Raw Data Research Articles

Brute force prey metabarcoding to explore the diets of small invertebrates.

Prey metabarcoding has become a popular tool in molecular ecology for resolving trophic interactions at high resolution, from various sample types and animals. To date, most predator-prey studies of small-sized animals (<1 mm) have met the problem of overabundant predator DNA in dietary samples by adding blocking primers/peptide nucleic acids. These primers aim to limit the PCR amplification and detection of the predator DNA but may introduce bias to the prey composition identified by interacting with sequences that are similar to those of the predator. Here we demonstrate the use of an alternative method to explore the prey of small marine copepods using whole-body DNA extracts and deep, brute force metabarcoding of an 18S rDNA fragment. After processing and curating raw data from two sequencing runs of varying depths (0.4 and 5.4 billion raw reads), we isolated 1.3 and 52.2 million prey reads, with average depths of ~15,900 and ~120,000 prey reads per copepod individual, respectively. While data from both sequencing runs were sufficient to distinguish dietary compositions from disparate seasons, locations, and copepod species, greater sequencing depth led to better separation of clusters. As computation and sequencing are becoming ever more powerful and affordable, we expect the brute force approach to become a general standard for prey metabarcoding, as it offers a simple and affordable solution to consumers that is impractical to dissect or unknown to science.

Testing the inhibition efficiency of the castor oil leaf as corrosion inhibitor of mild steel in H2SO4

This study focused on the testing of castor oil leaf as corrosion inhibitor of mild steel in H2SO4. It involved the examination of the dynamics of molecular forces at the inhibitor and mild steel interface. Findings of the study will promote industrial diversification of castor oil leaf. On the experiment, bio-molecular compounds of the leaf were identified by gas chromatography mass spectrophotometer. Then, gravimetric and potentiodynamic polarization techniques were employed, where the efficacy and type of the inhibitor were determined. Considering the gravimetric process, factors of inhibitor concentration (0.2 g/L - 1.0 g/L), temperature (303 K – 333 K) and time (8 hrs – 24 hrs) were considered, with the inhibitor efficiency as the response. The efficiency was determined and optimized using response surface methodology (RSM) and artificial neural network (ANN). On the other hand, potentiodynamic polarization process was used to identify the type of inhibitor. Adsorption quantities were ascertained through the subjection of the experimental raw data to various adsorption isotherms. The analyses showed that castor oil leaf contains methyl nicotinate and pyridine-4-carbohydrazide. The bio-molecular compounds possess heteroatoms (N, S and O), signifying strong corrosion inhibitive tendencies. High efficiency (96.25 %) was obtained, with ANN having better prediction capability than the RSM. Potentiodynamic polarization curves revealed castor oil leaf as mixed-type inhibitor. On the adsorption phenomenon, the process of the inhibition was best fitted by Frumkin isotherm, revealing physical adsorption mechanism of Gibb’s free energy less than -40 kJ/mole. As a mixed-type inhibitor, castor oil leaf should be applied for mitigation of anodic and cathodic corrosion.

Distribution and Properties of Molecular Gas toward the Monoceros OB1 Region

We perform a comprehensive CO study toward the Monoceros OB1 (Mon OB1) region based on the Milky Way Imaging Scroll Painting survey at an angular resolution of about 50″. The high-sensitivity data, together with the high dynamic range, show that molecular gas in the 8° × 4° region displays complicated hierarchical structures and various morphology (e.g., filamentary, cavity-like, shell-like, and other irregular structures). Based on Gaussian decomposition and clustering for 13CO data, a total of 263 13CO structures are identified in the whole region, and 88% of raw data flux is recovered. The dense gas with relatively high column density from the integrated CO emission is mainly concentrated in the region where multiple 13CO structures are overlapped. Combining the results of 32 large 13CO structures with distances from Gaia DR3, we estimate an average distance of 729−45+45pc for the giant molecular cloud (GMC) complex. The total mass of the GMC complex traced by 12CO, 13CO, and C18O is 1.1 × 105 M ⊙, 4.3 × 104 M ⊙, and 8.4 × 103 M ⊙, respectively. The dense gas fraction shows a clear difference between Mon OB1 GMC East (12.4%) and Mon OB1 GMC West (3.3%). Our results show that the dense gas environment is closely linked to the nearby star-forming regions. On the other hand, star-forming activities have a great influence on the physical properties of the surrounding molecular gas (larger velocity dispersion, higher temperatures, more complex velocity structures, etc.). We also discuss the distribution/kinematics of molecular gas associated with nearby star-forming activities.

Taxonomic identity of the Bacillus licheniformis strains used to produce food enzymes evaluated in published EFSA opinions.

Bacillus paralicheniformis, a species known to produce the antimicrobial bacitracin, could be misidentified as Bacillus licheniformis, depending on the identification method used. For this reason, the European Commission requested EFSA to review the taxonomic identification of formerly assessed B. licheniformis production strains. Following this request, EFSA retrieved the raw data from 27 technical dossiers submitted and found that the taxonomic identification was established by 16S rRNA gene analyses for 15 strains and by whole genome sequence analysis for 12 strains. As a conclusion, only these 12 strains could be unambiguously identified as B. licheniformis.

G2-MonoDepth: A General Framework of Generalized Depth Inference From Monocular RGB+X Data.

Monocular depth inference is a fundamental problem for scene perception of robots. Specific robots may be equipped with a camera plus an optional depth sensor of any type and located in various scenes of different scales, whereas recent advances derived multiple individual sub-tasks. It leads to additional burdens to fine-tune models for specific robots and thereby high-cost customization in large-scale industrialization. This article investigates a unified task of monocular depth inference, which infers high-quality depth maps from all kinds of input raw data from various robots in unseen scenes. A basic benchmark G2-MonoDepth is developed for this task, which comprises four components: (a) a unified data representation RGB+X to accommodate RGB plus raw depth with diverse scene scale/semantics, depth sparsity ([0%, 100%]) and errors (holes/noises/blurs), (b) a novel unified loss to adapt to diverse depth sparsity/errors of input raw data and diverse scales of output scenes, (c) an improved network to well propagate diverse scene scales from input to output, and (d) a data augmentation pipeline to simulate all types of real artifacts in raw depth maps for training. G2-MonoDepth is applied in three sub-tasks including depth estimation, depth completion with different sparsity, and depth enhancement in unseen scenes, and it always outperforms SOTA baselines on both real-world data and synthetic data.

A privacy-preserving federated learning protocol with a secure data aggregation for the Internet of Everything

Although there are significant advantages to the popular use of connected devices on the Internet of Everything, there remain distinct concerns surrounding privacy. Federated learning has been one of the solutions suggested to remediate such issues and looks promising given the collaborative model it provides, which is shared across local gradients Grds without the exposure of raw data. However, the shared Grds threaten the local data with a privacy risk. The central server could produce summarized results. Besides, in federated learning devices with resource constraints frequently dropout. There are only two possible solutions now: efficiency or privacy preservation. Designing a verifiable secure aggregation on the scale of federated learning is still a difficult test. Here, an efficient privacy-preserving federated learning protocol is proposed, with a secure data aggregation for the Internet of Everything. Aggregator encryption effectively masks the local Grds of clients. The central server aggregates the obscured Grds without exposing the local data. At the same time, clients can effectively verify whether the aggregated result is correct. Secondly, the suggested protocol employs a group management mechanism to tolerate the dropout of clients without negatively affecting their further participation in subsequent learning activities. Security analysis shows that the protocol guarantees privacy-preserving federated learning’s security demand. The results of experiments carried out on datasets reveal the highly efficient practical performance of the proposed protocol.

Organic Self-Talk Questionnaire for Sport: development and validation of a theory-based, state measure of athletes’ organic self-talk

ABSTRACT A contemporary, theory-based conceptualisation of self-talk in sport (Latinjak, Hatzigeorgiadis, et al., [2019]. Speaking clearly … 10 years on: The case for an integrative perspective of self-talk in sport. Sport, Exercise, and Performance Psychology, 8(4), 353–367. https://doi.org/10.1037/spy0000160) has distinguished between two main types of athletes’ organic self-talk: spontaneous self-talk (an uncontrolled type of self-talk) and goal-directed self-talk (a controlled type of self-talk). However, these contemporary organic self-talk distinctions have not yet been operationalised to allow assessment suitable for quantitative research. The aim of this investigation was to develop and validate the Organic Self-Talk Questionnaire for Sport (OSTQS), a new theory-grounded, self-report measure, designed to assess the content and structure of athletes’ organic, spontaneous and goal-directed self-talk at a state level during sport participation. Two studies were conducted. In Study 1, a prospective instrument was developed based upon the operational definitions of athletes’ spontaneous and goal-directed self-talk in sport, and raw data collected from athletes in this study through open-ended questionnaires and semi-structured interviews. In Study 2, the psychometric properties of the developed questionnaire were tested through examination of factorial validity, discriminant validity, and reliability. Confirmatory factor analyses (CFA) revealed an adequate fit of a 17-item four-factor correlated model for spontaneous self-talk and a 28-item seven-factor correlated model for goal-directed self-talk; as well as the superiority of these models over their respective single-factor and hierarchical models. Furthermore, discriminant validity and reliability analyses of the subscale scores provided further evidence regarding the psychometric integrity of the instrument. Overall, the OSTQS seems a valid and reliable scale that could help us further enhance our understanding regarding athletes’ organic self-talk, its antecedents, and its consequences.

Comparison of three mathematical models to estimate lactation performance in dairy cows

Milk yield dynamics and production performance reflect how dairy cows cope with their environment. To optimize farm management, time-series of individual cow milk yield have been studied in the context of precision livestock farming, and many mathematical models have been proposed to translate raw data into useful information for the stakeholders of the dairy chain. To gain better insights on the topic, this study aimed at comparing 3 recent methods that allow to estimate individual cow potential lactation performance, using daily data recorded by the automatic milking systems of 14 dairy farms (7 Holstein, 7 Italian Simmental) from Belgium, the Netherlands, and Italy. An iterative Wood model (IW), a perturbed lactation model (PLM), and a quantile regression (QR) were compared in terms of estimated total unperturbed (i.e., expected) milk production and estimated total milk loss (relative to unperturbed yield). The IW and PLM can also be used to identify perturbations of the lactation curve and were thus compared in this regard. The outcome of this study may help a given end-user in choosing the most appropriate method according to their specific requirements. If there is a specific interest in the post-peak lactation phase, IW can be the best option. If one wants to accurately describe the perturbations of the lactation curve, PLM can be the most suitable method. If there is need for a fast and easy approach on a very large data set, QR can be the choice. Finally, as an example of application, PLM was used to analyze the effect of cow parity, calving season, and breed on their estimated lactation performance.

Data reduction strategy in the PandaX-4T experiment

The PandaX-4T experiment is designed for multiple purposes, including searches for solar neutrinos, weakly interacting massive particles, and rare double beta decays of xenon isotopes. The experiment produces a huge amount of raw data that needs to be stored for related physical analyses in a wide energy range. With the upgrading of the PandaX-4T experiment, the doubled sampling rate resulted in a larger data size, which challenges both the cost and the data processing speed. To address this issue, we propose a data reduction strategy by removing the noise tail of large signals and downsampling the remaining parts of them. This strategy reduces the requirement for storage by 65% while increasing data processing speed. The influences on physical analyses on different topics at different energy regions are negligible.

Advancing data honesty in experimental biology.

The ease with which scientific data, particularly certain types of raw data in experimental biology, can be fabricated without trace begs urgent attention. This is thought to be a widespread problem across the academic world, where published results are the major currency, incentivizing publication of (usually positive) results at the cost of lax scientific rigor and even fraudulent data. Although solutions to improve data sharing and methodological transparency are increasingly being implemented, the inability to detect dishonesty within raw data remains an inherent flaw in the way in which we judge research. We therefore propose that one solution would be the development of a non-modifiable raw data format that could be published alongside scientific results; a format that would enable data authentication from the earliest stages of experimental data collection. A further extension of this tool could allow changes to the initial original version to be tracked, so every reviewer and reader could follow the logical footsteps of the author and detect unintentional errors or intentional manipulations of the data. Were such a tool to be developed, we would not advocate its use as a prerequisite for journal submission; rather, we envisage that authors would be given the option to provide such authentication. Only authors who did not manipulate or fabricate their data can provide the original data without risking discovery, so the mere choice to do so already increases their credibility (much like 'honest signaling' in animals). We strongly believe that such a tool would enhance data honesty and encourage more reliable science.

The Analysis of the Risk of Metabolic Syndrome and Physical Activity Participation in Stroke Patients: The National Health and Nutrition Survey (8th, 2019-2021)

OBJECTIVES The purpose of this study is to find out the correlation with the risk of metabolic syndrome in stroke patients using KNHNES data.METHODS The raw data from the 1st to 3rd years of the 8th National Health and Nutrition Survey (KNHANES) were used, and 290 stroke patients aged 60 or older (109 in 2019, 89 in 2020, 92 in 2021) were selected out of 22.559 participants. The average and standard deviation of the subjects' general characteristics were presented, the risk of stroke patients and metabolic syndrome and related variables were analyzed through Pearson’s correlation analysis(p&lt;.05).RESULTS Men's fasting blood glucose was correlated with triglycerides (r=.175, p&lt;.05), and HDL cholesterol was correlated with triglycerides (r=.-266, p&lt;.05). Women's waist circumference was correlated with blood glucose (r=.321, p&lt;.001), hypertension (r=.195, p&lt;.05), and HDL cholesterol (r=.-205, p&lt;.001). Fasting blood glucose was correlated with HDL cholesterol (r=.-265, p&lt;.001) and triglyceride (r=.340, p&lt;.001). HDL cholesterol was correlated with triglycerides (r=.-332, p&lt;.001) and hypertension (r=.-195, p&lt;.05).CONCLUSIONS In summary, the major independent factors for the risk of metabolic syndrome in stroke patients were triglyceride in men and fasting blood glucose and hypertension in women.

Area of Interest Tracking Techniques for Driving Scenarios Focusing on Visual Distraction Detection

On-road driving studies are essential for comprehending real-world driver behavior. This study investigates the use of eye-tracking (ET) technology in research on driver behavior and attention during Controlled Driving Studies (CDS). One significant challenge in these studies is accurately detecting when drivers divert their attention from crucial driving tasks. To tackle this issue, we present an improved method for analyzing raw gaze data, using a new algorithm for identifying ID tags called Binarized Area of Interest Tracking (BAIT). This technique improves the detection of incidents where the driver’s eyes are off the road through binarizing frames under different conditions and iteratively recognizing markers. It represents a significant improvement over traditional methods. The study shows that BAIT performs better than other software in identifying a driver’s focus on the windscreen and dashboard with higher accuracy. This study highlights the potential of our method to enhance the analysis of driver attention in real-world conditions, paving the way for future developments for application in naturalistic driving studies.

Digital wearable insole-based identification of knee arthropathies and gait signatures using machine learning.

Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and to monitor disease progression or intervention response. However, clinical gait analysis relies on subjective visual observation of walking, as objective gait analysis has not been possible within clinical settings due to the expensive equipment, large-scale facilities, and highly trained staff required. Relatively low-cost wearable digital insoles may offer a solution to these challenges. In this work, we demonstrate how a digital insole measuring osteoarthritis-specific gait signatures yields similar results to the clinical gait-lab standard. To achieve this, we constructed a machine learning model, trained on force plate data collected in participants with knee arthropathy and controls. This model was highly predictive of force plate data from a validation set (area under the receiver operating characteristics curve [auROC] = 0.86; area under the precision-recall curve [auPR] = 0.90) and of a separate, independent digital insole dataset containing control and knee osteoarthritis subjects (auROC = 0.83; auPR = 0.86). After showing that digital insole derived gait characteristics are comparable to traditional gait measurements, we next showed that a single stride of raw sensor time series data could be accurately assigned to each subject, highlighting that individuals using digital insoles can be identified by their gait characteristics. This work provides a framework for a promising alternative to traditional clinical gait analysis methods, adds to the growing body of knowledge regarding wearable technology analytical pipelines, and supports clinical development of at-home gait assessments, with the potential to improve the ease, frequency, and depth of patient monitoring.

Detection of Lane &amp; Speed Breakers for Autonomous Vehicles using CNN

Abstract: The advancement of autonomous vehicles necessitates robust systems for real-time detection of lane markings and speed breakers to ensure safe and efficient navigation. In this paper, we propose a novel approach for lane and speed breaker detection utilizing Convolutional Neural Networks (CNN). Our method achieves remarkable accuracy in identifying lane markings and speed breakers from input images captured by onboard cameras. By leveraging the capabilities of CNN, our system demonstrates superior performance in diverse environmental conditions, including varying lighting, weather, and road surface conditions. We present a comprehensive analysis of the CNN architecture employed in our system, highlighting its ability to extract meaningful features from raw image data and make accurate predictions. The training process involves a large dataset comprising annotated images of lane markings and speed breakers, enabling the CNN to learn discriminative patterns for effective detection. Furthermore, we evaluate the performance of our proposed system through extensive experimentation, quantifying its accuracy, precision, and recall rates across different scenarios. The results indicate the effectiveness and reliability of our approach in real-world settings, showcasing its potential for integration into autonomous vehicle platforms. Overall, this paper contributes to the field of autonomous driving by presenting a robust and efficient solution for lane and speed breaker detection, leveraging the power of machine learning algorithms, specifically CNN, to enhance the safety and autonomy of vehicles on the road.

Multiple instance regression for the estimation of leaf nutrient content in olive trees using multispectral data taken with UAVs

The rational fertilisation of olive trees, based on adding exclusively the nutrients that are actually needed, is important from both the economic and environmental sustainability points of view. This paper employs UAV-obtained multispectral data collected from five different orchards located in Southern Spain to build a set of models for the prediction of the leaf nutrient content of olive trees using Support Vector Regression. The paper shows the convenience of addressing the problem as a Multiple Instance Regression, and compares two strategies of data aggregation and different choices of feature vectors derived from the raw multispectral data. The models provided good results for N, P and K (r2 = 0.76, r2 = 0.87 and r2 = 0.91, respectively for the Hojiblanca model, and r2 = 0.79, r2 = 0.80 and r2 = 0.80 for the Picual model). The rest of nutrients studied also offered good results for both the Picual and Hojiblanca models, ranging from r2 = 0.69 for B to r2 = 0.93 for Cu. The results indicate a robust performance of the models and a potential for improvement with the addition of more data, along with an advantage of considering individual models for each cultivar variety. Overall, these results are very promising for the estimation of the leaf nutrient content of olives trees and the detection of spatial variability in the fertilisation needs of orchards.

An Analysis of Convolutional Neutral Network for Image Classification

Abstract: Convolutional Neural Networks (CNNs) have emerged as a powerful tool for image classification tasks, owing to their ability to automatically learn hierarchical representations from raw pixel data. This paper presents a comprehensive analysis of CNNs for image classification, focusing on their architecture, training process, and performance evaluation metrics. The study investigates various CNN architectures, including AlexNet, GoogLeNet, and ResNet, highlighting their respective strengths and weaknesses in different scenarios. Moreover, the paper explores the impact of hyperparameters such as network depth, filter size, and pooling strategies on classification accuracy and computational efficiency. Furthermore, the training process of CNNs, encompassing data preprocessing, augmentation techniques, and optimization algorithms, is scrutinized to elucidate best practices for achieving optimal performance. Additionally, the paper discusses commonly used evaluation metrics such as accuracy, precision, recall, and F1-score, elucidating their interpretation and significance in assessing model performance. Through a systematic review of existing literature and experimental validation, this analysis aims to provide insights into the underlying mechanisms of CNNs for image classification tasks. Finally, the paper outlines future research directions, emphasizing the need for exploring novel architectures, optimizing hyperparameters, and enhancing interpretability and robustness of CNN models. Overall, this study contributes to a deeper understanding of CNNs for image classification and provides valuable guidance for practitioners and researchers in the field.

Microsoft Power BI untuk Analisis Data Pembelajaran Siswa di SMK Reformasi Kota Jakarta Barat

Data analysis is very crucial nowadays. Educational Institutions in order to improve services and better understand the conditions of the learning process of their students need more in-depth data analysis in order to get an overview of the condition of student learning outcomes, obstacles experienced, and opportunities to improve the quality and service of Education for their students. Data analysis can be done quite easily using power BI, where the raw data to be used can be in the form of excel that has been used by SMK Reformasi before. This will make it easier for partners to improve condition assessments with minimal changes to existing systems and more optimal efforts. On the occasion of this community service activity, we will explain and guide the teachers of SMK Reformasi so that they can use power BI to make reports and analyze data on their students. With increased understanding and application of deeper data analysis, it is expected to be able to apply the most appropriate teaching method for each subject taught. This will certainly have an impact on student learning outcomes and student satisfaction and parents as well as increased competition and reputation of SMK Reformasi.

Improving disentanglement in variational auto-encoders via feature imbalance-informed dimension weighting

Using Variational Auto-Encoder (VAE) to learn disentangled representation holds great promise. But there is a feature imbalance in the learning process of VAEs, and the model usually concentrates on the learning of some dimensions at the expense of others. Instead of attempting to rectify it, we exploit feature imbalance to propose a Dimension Weighting method that can boost the disentanglement effect of VAE-based models. In order to conduct disentanglement learning under a fixed size of the latent space, the intrinsic dimension of raw data is estimated by a Dimension Number Estimator and set as the latent space size. Then, leveraging the feature imbalance, a Dimension Importance Evaluator is constructed to separate each dimension in the latent variable into important, unimportant, and general dimensions. By exerting different learning pressures on specific dimensions, we further optimize the variational lower bound of model and retrain it, thus promoting the disentanglement of important dimensions. The experiments on four benchmark datasets show that the Dimension Weighting can further improve the disentangling effect without compromising model performance, and in approximately 80% of cases, the results in disentangling metric evaluation experiments achieve better disentangling scores than the original models. This reveals that not all dimensions of the latent variables are equally influential. By focusing on the crucial dimensions within the latent representation, the model can achieve better performance.

An Empirical Study on Downstream Dependency Package Groups in Software Packaging Ecosystems

The role of focal packages in packaging ecosystems is crucial for the development of the entire ecosystem, as they are the packages on which other packages depend. However, the evolution of dependency groups in packaging ecosystems has not been systematically investigated. In this study, we examine the downstream dependency package groups (DDGs) in three typical packaging ecosystems—Cargo for Rust, Comprehensive Perl Archive Network for Perl, and RubyGems for Ruby—to identify their features and evolution. We also identify and analyze a special type of DDG, the collaborative downstream dependency package group (CDDG), which requires shared contributors. Our findings show that the overall development of DDGs, particularly CDDGs, is consistent with the status of the whole ecosystem, and the size of DDGs and CDDGs follows a power law distribution. Furthermore, the interaction mechanisms between focal packages and downstream packages differ between ecosystems, but focal packages always play a leading role in the development of DDGs and CDDGs. Finally, we investigate predictive models for the development of CDDGs in the next stage based on their features, and our results show that random forest and Gradient Boosting Regression Tree achieve acceptable prediction accuracy. We provide the raw data and scripts used for our analysis at https://github.com/onion616/DDG.

"Visualizing Success: Unveiling Sales Insights through Tableau's Lens"

In today's dynamic business environment, data-driven decision-making has become imperative for organizations aiming to stay competitive. The field of sales, in particular, relies heavily on insights derived from vast amounts of data to drive strategies and enhance performance. Leveraging advanced visualization tools such as Tableau presents an opportunity to transform raw data into actionable insights, empowering sales teams to make informed decisions efficiently. This research paper delves into the realm of sales insight using Tableau, exploring how visualization techniques can unlock hidden patterns, trends, and correlations within sales data. By harnessing Tableau's capabilities, businesses can gain a comprehensive understanding of their sales performance across various dimensions, including products, regions, customer segments, and sales channels. Through a combination of literature review, case studies, and practical demonstrations, this paper elucidates the power of Tableau in providing intuitive and interactive visualizations that facilitate deeper exploration of sales data.