Data Quality Research Articles

Evaluating AIS implementation to improve accounting information quality: the prospect in Jordanian family SMEs in the post-Covid-19 age

PurposeAs organisations rely more on systems to protect their accounting data, it is critical to investigate whether cutting-edge “Accounting Information Systems (AIS)” technologies increase “Accounting Information Quality (AIQ)” in post-Covid-19 era.Design/methodology/approachThe current study examined how Covid-19 modified the expected links between AIS implementation and AIQ factors. To make the “structural model”, a “web-based survey” was used. A “Multi-group Analysis” (MGA) with “AMOS.26” was employed to test the regulating effect. To look at their regression values, the 412 participants from “Family Small- and Medium-sized Enterprises FSMEs” in Jordan who took part in the MGA were split into two groups: those who had a high experience of Covid-19 impact (N = 143) and those who had a low experience of Covid-19 impact (N = 269). The study of 412 completed surveys shows that MGA uses a modified t-test to determine the significant difference between regression coefficients.FindingsThe results of the moderation effect confirm that Covid-19 has a substantial effect on AIS adoption and AIQ. All hypotheses about how AIS implementation affects AIQ in post-Covid-19 era were approved. The study finds that adopting AIS is critical for enterprises to maintain successful operations and efficiency.Practical implicationsThis model may assist businesses in comprehending the relationship between AIS and AIQ, how it affects it in the current technological adoption phase under extremely unpredictable economic conditions and what characteristics support its widespread usage. This research offered the theoretical underpinning for examining AIS’s ability to improve AIQ among Jordanian family firms. The results can also be applied to other Middle Eastern countries and AIQ, how it affects this relationship during this period of technological adoption under extraordinarily unpredictable economic conditions and what factors support its widespread use. Practitioners and scholars must recognise how AIS can develop organisational values and impacts as the AIS environment grows. This study recommends a holistic AIS-implementation methodology and evaluates organisational implications to address this empirical challenge.Originality/valueThis study investigates whether adopting AIS results in significantly higher AIQ in the post-Covid-19 era. To the best of the author’s knowledge, there have been a few attempts to investigate how AIS influences data quality. Still, there is little evidence of how this relationship has evolved in the aftermath of the Covid-19 tragedy. As a result, much work remains to be done, particularly in analytical decision-making culture, which is critical to the development of the growing economies of the Middle East and Jordan. To address this knowledge gap, the Covid-19 and AIQ criteria were incorporated into the model utilised in this study. This research adds to existing knowledge and may be valuable in areas with low AIS framework frequency.

The Serial Mediation Effect of COVID-19 Anxiety and COVID-19 Burnout on the Relationship Between the Fear of COVID-19 and COVID-19 Quality of Life in Nurses.

There is growing interest in the association between COVID-19-related stressors and quality of life. This study aimed to extend current knowledge by investigating the potential mediating roles of COVID-19 anxiety and COVID-19 burnout in the association between fear of COVID-19 and COVID-19 quality of life in nurses. A total of 300 nurses were selected by convenience sampling approach from a training and research hospital serving as a pandemic hospital in Turkey. COVID-19 anxiety, COVID-19 burnout, and COVID-19 quality of life data were collected using the self-reported questionnaires using both online and paper-and-pencil versions of the questionnaires. Structural equation modeling was used to conduct serial multiple mediation analysis. We found statistically significant associations among fear of COVID-19, COVID-19 anxiety, COVID-19 burnout, and COVID-19 quality of life. Serial multiple mediation analysis revealed that the association of fear of COVID-19 with COVID-19 quality of life was partially mediated by COVID-19 anxiety and COVID-19 burnout. These results indicate that the association of fear of COVID-19 with COVID-19 quality of life in nurses may be changed through COVID-19 anxiety and COVID-19 burnout, which appear to play serial multiple mediating roles in this association. These findings enrich our understanding of the associations among these psychological factors and suggest that focusing solely on fear of COVID-19 without considering the mediating effects of COVID-19 anxiety and COVID-19 burnout might not be adequate for reducing the COVID-19-impact on the quality of life among nurses.

Domestic groundwater wells in Appalachia show evidence of low-dose, complex mixtures of legacy pollutants.

Lack of water quality data for private drinking water sources prevents robust evaluation of exposure risk for communities co-located with historically contaminated sites and ongoing industrial activity. Areas of the Appalachian region of the United States (i.e., Pennsylvania, Ohio and West Virginia) contain extensive hydraulic fracturing activity, as well as other extractive and industrial technologies, in close proximity to communities reliant on private drinking water sources, creating concern over potential groundwater contamination. In this study, we characterized volatile organic compound (VOC) occurrence at 307 private groundwater well sites within Pennsylvania, Ohio, and West Virginia. The majority (97%) of water samples contained at least one VOC, while the average number of VOCs detected at a given site was 5 ± 3. The majority of individual VOC concentrations fell below applicable U.S. Environmental Protection Agency (EPA) Maximum Contamination Levels (MCLs), except for chloroform (MCL of 80 μg L-1; n = 1 at 98 μg L-1), 1,2-dibromoethane (MCL of 0.05 μg L-1; n = 3 ranging from 0.05 to 0.35 μg L-1), and 1,2-dibromo-3-chloropropane (MCL of 0.2 μg L-1; n = 7 ranging from 0.20 to 0.58 μg L-1). To evaluate well susceptibility to VOCs from industrial activity, distance to hydraulic fracturing site was used to assess correlations with contaminant occurrences. Proximity to closest hydraulic fracturing well-site revealed no statistically significant linear relationships with either individual VOC concentrations, or frequency of VOC detections. Evaluation of other known industrial contamination sites (e.g., US EPA Superfund sites) revealed elevated levels of three VOCs (chloroform, toluene, benzene) in groundwaters within 10 km of those Superfund sites in West Virginia and Ohio, illuminating possible point source influence. Lack of correlation between VOC concentrations and proximity to specific point sources indicates complex geochemical processes governing trace VOC contamination of private drinking water sources. While individual concentrations of VOCs fell well below recommended human health levels, the low dose exposure to multiple VOCs occurring in drinking supplies for Appalachian communities was noted, highlighting the importance of groundwater well monitoring.

A novel automated pipeline to assess MR spectroscopy quality control: Comparing current standards and manual assessment.

The absence of a consensus data quality control (DQC) process inhibits the widespread adoption of MR spectroscopy. Poor DQC can lead to unreliable clinical diagnosis and irreproducible research conclusions. Currently, manual visual assessment or the standard quantitative metrics of signal-to-noise, linewidth, and model fit are used as classifiers, but these measures may not be sufficient. To supplement standard metrics, this paper proposes a novel automated DQC pipeline named Visual Evaluative Control Technology Of Resonance Spectroscopy (VECTORS). Manual DQC ratings were conducted on 7180 spectra obtained from 110 young adults using short-echo chemical shift imaging at 3Tesla. Four reviewers conducted manual ratings on the presence of artifacts and location of metabolites. The ratings were labor intensive, taking over 180hours. VECTORS was developed to quantify their DQC criteria, detecting artifacts that present as duplicate peaks, vertical shifts, and glutamine+glutamate and myoinositol peak shapes. Run on the same data using a standard laptop, VECTORS only took 2hours. The manual ratings were not monotonic to the standard quantitative metrics. VECTORS correctly flagged spectra that the manual ratings missed. VECTORS accurately flagged an additional 126 poor DQ spectra that consensus cutoffs of the standard quantitative metrics deemed good DQ. Standard quantitative metrics may not account for all DQC artifacts as they are not monotonic to the manual ratings. However, manual ratings are labor intensive, subjective, and irreproducible. VECTORS addresses these issues and should be used in conjunction with standard quantitative metrics.

Integrating dynamic high-throughput phenotyping and genetic analysis to monitor growth variation in foxtail millet

BackgroundFoxtail millet [Setaria italica (L.) Beauv] is a C4 graminoid crop cultivated mainly in the arid and semiarid regions of China for more than 7000 years. Its grain highly nutritious and is rich in starch, protein, essential vitamins such as carotenoids, folate, and minerals. To expand the utilisation of foxtail millet, efficient and precise methods for dynamic phenotyping of its growth stages are needed. Traditional foxtail millet monitoring methods have high labour costs and are inefficient and inaccurate, impeding the precise evaluation of foxtail millet genotypic variation.ResultsThis study introduces a high-throughput imaging system (HIS) with advanced image processing techniques to enhance monitoring efficiency and data quality. The HIS can accurately extract a range of key growth feature parameters, such as plant height (PH), convex hull area (CHA), side projected area (SPA) and colour distribution, from foxtail millet images. Compared with traditional manual measurements, this HIS improved data quality and phenotyping of the key foxtail millet growth traits. High-throughput phenotyping combined with a genome-wide association study (GWAS) revealed genetic loci associated with dynamic growth traits, particularly plant height (PH), in foxtail millet. The loci were linked to genes involved in the gibberellic acid (GA) synthesis pathway related to PH.ConclusionThe HIS developed in this study enables the efficient and dynamic monitoring of foxtail millet phenotypic traits. It significantly improves the quality of data obtained for phenotyping key growth traits. The integration of high-throughput phenotyping with GWAS provides new insights into the genetic underpinnings of dynamic growth traits, particularly plant height, by identifying associated genetic loci in the GA synthesis pathway. This methodological advancement opens new avenues for the precise phenotyping and exploration of genetic resources in foxtail millet, potentially enhancing its utilisation.

Measles surveillance data analysis and serological survey in Quzhou, China, 2014–2024: an assessment of progress toward measles elimination

BackgroundMeasles is a disease that can be eliminated through vaccination. In recent years, measles incidence and mortality have been greatly reduced.MethodsAnalyze measles surveillance data from 2014 to 2023 and measles seroepidemiological characteristics of healthy populations in 2024 to assess progress toward measles elimination.ResultsA total of 35 measles cases were reported in the surveillance system from 2014–2023 in Quzhou, with an average annual incidence of 1.6/1 million. Since 2019, the incidence of measles has been lower than 0.5/1 million. A serological survey of 257 healthy people showed that the positive rate of measles IgG antibody was 90.3%, and the immunity of all age groups except 0–5 years old was lower than 95%, which did not reach the threshold of 95% herd immunity required for eliminating measles.ConclusionAlthough the incidence of measles in Quzhou is low, the immunity of healthy people to measles infection is insufficient. Measles is still in the control phase, not in the elimination phase. Inadequate immunity in the population may be due to the failure to achieve ≥95% vaccination coverage and low immunogenicity of the vaccine. Recommends that the quality of routine immunization data be assessed and monitored to verify reported vaccination coverage; at the same time, improve vaccination services and optimize vaccination policies to increase actual vaccination coverage. In addition, it is recommended to adjust the MMR immunization strategy, changing the time of the first MMR vaccination from 8 months of age to 12–15 months of age, and the second dose at 4 to 6 years of age.

Quadratic Unet for seismic random noise attenuation

In the field of seismic data processing, seismic denoising is essential to improve the quality of seismic data. Various deep learning methods have been proposed, showing promising performance for seismic denoising. However, most deep learning methods are based on lin ear neurons, resulting in limited expressive ability for complex seismic signals. To enhance denoising capability using non-linear neurons, we propose a supervised quadratic U-shaped network (QUnet) for seismic random noise attenuation. The quadratic neurons in QUnet are represented by a second-order polynomial of input data and weighted parameters. Nu merical synthetic seismic data experiments show that the proposed QUnet method achieves higher signal-to-noise ratio results and preserves the continuity of reflectors more effectively. We have also tested the effectiveness of QUnet on both prestack field data and post-stack seismic data. The detailed structures and weak signals of seismic data are better preserved by our proposed QUnet method.

Long-term outcomes of spinal fusion in adolescent idiopathic scoliosis: a literature review

Adolescent idiopathic scoliosis (AIS) is the most common form of spinal deformity in the younger population. The surgical management for these patients improved constantly over the last year and might not be comparable to modern treatment strategies. However, under this aspect the present investigation updates and discusses current evidence regarding the long-term outcome of the surgical management of AIS. All the clinical studies which evaluated the long-term outcomes of spinal fusion were considered. Level of evidence, clinical and radiological data, results of health-related questionnaires and surgery-associated complications during long-term follow-up, e.g., proximal and distal junctional kyphosis (PJK/DJK), and adjacent segment degeneration (ASD), are presented. Data concerning the following patient-reported outcomes measures were collected: Oswestry Disability Index (ODI), Scoliosis Research Society (SRS) Outcome Questionnaire, visual analogue scale (VAS), and short form-12 and 36 (SF-12/SF-36). Overall, data from 1115 patients were included. Of them, 324 underwent anterior and 791 posterior spinal fusion. One study focuses on a combined anterior/posterior fusions. The mean follow-up was 22.6 years (posterior fusion: 24.6 years, anterior fusion: 18.31 years). Seven studies focus on the thoracic segments, while 12 focus on the lumbar spine. Data on imaging was reported in 13 studies and those on PROMs in 15 investigations. In conclusion, there is low quality and paucity of long-term data on AIS. However, the long-term results of the implicated studies on AIS patients in this review appear to be satisfactory, although there are limitations in the outcome compared to healthy comparison cohorts. Adjacent degenerations appear to be the most common mechanical complication after long-segment fusions, despite their influence on the outcome remains unclear. With regard to pregnancies, there are slightly increased cesarean section rates, which could be explained by deviations in the sagittal profile.

Adaptive questionnaires for facilitating patient data entry in clinical decision support systems: methods and application to STOPP/START v2.

Clinical decision support systems are software tools that help clinicians to make medical decisions. However, their acceptance by clinicians is usually rather low. A known problem is that they often require clinicians to manually enter a lot of patient data, which is long and tedious. Existing solutions, such as the automatic data extraction from electronic health record, are not fully satisfying, because of low data quality and availability. In practice, many systems still include long questionnaire for data entry. In this paper, we propose an original solution to simplify patient data entry, using an adaptive questionnaire, i.e. a questionnaire that evolves during user interaction, showing or hiding questions dynamically. Considering a rule-based decision support systems, we designed methods for determining the relationships between rules and translating the system's clinical rules into display rules that determine the items to show in the questionnaire, and methods for determining the optimal order of priority among the items in the questionnaire. We applied this approach to a decision support system implementing STOPP/START v2, a guideline for managing polypharmacy. We show that it permits reducing by about two thirds the number of clinical conditions displayed in the questionnaire, both on clinical cases and real patient data. Presented to clinicians during focus group sessions, the adaptive questionnaire was found "pretty easy to use". In the future, this approach could be applied to other guidelines, and adapted for data entry by patients.

Navigating AI Transformation: Human Resource Development Strategies for Corporate Learning

Artificial Intelligence (AI) has revolutionised knowledge work through generative AI like ChatGPT, impacting coordination, creativity, and problem-solving. As AI adoption accelerates, understanding its implications for Human Resource Development (HRD) becomes crucial. This paper introduces a conceptual framework for HRD strategies in AI transformation, addressing the dearth of research in this area. Utilising a case study approach, we distinguish five strategies: Acceleration, Culture-driven Transformation, Data-Driven Agility, Personalized Learning, and Immersive, emotional Learning at the workplace. Each strategy caters to distinct organisational needs. Challenges include balancing rapid upskilling, managing cultural shifts, ensuring data quality, promoting self-directed learning, and implementing immersive technologies. The framework offers insights for organisations navigating AI-induced transformations in HRD practices. Further research and refinement are essential as AI technology evolves.

Assessment of unintentional acute pesticide poisoning among smallholder vegetable farmers in Trinidad and Jamaica

Poisoning caused by pesticides is widely recognized as a major public health problem among smallholder farmers and rural communities, including in the Caribbean. However, a lack of quality data impedes understanding of the problem and hampers the development of effective strategies for its management. To better understand the prevalence of unintentional acute pesticide poisoning (UAPP) in Trinidad and Tobago and Jamaica and the pesticides and practices involved, we conducted a cross-sectional survey of 197 and 330 vegetable farmers in Trinidad and Jamaica, respectively. The findings from this study revealed a high incidence of self-reported health effects from occupational pesticide exposure, with 48 and 16% of respondents, respectively, experiencing symptoms of UAPP within the previous 12 months. Furthermore, the substantial proportion of UAPP incidents were associated with a few highly hazardous pesticides (HHPs), particularly lambda-cyhalothrin, acetamiprid, and profenofos in Jamaica, and alpha-cypermethrin, paraquat and lambda-cyhalothrin in Trinidad. Given the well-documented adverse effects of these chemicals on human health, the results of this study should be of significant concern to health authorities in Jamaica and Trinidad. This clearly indicates an urgent need for improved regulation and safer alternatives to the use of HHPs, as well as the promotion of alternatives. We provide policy recommendations and identify alternatives to HHPs for tropical vegetable production.

Does Real-time Feedback Guide Devices Improve the Quality of Chest Compressions in the Bystander (Naves) Provider?

Abstract Introduction: Cardiopulmonary resuscitation (CPR) is an important lifesaving technique that must taught to everyone. As most cardiac arrests occur outside of the hospital, training the general population in the recognition of cardiac arrest and performing high-quality CPR is vital. Thus, this study aimed to compare the chest compression quality data (rate, depth, and fraction) with and without the use of feedback devices during CPR. Methods: A crossover observational study was carried out at a tertiary hospital in September 2021. Five hundred and seventeen 1st-year university students were recruited, and the response rate was 90% (465). A feedback device was used to collect data. The data were numerically coded and statistical software (SPSS 21) was used to perform descriptive and inferential statistical analysis, including a paired t-test. Results: The mean compression rate with feedback was statistically significantly lower after performing one cycle of CPR (mean difference: −5.610; 95% confidence interval [CI]= −7.987–−3.233; P < 0.001), the difference between mean compression depth with and without feedback was not statistically significant (mean difference: = −0.006; 95% CI = −0.190–0.177; P = 0.944), and the mean compression target (%) with feedback was statistically significantly higher (mean difference: −15.951; 95% CI = −17.894–−14.009; P < 0.001). Conclusions: We found that the use of feedback devices during layperson-simulated CPR scenarios significantly improved the quality of chest compression in terms of depth and rate and resulted in more guidelines adherence. Moreover, national programs for CPR education should be implemented.

Phytochemical Screening of Seaweed Flour (Eucheuma cottonii) Using Various Organic Solvents and Its Application in Tilapia Feed (Oreochromis niloticus)

This study aims to qualitatively analyze the bioactive compounds in Eucheuma cottonii seaweed flour extract and its application in commercial feed for tilapia (Oreochromis niloticus). The methodology employed is experimental and consists of two phases. Phase 1 involves the extraction of E. cottonii flour using three different organic solvents with varying polarity (ethanol, ethyl acetate, and a combination of ethanol + ethyl acetate). The testing parameters include qualitative phytochemical tests for alkaloids, steroids/triterpenoids, saponins, flavonoids, and tannins. Phase 2 consists of the application of the extract to tilapia over a 30-day rearing period with four treatments: control/commercial feed (P0), commercial feed + ethanol extract (P1), commercial feed + ethyl acetate extract (P2), and commercial feed + combined ethanol and ethyl acetate extract (P3). The parameters tested include absolute weight, specific growth rate, survival rate, and water quality. Phytochemical and water quality data are presented in table format. Meanwhile, growth and survival data are analyzed using ANOVA, followed by Duncan’s test for significant differences. The results of the researches carried out, it can be concluded that the phytochemical analysis of E. cottonii extract in ethanol and the ethanol + ethyl acetate combination yielded similar compounds: alkaloids, steroids/triterpenoids, and flavonoids. The ethyl acetate extract contained triterpenoids and flavonoids. Both saponins and tannins returned negative results across all organic solvent treatments. All treatments with the addition of E. cottonii seaweed extract in various organic solvents demonstrated better growth in tilapia compared to the control treatment.

A Passive-Source Ocean-Bottom Seismic Experiment in the South China Sea: Evaluations of Data Quality and Instrument Performance

Abstract This study presents a passive-source ocean-bottom seismograph (OBS) experiment conducted in the southwest subbasin of the South China Sea (SCS) aimed at exploring the geodynamic processes shaping this short-lived oceanic basin. The successful deployment and recovery of 24 passive-source OBS units, including 14 I-7C types and 10 Pankun OBS units, resulted in the acquisition of one of the most extensive passive-source seismic data sets ever recorded in the SCS. We provide comprehensive details of the experiment, with a focus on evaluating the data quality and performance of the Pankun OBS units. This evaluation includes assessments of horizontal orientation determination, leveling system effectiveness, timing accuracy, and ambient noise spectrum. In addition, we compared the waveforms recorded by the Pankun OBS units with those from land stations and I-7C units, as well as the noise spectra between Pankun and global OBSs using the same seismometer. Although these comparisons suggest that the Pankun OBS achieves satisfactory performance, we identified a few inadequacies with this new instrument, such as issues with the differential pressure gauge, clock shifts, and sensor package calibration.

Impact of light polarization on laser speckle contrast imaging with a custom phantom for microvascular flow

Laser speckle contrast imaging (LSCI) is a non-invasive, powerful, and cost-effective imaging technique that has seen widespread adoption across various medical fields, particularly for blood flow imaging. While LSCI provides physicians with valuable insights into changes or occlusions in blood flow, the technique is susceptible to various factors and parameters that can impact measurement sensitivity and signal-to-noise ratio (SNR). These include the scattering of light, which can affect the quality and reliability of the LSCI data. The polarization of light holds significant promise to enhance the performance of LSCI. In this study, we employed polarization manipulation of light to investigate its impact on the performance of LSCI for measuring flow. Focusing on the application of LSCI in microcirculation within capillaries, we examined the effect of polarization control on the technique's flow measurement capabilities using a custom-designed phantom system. This phantom consisted of three tubes with inner diameters of 1.1 mm, 1.6 mm, and 2.8 mm, embedded in a polydimethylsiloxane (PDMS) matrix with optical properties similar to biological tissue. By manipulating the polarization of both the incident and reflected light, alternating between parallel and perpendicular states, we compared the performance of our LSCI system in detecting flow for different tube diameters and depths within the phantom. Our study revealed that while depth is a critical parameter influencing flow detection using LSCI, employing perpendicular polarization (between incident and reflected light) resulted in the lowest measurement error and highest SNR compared to parallel polarization and the absence of polarization control.

Machine Learning-based Filtering system for fNIRS Signals’ Analysis Purpose

This paper presents a preliminary study delving into the application of machine learning-based methods for optimising parameter selection in filtering techniques. The authors focus on exploring the efficacy of two prominent filtering methods: smoothing and cascade filters, known for their profound impact on enhancing the quality of brain signals. The study specifically examines signals acquired through functional near-infrared spectroscopy (fNIRS), a non-invasive neuroimaging modality offering valuable insights into brain activity. Through meticulous analysis, the research underscores the potential of machine learning approaches in discerning optimal parameters for filtering, thereby leading to a significant enhancement in the quality and reliability of fNIRS-derived signals. The results demonstrate the effectiveness of machine learning-based methods in optimizing parameter selection for filtering techniques, particularly in the context of fNIRS signals. By leveraging these approaches, the study achieves notable improvements in the quality and reliability of brain signal data. This work sheds light on promising avenues for refining neuroimaging methodologies and advancing the field of signal processing in neuroscience. The successful application of machine learning-based techniques highlights their potential for optimizing neuroimaging data processing, ultimately contributing to a deeper understanding of brain function.

A Deep Learning Method to Integrate extracelluar miRNA with mRNA for cancer studies.

Extracellular miRNAs (exmiRs) and intracellular mRNAs both can serve as promising biomarkers and therapeutic targets for various diseases. However, exmiR expression data is often noisy, and obtaining intracellular mRNA expression data usually involves intrusive procedures. To gain valuable insights into disease mechanisms, it is thus essential to improve the quality of exmiR expression data and develop non-invasive methods for assessing intracellular mRNA expression. We developed CrossPred, a deep-learning multi-encoder model for the cross-prediction of exmiRs and mRNAs. Utilizing contrastive learning, we created a shared embedding space to integrate exmiRs and mRNAs. This shared embedding was then used to predict intracellular mRNA expression from noisy exmiR data and to predict exmiR expression from intracellular mRNA data. We evaluated CrossPred on three types of cancers and assessed its effectiveness in predicting the expression levels of exmiRs and mRNAs. CrossPred outperformed the baseline encoder-decoder model, exmiR or mRNA-based models, and variational autoencoder models. Moreover, the integration of exmiR and mRNA data uncovered important exmiRs and mRNAs associated with cancer. Our study offers new insights into the bidirectional relationship between mRNAs and exmiRs. The datasets and tool are available at https://doi.org/10.5281/zenodo.13891508. Supplementary data are available at Bioinformatics online.

Research on Oil Well Production Prediction Based on GRU-KAN Model Optimized by PSO

Accurately predicting oil well production volume is of great significance in oilfield production. To overcome the shortcomings in the current study of oil well production prediction, we propose a hybrid model (GRU-KAN) with the gated recurrent unit (GRU) and Kolmogorov–Arnold network (KAN). The GRU-KAN model utilizes GRU to extract temporal features and KAN to capture complex nonlinear relationships. First, the MissForest algorithm is employed to handle anomalous data, improving data quality. The Pearson correlation coefficient is used to select the most significant features. These selected features are used as input to the GRU-KAN model to establish the oil well production prediction model. Then, the Particle Swarm Optimization (PSO) algorithm is used to enhance the predictive performance. Finally, the model is evaluated on the test set. The validity of the model was verified on two oil wells and the results on well F14 show that the proposed GRU-KAN model achieves a Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2) values of 11.90, 9.18, 6.0% and 0.95, respectively. Compared to popular single and hybrid models, the GRU-KAN model achieves higher production-prediction accuracy and higher computational efficiency. The model can be applied to the formulation of oilfield-development plans, which is of great theoretical and practical significance to the advancement of oilfield technology levels.

Remote Sensing Data Quality in the Era of AI

Abstract. The era of Artificial Intelligence ‘AI’ with all the benefits brought along, has raised new and additional challenges to the ongoing efforts of assessing, defining, formulating, and implementing the quality aspects of geospatial remote sensing data. Developed practices using artificial intelligence leveraged techniques such as image interpretation, classification, thematic mapping, and even image quality enhancement, necessitating by that the reassessment and redevelopment of some of the related emerging quality aspects. Moreover, technology also made the generation of false images and false data possible, this matter constrained and increased precaution and doubtfulness, altogether making some practices based on that data almost halt to further notice. This paper presents the collaborative research work to assess and clarify the quality aspects that arose with the advent and implementation of AI and associated technologies; the concerns and issues that can accompany the generation of false satellite and aerial images including the generated geospatial data out of which, how the new emerged quality aspects fit into the currently existing methods through the lifecycle of remote sensing data production and usage, and consequently how the quality dimensions are affected and should be further developed and improved to tackle the changes and innovations. Also, lame a bit on investigating how to accommodate the new challenges in standards, and practical procedures and raise the awareness to users, the level of dependency on improved and enhanced satellite images when it comes to data collection interpretation and classification, and finally define the research gaps, future expected challenges and thus enclose suggestions and recommendations in that respect.

Multiplatform metabolomic interlaboratory study of a whole human stool candidate reference material from omnivore and vegan donors.

Human metabolomics has made significant strides in understanding metabolic changes and their implications for human health, with promising applications in diagnostics and treatment, particularly regarding the gut microbiome. However, progress is hampered by issues with data comparability and reproducibility across studies, limiting the translation of these discoveries into practical applications. This study aims to evaluate the fit-for-purpose of a suite of human stool samples as potential candidate reference materials (RMs) and assess the state of the field regarding harmonizing gut metabolomics measurements. An interlaboratory study was conducted with 18 participating institutions. The study allowed for the use of preferred analytical techniques, including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR). Different laboratories used various methods and analytical platforms to identify the metabolites present in human stool RM samples. The study found a 40% to 70% recurrence in the reported top 20 most abundant metabolites across the four materials. In the full annotation list, the percentage of metabolites reported multiple times after nomenclature standardization was 36% (LC-MS), 58% (GC-MS) and 76% (NMR). Out of 9,300 unique metabolites, only 37 were reported across all three measurement techniques. This collaborative exercise emphasized the broad chemical survey possible with multi-technique approaches. Community engagement is essential for the evaluation and characterization of common materials designed to facilitate comparability and ensure data quality underscoring the value of determining current practices, challenges, and progress of a field through interlaboratory studies.