scholarly journals Societal Digital Platforms for Sustainability: Agriculture

2021 ◽  
Vol 13 (9) ◽  
pp. 5048
Author(s):  
Srikanta Bhaskara ◽  
Kamaljit S. Bawa
Keyword(s):  
Real Time ◽  
Digital Literacy ◽  
Agricultural Research ◽  
Contextual Information ◽  
Cost Effective ◽  
Fixed Costs ◽  
Additional Increase ◽  
Digital Platforms ◽  
Long Run ◽  
Approximate Cost

During the last several decades, international and national agricultural research infrastructures have rapidly expanded, bringing the outputs of agricultural research to the world’s farmers. However, despite huge investments in agricultural research, there have been few systematic efforts to create digital platforms to meet the information requirements of farmers in a changing world. We describe an interactive information system in real time to provide agricultural information to farmers. The goals were to increase yields, reduce or optimize farm inputs, inform farmers about markets and government policies, and enable digital literacy among farmers, which (in the long run) would enhance rural incomes. Farmer clubs were created at the village level to increase engagement in the program and to access information. A call-in help center enabled farmers to get information in real time. In addition, a digital platform named eKisaan delivered relevant and contextual information in the local language, mostly in the video format via mobile and cloud technologies. The platform provided information about crop management and a variety of other parameters. The combined incremental savings and incremental earnings resulted in an estimated increase of 15% in income after 18 months, totaling INR₹26,250,000 (US$365,000), followed by an additional increase of 7% in the third year. The approximate cost of the information technology program and help center was INR₹15,000,000 (US$208,000). Over time, costs can decrease by spreading fixed costs over several years, with benefits reaching more farmers. Thus, the digital systems focused on information alone can be cost-effective, reduce inputs, increase productivity and income, and foster sustainability.

scholarly journals Health and Structural Integrity of Monitoring Systems: The Case Study of Pressurized Pipelines

2020 ◽  
Vol 10 (17) ◽  
pp. 6023
Author(s):  
Vladimír Chmelko ◽  
Martin Garan ◽  
Miroslav Šulko ◽  
Marek Gašparík
Keyword(s):  
Real Time ◽  
Structural Integrity ◽  
Human Life ◽  
Cost Effective ◽  
Monitoring Systems ◽  
Long Run ◽  
Operational Safety ◽  
Pipeline Systems ◽  
Safety Parameters ◽  
High Pressure Pipeline

In the operation of some structures, particularly in energy or chemical industry where pressurized pipeline systems are employed, certain unexpected critical situations may occur, which must be definitely avoided. Otherwise, such situations would result in undesirable damage to the environment or even the endangerment of human life. For example, the occurrence of such nonstandard states can significantly affect the safety of high-pressure pipeline systems. The following paper discusses basic physical prerequisites for assembling the systems that can sense loading states and monitor the operational safety conditions of pressure piping systems in the long-run. The appropriate monitoring system hardware with cost-effective data management was designed in order to enable the real-time monitoring of operational safety parameters. Furthermore, the paper presents the results obtained from the measurements of existing real-time safety monitoring systems for selected pipeline systems.

Application of Natural Dye Obtained from Sweet Indrajao on Fabrics

2020 ◽  
Vol 6 (3) ◽  
pp. 180-191
Author(s):  
Kavitha Chandrasekaran
Keyword(s):  
Potassium Dichromate ◽  
Bacterial Pathogens ◽  
Ultrasonic Method ◽  
Ferrous Sulphate ◽  
Fine Powder ◽  
Cost Effective ◽  
Metal Salts ◽  
Cow Dung ◽  
Long Run ◽  
Almost All

Background:: In the long run, synthetic tints were found to be harmful to the chemicals. As a result natural tints have come to be used for their many intrinsic values. The main reason being, then availability of local plants as the main source of natural colorants. Their easy availability in the country being zero cost – effective and planted for other purposes are the main reasons for utilizing them as natural tints. Almost all the parts of the plants, namely stem, leaves, fruits, seeds, barks etc. are used for extracting natural colour. In addition, they are antimicrobial antifungal, insect – repellant deodorant, disinfectant having medicinal values. Methods:: Sweet Indrajao leaves were cleaned by washing with water and dried under direct sunlight and ground as fine powder. A fine strainer was used to remove the wastages. After all these processes, 1-kilogram leaves weighed 318 grams. Then, it is put in 75% ethanol 25% water and heated in a breaker which in kept over a water bath for 2 hours. After this, the contents were filtered and kept in a separate beaker. Bleached fleece draperies stained with stain extract were made to become wet and put into different stain baths which contain the required amount of stain extract and water. Acetic acid was added to it after 20 minutes. The fleece drapery was stained for about one hour at 60oC. The draperies thus stained were removed, squeezed, and put to treatment with metal salts without washing. Different metal salts were used for the treatment using 3% of any one of the chemical mordants like alum, stannous chloride, potassium dichromate, ferrous sulphate, nickel sulphate, copper sulphate and natural mordants such as myrobolan, turmeric, cow dung, Banana sap juice at 60oC for 30 minutes with MLR of 1:30. The stained draperies were washed repeatedly in all the three methods in water and dried in air. At last, the stained draperies were put to soap with soap solution at 60oC for 10 minutes. The draperies were repeatedly washed in water and dried under the sun. Results:: Sweet Indrajao leaves discharged colour easily in alcoholic water. The fleece draperies were stained with chemical and natural mordants. It was observed that the stain uptake was found to be good in post-mordanting method. Ultrasonication has clearly improved the stainability of the draperies at pH 3 and 3.5 values. The pH decreases the stain ability under both Conventional and Ultrasonic conditions. The colour strength increases with an increase in staining temperature in both cases of US and CH methods. Conclusion:: Sweet Indrajao.L has been found to have good ultrasonic potential as a stain plant. The stain uptake as well as the fastness properties of the fleece drapery were found to enhance when metal mordant was used in conjugation with ultra-sonication for the extract of Sweet Indrajao. It was also found that the enhancement of staining ability was better without mordant draperies. The dye extract showed good antibacterial activity against the three bacterial pathogens. Among the three bacterial pathogens, dye extract showed more effective against Escherichia coli pathogens and dye extract showed more effective against Aspergillus pathogens. Hence, the ultrasonic method of drapery staining may be appropriate and beneficial for society at large in future.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

scholarly journals Achieving Sustainability in Food Systems: Addressing Changing Climate through Real Time Nitrogen and Weed Management in a Conservation Agriculture-Based Maize–Wheat System

2021 ◽  
Vol 13 (9) ◽  
pp. 5010
Author(s):  
Kapila Shekhawat ◽  
Vinod K. Singh ◽  
Sanjay Singh Rathore ◽  
Rishi Raj ◽  
T. K. Das
Keyword(s):  
Real Time ◽  
Weed Management ◽  
Agricultural Research ◽  
Indian Agricultural Research Institute ◽  
Interactive Effect ◽  
Maximum Yield ◽  
Conservation Agriculture ◽  
Wheat Crop ◽  
N Application ◽  
N Management

The proven significance of conservation agriculture (CA) in enhancing agronomic productivity and resource use efficiency across diverse agro-ecologies is often challenged by weed interference and nitrogen (N) immobilization. The collective effect of real-time N and weed management has been scarcely studied. To evaluate the appropriateness of sensor-based N management in conjunction with a broad-spectrum weed control strategy for the maize–wheat system, an experiment was conducted at ICAR—Indian Agricultural Research Institute—in New Delhi, India, during 2015–2016 and 2016–2017. Weed management in maize through Sesbania brown manure followed by post-emergence application of 2,4-D (BM + 2,4-D) in maize and tank-mix clodinafop-propargyl (60 g ha−1) and carfentrazone (20 g ha−1) (Clodi+carfentra) in wheat resulted in minimum weed infestation in both crops. It also resulted in highest maize (5.92 and 6.08 t ha−1) and wheat grain yields (4.91 and 5.4 t ha−1) during 2015–2016 and 2016–2017, respectively. Half of the N requirement, when applied as basal and the rest as guided by Optical crop sensor, resulted in saving 56 and 59 kg N ha−1 in the maize–wheat system, respectively, over 100% N application as farmers’ fertilizer practice during the two consecutive years. Interactive effect of N and weed management on economic yield of maize and wheat was also significant and maximum yield was obtained with 50% N application as basal + rest as per Optical crop sensor and weed management through BM+2,4-D in maize and Clodi+carfentra in wheat crop. The study concludes that real-time N management, complemented with appropriate weed management, improved growth, enhanced agronomic productivity and endorsed N saving under a CA-based maize–wheat system in Trans Indo-Gangetic Plains.

scholarly journals DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Collection ◽  
Stock Market ◽  
Real Time ◽  
Stock Prices ◽  
Contextual Information ◽  
Online News ◽  
Stock Prediction ◽  
Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

scholarly journals Cost-Effective Real-Time Metabolic Profiling of Cancer Cell Lines for Plate-Based Assays

Chemosensors ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 139
Author(s):  
Wiktoria Blaszczak ◽  
Zhengchu Tan ◽  
Pawel Swietach
Keyword(s):  
Real Time ◽  
Cell Lines ◽  
Acid Production ◽  
Metabolic Regulation ◽  
Cost Effective ◽  
Culture Conditions ◽  
Metabolic Inhibitors ◽  
Metabolic Phenotype ◽  
Ductal Adenocarcinoma ◽  
Oxygen Utilization

A fundamental phenotype of cancer cells is their metabolic profile, which is routinely described in terms of glycolytic and respiratory rates. Various devices and protocols have been designed to quantify glycolysis and respiration from the rates of acid production and oxygen utilization, respectively, but many of these approaches have limitations, including concerns about their cost-ineffectiveness, inadequate normalization procedures, or short probing time-frames. As a result, many methods for measuring metabolism are incompatible with cell culture conditions, particularly in the context of high-throughput applications. Here, we present a simple plate-based approach for real-time measurements of acid production and oxygen depletion under typical culture conditions that enable metabolic monitoring for extended periods of time. Using this approach, it is possible to calculate metabolic fluxes and, uniquely, describe the system at steady-state. By controlling the conditions with respect to pH buffering, O2 diffusion, medium volume, and cell numbers, our workflow can accurately describe the metabolic phenotype of cells in terms of molar fluxes. This direct measure of glycolysis and respiration is conducive for between-runs and even between-laboratory comparisons. To illustrate the utility of this approach, we characterize the phenotype of pancreatic ductal adenocarcinoma cell lines and measure their response to a switch of metabolic substrate and the presence of metabolic inhibitors. In summary, the method can deliver a robust appraisal of metabolism in cell lines, with applications in drug screening and in quantitative studies of metabolic regulation.

scholarly journals Traceable Air Baggage Handling System Based on RFID Tags in the Airport

2008 ◽  
Vol 3 (1) ◽  
pp. 106-115 ◽  
Author(s):  
Ting Zhang ◽  
Yuanxin Ouyang ◽  
Yang He
Keyword(s):  
Supply Chain ◽  
Real Time ◽  
Recognition Rate ◽  
Cost Effective ◽  
Object Identification ◽  
Rfid Tags ◽  
Handling System ◽  
International Airport ◽  
Management Efficiency ◽  
Baggage Handling

The RFID is not only a feasible, novel, and cost-effective candidate for daily object identification but it is also considered as a significant tool to provide traceable visibility along different stages of the aviation supply chain. In the air baggage handing application, the RFID tags are used to enhance the ability for baggage tracking, dispatching and conveyance so as to improve the management efficiency and the users’ satisfaction. We surveyed current related work and introduce the IATA RP1740c protocol used for the standard to recognize the baggage tags. One distributed aviation baggage traceable application is designed based on the RFID networks. We describe the RFID-based baggage tracking experiment in the BCIA (Beijing Capital International Airport). In this experiment the tags are sealed in the printed baggage label and the RFID readers are fixed in the certain interested positions of the BHS in the Terminal 2. We measure the accurate recognition rate and monitor the baggage’s real-time situation on the monitor’s screen. Through the analysis of the measured results within two months we emphasize the advantage of the adoption of RFID tags in this high noisy BHS environment. The economical benefits achieved by the extensive deployment of RFID in the baggage handing system are also outlined.

scholarly journals Simple, cost-effective technique for portable digital eletrocorticography

2000 ◽  
Vol 58 (2B) ◽  
pp. 424-427 ◽  
Author(s):  
PAULO R. M. DE BITTENCOURT ◽  
MARCOS C. SANDMANN ◽  
MARLUS S. MORO ◽  
JOÃO C. DE ARAÚJO
Keyword(s):  
Real Time ◽  
Epilepsy Surgery ◽  
Vascular Malformations ◽  
Cost Effective ◽  
New Method ◽  
Cortical Lesions ◽  
Effective Technique ◽  
Seizure Disorders ◽  
Operational Costs ◽  
Cost Effective Technique

We revised 16 patients submitted to epilepsy surgery using a new method of digital, real-time, portable electrocorticography. Patients were operated upon over a period of 28 months. There were no complications. The exam was useful in 13 cases. The low installation and operational costs, the reliability and simplicity of the method, indicate it may be useful for defining the epileptogenic regions in a variety of circumnstances, including surgery for tumors, vascular malformations, and other cortical lesions associated with seizure disorders.

Platform economy as a new form of capitalism: a Régulationist research programme

2019 ◽  
Vol 43 (4) ◽  
pp. 805-824 ◽  
Author(s):  
Matthieu Montalban ◽  
Vincent Frigant ◽  
Bernard Jullien
Keyword(s):  
Research Programme ◽  
Sharing Economy ◽  
Digital Platforms ◽  
Growth Regime ◽  
Long Run ◽  
New Process ◽  
New Form ◽  
Platform Economy ◽  
Mode Of Regulation

AbstractThe terms ‘platform economy’ or ‘sharing economy’ have become widespread with the development of digital platforms like Uber. This economy is transforming capitalism and raising important questions about its nature. Is it a new process of embeddedness or is it the next step for deregulation following the crisis of the financialised regime of accumulation (RA)? Is it a possible new Growth Regime? Using the approach of the French Régulation school of thought, we describe the nature and transformations of the form of competition inherent in platforms. Although this may favour some forms of re-embeddedness, we show that it will accelerate some of the trends and characteristics of the institutional forms of the financialised RA and that it is an endogenous product of its crisis. This raises further questions and uncertainties related to the ability of platforms to generate stable long run growth due to the dysfunctionality of the mode of régulation and the conflicts it could generate.

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