bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancing Consumer Benefit Via Special Tax Scheme for Social Enterprise
Authors: Binti Mohd Ariffin Rabiatul Adawiyah , Abd Ghadas Zuhairah Ariff , Bin Md Radzi Shahril Nizam 
Published: September 2023

In Malaysia, social enterprises (SEs) are still in their infancy, but they are growing in popularity as more people become aware of the beneficial effects on local communities. SEs in Malaysia are business models that combine the aspects of both non-profit and commercial entities to achieve social or environmental objectives. Business organizations, such as partnerships, corporations, and limited liability partnerships, are used to operate SEs because there is no specific legal entity for SEs in Malaysia. From a Malaysian perspective, tax-exempt status is one of the main challenges encountered by Malaysian social entrepreneurs. Despite the government’s efforts to recognize SEs, the problem of taxation for SE has not been resolved because SE still must pay taxes in a manner similar to that of commercial entities. This study aims to provide tax benefits to SE in Malaysia. Although Malaysia revised its laws and accredited SEs, no tax exemption has been provided exclusively for SEs. SEs contributions are meant to improve social welfare, but SEs are not exempt from taxes, such as charitable organizations. SEs business methods and organizational structures, which are comparable to those of commercial companies, lead to the conclusion that SEs are comparable to commercial organizations. The research concludes that by introducing tax exemptions permanently for SEs in Malaysia, customers can benefit from supporting businesses that align with their values, while SEs can benefit from increased support and growth opportunities. Governments can also benefit from a stronger economy and increased social and environmental impacts, making this an attractive policy option for Malaysian society.
Yield, Composition, Texture, and Sensory Characteristics of Cottage Cheese Produced with the Incorporation of Different Herb Extracts
Authors: Setyawardani Triana , Sumarmono Juni , Dwiyanti Hidayah , Arkan Naofal Dhia 
Published: September 2023

Herb cheese is cheese that contains herbs extracted for their antioxidant and bioactive properties. This study aims to determine the impact of adding herb leaf extracts to cottage cheese on its yield, composition, texture, and sensory characteristics. Three different herbs were used to prepare the extracts: bidara (Ziziphus mauritiana), moringa (Moringa oleifera), and bay (Syzygium polynthum). Cottage cheese was made in the the following process: cheese made of full-fat milk (FF), cheese made of low-fat milk (LF), cheese made of low-fat milk + 20% bidara extract (LB), cheese made of low-fat milk + 20% bay extract (LS), cheese made of low-fat milk + 20% moringa extract (LM), cheese made of low-fat milk + 10% bidara extract + 10% bay extract (LBS), cheese made of low-fat milk + 10% bidara extract + 10% moringa extract (LBM), cheese made of low-fat milk + 10% bay extract + 10% moringa extract (LSM) and cheese made of low-fat milk + 6.67% bidara + 6.67% bay + and 6.67% moringa extracts (LBSM). Each treatment was replicated three times. The variables included cheese yield, compositions, texture profile, and sensory characteristics. Herb extracts resulted in a slight variation in cheese yields ranging from 7.73 to 13.8%. Full-cream milk (FF) cheese contained the least moisture, while low-fat bay+moringa (LSM) cheese contained the most protein and the least fat. The texture profile showed a significant difference in hardness, cohesiveness, adhesiveness, gumminess, and chewiness but the springiness and resilience were similar. Herb extracts significantly contributed to the variation of sensory characteristics, including flavor, aroma, texture, and color. Adding herb extracts produces cheese with a slightly grassy aroma and bitter flavor. The addition of bidara, moringa, and bay leaf extracts during the manufacture of cottage cheese from cow milk resulted in the variation in yield, textures, composition, and sensory characteristics. Adding herbs produces cheese with higher moisture content but lower ash content than non-herb cheese. Cheese made with the addition of bay leaf extract had a higher hardness level than the control cheese and cheese added with other herbal extracts. The addition of herbal extracts makes a noticeable color change in cheese.
Authors: Syafi'i Imam 
Published: September 2023

Dengue Hemorrhagic Fever (DHF) is an endemic disease whose incidence continues to increase every year. Factors that influence the occurrence of Dengue Hemorrhagic Fever (DHF) include environmental factors, age, knowledge, attitudes, and behavior. This study aims to determine the relationship between family support (interpersonal) and risky behavior with the incidence of Dengue Hemorrhagic Fever (DHF) in Mayangrejo Village, Kalitidu District, Bojonegoro Regency. An analytic study with a cross sectional design with a population of 3.024 people. Sampling was carried out using the Lemeshow method and obtained a sample of 67 respondents. Collecting data using a questionnaire distributed to residents' homes and data analysis using the Chi-Square Test. Of the 67 respondents, the majority are residents with an elementary education category of 56.72%, ages 21-40 and 41-60 years respectively 44.78%, location of residence in Gempol 43.28%, female 62.69%, not working 61.19%, family support (interpersonal) high 79.10%, good risk behavior 76.12%. The results of the Chi-Square Test analysis between family support (interpersonal) with the incidence of Dengue Hemorrhagic Fever (DHF) in Mayangrejo Village, Kalitidu District, Bojonegoro Regency showed p-value = 0.12, which means that there is no relationship between the two because the p-value> 0 ,05. Meanwhile, between risk behavior and the incidence of Dengue Hemorrhagic Fever (DHF) in Mayangrejo Village, Kalitidu District, Bojonegoro Regency shows a p-value = 0.022, which means that there is a relationship between the two because the p-value <0.05.
Keywords: Dengue Hemorrhagic Fever (DHF), Family support (interpersonal), Risky behavior.
Peningkatan Hasil Belajar Menggunakan Media Ular Tangga Pintar pada Siswa Kelas IV
Authors: Sukmawati Laras , Mursidik Elly’s Mursina , Hardhinata Agung Yuli 
Published: September 2023

Penelitian ini berlatar belakang oleh hasil belajar matematika pada siswa kelas IV yang masih di bawah kriteria ketuntasan minimal (KKM). Studi awal menunjukkan penyebab rendahnya hasil nilai belajar matematika pada siswa adalah kurang optimalnya pembelajaran yang sudah dirancang guru dan penggunaan media pembelajaran di kelas yang kurang menarik dan bervariatif sehingga saat proses pembelajaran siswa kurang aktif dan kesulitan dalam memahami materi pelajaran. Dari permasalahan tersebut, peneliti memilih media ular tangga sebagai upaya untuk meningkatkan hasil belajar siswa. Tujuan dari penelitian ini adalah untuk meningkatkan hasil belajar siswa melalui penggunaan media ular tangga. Metode penelitian yang digunakan adalah penelitian tindakan kelas (PTK) secara kolaboratif yang mengacu pada rencana, tindakan, pengamatan, dan refleksi yang dilakukan sebanyak 2 siklus pembelajaran pada siswa kelas IV di SD Negeri 1 Temon. Hasil penelitian menunjukkan penggunaan media permainan ular tangga dapat meningkatkan hasil belajar siswa. Peningkatan keberhasilan nilai siswa pada pra siklus sebesar 23,0% meningkat menjadi 46,1% pada siklus I dan 76,9% pada siklus II, artinya terjadi peningkatan pada proses pembelajaran, sehingga disimpulkan bahwa media ular tangg mampu meningkatkan hasil belajar siswa kelas IV di SD Negeri 1 Temon.
AI Augmented Engineering Intelligence for Industrial Equipment
Authors: Santos P. , Aldren L. , Melvin E. , Lim J. , McMillan G. , Yang J. , Fraser C. , ODonnell J. 
Published: September 2023

Abstract
This paper presents an Artificial Intelligence (AI) based implementation for anomaly detection in critical industrial equipment at TotalEnergies. The approach was initially inspired by the work of Sipple, J. (2020), on AI anomaly detection in smart buildings. This approach has since then been adapted, applied, and extended to also handle multidimensional time series data effectively, whilst keeping model interpretability by design. The AI models have been successfully deployed in various North Sea assets and have been able to detect anomalous behaviour in the industrial rotating equipment, leading to improved visibility, efficiency, and reliability. The paper also discusses the motivations for the business case and change management processes associated with the implementation of the AI models.
Hybrid Workflows- A History of Tomorrow
Authors: Moradi B. , Ask K. K. , Alessio L. 
Published: September 2023

Abstract
This study provides an overview of the evolution of technology in the reservoir modeling field, discussing past, present, and future advancements. It explores how technologies such as central and graphics processing units have enabled engineers to increase the number of simulation grids from 500,000 to 1 trillion cells over the last 20 years. The paper also examines data-driven workflows that have accelerated the reservoir modeling process to a few weeks, highlighting their limitations. To address these limitations, the study introduces hybrid physics-guided data-driven models as a modern digital tool for mapping potential remaining oil in brownfields. These hybrid workflows combine the strengths of both physics-guided and data-driven models, overcoming the weaknesses of each approach. Two case studies, located in the North Sea and the Middle East, are reviewed to demonstrate the effectiveness of these hybrid workflows. The comparison of the water saturation map generated by the hybrid workflow with the contact movement calculated from 4D seismic data in the North Sea field shows a strong correlation, indicating a reliable match between the two maps. In the Middle East case study, the performance of four infill wells calculated by the hybrid workflow is evaluated against the actual outcomes of post-drilling activities. The results show that, except for one of wells, the actual outcomes align with the predicted trends, falling within the P10-P90 band. The paper emphasizes the novelty and benefits of using hybrid workflows, as they enable the generation of potential remaining oil maps in a few days—a task that is challenging with conventional numerical simulation workflows. These hybrid workflows offer significant time and cost savings while maintaining high fidelity. The advanced technologies discussed in this study facilitate the implementation of practical digitalization strategies across all disciplines in the industry, ushering in a new era of data-driven innovation.
Increased Efficiency on Plug and Abandonment Campaign Using New-Generation Ultrasonic Pulse-Echo and Pitch-Catch Configuration Technology
Authors: Bueno Jose , Govil Amit , Gundersen Sven 
Published: September 2023

Abstract
An operator on the Norwegian Continental Shelf oversaw the execution of a plug and abandonment (P&A) campaign comprising 32 wells in a mature field. The barrier evaluation plan was developed based on previous experiences in a field with similar properties and conducted by another operator that included the cement and solids evaluation using conventional ultrasonic technology. This paper describes how the introduction of the latest generation transducers aided in shortening the P&A campaign duration, saving days of rig time.
The entire logging plan for barrier evaluation within the32 well P&A campaign was custom developed, executed, and jointly analyzed by the operator and the service company. The logging intervals, well fluid and multiple casing size targets were considered during the strategic selection process. This enabled convergence to a solution that provided a comprehensive evaluation while avoiding multiple logging descents and achieving a faster acquisition speed without degradation of the results.
Acquisition capabilities have been constantly evolving over the past decades. The full data acquisition campaign described in this paper was improved from the original plan and adjusted to include a combination of ultrasonic pulse-echo and pitch-catch imaging measurements, third interface echo (TIE), an output of the pitch-catch method supported by and the traditional sonic technology response, enabling quick annular material identification as well as casing cut-and-pull decision making. The combination of all measurements allowed a robust solution that is independent from wellbore logging fluid and potentially unknown annular materials that segregates or changes through the life of these wells. The technology blend adopted in this field enabled a direct logging time reduction of 50% and allowed the acquisition of multiple nonconcentric casing sizes in a single descent, which is not possible with previous generation technologies. The overall result helped the operator to reduce their greenhouse gas (GHG) emissions during those operations by shortening the barrier evaluation campaign length by 10 rig days.
The operator was able to significantly reduce the rig time spent in the campaign the implementation of the latest ultrasonic barrier evaluation logging technology, thus reducing their overall GHG emissions without compromising the results of the original objectives.
Cement Integrity Assessment Using a Hydration-Coupled Thermo-Mechanical Model
Authors: Moghadam A. , Loizzo Matteo 
Published: September 2023

Abstract
Portland cement is commonly used in wells to provide zonal isolation in the annulus. A damaged cement sheath can expose the casing to corrosive fluids and open a leakage pathway to shallow freshwater aquifers and atmosphere. The leakage can manifest itself as sustained casing pressure (SCP) or lead to gas accumulation in shallower formations. The impact of pressure and temperature variation on cement stress has been widely studied in the literature. However, the hydration reactions of cement are not usually included in the mechanical models. This leads to incorrect assumptions about the initial state of stress in cement immediately after curing.
In this work, we have developed a 3D well integrity model that incorporates the cement hydration process. The model is verified using laboratory experiments on cement stress evolution. The model calculates the water consumption during the hydration reactions to predict the pore pressure change in cement. The evolution of cement's mechanical properties with the hydration degree is captured using a homogenization model. A case study is designed to represent a typical low-enthalpy geothermal well in the Netherlands, using well designs and inputs from publicly available data. The cement stresses are tracked over the life of the well, to understand the magnitude of the stress cycles and to assess the potential long-term damage to the cement sheath.
The results show that the pore pressure drop due to cement hydration causes an increase in shear stress in the cement sheath. The pore pressure drop during hydration can debond the cement from the formation. The level of destressing in cement is a function of cement properties, formation stiffness, and the depth of the top of cement. When placed against softer formations, the stress drop in cement is more muted leading to a better seal. During the temperature cycles, the shear stress in cement changes in a cyclical manner. Depending on the magnitude of the stress cycles, damage can be accumulated in the cement sheath. The stress evolution in cement can also vary depending on the presence of external water (formation permeability).
The modelling technique presented in this work provides a robust methodology to estimate the magnitude of cyclical stresses in the cement sheath. This is a critical input to design cement recipes that can withstand load cycles throughout the lifetime of the well. The results of this work indicate the need to assess the integrity of cement at various depths and against various formations. It may not be possible to guarantee the seal efficiency against all formations, however risk analysis can be conducted using the presented model to assess the seal integrity of critical locations in the well profile.
Optimisation of Well Scheduling with Loading and Minimum Turn-Down Constraints
Authors: Jones Peter 
Published: September 2023

Abstract
Breagh, a moderately tight gas field in the Southern North Sea started production in 2013. Until compression is available, Breagh has been constrained by minimum turn-down constraints and the need to keep wells flowing above their loading rates leading to complicated well scheduling optimisation.
This paper describes a tool that makes hourly forecasts for cycling and non-cycling wells used to rapidly assess a proposed well scheduling for loading and minimum turn-down compliance.
The Breagh short term forecasting tool uses empirical equations to match historical pressures and production rates. Parameter values have been found for each of the existing nine flowing wells which match build-up pressures, predict the initial gas rate via a pseudo-PI approach and decline the gas rate over its flowing period. By matching the wells’ hourly rates over multiple months, these same parameters are then used to predict future well flows and display the results in a number of graphics for easy history matching and well scheduling assessments.
Due to the steep decline of the cycling wells, an hourly forecast was required. Excellent matches were obtained over multiple month periods. A single framework was found to be sufficient for both cycling and non-cycling wells, but with different sets of parameters: the Breagh short term forecasting tool was born and has proved to be very useful for: short term forecasting over a few months; rapid assessment of well schedules, including cases where a well issue has required a well to be closed in at short notice and alternative well schedules devised to enable uninterrupted production; and to assess the need for additional production tests to further lower minimum turn-down.
A new framework is provided for the performance forecasting of cycling gas wells, some with significant declines on an hourly basis, thus enabling rapid response times to well schedule optimisation issues. The following are the key findings:Hourly data rather than daily were required to make quality forecasts for cycling wells with extending a few months ahead.New potential well cycling schedules could be rapidly assessed, and more optimal schedules selected.The Breagh Short Term Forecasting Tool was one of three key contributors to returning Breagh from weekly cyclic to continuous production.
Flexible Pipe Riser Torsion Detection in Underwater Video Images
Authors: Cesar V. M. , Reis B. , Maia M. G. , Kelner J. , Sadok D. H. , Marinho J. M. T. 
Published: September 2023

Abstract
When flexible pipelines are subjected to high axial loads and exposed to a mixture of CO2 and water, their metallic armor can corrode and crack, leading to a twisting deformation that, if left untreated, may cause collapse. Monitoring the entire length of the pipeline for twisting is crucial for early detection, but manual assessment is challenging due to the subtle nature of these anomalies and the analysis of video footage might take hours. This work aims to automate the inspection of flexible pipeline twisting by proposing an algorithm that processes video data obtained from standard pipeline inspection procedures and accurately detects sections of twisted pipelines. The algorithm was validated using simulated data and successfully detected twisting of a magnitude of 3mm on a 300mm pipeline. Results on real data were shown to be consistent with experts’ opinion, however further development is required to ensure reliability in the presence of other equipment and marine life.
Acquiring Accurate Real-Time Formation Fluid Properties to Provide In-Situ Fluid Analysis While Drilling
Authors: Cartellieri Ansgar , Schapotschnikow Philipp , Weinzierl Wolfgang , Denninger Johannes , Adams Alina 
Published: September 2023

Abstract
The composition of natural gas and crude oils specifies their calorific and commercial value. To determine required processing steps for intermediate and end products of the reservoir fluid and to define the layout of production facilities, an accurate fluid analysis is vital. Currently, the most reliable way of acquiring fluid compositional data is provided by recovering samples from the downhole environment with dedicated fluid sampling services for surface laboratory analysis. Specialized PVT (Pressure-Volume-Temperature) laboratories provide a detailed fluid characterization using advanced techniques such as gas chromatography (GC). This procedure is very costly and time consuming. Therefore, improved measurement systems and alternative methods with lower cost are commonly requested.
One way to hasten the analysis of reservoir fluids and to lower the cost is the introduction of sampling while drilling services. Even though, these products have been commercial for nearly a decade, most sampling jobs are still conducted with wireline services after the well has been drilled, delaying the acquisition, and incurring additional rig costs. One reason that has limited the application of logging-while-drilling (LWD) fluid sampling services is the reduced data resolution in real time. Whereas wireline services are able to transmit comprehensive fluid identification data in seconds, it takes minutes for a comparable LWD service to transmit the data even in significantly reduced resolution. The wireline data transmission rate is many thousand times higher than while drilling. A wired pipe application is available that can overcome this constraint, however, at much higher cost.
For a representative fluid evaluation and the selection of the optimal fluid samples for characterization in a dedicated PVT lab, an improved prediction of the fluid properties during pump-out, especially for LWD services, is required. Considering the limited real-time bandwidth available, the evaluation must be performed downhole. Therefore, new fluid type algorithms that combine multiple sensor readings into one model have been developed and implemented in the downhole electronics to provide a more sophisticated and thus conclusive in-situ fluid analysis.
This paper will discuss and present the most recent enhancements, achieved by adding machine learning algorithms and chemometrics to LWD fluid sampling services. Models are trained on data from multiple field studies and typical deepwater applications to simplify data interpretation and sample selection, resulting in comprehensive fluid type information being available at surface for real-time decisions and accurate in-situ fluid analysis.