Production Data Research Articles

Impacts of increasing compound hot-dry events on vegetation under the warming-wetting trend in Northwest China

In the warming world, climate extremes tend to be more frequent and intensive. The exceptional response of ecosystems triggered by extreme events under a warmer and wetter climate in northwest China (NWC) has aroused growing concern. However, understanding the responses of vegetation to climate extremes from a compound perspective remains challenging. In this study, we identify the climate dynamics in NWC during 1971–2020 based on daily meteorological observations, focusing on the changes in compound hot-dry events (CHDEs) during the warmer and wetter period. We further explore the effects of CHDEs on vegetation by examining vegetation anomalies and recovery time using daily gross primary productivity (GPP) data. The results show a clearly warmer and wetter period in NWC during 2000–2020. No signs of a stalled increase in CHDEs are observed during this period, and even the duration of CHDEs in western NWC keeps showing an increasing tendency. Vegetation in eastern NWC, with a lower probability of GPP anomalies, exhibits stronger resistance of ecosystems to CHDEs than in western NWC. In NWC, vegetation typically returns to its normal state in 5.50 days on average, but exhibits greater resilience in the western region, where it takes less recovery time (4.82 days). Vegetation in central region shows the lowest probability of GPP anomalies and relatively long recovery time, likely due to its higher altitudes. Our research underscores the imperative to address the considerable impacts of CHDEs on vegetation growth even as the regional climate tends to be warmer and wetter.

Hess’ ‘Drainage’ Wells Show Strong Production but Face Cost and Spacing Challenges

A new kind of horizontal well has emerged from an experiment in North Dakota’s Bakken and Three Forks shales. It uses an uncemented slotted liner to produce hydrocarbons by intercepting fractures that originate from offset wells. Hess Corp. revealed the concept over 2 years ago, confirming at an SPE conference that crude was flowing from the pilot well. The New York City-headquartered oil company also announced that additional trials were in progress, though it withheld specific production figures. The wait for those key details is now over. At the recent Unconventional Resources Technology Conference (URTeC) in Houston, Hess presented its first technical paper on the augmented drainage development (ADD) pilots which it claims have proven the approach to be, at the very least, a technical success. In URTeC 4044110, the company’s authors underlined their optimism in noting that ADD wells “could lead to a step change in productivity and recovery in unconventional developments.” Hess acknowledges that economic and technical hurdles currently stand in the way of that vision. But the upbeat assessment is backed by impressive production data from its inaugural pilot launched in 2021. Placed about 240 ft between two actively stimulated Middle Bakken wells, the first drainage well exceeded expectations, averaging nearly 1,700 B/D of crude in the first 8 days of flowback. Output fell to about 1,500 B/D over the next few days as the nearest actively stimulated well—called a “standard well” by Hess—began its flowback. This was one of several confirmations that the wells were in direct communication. “A lot of bets were lost on this well—maybe not by me, but by a lot of people,” remarked Craig Cipolla, a principle completions engineering advisor for Hess. Cipolla presented the company’s findings at the conference this past summer. He said the ADD well also pumped out about as much frac sand as is seen in a typical shale well, which in addition to tracer data, verified that it was well propped. During the initial production period, the first ADD well achieved about 85% of the output of the offsetting standard wells on the pad. Although this figure eventually dropped to just over 40% over the next 2 years, it was enough for Hess to consider the concept validated. Based on the results from all three pilots, Hess estimates ADD wells in the Bakken can be expected to achieve 10 to 45% of the output of typical hydraulically fractured horizontal wells. On the low end, the oil recovered from distant and low-conductivity fractures likely won’t justify drilling even a wellbore as barebones as the ADD concept calls for. Conversely, if a drainage well produces too much oil it is a sign that well spacing is too tight and that it is overly competing for resources with more-expensive offsets. This is likely the story of why the first ADD well came on so strong.

An assessment of the relationship between spring frost indicators and global crop yield losses

Reports on the influences of spring frost on crop losses are not consistent, which may be because insufficient indicators of spring frost were included in the analysis. To bridge this gap, we analyzed global temperature datasets and production data for the three major crops of maize, winter wheat, and rice from 1981 to 2016. Five indicators of spring frost events: temperature fluctuation (Tv), temperature difference (Td), duration (Thour), occurrence date (Tdate), and frequency (Tnum) were considered to assess their relationship with yield losses. Linear regression was employed to analyze the change trends in five indicators and random forest was utilized to investigate the relationship between yield loss and indicators of spring frost. Our findings reveal that, despite a decline in the number of spring frost events during global warming, not all the five indicators declined over time. Tv is the most important indicator for yield losses in maize and winter wheat, which shows an increasing trend in their growing regions and provides an explanation for the increasing yield losses of maize and winter wheat over time. Td is the most important indicator of rice yield losses but it shows a decreasing trend in rice-growing areas, which explains why rice yield losses from spring frosts in recent years are not significant.

Augmented Depletion Development Technology Assessed Across US Shale Plays

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 217792, “Assessment of Augmented Depletion Development Technology Across US Shale Plays,” by Ahmed Merzoug, SPE, Texas A&M University, and Vibhas J. Pandey, SPE, ConocoPhillips. The paper has not been peer reviewed. _ The concept of augmented depletion development (ADD) was introduced to the industry through a pilot project conducted in the Bakken play in the US. These wells are openhole wells drilled within the fractured network of a pad. They are not fracture stimulated deliberately but do produce and contribute to the overall production of the lease. In this study, numerical modeling is used to understand the potential performance of these wells in several plays across the US. The learnings from this study can be used as a benchmark for operators that are planning on implementing such wells in their field development programs. Theoretical Basis of Study The concept of ADD can be modeled using a similar approach to the analytical solutions developed for flow into a multistage fractured horizontal well. In these wells, several regimes can be identified, such as transient linear flow and boundary-dominated flow. In previous work in the literature that proposed a method for analyzing production data of linear flow of fractured gas wells, fractures were assumed to have infinite dimensionless conductivity. Those researchers proposed a solution for a constant bottomhole-pressure liquid flow that is described in the present complete paper by a series of equations. For the case used by the present authors, a well spacing of 300 ft is assumed, implying that the perpendicular distance from the wellbore to a corresponding boundary is 150 ft, which is half the spacing between the fractured well and the ADD well (assuming each well drains half the fracture length). To elaborate further for better understanding of production, a plot of product of cumulative dimensionless rate and formation permeability (as a proxy for cumulative rate) while assuming all other properties behave the same shows that, with higher permeability, the recovery potential not only increases but also appears to accelerate. This supports the concept that, during the time of production of ADD wells, it is preferable to have higher permeabilities that can support faster production in zones that the primary wells may not drain effectively. The results of the analytical equation are for only one fracture but can be readily extended to multifractured horizontal wells using superposition methods. Methodology This study uses physics-based numerical modeling to simulate the performance of ADD wells in different plays. The numerical code simulates wellbore flow, hydraulic fracturing, geomechanics, and reservoir flow, all in the same package. The fracture and reservoir mesh are independent. The flow between the matrix and the reservoir is calculated using the 1D submesh approximation to reduce the computation expenses with higher accuracy. The hydraulic fracturing propagation algorithm uses a tip-tracking algorithm to allow the use of larger grid size without compromising accuracy in layered formations. The fracture conductivity is calculated with stress changes. The conductivity profile changes from a propped fracture into an unpropped fracture. The unpropped fracture conductivity is an underlying assumption used in this study. The collision between fractures initiated from different wells also is an assumption in this study.

Predicting Oil Recovery Under Uncertainty for Huff ‘n’ Puff Gas Injection: A Field Case Study in the Permian Basin

Summary The extent and size of the fracture network connected to the wellbore are among the most uncertain parameters for multifractured horizontal wells. The uncertainty has a profound impact on production under primary depletion and has an even bigger impact on the recovery during huff ‘n’ puff (HnP) gas injection. In this paper, we quantify the uncertainty in reservoir properties and enhanced oil recovery (EOR) for a pair of wells in the Permian basin. In addition, we report on the lessons learned from modeling a field pilot HnP project involving four cycles using reservoir simulation. We create a base sector model for two wells completed in Wolfcamp B and C formations, where fractures are incorporated using an embedded discrete fracture model. For each well, we identify 13 uncertain parameters (describing fracture properties and compaction, initial conditions, and relative permeability). We use a Bayesian assisted-history-matching (AHM) method to match primary production data. Our study emphasizes the critical role of grid refinement for the accurate depiction of the gas-injection process by comparing three models with varying grid sizes. Additionally, we study the impact of molecular diffusion on oil and gas recovery using different diffusion coefficients. Another significant factor is the dynamic nature of the fracture network during cyclic injection, as indicated by evidence of communication between the wells. This makes the accessible fracture area during injection highly uncertain. To address interwell interference, we incorporate an additional fracture network activated during injection. To manage the uncertainty of active fracture area, we created four scenarios within the literature range, approximately 0.5, 1, 2, and 4 million ft² per stage, and tuned their properties to match historical HnP data. The production predictions from the AHM solutions were extended beyond the initial four HnP cycles to provide probabilistic forecasts. We demonstrate that grid refinement has a minimal effect on production under primary depletion but significantly influences HnP recovery. We attribute this disparity to numerical dispersion, which causes the artificial mixing of gas in the large gridblocks, leading to an overly optimistic recovery. Activating the molecular-diffusion mechanism results in a negligible impact on the recovery in our case. The contribution of molecular diffusion becomes noticeable only when diffusion coefficients exceed ten times the reported values. This leads us to conclude that molecular diffusion is of second-order importance compared with advection and can generally be neglected in our case. This finding should not be generalized to all shale formations. In reservoirs characterized by lower permeability and fluids with higher diffusion coefficients, molecular diffusion could still play a crucial role. We highlight that the contacted-fracture area, which is often underestimated in the reservoir simulation practice, is a critical factor for accurate modeling. Production for 37 filtered AHM solutions is extended under the four scenarios with variable fracture surface areas. Based on our analysis, we report that the HnP results in an average of 76% improvement in oil recovery compared with natural depletion.

Global Probabilistic Forecasting for Multiple Tight Gas Wells Using Deep Autoregressive Networks

Summary Traditional methods for forecasting production rate, such as Arps, analytical techniques, and recurrent neural network (RNN)–based deep learning, are mainly point prediction techniques developed within the framework of single-well forecasting. These methods often face limitations stemming from single-well historical production data and model assumptions, hindering their ability to consider the influence of development patterns of other production wells within the block on the target well. In addition, they struggle to predict the multiple production rate time series simultaneously and often fail to quantify uncertainty in predictions or adequately exploit extensive relevant historical production data. To tackle these challenges, we propose a model based on the deep autoregressive recurrent neural network (DeepAR), leveraging related multiwell production rate data to enable global modeling and probabilistic forecasting. This model incorporates dynamic covariate and static categorical variable data, integrating Bayesian inference and using gradient descent algorithms and maximum likelihood estimation methods to derive a comprehensive historical-future production probability evolution pattern shared across multiple wells. Leveraging data from 943 tight gas wells, a comprehensive evaluation of the DeepAR model’s performance was undertaken, encompassing the comparison of prediction accuracy with long short-term memory (LSTM), classification prediction, cold-start prediction, and single-well multitarget prediction scenarios, summarizing the applicability conditions for each. The research findings highlight that DeepAR integrates the acquired comprehensive production probability evolution pattern with specific production historical data of the target well to formulate a “comprehensive + specific” production probability prediction approach, resulting in improved stability and accuracy. On average, DeepAR demonstrates a 58.79% reduction in normalized deviation (ND) compared to the LSTM model, showcasing enhanced stability, particularly in scenarios involving frequent well shut-ins and openings. Moreover, DeepAR can learn static categorical features, with the classification model resulting in a 27.15% reduction in the ND compared to the unclassified model. Furthermore, DeepAR adeptly addresses the challenge of limited data availability, achieving cold-start prediction and facilitating multitarget single-well training and prediction while considering the interdependency among multiple variables over time and effectively mitigating the issue of missing auxiliary variables during the prediction phase. This study contributes to a broader understanding of production forecasting and analysis of production dynamics methods from a big data perspective.

Correlation between Comprehensive Evaluation Method for Fracture Complexity in Tight Reservoirs Post-Fracturing and Production Capacity

The complexity of post-fracturing fractures in tight reservoirs is critical for evaluating fracturing effectiveness and is also a significant factor influencing well productivity. Currently, some scholars have utilized an improved G function to study the complexity of hydraulic fractures after fracturing. However, in certain specific construction curves, the characteristics and values of the G function alone are insufficient for comprehensive interpretation. To conduct a more thorough analysis of fracture complexity, this study introduces net pressure and liquid efficiency—parameters related to fracture complexity—based on the G function, and performs statistical analysis. Since the G function curve morphology is image data, it is not suitable for quantitative analysis. Therefore, digital calibration was conducted based on its corresponding morphological features to render it amenable to data analysis. Using Principal Component Analysis (PCA), a comprehensive fracture complexity coefficient was proposed, which considers four parameters: the digital calibration of the G function curve, G function characteristic values, net pressure, and liquid efficiency. The relationship between this coefficient and productivity was examined through Pearson and Spearman correlation analyses, utilizing post-fracturing production data from 26 wells in the Dabei block for validation. The results indicate a significant positive correlation between the comprehensive fracture complexity coefficient and productivity, with a high degree of correlation. This method not only addresses the shortcomings in interpreting certain specific construction curves but also provides a more comprehensive quantitative characterization of fracture complexity. Preliminary validation confirms the applicability of the method, offering valuable insights for evaluating post-fracturing effectiveness.

Key technologies and applications of collaboration between digital power grid and Internet of Things

AbstractThe integration of electricity technology and information technology, such as the Internet of Things (IoT), enables the construction of new power systems, along with the innovation of application scenarios and business scope. The key technologies of power IoT and the data flow process are summarised first. The IoT technology and application scenario requirements of power generation, transmission, loading, and storage of new power systems are studied. Thus, the nature of the collaborative development of the digital power grid and the IoT is demonstrated from the perspective of data processing in power IoT and application requirements in power systems. The key problems and solutions faced by the power IoT under the digital transformation are described, and the cross‐integration of key technologies and promotion of application scenario innovation are prospected. Finally, the key issues of future technological development were discussed, providing reference ideas for fully leveraging the value of energy and electricity data production factors and promoting the construction of a digital electricity ecosystem.

Consistent Seasonal Hydrography From Moorings at Northwest Greenland Glacier Fronts

AbstractGreenland's marine‐terminating glaciers connect the ice sheet to the ocean and provide a critical boundary where heat, freshwater, and nutrient exchanges take place. Buoyant freshwater runoff from inland ice sheet melt is discharged at the base of marine‐terminating glaciers, forming vigorous upwelling plumes. It is understood that subglacial plumes modify waters near glacier fronts and increase submarine glacier melt by entraining warm ambient waters at depth. However, ocean observations along Greenland's coastal margins remain biased toward summer months which limits accurate estimation of ocean forcing on glacier retreat and acceleration. Here, we fill a key observational gap in northwest Greenland by describing seasonal hydrographic variation at glacier fronts in Melville Bay using in situ observations from moorings deployed year‐round, CTDs, and profiling floats. We evaluated local and remote forcing using remote sensing and reanalysis data products alongside a high‐resolution ocean model. Analysis of the year‐round hydrographic data revealed consistent above‐sill seasonality in temperature and salinity. The warmest, saltiest waters occurred in spring (April–May) and primed glaciers for enhanced submarine melt in summer when meltwater plumes entrain deep waters. Waters were coldest and freshest in early winter (November–December) after summer melt from sea ice, glacier ice, and icebergs provided cold freshwater along the shelf. Ocean variability was greatest in the summer and fall, coincident with increased freshwater runoff and large wind events before winter sea ice formation. Results increase our mechanistic understanding of Greenland ice‐ocean interactions and enable improvements in ocean model parameterization.

Marketplace Data Analysis Using Web Scraping

The rapid development of the internet in fast communication has provided significant benefits for business people, allowing them to offer and sell product information boldly. One of the main advantages is the ability to search and find a variety of products easily through the internet. This convenience has led to the proliferation of many online stores in Indonesia, so that the need for applications that can help users search and combine product data from the internet arises. This study aims to develop a web application for product data analysis by utilizing web scraping techniques on the Tokopedia marketplace website, using the BeautifulSoup4 (Bs4) method. The web application is designed using the Streamlit module from Python. The results of this study are web applications that process data analysis taken from the Tokopedia website, which are classified based on certain criteria such as price range, highest and lowest ratings, highest and lowest sales, highest and lowest prices, percentage of location distribution, percentage of seller stores, and product recommendations.

Performance of plant growth regulators in soybean cultivation under differente aplication methods

The utilization of biostimulants has emerged as a promising approach in agriculture, aiming to augment the physiological processes associated with germination and growth across diverse plant species, thereby leading to substantial and sustainable enhancements in agricultural productivity. Grounded in scientific insights, this study endeavored to juxtapose the efficacy of a biostimulant compound against conventional market-available products. The application methodologies encompassed seed treatment and foliar spraying at the V2 growth stage, with a primary focus on productivity augmentation. The investigation transpired at the Agricultural Technological Center of Paraguay (CETAPAR) in Colônia Iguazu, Paraguay, utilizing a randomized complete block design (RCBD) featuring 16 treatments replicated four times. Dosages were administered in adherence to label recommendations. Productivity data underwent rigorous statistical analyses, including the F-test and Dunnett's test, with a significance level set at 10%. Remarkably, foliar applications at the V2 phenological stage and seed treatments outperformed other modalities, yielding notable outputs of 4,431 kg.ha-1 and 4,419 kg.ha-1, respectively. Noteworthy is the observation that the tested biostimulant formulation exhibited equivalent or superior potential when juxtaposed with commercial counterparts. The findings underscore the viability of integrating biostimulants as a compelling strategy to optimize agricultural productivity, thereby furnishing farmers with an efficacious and sustainable alternative.

Data-driven visual model development and 3D visual analytics framework for underground mining

Data visualisation is broadly utilised to aid in presenting complex information, facilitating decision-making, and enhancing operational efficiency in the mining sector. However, due to the extensive utilisation of CAD files and the complexity and difficulty of converting 2D drawings into 3D models, it is challenging to realise (near) real-time 3D data visualisation in mining applications. The difficulties are including site drawing complexity, lack of standardisation, time-consuming cleaning, and imperative manual intervention. This paper addresses these challenges by focusing on an automatic, data-driven approach for 3D visual model development in mining, with an emphasis on lifetime support and sustainability. Therefore, we introduce standardised visual model data diagram and proposed conversion workflows to leverage digitised 2D drawings in automatic 3D visual model development. Moreover, to improve visualisation efficiency and facilitate visual analytics construction in mining, this paper also proposes a 3D tile index methodology for efficient visual model reconstruction and generic production data integration. To ensure lifetime support and sustainable development in visualisation, we propose two model expansion schemes: one based on dxf file-driven model development, and the other one is interaction-oriented 3D model design and development, which the two schemes share the same data exchange and conversion workflows. Furthermore, to facilitate data fusion among various datasets, we also provide insights into revealing spatial–temporal patterns among multimodality data by real-time visualisation. These advancements aim to propel the field of underground mining visualisation, offering significant insights into enhancing the efficiency and safety of mining operations.

A clustering-aided multi-agent deep reinforcement learning for multi-objective parallel batch processing machines scheduling in semiconductor manufacturing

Batch processing machines are often the bottleneck in semiconductor manufacturing and their scheduling plays a key role in production management. Pioneer researches on multi-objective batch machines scheduling mainly focus on evolutionary algorithms, failing to meet the online scheduling demand. To deal with the challenges confronted by incompatible job families, dynamic job arrivals, capacitated machines and multiple objectives, we propose a clustering-aided multi-agent deep reinforcement learning approach (CA-MADRL) for the scheduling problem. Specifically, to achieve diverse nondominated solutions, an offline multi-objective scheduling algorithm named Multi-Subpopulation fast elitist Non-Dominated Sorting Genetic Algorithm (MS-NSGA-II) is firstly developed to obtain the Pareto Fronts, and a clustering algorithm based on cosine distance is employed to analyze the distribution of Pareto frontier solution, which would be used to guide reward functions design in multi-agent deep reinforcement learning. To realize multi-objective optimization, several reinforcement learning base models are trained for different optimization directions, each of which composed of batch forming agent and batch scheduling agent. To alleviate time complexity of model training, a parameter sharing strategy is introduced between different reinforcement learning base model. By validating the proposed approach with 16 instances designed based on actual production data from a semiconductor manufacturing company, it has been demonstrated that the approach not only meets the high-frequency scheduling requirements of manufacturing systems for parallel batch processing machines but also effectively reduces the total job tardiness and machine energy consumption.

Assessment of the spaceborne EnMAP hyperspectral data for alteration mineral mapping: A case study of the Reko Diq porphyry Cu[sbnd]Au deposit, Pakistan

For over four decades, spaceborne multispectral data have played a crucial role in supporting mineral exploration and geologic mapping. The spaceborne multispectral datasets, however, have a restricted number of bands with coarse spectral resolution and, thus are very limited in mineral mapping. The advent of high-quality spaceborne imaging spectroscopic data like the Environmental Mapping and Analysis Program (EnMAP), has bridged this gap initiating a new era in global hyperspectral mineral mapping. The EnMAP satellite, operational since November 2022, covers the spectral range of 420 and 2450 nm in 224 bands, offering a spatial resolution of 30 m and a mean spectral sampling distance of 8.1 and 12.5 nm in the visible-near infrared and shortwave infrared regions, respectively. In this paper, we demonstrate the enhanced mapping capabilities of EnMAP using datasets acquired over the Reko Diq mining district, a cluster of Miocene porphyries located in Pakistan's Chagai Belt hosting an undeveloped world-class porphyry Cu-Au ± Mo deposit. The EnMAP's Level 2A data product was processed using the polynomial fitting technique to characterize the diagnostic absorption features of the alteration minerals in the Reko Diq porphyry system. This involved retrieving the minimum wavelength, depth, width, and asymmetry parameters for key absorption features and employing them interactively for mineral characterization. A diverse array of minerals were successfully mapped over the study area and validated by ground spectroscopy. This includes abundance/composition maps for white micas, chlorite, epidote, calcite, kaolinite, gypsum, jarosite, and ferric and ferrous iron minerals. The minimum wavelength of white mica was found to vary between 2195 and 2210 nm, with the shorter wavelengths (Al-rich) white mica occurring proximal to the known mineralized zones. The potassic alteration cores and the outer propylitic zones were identified by the ferrous iron and chlorite-epidote-calcite mineral maps, respectively. This study demonstrated the superiority of EnMAP hyperspectral data in delineating the alteration mineralogy and zonation pattern of porphyry copper systems. This capability can potentially contribute to the exploration of new deposits in exposed terrains worldwide.

Carbon emissions from global impervious surface expansion between 1985 and 2020

Impervious surface expansion (ISE) refers to the phenomenon that natural surfaces are covered by artificial materials, such as cement, asphalt, bricks, etc., usually due to human activities. Over recent decades, the rapid growth of the global population, economic development, and continuous urbanization have contributed to extensive ISE, which caused significant losses in terrestrial ecosystem carbon pools. A global assessment effort is lacking because of limited comprehensive data on carbon pools and uncertainties surrounding the extent of ISE. In this study, we aimed to quantify the carbon emissions resulting from global ISE between 1985 and 2020, following the method provided by the Intergovernmental Panel on Climate Change (IPCC) Guidelines. We first divided global land into 87 fine geographical zones by overlaying continental boundary information with an ecological zone map. Then, multiple time-series impervious surface data products, land cover dynamic monitoring product, global biomass data, and topsoil organic carbon (TSOC) information were integrated to build a lookup table (LUT) of biomass and TSOC density for these fine geographical zones. Last, we employed the IPCC method to estimate carbon emissions from ISE between 1985 and 2020. Our findings indicated a global ISE encompassing a total area of 58.12 Mha, with cropland encroachment accounting for 67.30 %. Over the past 35 years, cumulative committed carbon emissions from global ISE reached 1.14 ± 0.38 PgC, with TSOC representing 54.55 % and biomass carbon contributing 45.45 %.

From Do-It-Yourself Design to Discovery: A Comprehensive Approach to Hyperspectral Imaging from Drones

This paper presents an innovative, holistic, and comprehensive approach to drone-based imaging spectroscopy based on a small, cost-effective, and lightweight Unmanned Aerial Vehicle (UAV) payload intended for remote sensing applications. The payload comprises a push-broom imaging spectrometer built in-house with readily available Commercial Off-The-Shelf (COTS) components. This approach encompasses the entire process related to drone-based imaging spectroscopy, ranging from payload design, field operation, and data processing to the extraction of scientific data products from the collected data. This work focuses on generating directly georeferenced imaging spectroscopy datacubes using a Do-It-Yourself (DIY) imaging spectrometer, which is based on COTS components and freely available software and methods. The goal is to generate a remote sensing reflectance datacube that is suitable for retrieving chlorophyll-A (Chl-A) distributions as well as other properties of the ocean spectra. Direct georeferencing accuracy is determined by comparing landmarks in the directly georeferenced datacube to their true location. The quality of the remote sensing reflectance datacube is investigated by comparing the Chl-A distribution on various days with in situ measurements and satellite data products.

Financing health in sub-Saharan Africa 1990-2050: Donor dependence and expected domestic health spending.

In 2021, global life expectancy at birth was 74 years whereas in sub-Saharan Africa it was 66 years. Yet in that same year, $92 per person was spent on health in sub- Saharan Africa, which is roughly one fifth of what the next lowest geographic region-North Africa and Middle East-spent ($379). The challenges to healthy lives in sub-Saharan Africa are many while health spending remains low. This study uses gross domestic product, government, and health spending data to give a more complete picture of the patterns of future health spending in sub-Saharan Africa. We analyzed trends in growth in gross domestic product, government health spending, development assistance for health and the prioritization of health in national spending to compare countries within sub-Saharan Africa and globally.We found that while gross domestic product was projected to increase through 2050 in sub-Saharan Africa, the share of gross domestic product that goes to health spending is only expected to increase moderately. Our exploration shows that this tepid growth is expected because the percent of overall government spending that is dedicated to health 7·2% (6·3-8·3) compared to average of 12·4% (11·7-13·2) in other regions) is expected to stay low. Even if the amount, of resources provided from donors climbs some, it is not expected to keep pace with growing economies in sub-Saharan Africa and may transition towards other global public health goods. Critically, development assistance for health provided to sub-Saharan Africa is expected to decrease in some countries, and the expected growth in government health spending might not be enough to cover this expected decline. Increases in spending with a concordant prioritization of health and the appropriate health system governance and structural reforms are critical to ensure that people who live in sub-Saharan Africa are not left behind.

PENGEMBANGAN LEMBAR KERJA PESERTA DIDIK (LKPD) BERBASIS ETNOMATEMATIKA PADA MATERI TRANSFORMASI

This study aims to produce teaching materials in the form of ethnomathematics-based worksheets on Lampung batik motifs in understanding valid and practical geometric transformation concepts. The type of research used is Research and Development (R&D). This research produced a product in the form of ethnomathematics-based worksheets on transformation material at SMP PGRI Kesumadadi. The research procedure used is the Borg and Gall development model which goes through several stages including: potentials and problems, data collection, product design, design validation, design revision, product testing, and product revision. In the product trial stage, a limited trial was carried out involving 15 class IX students at SMP PGRI Kesumadadi. The research instruments used were material expert validation sheets, media expert validation sheets, and student response questionnaires. The results of this development research show that the assessment of the material expert validation obtained an average validation result of 88.12% with the validation criterion of "Very Valid". Furthermore, the results of the validation of media experts obtained an average validation result of 91.66% with the validation criterion of "Very Valid". The results of the analysis of the student response questionnaire at the limited trial stage obtained a percentage of 92.6% with the practicality criterion "Very Practical".

Snails, time, data: On the politics of mass-digitization and the possibility of data drift

In natural history museums around the globe, museum staff are working on making collections of specimens (such as insects on pins, snail shells, dried plants, mounted birds) digitally available through online databases en masse. The mass-digitization of specimens is urgent especially now, so museum actors and biodiversity scientists stress, as natural history collections could provide the biodiversity sciences with crucial data for research into the effects of climate-change induced biodiversity loss. This paper investigates the processes and practices through which these biodiversity data are produced. It argues that the work of data production requires the negotiation of- and grappling with different temporalities, and that attending to these temporalities contributes to a further understanding of the politics of mass-digitization. Building on ethnographic fieldwork in the Museum für Naturkunde Berlin's collection of snail shells, the paper brings into view the work required to make snail specimens and their metadata digital. Following steps in a mollusk digitization workflow—meant to streamline and scale up digitization processes—it attends to the misalignment of rhythms; the importance of informal cleaning labor; the negotiation of social, biological, and colonial temporalities in practices of label transcription; and the anticipatory and expansive logics of mass-digitization. Drawing on snails’ ability to “drift,” it raises data drift as a way of engaging with natural history collections and their data.

Testing a Typology of Data Journalism: Professional and Epistemological Orientations Across and Between Global Regions

ABSTRACT This article provides analysis of the growing data journalism subfield using data gathered from a cross-national survey of journalists in 68 countries. The study makes two contributions to the literature, first offering a test of a typological framework for categorizing data journalists based on their work routines and conditions, and second exploring similarities and differences between journalists working in Global South settings versus those in the Global North. The analysis provides empirical support for conceptual claims of contrasting paradigms revolving around traditional journalistic practices versus more computational approaches to using data in news production. At the same time, we find that rather than aligning neatly with one or the other professional or epistemological paradigm, data journalists tend to employ a wide range of practices, with variations in degree of adoption of individual approaches contingent on specific work contexts. Journalists in countries of the Global South, for instance, are likelier than their Global North counterparts to adopt strategies such as using non-expert sources of information to produce stories based on data. Overall, the data suggest a maturing global data journalism field characterized by broad diffusion of standardized practices that feature some regional variation likely tied to differing data access regimes.