Plant Efficiency Research Articles

Metagenomic investigation of antibiotic resistance genes and resistant bacteria contamination in pharmaceutical plant sites in China

Pharmaceutical plant sites play a significant role in the dissemination of antibiotic resistance genes (ARGs) into the environment. It is imperative to comprehensively monitor of ARGs across various environmental media at these sites. This study focused on three pharmaceutical plants, two located in North China and one in South China. Through metagenomic approaches, we examined the composition, mobility potential, and bacterial hosts of ARGs in diverse media such as process water, groundwater, topsoil, soil cores, and pharmaceutical fermentation residues across diverse environmental matrices, including topsoil, soil cores, process water, groundwater, and pharmaceutical fermentation residues. We identified a wide array of ARGs, comprising 21 types and 740 subtypes, with process water exhibiting the highest abundance and diversity. Treatment processes varied in their efficacy in eliminating ARGs, and the clinically relevant ARGs should also be considered when evaluating wastewater treatment plant efficiency. Geographical distinctions in groundwater ARG distribution between northern and southern regions were observed. Soil samples from the three sites showed minimal impact from pharmaceutical activity, with vancomycin-resistance genes being the most prevalent. High levels of ARGs in pharmaceutical fermentation residues underscore the necessity for improved waste management practices. Metagenomic assembly revealed that plasmid-mediated ARGs were more abundant than chromosome-mediated ARGs. Metagenome-assembled genomes (MAGs) analysis identified 166 MAGs, with 62 harboring multiple ARGs. Certain bacteria tended to carry specific types of ARGs, revealing distinct host-resistance associations. This study enhances our understanding of ARG dissemination across different environmental media within pharmaceutical plants and underscores the importance of implementing strict regulations for effluent and residue discharge to control ARG spread.

О влиянии неопределенностей на прогнозы ветрового ресурса и выработки электроэнергии

The annual power generation of a wind farm is one of the most important indicators determining the profitability of a wind power project. The methods used to estimate the annual power generation of a wind farm have to consider uncertainties at all stages of the project life cycle. When developing a financial model of a wind power project, it is required to consider information about uncertainties to reduce the error and increase the reliability of the project. Specialists of various countries are improving the efficiency of wind power plants, studying wind energy resources, as well as assessing the efficiency of their use. However, special studies do not pay due attention to the problem of assessing the impact of various uncertainties on the forecasts of wind resource and power generation. Two methods are proposed to calculate uncertainties. They are a deterministic method based on the assumption of independence of various uncertainties, and a Monte Carlo method that simulates the behavior of a physical system many times. The paper considers the uncertainties to be considered during the design of a wind farm and presents the variation ranges. The authors have presented the plots of power generation with various levels of probability of being reached or exceeded the total uncertainty for three variants. It is shown that considering the various uncertainties allows a power generation forecast to be made with increased accuracy. The results obtained are necessary to develop a wind measurement data processing model that allows us to forecast electricity generation by existing wind power plants with increased accuracy.

Measures to Improve Power Quality for Consumers to Improve the Operation Efficiency of Pressurization Plants

Measures to Improve Power Quality for Consumers to Improve the Operation Efficiency of Pressurization Plants

Role of malic acid in enhancing the efficiency of barley (Hordeum vulgare L.) and alfalfa (Medicago sativa L.) plants for phytoremediation of salt affected soil

Soil salinization is a growing global problem that influences plant growth and crop productivity. Most of the reclamation efforts in the past have focused on the installation of surface drainage systems. Other management approaches, such as excessive leaching and chemical amendments, have also been used on a limited scale to enhance the productivity of these soils. Phytoremediation can be cost-effective and environmentally sound technology.A laboratory experiment was carried out to study the role of malic acid which is low molecular weight organic acid (LMWOA) in enhancing the efficiency of barley and alfalfa plants for the phytoremediation of salt-affected soil. Seeds of barley and alfalfa were cultured in pots and irrigated with full strength Hoagland nutrient solution with three concentrations of seawater (SW) (10%, 20% and 30%) and a mixture of seawater with malic acid (MA) at 2, 4 and 6 mM l-1 (MA+SW), Hoagland solution was used as control. After twelve weeks, plants were harvested, and three types of soils (barley soil, alfalfa soil, and plant-free soil) were subjected to physical and chemical analysis for EC (electrical conductivity), TOC (total organic carbon), pH, potassium, sodium, and chloride ions. Results indicated a significant decrease was recorded in soil EC, pH, potassium, sodium, and chloride ions and a significant increase in soil TOC in barley and alfalfa soil compared with plant-free soil. Treatments with (MA+SW), especially at (2+10%) resulted in a significant increase in ions availability and phytoremediation activity in barley and alfalfa soils comparing with plant-free soil.

Verification Experiment of Vacuum Sprinkler Extinguishing System to Improve Performance Efficiency in Semiconductor Plants

Verification Experiment of Vacuum Sprinkler Extinguishing System to Improve Performance Efficiency in Semiconductor Plants

Optimizing water relations, gas exchange parameters, biochemical attributes and yield of water-stressed maize plants through seed priming with iron oxide nanoparticles

Drought poses significant risks to maize cultivation by impairing plant growth, water uptake and yield; nano priming offers a promising avenue to mitigate these effects by enhancing plant water relations, stress tolerance and overall productivity. In the current experiment, we tested a hypothesis that seed priming with iron oxide nanoparticles (n-Fe2O3) can improve maize performance under water stress by improving its growth, water relations, yield and biochemical attributes. The experiment was conducted on a one main plot bisected into two subplots corresponding to the water and drought environments. Within each subplot, maize plants were raised from n-Fe2O3 primed seeds corresponding to 0 mg. L− 1 (as control treatment), 25, 50, 75, and 100 mg. L− 1 (as trial treatments). Seed priming with n-Fe2O3 at a concentration of 75 mg. L− 1 improved the leaf relative water content, water potential, photosynthetic water use efficiency, and leaf intrinsic water use efficiency of maize plants by 13%, 44%, 64% and 17%, respectively compared to control under drought stress. The same treatments improved plant biochemical attributes such as total chlorophyll content, total flavonoids and ascorbic acid by 37%, 22%, and 36%, respectively. Seed priming with n-Fe2O3 accelerated the functioning of antioxidant enzymes such as SOD and POD and depressed the levels of leaf malondialdehyde and hydrogen peroxide significantly. Seed priming with n-Fe2O3 at a concentration of 75 mg. L− 1 improved cob length, number of kernel rows per cob, and 100 kernel weight by 59%, 27% and 33%, respectively, under drought stress. Seed priming with n-Fe2O3 can be used to increase maize production under limited water scenarios.

Low-Cost Imaging to Quantify Germination Rate and Seedling Vigor across Lettuce Cultivars.

The survival and growth of young plants hinge on various factors, such as seed quality and environmental conditions. Assessing seedling potential/vigor for a robust crop yield is crucial but often resource-intensive. This study explores cost-effective imaging techniques for rapid evaluation of seedling vigor, offering a practical solution to a common problem in agricultural research. In the first phase, nine lettuce (Lactuca sativa) cultivars were sown in trays and monitored using chlorophyll fluorescence imaging thrice weekly for two weeks. The second phase involved integrating embedded computers equipped with cameras for phenotyping. These systems captured and analyzed images four times daily, covering the entire growth cycle from seeding to harvest for four specific cultivars. All resulting data were promptly uploaded to the cloud, allowing for remote access and providing real-time information on plant performance. Results consistently showed the 'Muir' cultivar to have a larger canopy size and better germination, though 'Sparx' and 'Crispino' surpassed it in final dry weight. A non-linear model accurately predicted lettuce plant weight using seedling canopy size in the first study. The second study improved prediction accuracy with a sigmoidal growth curve from multiple harvests (R2 = 0.88, RMSE = 0.27, p < 0.001). Utilizing embedded computers in controlled environments offers efficient plant monitoring, provided there is a uniform canopy structure and minimal plant overlap.

Impact of Multi-Energy System and Different Control Strategies on a Generic Low-Voltage Distribution Grid

The rising electricity costs, cost of space heating, and domestic hot water end up driving consumers toward reducing expenses by generating their electricity through devices like photovoltaic systems and efficient combined heat and power plants. When coupled with thermal systems via an energy management system (EMS) in a Multi-Energy System (MES), this self-produced electricity can effectively lower electricity and heating bills. However, MESs with EMSs can serve various purposes beyond cost reduction via self-consumption, such as reacting to variable electricity prices, meeting special grid connection conditions, or minimizing CO2 emissions. These diverse strategies create unique prosumer profiles, deviating significantly from standard load profiles. The potential threat to the power grid arises as grid operators lack visibility into which consumers employ which control strategies. This paper investigates the impact of controlled MESs on the power grid compared to average households and answers whether new control strategies affect the planning strategies of low voltage grids. It proposes a comprehensive four-step toolchain for the detailed simulation of thermal–electrical load profiles, MES control strategies, and grid dynamics. It includes a new method for the grid impact analysis of extreme and average bulk values. As a result, this study identifies three primary factors influencing distribution power grids by MESs. Firstly, the presence and scale of photovoltaic (PV) systems significantly affect extreme values in the grid. Secondly, MESs incorporating combined heat and power (CHP) and heat pump (HP) units impact the overall grid performance, mainly reflected in bulk values. Thirdly, the placement of an MES with heating systems, especially when concentrated in one feeder, plays a crucial role in grid dynamics. Despite the three distinct factors identified as impactful on the power grid, this study reveals that the various control strategies, despite leading to vastly different grid profiles, do not exhibit divergent impacts on buses, lines, or transformers. Remarkably, the impact of MESs remains consistently similar across the range of control strategies studied. Therefore, different control strategies do not pose an additional challenge to the grid integration of MESs.

Heat transfer analysis in open pan heat exchanger (OPHE) with different cross‐sections of conduits for jaggery processing

AbstractIndia leads the world in jaggery production, with the jaggery industry being a vital rural‐based sector. The jaggery plants in the country face challenges in effectively utilizing energy, resulting in varying efficiency levels ranging from 20% to 65% across different plants. To address this, a modified approach employing open pan heat exchangers (OPHEs) in jaggery plants is proposed to enhance overall performance. The effectiveness of the heat exchanger (HE) relies on factors such as heat transfer coefficient (HTC), surface contact area, and temperature differences between surfaces and the warm liquid. Circular conduits are predominantly favored in HE design, but this study delves into the impact of conduit shapes specifically circular, semi‐circular, and square on the achievement of the HE. The study reveals that semi‐circular conduits outperform circular conduits by 25%–28% in terms of HTC, while square conduits show a 21%–24% improvement. Moreover, comparing the efficiency of a conventional open pan jaggery plant with a circular conduit OPHE and a modified version featuring semi‐circular conduits OPHE, significant improvements are noted. A one hp pump is enough to overcome the issue related to pumping power due to a change in conduit shape. Heat transfer rate (HTR) is very poor for circular conduit due to line contact, whereas HTR for semi‐circular conduit is 38%–39% better than the square conduit. Efficiency increases from 24.36% for the conventional open pan to 62.5% for the circular conduit and further to 83.22% for the semi‐circular conduit. Processing time for jaggery making also varies significantly, with 165 min for the conventional open pan, which is reduced by 33.33% for the circular conduit OPHE and 45.45% for the semi‐circular conduit OPHE. As compared to circular conduit processing time of semicircular conduit OPHE is reduced by 18.18%.

Lightweight convolutional neural network-based plant disease identification for protection and landscape design

Plant diseases significantly impact landscape design, necessitating comprehensive consideration and effective management measures to ensure the health, aesthetics, and sustainability of landscapes. Early detection and timely control of plant diseases are crucial, but traditional monitoring methods, which rely on manual observation and sample collection, are inadequate for covering large garden areas and may delay necessary treatments. This study addresses these challenges by constructing a small Rosa chinensis disease dataset through field collection and data augmentation techniques. We propose MixResCoAtNet, an improved model based on the lightweight MixNet framework, to identify and categorize diseases from plant leaf images using convolutional neural networks (CNNs). Comparison experiments with various classical convolutional network models demonstrate that MixResCoAtNet outperforms these models, offering more competitive performance. Additionally, due to its lighter structure, MixResCoAtNet shows greater potential for deployment on mobile devices, facilitating real-time and efficient plant disease monitoring and management in landscape design.

Transposase-assisted target-site integration for efficient plant genome engineering

The current technologies to place new DNA into specific locations in plant genomes are low frequency and error-prone, and this inefficiency hampers genome-editing approaches to develop improved crops1,2. Often considered to be genome ‘parasites’, transposable elements (TEs) evolved to insert their DNA seamlessly into genomes3–5. Eukaryotic TEs select their site of insertion based on preferences for chromatin contexts, which differ for each TE type6–9. Here we developed a genome engineering tool that controls the TE insertion site and cargo delivered, taking advantage of the natural ability of the TE to precisely excise and insert into the genome. Inspired by CRISPR-associated transposases that target transposition in a programmable manner in bacteria10–12, we fused the rice Pong transposase protein to the Cas9 or Cas12a programmable nucleases. We demonstrated sequence-specific targeted insertion (guided by the CRISPR gRNA) of enhancer elements, an open reading frame and a gene expression cassette into the genome of the model plant Arabidopsis. We then translated this system into soybean—a major global crop in need of targeted insertion technology. We have engineered a TE ‘parasite’ into a usable and accessible toolkit that enables the sequence-specific targeting of custom DNA into plant genomes.

Hydrochar preparation from wild weeds (Amaranthus sp.) and its application as artificial soil for hydroponic system

Hydroponics is a viable alternative for urban contexts with limited resources and space since it offers efficient and low-maintenance planting methods. This study specifically examines the development of synthetic soil by utilizing hydrochar obtained from wild weeds (Amaranthus sp.). X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and field-emission scanning electron microscopy (FE-SEM) were used to study the hydrochar after it was made using the hydrothermal carbonization method. The analysis showed that the hydrochar had an undefined crystal structure and an uneven, varied porosity. The expansion capacity test clearly showed a significant growth potential of 32%. Nevertheless, the water retention tests revealed a progressive decline in the capacity to retain water as time passed. The kinetics model was used to simulate the release of NPK from the counterfeit soil. The P and K components adhered to a first-order model, but the N elements adhered to the Kosmeyer-Peppas model. The use of artificial soil as a substrate for kale plants had excellent outcomes, with the highest growth rate of 0.75 cm reported on day 7. Subsequently, the growth stabilized and gradually decreased to 0.3 cm by day 21. Hydrochar generated from wild weeds (Amaranthus sp.) is a practical choice for hydroponic farming systems, as it provides fertilizer storage and slow-release advantages.

A systematic review on phytoremediation of indoor air pollution

Degradation of Indoor Air Quality (IAQ) due to confined spaces and insufficient ventilation has become a serious concern to human health. Published literature has established phytoremediation as an efficient removal mechanism of indoor air pollutants such as formaldehyde, Benzene, Toluene, Ethyl benzene, Xylene (BTEX), Volatile Organic Compounds (VOCs), and Particulate Matter (PM) using potted plants. This review discusses both conventional and enhanced phytoremediation for removing air pollutants and the parameters influencing the removal efficiencies. A literature review was conducted following the PRISMA guidelines to identify published literature on indoor air phytoremediation. After eliminating duplicates and reviewing articles, the articles related to indoor air phytoremediation from 2011 to the present were selected. The database was managed using Mendeley reference manager. Indoor air pollutants can be removed efficiently through phytoremediation using potted plants. Chlorophytum comosum removed the broadest range of contaminants, whereas Epipremnum aureum is the frequently used plant species for pollutant removal. Adding enhancing factors to the plant enhances their ability to remove pollutants. Inoculation of plants with soil bacteria such as Bacillus cereus ERBP is the most common enhancement method reported. The present study highlighted advancements in phytoremediation and factors affecting the pollutant removal efficiencies of plants. The findings demonstrated that enhanced phytoremediation is more effective at removing pollutants than the conventional method. Depending on the plant species used, the removal of indoor air pollutants may vary. The findings suggested that a combination of various plant species could be used to remove indoor air pollutants more efficiently.

Efficient enhancement of cryogenic processes: Extracting valuable insights with minimal effort

Cryogenic systems are widely used in industries but are known for their high energy consumption. Designing these systems involves numerous variables, objectives, and constraints. This study introduces an optimization approach applied to three cryogenic processes: Natural Gas Liquids (NGLs) recovery, Air Separation Unit (ASU), and propane mixed refrigerant (C3MR) liquefaction, focusing on minimizing energy input to enhance efficiency and LNG output in a mega LNG plant. Exergy analyses identified energy losses in compressors (47 %), columns (37 %), heat exchangers (10 %), and valves/mixers (5 %), highlighting the need for improved compressor design. Optimization showed that inefficiencies could be reduced by adding inter-cooled compression stages and isentropic/isothermal compression routes. The ASU optimization involved five independent variables with optimized conditions demanding 30 MW of compression power while achieving 51 % separation efficiency. Heat exchangers are the main source of exergy loss in the standalone C3MR cycle, accounting for 61.82 % of 69.0 MW followed by compressors ∼29.27 %. When simulated with utilities, total exergy loss increases to 249.0 MW, with compressors accounting for 78.34 %, exergy loss from heat exchangers drops to 17.94 %. Results affirm the practicality of the proposed method for optimizing complex cryogenic systems, providing valuable engineering insights and potential for significant energy savings.

Enhancing Energy Efficiency and Resource Recovery in Wastewater Treatment Plants

Enhancing Energy Efficiency and Resource Recovery in Wastewater Treatment Plants

Innovative stimuli-responsive membrane MSF brine rejection dilution by tertiary treated sewage effluent

In this study, treated wastewater and Multi-Stage Flash (MSF) brine were integrated into the Forward Osmosis (FO) system using pressure stimuli-responsive Nanofiltration (PSRNF) membranes to dilute magnesium, calcium, and sulfate MSF plant brine reject. The deposition of magnesium sulfate and calcium sulfate in the heat exchanger is one of the main issues affecting the performance and efficiency of MSF thermal desalination plants. Reducing the concentration of the divalent ions can minimize scale formation and deposition to a level that allows the MSF plant to operate at high top brine temperature (TBT) and without scale problems. The PSRNF membranes were chosen in the FO process because of their high water permeability, rejection of divalent and monovalent ions, small structure parameter (S), and inexpensiveness compared to commercial FO membranes. Three PSRNF membranes were tested in the FO process with the feed solution facing the active membrane layer to avoid active layer delamination. Although the PSRNF membrane exhibited negligible water flux at 0 bar, it increased when a 2–4 bar was applied to the feed solution. The wastewater temperature was set at 25 °C while 40 °C was the brine operational temperature to mimic the field situation. A maximum average water flux of 39.5 L/m2h was recorded at 4 bar feed pressure when the PSRNF membrane was used for the brine dilution, achieving up to 42% divalent ions dilution at 0.02 kWh/m3 specific power consumption. The average water flux in the PRSNF membrane was 35% higher than that in the commercial TFC FO membrane. Notably, the PSRNF membrane is ten times cheaper than commercial FO membranes. Notably, the PSRNF membrane is ten times cheaper than commercial FO membranes, achieving substantial cost reductions and pioneering advancements in FO purification technology.

The microbial-driven nitrogen cycle and its relevance for plant nutrition.

Nitrogen (N) is a vital nutrient and an essential component of biological macromolecules such as nucleic acids and proteins. Microorganisms are major drivers of N-cycling processes in all ecosystems, including the soil and plant environment. The availability of N is a major growth-limiting factor for plants and it is significantly affected by the plant microbiome. Plants and microorganisms form complex interaction networks resulting in molecular signaling, nutrient exchange, and other distinct metabolic responses. In these networks, microbial partners influence growth and N use efficiency of plants either positively or negatively. Harnessing the beneficial effects of specific players within crop microbiomes is a promising strategy to counteract the emerging threats to human and planetary health due to the overuse of industrial N fertilizers. However, in addition to N-providing activities (e.g. the well-known symbiosis of legumes and Rhizobium spp.), other plant-microorganism interactions must be considered to obtain a complete picture of how microbial-driven N transformations might affect plant nutrition. For this, we review recent insights into the tight interplay between plants and N-cycling microorganisms, focusing on microbial N-transformation processes representing N sources and sinks that ultimately shape plant N acquisition.

ANTIOXIDANT ENZYMES INVOLVED IN SEEDS’ GERMINATION MODULATED BY ACTIVE PRINCIPLES FROM PLANT EXTRACTS AND HYDROLYZED PROTEINS

Transition to organic farming requires the development of new methods to protect seeds from adverse factors. The germinative process and the post-germinative phase are strictly controlled by the oxidative balance involving lipid peroxidation, reactive oxygen species and enzymes activity, directing the plant viability and its further development. We focus our studies mainly on the seeds protection and fortification through natural solutions based on marigold and fenugreek extracts, steroid alkaloids from tomatoes and hydrolyzed proteins derived from leather waste industry. The structural configuration of the plant protection products proves significant effects fighting on different mechanisms of seeds’ oxidative stress. As well as the experimental design highlighted a tandem correlative mechanism between germination, decrease of lipid peroxidase and activation of catalase and superoxide-dismutase. SEM-PROTECT II was the most active biopesticide involved in these processes. Our findings directed complex research oriented to technological and experimental optimizations for the development of an innovative, efficient and competitive plant protection product to meet modern agriculture requirement.

Innovative and efficient integrations of desalination plants coupled absorption, adsorption, and humidification-dehumidification desalination units employing external heat recovery techniques

Thermal desalination technologies have been developed recently to solve some pressing problems, such as high energy expenditure, increasing fuel costs, and environmental problems related to conventional plants. However, these technologies still need to be enhanced in terms of water production and gain output ratio (GOR). This paper presents innovative and efficient combined thermal desalination plants consisting of absorption (ABDS), adsorption (ADDS), and humidification-dehumidification (HDH) desalination units to enhance energy utilization, water production, and overall system performance. Four innovative combined desalination plants employing different external heat recovery scenarios are presented and compared. The result from each scenario is evaluated and compared with those of the standalone ABDS and with other studies found in the literature at the same heat source temperatures. The heat source is mainly utilized to drive the ABDS plant. The ADDS and HDH are driven by the heat rejection from the ABDS components with different external heat recovery scenarios. Results illustrated that the maximum freshwater production generated from the proposed scenarios ranges between 1.57 and 2.94 m3.day−1 with a GOR of between 1.38 and 1.75 at 120 °C. Raising heat source temperatures from 55 to 120 °C decreased the improvement in the water productivity from 618.5 to 224 % and decreased the enhancement in the GOR from 222.8 to 99 %. In scenarios where the ADDS and HDH are driven by the heat rejection from the absorption and condensation process of the ABDS, the maximum freshwater production results from the ABDS unit. On the other hand, in scenarios where the ADDS and HDH are driven by the outlet hot water from the ABDS-generator, the maximum freshwater production is obtained from the HDH unit. The finding provided that the proposed innovative scenarios have an effective improvement in overall system performance indicators.

Automated Early Detection of Plant Diseases a Machine Learning Approach

This study introduces an innovative strategy for the early detection of plant diseases leveraging machine learning algorithms, a crucial step towards bolstering global food security. Despite the importance of early disease detection for mitigating crop losses, inadequate infrastructure often hinders swift and accurate identification. This paper addresses these challenges by utilizing the Random Forest algorithm to distinguish between healthy and diseased plant leaves using a specially curated dataset. Our proposed method involves comprehensive phases of dataset creation, feature extraction, classifier training, and subsequent classification. For the extraction of image features, the Histogram of Oriented Gradient (HOG) technique is utilized, offering a robust method for image analysis. Embracing the capabilities of publicly accessible, large-scale datasets, our method provides a scalable solution to plant disease detection. The empirical results demonstrate promising levels of accuracy in disease monitoring, presenting transformative potential for disease management practices in agriculture. Particularly in resourceconstrained settings, our machine learning approach paves the way for efficient and effective plant disease detection, carrying significant implications for future crop protection strategies.