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In this paper, the treatment efficiency of a small-scale wastewater treatment plant (WWTP) with activated sludge was analysed in order to examine the impact of variations in the composition of incoming raw municipal wastewater. The characteristics of the wastewater were analysed with respect to COD, BOD and TSS values and loading during the two years, 2018 and 2019. The mixed liquid suspended solid (MLSS), sludge volume index (SVI), food to microorganism ratio (F/M), sludge age and hydraulic retention time (HRT) were used for evaluation of the performance of WWTP. Removal percentage is in the order of TSS > BOD > COD during 2018, while in 2019 is in the order BOD > TSS > COD. However, better values of removal efficiency for COD, BOD and TSS are obtained in 2019, which is connected to lower oscillation values of MLSS and SVI index. Biodegradability ratio of raw and treated wastewater, plant reliability factor (RF) and equivalent number of inhabitant (ENI) were determined. In addition, the economic cost of small-scale wastewater treatment plant (WWTP) with activated sludge was evaluated and discussed.

The evaluation and prediction of reliability and testability of mining machinery and equipment are crucial, as advancements in mining technology have increased the importance of ensuring the safety of both the technological process and human life. This study focuses on developing a reliability model to analyze the controllability of mining equipment. The model, which examines the reliability of a mine cargo-passenger hoist, utilizes statistical methods to assess failures and diagnostic controlled parameters. It is represented as a transition graph and is supported by a system of equations. This model enables the estimation of the reliability of equipment components and the equipment as a whole through a diagnostic system designed for monitoring and controlling mining equipment. A mathematical and logical model is proposed to calculate availability and downtime coefficients for different structures within the mining equipment system. This analysis considers the probability of failure-free operation of the lifting unit based on the structural scheme, with additional redundancy for elements with lower reliability. The availability factor of the equipment for monitoring and controlling the mine hoisting plant is studied for various placements of diagnostic systems. Additionally, a logistic concept is introduced for organizing preventive maintenance systems and reducing equipment recovery time by optimizing spare parts, integrating them into strategies aimed at enhancing the reliability of mine hoisting plants.

Energy storage is widely introduced and integrated into generating systems involving renewable energy usage, as it can smooth power output, alleviate system volatility, and reduce renewable power spillage. This paper studies the reliability evaluation of remote wind-storage plants. The dispatch procedure is formulated to supply smooth power output during a long-time horizon. The concept of substitutable output capacity is introduced to evaluate the capability of a wind-storage plant that provides steady power during most time segments. The proposed model can provide meaningful inference on the reliability and stationarity of a renewable plant, reflecting the advantage of storage-assisted generation.

An ethnobotanical expedition was conducted to document the traditional ethnobotanical (TEB) uses of wild flora of Dawarian and Ratti Gali villages of District Neelam, Azad Jammu and Kashmir (AJK) Pakistan. District Neelam has rich plant diversity and is hub of many endemic plant species while the study areas are not yet explored. The research area: Dawarian and Rati Gali (DRG) area is mountaineous terrain and villages are located on far and farther distances. DRG area has rich biocultural and plant diversity comprising of different ethnic groups of Kashmir state. The current research was aimed to explore and document traditional medicines (TEMs) and other domestic and commercial uses of wild plants. This study will assist to evaluate conservation and commercial worth of wild flora which can be potential candidate for drug discovery through ethnopharmacological analysis. The current quantitative ethnobotanical research was carried out in 2018 by interviewing 150 indigenous informants (90 male and 60 female) of DRG area using questionnaire applying structured and semi structured interview methodology. Data analysis was analyzed by using quantitative ethnobotanical statistical tools such as fidelity level (FL), informant consensus factor (ICF), Spearman's rank correlation (SRC) and data matrix ranking (DMR). The indigenous people of DRG area use wild plants in their daily life to cope life necessities i.e. food, vegetables, fodder, fuel, shelter, timber and herbal medicines. TEMs are primarily used to cure different infirmities like diabetics, asthma, dysentery, constipation, cold, fever, joint pain, wound healing, cancer, cardiovascular disorders, epilepsy, kidney infections and many types of skin diseases. Current study revealed the data of 103 wild plants species belonging to 46 plant families from selected areas of District Neelum, AJK. Results depicted that Asteraceae ranked 1st (12 plants spp). Among plant parts used leaf ranked 1st (18%), followed by seed (17%) and root (13%). While prevalent form recipe mode was decoction (20%), followed by powder (17%) and extract (14%) and fodder was highest (37%) EB use-form fodder, followed by food (32%) and fuel (17%). Quantitative ethnobotanical analysis (QEA) was carried to find the reliability and novelty of the study. Five plant species including Berberis lyceum (FL = 97.78%), Isodon rugosus (FL = 95.71%), Saussurea lappa (FL = 94.74%), Aconitum heterophyllum (FL = 92.71%) and Taxus baccata (91.58%) had shown high fidelity level which confirmed that these plants have high medicinal worth in study area. The highest value (0.94) of ICF was for diseases group \"tuberculosis and leucorrhea\", followed by stomachache and flatulence (0.93), diabetics and blood pressure (0.92) and asthma and chest infections (0.88). For other uses fuel with ICF (0.83) ranked first and second was hedging and thatching (ICF = 0.82) where people use plants or their parts for construction. Spearman's rank correlation (SRC) test indicated that number of TEB uses increases if number of species is increased. Jaccard index (JI) analysis depicted that 56.31% plants are being used as TEMs which are first time explored from the study area. While 26.21% plants are being used in different TEB uses which are different from past cited literature. These novel findings of research indicate that wild flora of the study area has great potential for novel drug discovery and provision of materialist services for the indigenous communities. The present research revealed that TEMs uses of 58 plants are novel being first time reported from the study area (DRG) of District Neelam of AJK. The results showed that plants like Acer cappadocicum, Ajuga bracteosa and Swertia paniculata are used to cure diabetes, Viscum album, Viola canescens, Taxus baccata are used for cure of cancer, Isodon rugosus, Polygala chinensis are used in TEMs for treating cardiovascular disorders and Anaphalis triplinervis is used for epilepsy. Berberis lyceum, Ajuga bracteosa, Aconitum heterophyllum, Bistorta amplexicaule, Saussurea lapa and Jurinea dolomiaea are severely threatened and there is urgent need to do conservation measures for available of valuable MPs to the indigenous communities for life necessities and for future research. The current study will also be useful addition in ethnobotanical database, preservation of traditional culture and drug discovery and drug development through future ethnopharmacological research.

Accurately representing flow across the mesoscale to the microscale is a persistent roadblock for completing realistic microscale simulations. The science challenges that must be addressed to coupling at these scales include the following: 1) What is necessary to capture the variability of the mesoscale flow, and how do we avoid generating spurious rolls within the terra incognita between the scales? 2) Which methods effectively couple the mesoscale to the microscale and capture the correct nonstationary features at the microscale? 3) What are the best methods to initialize turbulence at the microscale? 4) What is the best way to handle the surface-layer parameterizations consistently at the mesoscale and the microscale? 5) How do we assess the impact of improvements in each of these aspects and quantify the uncertainty in the simulations? The U.S. Department of Energy Mesoscale-to-Microscale-Coupling project seeks to develop, verify, and validate physical models and modeling techniques that bridge the most important atmospheric scales determining wind plant performance and reliability, which impacts many meteorological applications. The approach begins with choosing case days that are interesting for wind energy for which there are observational data for validation. The team has focused on modeling nonstationary conditions for both flat and complex terrain. This paper describes the approaches taken to answer the science challenges, culminat­ing in recommendations for best approaches for coupled modeling.

Existing methods for optimizing wind array layouts typically use power or cost objectives and rarely consider reliability‐based objectives. Component and system failure rates, however, are dependent on location‐specific wind conditions, are influenced by array layout and wake interactions, and have a direct and significant impact on capital costs, operational costs, and power production. Although wind power plant models exist that calculate wind loads with sufficient resolution to capture component loading dynamics from wind conditions, they are computationally expensive and thus not suitable for research applications requiring many evaluations, particularly optimization. This study describes the development of computationally efficient, reliability‐based layout optimization methods, enabling us to explore the relationship between component reliability and layout optimization. These methods include the surrogate modeling of the planet bearing life based on varying wind conditions simulated in FAST.Farm and the formulation of reliability‐based objectives based on failure cost and power production models. Through demonstration of this method, we explore how wind conditions, objective functions, and capacity density influence reliability‐based layout optimization. Results indicate that considering reliability alongside power production can reduce failure costs associated with replacement costs and downtime whilemaintaining or improving power production. Our conclusions highlight the opportunity for wind power plant developers to integrate reliability and operational expenditures alongside performance and capital expenditure objectives in plant design and development to improve plant performance and costs.

Suppressive composts represent a sustainable approach to combat soilborne plant pathogens and an alternative to the ineffective chemical fungicides used against those. Nevertheless, suppressiveness to plant pathogens and reliability of composts are often inconsistent with unpredictable effects. While suppressiveness is usually attributed to the compost's microorganisms, the mechanisms governing microbial recruitment by the roots and the composition of selected microbial communities are not fully elucidated. Herein, the purpose of the study was to evaluate the impact of a compost on tomato plant growth and its suppressiveness against f. sp. (Foxl) and (Vd). First, growth parameters of tomato plants grown in sterile peat-based substrates including 20 and 30% sterile compost (80P/20C-ST and 70P/30C-ST) or non-sterile compost (80P/20C and 70P/30C) were evaluated in a growth room experiment. Plant height, total leaf surface, and fresh and dry weight of plants grown in the non-sterile compost mixes were increased compared to the plants grown in the sterile compost substrates, indicating the plant growth promoting activity of the compost's microorganisms. Subsequently, compost's suppressiveness against Foxl and Vd was evaluated with pathogenicity experiments on tomato plants grown in 70P/30C-ST and 70P/30C substrates. Disease intensity was significantly less in plants grown in the non-sterile compost than in those grown in the sterile compost substrate; AUDPC was 2.3- and 1.4-fold less for Foxl and Vd, respectively. Moreover, fungal quantification demonstrated reduced colonization in plants grown in the non-sterile mixture. To further investigate these findings, we characterized the culturable microbiome attracted by the roots compared to the unplanted compost. Bacteria and fungi isolated from unplanted compost and the rhizosphere of plants were sequence-identified. Community-level analysis revealed differential microbial communities between the compost and the rhizosphere, suggesting a clear effect of the plant in the microbiome assembly. Proteobacteria and Actinobacteria were highly enriched in the rhizosphere whereas Firmicutes were strongly represented in both compartments with being the most abundant species. Our results shed light on the composition of a microbial consortium that could protect plants against the wilt pathogens of tomato and improve plant overall health.

Phenotypic traits like plant height are crucial in assessing plant growth and physiological performance. Manual plant height measurement is labor and time-intensive, low throughput, and error-prone. Hence, aerial phenotyping using aerial imagery-based sensors combined with image processing technique is quickly emerging as a more effective alternative to estimate plant height and other morphophysiological parameters. Studies have demonstrated the effectiveness of both RGB and LiDAR images in estimating plant height in several crops. However, there is limited information on their comparison, especially in soybean ( Glycine max [L.] Merr.). As a result, there is not enough information to decide on the appropriate sensor for plant height estimation in soybean. Hence, the study was conducted to identify the most effective sensor for high throughput aerial phenotyping to estimate plant height in soybean. Aerial images were collected in a field experiment at multiple time points during soybean growing season using an Unmanned Aerial Vehicle (UAV or drone) equipped with RGB and LiDAR sensors. Our method established the relationship between manually measured plant height and the height obtained from aerial platforms. We found that the LiDAR sensor had a better performance (R 2 = 0.83) than the RGB camera (R 2 = 0.53) when compared with ground reference height during pod growth and seed filling stages. However, RGB showed more reliability in estimating plant height at physiological maturity when the LiDAR could not capture an accurate plant height measurement. The results from this study contribute to identifying ideal aerial phenotyping sensors to estimate plant height in soybean during different growth stages.

How to evaluate the accuracy of quantitative trait prediction is crucial to choose the best model among several possible choices in plant breeding. Pearson’s correlation coefficient (PCC), serving as a metric for quantifying the strength of the linear association between two variables, is widely used to evaluate the accuracy of the quantitative trait prediction models, and generally performs well in most circumstances. However, PCC may not always offer a comprehensive view of predictive accuracy, especially in cases involving nonlinear relationships or complex dependencies in machine learning-based methods. It has been found that many papers on quantitative trait prediction solely use PCC as a single metric to evaluate the accuracy of their models, which is insufficient and limited from a formal perspective. This study addresses this crucial issue by presenting a typical example and conducting a comparative analysis of PCC and nine other evaluation metrics using four traditional methods and four machine learning-based methods, thereby contributing to the improvement of practical applicability and reliability of plant quantitative trait prediction models. It is recommended to employ PCC in conjunction with other evaluation metrics in a targeted manner based on specific application scenarios to reduce the likelihood of drawing misleading conclusions.

Generator is key unit of steam turbine power plant which is used to convert mechanical energy into electrical energy. This energy is further used for completing the requirement generated by various sectors of country. Generator is one of the most important component of steam turbine power plant configured with seven subsystems in series configuration. So, it is necessary to ensure its smooth functioning which is attain by the proper functioning of its subcomponents. It is important to increase operational availability of subsystem. Hence, present study is proposed to investigate various reliability measures of generator used in STP through RAMD approach at component level. For this purpose, mathematical models using Markovian birth death process have been developed for all subsystems of generator. These models are very helpful in reliability, maintainability, and availability analysis of generators. RAMD analysis will play significant role in this direction. This will help to find critical component of system so that proper maintenance strategies can be proposed. It is assumed that all failure and repair rates associated with each component followed exponential distribution and adequate repair facility always remains available with system. Various system performance measures like reliability function, maintainability function, dependability function and steady state availability for different components as well as system have been evaluated. The numerical results have been derived to highlight the importance of study.