Reliable Data Research Articles

Long-term monitoring of citizen science: driving factors and pandemic impacts

ABSTRACT In recent years, the importance of citizen science in biodiversity monitoring has become increasingly evident. Citizen science provides valuable data to understand the dynamic changes in biodiversity across different cities. Although long-term monitoring is crucial for understanding these dynamic changes, previous research has primarily focused on short-term data analysis. Therefore, this study analysed data from the iNaturalist platform for 11 Japanese cities from 2016 to 2023, focusing on the driving factors of observation changes and the impact of the COVID-19 pandemic using the Logarithmic Mean Divisia Index (LMDI) method. The results revealed that the population (total number of active users) metric had the greatest impact, followed by intensity (the number of observations per active day), frequency (active days per active user), and structure (proportion of regular users). To ensure data continuity and reliability, optimizing user structure by increasing the proportion of regular users is essential. Although observation volumes fluctuated during the pandemic, they rebounded significantly post-pandemic, demonstrating the resilience, and adaptability of citizen science projects. The iNaturalist platform provides a stable foundation for long-term data collection, aiding in the comprehensive understanding of biodiversity dynamics. Our study also highlighted differences in citizen science participation and biodiversity monitoring across cities, indicating the need for tailored biodiversity conservation policies. To effectively address similar crises in the future, developing contingency plans is recommended to provide continuous support for biodiversity conservation and public engagement in environmental management.

Comparative Analysis of Reliability in On-Time Monitoring Data for NO3–N, BTEX, and TOC: Commercialized Sensors versus Spectroscopic Methods

Comparative Analysis of Reliability in On-Time Monitoring Data for NO₃–N, BTEX, and TOC: Commercialized Sensors versus Spectroscopic Methods

Effect of Inflation Rate on government bond yields listed at the Nairobi Securities Exchange.

This study aimed to determine the effect of inflation rates on government bond yields listed on the Nairobi Securities Exchange (NSE). The primary objective was to assess how fluctuations in inflation impact the yields of 15-year government bonds over a 16-year period, from the first quarter of 2007 to the first quarter of 2023. The study applied liquidity premium theory, as posited by Brennan (1979), which suggests that investors require a liquidity premium, or higher yield, during inflationary periods to compensate for the erosion of purchasing power in fixed-income investments. Given this theoretical framework, the research sought to quantify the relationship between inflation rates and bond yields, considering how rising inflation prompts higher yields to maintain purchasing power.The study employed a causal-comparative research design, utilizing secondary data on bond yields and inflation rates sourced from NSE and Central Bank of Kenya records. Key diagnostic tests, such as the Jarque-Bera test for normality, Breusch-Pagan-Godfrey test for heteroscedasticity, and Augmented Dickey-Fuller test for stationarity, ensured data validity and reliability. The Vector Error Correction Model (VECM) was used to analyze both short- and long-term relationships between inflation and bond yields. Findings revealed a statistically significant positive correlation between inflation rates and government bond yields, with inflation accounting for 45% of the variance in bond yields. The standardized coefficient of 0.67 suggested a moderate-to-strong association, highlighting inflation’s critical role in bond pricing. Based on these results, it is recommended that investors and policymakers closely monitor inflation trends as they influence bond yields and market performance. Further research could explore other macroeconomic factors, such as interest rates, to offer a more comprehensive view of bond market dynamics in emerging economies.

Brazil and regional fisheries management organizations - What is at stake?

This study compares Brazil's participation in two distinct Regional Fisheries Management Organizations (ICCAT and CCAMLR) from the mid-2000s to 2019 to reveal how domestic governance issues such as substitutability and regulatory capture shape its international negotiation strategies. By contrasting Brazil's active fishing role in ICCAT with its conservation-focused stance in CCAMLR, the study demonstrates how varying domestic interests influence the country’s international engagement and sustainability commitments. By analyzing primary and secondary sources, including government documents, international submissions, and reports from non-governmental organizations, this study offers insights into the complexities of fisheries management in Brazil. The findings reveal that Brazil's contributions to and stance on RFMOs are significantly shaped by internal challenges, including governance instability and insufficient fisheries data, which hinder the formulation of coherent and effective international fishery policies. This study highlights the need for improved governance framework, data reliability, and a balanced approach to environmental and economic objectives to enhance the sustainability of fishery management practices. It provides critical perspectives for policymakers, stakeholders, and researchers concerned with advancing sustainable fisheries governance in Brazil and similar global contexts.

Cross-validation of pharmacokinetic assays post-ICH M10 is not a pass/fail criterion.

The ICH M10 guideline establishes global standards for bioanalytical method validation for pharmacokinetic assays, focusing on data reliability and accuracy across studies. A significant component is cross-validation, which should be performed to ensure data comparability when multiple methods or laboratories are involved in a single study or across studies where comparison will be performed. However, ICH M10 does not specify acceptance criteria for cross-validation, creating challenges for the industry because traditionally many laboratories have always utilized acceptance criteria to "pass" or "fail" the study. This editorial discusses how bioanalytical labs should conduct cross-validation for PK assays post-ICH M10, highlighting the role of statistical methods and the need for close collaboration with clinical pharmacology and biostatistics departments. Proper implementation and strategic focus on relevant studies are essential for effective cross-validation.

Innovative machine learning for drilling fluid density prediction: a novel central force search-adaptive XGBoost in HPHT environments

Oil and gas industries are facing a special dilemma when it comes to high-pressure, high-temperature (HPHT) drilling as the accurate forecasting of the drilling fluid density (DFD) is a vital factor for safe and efficient operations. Complicated relationships and inconsistencies in HPHT situations are rarely mapped by current forecasting models, while their buggy performance and safety risks during drilling can be underestimated. In this research, we propose a novel machine learning (ML) approach to enhance the accuracy of DFD anticipation under HPHT conditions: central force search-adaptive extreme gradient boosting (CFS-XGB). This paper uses a dataset that has drilling variables together with the DFD for HPHT situations to examine the accuracy of the CFS-XGB model. Excluding the abnormalities of data or mistakes, the reliability of the original data is maintained by applying min–max normalization. After that, finding the important features with the help of the boosted principal component analysis (BPCA) approach to the normalized data will ensure a major improvement in the CFS-XGB methodology’s prediction efficacy. This research is experimented in the Python platform, and the performance of the proposed CFS-XGB method is analyzed in terms of MSE, R2, and AAPRE metrics. The suggested approach performs better than the current methods in forecasting the drilling fluid concentration in HPHT settings, according to the experimental data. This development in predictive modeling helps increase the productivity and safety of drilling operations, which will eventually help the oil and gas sector manage the challenges posed by HPHT drilling settings.

Wearable device data-driven athlete injury detection and rehabilitation monitoring algorithm

In recent years, sports wearable technology has completely changed the way athletes prepare, compete, and recover. Wearable technology has a lot to offer in the rehabilitation process, which is essential to an athlete’s return to their best performance. Wearable devices for athlete injury detection pose potential challenges like data quality, security, and privacy, impacting accuracy, reliability, and effectiveness. To solve these problems, an innovative injury detection and rehabilitation monitoring (IDRM) system was proposed for athletes. By employing an adjustable recurrent neural network (ARNN) to detect anomalies in injury risks such as abnormal joint movements in athletes. In this study, biomechanics data was collected from sports athletes through wearable devices, and the wearable system provided feedback to the user. A redefined convolutional neural network (RCNN) was utilized to monitor the rehabilitation process. This system tracks athlete’s rehabilitation progress and ensures that progress monitors were performed correctly, and the system, feasibility was evaluated on 10 healthy subjects performing 4 different rehabilitation exercises. Each exercise was performed four times monitoring and validation. The data was preprocessed using a Gaussian filter to remove noise from the obtained data. Then the features are extracted using independent component analysis (ICA) for dimensionality reduction from preprocessed data. The proposed method is implemented using Python software. In comparative analysis, the performance of ARNN showed high performance, with an F1-measure of 91.6%, accuracy of 93.5%, recall of 92.8%, and precision of 91.4%. With a 95% accuracy rate, 98% F1 measure, 94% precision, and 93% recall, the RCNN model functioned effectively. The result showed the proposed method achieved better performance in athlete injury detection and accurately recognizing all rehabilitation monitoring. This study provides a complete approach to athlete health management by highlighting the integration of rehabilitation monitoring and injury detection into an overall structure.

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.

On the problem of estimating the snowiness of winters by different methods by the example of the Yuzhno-Sakhalinsk city

The problem of estimating a snowiness (a snow coverage) of a territory is considered by many authors, but still nowadays no common approach to its solution is reached. Different authors use different characteristics in their works for estimating the winter snowiness. In this work the snowiness of winters was estimated using different parameters to determine the most representative one. The area ща study is the Yuzhno-Sakhalinsk urban district. The data for the past 36 years (1986–2022) were used to determine the types of the winter snowiness in this district. The following nine methods have been applied: the maximum winter snow reserve; by the amount of solid precipitation; by the average winter thickness of snow cover from the weather station and by snow survey; by the average greatest ten-day thickness of snow cover; by the amount of precipitation in the form of snow; the method of N.N. Galakhov; the Schultz coefficient; and the maximum winter snow cover thickness. The results obtained are very contradictory. Thus, the types of snowiness completely coincided in only 17% of winters; while in 58% of winters the types of snowiness coincided by 2/3 of the above indicators. Estimation of snowiness using various parameters gives closer results when using data on snow reserve at the beginning of snowmelt and the average winter thickness of snow cover (coincidence in 78% of cases). The reason is that a major part of the above methods uses the values of only one parameter. But given that snowiness is a complex indicator, it would be reasonable to consider all possible parameters at the same time. It is impossible to develop a unified approach to estimating winter snowiness, since the parameters chosen for this problem depend on the goal of the determination. It would also be worthwhile to select a methodology for estimating winter snowiness depending on the tasks set, and on the availability and reliability of the initial meteorological data for the analysis. Such work is also complicated by the insufficient volume of meteorological data, as well as due to gaps in them.

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.

Formulation of Envelope Correlation Coefficient for Multiple Sensors Based on Scattering Parameter

The Envelope Correlation Coefficient (ECC) is crucial for assessing multiple sensor systems in wireless communication, sensing, and microwave imaging. ECC measures signal similarity between sensors, with higher values (close to 1.00) indicating strong correlation. Efficient power transmission, reception, and multiband path transmission enhance sensor network performance by improving data rates and reliability through multiple frequency bands. This study uses scattering parameters to calculate ECC for four different sensors in a single arrangement. Most of the sensors exhibit higher correlation at f2 and f3, while f1 shows a low ECC.

Electronic Health Record Serious Illness Conversation Dashboards: An Implementation Case Series

IntroductionDashboards are used to track and visualize quality indicators within health systems to improve clinical performance. Structured serious illness conversation (SIC) documentation templates within electronic health records (EHR) have enabled the development of SIC dashboards for quality improvement. Little is known about the successes and challenges of SIC dashboards. MethodsThis implementation case series describes findings from semi-structured interviews and surveys with nine palliative care leaders in eight health systems that implemented SIC dashboards. Interviews and case information were analyzed to identify themes. ResultsFive themes were identified. First, dashboards focus on process metrics. By creating transparent and efficient access to data on EHR-documented SICs, dashboards facilitated monitoring of the results of clinician training and quality improvement efforts. Second, palliative care leaders used dashboard data on documented SICs to implement clinician practice change strategies (e.g. data feedback; quality incentives), but clinicians had mixed reactions to data. Third, dashboards facilitated leadership investment in SIC efforts and required financial and technical resources to build and maintain. Fourth, while dashboards streamlined data collection for implementation teams, participants noted challenges with data reliability, including inadequate clinician use of structured SIC documentation templates (which most dashboards rely on for measurement). Fifth, needs and tensions arose with integrating patient-centered outcome measures as part of dashboards. ConclusionDashboards can be powerful tools for identifying gaps in SIC and driving interventions for clinician practice change. However, challenges related to clinician adoption of structured templates for SIC documentation and mixed clinician receptivity to data feedback may limit their reliability and use.

Evaluating the effectiveness of elastic net model in predicting stock closing price

Abstract. Accurate stock price prediction is important for financial investors, as it directly influences investment strategies and decision-making. In this study, the author focuses on evaluating the effectiveness of the Elastic Net model in forecasting the closing prices of Apple Inc.'s stock. The dataset, spanning from 2014 to 2023, is sourced from Kaggle and includes both historical prices and a range of technical indicators, thereby ensuring the accuracy and reliability of the research data. To enhance the predictive accuracy of the model, this paper carefully selects the 14-day moving average of the closing prices along with a set of relevant technical indicators as the key input variables. This selection aims to capture the underlying trends and patterns in the stock's price. The predictive performance of the Elastic Net model is evaluated using multiple evaluation metrics, including Mean Squared Error (MSE) and R-squared values, which quantify the model's accuracy and explanatory power. Additionally, this paper utilizes the Autocorrelation Function (ACF) plot to analyze the residuals and further validate the model's effectiveness. The results demonstrate that the Elastic Net model is well-suited for handling high-dimensional financial data, exhibiting strong performance in predicting Apple Inc.'s stock prices. However, it also underscores the model's potential problem as a valuable tool for investors seeking to make proper decisions in real-life conditions.

Effects of List-Mode-Based Intraframe Motion Correction in Dynamic Brain PET Imaging.

Motion is unavoidable in dynamic [18F]-MK6240 Positron Emission Tomography (PET) imaging, especially in Alzheimer's disease (AD) research requiring long scan duration. To understand how motion correction affects quantitative analysis, we investigated two approaches: II-MC, which corrects for both inter-frame and intra-frame motion, and IO-MC, which only corrects for inter-frame motion. These methods were applied to 83 scans from 34 subjects, and we calculated distribution volume ratios (DVR) using the multilinear reference tissue model with two parameters (MRTM2) in tau-rich brain regions. Most of the studies yielded similar DVR results for both II-MC and IO-MC. However, in one scan of an AD subject, the inferior temporal region showed 14% higher DVR with II-MC compared to IO-MC. This difference was reasonable given the AD diagnosis, although similar results were not observed in other regions. Although discrepancies between IO-MC and II-MC results were rare, they underscore the importance of incorporating intra-frame motion correction for more accurate and dependable PET quantitation, particularly in the context of dynamic imaging. These findings suggest that while the overall impact of intra-frame motion correction may be subtle, it can improve the reliability of longitudinal PET data, ultimately enhancing our understanding of tau protein distribution in AD pathology.

Enhancing flexibility and system performance in 6G and beyond: A user-based numerology and waveform approach

A Mixed Numerology OFDM (MN-OFDM) system is essential in 6G and beyond. However, it encounters challenges due to Inter-Numerology Interference (INI). The upcoming 6G technology aims to support innovative applications with high data rates, low latency, and reliability. Therefore, effective handling of INI is crucial to meet the diverse requirements of these applications. To address INI in MN-OFDM systems, this paper proposes a User-Based Numerology and Waveform (UBNW) approach that uses various OFDM-based waveforms and their parameters to mitigate INI. By assigning a specific waveform and numerology to each user, UBNW mitigates INI, optimizes service characteristics, and addresses user demands efficiently. Guard Band (GB) requirements, expressed as a ratio of user bandwidth, vary significantly across different waveforms at an SIR of 25 dB. For instance, OFDM-FOFDM needs only 2.5%, while OFDM-UFMC, OFDM-WOLA, and conventional OFDM require 7.5%, 24%, and 40%, respectively. The time-frequency efficiency also varies between the waveforms. FOFDM achieves 85.6%, UFMC achieves 81.6%, WOLA achieves 70.7%, and conventional OFDM achieves 66.8%. The simulation results demonstrate that the UBNW approach not only effectively mitigates INI but also enhances system flexibility and time-frequency efficiency, while simultaneously reducing the required GB.

Pioneering diabetes screening tool: machine learning driven optical vascular signal analysis

The escalating prevalence of diabetes mellitus underscores the critical need for non-invasive screening tools capable of early disease detection. Present diagnostic techniques depend on invasive procedures, which highlights the need for advancement of non-invasive alternatives for initial disease detection. Machine learning in integration with the optical sensing technology can effectively analyze the signal patterns associated with diabetes. The objective of this research is to develop and evaluate a non-invasive optical-based method combined with machine learning algorithms for the classification of individuals into normal, prediabetic, and diabetic categories. A novel device was engineered to capture real-time optical vascular signals from participants representing the three glycemic states. The signals were then subjected to quality assessment and preprocessing to ensure data reliability. Subsequently, feature extraction was performed using time-domain analysis and wavelet scattering techniques to derive meaningful characteristics from the optical signals. The extracted features were subsequently employed to train and validate a suite of machine learning algorithms. An ensemble bagged trees classifier with wavelet scattering features and random forest classifier with time-domain features demonstrated superior performance, achieving an overall accuracy of 86.6% and 80.0% in differentiating between normal, prediabetic, and diabetic individuals based on the optical vascular signals. The proposed non-invasive optical-based approach, coupled with advanced machine learning techniques, holds promise as a potential screening tool for diabetes mellitus. The classification accuracy achieved in this study warrants further investigation and validation in larger and more diverse populations.

Design and Investigation of a High-Performance Quartz-Based SAW Temperature Sensor

In this work, a surface acoustic wave (SAW) temperature sensor based on a quartz substrate was designed and investigated. Employing the Coupling-of-Modes (COM) model, a detailed analysis was conducted on the effects of the number of interdigital transducers (IDTs), the number of reflectors, and their spacing on the performance of the SAW device. The impact of the transversal mode of quartz SAWs on the device was subsequently examined using the finite element method (FEM). The simulation results indicate that optimizing these structural parameters significantly enhances the sensor’s sensitivity and frequency stability. SAW devices with optimal structural parameters were fabricated, and their resonant frequencies were tested across a temperature range of 25–150 °C. Experimental results demonstrate that the SAW temperature sensor maintains high performance stability and data reliability throughout the entire temperature range, achieving a Bode-Q of 7700. Furthermore, the sensor exhibits excellent linearity and repeatability. An analysis of the sensor’s response under varying temperature conditions reveals a significant temperature dependency on its Temperature Coefficient of Frequency (TCF). This feature suggests that the sensor possesses potential advantages for applications in industrial process control and environmental monitoring.

Early Detection of Wildlife Disease Pathogens Using CRISPR-Cas System Methods.

Wildlife diseases are a considerable threat to human health, conservation, and the economy. Surveillance is a critical component to mitigate the impact of animal diseases in these sectors. To monitor human diseases, CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR-associated protein) biosensors have proven instrumental as diagnostic tools capable of detecting unique DNA and RNA sequences related to their associated pathogens. However, despite the significant advances in the general development of CRISPR-Cas biosensors, their use to support wildlife disease management is lagging. In some cases, wildlife diseases of concern could be rapidly surveyed using these tools with minimal technical, operational, or cost requirements to end users. This review explores the potential to further leverage this technology to advance wildlife disease monitoring and highlights how concerted standardization of protocols can help to ensure data reliability.

Causal relationship between hypothyroidism and ulcerative colitis: a bidirectional Mendelian randomization study.

Ulcerative colitis (UC) and Hashimoto's thyroiditis frequently cooccur in patients with multiple autoimmune conditions, but the specific association between UC and hypothyroidism is unknown. We used Mendelian randomization (MR) methods to determine the causal relationship between UC and hypothyroidism. We obtained single nucleotide polymorphisms (SNPs) related to ulcerative colitis (UC) and hypothyroidism from genome-wide association studies (GWAS) available in the public database of the Integrated Epidemiology Unit (IEU). To assess the causal relationship between UC and hypothyroidism, we employed MR-Egger, weighted median, inverse variance weighted (IVW), simple mode, and weighted mode methods. Sensitivity analyses were performed using Cochran's Q test, the horizontal pleiotropy test, and the leave-one-out (LOO) method to assess the reliability of the MR data. The genes corresponding to instrumental variables (IVs) were subjected to Gene Ontology (GO) functional annotation, Kyoto Encyclopedia of the Genome (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) analysis to explore the mechanisms behind the causal relationships at the gene level. Forward MR analysis indicated that hypothyroidism was associated with an increased risk of UC (IVW: P = 0.02, OR = 9.71, 95% confidence interval (CI) = 1.36-69.46). In contrast, reverse MR did not demonstrate a causal relationship between UC and hypothyroidism (IVW: P = 0.53). Sensitivity analysis proved the reliability of the results. The PPI network revealed CD247, CD80, and STAT4 as central genes. GO and KEGG analyses revealed significant enrichment of the T cell, gamma interferon (IFN-γ), and programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) pathways. Hypothyroidism was a risk factor for UC. The balance of T-cell differentiation played an important role in the process of hypothyroidism-induced UC, and IL-21 might be the key to finding a cure. Enrichment of PD-1/PD-L1 might attenuate inflammation by suppressing the immune action of T cells.

Accurate and efficient AI-assisted paradigm for adding granularity to ERA5 precipitation reanalysis

Scientific inquiry has long relied on deterministic algorithms for systematic problem-solving and predictability. However, the rise of artificial intelligence (AI) has revolutionized data analysis, allowing us to uncover complex patterns in large datasets. In this study, we combine these two approaches by using AI to improve the reconstruction of past precipitation events, which is crucial for understanding climate change. Our objective is to leverage AI to map large-scale atmospheric proxies from the ERA5 climate reanalysis and multi-satellite historical precipitation data from the NASA-IMERG GPM constellation to observed precipitation, enhancing the accuracy and the resolution of climate reanalysis. Accurate climate reanalyses are essential, as they provide the most realistic representations of past atmospheric conditions, serving as benchmarks against which climate models are validated. Our AI-enhanced method offers a more accurate and computationally efficient solution compared to deterministic high-resolution precipitation downscaling methods. Additionally, it shows the capability to generalize predictions to new, previously unobserved locations, making it applicable across various regions. By integrating AI with traditional reanalysis techniques, we open up new opportunities for climate science and geosciences, with the potential to improve the accuracy and reliability of climate data, contributing to a better understanding of climate dynamics.