Extra Data Research Articles

Grid Search Optimized Machine Learning based Modeling of CO2 Emissions Prediction from Cars for Sustainable Environment

Carbon emissions have increased dramatically because of industrialization, trapping heat in the atmosphere and hastening climate change. This is a serious threat to the wealth, security, and well-being of the world. The effects are extensive, ranging from severe weather, disease outbreaks, and economic disruption to food insecurity and water scarcity. The World Health Organization (WHO) has determined that climate change poses the greatest threat to public health in the twenty-first century. Thus, precise CO2 emissions have emerged as a crucial concern in recent times. Several studies have tried to forecast the amount CO2 from industry and power plant using statistical analysis. Efficiency, robustness and diverse application was the limitation of the study. In this study, we have proposed an AI based model that is able to predict the amounts of CO2 emissions from cars. We applied a grid search-optimized machine learning approach using the publicly available Canadian dataset. Incorporation of different statistical analyses and preprocessing techniques such as duplicate data management, outlier rejection, scaling contributed to enhance the quality of the dataset. Later, grid search techniques were applied to tune the KNN, RF, and SVR models. The approach has enhanced the performance of CO2 emissions prediction. In the study, we further used the explainability of the random forest model to check the bias and fairness of predictability. MSE, RMSE, and R-squared metrics of the proposed approach were the highest as the state of the art.

Obstetric care provider’s knowledge about the use of low dose aspirin for preeclampsia prevention in low and middle income countries: a systematic review and meta-analysis

Introduction: Preeclampsia can elevate the likelihood of unfavorable consequences for a mother, such as severe morbidity and mortality. World Health Organization recommends low dose acetylsalicylic acid (aspirin, 75 mg per day) for the prevention of preeclampsia in women at moderate or high risk of developing the condition. The use of low dose aspirin is dependent on the knowledge of health care providers working in the antenatal care units. We found inconsistent figures regarding the knowledge level of health care providers on low dose aspirin for preeclampsia prevention around different low and middle income countries in the world. Thus, determining the pooled knowledge level of health care providers is very important.MethodsThis systematic review and meta-analysis (SRMA) was conducted on the knowledge level of among obstetric care providers towards preeclampsia prevention in low and middle income countries. We identified relevant literature in the English language only. A comprehensive search was conducted on databases such as PubMed, Google Scholar, HINARI, and Scopus. Subsequently, all datasets were exported to Mendeley reference manager and transferred to a Microsoft Excel spreadsheet to eliminate duplicate data during the review process. The extracted Microsoft Excel spreadsheet format data was imported to STATA software version 17 (STATA corporation, Texas, USA) for analysis. Then random effect model was used to estimate the pooled level of knowledge of health care providers on low dose aspirin for preeclampsia prevention in low income countries. Cochrane Q-test and I2 statistics were computed to assess heterogeneity among all the studies included in this SRMA.ResultA total of 1231 articles were identified through our search strategies, including Google Scholar, PubMed, Hinari and Scopus. Ultimately, six articles met the eligibility criteria for inclusion in the final SRMA. The pooled knowledge level of healthcare providers regarding the use of low-dose aspirin for preeclampsia prevention in low-income countries was found to be 16.38% (95% CI: 4.36–28.40). The Cochrane heterogeneity index, with a substantial I2 value of 98.89% and a significant P-value of 0.01, indicated significant heterogeneity among the primary studies included.Conclusionthe knowledge level of obstetric care providers in low and middle income countries is found very low and all the governmental and non-governmental organizations should strive to enhance the knowledge of obstetric care providers on the use of low dose aspirin for preeclampsia prevention in low and middle income countries.

Geographical Entity Management Model Based on Multi-Classification

Scientific and logical classification is crucial for efficient information storage, management, and sharing. However, there are numerous existing classification systems for geographical entities, and the categories to which the same geographical entity belongs are often different in the business databases constructed according to different classification systems, which brings great obstacles to the management and sharing of geographical information. This study analyzes the complexities of multiple classifications of geographical entities and proposes a multi-classification model for geographical entities based on directed hypergraph theory. This model integrates and transforms different classification systems for the same geographical entity, creating a unified method for expressing multiple classifications. We also designed a data structure to support this unified expression. By implementing this model, the study enables the effective management of geographical entity data, facilitating improved sharing and the exchange of geographical information across different industries and applications. In practical, the multi-classification model proposed in this paper allows geographical entities from different classification systems to be stored and managed within a single geographical database. Data views are then used to provide tailored services to various industry sectors and business applications. This approach effectively reduces data duplication and enhances the efficiency of managing and sharing geographical information. Using land use classification as an example, this study constructs a unified expression of three different land use classification systems based on the multi-classification model. An experiment managing land use data for a specific city was conducted using this model in PostgreSQL. The results indicate that the proposed method not only reduces data redundancy but also improves the query efficiency by over 10% on average compared to the mainstream relational database management mode. This confirms the effectiveness and practical value of the proposed method.

CONTROL ALGORITHM FOR IMPLEMENTING A MATHEMATICAL MODEL FOR ASSESSING THE EFFECTIVENESS OF SCENARIO MANAGEMENT AS A TOOL FOR ENSURING THE SECURITY OF A STRATEGIC FACILITY

Ensuring the safety of critical infrastructure facilities and other strategic objects is one of the main tasks of Ukraine as a state defending itself against military aggression. To accomplish this task, the authors developed a mathematical model of scenario management as a tool to ensure a strategic object’s security. However, the problem arises that, due to the nonlinearity of the processes under consideration, the theoretical solutions rely on assessing various controlling influences and, if necessary, their corrections to obtain the planned result. This article aims to develop a control algorithm for implementing a mathematical model to assess the effectiveness of scenario management as a tool for ensuring the security of a strategic object. For this, it is necessary to complete the following tasks: to consider a mathematical model for assessing the effectiveness of scenario management as a tool for ensuring the safety of a strategic object; develop the structure of the control algorithm for the implementation of this mathematical model; analyse the structure of the algorithm and make recommendations about the need to create the basic procedures for its implementation. A proposed control algorithm for implementing a mathematical model for assessing the effectiveness of scenario management as a tool for ensuring the security of a strategic object is a hierarchical structure of nine blocks (or modules) located at seven hierarchical levels, connected by direct and feedback links. It assesses the effectiveness of solving private tasks to ensure the security of a strategic object under protection, adjusting control influences aimed at implementing the selected scenario management option. At the first hierarchical level is the module for determining the parameters of systematisation of critical infrastructure conglomeration security data. The second level includes the object conglomeration module, which consists of submodules. The third hierarchical level contains the module for collecting data by parameters. The fourth level has two modules. The first is for performing calculations, and the second is for selecting evaluation parameters. It also aims to supplement or reject duplicate data. The fifth hierarchical level also contains two modules. The first is evaluating results according to particular criteria, and the second is the selection of evaluation criteria. The sixth level is the results interpretation module. This module provides the final assessment of the facility’s safety. The seventh hierarchical level is the module for making decisions on adjusting control actions. For the correct, practical application of this control algorithm to assess the effectiveness of solving private tasks to ensure the security of strategic and critical infrastructure objects under protection, it is necessary to develop the procedures for its application in detail. Keywords: critical infrastructure object, scenario management, algorithm, mathematical model, emergency.

Fog-assisted de-duplicated data exchange in distributed edge computing networks

The Internet of Things (IoT) generates substantial data through sensors for diverse applications, such as healthcare services. This article addresses the challenge of efficiently utilizing resources in resource-scarce IoT-enabled sensors to enhance data collection, transmission, and storage. Redundant data transmission from sensors covering overlapping areas incurs additional communication and storage costs. Existing schemes, namely Asymmetric Extremum (AE) and Rapid Asymmetric Maximum (RAM), employ fixed and variable-sized windows during chunking. However, these schemes face issues while selecting the index value to decide the variable window size, which may remain zero or very low, resulting in poor deduplication. This article resolves this issue in the proposed Controlled Cut-point Identification Algorithm (CCIA), designed to restrict the variable-sized window to a certain threshold. The index value for deciding the threshold will always be larger than the half size of the fixed window. It helps to find more duplicates, but the upper limit offset is also applied to avoid the unnecessarily large-sized window, which may cause extensive computation costs. The extensive simulations are performed by deploying Windows Communication Foundation services in the Azure cloud. The results demonstrate the superiority of CCIA in various metrics, including chunk number, average chunk size, minimum and maximum chunk number, variable chunking size, and probability of failure for cut point identification. In comparison to its competitors, RAM and AE, CCIA exhibits better performance across key parameters. Specifically, CCIA outperforms in total number of chunks (6.81%, 14.17%), average number of chunks (4.39%, 18.45%), and minimum chunk size (153%, 190%). These results highlight the effectiveness of CCIA in optimizing data transmission and storage within IoT systems, showcasing its potential for improved resource utilization and reduced operational costs.

A FORENSICS ACTIVITY LOGGER TO EXTRACT USER ACTIVITY FROM MOBILE DEVICES

Mobile devices have become one of the most often used tools in everyday life, mostly because of the importance of its apps. In this case, mobile devices become personal trackers for daily activities that provide important information about the user by recording extra data in addition to the user's personal information. As a consequence of this information gathering, several tools are now accessible for use on mobile devices, however each tool is only able to provide discrete details about a certain application or activity. Consequently, the present research proposes a technology that allows investigators to get a detailed report and time line of all operations performed on the device. This report combines data from several sources to generate a unique collection of facts. Furthermore, an example is provided to illustrate how the solution works, highlighting the practicality of the instrument as well as the way in which investigators need to use it.

An Analysis on Attribute Selection and Token Formation used for Duplicate Record Detection

The data mining method relies heavily on data pre-processing. The data cleansing methods that work for some types of data may not work for others. Extensive experiments are conducted to analyze & assess a newly constructed method for attribute selection. The data cleaning processes involve reducing the amount of attributes to deal with noisy data & duplicate data. The experimental findings demonstrate that it is an extremely efficient and straightforward method for attribute selection by significantly reducing the attributes. Efficiently reducing the time required for subsequent data cleaning processes, such as token synthesis, record similarity, & deletion, is the primary goal of attribute selection for data cleaning. Smart tokens for data cleansing are formed using the token generation algorithm, which is appropriate for data that consists of numeric, alphabetic, & non-numerical elements. Duplicate data can be efficiently removed using token-based data cleaning. Attribute selection & token-based technique will both shorten the time required.

A Study the review of Duplicate Data in Cloud Computing

In the realm of cloud computing, the management of data redundancy, often referred to as duplicate data, poses a significant challenge due to its implications on storage efficiency, data integrity, and overall system performance. This study provides a comprehensive review of the current strategies employed to detect, manage, and eliminate duplicate data in cloud environments. Duplicate and nearly duplicate web pages cause major issues for web search engines. Duplicate document identification, or the process of finding document pairs that represent the same entity, is a basic component of data cleansing. In order to address the structural heterogeneity issue, data must first undergo a data preparation step that involves parsing, data transformation, and data standardization. The findings aim to aid cloud service providers and users in understanding the importance of effective data deduplication strategies to optimize storage resources and enhance cloud computing performance.

Analisa Pengendalian Persediaan Bahan Baku pada CV. Es Mitra Jaya Surabaya

Manual data collection of raw materials causes inventory problems such as data collection errors, duplicate data, and warehouse loading processes that consume a lot of time. This research was conducted on a milktea boba frenchise company that is very maintaining quality. Raw materials are provided and arranged by the central company, the time of marking goods can only be ordered according to a predetermined schedule. By implementing the Qr-Code Scan system using the Scan-IT to Office application, the warehouse goods data collection process becomes more effective. The results of system implementation were evaluated using the PIECES method with an average satisfaction of 3.58 (SATISFIED) from the six domains tested. The results of the total cost calculation that have been carried out prove that the decrease in inventory costs using Material Requirement Planning (MRP) with the Economic Order Quantity (EOQ) method has decreased. The calculation carried out by the company shows the total inventory cost of Rp.32,346,426.25. After calculations using the MRP method, it decreased by Rp.1,460,780.23.

An efficient data compression and storage technique with key management authentication in cloud space

Cloud computing is one of the promising technologies that offers cost-effective choices for processing and storing the huge volumes of data. In today’s world, data is the most important asset that one can have but it needs to be handled and protected properly. Portability of data can be increased by reducing the size of the data to be stored because of the limited storage space. As a result, data compression has arisen significantly. Data compression is a useful technique for reducing data size and increasing the effectiveness of data transit and storage. Data compression reduces the size of a data file while using lossy or lossless compression. One of the newest techniques for data compression is data duplication, which can reduce the amount of data saved while removing unnecessary data and maintaining an exact copy of the data. This analysis presents an Efficient data compression and storage technique with key management authentication in cloud space. This approach uses Regressive probabilistic key encryption (RPKE) to encrypt the cloud data and Lempel-Ziv-77-Huffman coding (LZ77-HM) is used to compress the huge amounts of cloud data. The Performance of presented approach is evaluated in terms of compression ratio and compression rate.

Winter Targeted Observing Periods during the Year of Polar Prediction in the Southern Hemisphere (YOPP-SH)

Abstract The Year of Polar Prediction in the Southern Hemisphere (YOPP-SH) held seven targeted observing periods (TOPs) during the 2022 austral winter to enhance atmospheric predictability over the Southern Ocean and Antarctica. The TOPs of 5–10-day duration each featured the release of additional radiosonde balloons, more than doubling the routine sounding program at the 24 participating stations run by 14 nations, together with process-oriented observations at selected sites. These extra sounding data are evaluated for their impact on forecast skill via data denial experiments with the goal of refining the observing system to improve numerical weather prediction for winter conditions. Extensive observations focusing on clouds and precipitation primarily during atmospheric river (AR) events are being applied to refine model microphysical parameterizations for the ubiquitous mixed-phase clouds that frequently impact coastal Antarctica. Process studies are being facilitated by high-time-resolution series of observations and forecast model output via the YOPP Model Intercomparison and Improvement Project (YOPPsiteMIIP). Parallel investigations are broadening the scope and impact of the YOPP-SH winter TOPs. Studies of the Antarctic tourist industry’s use of weather services show the scope for much greater awareness of the availability of forecast products and the skill they exhibit. The Sea Ice Prediction Network South (SIPN South) analysis of predictions of the sea ice growth period reveals that the forecast skill is superior to the sea ice retreat phase.

Graph Deep Active Learning Framework for Data Deduplication

Graph Deep Active Learning Framework for Data Deduplication

Retracted] Tanshinone IIA regulates colorectal cancer apoptosis via attenuation of Parkin‑mediated mitophagy by suppressing AMPK/Skp2 pathways.

Following the publication of this paper, it was drawn to the Editor's attention by a concerned reader that certain of the immunofluorescence assay data shown in Fig. 4A on p. 1698 were strikingly similar to data that had already been submitted for publication in different form in another article written by different authors at different research institutes. In addition, there was an instance of apparent duplication of western blot data comparing between Fig. 5A and 5G, and the reader also had concerns regarding the presentation of the flow‑cytometry cell‑count histograms in Fig. 2A. Owing to the fact that the contentious data in the above article had already been submitted for publication elsewhere prior to its submission to Molecular Medicine Reports, the Editor has decided that this paper should be retracted from the Journal. The authors were asked for an explanation to account for these concerns, but the Editorial Office did not receive a reply. The Editor apologizes to the readership for any inconvenience caused. [Molecular Medicine Reports 18: 1692‑1703, 2018; DOI: 10.3892/mmr.2018.9087].

Statistical Modeling of Within-Laboratory Precision Using a Hierarchical Bayesian Approach.

Reproducibility has been well-studied in the field of food analysis; the relative standard deviation is said to follow the Horwitz curve with certain exceptions. However, little systematic research has been done on predicting repeatability or intermediate precision. We developed a regression method to estimate within-laboratory standard deviations using hierarchical Bayesian modeling and analyzing duplicate measurement data obtained from actual laboratory tests. The Hamiltonian Monte Carlo method was employed and implemented using R with Stan. The basic structure of the statistical model was assumed to be a chi-squared distribution, the fixed effect of the predictor was assumed to be a nonlinear function with a constant term and a concentration-dependent term, and the random effects were assumed to follow a lognormal distribution as a hierarchical prior. By analyzing over 300 instances, we obtained regression results that fit well with the assumed model, except for moisture, which was a method-defined analyte. The developed method applies to a wide variety of analytes measured using general principles, including spectroscopy, GC, and HPLC. Although the estimated precisions were within the HorRat(r) criteria, some cases using high-sensitivity detectors, such as mass spectrometers, showed standard deviations below that range. We propose utilizing the within-laboratory precision predicted by the model established in this study for internal quality control and measurement uncertainty estimation without considering the sample matrices. Performing statistical modeling on data from double analysis, which is conducted as a part of internal quality controls, will simplify the estimation of the precision that fits each analytical system in a laboratory.

Review on Temporal Convolutional Networks for Electricity Theft Detection with Limited Data

Electricity theft detection using artificial intelligence (AI) and machine learning techniques have shown significant promise in recent research. However, practical implementation and widespread adoption of these advanced methods face several persistent challenges, particularly when dealing with limited data. This review delves into the computational complexity, data requirements, overfitting issues, and scalability and generalizability concerns associated with popular techniques such as Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM), Deep Convolutional Neural Networks (DCNN), Multi-Layer Perceptron (MLP), Gated Recurrent Unit (GRU), and Artificial Neural Networks (ANN). Computational complexity and resource constraints affect the training times and convergence of TCN, LSTM, and DCNN, while high data needs and parameter tuning hinder MLP and GRU. The ANN-based method utilized by the Electricity Company of Ghana underscores overfitting and data duplication, further exacerbated by limited data availability. Moreover, the scalability and generalizability of TCN, LSTM, and DCNN across different regions and larger datasets are limited, with effectiveness varying based on electricity consumption patterns and theft tactics. Addressing these challenges through optimizing computational efficiency, improving data quality and utilization, and enhancing scalability and generalizability is crucial, especially in data-constrained environments. Continued research and development in these areas will be essential for realizing the full potential of AI-based electricity theft detection systems with limited data.

Reaction network and kinetic modeling for the direct catalytic cracking of Arabian light crude oil to chemicals

We investigate the catalytic cracking of Arabian light crude oil into chemicals. A kinetic model has been established for an in-house developed catalyst using a micro-activity testing unit under a wide temperature range (525–650 °C) and various catalyst-to-oil ratios (0–4.14). Our conditions are adjusted to promote light olefins and propylene-to-ethylene ratio. We trained different reaction networks with the experimental data to calculate the kinetic parameters and, at the same time, select a statistically relevant model. The most representative model, with eight lumps and forward–backward first-order reactions, enables the acceptable prediction of the yield of chemicals (light olefins). The obtained kinetic parameters suggest that diesel and gasoline fractions are important intermediates in which forward reactions are preferred due to lower activation energy requirements and faster rate constant. The model was validated by reparametrization using Arabian extra light data.

Compositional Kronecker Context Optimization for vision–language models

Context Optimization (CoOp) has emerged as a simple yet effective technique for adapting CLIP-like vision–language models to downstream image recognition tasks. Nevertheless, learning context with satisfactory base-to-new, domain and cross-task generalization ability simultaneously while adapting to new tasks is a challenge. To tackle such a challenge, existing methods mainly exploit knowledge distillation with auxiliary text data written by human experts. However, we instead explore a new technique route by structuring the prompts without resorting to extra text data. As a result, we obtain a new lightweight yet generalizable approach termed Compositional Kronecker Context Optimization (CK-CoOp). Technically, the prompt’s context words in CK-CoOp are learnable vectors, which are crafted by linearly combining base vectors from a dictionary. These base vectors consist of a non-learnable component obtained by quantizing the weights in the token embedding layer, and a learnable component constructed by applying Kronecker product on several learnable tiny matrices. Intuitively, the compositional structure mitigates the risk of overfitting on training data and the Kronecker product breaks the non-learnable restrictions of the dictionary, thereby enhancing representation ability with minimal additional parameters. Extensive experiments confirm that, compared with existing methods, CK-CoOp can not only achieve comparable or even better performance under base-to-new, domain and cross-task generalization evaluation without the help of auxiliary text data, but also has the merits of fewer learnable parameters and efficient training and inference speed.

Revolutionary Data Deduplication with Fuzzy C-Means: Advancing Data Quality Management

Revolutionary Data Deduplication with Fuzzy C-Means: Advancing Data Quality Management

Visible–infrared person re-identification via patch-mixed cross-modality learning

Visible–infrared person re-identification (VI-ReID) aims to retrieve images of the same pedestrian from different modalities, where the challenges lie in the significant modality discrepancy. To alleviate the modality gap, recent methods generate intermediate images by GANs, grayscaling, or mixup strategies. However, these methods could introduce extra data distribution, and the semantic correspondence between the two modalities is not well learned. In this paper, we propose a Patch-Mixed Cross-Modality framework (PMCM), where two images of the same person from two modalities are split into patches and stitched into a new one for model learning. A part-alignment loss is introduced to regularize representation learning, and a patch-mixed modality learning loss is proposed to align between the modalities. In this way, the model learns to recognize a person through patches of different styles, thereby the modality semantic correspondence can be inferred. In addition, with the flexible image generation strategy, the patch-mixed images freely adjust the ratio of different modality patches, which could further alleviate the modality imbalance problem. On two VI-ReID datasets, we report new state-of-the-art performance with the proposed method.

OBCTeacher: Resisting labeled data scarcity in oracle bone character detection by semi-supervised learning

Oracle bone characters (OBCs) are ancient ideographs for divination and memorization, as well as first-hand evidence of ancient Chinese culture. The detection of OBC is the premise of advanced studies and was mainly done by authoritative experts in the past. Deep learning techniques have great potential to facilitate OBC detection, but the high annotation cost of OBC brings the scarcity of labeled data, hindering its application. This paper proposes a novel OBC detection framework called OBCTeacher based on semi-supervised learning (SSL) to resist labeled data scarcity. We first construct a large-scale OBC detection dataset. Through investigation, we find that spatial mismatching and class imbalance problems lead to decreased positive anchors and biased predictions, affecting the quality of pseudo labels and the performance of OBC detection. To mitigate the spatial mismatching problem, we introduce a geometric-priori-based anchor assignment strategy and a heatmap polishing procedure to increase positive anchors and improve the quality of pseudo labels. As for the class imbalance problem, we propose a re-weighting method based on estimated class information and a contrastive anchor loss to achieve prioritized learning on different OBC classes and better class boundaries. We evaluate our method by using only a small portion of labeled data while using the remaining data as unlabeled and all labeled data with extra unlabeled data. The results demonstrate the effectiveness of our method compared with other state-of-the-art methods by superior performance and significant improvements of an average of 11.97 in AP50:95 against the only supervised baseline. In addition, our method achieves comparable performance using only 20% of labeled data to the fully-supervised baseline using 100% of labeled data, demonstrating that our method significantly reduces the dependence on labeled data for OBC detection.