Simple Data Research Articles

Reversible data hiding in encrypted images using Pixel Shifting Approach (PSA)

This study proposes a novel technique for reversibly embedding data within encrypted images, specifically utilizing a Pixel Shifting Approach (PSA). Traditional data hiding methods commonly employ the modification of the least significant bits (LSBs) of pixels in the original image. In contrast, methods using encrypted cover images offer a wider range of data hiding techniques that are not constrained by LSB modifications. The proposed data hiding method embeds data into an encrypted cover image by dividing it into non-overlapping blocks and then permuting pixels within each block according to a defined rule, rather than modifying the LSBs of pixels. The entropy of the image remains preserved after data embedding, making it difficult to detect the location of hidden data. To extract the hidden data, the encrypted image is decrypted, and the smoothness of each block is measured. The block with the lowest smoothness is identified as the one containing the hidden data, which is also the original block. This is because the pixels within each block have different statistical properties compared to other blocks, allowing for data recovery. The proposed method offers the advantage of embedding 4 bits of data per block, surpassing the performance of the existing Modified Pixel Shifting Approach (MPSA). Moreover, the entropy preservation of the stego-image is comparable to MPSA, while the algorithm’s execution time is more efficient. The simpler data hiding rule compared to MPSA enables faster encoding and decoding. Reversibility is guaranteed by preserving the correlation among pixels within each block when extracting data from the stego-image and restoring the original image. Experimental results demonstrate that the proposed method significantly outperforms existing techniques in terms of data hiding efficiency and security.

CovSTATIS: A multi-table technique for network neuroscience

Similarity analyses between multiple correlation or covariance tables constitute the cornerstone of network neuroscience. Here, we introduce covSTATIS, a versatile, linear, unsupervised multi-table method designed to identify structured patterns in multi-table data, and allow for the simultaneous extraction and interpretation of both individual and group-level features. With covSTATIS, multiple similarity tables can now be easily integrated, without requiring a priori data simplification, complex black-box implementations, user-dependent specifications, or supervised frameworks. Applications of covSTATIS, a tutorial with Open Data and source code are provided. CovSTATIS offers a promising avenue for advancing the theoretical and analytic landscape of network neuroscience.

Development of Predictive Statistical Model for Gaining Valuable Insights in Pharmaceutical Product Recalls.

The rapid progress in artificial intelligence (AI) has revolutionized problem-solving across various domains. The global challenge of pharmaceutical product recalls imposes the development of effective tools to control and reduce shortage of pharmaceutical products and help avoid such recalls. This study employs AI, specifically machine learning (MI), to analyze critical factors influencing formulation, manufacturing, and formulation complexity which could offer promising avenue for optimizing drug development processes. Utilizing FDAZilla and SafeRX tools, an open database model was constructed, and predictive statistical models were developed using Multivariate Analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) Approach. The study focuses on key descriptors such as delivery route, dosage form, dose, BCS classification, solid-state and physicochemical properties, release type, half-life, and manufacturing complexity. Through statistical analysis, a data simplification process identifies critical descriptors, assigning risk numbers and computing a cumulative risk number to assess product complexity and recall likelihood. Partial Least Square Regression and the LASSO approach established quantitative relationships between key descriptors and cumulative risk numbers. Results have identified key descriptors; BCS Class I, dose number, release profile, and drug half-life influencing product recall risk. The LASSO model further confirms these identified descriptors with 71% accuracy. In conclusion, the study presents a holistic AI and machine learning approach for evaluating and forecasting pharmaceutical product recalls, underscoring the importance of descriptors, formulation complexity, and manufacturing processes in mitigating risks associated with product quality.

RETRACTED: Theoretical development and validation of asphalt concrete density measurement using non-contact interdigital coplanar capacitive sensor

Asphalt concrete (AC) density serves as a critical criterion for quality control and quality assurance in asphalt pavement construction. Current non-destructive testing (NDT) approaches have shown potential for AC density measurement, but they present disadvantages such as inefficiency and unstable accuracy. This paper developed a novel NDT approach for AC density measurement based on non-contact interdigital coplanar capacitive sensor (ICCS). The feasibility of this approach was evaluated through both numerical and experimental validations. Firstly, the theoretical model of non-contact ICCS for AC density measurement was established based on conformal mapping technique, partial capacitance technique, and dielectric mixing model. Secondly, numerical simulations were performed to validate the developed theoretical model under two scenarios. Thirdly, laboratory experiments were conducted to measure capacitance data and predict density for three common types of ACs. Finally, the error of non-contact ICCS was discussed and compared with that of other NDT instruments. The results indicated that the developed theoretical model could establish the relationship between AC density and capacitance value, which provided the basis for AC density measurement using non-contact ICCS. It was also observed that the capacitance data obtained by the proposed model closely matched those computed by numerical simulations. The maximum errors between theoretical and numerical data were 4.10% and 1.08% for two cases respectively, which proved the feasibility of the theoretical model. Furthermore, experimental results indicated that the capacitance value of AC specimens increased by about 1 pF as the rolling time increases due to a decrease in the volume of air in AC, which reaffirms the validity of AC density measurement using non-contact ICCS. Additionally, the non-contact ICCS demonstrated excellent accuracy with a maximum error of 6.69%, which is comparable to the current mainstream NDT approaches. And it has the advantages of lower cost, simpler data processing and higher efficiency. The findings of this paper offer a new and reliable method and tool for non-destructive and large-scale AC density measurement.

Analisis Pemanfaatan Artificial Intelligence sebagai Sarana Efisiensi Komunikasi Publik di Era BANI

This article explores the utilization of Artificial Intelligence (AI) as a tool for enhancing efficiency in public communication within the BANI era (Brittle, Anxious, Nonlinear, Incomprehensible). The aim of this research is to investigate how AI can improve the effectiveness of public communication amidst uncertainty and complexity. The research employs a descriptive qualitative approach with a literature review, analyzing data from relevant journal articles, books, and case studies. The study finds that AI plays a crucial role in addressing system fragility through misinformation detection, reducing public anxiety by providing personalized and responsive information, and managing uncertainty and complexity through predictive analysis and data simplification. The results indicate that AI can enhance the efficiency and clarity of public communication but must be complemented by stringent regulations and ethical considerations to ensure responsible use. With the right approach, AI can be an effective tool in improving public communication in this challenging era.

Scalable data fusion via a scale-based hierarchical framework: Adapting to multi-source and multi-scale scenarios

Multi-source information fusion addresses challenges in integrating and transforming complementary data from diverse sources to facilitate unified information representation for centralized knowledge discovery. However, traditional methods face difficulties when applied to multi-scale data, where optimal scale selection can effectively resolve these issues but typically lack the advantage of identifying the optimal and simplest data from different data source relationships. Moreover, in multi-source, multi-scale environments, heterogeneous data (where identical samples have different features and scales in different sources) is prone to occur. To address these challenges, this study proposes a novel approach in two key stages: first, aggregating heterogeneous data sources and refining datasets using information gain; second, employing a customized Scale-based Tree (SbT) structure for each attribute to help extract specific scale information value, thereby achieving effective data fusion goals. Extensive experimental evaluations cover ten different datasets, reporting detailed performance across multiple metrics including Approximation Precision (AP), Approximation Quality (AQ) values, classification accuracy, and computational efficiency. The results highlight the robustness and effectiveness of our proposed algorithm in handling complex multi-source, multi-scale data environments, demonstrating its potential and practicality in addressing real-world data fusion challenges.

Policy brief Belgian EBCP mirror group ‘prevention’ and ‘early detection & screening’ in cancer

The availability and linkage of comprehensive data on cancer mortality, incidence, screening and vaccination (HPV/HB) are essential for monitoring the process and impact of all screening and vaccination programmes, and to generate evidence to improve prevention policies. Linkage of data registries will allow better exploitation of real-world data. Simplification of data sharing for epidemiological research is of utmost importance. Human, as well as technical/technological and legal/regulatory barriers need to be tackled in Belgium to aid actors involved in screening, to facilitate organised linkage of data. Rapid advances in technologies for early detection and screening for a broad range of cancer types provide significant opportunities. Such tests are not yet ready for adoption in screening programmes. Emerging evidence should be monitored to ensure that promising innovations can transition to implementation studies in a timely manner. More and sustained research is needed to generate the necessary scientific relevance for new screening techniques in relevant cancer types.

The Needs of Science Major Students at Madrasah Aliyah Al-Bairuni Jombang for Instagram Content-Based Nahwu Materials

Nahwu material tends to be difficult, making students feel bored quickly and less interested in learning it more deeply. The thing that needs to be considered is variation in learning. One of the variations in learning is using social media based on Instagram content so that students can understand and make the learning atmosphere interesting. This study aims to uncover the requirements of students with regard to Instagram content focused on nahwu. Furthermore, the intention is to demonstrate that Instagram, typically utilized for sharing photos and videos, is also possibly utilized effectively as a learning tool. The study utilized a descriptive qualitative research approach as its research method. Observation interviews served as the approach for collecting data, including up to 5 individuals who met the criteria of being class XII students and had studied nahwu material. The Miles and Huberman model is utilized in the method for analyzing data, which involves Data collection, data simplification, data display, and drawing/verifying final thoughts. The findings of this research show that students need to have a sense of urgency in Instagramming content related to Nahwu studies. The reason for displaying nahwu content on Instagram is to make the learning process more engaging and easier to comprehend. Hence, the media must communicate nahwu material.

Quadrature Based Neural Network Learning of Stochastic Hamiltonian Systems

Hamiltonian Neural Networks (HNNs) provide structure-preserving learning of Hamiltonian systems. In this paper, we extend HNNs to structure-preserving inversion of stochastic Hamiltonian systems (SHSs) from observational data. We propose the quadrature-based models according to the integral form of the SHSs’ solutions, where we denoise the loss-by-moment calculations of the solutions. The integral pattern of the models transforms the source of the essential learning error from the discrepancy between the modified Hamiltonian and the true Hamiltonian in the classical HNN models into that between the integrals and their quadrature approximations. This transforms the challenging task of deriving the relation between the modified and the true Hamiltonians from the (stochastic) Hamilton–Jacobi PDEs, into the one that only requires invoking results from the numerical quadrature theory. Meanwhile, denoising via moments calculations gives a simpler data fitting method than, e.g., via probability density fitting, which may imply better generalization ability in certain circumstances. Numerical experiments validate the proposed learning strategy on several concrete Hamiltonian systems. The experimental results show that both the learned Hamiltonian function and the predicted solution of our quadrature-based model are more accurate than that of the corrected symplectic HNN method on a harmonic oscillator, and the three-point Gaussian quadrature-based model produces higher accuracy in long-time prediction than the Kramers–Moyal method and the numerics-informed likelihood method on the stochastic Kubo oscillator as well as other two stochastic systems with non-polynomial Hamiltonian functions. Moreover, the Hamiltonian learning error εH arising from the Gaussian quadrature-based model is lower than that from Simpson’s quadrature-based model. These demonstrate the superiority of our approach in learning accuracy and long-time prediction ability compared to certain existing methods and exhibit its potential to improve learning accuracy via applying precise quadrature formulae.

Exploring Burnout among Nursing Students in Bangalore: A t-Distributed Stochastic Neighbor Embedding Analysis and Hierarchical Clustering in Cross-Sectional Data.

This study explores burnout among nursing students in Bangalore, India, focusing on Exhaustion and Disengagement scores. A cross-sectional design was applied using the Oldenburg Burnout Inventory modified for nursing students, collecting data using a survey that was conducted between October and December 2023. The sample consisted of 237 female nursing students from the Bachelor of Science in Nursing program at Bangalore College of Nursing, South India. The study integrated the t-distributed Stochastic Neighbor Embedding (t-SNE) procedure for data simplification into three t-SNE components, used in a hierarchical clustering analysis, which identified distinct student profiles: "High-Intensity Study Group" and "Altruistic Aspirants". While burnout scores were generally high, students with high study hours ("High-Intensity Study Group") reported greater Exhaustion, with a mean score of 26.78 (SD = 5.26), compared to those in the "Altruistic Aspirants" group, who reported a mean score of 25.00 (SD = 4.48), demonstrating significant differences (p-value = 0.005). Conversely, those motivated by altruism ("Altruistic Aspirants") showed higher Disengagement, with a mean score of 19.78 (SD = 5.08), in contrast to "High-Intensity Study Group", which reported a lower mean of 17.84 (SD = 4.74) (p-value = 0.002). This segmentation suggests that burnout manifests differently depending on the students' academic load and intrinsic motivations. This study underscores the need for targeted interventions that address specific factors characterizing the clusters and provide information for designing future research and interventions. This study was not registered.

The Role of Mobile Language Learning Applications Duolingo in Enhancing English Language Proficiency: A Study on Hospitality Students

The rapid development of technology has encouraged the increasing variety of educational applications that are increasingly interesting and innovative as a means of online-based learning, especially for English language learning. One of the applications that students are interested in is the Duolingo application, which applies ease, comfort, and effectiveness to students learning English. Several studies have been conducted to determine the impact of the Duolingo app on the education system. Therefore, the researchers were interested in knowing how students perceive the use of the Duolingo application in the process of learning English. This research uses a qualitative descriptive method by involving students at IPB International as participants. To collect researcher data using written text from interview transcripts and documentation, there are three steps in analyzing this research data; the first step is the process of selection, concentration, attention, simplification, abstraction, and transformation of raw data arising from interview results, and documentation of student learning process using Duolingo. The results of this study show that most students prefer to learn English using the Duolingo application as a Foreign language learning media, especially English; the results of this study provide positive perceptions and results of the Duolingo application in improving students' ability to learn English.

Legal Effectiveness in the Application of Halal Product Assurance Certification to MSME Business Actors According to Law Number 33 of 2014 concerning Halal Product Assurance

Law Number 33 of 2014 concerning Halal Product Assurance (JPH Law) aims to protect consumers and increase the competitive ability of Indonesian halal products in the world market, which is included in the aspects listed in the JPH Law, namely the obligation for business actors to obtain halal certificates for goods that business actors produce and trade. The focus of the study aims to evaluate the extent of legal effectiveness in the application of halal product assurance certification by MSME business actors. The research method used is qualitative with techniques in collecting data, namely literature studies. After the data is collected, data analysis is carried out which includes the stages of data simplification, data presentation and conclusion making. The conclusion of the study explains that the effectiveness of the law in implementing halal product assurance certification for MSME business actors is still at a low level. Several obstacles were identified, such as lack of knowledge about the JPH Law and the halal certification process, the relatively expensive cost of halal certification, the halal certification process which is considered complicated and convoluted and lack of assistance and socialization from the government.

Innovative Strategies In Human Resource Management: Optimizing Organizational Performance In The Digital Age

In the ongoing digital era, organizations are faced with various changes that require them to optimize performance in order to remain competitive and sustainable. One approach that can be used to improve organizational performance is through human resource management, given the important role of humans as assets for organizations. The focus of this research is to analyze innovative strategies in human resource management that can improve organizational performance in the digital era. This research uses qualitative methods with data collection techniques through literature studies. After the data is collected, data analysis is carried out which includes the stages of data simplification, data presentation, and conclusion drawing. The conclusion of the research explains that innovative strategies in HRM such as leveraging technology to improve HRM implementation, establishing an innovative and adaptive organizational culture, focusing on developing employees' digital skills, implementing effective talent recruitment and retention strategies, and conducting regular evaluations of these strategies, are able to overcome challenges that may arise during HRM implementation in organizations. Thus, these innovative strategies will help improve HRM implementation, and ultimately optimize organizational performance in the digital era.

Generating realistic data through modeling and parametric probability for the numerical evaluation of data processing algorithms in two-dimensional chromatography

BackgroundComprehensive two-dimensional chromatography generates complex data sets, and numerous baseline correction and noise removal algorithms have been proposed in the past decade to address this challenge. However, evaluating their performance objectively is currently not possible due to a lack of objective data. ResultTo tackle this issue, we introduce a versatile platform that models and reconstructs single-trace two-dimensional chromatography data, preserving peak parameters. This approach balances real experimental data with synthetic data for precise comparisons. We achieve this by employing a Skewed Lorentz-Normal model to represent each peak and creating probability distributions for relevant parameter sampling. The model's performance has been showcased through its application to two-dimensional gas chromatography data where it has created a data set with 458 peaks with an RMSE of 0.0048 or lower and minimal residuals compared to the original data. Additionally, the same process has been shown in liquid chromatography data. SignificanceData analysis is an integral component of any analytical method. The development of new data processing strategies is of paramount importance to tackle the complex signals generated by state-of-the-art separation technology. Through the use of probability distributions, quantitative assessment of algorithm performance of new algorithms is now possible. Therefore, creating new opportunities for faster, more accurate, and simpler data analysis development.

Developing a Blockchain-Based Framework for Digital Archiving of BIM Using Axiomatic Design

Building information modeling (BIM) has been attracting increasing attention in the architecture, engineering, and construction (AEC) industry in recent years. The wide spread of BIM marks the transition from 2D and paper-based archiving to 3D and digital archiving of project information. However, the commonly used centralized managing approaches for BIM data have high risks of data loss and data tampering. With cryptographic algorithms and distributed databases, blockchain has the potential to address the limitations of data loss and trust in conventional BIM management methods. Therefore, this paper proposes a blockchain-based framework for digital archiving of BIM data. In this paper, the axiomatic design approach is utilized to design the archiving framework in order to systematically map the design parameters with the functional requirements and minimize the information contents. The proposed framework contains five modules, including the BIM module, building plan approval (BPA) module, building data simplification (BDS) module, distributed data storage (DDS) module, and digital document verification (DDV) module. Hyperledger Fabric is utilized to develop the blockchain system based on the proposed framework. In the end, a simple BIM model with 240 components and around 11,000 lines of content in the Industry Foundation Classes (IFC) file is taken as an illustrative example to validate the proposed blockchain approach. By storing the BIM model on to the developed blockchain, it is found that each transaction of a blockchain can store over 5000 lines of IFC contents with 0.09 s of uploading latency. The results show that the proposed blockchain-based approach can effectively and securely archive BIM data.

Design of storage benchmark kit framework for supporting the file storage retrieval

An open-source software framework called the storage benchmark kit (SBK) is used to store the system benchmarking performance framework. The SBK is designed to perform any storage client or device using any data type as a payload. SBK simultaneously helps number of readers as well as writes to the storage system of large amounts of data as well as allows end-to-end latency benchmarking for multiple writers and readers. The SBK uses standardized performance measures for comparing and evaluating various storage systems and their combinations. Distributed file systems, distributed database systems, single or local node databases, systems of object storage, platforms of distributed streaming and messaging, and systems of key-value storage are the storage solutions supported by SBK. The SBK supports various storage systems like XFS, Kafka streaming storage systems, and Hadoop distributed file system (HDFS) performance benchmarking. The experimental results show that a proposed method achieves execution time of 65.530 s, 40.826 s and 30.351 s for the 100k, 500k and 1000k files respectively which ensures better improvement than the existing methods such as simple data interface and distributed data protection system.

Penguatan Tata Kelola Capacity Building Dalam Mitigasi Pekerja Migran Indonesia di Perbatasan Entikong Kalimantan Barat

This study aims to determine the governance of State Cross Border Posts in the field of capacity building and governance through border diplomacy during the handling of Covid-19 at the Entikong border in West Kalimantan. This study uses a qualitative approach with a descriptive study method within the framework of border governance theory and the concept of vitalization function. Data collection techniques used interviews and literature study. The data sources for this study are the Coordinator of the Entikong Indonesian Migrant Worker Protection Agency, the head of class II immigration, Entikong, West Kalimantan and the Head of Entikong District, as well as staff from the Entikong State Cross Border Post, as well as literature studies that come from reliable sources. Data analysis includes several stages, namely data collection, data simplification, presentation and drawing conclusions. Data validity was carried out using triangulation techniques. The results of this study are that the governance of the Entikong State Cross-Border Post already has fairly good infrastructure, such as the State Cross-Border Post office, International Land Terminal and other infrastructure. Improving governance in the field of capacity building at the Entikong State Cross-Border Post is carried out by increasing cooperation, facilities and in general it can be said that the capacity of the Entikong State Cross-Border Post has, especially when facing the Covid-19 pandemic. Another strategy is border diplomacy or cooperation between Indonesia and Malaysia.

Multilevel data simplification methods for flood process visualizations based on visual perceptions

ABSTRACT Visualizations of flood processes require a balance between scientific authenticity and realistic expressions. As the flood area increases, the amounts of time-series data that are produced by flood models often exceed the capacity of computer rendering, leading to poor user experiences and low visualization effectiveness. Therefore, this study carried out visual perception experiments and proposed a multilevel simplification and scheduling method for flood process data based on visual perceptions. This method first simplifies flood data by flood feature point selection according to conclusions related to visual perception, then triangulated irregular networks are built, and finally, flood data scheduling and rendering are achieved through multiple levels of detail combined with visual ranges. An experimental analysis using a 3D visualization prototype system shows that the proposed method can improve the efficiency of visual expressions while preserving flood morphology features.

The role of correspondence analysis in medical research.

Correspondence analysis (CA) is a multivariate statistical and visualization technique. CA is extremely useful in analyzing either two- or multi-way contingency tables, representing some degree of correspondence between columns and rows. The CA results are visualized in easy-to-interpret "bi-plots," where the proximity of items (values of categorical variables) represents the degree of association between presented items. In other words, items positioned near each other are more associated than those located farther away. Each bi-plot has two dimensions, named during the analysis. The naming of dimensions adds a qualitative aspect to the analysis. Correspondence analysis may support medical professionals in finding answers to many important questions related to health, wellbeing, quality of life, and similar topics in a simpler but more informal way than by using more complex statistical or machine learning approaches. In that way, it can be used for dimension reduction and data simplification, clustering, classification, feature selection, knowledge extraction, visualization of adverse effects, or pattern detection.

Insights into charge transfer dynamics of Li batteries through temperature-dependent electrochemical impedance spectroscopy (EIS) utilizing symmetric cell configuration

Studying charge transfer processes in Li batteries poses a challenge due to their complicated nature and the battery's inherent inertness. While electrochemical impedance spectroscopy (EIS) offers a promising approach to investigating these processes, its effectiveness is hindered by the complexity of the data generated and the ambiguity in the analyses. Here, we demonstrate that the assignment process is not straightforward for complex secondary Li batteries, and it necessitates additional measurements and in-depth analysis. We first performed simplification of the impedance data by employing symmetric cell configurations to obtain charge transfer resistance values for each electrode. We second took advantage of altering three parameters during the EIS measurement: electrolyte composition, measurement temperature, and the state of health to extract important parameters related to both the anodic and cathodic charge transfer mechanisms. The analysis enabled the correlation of the observed charge transfer resistances to the corresponding electrochemical process and to obtain the kinetic parameters of the studied process. Our work illustrates how EIS can be used to study and understand the intricate electrochemical charge transfer processes in Li batteries, which are difficult to explore using alternative techniques.