Management of paddy soaking water: as a source for enriched compost making

The effluents discharged from rice mills do not contain toxic compounds, but continuous discharge in to soil or surrounding water bodies cause adverse environmental effects. Hence, the aim of the research was selected as management of paddy soaking water with its composition of nutrients as source for preparation of enriched compost and also is useful for the control of wastewater pollution and make safe the environment with effective usage. The characteristics of the effluent generated from cold soaking were acidic pH of 4.0 with concentration of nitrogen as 98 mg/l, phosphorous (91mg/l), potassium (98 mg/l), and reducing sugar (76 mg/l) and high concentration of COD (2760 mg/l), total dissolved solid (2800 mg/l) and electrical conductivity of 6mS/cm. Wastewater from the paddy soaking was then used as a potential source to an anaerobic composting. The digester with organic solid waste of 5 kg was mixed with 30 lit of rice mill effluent and other with 30 lit of water as control. The experiment was conducted in complete randomize design with three replicates. The use of rice mill wastewater significantly increased the available nitrogen, phosphorous and potassium after completion of 42nd days. Chemical analysis of digestate revealed that nutrition profile for anaerobic compost making of paddy soak water with waste was better than that of water. The C/N ratio decreases with days of composting but the reduction rate was high in paddy soak water than the water treated. Therefore, the rice mill wastewater is useful for compost making in anaerobic condition with the production of enriched compost

Ameliorative effects of salt resistance on physiological parameters in the halophyte Salicornia bigelovii torr. with plant growth-promoting rhizobacteria

Salicornia bigelovii is a promising resource to cultivate under extreme climatic conditions of arid-desert regions. However, the production of Salicornia depends on the appropriate supplementation of nitrogen rich synthetic fertilizers. Application of specific halotolerant nitrogen-fixing bacteria associated with S. bigelovii could be an important practice for crop production in salt-affected regions. Seedlings of S. bigelovii were inoculated and developed with plant growth promoting rhizobacteria (Klebsiella pnseumoniae) at different salinities (0 and 0.25 M NaCl) grown under in vitro conditions. The inoculation increased growth and physiological activity at a high salinity. The major benefits of inoculation were observed on total seedlings biomass (320 and 175 g at 0 and 0.25 M NaCl, respectively) and adjacent branches of stem biomass (150 and 85 g at 0 and 0.25 M NaCl, respectively). The inoculation with Klebsiella pneumoniae also significantly improved seedlings salinity tolerance compared to the noninoculated controls. In non-salinity conditions, the inoculated seedlings enhanced the CO2 fixation and O2 evolution. The non-inoculated controls were more sensitive to salinity than inoculated seedlings exposed to salinity, as indicated by several measured parameters. Moreover, inoculated seedlings had significantly increase on proline, phenolics content, but not significant in starch compared to noninoculated controls. In conclusion, K. pneumoniae inoculation mitigates the salinity effects and promotes the Salicornia growth.Keywords: Salicornia bigelovii, Klebsiella pneumoniae, halophyte, ecotype, stress salinity. African Journal of Biotechnology Vol. 12(34), pp. 5278-528

Concurrent tolerance allocation and scheduling for complex assemblies

Traditionally, tolerance allocation and scheduling have been dealt with separately in the literature. The aim of tolerance allocation is to minimize the tolerance cost. When scheduling the sequence of product operations, the goal is to minimize the makespan, mean flow time, machine idle time, and machine idle time cost. Calculations of manufacturing costs derived separately using tolerance allocation and scheduling separately will not be accurate. Hence, in this work, component tolerance was allocated by minimizing both the manufacturing cost (sum of the tolerance and quality loss cost) and the machine idle time cost, considering the product sequence. A genetic algorithm (GA) was developed for allocating the tolerance of the components and determining the best product sequence of the scheduling. To illustrate the effectiveness of the proposed method, the results are compared with those obtained with existing wheel mounting assembly discussed in the literature

Multi-objective optimization for optimum tolerance synthesis with process and machine selection using a genetic algorithm

This paper presents a new approach to the tolerance synthesis of the component parts of assemblies by simultaneously optimizing three manufacturing parameters: manufacturing cost, including tolerance cost and quality loss cost; machining time; and machine overhead/idle time cost. A methodology has been developed using the Genetic Algorithm (GA) technique to solve this multi-objective optimization problem. The effectiveness of the proposed methodology has been demonstrated by solving a wheel mounting assembly problem consisting of five components, two subassemblies, two critical dimensions, two functional tolerances, and eight operations. Significant cost saving can be achieved by employing this methodology

A standard set of person-centred outcomes for diabetes mellitus: results of an international and unified approach

AIMS To select a core list of standard outcomes for diabetes to be routinely applied internationally, including patient-reported outcomes. METHODS We conducted a structured systematic review of outcome measures, focusing on adults with either type 1 or type 2 diabetes. This process was followed by a consensus-driven modified Delphi panel, including a multidisciplinary group of academics, health professionals and people with diabetes. External feedback to validate the set of outcome measures was sought from people with diabetes and health professionals. RESULTS The panel identified an essential set of clinical outcomes related to diabetes control, acute events, chronic complications, health service utilisation, and survival that can be measured using routine administrative data and/or clinical records. Three instruments were recommended for annual measurement of patient-reported outcome measures: the WHO Well-Being Index for psychological well-being; the depression module of the Patient Health Questionnaire for depression; and the Problem Areas in Diabetes scale for diabetes distress. A range of factors related to demographic, diagnostic profile, lifestyle, social support and treatment of diabetes were also identified for case-mix adjustment. CONCLUSIONS We recommend the standard set identified in this study for use in routine practice to monitor, benchmark and improve diabetes care. The inclusion of patient-reported outcomes enables people living with diabetes to report directly on their condition in a structured way

A dimensioning and tolerancing methodology for concurrent engineering applications II: comprehensive solution strategy

Dimensioning and tolerancing (D&T) is a multidisciplinary problem which requires the fulfillment of a large number of dimensional requirements. However, almost all of the currently available D&T tools are only intended for use by the designer. In addition, they typically provide solutions for the requirements one at time. This paper presents a methodology for determining the dimensional specifications of the component parts and sub-assemblies of a product by satisfying all of its requirements. The comprehensive solution strategy presented here includes: a strategy for separating D&T problems into groups, the determination of an optimum solution order for coupled functional equations, a generic tolerance allocation strategy, and strategies for solving different types of D&T problems. A number of commonly used cost minimization strategies, such as the use of standard parts, preferred sizes, preferred fits, and preferred tolerances, have also been incorporated into the proposed methodology. The methodology is interactive and intended for use in a concurrent engineering environment by members of a product development team

Assessment of Wet and Dry Spells Over North Western Zone of Tamil Nadu Using WASP

Rainfall is the most dependent weather factor that decides the success or failure of a crop in a location. The behaviour of rainfall plays a major role in the selection of crop, cropping system and cropping pattern. It is also essential for planning of water resource management structures. Hence it is essential to study the anomaly in rainfall over a long period of time to identify the deviation in rainfall pattern over the region. Weighted Anomaly of Standardized Precipitation Index (WASP) is an index that can help in identifying the occurrence of dry or wet spells over different time scales (tri, hexa, nona and dodeca-monthly). The analysis was carried out for Salem district of Tamil Nadu over a period of 30 years (1991-2020) which has an average rainfall of 990 mm per year with bimodal distribution. The analysis shows an increase in near normal events, decrease in wet as well as dry events with 3 m, 6 m, 9 m and 12 m WASP. The 3 m-WASP indicates the north east monsoon have more variability in rainfall since more number of wet as well as dry events have occurred during this season. With 6 m WASP During, in all the 3 decades, the consecutive wet as well as dry events have been occurred during the winter and NE monsoon seasons. This shows the risk in crop cultivation during NEM season because of higher instability in rainfall. Considering the longer time scale of 9 m and 12 m WASP indicates the occurrence of prolonged extreme dry or extreme wet events to be very minimal. The negative effects of these events can be handled easily by adopting proper drainage, water harvesting and storage structure within the farms. These structures can also help in recharging the ground water table in addition to supplementing water needs for the crops

Characteristics of Fly Ash and its Influence on the Germination of Amaranthus Seeds

The physical and chemical properties of fly ash collected from three different thermal power stations were studied and the fly ash with neutral to alkaline pH was utilized as a medium for the germination of amaranthus seeds. The germination test was carried out using a completely randomized design with two replications at Agricultural College and Research Institute, Madurai. The various fly ash was analyzed for its physical and chemical properties with standard methods. The fly ash with a slightly alkaline pH was selected and media was prepared with soil, vermicompost and farm yard manure. The seeds of an amaranthus crop were sown and the germination percentage, days to germination, shoot length, root length and vigour index were recorded. The collected fly ashes were recorded with low bulk density, alkaline pH with quantities of micro and macronutrients. The fly ash of Tuticorin has a slightly alkaline pH (8.03) and was utilized for media preparation. Among the different media, the media with treatment Fly ash + Soil + Vermicompost (1:1:4) + Bio Fertilizer has recorded higher germination percentage, shoot length, root length, vigour index, lower days to germination with 94.6%, 18.3 cm, 4.8 cm,16.17 and 2.8 respectively. The vermicompost is best for the enrichment of fly ash compared to farm yard manure as it is completely decomposed organic manure

Soil Texture Prediction Using Machine Learning Approach for Sustainable Soil Health Management

Soil in the earth acts as a foothold for all crops. Soil texture is the most important soil health indicator being used for the selection of crops, mechanical manipulation, irrigation management, and fertilizer management. The texture of the soil influences the storage and flow of air and water within the soil, as well as root development, the accessibility of plant nutrients, and the activities of different microorganisms. These factors collectively impact the soil's fertility, quality, and soil health. A conventional method of soil texture analysis is cumbersome, time-consuming, and labor-intensive. Machine learning (ML) is a newly emerging technique being used to assess the soil's physical, chemical, and biological properties quickly in real-time. This is an eco-friendly approach since it does not involve any hazardous chemicals. Machine learning can learn complex features and predict nonlinear properties. Convolutional Neural Networks (CNN) employs convolutional layers to automatically learn features from the input data and is widely used in image classification, object detection, and image generation tasks in a short time. Soil texture images are given as input dataset after the completion of image subsetting, data preprocessing, and Image augmentation. This gives a CNN-based soil texture predictive model with a reliable accuracy of 87.50% at a lower cost