Search Results (17,885)

Increasing water demand driven by population growth and climate change strains water resources, especially in arid regions. The effectiveness of rainwater harvesting (RWH) as a viable solution is contingent upon the meticulous selection of appropriate sites. Contemporary efforts have increasingly utilized Geographic Information Systems (GIS) and remote sensing technologies to optimize the identification of ideal locations for implementing RWH infrastructure. However, inconsistencies in rainfall classification methodologies can compromise the accuracy of the resulted suitability maps. Consequently, a standardized approach to grading rainfall depth for mapping RWH sites becomes imperative. This study presents an innovative rainfall classification method tailored for both micro and macro catchment areas, offering a reliable and adaptable approach to rainfall analysis. By refining classification criteria, this method aims to improve the consistency and precision of RWH mapping, addressing a gap in existing methodologies and providing a more standardized approach. Through the application of FAHP and Fuzzy overlay techniques in ArcGIS 10.4, the study compares traditional rainfall classification with the proposed new classification method to assess RWH suitability in Kalar. The comparison highlights that the new rainfall classification-based map yielded higher accuracy and realism compared to traditional methods. Full article

Magnetohydrodynamic (MHD) generators are direct energy conversion devices that transform the motion of an electrically conducting fluid into electricity through interaction with a magnetic field. Developed as an alternative to conventional turbine-generator systems, MHD generators evolved through the 20th century from large units, which are intended to transform thermal energy into electricity using plasma as a working fluid, to smaller units that can harness heat from a variety of sources. In the last few decades, an effort has been made to develop energy conversion systems that incorporate MHD generators to harvest renewable sources such as solar and ocean energy, strengthening the sustainability of this technology. This review briefly synthesizes the main steps in the evolution of MHD technology for electricity generation, starting by outlining its physical principles and the proposals to convert thermal energy into electricity, either using a high-temperature plasma as a working fluid or a liquid metal in a one- or two-phase flow at lower temperatures. The use of wave energy in the form of acoustic waves, which were obtained from the conversion of thermal energy through thermoacoustic devices coupled to liquid metal and plasma MHD generators, as well as alternatives for the transformation of environmental energy resources employing MHD transducers, is also assessed. Finally, proposals for the conversion of ocean energy, mainly in the form of waves and tides, into electric energy, through MHD generators using either seawater or liquid metal as working fluids, are presented along with some of the challenges of MHD conversion technology. Full article

Aiming to meet the growing need for automated harvesting, an automatic alignment method based on Euclidean clustering and K-means clustering is proposed to address issues of driver fatigue and inaccurate driving in manually operated corn harvesters. Initially, the corn field environment is scanned using LiDAR to obtain point cloud data, which are then subjected to pass-through filtering and statistical filtering to remove noise and non-corn contour points. Subsequently, Euclidean clustering and K-means clustering methods are applied to the filtered point cloud data. To validate the impact of Euclidean clustering on subsequent clustering, two separate treatments of the obtained point cloud data were conducted during experimental validation: the first used the K-means clustering algorithm directly, while the second involved performing Euclidean clustering followed by K-means clustering. The results demonstrate that the combined method of Euclidean clustering and K-means clustering achieved a success rate of 81.5%, representing a 26.5% improvement over traditional K-means clustering. Additionally, the Rand index increased by 0.575, while accuracy improved by 57% and recall increased by 61%. Full article

Pursuing sustainable energy solutions has prompted researchers to focus on optimizing energy extraction from renewable sources. Control laws that optimize energy extraction require accurate modeling, often resulting in time-varying, nonlinear differential equations. An energy-maximizing optimal control law is derived for time-varying, nonlinear, second-order, energy harvesting systems. We demonstrate that sustaining periodic motion under this control law when subjected to periodic disturbances necessitates identifying appropriate initial conditions, inducing the system to follow a limit cycle. The general optimal solution is applied to two point absorber wave energy converter models: a linear model where the analytical derivation of initial conditions suffices and a nonlinear model demanding a numerical approach. A stable limit cycle is obtained for the latter when the initial conditions lie within an ellipse centered at the origin of the phase plane. This work advances energy-maximizing optimal control solutions for nonautonomous nonlinear systems with application to point absorbers. The results also shed light on the significance of initial conditions in achieving physically realizable periodic motion for periodic energy harvesting systems. Full article

The increasing use of tractors and implements is replacing manual labor, but adds financial burdens on small-scale farmers due to rising costs. Many farmers have turned to leasing and renting machinery to mitigate these expenses, while repair and maintenance costs remain significant. Government interventions aim to alleviate these burdens, but income disparities between urban and rural areas persist, and the impact of machinery use on climate change and the environment poses further challenges. Strategies like omitting some operation steps and adopting versatile machinery are proposed to cut costs and promote economic sustainability for small-scale farmers. Therefore, this study assessed the economic benefits of using versatile machinery in farming, especially for small-scale rural farmers. Farming processes were divided into field preparation and crop season activities. Field preparation included rotary tillage, ridge formation, and mulching, whereas crop season activities included harvesting and transportation. Annual usage and production cost analyses per hectare, including labor, fuel, and interest, alongside purchasing cost surveys, were conducted. Versatile machinery reduced annual usage costs for field preparation and crop season activities by 63.54% and 71.71%, respectively. This effect was more pronounced for farms under 2 ha, especially those employing manual harvest and transportation. Small-scale farmers, such as those cultivating hot pepper farms, are strongly encouraged to adopt versatile machinery to mitigate expenses and labor costs. The significance of adopting studied methodology will be amplified with the rising cost of labor. Consequently, utilization of versatile machinery in field farming for small-scale farms is projected to increase incomes not through enhanced production, but by significantly reducing the annual usage costs associated with agricultural machinery. This approach not only alleviates financial burdens but also enhances the sustainability of farm management, ensuring long-term viability and environmental stewardship. Full article

Mango production is fundamental to the agricultural economy, generating income and employment in various communities. Accurate estimation of its production optimizes the planning and logistics of harvesting; traditionally, manual methods are inefficient and prone to errors. Currently, machine learning, by handling large volumes of data, emerges as an innovative solution to enhance the precision of mango production estimation. This study presents an analysis of mango fruit detection using machine learning algorithms, specifically YOLO version 8 and Faster R-CNN. The present study employs a dataset consisting of 212 original images, annotated with a total of 9604 labels, which has been expanded to include 2449 additional images and 116,654 annotations. This significant increase in dataset size notably enhances the robustness and generalization capacity of the model. The YOLO-trained model achieves an accuracy of 96.72%, a recall of 77.4%, and an F1 Score of 86%, compared to the results of Faster R-CNN, which are 98.57%, 63.80%, and 77.46%, respectively. YOLO demonstrates greater efficiency, being faster in training, consuming less memory, and utilizing fewer CPU resources. Furthermore, this study has developed a web application with a user interface that facilitates the uploading of images from mango trees considered samples. The YOLO-trained model detects the fruits of each tree in the representative sample and uses extrapolation techniques to estimate the total number of fruits across the entire population of mango trees. Full article

Teaming draught animals with modern forest machines may offer an innovative low-impact solution to biomass harvesting in protected areas. Machine traffic only occurs on pre-designated access corridors set 50 m apart, while trees are cut with chainsaws and dragged to the corridor’s edge by draught horses. The operation presented in this study included one chainsaw operator, two draught horses with their driver, an excavator-based processor with its driver and a helper equipped with a chainsaw for knocking off forks and large branches, and a light forwarder (7 t) with his driver. Researchers assessed work productivity and harvesting cost through a time study repeated on 20 sample plots. Descriptive statistics were used to estimate productivity and cost benchmark figures, which were matched against the existing references for the traditional alternatives. The new system achieved a productivity in excess of 4 m³ over bark per scheduled hour (including delays). Harvesting cost averaged EUR 53 m⁻³, which was between 15% and 30% cheaper than the traditional alternatives. What is more, the new system increased labor and horse productivity by a factor of 2 and 7, respectively, which can effectively counteract the increasingly severe shortage of men and animals. Full article

This study models two alternative downstream processes based on magnetic separation with the objective of understanding the economic feasibility of these processes compared to the traditional mAb process. The key focus lies in the economic understanding of the cell harvest and capture steps in the models. Here, the models revealed that integrating cell removal and product capture in a single operation is the main factor driving the unified productivity between USP and the magnetic bead-based processes. This results in significant economic benefits, such as savings in both the cost of goods per gram of mAb and fixed costs, as well as increasing annual facility output. The predicted savings potential approaches 38% for COGs, 17% for capital investment, and 40% for annual facility output. For mammalian cell-based manufacturing, the magnetic separation-based DSP provides a highly valuable option due to its integration of several individual unit operations compared to the traditional process both in reducing process time and cost and accommodating higher demands. Full article

In the context of planning and construction of the new capital city of Indonesia, referred to as Ibu Kota Negara (IKN), this article addresses the spatial risks and opportunities of rainwater resources in the area where IKN is planned. The article relies on an inventory of various physical data, which were used to derive a flood susceptibility map, as well as rainfall data derived from public and open sources. The geospatial study drew on geospatial software (ArcGIS Pro, 2.1.) and the Google Earth Engine platform (GEE). After this analysis, we followed a management design, which took IPCC climate change scenarios into account. The results demonstrated that the southern coast has higher precipitation than the northern coast in the IKN area. To enhance the efficacy of rainwater management planning, a grid is proposed to mitigate the flood risk and to harvest rainwater. Although rainwater varies throughout the IKN area, and may vary even more with different climate change predictions, it is possible to capture rainwater and create a system to reduce reliance on traditional water sources, alleviate stormwater runoff and mitigate the impact of urban flooding. While IKN will be developed by both regulated planning and other population-driven developments, monitoring and reflecting on existing plans will still be necessary to make IKN sufficiently resilient and sustainable. Full article

The rise of high-rise vertical farms in cities is helping to mitigate urban constraints on crop production, including land, transportation, and yield requirements. However, separate issues arise regarding energy consumption. The utilisation of wind energy resources in high-rise vertical farms is therefore on the agenda. In this study, we investigate the aerodynamic performance of an ellipsoidal tall building with large openings to determine, on the one hand, the threshold income wind that could impact human comfort, and on the other, the turbulence intensity at specific locations on the roof and façade where micro-wind turbines could operate. To this end, we calculate the wind pressure coefficient and turbulence intensity of two scale models tested within a wind tunnel facility and compare the results with a separate CFD simulation completed in the past. The results confirm that the wind turbines installed on the building façade at a height of at least z/h = 0.725 can operate properly when the inlet wind speed is greater than 7 m/s. Meanwhile, the wind regime on the roof is more stable, which could yield higher energy harvesting via wind turbines. Furthermore, we observe that the overall aerodynamic performance of the models tested best under wind flowing at angles of 45° and 60° with respect to their centreline, whereas the turbulence at the wind envelope compares to that of the free wind flow at roof height. Full article

Climate change has significant implications for agriculture, especially in viticulture, where temperature plays a crucial role in grapevine (Vitis vinifera) growth. Mendoza’s climate is ideal for producing high-quality wines, but 21st-century climate change is expected to have negative impacts. This study aimed to evaluate the effects of increased temperature on the phenology, physiology, and yield of Malbec, Bonarda, and Syrah. A field trial was conducted over two seasons (2019–2020 and 2020–2021) in an experimental vineyard with an active canopy heating system (+2–4 °C). Phenological stages (budburst, flowering, fruit set, veraison, harvest), shoot growth (SG), number of shoots (NS), stomatal conductance (gs), chlorophyll content (CC), chlorophyll fluorescence (CF), and water potential (ψa) were measured. Additionally, temperature, relative humidity, light intensity, and canopy temperature were recorded. Heat treatment advanced all phenological stages by approximately two weeks, increased SG and NS, and reduced gs and ψa during the hottest months. CC and CF remained unaffected. The treatment also resulted in lower yields, reduced acidity, and increased °Brix in both seasons. Overall, rising temperatures due to climate change advance the phenological phases of Malbec, Syrah, and Bonarda, leading to lower yields, higher °Brix, and lower acidity, although physiological variables remained largely unchanged. Full article

Effective lettuce cultivation requires precise monitoring of growth characteristics, quality assessment, and optimal harvest timing. In a recent study, a deep learning model based on multimodal data fusion was developed to estimate lettuce phenotypic traits accurately. A dual-modal network combining RGB and depth images was designed using an open lettuce dataset. The network incorporated both a feature correction module and a feature fusion module, significantly enhancing the performance in object detection, segmentation, and trait estimation. The model demonstrated high accuracy in estimating key traits, including fresh weight (fw), dry weight (dw), plant height (h), canopy diameter (d), and leaf area (la), achieving an R² of 0.9732 for fresh weight. Robustness and accuracy were further validated through 5-fold cross-validation, offering a promising approach for future crop phenotyping. Full article

This study investigated the combined effects of novel plant growth-promoting rhizobacteria (PGPR)—Agrobacterium pusense NC2, Kosakonia oryzae WN104, and Phytobacter sp. WL65—and Ascophyllum nodosum seaweed extract (ANE) as biostimulants (PGPR-ANE) on rice growth, yield, and rhizosphere bacterial communities using the RD79 cultivar. The biostimulants significantly enhanced plant growth, shoot and root length, and seedling vigour; however, seed germination was not affected. In pot experiments, biostimulant application significantly increased the richness and evenness of bacterial communities in the rhizosphere, resulting in improvements in rice growth and yield, with increases in plant height (9.6–17.7%), panicle length (14.3–17.9%), and seeds per panicle (48.0–53.0%). Notably, biostimulant treatments also increased post-harvest soil nutrient levels, with nitrogen increasing by 7.7–19.2%, phosphorus by 43.4–161.4%, and potassium by 16.9–70.4% compared to the control. Principal coordinate analysis revealed distinct differences in bacterial composition between the tillering and harvesting stages, as well as between biostimulant treatments and the control. Beneficial bacterial families, including Xanthobacteraceae, Beijerinckiaceae, Acetobacteraceae, Acidobacteriaceae, and Hyphomicrobiaceae, increased in number from the tillering to harvesting stages, likely contributing to soil health improvements. Conversely, methanogenic bacterial families, such as Methanobacteriaceae and Methanosarcinaceae, decreased in number compared to the control. These findings highlight the dynamic responses of the rhizosphere microbiome to biostimulant treatments and underscore their potential benefits for promoting sustainable and productive agriculture. Full article

Aiming at the problem of floating shellfish boxes interfering with kelp harvesting when mechanized kelp harvesting is based on shellfish–kelp mixed culture mode, this paper combines the structural characteristics of the shellfish box itself, designs the kelp harvesting unit test bench and develops a shellfish box separator device. The key factors affecting the box separator’s separation effect were derived through the theoretical analysis. The process of separation of a shellfish box by the box separator is simulated and optimized under the derived boundary conditions. The single-factor test for the separating effect of the box separator was conducted with ADAMS kinematics simulation software. The test showed the optimal utility intervals for the key factors under consideration. Further orthogonal tests were conducted for the three key factors, which were ranked in descending order of importance as box separator separation angle θ, box separator taper angle β and box separator placement depth h. The optimal parameter combination is the box separator separation angle of 31°, the box separator taper angle of 30° and the box separator placement depth of 550 mm. Verification experiments have shown that both indicators, the farthest horizontal distance of the shellfish box and the angle of the shellfish box deviating from the box separator, meet the actual production requirements. In summary, the separator can effectively separate the shellfish box from the kelp, and the device is simple in design, quick in operation, and accomplished separation without disturbing shellfish. This study can provide a theoretical basis for the separation technology of kelp and shellfish box under shellfish–kelp mixed culture mode. Full article

The excessive mineralization of organic molecules during anaerobic fermentation increases the availability of nitrogen and carbon. For this reason, the development of downstream processing technologies is required to better manage ammonia and carbon dioxide emissions during the storage and land application of the resulting soil organic amendment. The present work investigated classical distillation as a technology for valorizing ammoniacal nitrogen (NH₄⁺-N) in anaerobic digestate. The results implied that the direct isolation of ammonium bicarbonate (NH₄HCO₃) was possible when applying the reactive distillation to the food waste digestate (FWD) with a high content of NH₄⁺-N, while the addition of antifoam to the agrowaste digestate (AWD) was necessary to be able to produce an aqueous solution of NH₄HCO₃ as the distillate. The reason was that the extraction of NH₄HCO₃ from the AWD required a higher temperature (>95 °C) and duration (i.e., steady state in batch operation) than the recovery of the inorganic fertilizer from the FWD. The titration method, when applied to the depleted digestate, offered the quickest way of monitoring the reactive distillation because the buffer capacity of the distillate was much higher. The isolation of NH₄HCO₃ from the FWD was attained in a transient mode at a temperature below 90 °C (i.e., while heating up to reach the desired distillation temperature or cooling down once the batch distillation was finished). For the operating conditions to be regarded as techno-economically feasible, they should be attained in the anaerobic digestion plant by integrating the heat harvested from the engines, which convert the biogas into electricity. Full article