall operational efficiency of the railway system and optimized resource utilization. The Pareto front analysis provides decision makers with a range of scheduling alternatives, offering flexibility in balancing service quality and cost. In conclusion, the proposed approach is highly applicable in real-world railway operations, especially under complex and uncertain conditions, as it not only reduces operational costs but also aligns railway operations with broader sustainability goals. Full article

Pedestrians, as the most vulnerable road users in traffic crashes, prompt transportation researchers and urban planners to prioritize pedestrian safety due to the elevated risk and growing incidence of injuries and fatalities. Thorough pedestrian crash data are indispensable for safety research, as the most detailed descriptions of crash scenes and pedestrian actions are typically found in crash narratives and diagrams. However, extracting and analyzing this information from police crash reports poses significant challenges. This study tackles these issues by introducing innovative image-processing techniques to analyze crash diagrams. By employing cutting-edge technological methods, the research aims to uncover and extract hidden features from pedestrian crash data in Michigan, thereby enhancing the understanding and prevention of such incidents. This study evaluates the effectiveness of three Convolutional Neural Network (CNN) architectures—VGG-19, AlexNet, and ResNet-50—in classifying multiple hidden features in pedestrian crash diagrams. These features include intersection type (three-leg or four-leg), road type (divided or undivided), the presence of marked crosswalk (yes or no), intersection angle (skewed or unskewed), the presence of Michigan left turn (yes or no), and the presence of nearby residentials (yes or no). The research utilizes the 2020–2023 Michigan UD-10 pedestrian crash reports, comprising 5437 pedestrian crash diagrams for large urbanized areas and 609 for rural areas. The CNNs underwent comprehensive evaluation using various metrics, including accuracy and F1-score, to assess their capacity for reliably classifying multiple pedestrian crash features. The results reveal that AlexNet consistently surpasses other models, attaining the highest accuracy and F1-score. This highlights the critical importance of choosing the appropriate architecture for crash diagram analysis, particularly in the context of pedestrian safety. These outcomes are critical for minimizing errors in image classification, especially in transportation safety studies. In addition to evaluating model performance, computational efficiency was also considered. In this regard, AlexNet emerged as the most efficient model. This understanding is precious in situations where there are limitations on computing resources. This study contributes novel insights to pedestrian safety research by leveraging image processing technology, and highlights CNNs’ potential use in detecting concealed pedestrian crash patterns. The results lay the groundwork for future research, and offer promise in supporting safety initiatives and facilitating countermeasures’ development for researchers, planners, engineers, and agencies. Full article

The generation of solid waste and the use of non-renewable natural resources in the foundry industry are environmental challenges that require the search for solutions that guarantee the application of circular economy and cleaner production principles. Studies on the reuse of Foundry Sand Waste (FSW) generated in this process can guarantee the minimization of the current environmental impact and contribute to the achievement of sustainability in the industrial sector. The objective of this study is to assess the feasibility of utilizing WFS in the construction of pavement bases and sub-bases, in combination with sandy soil and hydrated lime. The laboratory experimental program included the evaluation of compaction characteristics, California Bearing Ratio (CBR), compressive strength, and resilient modulus. The results indicate that the addition of 25% and 50% WFS yields predicted performance levels ranging from good to excellent. The inclusion of hydrated lime enables the mixtures to be employed in sub-bases and bases, while the increased WFS content further enhances load-bearing capacity by up to 60% and 75% for 25% and 50% WFS, respectively. Full article

Water environment evaluation is the basis of water resource planning and sustainable utilization. As a successful case of the coordinated progress of ecological protection and economic development, the Xin’an River Basin is a model for exploring the green development model. However, there are still some problems in the synergistic cooperation between the two provinces. Exploring the differences within the basin is a key entry point for solving the dilemma of synergistic governance in the Xin’an River Basin, optimizing the allocation of resources, and improving the overall effectiveness of governance. Based on the DPSIR model, 21 water environment–related indicators were selected, and the entropy weight–TOPSIS method and gray correlation model were used to evaluate the temporal and spatial status of water resources in each county of the Xin’an River Basin. The results show that (1) The relative proximity of the water environment in Xin’an River Basin fluctuated in “M” shape during the ten years of the study period, and the relative proximity reached the optimal solution of 0.576 in 2020. (2) From the five subsystems, the state layer and the corresponding layer are the most important factors influencing the overall water environment of the Xin’an River Basin. In the future, it is intended to improve the departmental collaboration mechanism. (3) The mean values of relative proximity in Qimen County, Jiande City, and Chun’an County during the study period were 0.448, 0.445, and 0.439, respectively, and the three areas reached a moderate level. The water environment in Huizhou District and Jixi County, on the other hand, is relatively poor, and the mean values of proximity are 0.337 and 0.371, respectively, at the alert level. The poor effect of synergistic development requires a multi–factor exploration of reasonable ecological compensation standards. We give relevant suggestions for this situation. Full article

Lowering dietary protein content is one of the effective ways to reduce nitrogen (N) emissions and conserve protein feed resources. However, it is unclear how reducing dietary protein levels affects milk quality and the efficiency of N utilization in lactating goats. It is therefore difficult to determine exactly how much reduction in dietary protein levels is optimal. The objective of this study was to evaluate the effects of low-protein diets on milk quality, N balance and rumen microbiota in lactating goats. A total of 50 lactating goats were enrolled in a completely randomized design and maintained on either a diet with 15.82% protein level as the control group (CON) or reduced protein levels with 13.85% (R2 group), 11.86% (R4 group), 9.84% (R6 group) and 7.85% (R8 group), respectively. The results showed that the dry matter intake, milk yield, fecal and urinary N excretion and utilization efficiency of N of lactating goats decreased linearly with reduced dietary protein levels. Specifically, the milk yield was reduced by the R8 group (p < 0.05). Furthermore, the R8 group reduced the contents of protein, fat and lactose (p < 0.05), but R2 and R4 have no influence (p > 0.05). The R6 group decreased protein content only at the 4th week. Fecal and urinary N excretion and utilization efficiency of N reduced linearly with decreasing dietary protein levels (p < 0.05). The R8 group affected the relative abundance of rumen microbiota including Christensenellaceae_R-7_group, NK4A214_group and UCG-005 (p < 0.05). In conclusion, lowering dietary protein levels decreased milk quality and N excretion by altering rumen microbiota in goats during lactation. This phenomenon was most pronounced when the dietary protein level was reduced by 8 percentage points. Nevertheless, dietary protein levels should not be reduced by more than 6 percentage points to ensure normal performance of the goat during lactation. Full article

Phytosulfokine (PSK) is a tyrosine-sulfated pentapeptide found throughout the plant kingdom, playing key roles in plant growth, development, and responses to biotic and abiotic stresses. However, there is still a lack of a comprehensive analysis of the CsPSK gene family in Camellia sinensis. In this study, we conducted a genome-wide identification and characterized 14 CsPSK genes in tea plants, which are unevenly distributed across seven chromosomes. CsPSK genes encode proteins ranging from 75 to 124 amino acids in length, all belonging to the PSK-α type and containing conserved PSK domains. A synteny analysis revealed that the expansion of the CsPSK gene family is primarily attributed to whole-genome duplication, with homology to Arabidopsis thaliana PSK genes. A promoter region analysis identified cis-regulatory elements related to hormone and stress responses. An expression profile analysis showed that CsPSK genes are highly expressed in roots, stems, flowers, and leaves, and are induced by both biotic and abiotic stresses. Furthermore, an RT-qPCR assay demonstrated that the expression levels of CsPSK8, CsPSK9, and CsPSK10 are significantly upregulated following Discula theae-sinensis infection. These findings establish a basis for further research into the role of the CsPSK gene family in tea plant disease resistance and underlying molecular mechanisms, offering valuable perspectives for developing novel antimicrobial peptides. Full article

During the extraction and utilization of coal resources, a large amount of CO₂ and coal-based solid wastes (CBSW), such as coal gangue, are generated. To reduce the carbon and waste emissions, an effective approach is to mineralize the CO₂ with the CBSW and then backfill the mineralized materials into the goaf area. However, efficient CO₂ mineralization is challenging due to the low reactivity of coal gangue. To this end, mechanical activation was used for the modification of coal gangue, and the mechanical activation mechanism of coal gangue was revealed from a microcosmic perspective by dry powder laser particle size testing (DPLPST), X-ray diffractometer (XRD) analysis, Fourier-transform infrared spectrometer (FTIR) analysis, and scanning electron microscopy (SEM). The results showed that compared with the unground coal gangue, the average particle size of coal gangue after 0.5 h, 1 h, and 1.5 h milling decreases by 94.3%, 95%, and 95.3%, respectively; additionally, the amorphous structures of the coal gangue after milling increase, and their edges and corners gradually diminish. After the pressure mineralization of coal gangues with different activation times, thermogravimetric (TG) analysis was performed, and the CO₂ mineralization effect of the mechanically activated coal gangue was explored. It is found that the carbon fixation capacity of the coal gangue after 0.5 h, 1.0 h, and 1.5 h mechanical activation is increased by 1.18%, 3.20%, and 7.57%, respectively. Through the XRD and SEM, the mechanism of CO₂ mineralization in coal gangue was revealed from a microcosmic perspective as follows: during the mineralization process, alkali metal ions of calcium and magnesium in anorthite and muscovite are leached and participate in the mineralization reaction, resulting in the formation of stable carbonates such as calcium carbonate. Full article

In hot-forming, integrating in situ quality monitoring is essential for the early detection of thermally induced geometric deviations, especially in the production of hybrid bulk metal parts. Although hybrid components are key to meeting modern technical requirements and saving resources, they exhibit complex shrinkage behavior due to differing thermal expansion coefficients. During forming, these components are exposed to considerable temperature gradients, which result in density fluctuations in the ambient air. These fluctuations create an inhomogeneous refractive index field (IRIF), which significantly affects the accuracy of optical geometry reconstruction systems due to light deflection. This study utilizes existing simulation IRIF data to predict the magnitude and orientation of refractive index fluctuations. A light deflection simulation run on a GPU-accelerated ray tracing framework is used to assess the impact of IRIFs on optical measurements. The results of this simulation are used as a basis for selecting optimized measurement positions, reducing and quantifying uncertainties in surface reconstruction, and, therefore, improving the reliability of quality control in hot-forming applications. Full article

Spacecraft catering to the Lunar Gateway or other “permanent” stations in the lunar vicinity would require frequent travel between periodic orbits around the Earth–Moon $L_1$ and $L_2$ Lagrange points. The transition through the Hill sphere is often characterized by close passages of our nearest neighbor—rendering the optimization problem numerically challenging due to the increased local sensitivities. Depending on the mission requirements and resource constraints, transfer architectures must be studied, and trade-offs between flight time and fuel consumption quantified. While direct low-thrust transfers between the circular restricted three-body problem periodic orbit families have been studied, the asymptotic flow in the neighborhood of the periodic orbits could be leveraged for expansion and densification of the solution space. This paper presents an approach to achieve a dense mapping of manifold-assisted, low-thrust transfers based on initial and terminal coast segments. Continuation schemes are utilized to attain the powered intermediate time-optimal segment through a multi-shooting approach. Interesting insights regarding the linear correlation between $\Delta$V and change in reduced two-body osculating elements associated with the initial-terminal conditions are discussed. These insights could inform the subsequent filtering of the osculating selenocentric periapsis map and provide additional interesting and efficient solutions. The described approach is anticipated to be extremely useful for future crewed and robotic cis-lunar operations. Full article

Compressive Sensing (CS) is a transformative signal processing framework that enables sparse signal acquisition at rates below the Nyquist limit, offering substantial advantages in data efficiency and reconstruction accuracy. This survey explores the theoretical foundations of CS, including sensing matrices, sparse bases, and recovery algorithms, with a focus on its applications in power engineering. CS has demonstrated significant potential in enhancing key areas such as state estimation (SE), fault detection, fault localization, outage identification, harmonic source identification (HSI), Power Quality Detection condition monitoring, and so on. Furthermore, CS addresses challenges in data compression, real-time grid monitoring, and efficient resource utilization. A case study on smart meter data recovery demonstrates the practical application of CS in real-world power systems. By bridging CS theory and its application, this survey underscores its potential to drive innovation, efficiency, and sustainability in power engineering and beyond. Full article

This study was conducted to achieve simple and feasible secondary-side independent power control for wireless power transfer (WPT) systems with a hybrid energy storage system (HESS) and to minimize the power loss introduced by the added converter. We propose a novel operation mode tailored to a WPT system with a HESS load composed of an LCC-compensated WPT system and a Buck/Boost bidirectional converter. Its power control is based on insights into the characteristics of LCC–LCC compensation. Since this control method requires the cooperation of a DC converter, control of the converter’s efficiency is the focus of this paper. Building on this framework, several parasitic parameters such as the equivalent series resistance (ESR) of inductors and switches are taken into account. An improved operation mode is proposed to address the efficiency degradation and control imbalance caused by ESR. By meticulously controlling the behavior of the components of the converter, the devices operate in zero-voltage switching (ZVS) mode, thereby reducing switching losses. Additionally, fuzzy control is utilized in this study to enhance robustness. The analyses are verified through a prototype system. The results of the experiments illustrate that the analytical approach proposed in this study achieves reliable power control and efficient converter operation. The results of this study show that the efficiency of the devices is improved and reached up to 99% with the converter. This study explores the efficiency optimization of the WPT system, which directly supports sustainable practices by reducing resource consumption and minimizing environmental impact. The findings offer valuable insights into sustainable applications and policy implications, aligning with the goals of socio-economic and environmental sustainability. Full article

Although safety technology has recently been shown to prevent occupational incidents, a systematic approach to identifying technological opportunities is still lacking. Incident report documents, containing large volumes of narrative text, are considered valuable resources for predetermining incident factors. Additionally, patent data, as a form of big data from technological sources, is widely utilized to explore potential technology solutions. In this context, this study aims to identify technology opportunities by integrating two types of textual big data: incident documents and patent documents. Text mining and self-organizingmaps are employed to discover applicable technologies for incident prevention, grouping them into five categories, as follows: machine tool work, high-place work, vehicle-related facilities, hydraulic machines, and miscellaneous tools. A gap analysis between incidents and patents is also conducted to assess feasibility and develop a technology strategy. The findings, derived from both types of big data, provide technology solutions that are essential for improving workplace safety and that can be used by business owners and safety managers. Full article

Water is an essential necessity for maintaining the life cycle on Earth. These resources are continuously changing because of human activities and climate-related factors. Hence, adherence to effective water management and consistent water policy is vital for the optimal utilization of water resources. Water resource monitoring can be achieved by precisely delineating the borders of water surfaces and quantifying the variations in their areas. Since Lake Van is the largest lake in Turkey, the largest alkaline lake in the world, and the fourth largest terminal lake in the world, it is very important to determine the changes in water surface boundaries and water surface areas. In this context, the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI) and Automatic Water Extraction Index (AWEI) were calculated from Landsat-8 satellite images of 2014, 2017, 2020 and 2023 in June, July, and August using the Google Earth Engine (GEE) platform. Water pixels were separated from other details using the Canny edge detection algorithm based on the calculated indices. The Otsu thresholding method was employed to determine water surfaces, as it is the most favored technique for calculating NDWI, AWEI, and MNDWI indices from Landsat 8 images. Utilizing the Canny edge detection algorithm and Otsu threshold detection approaches yielded favorable outcomes in accurately identifying water surfaces. The AWEI demonstrated superior performance compared to the NDWI and MNDWI across all three measures. When the effectiveness of the classification techniques used to determine the water surface is analyzed, the overall accuracy, user accuracy, producer accuracy, kappa, and f score evaluation criteria obtained in 2014 using CART (Classification and Regression Tree), SVM (Support Vector Machine), and RF (Random Forest) algorithms as well as NDWI and AWEI were all 100%. In 2017, the highest producer accuracy, user accuracy, overall accuracy, kappa, and f score evaluation criteria were all 100% with the SVM algorithm and AWEI. In 2020, the SVM algorithm and NDWI produced the highest evaluation criteria values of 100% for producer accuracy, user accuracy, overall accuracy, kappa, and f score. In 2023, using the SVM and CART algorithms as well as the AWEI, the highest evaluation criteria values for producer accuracy, user accuracy, overall accuracy, kappa, and f score were 100%. This study is a case study demonstrating the successful application of machine learning with Canny edge detection and the Otsu water surfaces thresholding method. Full article