Search results for: courts of accounts

Authors: Harold M. Carag, April Charmaine D. Camacho, Girlie Nora A. Abrigo, Florencia G. Palis, Ma. Larissa Lelu P. Gata

Abstract:

A technology impact assessment on the use of microbial inoculants in the reforestation and rehabilitation of forest ancestral lands of the Magbukün Aytas in Morong, Bataan was conducted. This two-year rainforestation technology aimed to determine the optimum condition for the improvement of seedling survival rate in the nursery and in the field to hasten the process of forest regeneration of Magbukün Ayta’s ancestral land. A combination of qualitative methods (key informant interviews, focus groups and participant observation), participated by the farmers who were directly involved in the project, community men and women, the council of elders and the project staff, was employed to complete this impact assessment. The recorded data were transcribed, and the accounts were broadly categorized on the following aspects: social (gender, institutional, anthropological), economic and environmental. The Australian Center for International Agricultural Research (ACIAR) framework was primarily used for the impact analysis while the Harvard Analytical Framework was specifically used for the gender impact analysis. Through this technology, a wildling nursery with more than one thousand seedlings was successfully established and served as a good area for the healthy growth of seedlings that would be planted in the forest. Results showed that this technology affected positively and negatively the various gender roles present in the community although household work remained to be the women’s responsibility. The technology introduced directly added up to the workload done by the men and women (preparing and applying fertilizer, making pots etc.) but this, in turn, provided ways to increase their sources of livelihood. The gender roles that were already present were further strengthened after the project and men remained to be in control. The technology or project in turn also benefited from the already present roles since they no longer have to assign things to them, the execution of the various roles was smoothly executed. In the anthropological aspect, their assigned task to manage the nursery was an easy responsibility because of their deep connection to the environment and their fear and beliefs on ‘engkato’ and ‘anito’ was helpful in guarding the forest. As the cultural value of these trees increases, their mindset of safeguarding the forest also heightens. Meanwhile, the welfare of the whole tribe is the ultimate determinant of the swift entry of projects. The past institutions brought ephemeral reliefs on the subsistence of the Magbukün Aytas. These were good ‘conditioning’ factors for the adoption of the technology of the project. As an attempt to turn away from the dependent of harmful chemical, the project’s way of introducing organic inputs was slowly gaining popularity in the community. Economically, the project was able to provide additional income to the farmers. However, the slow mode of payment dismayed other farmers and abandoned their roles. Lastly, major environmental effects weren’t that much observed after the application of the technology. The minor effects concentrated more on the improved conditions of the soil and water in the community. Because of the introduced technology, soil conditions became more favorable specifically for the species that were planted. The organic fertilizers used were in turn not harmful for the residents living in Sitio Kanawan. There were no human diseases caused by the technology. The conservation of the biodiversity of the forest is clearly the most evident long-term result of the project.

Keywords: ancestral lands, impact assessment, microbial inculants, reforestation

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Authors: Jai Bakliya, Palak Dhamecha

Abstract:

Today customer satisfaction is given utmost priority be it any industry. But when it comes to hospitality industry this applies even more as they come in direct contact with customers while providing them services. Employee engagement is new concept adopted by Human Resource Department which impacts customer satisfactions. To satisfy your customers, it is necessary to see that the employees in the organisation are satisfied and engaged enough in their work that they meet the company’s expectations and contribute in the process of achieving company’s goals and objectives. After all employees is human capital of the organisation. Employee engagement has become a top business priority for every organisation. In this fast moving economy, business leaders know that having a potential and high-performing human resource is important for growth and survival. They recognize that a highly engaged manpower can increase innovation, productivity, and performance, while reducing costs related to retention and hiring in highly competitive talent markets. But while most executives see a clear need to improve employee engagement, many have yet to develop tangible ways to measure and tackle this goal. Employee Engagement is an approach which is applied to establish an emotional connection between an employee and the organisation which ensures the employee’s commitment towards his work which affects the productivity and overall performance of the organisation. The study was conducted in hospitality industry. A popular branded hotel was chosen as a sample unit. Data were collected, both qualitative and quantitative from respondents. It is found that employee engagement level of the organisation (Hotel) is quite low. This means that employees are not emotionally connected with the organisation which may in turn, affect performance of the employees it is important to note that in hospitality industry individual employee’s performance specifically in terms of emotional engagement is critical and, therefore, a low engagement level may contribute to low organisation performance. An attempt to this study was made to identify employee engagement level. Another objective to take this study was to explore the factors impeding employee engagement and to explore employee engagement facilitation. While in the hospitality industry where people tend to work for as long as 16 to 18 hours concepts like employee engagement is essential. Because employees get tired of their routine job and in case where job rotation cannot be done employee engagement acts as a solution. The study was conducted at Trident Hotel, Udaipur. It was conducted on the sample size of 30 in-house employees from 6 different departments. The various departments were: Accounts and General, Front Office, Food & Beverage Service, Housekeeping, Food & Beverage Production and Engineering. It was conducted with the help of research instrument. The research instrument was Questionnaire. Data collection source was primary source. Trident Udaipur is one of the busiest hotels in Udaipur. The occupancy rate of the guest over there is nearly 80%. Due the high occupancy rate employees or staff of the hotel used to remain very busy and occupied all the time in their work. They worked for their remuneration only. As a result, they do not have any encouragement for their work nor they are interested in going an extra mile for the organisation. The study result shows working environment factors including recognition and appreciation, opinions of the employee, counselling, feedback from superiors, treatment of managers and respect from the organisation are capable of increasing employee engagement level in the hotel. The above study result encouraged us to explore the factors contributed to low employee engagement. It is being found that factors such as recognition and appreciation, feedback from supervisors, opinion of the employee, counselling, feedback from supervisors, treatment from managers has contributed negatively to employee engagement level. Probable reasons for the low contribution are number of employees gave the negative feedback in accordance to the factors stated above of the organisation. It seems that the structure of organisation itself is responsible for the low contribution of employee engagement. The scope of this study is limited to trident hotel situated in the Udaipur. The limitation of the study was that that the results or findings were only based on the responses of respondents of Trident, Udaipur. And so the recommendations were also applicable in Trident, Udaipur and not to all the like organisations across the country. Through the data collected was further analysed, interpreted and concluded. On the basis of the findings, suggestions were provided to the hotel for improvisation.

Keywords: human resource, employee engagement, research, study

Procedia PDF Downloads 285

Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Mina Naeini, Thomas A. Adams II

Abstract:

Solid Oxide Fuel Cells (SOFCs) feature high electrical efficiency and generate substantial amounts of waste heat that make them suitable for integrated community energy systems (ICEs). By harvesting and distributing the waste heat through hot water pipelines, SOFCs can meet thermal demand of the communities. Therefore, they can replace traditional gas boilers and reduce greenhouse gas (GHG) emissions. Despite these advantages of SOFCs over competing power generation units, this technology has not been successfully commercialized in large-scale to replace traditional generators in ICEs. One reason is that SOFC performance deteriorates over long-term operation, which makes it difficult to find the proper sizing of the cells for a particular ICE system. In order to find the optimal sizing and operating conditions of SOFCs in a community, a proper knowledge of degradation mechanisms and effects of operating conditions on SOFCs long-time performance is required. The simplified SOFC models that exist in the current literature usually do not provide realistic results since they usually underestimate rate of performance drop by making too many assumptions or generalizations. In addition, some of these models have been obtained from experimental data by curve-fitting methods. Although these models are valid for the range of operating conditions in which experiments were conducted, they cannot be generalized to other conditions and so have limited use for most ICEs. In the present study, a general, detailed degradation-based model is proposed that predicts the performance of conventional SOFCs over a long period of time at different operating conditions. Conventional SOFCs are composed of Yttria Stabilized Zirconia (YSZ) as electrolyte, Ni-cermet anodes, and LaSr₁₋ₓMnₓO₃ (LSM) cathodes. The following degradation processes are considered in this model: oxidation and coarsening of nickel particles in the Ni-cermet anodes, changes in the pore radius in anode, electrolyte, and anode electrical conductivity degradation, and sulfur poisoning of the anode compartment. This model helps decision makers discover the optimal sizing and operation of the cells for a stable, efficient performance with the fewest assumptions. It is suitable for a wide variety of applications. Sulfur contamination of the anode compartment is an important cause of performance drop in cells supplied with hydrocarbon-based fuel sources. H₂S, which is often added to hydrocarbon fuels as an odorant, can diminish catalytic behavior of Ni-based anodes by lowering their electrochemical activity and hydrocarbon conversion properties. Therefore, the existing models in the literature for H₂-supplied SOFCs cannot be applied to hydrocarbon-fueled SOFCs as they only account for the electrochemical activity reduction. A regression model is developed in the current work for sulfur contamination of the SOFCs fed with hydrocarbon fuel sources. The model is developed as a function of current density and H₂S concentration in the fuel. To the best of authors' knowledge, it is the first model that accounts for impact of current density on sulfur poisoning of cells supplied with hydrocarbon-based fuels. Proposed model has wide validity over a range of parameters and is consistent across multiple studies by different independent groups. Simulations using the degradation-based model illustrated that SOFCs voltage drops significantly in the first 1500 hours of operation. After that, cells exhibit a slower degradation rate. The present analysis allowed us to discover the reason for various degradation rate values reported in literature for conventional SOFCs. In fact, the reason why literature reports very different degradation rates, is that literature is inconsistent in definition of how degradation rate is calculated. In the literature, the degradation rate has been calculated as the slope of voltage versus time plot with the unit of voltage drop percentage per 1000 hours operation. Due to the nonlinear profile of voltage over time, degradation rate magnitude depends on the magnitude of time steps selected to calculate the curve's slope. To avoid this issue, instantaneous rate of performance drop is used in the present work. According to a sensitivity analysis, the current density has the highest impact on degradation rate compared to other operating factors, while temperature and hydrogen partial pressure affect SOFCs performance less. The findings demonstrated that a cell running at lower current density performs better in long-term in terms of total average energy delivered per year, even though initially it generates less power than if it had a higher current density. This is because of the dominant and devastating impact of large current densities on the long-term performance of SOFCs, as explained by the model.

Keywords: degradation rate, long-term performance, optimal operation, solid oxide fuel cells, SOFCs

Procedia PDF Downloads 110