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The main purpose of this study is to investigate the influence of eWOM on consumer purchase intention. Moreover, this study examines the extent to which relationship exists between eWOM credibility, eWOM review quantity, eWOM perceived usefulness and consumer purchase intention in cosmetic products in Sri Lanka. The primary data was collected through an online survey with the help of a standardized questionnaire from 400 cosmetic product consumers in Sri Lanka. The items of the questionnaire extracted from previous research and altered them as per study requirement. The result of this research implies that eWOM Credibility, eWOM Review Quantity and eWOM Perceived Usefulness has significant impact on Purchase Intention. The element that has the highest impact is eWOM perceived usefulness which entirely contributes to the effectiveness of eWOM while eWOM credibility and eWOM review quantity have less impact. These three elements thoroughly perform different roles in influencing consumer purchase intention. This research is limiting only three main determinants such as eWOM credibility, eWOM review quantity and eWOM perceived usefulness which determine the purchase intention. There may be other factors also which are affecting that can be considered for further research. Companies could use tactics to encourage potential customers to thoroughly read eWOM provided in social media or consumer reviews. This study provides valuable insights into the eWOM which affects consumer purchase intention.

Food is one of the basic necessities of human, and there are manypeople in this world who do not have enough food for their basic requirements. We often see many food waste from parties, luxury weddings which mostly goes waste. Most consider to donate the surplus food to the poor or required institution but these do not go as planned as there is no available mechanism to implement these functions. So in this project we come up with a solution to tackle both surplus as well as thrown away food. It is done through a charitable app for donating surplus food along witha IoT based smart bin that measures the level of thrown away food in the bin and send it to the app used by bioplant users.

Almost every household and restaurants use Liquified Petroleum Gas (LPG) in their kitchens as it is an essential part of cooking. As important as it becomes it is often difficult to maintain it as there are a lot of difficulties for the users and the igneous gas can cause hazards at any time. The main issue arises when we cannot detect gas leakage in our LPG and there is no automated booking yet. These leakages can cause the life of the user. We can only understand that the gas cylinder is over when it is empty and stops working which is very inefficient for the users. To rectify these issues we developed Auto-Gaz, a hardware and software combination product that helps us to identify the current filling of the cylinder, the correct time for slot booking, and gives information about gas leakage if any occurs. In this product load cell senses the weight of the cylinder and sends the data to node MCU which helps us identify the current status of the gas. MQ6 sensor detects the gas leakage and the analog data is sent to the node MCU WIFI module. Node MCU is connected to the software application via firebase and the data is stored in the application. Notifications of current updates are shown in the application. If leakage occurs the system detects it and sends a warning alert to the mobile application. When the gas reaches a minimum level, the user is notified of a new booking. In addition, this device promises automatic working with no need for human interference and assures reliability, efficiency along with cost-effectiveness. AutoGaz is easily installable and low maintenance. This project aims to aid in home automation with the help of IoT.

Humans now spend the bulk of their time on the Internet, thanks to the huge rise of social media and other applications. Organizations and individuals have a shared platform to express their thoughts and ideas on any entity in the globe. Every website has a massive amount of information about products and services. Through social media tools, we, as humans, may remark on any subject or incident. Most of the time, these public opinions are considered by organizations and individuals to better corporate operations and strategies, or in the decision-making process. Although these user opinions are practical in decision-making processes, manually analyzing and summarizing them is a difficult effort. It is a lengthy process. As a result, having automated opinion mining techniques to analyze user sentiments is critical. Since the beginning of social media, sentiment analysis has been a hot research area. Many studies have been conducted in various fields. The tourism sector is highlighted as one of the important topics for diversification in Oman Vision 2040. This study suggests an analysis of user reviews of Omani restaurants, categorizing them as positive or bad, and ranking the restaurants according to the reviews. The reviews are analyzed using opinion mining methods

ABSTRACT the aim of the study was to study the effectiveness of Low Energy Laser Application or piezocisions in accelerating tooth movement, during canine retraction, compared to conventional canine retraction. Patients and Methods: A split mouth study design was done with two groups A and B. Each group confined 10 patients; in each patient one side was used as a control side and the contralateral side received either low-level laser therapy (LLLT) (group A) or piezocisions (group B). The evaluation data was gathered by Intra-oral measurements directly, every 2 weeks, over a 3 months retraction period. Results: Statistical analysis using Repeated measure ANOVA test was used to compare between more than two groups in related samples. Paired sample t-test was used to compare between two groups in related samples. Independent sample t-test was used to compare between two groups in non-related samples. direct intra-oral measurements from Group A illustrated that there was a statistically significant difference between the study and control sides (p

Cancer is a major health problem with a significant impact on society and healthcare systems. In 2018, approximately 18.1 million cases of cancer were diagnosed and 9.6 million deaths were documented. Urological cancers account for 12.9% of new cases recorded and 8% of deaths due to cancer worldwide. The latest cancer registries covering the Gulf Cooperation Council (GCC) countries report that 4078 cases of renal cell carcinoma were diagnosed from 1998 to 2012. Urological cancers comprised 9.4% of all cases with an incidence rate of 16.1% in males and 3.2% in females. All renal cancer cases documented in Salmaniya Medical Complex (SMC) from 2014 to 2018 were reviewed. Data collected for all patients from the electronic health record system included age at diagnosis, gender, laterality of cancer (where applicable), histological type, and TNM (tumor, node, metastasis) classification and staging. Furthermore, World Health Organization (WHO) grade and data were collected for kidney cancer cases. Statistical analysis was carried out using Statistical Package for the Social Sciences (SPSS) version 23 (IBM Corp, Armonk, NY). From 2014 to 2018, there were 65 documented cases of kidney cancer with an average caseload of 13 cases per year. The mean age at diagnosis was 57.6 years. Clear cell carcinoma was the most common histological subtype (37.5%). Stage 1 was the most common stage at diagnosis (35.4%) and the age-standardized mortality rate for males and females were 4.59 and 4.58 in 100,000, respectively. Kidney cancer is a urological malignancy that can pose a burden on both the patient and the healthcare system. There should be a national effort to better understand the etiology and epidemiology of this disease entity with regard to our population. Such efforts would make data regarding diagnosis, management, and follow-up more accessible and would add positively to our healthcare system.

In the normal life of an oil or gas well, sometimes the well produce water in conjunction with the desired hydrocarbons. In the initial stage of life of the wells, the wells have sufficient energy to carry undesired water through the well bore and out of well to the surface. With the passage of time, the reservoir pressure depletes, and it leads to a decrease in well production. When this occurs the flowing pressure decrease and have an adverse effect on the well to carry and remove the undesired water being produced by the well. When the well can't carry said water out of the well bore it builds up in the production tubing and the well shuts off flow to the surface. Soap sticks are deployed on the wells to generate foam and to overcome the liquid loading, thereby reducing the surface tension and lowering the fluid density. The customer was experiencing liquid loading in many of its older gas wells which has a considerable impact on production targets. The liquid loading was a major challenge for the customer. The customer was using coil tubing and nitrogen kick off to offload the liquid loaded wells. The cost associated with nitrogen kick off was vey high. The customer was looking for a cost-effective solution to offload liquid loaded wells. This paper describes the application of Soap Sticks on liquid loaded wells and demonstrates that the use of Soap Sticks is cost effective and sustainable alternative to unloading by coil tubing. Initially, two liquid loaded wells were selected for Soap Sticks treatment. The data for two wells, Well X-1 and Well X-2 was used for analysis and to prepare best treatment plan. The Well X-1 was a deviated well with 4-1/2-inch single string completion. The well was producing 4944Mscf/d and 39 bbl/day liquids. The well was loaded up with water and was unable to deliver the desired production rates. As per the analysis, the Soap sticks were launched, and the well was unloaded successfully. The Well X-2 located in the same field and was a vertical well with 4-1/2" single string completion. Based on historical data, this well was producing around 4310 Mscf/day. The production from this well dropped to 1704Mscf/day and it was kept dropping continusly. Prior to the field trial the liquid column height was determined, and the required number of Soap Sticks was calculated for the candidate well. Commencing the field trial, Soap Sticks were dropped in the well and wellhead pressure was building up. After several hours of pressure build up the well was opened whilst monitoring the flow rate and wellhead pressure.

One of the key aspects of production optimization and monitoring towards digital transformation requirements is to have reliable and accurate data / measurement. This submission demonstrates how the smart technologies for direct wellhead measurement using Coriolis meter in gas field significantly help asset in production monitoring and Opex optimization while accurately regulating the field rates. The use of the smart technology in direct well head measurement provided real time insight into well production, with continuous and reliable quality data on production enabling Reservoir and Production Engineers to take timely decisions to optimize production, improve mass balance, better reservoir management, and reduce operating cost. The technology also had built in advanced diagnostics and connectivity features paving way for digitalization of oil fields and remote online access of data. The high operating range and flexibility in switching between wells offered ease of adaptability, reduced engineering, and commissioning costs as well as reduced maintenance and inventory requirement in mass implementation. Prior to scale-up, a trial / pilot was conducted in collaboration between ADNOC TC, Bab site Production & Instrumentation team and vendor for the technology over a period of 2 months. First, Multiple wells were selected from different operating envelop aiming different reservoirs. Then, the technology was tested by installing the Wet-Gas Coriolis meter in series with a portable well testing package provider. The measurement of the well tests from both techniques were compared. This is to evaluate the performance of the technology in having a reliable data compared with the current periodic measurement. Finally, evaluation report was produced to show the challenges, data verification and validation, then plan for wide field implementation. The technology provides real time insight and continuous monitoring of well production and its profile resulting in better production management. With direct real time well head measurement, significant CAPEX and OPEX savings can be achieved in additional to optimizing production with the use of digital transformation capable technology. Additionally, Support the asset digital and back allocation initiatives. Significant optimization on periodic portable separator well testing requirement. Permanent installation of the Wet Gas Coriolis meter on all wells will empower continuous production monitoring capabilities by increasing the availability of the real time multivariable well data. Continuous data and actionable insights will be used to identify new development opportunity to maximize recovery. Advanced diagnostics capabilities enable predictive maintenance and reducing operating costs. Improved Reservoir and well modeling and production management. No intervention cost required and enhanced HSE measures. Well behaviors change over time and can't be accurately predicted specially with the depleted wells. The current method of relying on intermittent well testing data is insufficient, expensive, labor intensive and increases safety risk. Continuous real time insight into production is essential in optimizing production, improving mass balance, better reservoir management and reducing optimizing cost. The implantation in 60 wells have a value proposition of $5.98 M over a period of 5 years.

With a vast reservoir with a complex and dynamic system production system containing more than 300 wells, both producers and injectors, keeping track of the field operational activity like Reservoir Monitoring Plan (RMP) jobs can lead to sub-utilization, confusion, lack of efficiency, and loss of time. This paper describes an integrated and collaborated method that supports monitoring and execution of field activity tasks utilizing the integrated production optimization platform and the business intelligence tools. Reservoir monitoring planning (RMP) is one of the critical workflows used to ensure the smooth execution of tasks at the required time. This enables the user to plan future tasks based on the reservoir behavior and have a quick comparison between actual and planned tasks. The process starts with inputting the planned tasks into the integrated system, categorizing the tasks based on types, and assigning the executors. The system sends reminders/notifications of the planned task approaching the task due date to all the stakeholders. It also provides an automated direct summary/bird's eye view utilizing the business intelligence tool. Using an integrated asset operation model (IAOM) solution in a digital platform, this planning and monitoring workflow has enabled the users to establish a standardized and unified central repository for the tasks to ensure the single source of truth. With the help of this advanced workflow, inter-departmental communication gaps have been reduced tremendously, thus enabling better execution, analysis, gaps, or bottleneck identification. The automated summary dashboard contains the comparison of the actual status of tasks versus planned tasks. This helps in optimal facility utilization based on dynamic RMP monitoring. Additionally, the integrated solution for planner, performer & approver enabled the users to prioritize the activity based on bottlenecks faced during the past months and reduce the times used to update the monitoring Excel sheets. This outlining process provides a standardized approach across the assets, leading to improved tracking efficiency, minimizing the time spent on manual monitoring, planning, and receiving automatic reminders to avoid delay in the planned tasks, which assisted the users in focusing on production optimization and solving different bottlenecks. This reservoir monitoring and planning approach aligns with the overall corporate strategy of using an integrated asset operation Model (IAOM) for providing end users with tremendous opportunities related to system optimization. This also supports the users’ drive to switch the approach from individual people oriented to standardized process oriented. This approach supports standardization of the work process across the organization and a minimum of $ 700K value proposition from manpower time saving over five years.

Submission Summary A successful achievement by ASR ADNOC onshore started by accepting the challenge of testing Ultrasour gas wells. We are taping on additional reserve by carrying out the appraisal testing of sour gas wells with high H2S that contain H2S ranging from 20 to 40 % and CO2 11% approximately that is creating multiple challenges during testing and tie in the well. And due to high H2S compounded with extremely high temp and pressure the use of conventional material is not possible thus, creating multiple challenging while testing the gas wells in ADNOC Onshore fields. Another milestone of this project is evaluating the exotic material selection for sour wells, as well as HSE protocol modifications and utilizing the existing facilities in order to reduce the CAPEX & OPEX for the production of highly sour gas using the blending studies development. So, this project is an example of success story that successfully managing the ultra-sour gas wells with high temp and pressure safely during drilling, tie-in and well testing operations. And once its potential is untapped the additional TCF (Trilliant Cubic Feet) gas will be available for the future generations.

This paper describes accurate, efficient, and time-saving methodology for achieving the Business target by determining well allowable using advanced, integrated, and automated work-process for a gas condensate field with more than 350 (producing and injecting) well strings from a multi-layered reservoir, having varied reservoir characteristics. This paper will also illustrate challenges and enhancement opportunities toward full smart field applications. Integrated asset operation modeling (IAOM) within a digital framework provides automation to the engineering and analytical approach of allowable rate calculations. The approach comprises 3 step calculation process to determine the Well targets/allowable. Firstly, using the shareholder/reservoir management guideline along with calibrated well models for calculating the well's technical rate. Secondly, calculation of the well and reservoir available/potential rate using the well technical rates, reservoir target, and an inbuilt analytical solver. Thirdly, determination of the well allowable rate by conjugation of various well production components, including wellbore dynamics (Inflow performance and Well performance) and surface constraints. In a digital platform, this automated "Well allowable" workflow has enabled engineers and operators to determine the true potential of wells and reservoirs, thus overcoming potential challenges of computational time saving and identification of cost improvement opportunities. The use of the automated workflow has reduced the time to compute well allowable rates by more than 90% for a gas condensate field with more than 350 (producing and injecting) strings. Implementing this workflow prevented engineers from performing a tedious manual calculation on a well-by-well basis, allowing engineers to focus on engineering and analytical problems. Additionally, this efficient engineering approach provided the user with key information associated with the well's performance under various guideline indexes such as well available/potential rates, well technical rate, reservoir available rate, and rate to maintain drawdown/ minimum Bottom-hole Pressure. This advanced workflow computes the rate that can be delivered from each well corresponding to each guideline and constraint, thereby providing key inputs to various business objective scenarios for production efficiency improvement. Post-implementation, some challenges turned into opportunities to ensure the full and smooth implementation of the generated production scenarios adhering to the gas demand fluctuation. The accuracy and robustness of advanced and automated workflow of setting well allowable /production scenarios empower users to establish well performance and deliverability with a solid engineering analysis base, thereby providing key opportunities for saving cost computational time and assuring short-term production mandate deliverables. This approach supports standardization of the work process across the organization and a minimum of $ 2.8M value proposition from manpower time saving over 5 years.

This paper demonstrates the use of an integrated production optimization platform to determine the well performance for gas condensate wells in a statistical approach to increase the data accuracy for reservoir studies, simulate the field limitations, and provide recommendations for production optimization in a multilayered large carbonate reservoir field. This case involves wells under recycle and natural decline with challenges in the evaluation of well performance where the bulk of the information is available in multiple data sources The first elemental block in establishing the well performance of a gas condensate well is to determine and simulate its fluid behavior. Based on the PVT reports and subsurface fluid studies, compositional PVT models are built and matched with experimental data analyzing representative phase envelop properties and relevant Equation of State (EOS). The next step incorporates the utilization of representative physics-based well models in an integrated system to determine the reservoir and well deliverability. Finally, by applying a detailed statistical approach to the production well test history, models are calibrated in order to predict the performance of the gas condensate wells. Tuning of compositional PVT models established the EOS to be incorporated in predicting the fluid behavior and integrating representative PVT models with well models to determine such behavior along the fluid path. Using the statistical approach, the poor well measurements were identified, facilitating the well-performance and deliverability calculation. In addition, the use of representative models helped in increasing the accuracy of identifying well performance. During this study, two different methodologies were identified based on the reservoir management guidelines. Firstly, for the recycle reservoir in which, the decline of reservoir pressure is arrested using gas Injection. Secondly, for the depletion reservoir, in which the reservoir pressure declines rapidly. For the recycle reservoir, it was statistically identified that the reservoir pressure was declining at less than 4%. Therefore, the acceptance criteria for the operating envelope for each well was defined using the reservoir decline of less than 4%. Similarly, for the depletion reservoir, the pressure was declining between 7% and 10%. Thus, the operating envelope's acceptance criteria were defined using the max reservoir decline tolerance of 10%. The above-identified criteria were incorporated into the integrated model framework to validate the well performance generated from the well tests. Implementing this specialized engineering approach in an integrated model framework considerably reduces the time required by engineers to validate the production well tests and provides higher levels of accuracy for production optimization, voidage replacement ratio calculation, daily rate estimation, and surveillance.

One of the critical aspects of production optimization and planning is to meet the production targets or to meet some operational requirements such as workovers or maintenance activities. This paper demonstrates how an advanced integration in a digital platform, coupled with a predictive analytical model for choke performance and intelligent alarming, can significantly help asset in production planning and cost optimization while accurately regulating the field rates. First, the bulk of well-test data from corporate databases is integrated into an advanced digital platform with an automated well-test validation workflow. The workflow output provides the choke tuning factors for each test while validating the well-test parameters. The digital platform provided the initial data check to ensure the validated tests with choke tuning factors were processed for further regression analysis. The network model in the digital platform for the entire asset was run for a predefined set of iterations to generate the representative choke tuning factors for each well, based on production test parameters and flow line pressure constraints. The regression analysis output was used to predict the choke sizes for different inflow performance rates and various operating wellhead pressures and vice versa. The predictive choke analytical model outputs were utilized to predict the choke size for a set of well parameters, such as rates and wellhead pressures, based on historical well performance. The choke sizes predicted could be used to identify preferred wells in an area to be controlled to achieve production targets, minimizing the operational effort and time. The predictive choke model with intelligent alarm feature provided users instantaneous insight into underperforming and overperforming wells, assisting them to take further actions in an effective way. The other intelligent alarms worked in combination to detect lifting problems associated with wells more efficiently, such as the liquid loading intelligent alarm. The predictive model was also valuable for efficient production planning in terms of setting the quarterly well allowable, choke sizes, or performing field capacity tests to meet the business production target on field & well level and to analyze short-term and medium-term forecast cases using an automated reservoir integration workflow in the digital platform. This was helpful in planning ahead of time for future operations and saving a significant amount of time and effort for engineers and the operation team. This specialized approach of predictive choke performance modeling in a digital platform provided asset a robust tool to plan and optimize their field production while leveraging the power of data-driven digital platforms consisting of closed-loop automated engineering workflows. The accuracy of prediction proved significant cost optimization and proactive planning, where the bulk of data was handled effectively and efficiently to identify production optimization opportunities and field bottlenecks.

AIM: The aim of the study was to investigate the effectiveness of using micro-osteoperforations (MOPs) or piezocision in accelerating tooth movement, during canine retraction, compared to standard canine retraction. PATIENTS AND METHODS: A split-mouth study design was carried out with two Groups A and B. Each group contained 10 patients; in each patient, one side was used as a control side and the contralateral side received either MOPs (Group A) or piezocision (Group B). The assessment data were collected by direct intraoral measurements, every 2 weeks, over a 3 months retraction period. RESULTS: Independent t-test, paired t-test, and ANOVA were used to analyze the results. In Group A, there was a statistically significant difference between the study and control sides (p < 0.001) with a total of 4.2 ± 0.5 mm canine retraction in the MOPs assisted canine retraction side versus a 2.8 ± 0.2 mm total canine retraction in the control side. For Group B, there was a statistically significant difference between the study and control sides (p < 0.001) with a total of 3.6 ± 0.4 mm canine retraction in the piezocision-assisted canine retraction side versus a 2.8 ± 0.2 mm total canine retraction in the control. CONCLUSION: MOPs and piezocision techniques accelerated the rate of canine retraction during orthodontic treatment, with the MOPs being slightly more effective.