Height Data Research Articles

Lumbar spine MRI annotation with intervertebral disc height and Pfirrmann grade predictions.

Many lumbar spine diseases are caused by defects or degeneration of lumbar intervertebral discs (IVD) and are usually diagnosed through inspection of the patient's lumbar spine MRI. Efficient and accurate assessments of the lumbar spine are essential but a challenge due to the size of the clinical radiologist workforce not keeping pace with the demand for radiology services. In this paper, we present a methodology to automatically annotate lumbar spine IVDs with their height and degenerative state which is quantified using the Pfirrmann grading system. The method starts with semantic segmentation of a mid-sagittal MRI image into six distinct non-overlapping regions, including the IVD and vertebrae regions. Each IVD region is then located and assigned with its label. Using geometry, a line segment bisecting the IVD is determined and its Euclidean distance is used as the IVD height. We then extract an image feature, called self-similar color correlogram, from the nucleus of the IVD region as a representation of the region's spatial pixel intensity distribution. We then use the IVD height data and machine learning classification process to predict the Pfirrmann grade of the IVD. We considered five different deep learning networks and six different machine learning algorithms in our experiment and found the ResNet-50 model and Ensemble of Decision Trees classifier to be the combination that gives the best results. When tested using a dataset containing 515 MRI studies, we achieved a mean accuracy of 88.1%.

Airborne GNSS reflectometry for coastal monitoring of sea state

Sea level rise and sea state variability, resulting from climate change and global warming, are critical research areas. However, current techniques for observing and monitoring these phenomena have limitations in terms of spatial and temporal resolution, particularly in dynamic coastal zones. GNSS Reflectometry (GNSS-R) is an emerging bistatic radar-based technique that utilizes the GNSS direct (transmitter-receiver) and reflected (transmitter-reflection point-receiver) signals to extract properties of the reflecting surface. This study explores the potential of airborne GNSS-R as a means to monitor sea state in coastal areas by using the Doppler spread and reflectivity as observables. The paper aims to derive a sea state factor from the reflected signal power and the Doppler shift distribution to analyze its correlation with wind speed and significant wave height data obtained from the ERA5 model. The experiment involved four flights conducted along the coast between Calais and Boulogne-sur-Mer, France, in July 2019. A GNSS software receiver processes the direct and reflected signals, tracking and re-tracking the reflected signals with the aid of a specular reflection model. The resulting in-phase and quadrature components are analyzed in the spectral domain every minute to estimate the power, the surface reflectivity, and the relative Doppler shift. The findings reveal that the sea state factor and Doppler spreading are sensitive to sea state conditions, correlated with the ERA5 parameters, and influenced by the elevation angle of GNSS satellites. At low elevations (E<10°), the sea state factor demonstrates an inverse relationship (anti-correlation) with the wind speed and significant wave height, while the Doppler distribution shows a correlation with these parameters. Both correlations decrease with increasing elevation angle. This research underscores the potential of airborne GNSS-R for monitoring sea state variability in coastal areas enhancing our understanding of the relationships between GNSS-R measurements and sea state parameters.

Clinical impact of body mass index on outcomes of ischemic and hemorrhagic strokes.

To investigate the prognostic implication of body mass index (BMI) on clinical outcomes after acute ischemic and hemorrhagic stroke. The subjects of the study included adult patients with available baseline body weight and height data who had suffered an acute stroke and were registered in the Japan Stroke Data Bank-a hospital-based, multicenter stroke registration database-between January 2006 and December 2020. The outcome measures included unfavorable outcomes defined as a modified Rankin Scale (mRS) score of 5-6 and favorable outcomes (mRS 0-2) at discharge, and in-hospital mortality. Mixed effects logistic regression analysis was conducted to determine the relationship between BMI categories (underweight, normal weight, overweight, class I obesity, class II obesity; <18.5, 18.5-23.0, 23.0-25.0, 25-30, ⩾30 kg/m2) and the outcomes, after adjustment for covariates. A total of 56,230 patients were assigned to one of the following groups: ischemic stroke (IS, n = 43,668), intracerebral hemorrhage (ICH, n = 9741), and subarachnoid hemorrhage (SAH, n = 2821). In the IS group, being underweight was associated with an increased likelihood of unfavorable outcomes (odds ratio, 1.47 (95% confidence interval (CI):1.31-1.65)) and in-hospital mortality (1.55 (1.31-1.83)) compared to outcomes in those with normal weight. Being overweight was associated with an increased likelihood of favorable outcomes (1.09 (1.01-1.18)). Similar associations were observed between underweight and these outcomes in specific IS subtypes (cardioembolic stroke, large artery stroke, and small-vessel occlusion). Patients with a BMI ⩾30.0 kg/m2 was associated with an increased likelihood of unfavorable outcomes (1.44 (1.01-2.17)) and in-hospital mortality (2.42 (1.26-4.65)) in large artery stroke. In patients with ICH, but not those with SAH, being underweight was associated with an increased likelihood of unfavorable outcomes (1.41 (1.01-1.99)). BMI substantially impacts functional outcomes following IS and ICH. Lower BMI consistently affected post-stroke disability and mortality, while higher BMI values similarly affected these outcomes after large artery stroke.

Re-examining extreme poverty before and after the transition to capitalism, 1300s–1900s: the issue of definition, periodization, measurement, and causal responsibility

ABSTRACT The ‘standard narrative’ about capitalism and extreme poverty holds that the transition to the former was associated with a staggering reduction of the latter. Recently, this has been strongly called into question by a critical narrative grounded in a battery of non-GDP-based measures of extreme poverty and living standards, such as subsistence welfare ratios, skeletal height data, and famine reports. This critical narrative holds that extreme-poverty conditions were + rare in normal circumstances before the transition to capitalism, and that a significant deterioration amounting to extreme poverty happened in Europe and globally only afterwards. The present paper re-examines the issue, drawing on these and additional non-GDP-based historical sources of living standards, including formerly overlooked cases, such as pre-conquest Aztec Empire and Tokugawa-era Japan. It finds that the standard narrative’s portrayal of the prevalence of extreme poverty before capitalism is indeed mistaken. However, it also takes issue with parts of the critical narrative, mainly the idea that capitalism caused severe deterioration in living standards. On a widely shared definition of capitalism, and a quantitatively demonstrated periodization of the transition, the notion that living standards were generally poor before a society’s transition to capitalism and only started systematically improving afterwards has better support.

CORRELATION BETWEEN HEIGHT, BMI, MUAC WITH ANEMIA STATUS IN ADOLESCENT GIRLS

Background: Anemia in adolescents is a health problem that needs immediate intervention in Indonesia. Anemia can caused by malnutrition. protein and iron deficiency will cause nutritional problems including stunted, thinness, and Mid Upper Arm Circumference (MUAC) &lt; 23,5 cm. Protein and iron deficiency can interfere with the formation of hemoglobin resulting in anemia. This study aims to analyze the relationship between status based on height, Body Mass Index (BMI) and MUAC with anemia status at Mambaus Sholihin Islamic Boarding School in Gresik Regency. Method: This research is a quantitative observational analytic with the cross sectional method. The population was adolescent girls in Mambaus Sholihin Islamic Boarding School in Gresik Regency. The sample was 11th and 12th grade senior high school girls with a total of 141 respondents using the total sampling technique. The variables used in this research consisted of height, BMI, MUAC, and anemia. Height data was obtained by using microtoise, BMI using microtoise and scales, MUAC using tape measure plastic, and hemoglobin using the digital tool of Point of Care Testing (POCT) method. The research data were analyzed by chi square test with a contingency coefficient of 95%. Result: The results showed that out of 141 adolescent girls, 66.7% had normal height status, 23.4% were stunted, and 9.9% were severely stunted. In BMI status, 55.3% normal, 14.9% thinness, 15.6% overweight, and 14.2% obese. MUAC status was 79.4% with MUAC ≥ 23.5 cm and 20.6% with MUAC &lt; 23.5 cm. Anemia status was 51.8% frail and 48.2% not anemic. After the chi square test, the significance value was obtained (p=0.006), which means that statistically there was a significant relationship between height and anemia, (p=0.003) there was a significant relationship between BMI and anemia, and (p=0.01) there was a significant relationship between MUAC and anemia. Conclusion: There is a relationship between nutritional status based on height, BMI, and MUAC with anemia status in adolescent girls at Mambaus Sholihin Islamic Boarding School in Gresik Regency.

Temperature Structure Inversion of Mesoscale Eddies in the South China Sea Based on Deep Learning

Mesoscale eddies are common in global oceans, playing crucial roles in ocean dynamics, ocean circulation, and heat transport, and their vertical structures can affect the water layers from tens to thousands of meters. In this study, we integrated sea surface height and sea surface temperature data into deep learning methods to study the mesoscale eddy subsurface temperature structure and to explore the relationship between sea surface data and eddy subsurface layers. In this study, we introduce Dual_EddyNet, a deep learning algorithm designed to invert the subsurface temperature structure of mesoscale eddies. Using this algorithm, we explore the impact of the sea surface height and sea surface temperature on the subsurface temperature structure inversion of mesoscale eddies. Furthermore, we compare different data fusion strategies, namely single-stream neural networks and dual-stream neural networks, to validate the effectiveness of the dual-stream model. To capture the interrelations among surface data and integrate feature information across various dimensions, we introduce the Triplet Attention Mechanism. The experimental results demonstrate that the proposed Dual_EddyNet performs well in reconstructing the three-dimensional structure of mesoscale eddies in the South China Sea (within a depth of 1000 m), with an inversion accuracy of 91.44% for cyclonic eddies and 95.25% for anticyclonic eddies. This algorithm provides a new method for inverting the subsurface temperatures of mesoscale eddies, and can not only be directly deployed in systems, embedded in ship moving platforms, etc., but can also provide a data reference for assimilations and numerical simulations, demonstrating its rich application potential.

Surface Elevation Change Monitoring Based on ICESat-2 Satellite Altimetry Data

The ICESat-2 (Ice, Cloud and Land Elevation Satellite-2), launched by NASA on September 18, 2018, offers higher precision in surface elevation monitoring. This study aims to investigate the surface elevation changes over different time periods in the Delong and Bulianta open-pit mines in Wulanmuren Township, Ordos City, using ICESat-2 laser data. The study utilizes the spatial distribution information of each photon from ATL03 (terrain elevation data) and the classification information of each photon from ATL08 (vegetation canopy height and surface elevation data) to extract surface photon elevation data for different periods in the same area. The precision of ICESat-2 satellite elevation data is validated using the on-board radar data from Henan Polytechnic University's Feima unmanned aerial vehicle D2000 in Jiaozuo City, Henan Province.Results indicate that in the No.1 area of the Delong open-pit mine, the surface subsided by approximately 16.3 meters between November 14, 2019, and November 10, 2021, due to soil removal operations. In the No.2 and No.3 areas of the Bulianta open-pit mine, the surface was uplifted by 3.1 meters and 13.6 meters, respectively, between May 16, 2019, and August 13, 2020, due to backfilling after mining. Additionally, the No.4 area was uplifted by about 10.5 meters between November 14, 2019, and November 10, 2021.Furthermore, the accuracy assessment yielded the following results: R2=0.956, RMSE=0.324, and MAE=0.105 meters, indicating that ICESat-2 data can be used as scientific data for inverting surface elevation changes.

A numerical method for generation of shot peened surfaces

Shot peening is a widely utilized surface modification technique, and the surface morphology of the treated materials plays a crucial role in determining their friction and fatigue properties. Surface morphology generation is a pivotal process in simulating shot-peened surface friction and other performance characteristics. However, there are limited available methods for shot-peened surfaces until now. In this paper, we present a method that directly specifies the height probability distribution and power spectrum to generate shot-peened surfaces. Compared to methods that specify height parameters, this method has more flexibility. This approach replaces the traditional Johnson transformation method with an optimization algorithm to generate height data that follows a high probability distribution, while the power spectrum is derived from the Fourier transform of an exponential autocorrelation function. The results show that this approach can generate shot-peened surfaces using any conventional height parameters, with a maximum difference in height parameters between the reconstructed surface and the original surface of no more than 18.63%. Compared to the Johnson transformation method, this method significantly reduces the errors in skewness and kurtosis of the generated surface. It offers rapid surface generation for shot-peened surfaces, leading to significant time and cost savings in experiments.

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Wheat-Crop Height Monitoring Using Machine Learning

Monitoring crop height during different growth stages provides farmers with valuable information important for managing and improving expected yields. The use of synthetic aperture radar Sentinel-1 (S-1) and Optical Sentinel-2 (S-2) satellites provides useful datasets that can assist in monitoring crop development. However, studies exploring synergetic use of SAR S-1 and optical S-2 satellite data for monitoring crop biophysical parameters are limited. We utilized a time-series of monthly S-1 satellite data independently and then used S-1 and S-2 satellite data synergistically to model wheat-crop height in this study. The polarization backscatter bands, S-1 polarization indices, and S-2 spectral indices were computed from the datasets. Optimized Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), Decision Tree Regression (DTR), and Neural Network Regression (NNR) machine-learning algorithms were applied. The findings show that RFR (R2 = 0.56, RMSE = 21.01 cm) and SVM (R2 = 0.58, RMSE = 20.41 cm) produce a low modeling accuracy for crop height estimation with S-1 SAR data. The S-1 and S-2 satellite data fusion experiment had an improvement in accuracy with the RFR (R2 = 0.93 and RMSE = 8.53 cm) model outperforming the SVM (R2 = 0.91 and RMSE = 9.20 cm) and other models. Normalized polarization (Pol) and the radar vegetation index (RVI_S1) were important predictor variables for crop height retrieval compared to other variables with S-1 and S-2 data fusion as input features. The SAR ratio index (SAR RI 2) had a strong positive and significant correlation (r = 0.94; p &lt; 0.05) with crop height amongst the predictor variables. The spatial distribution maps generated in this study show the viability of data fusion to produce accurate crop height variability maps with machine-learning algorithms. These results demonstrate that both RFR and SVM can be used to quantify crop height during the growing stages. Furthermore, findings show that data fusion improves model performance significantly. The framework from this study can be used as a tool to retrieve other wheat biophysical variables and support decision making for different crops.

A Metadata-Enhanced Deep Learning Method for Sea Surface Height and Mesoscale Eddy Prediction

Predicting the mesoscale eddies in the ocean is crucial for advancing our understanding of the ocean and climate systems. Establishing spatio-temporal correlation among input data is a significant challenge in mesoscale eddy prediction tasks, especially for deep learning techniques. In this paper, we first present a deep learning solution based on a video prediction model to capture the spatio-temporal correlation and predict future sea surface height data accurately. To enhance the performance of the model, we introduced a novel metadata embedding module that utilizes neural networks to fuse remote sensing metadata with input data, resulting in increased accuracy. To the best of our knowledge, our model outperforms the state-of-the-art method for predicting sea level anomalies. Consequently, a mesoscale eddy detection algorithm will be applied to the predicted sea surface height data to generate mesoscale eddies in future. The proposed solution achieves competitive results, indicating that the prediction error for the eddy center position is 5.6 km for a 3-day prediction and 13.6 km for a 7-day prediction.

How natural are the forests in Rajiv Gandhi (Nagarhole) Tiger Reserve? A multi-source data approach.

This study aimed to monitor long-term land use dynamics for understanding the natural forestintegrity and intactness of the Rajiv Gandhi (Nagarhole) Tiger Reserve (RTR) pre- and post-declarations as TR. We employed multi-source data from Survey of India Toposheets (1:50k), Landsat-7, and Sentinel-2A along with Global Ecosystem Dynamics Investigation (GEDI) vegetation canopy height (10m) data, a high-spatial resolution CORONA (1970) images and temporal Google Earth Pro images for mapping and validation. To map vegetation type, land use and land cover (LULC) transitions, and fragmentation (1980-2022) we employed a hybrid classification approach. This study also analyzed decadal forest dynamics within TRsusing India's State of Forest Reports (ISFR). Findings reveal significant forest fragmentation and habitat loss due to anthropogenic activities in the TR. Mono-plantations (teak and eucalyptus) were found inside TR, while the buffer (1km) was occupied mainly with coffee and orange plantations which indicates the prevalence of human footprint. The overall accuracy of the current period (2022) is 92.0% with a kappa coefficient value of 0.90. These plantations were established during the British colonial period (early 1900s) for commercial purposes by clearing natural forests. The present study highlights that mono-plantations have not transitioned into natural forests even after a century. This lack of transformation could potentially compromise the integrity of the native forest. Despite its ecological significance, RTR has experienced disturbance due to human footprint. Hence, there is a need for an action plan to protect this vital landscape by replacing mono-plantations with suitable species to preserve the integrity of the forest. These issues extend beyond the protected areas, impacting surrounding regions and require regular monitoring. The proposed methods can be applied to other protected areas facing similar problems in the countryand world.

Height and Weight Measurement and Communication With Families in Head Start: Developing a Toolkit and Establishing Best Practices.

Background: Head start (HS) programs are required to collect children's height and weight data. Programs also communicate these results to families. However, no standardized protocol exists to guide measurements or communicate results. The purpose of this article was to describe the development of a measurement toolkit and best practices for communication. Methods: HS programs contributed to the development and pilot testing of a toolkit for HS staff to guide child measurement. We used a three-phase iterative approach and qualitative methods to develop and test the toolkit, which included a video and handout. In addition, we convened an advisory group to draft best practices for communication. Results: HS program staff appreciated the toolkit materials for their simplicity and content. The advisory group highlighted the importance of weight stigma and the need to be cautious in the way that information is communicated to families. The group underscored the role of emphasizing health behavior change, instead of focusing solely on BMI. Best practices were organized into (1) Policies and procedures for communicating screening results, (2) training for HS program staff to improve communication related to screening and health behaviors, and (3) other best practices to promote health behaviors and coordinate data systems. Conclusions: Our toolkit can improve anthropometric measurements of HS to ensure that potential surveillance data are accurate. Advisory group best practices highlight opportunities for HS to develop and implement policies, procedures, and trainings across the country to improve communication with HS families. Future research should test the implementation of these best practices within HS.

Investigation of coherent Fourier scatterometry as a calibration tool for determination of steep side wall angle and height of a nanostructure

Nanostructures with steep side wall angles (swa) play a pivotal role in various technological applications. Accurate characterization of these nanostructures is crucial for optimizing their performance. In this study, we propose a far-field detection method based on coherent Fourier scatterometry (CFS) for accurate quantification of steep swa and heights in cliff-like nanostructures. Our approach introduces a parameter termed ‘visibility’, derived from the unique far-field signatures of cliff-like nanostructures. This parameter serves as a quantitative metric for the calibration of swa and heights. The heightened sensitivity of our method is demonstrated, particularly when the incident polarization is perpendicular to the invariant direction of the nanostructure for swa calibration, while both polarization states exhibit sensitivity to height calibration. Furthermore, a comprehensive sensitivity analysis reveals the stable nature of our method, showcasing that even with fluctuations of ±10 nm in the position of the nanostructure, the resulting swa remains stable within a range of ±0.5∘ . The exponential variation of the visibility parameter with edge roundness is observed, with fluctuations in edge roundness within 10 nm resulting in swa variations within 1.7∘ for both polarization states. In experimental validations, our results demonstrate reasonable agreement between CFS-derived and AFM measurements. The AFM data for swa ( 77.99∘±1.37∘ ) and height (148.35 nm ±2.11 nm) are corroborated with CFS-derived value of swa ( 77.75∘±3.61∘ , 78.36∘±3.89∘ ) and height (149.42 nm ±1.66 nm, 150.05 nm ±1.04 nm) obtained from calibration curves for TM and TE incident beams, respectively. Overall, our findings underscore CFS as a potential and reliable tool for nanostructure characterization, offering precise measurements that are pivotal for advancing nanotechnology.

Utilization of Multi-Mission Satellite Altimetry for Wave Energy with Site Suitability Analysis using the Analytic Hierarchy Process

Space technology advancements have enabled the acquisition of marine data that support the research on wave energy as an alternative to reduce fossil fuel dependency and mitigate climate change. Malaysia's ocean renewable energy potential lacks attention from local authorities due to insufficient in-situ data, posing challenges in investigating ocean characteristics, such as wave heights. This study investigated Malaysia's wave energy potential using extensive significant wave height data from multiple altimetry missions. The former assessed site suitability using the Analytical Hierarchy Process (AHP) multicriteria analysis, incorporating marine constraints, namely socioeconomic, physical, and environmental factors. The multicriteria findings were integrated into a Geographical Information System (GIS) to improve the site suitability analysis and generate a localized suitability index for wave energy. Validation of satellite altimeter data with in-situ measurements showed a strong correlation and low RMSE. AHP analysis indicated good consistency in the criteria analysis, with a consistency ratio of 0.045, which falls below the limit of 0.1. The coastal and offshore regions of the Malaysian seas are suitable for harnessing wave energy with energy ranges up to 4.21 kW/m. Therefore, this study provides valuable information to stakeholders and the government to increase their interest in wave energy.

Normative data of corneal diameter and palpebral fissure height in a large cohort of South Indian children.

The purpose of this study was to establish the normative data of horizontal visible iris diameter (HVID), vertical visible iris diameter (VVID), and palpebral fissure height (PFH) in a cohort of South Indian children. The study included 1,234 children from six schools of different regions of Tamil Nadu state, India. HVID, VVID, and PFH were measured using a simple millimeter ruler by three optometrists. Based on their age, the children were divided into three groups: preprimary school children (4-5 years), primary school children (6-10 years), and high school children (11-15 years). Mean age was 4.49 ± 0.50 years, 8.00 ± 1.41 years, and 12.87 ± 1.42 years in the three groups, respectively. Mean HVID was 10.45 mm, 10.54 mm, and 10.73 mm, respectively. Mean VVID was 9.18 mm, 9.32 mm, and 9.57 mm, respectively. Similarly, mean PFH was 8.15 mm, 8.30 mm, and 8.52 mm, respectively. There was a significant difference in HVID, VVID, and PFH among the three age groups (P ≤ 0.001), as well as among male and female children in the 6-10 years age group (P ≤ 0.05) but not in the other groups. Intraclass correlation coefficient values (0.78-0.95) show good agreement among the three optometrists for all parameters. The normal range of HVID, VVID, and PFH presented in the current study can help practitioners in the diagnosis of corneal disorders, serve as a basis for the design of contact lenses, and enable accurate intraocular lens power calculations for South Indian children.

Obesity prevalence in adults and patients with hepatitis C: results from screening a population of 50 million in Egypt

Obesity is a global health problem and has been increasing in Egypt over the last several decades. A national population screening program for hepatitis C included screening for obesity. Here we report the outcome of body mass index (BMI) calculation for the screened population and for patients with hepatitis C.Methods Adults 18 years and older (a target population of 62.5 million) were invited to participate in a screening program between October 2018 and April 2019. Persons had their BMI calculated. All data were entered in real-time to a central database via cellular networks. BMI data for patients with hepatitis C virus (HCV) infection before starting direct antiviral therapy were obtained from the National Committee for the Control of Viral Hepatitis (NCCVH)Findings 49.6 million persons (25.6 million females and 24.0 million males) voluntarily participated and had valid height and weight data. 12.7 million females (49.51%) and 7.09 million males (29.53%) were affected by obesity (BMI ≥ 30kg/m2). Obesity increased with age, was more prevalent in females, was more prevalent in the Nile Delta states and in urban areas. Detailed district-level prevalence for the whole country is identified. Prevalence of obesity among 335,504 patients with HCV was higher in females, increased with age, and was significantly lower overall and in males and females than in the screened general population.Conclusion This is the largest population screening program for obesity. We show that obesity prevalence in the general population is currently among the highest in the world, starting in young adults, and highlight the high prevalence areas. Prevalence is lower in patients with HCV.

Quasi‐5‐Day Oscillations During Arctic Major Sudden Stratospheric Warmings From 2005 to 2021

AbstractTraveling quasi‐5‐day oscillations (Q5DOs) with different wavenumbers are independently observed in the mesosphere and the lower thermosphere during many recent sudden stratospheric warming (SSW) events, but their common activities during SSWs are still unclear. Based on the geopotential height data measured by the Aura/Microwave Limb Sounder (MLS) from August 2004 to March 2021, we statistically investigate the characteristics of the Q5DOs during eight Arctic major SSW events. The amplitudes of the Q5DOs are obtained by a new fitting method, which inhibits the effect of rapid changes in stationary planetary waves during SSWs. Our results reveal a robust feature that Q5DOs were enhanced during all interested SSW events. Our analysis indicates that eastward and westward propagating Q5DOs can be simultaneously enhanced during SSWs. Additionally, the wavenumbers of the enhanced Q5DOs are found to be associated with the geometry of polar vortices. Extremely strong westward Q5DOs with wavenumber 2 are consistently observed during split‐type major SSWs.

Towards an Annually Updated Oil Palm Age Database (OPAD) with Multimodal Remote Sensing Features: Design and Preliminary Result

The cultivation of oil palm (Elaeis guineensis) is globally significant, particularly in tropical regions, due to the diverse applications of palm oil and palm kernel oil. This necessitates precise monitoring for sustainable management amidst escalating demand. This study aims to develop an annually updated Oil Palm Age Database (OPAD) using multisource remote sensing data to predict the oil palm age, a critical factor affecting productivity and environmental sensitivity. By comprehensively analyzing multisource remote sensing data from 1982 to 2023, this study establishes OPAD and validates its reliability using a random forest classifier. The findings reveal the relationship between remote sensing data features and changes in the oil palm age, offering insights into sustainable management practices. The results indicate an overall accuracy of 86.86 ± 0.92% for grouped age (i.e., seed (1-2 years), young (3-8 years), teen (9-14 years), mature (15-25 years), and post-mature (over 25 years)), with features such as GEDI forest height, ScanSAR HH SAVG, Planet NIR, and Planet Blue being instrumental in age prediction. Integrating optical, synthetic aperture radar (SAR), and canopy height data enables effective age prediction, which is essential for optimizing management strategies, assessing yield potential, and mitigating environmental impacts.

POTENSI PLTMH DI BENDUNGAN SIDAN, DESA BELOK SIDAN, KABUPATEN BADUNG

The potential of new renewable energy is highly needed to support Clean Energy Bali, and one of the viable energy sources is Micro Hydro Power Plant (PLTMH). The design of the Sidan Dam PLTMH is based on the construction of the Sidan Dam area in Belok Sidan Village, Badung Regency. The planning of the Sidan Dam PLTMH only extends to the construction of the penstock. In this Capstone Project, the design of the Sidan Dam PLTMH utilizes water discharge and head height data from the Sidan Dam planning. This data is used to determine the design, type of water turbine, dimensions of the water turbine, and to determine the appropriate generator capacity. The calculation results indicate that the Sidan Dam PLTMH has a potential capacity of 290.83 kW with a Francis turbine type and a generator capacity of 472.59 Kva. It is estimated to generate an annual energy output of 1,783,396 kWh with a capacity factor 70%.

Alterations in paraffin wax crystal networks induced by asphaltenes and pour point depressants, investigated by atomic force microscopy

Wax crystallization in pipelines in cold environments is a key factor affecting flow assurance during crude oil production. Polar crude oil components, such as asphaltenes, and polymeric pour point depressants (PPDs) have been demonstrated to interact with wax during gelation and crystallization through complex formation during nucleation or crystal growth alteration which leads to co-crystallization. This study introduces a methodology based on atomic force microscopy (AFM) to characterize features of solid surfaces precipitated from model wax, wax-PPD, wax-asphaltene and asphaltene systems. Height data collected from AFM measurements enabled the quantification of modifications in crystal sizes and shapes induced by the presence of asphaltenes and PPDs. The 2D Fourier transform of the height was used to generate statistics for the frequency of height amplitude, revealing information about the pore size and the height patterns. A stiffness/elasticity analysis was used to calculate the Young Modulus of the upper layers. In wax and wax-asphaltene systems, two nano-scale layers with different Young Modulus profiles emerged. The nano-scale layer on the top was softer and corresponded to more amorphous wax crystal networks with trapped dissolved wax. The nano-scale layer on the bottom was stiffer, comprising of more crystalline structures, corresponding to wax crystals with lower deviations from full crystallinity, with properties independent of asphaltene concentration. A bilayer did not form in the presence of PPD, which prevented the formation of amorphous phases with trapped dissolved wax or gel. After a methodology for model systems was established, the focus moved to systems based on production wax i.e. recovered from deposits. This was complemented by a comparison between model precipitation conditions and precipitation by cold finger, resembling industrial processes. Results highlighted the effect of change in chemical composition on topography, elasticity and adhesion of the surface. AFM provided new insights into wax crystallization patterns in the presence of inhibitors, which can be used to quantify the extent of wax-inhibitor interactions and project it on production systems. The method could be used to explain macro-scale rheological properties of oils during flow assurance in the future.