Data Structure Research Articles

Three new members of the hakite series, Cu6(Cu4Me2+2)Sb4Se13: hakite-(Cd), hakite-(Fe) and hakite-(Zn) from the Bytíz deposit, uranium and base-metal Příbram ore district, Czech Republic

Abstract Hakite-(Cd), hakite-(Fe) and hakite-(Zn) are new minerals belonging to the tetrahedrite group and forming, along with hakite-(Hg), the hakite series. They have been discovered in samples collected from the Bytíz deposit, in the uranium and base-metal Příbram ore district, Central Bohemia, Czech Republic. They occur as anhedral grains, up to 300 μm in size, in a calcite gangue, associated with clausthalite, cadmoselite, hakite-(Hg) [for hakite-(Cd)], berzelianite, bukovite, bytízite, crookesite, chaméanite, eskebornite, příbramite, the not yet approved giraudite-(Hg) and giraudite-(Cu), hakite-(Hg), umangite, chalcopyrite, tetrahedrite-(Zn) and a new Cu–As selenide [for hakite-(Fe) and -(Zn)]. The three new species are black, with a metallic lustre. Mohs hardness is ca. 3½–4; calculated density is 6.019 (Hak-Cd), 6.011 (Hak-Fe) and 6.081 g.cm–3 (Hak-Zn). In reflected light, they are isotropic, pale grey with bluish (Hak-Cd) or brownish (Hak-Fe and Hak-Zn) shades. Empirical formulae of hakite-(Cd), hakite-(Fe), and hakite-(Zn) are Cu9.71Ag0.24Cd1.51Hg0.43Zn0.03(Sb3.94As0.13)Σ4.07Se11.35S1.57, Cu10.11Ag0.18Fe0.81Zn0.50Hg0.26(Sb3.72As0.41)Σ4.13Se12.65S0.12, and Cu10.03Ag0.24Zn0.61Fe0.53Hg0.45(Sb3.55As0.60)Σ4.15Se12.82S0.08, respectively. These formulae correspond to the end-member formulae Cu6(Cu4Cd2)Sb4Se13 (Hak-Cd), Cu6(Cu4Fe2)Sb4Se13 (Hak-Fe), and Cu6(Cu4Zn2)Sb4Se13 (Hak-Zn). All these new members of the hakite series are cubic, I $\bar{4}$ 3m, Z = 2, with unit-cell parameters a = 10.8860(6) Å, V = 1290.0(2) Å3 (Hak-Cd); a = 10.7983(4) Å, V = 1259.12(14) Å3 (Hak-Fe); and a = 10.8116(14) Å, V = 1263.8(5) Å3 (Hak-Zn). These species are isotypic with the other members of the tetrahedrite group, and their crystal structures have been refined on the basis of single-crystal X-ray diffraction data down to R1 values of 0.0230 (Hak-Cd), 0.0254 (Hak-Fe), and 0.0302 (Hak-Zn). These structural data allow us to describe the S-to-Se partitioning in hakite-series minerals and to understand the mechanisms avoiding too short Me–Se distances in these selenides.

StaPep: An Open-Source Toolkit for Structure Prediction, Feature Extraction, and Rational Design of Hydrocarbon-Stapled Peptides.

All-hydrocarbon stapled peptides, with their covalent side-chain constraints, provide enhanced proteolytic stability and membrane permeability, making them superior to linear peptides. However, tools for extracting structural and physicochemical descriptors to predict the properties of hydrocarbon-stapled peptides are lacking. To address this, we present StaPep, a Python-based toolkit for generating 3D structures and calculating 21 features for hydrocarbon-stapled peptides. StaPep supports peptides containing two non-standard amino acids (norleucine and 2-aminoisobutyric acid) and six non-natural anchoring residues (S3, S5, S8, R3, R5, and R8), with customization options for other non-standard amino acids. We showcase StaPep's utility through three case studies. The first generates 3D structures of these peptides with a mean RMSD of 1.62 ± 0.86, offering essential structural insights for drug design and biological activity prediction. The second develops machine learning models based on calculated molecular features to differentiate between membrane-permeable and non-permeable stapled peptides, achieving an AUC of 0.93. The third constructs regression models to predict the antimicrobial activity of stapled peptides against Escherichia coli, with a Pearson correlation of 0.84. StaPep's pipeline spans data retrieval, structure generation, feature calculation, and machine learning modeling for hydrocarbon-stapled peptides. The source codes and data set are freely available on Github: https://github.com/dahuilangda/stapep_package.

FinSafeNet: securing digital transactions using optimized deep learning and multi-kernel PCA(MKPCA) with Nyström approximation.

With the swift advancement of technology and growing popularity of internet in business and communication, cybersecurity posed a global threat. This research focuses new Deep Learning (DL) model referred as FinSafeNet to secure loose cash transactions over the digital banking channels. FinSafeNet is based on a Bi-Directional Long Short-Term Memory (Bi-LSTM), a Convolutional Neural Network (CNN) and an additional dual attention mechanism to study the transaction data and influence the observation of various security threats. One such aspect is, relying these databases in most of the cases imposes a great technical challenge towards effective real time transaction security. FinSafeNet draws attention to the attack and reproductive phases of Hierarchical Particle Swarm Optimization (HPSO) feature selection technique simulating it in a battle for extreme time performance called the Improved Snow-Lion optimization Algorithm (I-SLOA). Upon that, the model then applies the Multi-Kernel Principal Component Analysis (MKPCA) accompanied by Nyström Approximation for handling the MKPCA features. MKPCA seeks to analyze and understand a non-linear structure of data whereas, Nyström Approximation reduces the burden on computational power hence allows the model to work in situations where large sizes of datasets are available but with no loss of efficiency of the model. This causes FinSafeNet to work easily and still be able to make accurate forecasts. Besides tackling feature selection and dimensionality reduction, the model presents advanced correlation measures as well as Joint Mutual Information Maximization to enhance variable correlation analysis. These improvements further help the model to detect the relevant features in transaction data that may present a threat to the security of the system. When tested on commonly used database for testing banks performance, for instance the Paysim database, FinSafeNet significantly improves upon the previous and fundamental approaches, achieving accuracy of 97.8%.

A Monte Carlo Approach for Simulating Electrical Conductivity in Highly Porous Ceramic Composites: Impact of Internal Structure.

Porous ceramic composites play an important role in several applications. This is due to their unique properties resulting from a combination of various materials. Determination of the composite properties and structure is crucial for their further development and optimization. However, composite analysis often requires complex, expensive, and time-demanding experimental work. Mathematical modeling represents an effective tool to substitute experimental approach. The present study employs a Monte Carlo 3D equivalent electronic circuit network model developed to analyze a highly porous composite on the basis of minimum easily obtainable input parameters. Solid oxide cell electrodes were used as a model example, and this study focuses primarily on materials with a porosity of 55% and higher, characterized by deviation of behavior from those of lower void fraction share. This task is approached by adding to the original Monte Carlo model an additional parameter defining the void phase coalescence phenomenon. The enhanced model accurately simulates electrical conductivity for experimental samples of up to 75% porosity. Using sample composition, single-phase properties, and experimentally determined conductivity, this model allows us to estimate data of the internal structure of the material. This approach offers a rapid and cost-effective method to study material microstructure, providing insights into properties, such as electrical conductivity and heat conductivity. The present research thus contributes to advancing predictive capabilities in understanding and optimizing the performance of composite materials with potential in various technological applications.

A Real-Time Associative Feature-Based Customer Relationship Management and Enterprise Resource Planning Integration Model for Small- and Medium-Sized Enterprises

Abstract Customer relationship management (CRM) and enterprise resource planning (ERP) have been extensively discussed in the research literature respectively. However, existing studies do not reach a complete agreement on the CRM/ERP integration method, especially for small- and medium-sized enterprises (SMEs). Generally, this work proposed a novel method for CRM/ERP integration approach via associative feature technology. A new feature type, Real-time Data Link Board (RDLB), is developed and detailed as a generic information carrier solution for multisystem integration between CRM and ERP packages. Valuable data elements are mapped into such data boards dynamically and synchronized across different data sources, structures, and databases. The contribution of this work is to present a well-defined and generically reusable data carrier definition, alongside related methods for ensuring consistent data modeling during the system integration phase across various digitalization engineering implementation projects. With this approach, the consistent management of data integration across intricate systems is achievable throughout application lifecycles, supported by the object-oriented software engineering foundation.

Efficient generation of grids and traversal graphs in compositional spaces towards exploration and path planning

AbstractDiverse disciplines across science and engineering deal with problems related to compositions, which exist in non-Euclidean simplex spaces, rendering many standard tools inaccurate or inefficient. This work explores such spaces conceptually in the context of materials discovery, quantifies their computational feasibility, and implements several essential methods specific to simplex spaces through a new high-performance open-source library . Most significantly, we derive and implement an algorithm for constructing a novel n-dimensional simplex graph data structure, containing all discretized compositions and possible neighbor-to-neighbor transitions. Critically, no distance or neighborhood calculations are performed, instead leveraging pure combinatorics and order in procedurally generated simplex grids, keeping the algorithm $${\mathcal{O}}(N)$$ O ( N ) , with minimal memory, enabling rapid construction of graphs with billions of transitions in seconds. Additionally, we demonstrate how such graph representations can be combined to homogeneously express complex path-planning problems, while facilitating efficient deployment of existing high-performance gradient descent, graph traversal, and other optimization algorithms.

Bulk Single Crystals and Physical Properties of Rutile GeO2 for High‐Power Electronics and Deep‐Ultraviolet Optoelectronics

The top‐seeded solution growth for rutile GeO2 single crystals using alkali carbonates or fluorides as flux is applied. Structural data of obtained single crystals confirm the rutile phase with a = b = 4.3966 Å and c = 2.8612 Å. The crystals with diameter of 5–15 mm are either undoped or intentionally doped with Sb5+, Sn4+, Al3+, Ga3+, and F− ions. It is found that Sb5+ is a very efficient n‐type donor enabling free electron concentration even above 1020 cm−3; thus, Sb‐doped GeO2 is a potential substrate for vertical power devices. In contrast, crystals doped with Al and Ga do not show p‐type conductivity suggested by the theory. The onset of the absorption occurs at 5.0 and 5.5 eV perpendicular and parallel to the c‐axis, respectively. Rutile GeO2 shows a very intense photoluminescence peaking at 420 nm (blue) and 520 nm (green). Raman spectra show narrow lines, in particular at high phonon energy (B1g, 170 cm−1). Prepared wafers show FWHM values of rocking curves below 30 arcsec and polishing is achieved down to RMS roughness of 0.15 nm. Transmission electron microscopy images do not show point or extended structural defects with uniform Sb distribution.

Application of Machine Learning for Predictive Analysis and Management of Mediterranean-Farmed Fish Mortalities: A Risk Management Case Study Using Apache Spark

The current study evaluates the performance of three machine learning models—Decision Trees, Random Forest, and Linear Regression—applied to aquaculture data to mitigate risks in aquaculture management. The performances of these models are analyzed and properly demonstrated using metrics including the Mean Squared Error (MSE), R-squared (R2), Root Mean Squared Error (RMSE), and Concordance Index (C-index). The Random Forest model achieved the highest prediction accuracy among all machine learning models, followed by Linear Regression and the Decision Trees. The scatter plot for Linear Regression demonstrates good predictive accuracy for mid-range values. However, it shows significant deviations at the extremes, indicating that the model struggles to capture the full range of variability in the data. The bar chart of coefficients pinpoints the variables with the greatest impact on the predictions, providing suggestions for potential areas that can be improved and providing model interpretability. Future work could incorporate more predictive statistics models focusing on improving the models for extreme values by assessing non-linear models, feature engineering methods, and expanding research into less influential variables. The results greatly impact several sections, including aquaculture management, policy-making, and operational strategies, providing valuable insights for stakeholders and decision-makers. Apache Spark was used for data processing and machine learning model implementation; Apache Cassandra was also used for data storage, ensuring efficient large dataset management and SQL tools for structured data handling; Oracle VM VirtualBox for cross-platform virtualization; and Spark Connector was also used.

Atmospheric Pressure Photoionization with Halogen Anion Attachment for Mass Spectrometric Analysis of Hydrocarbons and Hydrocarbon-Based Polymers.

Aliphatic hydrocarbons and hydrocarbon-based synthetic polymers are of interest in many fields, but their characterization by mass spectrometric methods is generally limited due to their poor ionizability. Recently, atmospheric pressure photoionization (APPI), combined with halogen anion attachment in negative-ion mode, has drawn attention as a potential method for ionizing various polymers without extensive fragmentation or other unwanted side reactions. In this work, the applicability of halogen anion attachment with APPI was studied using several synthetic polymers, including polyethylene, polypropylene, polyisoprene, and polystyrene, as well as simple n-alkanes of various chain lengths. For hydrocarbon-based polymers, the method produced clear distributions of intact polymer adduct ions when different halogen anions were used. It was found that increasing the halogen anion size decreased ionization efficiency, particularly in the absence of π-bonds in the polymer structure. Testing with simple n-alkanes showed that only molecules containing fifty or more carbon atoms formed detectable halogen adducts, possibly due to the low gas-phase stabilities of the lighter n-alkane adduct ions. In conclusion, halogen anion attachment with negative-ion APPI appears to be a highly promising method for polymer analysis, providing structural data and clean polymer mass spectra with minimal fragmentation, which can be useful for the identification of unknown samples.

The Future of Accounting: Efficacy of Big Data on Accountant’s Functions in the Accounting Information Systems

Before the advent of big data, accounting operations were performed mainly using historical and structured data. The emergence, however, introduced the use of big data to enhance and complement functions in the accounting information system, which improves the real-time and effective management of accounting data for varying uses of stakeholders. This study examined the efficacy of big data on accountant’s functions in the accounting information system in Nigeria. The study utilised survey research design method by administering structured Likert scaled questionnaire. This study was rooted in resource-based view theory and agency theory. The targeted population and the sample size was 283, which comprised accountants with experience in information systems. A purposive sampling technique was adopted to determine the respondent. The collected data were analysed using descriptive statistics and regression analysis. The empirical findings revealed that big data proxied with prescriptive analytics, predictive analytics, machine learning, and the Hadoop ecosystem had significant but negative effect on accountant’s functions in accounting information systems in Nigeria. This suggests that although these technologies are relevant and influential, their integration may be challenging or counterproductive to accountants' traditional roles. The negative effect could be due to factors like complexity, the need for new skill sets, inefficiencies in system adaptation, or resistance to change, which may hinder the effectiveness of accountants in fully leveraging these technologies within their work processes. This study concluded that while big data has the potential to significantly enhance accounting information systems, its integration into the accounting profession in Nigeria has been problematic. It was recommended that organizations should invest in comprehensive training programs for accountants, focusing on developing the necessary skills to handle big data tools effectively.

SOHUPDS+: An Efficient One-phase Algorithm for Mining High Utility Patterns over a Data Stream

Existing algorithms for mining high utility patterns over a data stream are two-phase algorithms that are not scalable due to the large number of candidates generation in the first phase, particularly when the minimum utility threshold is low. Moreover, in the second phase, the algorithm needs to scan the database again to find out actual utility for candidates. In this paper, we propose one-phase algorithm SOHUPDS+ to mine high utility itemsets in the current sliding window of the data stream with respect to absolute or relative minimum utility threshold. To facilitate SOHUPDS+, we propose a data structure IUDataListSW+ , which stores and maintains utility and upper-bound values of the items in the current sliding window when sliding window advances. In addition, we propose a transaction merging strategy, called BitmapTransactionMerging , which saves execution time for utility and upper-bound values computations in denser datasets. Moreover, we propose update strategies to utilize mined high utility patterns from the previous sliding window to update high utility patterns in the current sliding window. The results of experiments illustrate that SOHUPDS+ is more efficient than the state-of-the-art algorithms in terms of execution time as well as memory usage in most of the experiments on various datasets.

Reinforcing cybersecurity with Bloom filters: a novel approach to password cracking efficiency

Safeguarding digital information against unauthorized access is critical in the industry context. Techniques for cracking passwords are essential tools for both attackers and defenders. This study explores the utilization of Bloom filters, a probabilistic data structure known for its space and time efficiency, to refine the password-cracking process. We demonstrate that by employing Bloom filters, it is possible to significantly enhance the performance of password-cracking techniques in terms of speed and memory consumption. By conducting a comparative analysis with prevalent techniques such as hash tables and binary search, we demonstrate the superior performance of Bloom filters. The experimentation, utilizing a publicly available dataset of leaked password hashes, indicates a significant improvement in cracking efficiency. The findings contribute to the broader cybersecurity goal of developing resilient systems against password-related breaches, underscoring the importance of integrating cutting-edge research and practical applications to fortify digital defenses.

A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques

In an era where buildings are increasingly becoming multifaceted entities, the paradigm of smart buildings has witnessed significant evolution. This advancement integrates sophisticated communication technologies, the Internet of Things (IoT), artificial intelligence (AI), and data analytics. Intending to design an effective smart building monitoring system, this research paper explores and compares various solutions for measuring building parameters by identifying a broad spectrum of review articles considering building occupant behavior, sensor deployment, and implementation complexity. The objective of our paper is to compile diverse information on various sensors used for monitoring building conditions and provide a comprehensive overview of data structuring and processing, all within a single article. Additionally, this paper addresses the challenges of combining data from decentralized systems and the need for managerial tools to optimize user experiences. The findings contribute to the advancement of smart building management, offering valuable insights for improving building performance and user experience as well as evaluating future research directions in this field. This review is designed to serve as an introduction for anyone venturing into the field of building monitoring.

Adaptive Weighted Multiview Kernel Matrix Factorization and Its Application in Alzheimer’s Disease Analysis

Recent technology and equipment advancements have provided us with opportunities to better analyze Alzheimer’s disease (AD), where we could collect and employ the data from different image and genetic modalities that may potentially enhance the predictive performance. To perform better clustering in AD analysis, in this paper, we propose a novel model to leverage data from all different modalities/views, which can learn the weights of each view adaptively. Different from previous vanilla Non-negative matrix factorization which assumes data is linearly separable, we propose a simple yet efficient method based on kernel matrix factorization, which is not only able to deal with non-linear data structure but also can achieve better prediction accuracy. Experimental results on the ADNI dataset demonstrate the effectiveness of our proposed method, which indicates promising prospects for kernel application in AD analysis.

Blockchain and NoSQL: Enhancing Throughput via a Deep Learning‐Based Hybrid Architecture

ABSTRACTAny data structure used to store information can be considered a database. Blockchain technology, at its core, is no more than a ledger to store information about transactions. In recent years, such technology has gained immense popularity in a wide range of applications. However, scalability and throughput are major challenges of the Blockchain technology since applications and APIs have much lower throughput compared to non‐Blockchain ones. Several studies showed that the integration of the NoSQL paradigm along with a Blockchain pipeline enhances the overall throughput and scalability of the system, as well as the possibility of handling both on‐chain and off‐chain data. The goal of this paper is therefore to study how the advantages of both technologies can be capitalized on to enhance the system capabilities and to finally put forward a novel hybrid architecture that mixes NoSQL and Blockchain characteristics using Deep Learning techniques.

An Approach to Rapidly Obtaining Rock Mass Structure Data During Highway Tunnel Construction and its Application

An Approach to Rapidly Obtaining Rock Mass Structure Data During Highway Tunnel Construction and its Application

The ECP SICM project: Managing complex memory hierarchies for exascale applications

The Exascale Computing Project (ECP)’s Simplified Interface to Complex Memories (SICM) effort focuses on developing universal interfaces for discovering, managing, and sharing data across complex memory hierarchies. These facilitate the exploitation of emerging memory technologies and support precise control over their various trade-offs such as high-bandwidth versus low-latency, persistent versus ephemeral, high-capacity versus low-capacity, and near-CPU versus near-GPU. SICM comprises three interrelated components: a low-level interface, a high-level interface, and a persistent-heap interface. The low-level SICM interface is intended for system and run-time developers as well as expert application developers who prefer full control of the memory objects used within their application. The high-level SICM interface builds upon the low-level interface, employing application-level profiling and analysis to optimize data management for complex memory hierarchies. The persistent-heap interface provides applications with a persistent memory allocator that can allocate custom C++ data structures in both block-storage and byte-addressable persistent memories.

Pengaruh Citra Destinasi dan Komunikasi Pemasaran Terhadap Loyalitas melalui Kepuasan Pengunjung Destinasi Pantai Lon Malang

This study aims to examine the effect of destination image and marketing communication on loyalty through visitor satisfaction. This type of research is quantitative research with Structural Equation Modeling data analysis techniques using the SmartPLS version 4.0 statistical program. In this study, the population is not known for certain (infinite) and the sample used is tourists visiting Lon Beach, Malang. This study uses a questionnaire with a Likert Scale. The results of the study obtained that destination image affects loyalty with a p-value <0.05 and t-statistic> 1.96, while destination image does not affect satisfaction with a p-value> 0.05 and t-statistic <1.96, but marketing communication has an effect on loyalty with a p-value <0.05 and t-statistic> 1.96, and communication also has an effect on satisfaction with a p-value <0.05 and t-statistic> 1.96.

Machine-Learning-Assisted Materials Discovery from Electronic Band Structure.

Traditional methods of materials discovery, often relying on intuition and trial-and-error experimentation, are time-consuming and limited in their ability to explore the vast design space effectively. The emergence of machine learning (ML) as a powerful tool for pattern recognition has opened exciting opportunities to revolutionize materials discovery. This work explores the application of ML techniques to assist in the discovery of materials using band structure data. The electronic band structure, which describes the energy levels of electrons in a material, holds vital information regarding its electronic and optical properties. The band structure data of 63,588 materials, including metals and insulators, have been retrieved from the Materials Project database. The data were grouped into 85 batches based on the band path in the first Brillouin zone. Three ML clustering algorithms were trained on the band structure data after performing feature selection and engineering, followed by noise reduction. The models were validated by comparing the materials' properties in a cluster.

Reconstructing gearing ratios and digital-based service rates to mitigate financial risks for sustainable development in Indonesia’s credit guarantee industry

This research examines the role of the credit guarantee industry in supporting the growth of MSMEs in Indonesia through guarantee programs that aim to reduce information asymmetry between banks and MSMEs. Using structured interview methods and data triangulation techniques from various sources, this study analyzes the management of gearing ratio and tariff risks that significantly impact the financial stability of guarantee industry. The analysis results with NVIVO 12 and the Analytical Hierarchy Process (AHP) reveal digital transformation’s importance in efficiency and transparency in risk management. This research also produces a digitalization design for the Program Gearing Ratio (PGR) and offers practical solutions through SWOT analysis to improve the guarantee capacity and sustainability of the industry. The findings contribute to the literature by proposing an integrated internal audit approach for financial risk mitigation in the credit guarantee industry.