Commission Errors Research Articles

Ensemble automated approaches for producing high‐quality herbarium digital records

AbstractPremiseOne of the slowest steps in digitizing natural history collections is converting labels associated with specimens into a digital data record usable for collections management and research. Here, we address how herbarium specimen labels can be converted into digital data records via extraction into standardized Darwin Core fields.MethodsWe first showcase the development of a rule‐based approach and compare outcomes with a large language model–based approach, in particular ChatGPT4. We next quantified omission and commission error rates across target fields for a set of labels transcribed using optical character recognition (OCR) for both approaches. For example, we find that ChatGPT4 often creates field names that are not Darwin Core compliant while rule‐based approaches often have high commission error rates.ResultsOur results suggest that these approaches each have different strengths and limitations. We therefore developed an ensemble approach that leverages the strengths of each individual method and documented that ensembling strongly reduced overall information extraction errors.DiscussionThis work shows that an ensemble approach has particular value for creating high‐quality digital data records, even for complicated label content. While human validation is still needed to ensure the best possible quality, automated approaches can speed digitization of herbarium specimen labels and are likely to be broadly usable for all natural history collection types.

Response inhibition as a critical executive function in differentiating attention-deficit/hyperactivity disorder from autism spectrum disorder: a comprehensive attention test study

BackgroundAutism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) are both associated with impairment in executive function, particularly in complex attention. Although previous studies using clinical assessments have attempted to delineate differences between these disorders, the findings have been inconclusive. Our study aims to elucidate the differences of endophenotype between ASD, ADHD, and their co-occurring condition utilizing a uniform computerized test.MethodsThe study included children diagnosed with ASD, ASD co-occurring with ADHD (ASD+ADHD), or ADHD who completed the comprehensive attention test (CAT) at Severance Hospital between October 2013 to May 2023. We excluded children with intellectual disability and comorbid major psychiatric or neurologic disorders possibly affecting attention measurement. The participants were categorized into three groups for the comparative analysis of CAT measures: (a) ASD (n=112), (b) ASD+ADHD (n=155), and (c) ADHD (n=104). The study also conducted an exploratory analysis utilizing multivariate linear regression analysis to examine the association between the CAT measures and parent-reported scales.ResultsNotably, the ASD+ADHD and ADHD groups exhibited higher frequency of commission errors (CE) and perseveration errors (PE) compared to the ASD group. In the exploratory analysis, a significant negative association was observed between reaction time (RT) and both the social communication questionnaire (SCQ) and the child behavior checklist (CBCL) externalization scores in the ASD+ADHD and ADHD groups. The ASD+ADHD group tended to show higher standard deviation of reaction time (RTSD) compared to the ASD group.ConclusionsOur findings suggest that impaired response inhibition is more pronounced in ADHD compared to ASD. We propose altered visual attention, reflecting response inhibition, may serve as potential endophenotypic markers differentiating ADHD from ASD in attentional assessment. Elevated RTSD in the ASD+ADHD group demonstrates additive pathology, suggesting that the neurological mechanisms underpinning impaired sustained attention may differ between the two conditions.

Gradually increasing context-sensitivity shapes the development of children's verb marking: A corpus study.

There is substantial evidence that children's apparent omission of grammatical morphemes in utterances such as "She play tennis" and "Mummy eating" is in fact errors of commission in which contextually licensed unmarked forms encountered in the input are reproduced in a context-blind fashion. So how do children stop making such errors? In this study, we test the assumption that children's ability to recover from error is related to their developing sensitivity to longer-range dependencies. We use a pre-registered corpus analysis to explore the predictive value of different cues with regards to children's verb-marking errors and observe a developmental pattern consistent with this account. We look at context-independent cues (the identity of the specific verb being used) and at the relative value of context-dependent cues (the identity of the specific subject+verb sequence being used). We find that the only consistent effect across a group of 2- to 3-year-olds and a group of 3- to 4-year-olds is the relative frequency of unmarked forms of specific subject+verb sequences being used. The relative frequency of unmarked forms of the verb alone is predictive only in the younger age group. This is consistent with an account in which children recover from making errors by becoming progressively more sensitive to context, at first the immediately preceding lexical contexts (e.g., the subject that precedes the verb) and eventually more distant grammatical markers (e.g., the fronted auxiliary that precedes the subject in questions). RESEARCH HIGHLIGHTS: We provide a corpus analysis investigating input effects on young children's verb-marking errors (e.g., Mummy go) across development (between 2 and 4 years of age). We find evidence that these apparent errors of omission are in fact input-driven errors of commission that persist into the third year of life. We compare the relative effect on error rates of context-independent (e.g., verb) and context-dependent (e.g., subject+verb sequence) cues across developmental time. Our findings support the proposal that children recover from making verb-marking errors by becoming progressively more sensitive to preceding context.

E-Tendering and Performance of Humanitarian United Nations Agencies and Non-Governmental Organizations in South Sudan

Purpose: Manual procurement processes are expensive in terms of human resource cost, purchasing cost, wastage due to errors of commission or omissions, cause of delays in delivery of goods, services, and works which accounts for 40% of losses due to inefficiencies, and attracts corruption in the system amongst others. The study aimed to assess the effect of e-tendering on performance of humanitarian United Nations agencies and non-governmental organizations in South Sudan. Methodology: The study methodology adopted an explanatory research design of cross-sectional type for the population under study. There were over 235 National Non-governmental organizations, 150 International organizations in addition to over 15 United Nations agencies, bringing the total target population to 400 organizations. This study targeted 400 humanitarian organizations operating in South Sudan. Therefore, the sample size of 200 humanitarian organizations operating in South Sudan was selected using a stratified random sampling technique. Data was collected using structured questionnaires and analyzed using descriptive and inferential statistics. Results: Reliability findings indicated that e-tendering had a Cronbach Alpha Coefficient of 0.873, and performance had 0.859. The study established that e-tendering had a positive and significant relationship with performance significant (β=0.282, P=0.000). Conclusion: The study concluded that e-tendering has a positive and significant effect on performance of humanitarian United Nations agencies and non-governmental organizations. This study hence suggested that all NGOs and humanitarian agencies should ensure they adopt the use of e-tendering in their solicitation and sourcing processes in conducting procurement activities. The study cited advantages such saving time, wide coverage of markets and reaching more potential suppliers and finally save environment from printing more paper works attaining sustainable procurement practice.

Wildfire CNN: An Enhanced Wildfire Detection Model Leveraging CNN and VIIRS in Indian Context

Introduction Wildfires are an unexpected global hazard that significantly impact environmental change. An accurate and affordable method of identifying and monitoring on wildfire areas is to use coarse spatial resolution sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). Compared to MODIS, wildfire observations from VIIRS sensor data are around three times as extensive. Objective The traditional contextual wildfire detection method using VIIRS data mainly depends on the threshold value for classifying the fire or no fire which provides less performance for detecting wildfire areas and also fails in detecting small fires. In this paper, a wildfire detection method using Wildfiredetect Convolution Neural Network model is proposed for an effective wildfire detection and monitoring system using VIIRS data. Methods The proposed method uses the Convolutional Neural Network model and the study area dataset containing fire and non-fire spots is tested. The performance metrics such as recall rate, precision rate, omission error, commission error, F-measure and accuracy rate are considered for the model evaluation. Results The experimental analysis of the study area shows a 99.69% recall rate, 99.79% precision rate, 0.3% omission error, 0.2% commission error, 99.73% F-measure and 99.7% accuracy values for training data. The proposed method also proves to detect small fires in Alaska forest dataset for the testing data with 100% recall rate, 99.2% precision rate, 0% omission error, 0.7% commission error, 99.69% F-measure and 99.3% accuracy values. The proposed model achieves a 26.17% higher accuracy rate than the improved contextual algorithm. Conclusion The experimental findings demonstrate that the proposed model identifies small fires and works well with VIIRS data for wildfire detection and monitoring systems.

Land use and land cover mapping of the Saco River’s watershed, State of Maranhão, Brazil

Land use and land cover mapping benefits landscape understanding and its changes, especially those resulting from anthropogenic actions in the physical environment. The present study aimed at mapping land use and coverage of the Saco River’s watershed (located in Codó, state of Maranhão). The area comprises the coconut forest (named Mata dos Cocais), a region that encompasses the characteristics of different biomes on the same temporal and spatial scale. The methodology applied was based on remote sensing techniques developed in a Geographic Information System (GIS) environment, processing data from Landsat 8 and supervised classification. The results showed a predominance of dense vegetation in the basin under study, in addition to a reduced occupation by classes of urban area and water bodies. The overall accuracy was 79% and dense vegetation presented a higher user and producer accuracy than the general, with 91 and 87%, respectively. On the other hand, the highest commission and omission errors were those in urban areas and water bodies, which coincided with the lowest occupation classes in the Saco River basin. These results are pioneering for the coconut forest and provide data for strategic planning of environmental actions.

Clinical Utility of Virtual Kitchen Errand Task for Children (VKET-C) as a Functional Cognition Evaluation for Children with Developmental Disabilities

Background/Objectives: This study evaluated the clinical utility of a virtual reality (VR)-based kitchen error task for children (VKET-C) to assess functional cognition in children. Methods: In total, 38 children aged 7–12 years were included, comprising 23 typically developing (TD) children and 15 children with developmental disabilities (DDs), including autism spectrum disorder, attention deficit hyperactivity disorder, and intellectual disability. While performing the VKET-C, performance errors were analyzed. The Stockings of Cambridge (SOC) and Spatial Working Memory (SWM) tasks from the Cambridge Neuropsychological Test Automated Battery (CANTAB) were used to assess cognitive function. The Brunner–Munzel test was performed to compare performance errors between the TD and DD groups, and correlations between performance errors and cognitive measures were analyzed. Results: Omission and commission errors were significantly different between the groups (p < 0.001), with no significant difference in motor errors (p > 0.05). Omission errors were correlated with the initial thinking time mean (ITMN) in all items of the SOC task and the between errors (BE) of the SWM task. Commission errors were correlated with the ITMN in the difficult items of the SOC task and the BE of the SWM task. Additionally, motor errors were significantly correlated with problems solved in minimum moves (PSMM) and ITMN in the difficult items of the SOC task and BE in the SWM task. Conclusions: The VKET-C shows promise as an effective tool for assessing executive function and working memory in children with DDs, offering an engaging and ecologically valid alternative to traditional methods.

Incidental Physical Activity and Physical Fitness Associate with Sustained Attention and Impulse Control in Older Adults

Age-related changes have been observed in sustained attention and impulse control. Deficits in these functions are related to pathological aging. However, research suggests that an active lifestyle may exert a protective effect on cognition in aging. While the association between physical activity and cognitive functioning in older adults has been previously described, few studies have explored the relationship between incidental physical activity and fitness with sustained attention and impulse control in this population. Therefore, this study aimed to evaluate the association between incidental physical activity and various components of fitness and sustained attention and impulsivity in older adults. Fifty-four older adults were recruited (41 females and 13 males) with a mean age of 76.76 years (SD = 8.94). Physical activity levels were assessed using the Yale Physical Activity Survey (YPAS), physical function in healthy older adults was evaluated with the Senior Fitness Test, and sustained attention and impulsivity were measured using a computerized continuous performance task. Significant correlations were found between higher energy expenditure during housework and yardwork activities, and fewer commission errors and shorter reaction times during sustained attention task, and higher sensitivity during impulse control task (rho between 0.35 and 0.40, p < .01, β = 0.80). Additionally, higher scores between agility and balance were associated with better accuracy during the sustained attention task, and faster speed processing correlated to better performance impulse control task (rho between 0.33 and 0.36, p < .05, β = 0.80). Our study highlights the importance of incidental physical activity and various components of fitness on sustained attention and impulse control in aging.

Differential reconfiguration of brain networks in children in response to standard versus rewarded go/no-go task demands.

Response inhibition and sustained attention are critical for higher-order cognition and rely upon specific patterns of functional brain network organization. This study investigated how functional brain networks reconfigure to execute these cognitive processes during a go/no-go task with and without the presence of rewards in 26 children between the ages of 8 and 12 years. First, we compared task performance between standard and rewarded versions of a go/no-go task. We found that the presence of rewards reduced commission error rate, a measure considered to indicate improved response inhibition. Tau, thought to index sustained attention, did not change across task conditions. Next, changes in functional brain network organization were assessed between the resting state, the standard go/no-go task, and the rewarded go/no-go task. Relative to the resting state, integration decreased and segregation increased during the standard go/no-go task. A further decrease in integration and increase in segregation was observed when rewards were introduced. These patterns of reconfiguration were present globally and across several key brain networks of interest, as well as in individual regions implicated in the processes of response inhibition, attention, and reward processing. These findings align with patterns of brain network organization found to support the cognitive strategy of sustained attention, rather than response inhibition, during go/no-go task performance and suggest that rewards enhance this organization. Overall, this study used large-scale brain network organization and a within-subjects multi-task design to examine different cognitive strategies and the influence of rewards on response inhibition and sustained attention in late childhood.

Accuracy Assessment of NOAA IMS 4 km Products on the Tibetan Plateau with Landsat-8 OLI Images

The NOAA IMS (Interactive Multisensor Snow and Ice Mapping System) is a blended snow and ice product based on active and passive satellite sensors, ground observation, and other auxiliary information, providing the daily cloud-free snow cover extent in the Northern Hemisphere (NH) and having great application potential in snow cover monitoring and research in the Tibetan Plateau (TP). However, accuracy assessment of products is crucial for various aspects of applications. In this study, Landsat-8 OLI images were used to evaluate and validate the accuracy of IMS products in snow cover monitoring on the TP. The results show that (1) average overall accuracy of IMS 4 km products is 76.0% and average mapping accuracy is 88.3%, indicating that IMS 4 km products are appropriate for large-scale snow cover monitoring on the TP. (2) IMS 4 km products tend to overestimate actual snow cover on the TP, with an average commission rate of 45.4% and omission rate of 11.7%, and generally present that the higher the proportion of snow-covered area, the lower the probability of omission rate and the higher the probability of commission rate. (3) Mapping accuracy of IMS 4 km snow cover on the TP generally is higher at the high altitudes, and commission and omission errors increase with the decrease of elevation. (4) Compared with less regional representativeness of ground observations, the spatial characteristics of snow cover based on high-resolution remote sensing data are much more detailed, and more reliable verification results can be obtained. (5) In addition to commission and omission error metrics, the overall accuracy and mapping accuracy based on the reference image instead of classified image can better reveal the general monitoring accuracy of IMS 4 km products on the TP area.

Analysis of the Biennial Productivity of Arabica Coffee with Google Earth Engine in the Northeast Region of São Paulo, Brazil

Numerous challenges are associated with the classification of satellite images of coffee plantations. The spectral similarity with other types of land use, variations in altitude, topography, production system (shaded and sun), and the change in spectral signature throughout the phenological cycle are examples that affect the process. This research investigates the influence of biennial Arabica coffee productivity on the accuracy of Landsat-8 image classification. The Google Earth Engine (GEE) platform and the Random Forest algorithm were used to process the annual and biennial mosaics of the Média Mogiana Region, São Paulo (Brazil), from 2017 to 2023. The parameters evaluated were the general hits of the seven classes of land use and coffee errors of commission and omission. It was found that the seasonality of the plant and its development phases were fundamental in the quality of coffee classification. The use of biennial mosaics, with Landsat-8 images, Brightness, Greenness, Wetness, SRTM data (elevation, aspect, slope), and LST data (Land Surface Temperature) also contributed to improving the process, generating a classification accuracy of 88.8% and reducing coffee omission errors to 22%.

Improved phenology-based rice mapping algorithm by integrating optical and radar data

Information on rice planting areas is critically important for food and water security, as well as for adapting to climate change. Mapping rice globally remains challenging due to the diverse climatic conditions and various rice cropping systems worldwide. Synthetic Aperture Radar (SAR) data, which is immune to climatic conditions, plays a vital role in rice mapping in cloudy, rainy, low-latitude regions but it suffers from commission errors in high-latitude regions. Conversely, optical data performs well in high-latitude regions due to its high observation frequency and less cloud contamination but faces significant omission errors in low-latitude regions. An effective integrated method that combines both data types is key to global rice mapping. Here, we propose a novel adaptive rice mapping framework named Rice-Sentinel that combines Sentinel-1 and Sentinel-2 data. First, we extracted key phenological phases of rice (e.g., the flooding and transplanting phase and the rapid growth phase), by analyzing the characteristic V-shaped changes in the Sentinel-1 VH curve. Second, we identified potential flooding signals in rice pixels by integrating the VH time series from Sentinel-1 with the Land Surface Water Index (LSWI) and Enhanced Vegetation Index (EVI) from Sentinel-2, utilizing the generated phenology phases. Third, the rapid growth signals of rice following its flooding phase were identified using Sentinel-2 data. Finally, rice fields were identified by integrating flooding and rapid growth signals. The resultant rice maps in six different case regions of the world (Northeast and South China, California, USA, Mekong Delta of Vietnam, Sakata City in Japan, and Mali in Africa) showed overall accuracies over 90 % and F1 scores over 0.91, outperforming the existing methods and products. This algorithm combines the strengths of both optical and SAR time series data and leverages biophysical principles to generate robust rice maps without relying on any prior ground truth samples. It is well-positioned for global applications and is expected to contribute to global rice monitoring efforts.

Super-resolution cropland mapping with Sentinel-2 images based on a self-training learning network

ABSTRACT The Sentinel-2 image is widely used for cropland mapping, but the limitation of its 10 m spatial resolution may significantly impact the accuracy of result in areas characterized by severe cropland fragmentation. To address the issue, this letter proposes a super-resolution cropland mapping model for Sentinel-2 images. The proposed model improves the spatial resolution of Sentinel-2 images to 2.5 m through self-training learning with the Swin Transformer for Image Restoration (SwinIR) model without needing fine-resolution training samples. Then, the random forest classification is applied to map cropland from the super-solved 2.5 m resolution image. The proposed model was assessed in the Jianghan Plain, China, and the results show that the super-resolution images produce cropland maps with higher accuracy than the original Sentinel-2 images. Comparing cropland mapping results before and after super-resolution, the overall accuracy improves from 0.82 to 0.89, while the commission error and omission error decrease from 0.09 to 0.05 and 0.17 to 0.11, respectively. This method addresses the shortage of training samples collection and improves the accuracy of cropland mapping, contributing to more reliable agricultural remote sensing applications.

Attention improvement to transcranial alternating current stimulation at gamma frequency over the right frontoparietal network: a preliminary report.

Applying transcranial alternating current stimulation (tACS) at 40 Hz to the frontal and parietal regions, either unilaterally (left or right) or bilaterally, can improve cognitive dysfunctions. This study aimed to explore the influence of tACS at gamma frequency over right fronto-parietal (FP) region on attention. The analysis is based on retrospective data from a clinical intervention. We administered test of variables of attention (TOVA; visual mode) to 44 participants with various neuropsychiatric diagnoses before and after 12 sessions of tACS treatment. Alternating currents at 2.0 mA were delivered to the electrode positions F4 and P4, following the 10-20 EEG convention, for 20 mins in each session. We observed significant improvement across 3 indices of the TOVA, including reduction of variability in reaction time (p = 0.0002), increase in d-Prime (separability of targets and non-targets; p = 0.0157), and decrease in commission error rate (p = 0.0116). The mean RT and omission error rate largely remained unchanged. Artificial injection of tACS at 40 Hz over right FP network may improve attention function, especially in the domains of consistency in performance, target/non-target discrimination, and inhibitory control.

Impaired Reactive Control But Preserved Proactive Control in Hyperactive Children

Objective: To examine the manifestation of cognitive control deficit of children with different levels of hyperactivity, an “at risk” dimension for ADHD. Method: A group of children with high hyperactivity (N = 40) and another group of children with low levels of hyperactivity (N = 38) performed a modified stop-signal anticipation task, a revised Go/NoGo task, and the AX-continuous performance test (AX-CPT). Results: Children with higher levels of hyperactivity displayed: (1) significantly prolonged stop signal reaction time (SSRT) in the modified stop-signal anticipation task; (2) no notable differences in commission errors in the revised Go/NoGo task; (3) increased reaction time (RT) in stop-signal task and Go/NoGo task with increased probabilities of stop or NoGo signal; and (4) positive proactive behavioral index scores in AX-CPT. Conclusion: The results suggested that children with heightened hyperactivity exhibited impaired reactive control, especially for responses already underway, but preserved proactive control. Further studies concerning these children are warranted.

Estimating the distribution of reedbed in Britain demonstrates challenges of remotely sensing rare land cover types at large spatial scales

Common Reed Phragmites australis, globally one of the mostly widely distributed wetland plants, is important for biodiversity and for humans. However, like most wetland plant communities, reedbed has rarely been mapped at large geographical scales, restricting the information available to study reed’s range dynamics or inform its management. Using Sentinel-2 data and machine learning, we aimed to produce the first published remotely-sensed reedbed map of Britain; however, accuracy as assessed by field validation was relatively low (AUC = 0.671), with many false positives (commission error of 93.4%). A similar workflow carried out in Google Earth Engine, using nearly an order of magnitude more images, gave a lower commission error but a disproportionately higher omission error. Using the known commission and omission error, we estimate that in 2015–2017 ~ 7800 ha of Britain was reedbed. Our study highlights the enduring barriers to accurate land cover classification at large spatial scales. Even with a ‘big data’ approach, reflectance error and ecological factors such as confusion land cover types and geographical variation in temporal reflectance function will probably continue to limit the size of area for which land cover can be classified accurately, therefore limiting the utility of remote sensing for ecologists.

Multi-Scale Variation in Surface Water Area in the Yellow River Basin (1991–2023) Based on Suspended Particulate Matter Concentration and Water Indexes

Surface water is a crucial part of terrestrial ecosystems and is crucial to maintaining ecosystem health, ensuring social stability, and promoting high-quality regional economic development. The surface water in the Yellow River Basin (YRB) has a high sediment content and spatially heterogeneous sediment distribution, presenting a significant challenge for surface water extraction. In this study, we first analyze the applicability of nine water indexes in the YRB by using the Landsat series images (Landsat 5, 7, 8) and then examine the correlation between the accuracy of the water indexes and suspended particulate matter (SPM) concentrations. On this basis, we propose a surface water extraction method considering the SPM concentrations (SWE-CSPM). Finally, we examine the dynamic variations in the surface water in the YRB at four scales: the global scale, the secondary water resource zoning scale, the provincial scale, and the typical water scale. The results indicate that (1) among the nine water indexes, the MBWI has the highest water extraction accuracy, followed by the AWEInsh and WI2021, while the NDWI has the lowest. (2) Compared with the nine water indexes and the multi-index water extraction rule method (MIWER), the SWE-CSPM can effectively reduce the commission errors of surface water extraction, and the water extraction accuracy is the highest (overall accuracy 95.44%, kappa coefficient 90.62%). (3) At the global scale, the maximum water area of the YRB shows a decreasing trend, but the change amount is small. The permanent water area shows an uptrend, whereas the seasonal water area shows a downtrend year by year. The reason may be that the increase in surface runoff and the construction of reservoir projects have led to the transformation of some seasonal water into permanent water. (4) At the secondary water resource zoning scale, the permanent water area of other secondary water resource zonings shows an increasing trend in different degrees, except for the Interior Drainage Area. (5) At the provincial scale, the permanent water area of all provinces shows an uptrend, while the seasonal water areas show a fluctuating downtrend. The maximum water area of Shandong, Inner Mongolia Autonomous Region, and Qinghai increases slowly, while the other provinces show a decreasing trend. (6) At the typical water scale, there are significant differences in the water area variation process in Zhaling Lake, Eling Lake, Wuliangsuhai, Hongjiannao, and Dongping Lake, but the permanent water area and maximum water area of these waters have increased over the past decade. This study offers significant technical support for the dynamic monitoring of surface water and helps to deeply understand the spatiotemporal variations in surface water in the YRB.

Enhancing burned area monitoring with VIIRS dataset: A case study in Sub-Saharan Africa

Since 2001, the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board the Aqua and Terra platforms has made great strides in providing information on global burned areas (BA). However, the MODIS mission is nearing its end. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensors, presented as the MODIS Aqua heritage, could be an excellent alternative to ensure the temporal continuity of this information at a moderate resolution. This paper describes and evaluates the effectiveness of our developed hybrid algorithm, which utilizes VIIRS reflectance and active fire products on the Google Earth Engine platform, in producing efficient information about BA. The study investigates the algorithm's performance in sub-Saharan Africa as the region of interest in 2019, using biweekly outputs and a spatial resolution of 250 m. The algorithm encompasses several steps, including pre-processing individual scenes, creating cloud-free composites, generating binary reference data for burned and non-burned areas, conducting a supervised classification using random forest, and performing region shaping. The VIIRS-BA final product, which includes three confidence levels (low, moderate, and high) known as the uncertainty layer, is compared to four other burned area products. The validation is conducted against 27 reference sampling units from the Sentinel-2 Burned Area Reference Database dataset, allowing for a comprehensive uncertainty assessment across five various biomes. The VIIRS-BA product identified 5.1 million km2 of BA, which was significantly larger than other global coarse resolution BA products such as FireCCI51, FireCCIS310, and MCD64A1 and close to the fine resolution FireCCISFD20 with a difference of 7.3%. The differences were less significant in biomes such as “Tropical Savannas” and “Temperate Grasslands” which are characterized by persistent biomass burning. Based on a stratified random sampling, the validation results demonstrate varying levels of accuracy for the VIIRS-BA product across different confidence levels. The commission error (CE) ranges from 7.8% to 23.4%, while the omission error (OE) falls between 29.4% and 58.8%. Notably, there is a significant reduction in OE (ranging from 40.7% to 50.5%) compared to global BA products like FireCCI51, FireCCIS310, and MCD64A1. When compared to VIIRS-BA, the FireCCISFD20 regional product has a 37% better OE performance. While VIIRS-BA shows great potential in detecting fires that global products miss, the VIIRS-BA with low confidence level tends to overestimate BA in regions with high fire activity. To address this, future versions of the algorithm will integrate the updated VIIRS reflectance data alongside VIIRS active fire from the National Oceanic and Atmospheric Administration to reduce CE and improve understanding spatial patterns.

E-Procurement and Performance of Humanitarian United Nation Agencies and Non-Governmental Organizations in South Sudan

Manual procurement processes are expensive in terms of human resource cost, purchasing cost, wastage due to errors of commission or omissions, cause of delay in delivery of goods, services and works which accounts for 40% of losses to inefficiencies, and attracts corruption in the system amongst others. The study was guided by four objectives: To assess the role of e-tendering on performance of humanitarian United Nations agencies and non-governmental organizations in South Sudan. To examine effect of e-contract management on performance of humanitarian United Nations agencies non-governmental organizations in South Sudan. To evaluate effect of e-order processing on performance of humanitarian United Nations agencies non-governmental organizations in South Sudan and to explore influence of e-supplier management on performance of humanitarian United Nations agencies non-governmental organizations in South Sudan. The study methodology adopted explanatory research design of cross-sectional type for the population under study. There were over 235 National Non-governmental organizations, 150 International organizations in addition to over 15 United Nations agencies, bringing the total target population to 400 organizations. This study targeted 400 humanitarian organizations operating in South Sudan. Therefore, the sample size of 200 humanitarian organizations operating in South Sudan were selected using stratified random sampling technique. Data was collected using structured questionnaires which was dropped and picked later or emailed. The data collected from two hundred (200) respondents was coded into statistical package of social sciences (IBM SPSS v29) data analysis tool. The researcher covered a total of 20 respondents for pilot testing. Content validity was also conducted while reliability was conducted using Cronbach alpha. The data was presented in inform of graphs, charts, bars for descriptive analysis. Reliability findings indicated that e-tendering had Cronbach Alpha Coefficient of 0.873, e-contract management had 0.852, e-order processing had 0.872, e-supplier management had 0.736, and performance had 0.859. All the variables had Cronbach Alpha Coefficients greater than 0.7 and this denoted that the questionnaire was reliable. Validity findings reported that KMO value was 0.623 which was above the recommended minimum threshold of 0.5. This statistic implies that the statements in the questionnaire were adequate. In addition, results of the Bartlett roundness test yielded a chi-square statistic of 41.373 and a p-value of 0.000&lt;0.05. These statistics indicate that the statements were correlated and therefore suitable for further analysis. The study found that e-tendering, e-contract management, e-order processing, and e-supplier management have a positive and significant relationship with performance. The study hence concluded that e-tendering, e-contract management, e-order processing, and e-supplier management have a positive and significant effect on performance of humanitarian United Nations agencies non-governmental organizations in South Sudan. Based on these findings, the study suggested that all NGOs and humanitarian agencies should ensure they adopt the use of e-platforms in conducting activities relating to procurement. The study also recommends that the policy makers that make policies relating to humanitarian agencies and NGOs should formulate for them a policy that will enable them make use of the e-platform in conducting procurement activities without any limitations.

Disentangling inhibition toward food and non-food stimuli across two hunger levels: An fNIRS study

While individuals tend to display poorer inhibitory control toward food-related than neutral stimuli, it is unclear whether this challenge is specific to food or extends to other pleasant stimuli. Uncertainty also remains regarding the general impact of hunger on inhibition. To address these questions, we used a within-subjects design whereby 44 healthy adults completed two go/no-go tasks including no-go images of highly palatable foods and no-go images of animals matched for valence and physical properties. Both tasks were completed one week apart in either a fed or a fasted state. Prefrontal cortex activity was measured using functional near-infrared spectroscopy. Poorer behavioral inhibition was observed when participants needed to withhold their response to images of food compared to animals, regardless of hunger state. In addition, more commission errors were made in the fasted compared to the fed condition, regardless of the type of image to avoid responding to. Responses to go trials were slower when these trials were interspersed with food compared to animal no-go trials. However, hunger did not influence go response time. Greater activation was observed in the dorsolateral prefrontal cortex during blocks of trials with (vs. without) no-go images, but brain activity did not differ according to the type of no-go image. The effect of hunger on prefrontal brain activity was also not significant. Exploratory correlations showed that food-related inhibition deficits were positively related to self-reported impulsivity, but unrelated to body mass index. This study suggests that even among healthy adults, food-related inhibitory control may have a unique behavioral signature beyond general inhibition toward pleasant stimuli. Hunger also exerts an independent influence on general inhibitory capabilities, highlighting the importance of carefully controlling hunger levels in inhibition studies.