Operational Products Research Articles

Solvent-Controlled Divergent Cyclization of 3-Ylideneoxindoles with Ethyl 2-Diazoacetate: Access to Spirocyclopropyl and Spiropyrazolines Oxindoles.

Herein a catalyst-free solvent-controlled method for the divergent synthesis of spirocyclopropyl and spiropyrazoline oxindoles from 3-ylideneoxindoles and ethyl diazoacetate was developed. With ClCH2CH2Cl as the solvent, spirocyclopropyl oxindoles were obtained in moderate to excellent yields, whereas the use of MeOH as solvent afforded spiropyrazoline oxindoles in moderate to good yields. The readily available substrates, simple operation and various product transformations further highlighted the utility of this method.

Perencanaan Biaya Tahun 2025 pada Kegiatan Reklamasi di Banko Barat PT Bukit Asam Tbk Tanjung Enim Sumatera Selatan

Based on Law no. 3 of 2020 article 140 concerning Mineral and Coal Mining which states that "Every holder of a Mining Business Permit (IUP) and Special Mining Business Permit (IUPK) is required to submit a reclamation plan and post-mining plan when applying for a Production Operation IUP or Production Operation IUPK as basis for placing reclamation guarantees. The aim of this research is to calculate the direct and indirect costs required for PT Bukit Asam's reclamation plan. The research method used in this research is using activities in the form of visits and direct observations in the field and also collecting articles as support. Direct costs include recountouring costs, revegetation costs and maintenance costs, which are planned at Rp. 12,889,010,229. Meanwhile for indirect costs, the amount is determined from the indirect cost graph, the plan is Rp. 3,054,695,424, so the total cost of the reclamation plan for the West Banko Pit is Rp. 15,943. ,705,653 with an escalation calculation of 5.51%, it becomes IDR 19,758,945,354.

Aeolus Winds Improve Arctic Weather Prediction

AbstractIt has been proven that assimilating winds from the Aeolus global Doppler Wind Lidar (DWL) would enhance the predictive skill of weather forecast models. In this study, we use a series of observing system experiments to examine how currently assimilated “operational” wind products and Aeolus winds impact environment and climate change Canada's global forecast system over the data‐sparse Arctic region. Aeolus winds improve the tropospheric wind and temperature forecasts by about 0.7%–0.9% of error reduction (a 15%–20% effect compared to the impact of operational wind products) while having little impact on specific humidity. In particular, Aeolus winds have an impact on forecasts of strong wind days on the wind and temperature fields that is double the impact for weaker wind days. On the other hand, the operational wind products do not show as large a discrepancy in impact between forecasts of disturbed and normal days. These findings suggest significant potential for global DWL observations to enhance severe weather prediction in polar regions.

Three-dimensional distribution of aerosols of multiple types at daily scale using TROPOMI spaceborne observations

We present new satellite-based daily observations of the 3D distribution of aerosols from most common types and origins, both over land and ocean, derived from TROpospheric Ozone Monitoring Instrument (TROPOMI) measurements. This is done using the so-called AEROS5P (AEROsol Sentinel 5 Precursor) approach which retrieves vertical profiles of aerosol extinction from passive measurements of high-resolution spectral reflectance in the visible and near infrared for cloud-free conditions. Previous work demonstrated the potential of this approach, but it was initially applicable only to biomass burning aerosols with limited spatial coverage over ocean. The new methodology presented here generalizes the method for the most common aerosol types and improves spatial coverage. We take advantage of an operational aerosol type product derived from Visible Infrared Imaging Radiometer Suite (VIIRS) observations, combined with those from TROPOMI, to choose a priori aerosol set of intensive properties (particle size and refractive index). AEROS5P shows good agreement in aerosol optical depth, when compared to observations from AErosol RObotic NETwork and other widely used satellite measurements. Additionally, the vertical distribution of aerosol plumes of different types derived from AEROS5P agrees well with lidar coincident observations from the Advanced Topographic Laser Altimeter System spaceborne sensor and ground-based measurements. We utilize AEROS5P to analyze the three-dimensional distribution of aerosols for four different scenarios. It traces the three-dimensional pathways of smoke from Canadian wildfires reaching the Eastern USA in July 2023, desert dust from the Namibian deserts over the southeast Atlantic Ocean confined near the surface during the same month, fine aerosol pollution over Western Europe during June 2019, and an extreme dust event mixed with fine aerosol pollution over Northern China in March 2021. These observations provide valuable insights into aerosol transport patterns, surface-level air quality impacts, and can potentially be used to assess the radiative effects at various atmospheric layers.

Customer-Centric quality management: A framework for organizational excellence in SMEs

In an era where customer expectations are continually evolving, Small and Medium Enterprises (SMEs) must adopt innovative quality management practices to achieve organizational excellence. This review presents a customer-centric quality management framework designed specifically for SMEs, emphasizing the pivotal role of customer satisfaction in enhancing operational efficiency, product quality, and overall business performance. The framework is built on the premise that customer feedback and engagement are integral to quality management processes. By prioritizing customer needs and expectations, SMEs can develop quality assurance strategies that not only meet but exceed customer requirements. This approach involves systematic collection and analysis of customer feedback, enabling organizations to identify areas for improvement and innovate their offerings accordingly. Central to the framework are key components such as continuous improvement, employee involvement, and process optimization. Continuous improvement methodologies, like Lean and Six Sigma, are tailored to suit the unique challenges faced by SMEs, fostering a culture of quality that empowers employees at all levels. Engaging employees in quality initiatives cultivates a sense of ownership and accountability, essential for driving organizational change and enhancing service delivery. Furthermore, the framework emphasizes the importance of aligning quality management practices with customer-centric strategies. This alignment ensures that quality initiatives are not only reactive but also proactive, anticipating customer needs and adapting processes to deliver superior value. Incorporating customer relationship management (CRM) tools enhances communication, allowing SMEs to gather insights that inform quality improvements and strengthen customer loyalty. Implementing a customer-centric quality management framework can significantly impact an SME's competitiveness by enhancing product reliability, reducing defects, and improving customer satisfaction. Ultimately, this approach fosters organizational excellence, leading to increased market share and long-term sustainability. In conclusion, this framework equips SMEs with the necessary tools to integrate customer-centricity into their quality management processes, driving continuous improvement and excellence. Keywords: Customer-Centric Quality Management, Smes, Organizational Excellence, Customer Satisfaction, Continuous Improvement, Lean, Six Sigma, Employee Involvement, Process Optimization, CRM Tools, Competitiveness.

Tropospheric NO2 retrieval algorithm for geostationary satellite instruments: applications to GEMS

Abstract. In this study, we develop an advanced retrieval algorithm for tropospheric nitrogen dioxide (NO2) from the geostationary satellite instruments and apply it to Geostationary Environment Monitoring Spectrometer (GEMS) observations. Overall, the algorithm follows previous heritage for the polar-orbiting satellites Global Ozone Monitoring Experiment-2 (GOME-2) and Tropospheric Monitoring Instrument (TROPOMI), but several improvements are implemented to account for specific features of geostationary satellites. The DLR GEMS NO2 retrieval employs an extended fitting window compared to the current fitting window used in GEMS operational v2.0 NO2 retrieval, which results in improved spectral fit quality and lower uncertainties. For the stratosphere–troposphere separation in GEMS measurements, two methods are developed and evaluated: (1) STRatospheric Estimation Algorithm from Mainz (STREAM) as used in the DLR TROPOMI NO2 retrieval and adapted to GEMS and (2) estimation of stratospheric NO2 columns from the Copernicus Atmosphere Monitoring Service (CAMS) Integrated Forecast System (IFS) cycle 48R1 model data, which introduce full stratospheric chemistry as it will be used in the operational Sentinel-4 NO2 retrieval. While STREAM provides hourly estimates of stratospheric NO2, it has limitations in describing small-scale variations and exhibits systematic biases near the boundary of the field of view. In this respect, the use of estimated stratospheric NO2 columns from the CAMS forecast model profile demonstrates better applicability by describing not only diurnal variation but also small-scale variations. For the improved air mass factor (AMF) calculation, sensitivity tests are performed using different input data. In our algorithm, cloud fractions retrieved from the Optical Cloud Recognition Algorithm (OCRA) adapted to GEMS level 1 data are applied instead of the GEMS v2.0 cloud fraction. OCRA is used operationally in TROPOMI and Sentinel-4. Compared to the GEMS level 2 cloud fraction which is typically set to around 0.1 for clear-sky scenes, OCRA sets cloud fractions close to or at 0. The OCRA-based cloud corrections result in increased tropospheric AMFs and decreased tropospheric NO2 vertical columns, leading to better agreement with results from existing TROPOMI observations. The effects of surface albedo on GEMS tropospheric NO2 retrievals are assessed by comparing the GEMS v2.0 background surface reflectance (BSR) and TROPOMI Lambertian-equivalent reflectivity (LER) climatology v2.0 product. The differences between the two surface albedo products and their impact on tropospheric AMF are particularly pronounced over snow/ice scenes during winter. A priori NO2 profiles from the CAMS forecast model, applied in the DLR GEMS algorithm, effectively capture variations in NO2 concentrations throughout the day with high spatial resolution and the advanced chemical mechanism, which demonstrates its suitability for geostationary satellite measurements. The retrieved DLR GEMS tropospheric NO2 columns show good capability for capturing hotspot signals at the scale of city clusters and describe spatial gradients from city centres to surrounding areas. Diurnal variations of tropospheric NO2 columns over Asia are well described through hourly sampling of GEMS. Evaluation of DLR GEMS tropospheric NO2 columns against TROPOMI v2.4 and GEMS v2.0 operational products shows overall good agreement. The uncertainty of DLR GEMS tropospheric NO2 vertical columns varies based on observation scenarios. In regions with low pollution levels such as open-ocean and remote rural areas, retrieval uncertainties typically range from 10 % to 50 %, primarily due to uncertainties in slant columns. For heavily polluted regions, uncertainties in tropospheric NO2 columns are mainly driven by errors in tropospheric AMF calculations. Notably, the total uncertainty in GEMS tropospheric NO2 columns is most significant in winter, particularly over heavily polluted regions with low-level clouds below or near the NO2 peak.

Spectral induced polarization measurements to detect different scales in oil and gas production tubing

Different varieties of scales, such as iron sulfide (FeS) scale, and calcium carbonate (CaCO3), commonly precipitate within oil and gas production tubing, posing operational challenges and production losses. It is essential to implement efficient mitigation strategies and preserve production integrity to detect and characterize these scales in an accurate and efficient manner. Prior studies have explored Spectral induced Polarization (SIP) for the early detection of FeS within porous media, mainly for environmental applications or for identifying FeS in carbonate formations from the perspective of reservoir characterization. This study focuses on applying SIP to address production-related issues in production tubing. For this purpose, six actual subsurface scale samples were analyzed using X-ray Diffraction, X-ray Fluorescence, and SIP techniques. These samples were categorized into three primary groups based on their composition: Pyrrhotite, Goethite, and Calcium Sulfate Hydrate or Calcite. The results indicate that combining phase shift and conductivity as frequency functions makes it possible to develop an approach to classify and differentiate between various scale types. This information could be instrumental in developing methods that can be potentially integrated into tubing systems to detect and characterize the different scale types.

Automated methodology for calculating key production indicators of oil field development for business planning model

Background: In order to secure the stable and efficient operations of oil-producing companies, it is essential to forecast the profitability and ROI of the enterprise, as well as to regulate development for the next 5 years. Accurate forecasts facilitate more informed planning and decision-making, directly influencing the economic sustainability and competitiveness of the enterprise. Aim: The purpose of this study is to develop an innovative approach to automate the methodology for calculating key development indicators in a business planning model. Materials and methods: The study utilizes methods for collecting and analyzing production and geological data, empirical forecasting models, and statistical analysis techniques to enchance the accuracy and reliability of forecasts. This approach employs modern algorithms and technologies to process large volumes of data, which allows for more accurate and reasonable forecasts of key production indicators of the field development. Results: This methodology can be applied to forecast a five-year business plan and evaluate its expected implementation. It is integrated into the ‘Production planning and monitoring’ of the ABAI information system, which allows direct data export from the database, automates the monthly monitoring of production indicators, and generate reports for further export. Conclusion: The proposed method for automated planning of key production indicators of the development enhances the accuracy and efficiency of forecasting, thereby improving the quality of planning and evaluating the implementation of the business plan. This contributes to more informed and strategically validated management of oil production processes. Automation of planning processes reduces the labor costs traditionally associated with manual analysis and calculations, freeing up resources for more strategic purposes. This enables rapid responses to changes in production conditions and prompt adjustments of plans. As a result, managers can allocate resources more efficiently, minimize risks, and increase the overall productivity of oil production operations.

Analysis of the Implementation of Electronic Medical Records in Efficiency, Productivity, and Performance of Health Services at the Sriamur Bekasi Health Center with the Wellbeing Method

The application of digital technology in health services has significantly improved efficiency and service quality. Electronic Medical Record (E.M.R.) is a technology implementation that records, stores, and manages patient medical data electronically. This study aims to analyze the impact of using E.M.R. on the productivity and efficiency of health services at the Sriamur Bekasi Community Health Center. This research uses the Wellbeing method with a qualitative approach. Primary data was collected through in-depth interviews and questionnaires distributed to the medical and administrative staff of the Sriamur Community Health Center. Secondary data on the number of patients before and after E.M.R. implementation was also analyzed. The leading indicators measured include service time, quality, error rate, and human resource utilization. The research results show a significant increase in productivity and efficiency after E.M.R. implementation. The number of patients served per day increases, and the time required for recording and retrieving patient data is reduced. Respondents indicated high satisfaction with using E.M.R., with the majority assessing that the system helps speed up administrative processes and improve the accuracy of medical data. Implementing E.M.R. at the Sriamur Bekasi Community Health Center has increased the medical staff's operational efficiency and productivity. This technology makes it easier to access and manage patient data and reduces the administrative burden so that medical personnel can focus more on health services. To maximize the benefits of E.M.R., ongoing training and regular evaluation of existing systems and procedures are required.

The role of big data analytics in the footwear supply chains: a multiple case study perspective

Purpose This paper aims to analyze the importance of big data analytics (BDA) and supply chain analytics for sustainable supply chain management in all types of enterprises in the footwear industry. The importance and implementation of BDA in supply chain management have been discussed in this research paper, and a conceptual framework is proposed for the implementation. Design/methodology/approach The previous research indicates that the application of BDA and supply chain analytics is the key point to establishing sustainable supply chain management. Even though there are several types of research on the implications of BDA, there is still a gap in practical implications in organizations. For a better understanding, comparison of frameworks has been done for literature support. Further, 11 in-depth, semi-structured interviews have been conducted with four organizations from the footwear industry, and a conceptual framework has been proposed. Findings This research demonstrates an important gap in the literature through a thorough investigation of how firms can improve operational efficiency and productivity through the framework application and whether it is feasible to apply this within the global footwear industry or not. In conclusion, the research has denoted that despite lack of understanding related to sustainability agendas and not having proper ERP or BDA systems, the organizations operating in Pakistan still met some standards of sustainability and are moving toward achieving more. Research limitations/implications The research has pointed out the importance of the integration of all stakeholders in the supply chain through a proposed framework that aligns and guides the stakeholders so that the policies and other constraints may be taken into consideration while revising sustainable supply chain strategies. Practical implications This study will help increase sustainability through the application of BDA technology, which will benefit various stakeholders, particularly those in the footwear industry. Originality/value This study has proposed a framework based on three stages of the implementation of BDA in the existing supply chain to maintain and achieve sustainability in the organization in the footwear industry. This three-stage framework covers many aspects that were not covered in the literature before.

Extracting Wetlands in Coastal Louisiana from the Operational VIIRS and GOES-R Flood Products

Visible Infrared Imaging Radiometer Suite (VIIRS) and Advanced Baseline Imager (GOES-R ABI) flood products have been widely used by the National Weather Service (NWS) for river flood monitoring, and by the Federal Emergency Management Agency (FEMA) for rescue and relief efforts. Some water bodies, like wetlands, are detected as water but not marked as permanent or normal water, which may result in their misclassification as floodwaters by VIIRS and GOES-R flood products. These water bodies generally do not cause significant property damage or fatalities, but they can complicate the identification of truly hazardous floods. This study utilizes the severe Louisiana flood event caused by Hurricane Ida to demonstrate how to differentiate wetlands from real-hazard flooding. Since Hurricane Ida made landfall in 2021, and there was no major flood event in 2022, VIIRS and ABI flood data from 2021 and 2022 were selected. The difference in annual total flooding days between 2021 and 2022 was calculated and combined with long-time flood frequency to distinguish non-hazard floodwaters due to wetlands identified from real-hazard floods caused by the hurricane. The results were compared with the wetlands from the change detection analysis. The confusion matrix analysis indicated an accuracy of 91.58%, precision of 89.97%, and F1-score of 76.63% for the VIIRS flood products. For the GOES-R ABI flood products, the confusion matrix analysis yielded an accuracy of 86.88%, precision of 97.49%, and F1-score of 75.21%. The accuracy and F1-score values for the GOES-R ABI flood products are slightly lower than those for the VIIRS flood products, possibly due to their lower spatial resolution, but still within a feasible range.

Predictive modeling for coiled tubing fatigue using advanced machine learning techniques

Coiled tubing CT plays Pivotal role in oil and gas well intervention operations due to its advantages such as flexibility, fast mobilization, safe, low cost and wide range of applications, including: well intervention, cleaning, stimulation, fluid displacement, cementing, and drilling. However, CT is subject of fatigue and mechanical damage caused by repeated bending cycles, internal pressure and environmental factors which can lead to a premature failure, high operational cost and production downtime. With the development of CT proprieties and application traditional fatigue life prediction methods based on analytical models integrated in tracking process showed in some cases an underestimate or overestimate of the actual fatigue life of CT particularly when complex factor like welding type, corrosive environment, and high-pressure variation are involved. This study addresses this limitation by introducing a comprehensive machine learning-based approach to improve the accuracy of CT fatigue life prediction, using a data set derived from over 350 tests both lab scale and full-scale fatigue test. The study has incorporated the impact of different parameters such as CT, grades, wall, thickness, CT diameter, internal pressure and welding types. By using advanced machine learning techniques such as artificial network (ANN), Gradient boosting regressor we got a more precise estimation of the number of cycles to failure than traditional models. The results of this study shown the importance of integration of machine learning for CT fatigue life analysis and demonstrating its capacity to enhance the prediction accuracy and reduce the uncertainty. A detailed machine Learning models is presented, emphasizing their capability to handle complex data and improve prediction under diverse operational conditions. This study contributes to more reliable, CT management and safer more cost-efficient well intervention operations.

Systematic Analysis of Packaging Production in the Electric Motors Industry: A Multi-Criteria Approach through the SAPEVO-M Method

Market competitiveness drives the electric motors industry, which in turn necessitates the selection of optimal production scenarios, particularly in the context of packaging. This is crucial for maintaining competitiveness and meeting the rigorous quality and logistical demands that are characteristic of this industry. This paper presents a systematic analysis of the packaging production chain for electric motors, employing the SAPEVO-M method as a decision aid tool. The study examines various strategic options, including outsourcing and internalizing processes, with a particular focus on their impacts on logistics, quality control, and overall supply-chain efficiency. The research conducts a comprehensive evaluation of these strategies to ascertain the most effective approach for managing the complexities of packaging production. The SAPEVO-M method facilitated a structured decision-making process, allowing for the aggregation and prioritization of diverse criteria such as cost, quality, flexibility, environmental impact, and supply risk. A sensitivity analysis was performed to validate the robustness of the decision-making outcomes under varying alternatives. The findings highlight the benefits of internalizing certain processes, particularly the assembly (with a score of 43.27%), to gain direct control over production variables, leading to enhanced operational efficiency and product competitiveness. This paper contributes to the literature by demonstrating the application of MCDA in enhancing strategic decisions within the electric motors industry, providing insights for analyzing other manufacturing factors in the improvement of supply-chain processes.

Development of an endurance training model in athletic performance: a case study on long distance running in Indonesia

This research aims to create innovative and effective endurance training materials tailored for long-distance runners aged 15-18. Utilizing the research and development (RnD) approach with the Borg and Gall model, the study incorporates feedback from expert validators and athletes along with their coaches from Sorong, West Papua. The research procedure encompasses ten stages, starting with problem identification and culminating in the implementation of the final product, involving 80 athletes. These stages include initial analysis, planning, initial product development, preliminary field testing, primary product revision, main field testing, operational product revision, operational field testing, final product revision, and dissemination and implementation. The involvement of both experts and practitioners ensures the training materials are theoretically sound and practically applicable. Data analysis was conducted using SPSS version 21, employing a normality test, a homogeneity test, and a paired-sample t-test to ensure the reliability and validity of the findings. The normality test confirmed the data followed a normal distribution, while the homogeneity test ensured the sample groups were comparable. The paired-sample t-test results indicated the newly developed book-based endurance training model significantly enhanced athletes' performance (p-value 0.000), showing a more substantial improvement in training outcomes compared to conventional methods (p-value 0.014). This improvement is attributed to the structured and comprehensive nature of the training materials, which include interactive elements and technology integration, making the training process more engaging and effective for young athletes. In conclusion, adopting interactive and technology-based training models can significantly aid in developing endurance skills in young long-distance runners, addressing specific needs and optimizing their training regimen. Keywords: Athletics, Endurance Training, Exercise Media, Physical Education

Studying infrasound propagation in the middle atmosphere with ICON and UA-ICON: comparison with the IFS and ground-based remote sensing

Infrasound signals are used to monitor various anthropogenic and natural sources. In particular, infrasound is one of the four verification technologies used by the International Monitoring System (IMS) of the Comprehensive Test-Ban Treaty organisation (CTBTO). To determine accurate source locations and energies, an accurate model of wind and temperature up to the lower thermosphere is necessary, hence operational NWP products are of great importance for routine infrasound monitoring activities. However, many of these models focus on tropospheric conditions, and the middle atmosphere (MA) is not well represented. UA-ICON is an upper atmosphere version of the ICON model that provides modelled atmospheric parameters up to 150 km. First, to assess ICON and IFS operational analysis products, comparisons to lidar observations are made. The main differences between both products were analysed with respect to winds and temperatures in the MA. Second, UA-ICON simulations outputs were compared to ICON and IFS fields to demonstrate the increased wave activity above ~30 km with UA-ICON. The added value of UA-ICON with respect to ICON and IFS for infrasound propagation simulations is discussed. The comparisons between the instrumental observations and the models will be presented, as well as comparisons between modelled and measured infrasound propagation.

Industrial data-driven machine learning soft sensing for optimal operation of etching tools

Smart Manufacturing, or Industry 4.0, has gained significant attention in recent decades with the integration of Internet of Things (IoT) and Information Technologies (IT). As modern production methods continue to increase in complexity, there is a greater need to consider what variables can be physically measured. This advancement necessitates the use of physical sensors to comprehensively and directly gather measurable data on industrial processes; specifically, these sensors gather data that can be recontextualized into new process information. For example, artificial intelligence (AI) machine learning-based soft sensors can increase operational productivity and machine tool performance while still ensuring that critical product specifications are met. One industry that has a high volume of labor-intensive, time-consuming, and expensive processes is the semiconductor industry. AI machine learning methods can meet these challenges by taking in operational data and extracting process-specific information needed to meet the high product specifications of the industry. However, a key challenge is the availability of high quality data that covers the full operating range, including the day-to-day variance. This paper examines the applicability of soft sensing methods to the operational data of five industrial etching machines. Data is collected from readily accessible and cost-effective physical sensors installed on the tools that manage and control the operating conditions of the tool. The operational data are then used in an intelligent data aggregation approach that increases the scope and robustness for soft sensors in general by creating larger training datasets comprised of high value data with greater operational ranges and process variation. The generalized soft sensor can then be fine-tuned and validated for a particular machine. In this paper, we test the effects of data aggregation for high performing Feedforward Neural Network (FNN) models that are constructed in two ways: first as a classifier to estimate product PASS/FAIL outcomes and second as a regressor to quantitatively estimate oxide thickness. For PASS/FAIL classification, a data aggregation method is developed to enhance model predictive performance with larger training datasets. A statistical analysis method involving point-biserial correlation and the Mean Absolute Error (MAE) difference score is introduced to select the optimal candidate datasets for aggregation, further improving the effectiveness of data aggregation. For large datasets with high quality data that enable model training for more complex tasks, regression models that predict the oxide thickness of the product are also developed. Two types of models with different loss functions are tested to compare the effects of the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) loss functions on model performance. Both the classification and regression models can be applied in industrial settings as they provide additional information regarding the process outcome. Individually, these models can reduce the number of metrology steps in semiconductor factories, and when developed further, can empower the development of advanced process control strategies.

FORECASTING AND OPTIMIZATION OF CATALYTIC CRACKING UNIT OPERATION UNDER CONDITIONS OF FUZZY INFORMATION

This paper discusses the application of nonlinear regression to forecast and optimize the operation of catalytic cracking units under conditions of fuzzy information. Catalytic cracking is a crucial process in oil refining that produces high-quality gasoline and other light hydrocarbon products. However, the complexity of the process and the uncertainty of initial data complicate the modeling and optimization of plant operations. To address this issue, a nonlinear regression method is proposed that accommodates the fuzziness of input and output parameters described by linguistic variables. The methodology includes the collection and formalization of expert knowledge, the construction of fuzzy models, and their integration into the process control system. Forecasting is performed by creating regression models that describe the relationships between operational parameters and product quality characteristics. The paper presents a procedure for developing and applying nonlinear regression models, describes algorithms for synthesizing linguistic models, and provides examples of their use to optimize the operation of catalytic cracking units. The modeling results demonstrate the high adequacy and accuracy of the proposed method, as well as its advantages over traditional approaches in conditions of uncertainty and data scarcity. The scientific novelty of the research lies in the development and testing of advanced nonlinear regression models adapted for analyzing and optimizing catalytic cracking processes based on fuzzy data. These methods take into account the specificity and uncertainty of process data, improving the accuracy and reliability of forecasts, which facilitates more effective management of production processes in the petrochemical industry. The main reason for conducting this study is the need to improve the control of oil refining processes, particularly catalytic cracking, which plays an important role in producing high-quality gasoline. The complexity of this process and the presence of fuzzy information caused by fuzzy initial data require the development of new modeling and optimization methods. Existing traditional models based on deterministic methods are often insufficient under uncertainty. This leads to a decrease in the accuracy of process control, which can negatively affect the quality of the final product and production efficiency. The use of nonlinear regression in combination with fuzzy logic is a more flexible and adaptive approach that allows you to take into account the fuzziness and uncertainty of data and use expert knowledge to build models that match the actual operating conditions of the units. Thus, this study aims to solve the key problems associated with data uncertainty and the complexity of the catalytic cracking process, which will improve the accuracy of forecasting and optimization of the units. The main contribution is creating a model that uses nonlinear regression methods in combination with fuzzy logic. This allows uncertainty in input data (such as reactor temperature or pressure) to be effectively considered and processed to improve gasoline and other product yield forecasts. It is shown that using nonlinear regression combined with fuzzy logic significantly improves the management of technological processes, increases the output and quality of products, and reduces production costs. The conclusion of the paper discusses the prospects for further development of the methodology and its application to solve similar tasks in other areas of chemical technology.

광양항 항만배후단지 운영성과와 활성화방안 연구

Purpose: This study analyzes the supply plans and performance of the Gwangyang Port hinterland, and proposes activation strategies based on these findings. Research design, data, and methodology: A review of previous research was conducted to examine the definitions, functional changes, types, and designation criteria of port hinterlands. The current supply plans and development status of the Gwangyang Port hinterland were also analyzed. Results: The results show that while the volume of cargo in the hinterland is increasing, its contribution to the total volume of port cargo remains stagnant. Key issues identified include terminal operator productivity, lack of shipping routes, and excessive competition among logistics companies. Conclusions: The study proposes several activation strategies: improving terminal operator efficiency, enhancing support for high-value-added companies, promoting specialized hinterland areas for lifestyle logistics services, and strengthening the role of the Yeosu Gwangyang Port Authority.

Efficient Fabric Classification and Object Detection Using YOLOv10

The YOLO (You Only Look Once) series is renowned for its real-time object detection capabilities in images and videos. It is highly relevant in industries like textiles, where speed and accuracy are critical. In the textile industry, accurate fabric type detection and classification are essential for improving quality control, optimizing inventory management, and enhancing customer satisfaction. This paper proposes a new approach using the YOLOv10 model, which offers enhanced detection accuracy, processing speed, and detection on the torn path of each type of fabric. We developed and utilized a specialized, annotated dataset featuring diverse textile samples, including cotton, hanbok, cotton yarn-dyed, and cotton blend plain fabrics, to detect the torn path in fabric. The YOLOv10 model was selected for its superior performance, leveraging advancements in deep learning architecture and applying data augmentation techniques to improve adaptability and generalization to the various textile patterns and textures. Through comprehensive experiments, we demonstrate the effectiveness of YOLOv10, which achieved an accuracy of 85.6% and outperformed previous YOLO variants in both precision and processing speed. Specifically, YOLOv10 showed a 2.4% improvement over YOLOv9, 1.8% over YOLOv8, 6.8% over YOLOv7, 5.6% over YOLOv6, and 6.2% over YOLOv5. These results underscore the significant potential of YOLOv10 in automating fabric detection processes, thereby enhancing operational efficiency and productivity in textile manufacturing and retail.

Two Years of Cotton (Gossypium hirsutum L.) Data from the Georgia Coastal Plain, USA

The sustainable management of Earth’s complex ecosystems requires an abundance of field data to support long term stewardship. Remotely sensed satellite data provide crucial supplements to field measurements and are essential for deriving key operational products for monitoring Earth systems. However, to accurately calibrate and validate the models used to develop monitoring datasets, coincident field measurements are required. In 2018 and 2019, data related to cotton (Gossypium hirsutum L.) crops were collected from five fields in two farms located in Georgia, USA. Collections were timed to coincide with satellite overpasses to support the development of remote sensing-based crop and soil data products. Data collected include soil moisture, plant water content, above ground biomass, crop height, plant phenology, and field management practices (row direction, row spacing, and plant density). The datasets include 512 records collected in 2018 and 303 records collected in 2019. The data are archived in the National Agricultural Library Ag Data Commons repository and are available for use by researchers seeking crop and soil validation data.