Key Challenges Research Articles

A Short Appraisal of Magnetic Nanoparticles for Breast Cancer: In vitro and In vivo Research.

The increasing incidence of breast cancer and the associated morbidity due to higher metastasis created the urge to develop a nanocarrier that can be used as a potent therapeutic carrier with targeting efficacy. The use of superparamagnetic nanoparticles in breast cancer research and treatment has gained considerable attention in recent years. Magnetic nanoparticles (MNPs) can be used to construct nanocarriers since they possess superior properties such as superparamagnetism, easy surface functionalization to attach ligands, and non-immunogenic. MNPs are superior carriers that are used to target cancer cells without harming the normal cells in the body, which leads to therapeutic efficacy in the body. Along with their established anticancer potential and enhanced drug concentration at endosomal pH, the superparamagnetic property of MNPs was further exploited for their applications in reticuloendothelial uptake, drug delivery, medical imaging, and theranostics applications in breast cancer. Moreover, the clinical translational of MNPs, along with future prospects and key challenges in vivo, have been duly presented in the final review. The scientists preferred the ongoing research in MNPs due to their high biocompatibility and ease of targeting at molecular and cellular levels. The review highlighted the in vitro and in vivo research and patent supported data for potential use of MNPs for the treatment of breast cancer.

A study of concept similarity in Wikidata

Robust estimation of concept similarity is crucial for applications of AI in the commercial, biomedical, and publishing domains, among others. While the related task of word similarity has been extensively studied, resulting in a wide range of methods, estimating concept similarity between nodes in Wikidata has not been considered so far. In light of the adoption of Wikidata for increasingly complex tasks that rely on similarity, and its unique size, breadth, and crowdsourcing nature, we propose that conceptual similarity should be revisited for the case of Wikidata. In this paper, we study a wide range of representative similarity methods for Wikidata, organized into three categories, and leverage background information for knowledge injection via retrofitting. We measure the impact of retrofitting with different weighted subsets from Wikidata and ProBase. Experiments on three benchmarks show that the best performance is achieved by pairing language models with rich information, whereas the impact of injecting knowledge is most positive on methods that originally do not consider comprehensive information. The performance of retrofitting is conditioned on the selection of high-quality similarity knowledge. A key limitation of this study, similar to prior work lies in the limited size and scope of the similarity benchmarks. While Wikidata provides an unprecedented possibility for a representative evaluation of concept similarity, effectively doing so remains a key challenge.

Constrained Tiny Machine Learning for Predicting Gas Concentration with I4.0 Low-cost Sensors

Low-cost gas sensors (LCS) often produce inaccurate measurements due to varying environmental conditions that are not consistent with laboratory settings, leading to inadequate productivity levels compared to high-quality sensors. To address this issue, we propose the use of Machine Learning (ML) to predict accurate concentrations of pollutant gases acquired by LCS integrated into an embedded Internet of Things platform. However, a key challenge is to optimize an accurate ML design under low memory and computation power constraints of microcontrollers (MCUs) while maintaining accurate ML scores. After data analysis and pre-processing, we assess and analyze the performance of five ML algorithms to predict the concentration of pollutants gases from multiple specifications (weather, presence of other gases, etc.). To support the experiments, datasets from three sources are used: (1) VOCSens, (2) Belgian Interregional Environment Agency cell, and (3) Visual-Crossing. Once the best model was optimized and validated, multiple hard constraints were added to the selected ML structure to satisfy material and expert requirements. Trained models were ported to be implemented locally in a MCU after comparing several porting libraries. The assembled code obtained is evaluated based on two metrics: storage memory consumption and inference time, relative to the highest attainable capacities. The improved random forest is the best ML model for the used dataset with an R2 score meeting of 0.72 and Root Means Square Error of 0.0028 ppm. The best generated Tiny-ML model needs 3% of RAM and 98% of Flash storage. The empirical results prove that the developed ML algorithm applied to LCS provides high accuracy to predict pollutant gases. This algorithm can also be used to adjust the LCS systems to provide calibrated data in real time, even if the platform being used is not particularly advanced or powerful.

Corr-Track: Category-Level 6D Pose Tracking with Soft-Correspondence Matrix Estimation.

Category-level pose tracking methods can continuously track the pose of objects without requiring any prior knowledge of the specific shape of the tracked instance. This makes them advantageous in augmented reality and virtual reality applications. The key challenge is how to train neural networks to accurately predict the poses of objects they have never seen before and exhibit strong generalization performance. We propose a novel category-level 6D pose tracking method Corr-Track, which is capable of accurately tracking objects belonging to the same category from depth video streams. Our approach utilizes direct soft correspondence constraints to train a neural network, which estimates bidirectional soft correspondences between sparsely sampled point clouds of objects in two frames. We first introduce a soft correspondence matrix for pose tracking tasks and establish effective constraints through direct spatial point-to-point correspondence representations in the sparse point cloud correspondence matrix. We propose the "point cloud expansion" strategy to address the "point cloud shrinkage" problem resulting from soft correspondences. This strategy ensures that the corresponding point cloud accurately reproduces the shape of the target point cloud, leading to precise pose tracking results. We evaluated our approach on the NOCS-REAL275 and Wild6D dataset and observed superior performance compared to previous methods. Additionally, we conducted cross-category experiments that further demonstrated its generalization capability.

Compressed-SDR to HDR Video Reconstruction.

The new generation of organic light emitting diode display is designed to enable the high dynamic range (HDR), going beyond the standard dynamic range (SDR) supported by the traditional display devices. However, a large quantity of videos are still of SDR format. Further, most pre-existing videos are compressed at varying degrees for minimizing the storage and traffic flow demands. To enable movie-going experience on new generation devices, converting the compressed SDR videos to the HDR format (i.e., compressed-SDR to HDR conversion) is in great demands. The key challenge with this new problem is how to solve the intrinsic many-to-many mapping issue. However, without constraining the solution space or simply imitating the inverse camera imaging pipeline in stages, existing SDR-to-HDR methods can not formulate the HDR video generation process explicitly. Besides, they ignore the fact that videos are often compressed. To address these challenges, in this work we propose a novel imaging knowledge-inspired parallel networks (termed as KPNet) for compressed-SDR to HDR (CSDR-to-HDR) video reconstruction. KPNet has two key designs: Knowledge-Inspired Block (KIB) and Information Fusion Module (IFM). Concretely, mathematically formulated using some priors with compressed videos, our conversion from a CSDR-to-HDR video reconstruction is conceptually divided into four synergistic parts: reducing compression artifacts, recovering missing details, adjusting imaging parameters, and reducing image noise. We approximate this process by a compact KIB. To capture richer details, we learn HDR representations with a set of KIBs connected in parallel and fused with the IFM. Extensive evaluations show that our KPNet achieves superior performance over the state-of-the-art methods.

Bioactive Exploration in Functional Foods: Unlocking Nature's Treasures.

Functional foods offer an appealing way to improve health and prevent chronic diseases, and this subject has received much attention lately. They are effective in preventing chronic diseases like cancer, diabetes, heart disease, and obesity, according to research. This work presents an in-depth analysis of functional foods, covering key challenges from a scientific, legal, and commercial perspective. Multiple databases were searched to find studies on functional foods included in the systematic literature review. Various aspects of functional foods, from their classification, impact on human wellness, effectiveness in inhibiting chronic diseases, the regulatory environment, global market trends, and industry challenges, are all clarified in this thorough review. This study aims to enhance understanding and establish a pathway for functional foods to be acknowledged as valid choices in the field of dietary supplements. It provides a thorough investigation of bioactive compounds present in functional foods, including but not limited to polyphenols, carotenoids, omega fatty acids, prebiotics, probiotics, and dietary fiber, along with an overview of their potential to mitigate chronic illnesses. We engage in an in-depth exploration of regulatory frameworks, shed light on groundbreaking research advancements, and meticulously examine strategies for commercialization and the variety of global challenges that accompany them. Establishing scientific consensus, navigating complex regulatory processes, dealing with skeptical consumers, and rising levels of competition are all problems that need to be solved in this field. The field of functional foods can advance further, promoting better public health outcomes, by deeply comprehending and addressing these complex dimensions.

Nanomedicine Tumor Targeting.

Nanomedicines are extensively explored for cancer therapy. By delivering drug molecules more efficiently to pathological sites and by attenuating their accumulation in healthy organs and tissues, nanomedicine formulations aim to improve the balance between drug efficacy and toxicity. More than 20 cancer nanomedicines are approved for clinical use, and hundreds of formulations are in (pre)clinical development. Over the years, several key pitfalls have been identified as bottlenecks in nanomedicine tumor targeting and translation. These go beyond materials- and production-related issues, and particularly also encompass biological barriers and pathophysiological heterogeneity. In this manuscript, the author describes the most important principles, progress, and products in nanomedicine tumor targeting, delineates key current problems and challenges, and discuss the most promising future prospects to create clinical impact.

Fairness-Aware Graph Neural Networks: A Survey

Graph Neural Networks (GNNs) have become increasingly important due to their representational power and state-of-the-art predictive performance on many fundamental learning tasks. Despite this success, GNNs suffer from fairness issues that arise as a result of the underlying graph data and the fundamental aggregation mechanism that lies at the heart of the large class of GNN models. In this article, we examine and categorize fairness techniques for improving the fairness of GNNs. We categorize these techniques by whether they focus on improving fairness in the pre-processing, in-processing (during training), or post-processing phases. We discuss how such techniques can be used together whenever appropriate and highlight the advantages and intuition as well. We also introduce an intuitive taxonomy for fairness evaluation metrics, including graph-level fairness, neighborhood-level fairness, embedding-level fairness, and prediction-level fairness metrics. In addition, graph datasets that are useful for benchmarking the fairness of GNN models are summarized succinctly. Finally, we highlight key open problems and challenges that remain to be addressed.

Direct C-H Sulfuration: Synthesis of Disulfides, Dithiocarbamates, Xanthates, Thiocarbamates and Thiocarbonates.

In light of the important biological activities and widespread applications of organic disulfides, dithiocarbamates, xanthates, thiocarbamates and thiocarbonates, the continual persuit of efficient methods for their synthesis remains crucial. Traditionally, the preparation of such compounds heavily relied on intricate multi-step syntheses and the use of highly prefunctionalized starting materials. Over the past two decades, the direct sulfuration of C-H bonds has evolved into a straightforward, atom- and step-economical method for the preparation of organosulfur compounds. This review aims to provide an up-to-date discussion on direct C-H disulfuration, dithiocarbamation, xanthylation, thiocarbamation and thiocarbonation, with a special focus on describing scopes and mechanistic aspects. Moreover, the synthetic limitations and applications of some of these methodologies, along with the key unsolved challenges to be addressed in the future are also discussed. The majority of examples covered in this review are accomplished via metal-free, photochemical or electrochemical approaches, which are in alignment with the overraching objectives of green and sustainable chemistry. This comprehensive review aims to consolidate recent advancements, providing valuable insights into the dynamic landscape of efficient and sustainable synthetic strategies for these crucial classes of organosulfur compounds.

An All-Inclusive Taxonomy and Critical Review of Blockchain-Assisted Authentication and Session Key Generation Protocols for IoT

Authentication and Session Key Generation Protocols (SKGPs) play an essential role in securing the communication channels of connected Internet of Things (IoT) devices. Recently, through blockchain integration, scholars have tried to enhance the security and applicability of SKGPs. In brief, blockchain is a distributed ledger technology that can provide interesting features such as immutability, transparency, and accountability without any need for the active participation of trusted parties. This survey presents a comprehensive critical review of blockchain-assisted authentication and SKGPs, suggested for different IoT domains, including Internet of Vehicles, Internet of Drones, and Industrial IoT. Our survey categorizes existing schemes based on several criteria, including IoT application domains, security aspects, and blockchain components. By presenting an unbiased critical review and taxonomy of protocols, we aim to clarify the key challenges. Our review will specifically indicate what properties authors gained or lost through the integration of blockchain. To our best knowledge, this survey is the only one that offers all prerequisites for interested readers in blockchain-integrated SKGPs, such as security features and attacks, attack models, verification tools, blockchain types, blockchain platforms, and consensus mechanisms. Further, our survey elaborates existing research gaps in blockchain-assisted SKGPs. In doing so, we aim to guide future research in this field and provide researchers with the essential information they require.

Single-Transmitter-Controlled Multiple-Channel Constant Current Outputs for In-Flight Wireless Charging of Drones

This paper proposes a single-transmitter-controlled multiple-channel constant current outputs scheme, which can energize multiple hovering drones over the air simultaneously. As an unmanned and cost-effective solution to extend the cruising range of drones, the multiple-drone in-flight wireless charging has to deal with two key technical challenges, namely the continuous disturbance of multiple mutual inductances ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> ) caused by the hovering attitude of drones, as well as the limit of drone-side payload. Accordingly, this paper adopts the multi-frequency resonating compensation (MFRC) to establish multiple transmission channels and achieves the single-transmitter-controlled multiple-channel constant current outputs based on a communication-free multiple- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> estimation method. The simulated and experimental results are both given in this paper, which shows that the accuracy of the multiple- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> estimation is up to 97% and the steady-state accuracy of the current output control is up to 95%. What is more, the current output control can effectively restrain the output current fluctuation of the battery load under continuous multiple- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> disturbance, indicating that the proposed scheme is feasible to improve the stability and reliability of the multiple-drone in-flight wireless charging system.

SwinPA-Net: Swin Transformer-Based Multiscale Feature Pyramid Aggregation Network for Medical Image Segmentation.

The precise segmentation of medical images is one of the key challenges in pathology research and clinical practice. However, many medical image segmentation tasks have problems such as large differences between different types of lesions and similar shapes as well as colors between lesions and surrounding tissues, which seriously affects the improvement of segmentation accuracy. In this article, a novel method called Swin Pyramid Aggregation network (SwinPA-Net) is proposed by combining two designed modules with Swin Transformer to learn more powerful and robust features. The two modules, named dense multiplicative connection (DMC) module and local pyramid attention (LPA) module, are proposed to aggregate the multiscale context information of medical images. The DMC module cascades the multiscale semantic feature information through dense multiplicative feature fusion, which minimizes the interference of shallow background noise to improve the feature expression and solves the problem of excessive variation in lesion size and type. Moreover, the LPA module guides the network to focus on the region of interest by merging the global attention and the local attention, which helps to solve similar problems. The proposed network is evaluated on two public benchmark datasets for polyp segmentation task and skin lesion segmentation task as well as a clinical private dataset for laparoscopic image segmentation task. Compared with existing state-of-the-art (SOTA) methods, the SwinPA-Net achieves the most advanced performance and can outperform the second-best method on the mean Dice score by 1.68%, 0.8%, and 1.2% on the three tasks, respectively.

Computational Experiments for Complex Social Systems—Part III: The Docking of Domain Models

Powered by advanced information technology, more and more complex systems are exhibiting characteristics of the cyber–physical–social systems (CPSS). In consideration of the cost, legal, and institutional constraints on the study of CPSS in real world, computational experiments have emerged as a new method for quantitative analysis of CPSS. However, with the increase of application scenarios, how to map complex and diverse domain models to artificial society models has become a key challenge to hinder the wide use of computational experiments. In this article, the docking framework between the domain model and the artificial society model was proposed in this article, and the model docking specification is given from three aspects: the agent model, the environmental model, and the rules model. In addition, the effectiveness of the framework was verified by two classic cases: artificial stock market and epidemic prevention and control. The result showed that the proposed model docking framework can provide technical support for the multidisciplinary applications of computational experiments and significantly reduce the difficulty of using the method.

Generative Listener EEG for Speech Emotion Recognition Using Generative Adversarial Networks with Compressed Sensing.

Currently, emotional features in speech emotion recognition are typically extracted from the speeches, However, recognition accuracy can be influenced by factors such as semantics, language, and cross-speech datasets. Achieving consistent emotional judgment with human listeners is a key challenge for AI to address. Electroencephalography (EEG) signals prove to be an effective means of capturing authentic and meaningful emotional information in humans. This positions EEG as a promising tool for detecting emotional cues conveyed in speech. In this study, we proposed a novel approach named CS-GAN that generates listener EEGs in response to a speaker's speech, specifically aimed at enhancing cross-subject emotion recognition. We utilized generative adversarial networks (GANs) to establish a mapping relationship between speech and EEGs to generate stimulus-induced EEGs. Furthermore, we integrated compressive sensing theory (CS) into the GAN-based EEG generation method, thereby enhancing the fidelity and diversity of the generated EEGs. The generated EEGs were then processed using a CNN-LSTM model to identify the emotional categories conveyed in the speech. By averaging these EEGs, we obtained the event-related potentials (ERPs) to improve the cross-subject capability of the method. The experimental results demonstrate that the generated EEGs by this method outperform real listener EEGs by 9.31% in cross-subject emotion recognition tasks. Furthermore, the ERPs show an improvement of 43.59%, providing evidence for the effectiveness of this method in cross-subject emotion recognition.

Digital Twin-Based Cyber-Attack Detection Framework for Cyber-Physical Manufacturing Systems

Smart manufacturing (SM) systems utilize run-time data to improve productivity via intelligent decision-making and analysis mechanisms on both machine and system levels. The increased adoption of cyber-physical systems in SM leads to the comprehensive framework of cyber-physical manufacturing systems (CPMS) where data-enabled decision-making mechanisms are coupled with cyber-physical resources on the plant floor. Due to their cyber-physical nature, CPMS are susceptible to cyber-attacks that may cause harm to the manufacturing system, products, or even the human workers involved in this context. Therefore, detecting cyber-attacks efficiently and timely is a crucial step toward implementing and securing high-performance CPMS in practice. This paper addresses two key challenges to CPMS cyber-attack detection. The first challenge is distinguishing expected anomalies in the system from cyber-attacks. The second challenge is the identification of cyber-attacks during the transient response of CPMS due to closed-loop controllers. Digital twin (DT) technology emerges as a promising solution for providing additional insights into the physical process (twin) by leveraging run-time data, models, and analytics. In this work, we propose a DT framework for detecting cyber-attacks in CPMS during controlled transient behavior as well as expected anomalies of the physical process. We present a DT framework and provide details on structuring the architecture to support cyber-attack detection. Additionally, we present an experimental case study on off-the-shelf 3D printers to detect cyber-attacks utilizing the proposed DT framework to illustrate the effectiveness of our proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by developing a general-purpose and extensible digital twin-enabled cyber-attack detection framework for manufacturing systems. Existing works in the field consider specialized attack scenarios and models that may not be extensible in practical manufacturing scenarios. We utilize digital twin (DT) technology as a key enabler to develop a systematic and extensible framework where we identify the abnormality of a resource and detect if the abnormality is due to an attack or an expected anomaly. We provide several remarks on how our proposed framework can extend existing industrial control systems (ICS) and can accommodate further extensions. The presented DTs utilize data-driven machine learning models, physics-based models, and subject matter expert knowledge to perform detection and differentiation tasks in the context of expected anomalies and model-based controllers that control the manufacturing process between multiple setpoints. We utilize a model predictive controller on an off-the-shelf 3D printer to run the process, and stage anomalies and cyber-attacks that are successfully detected by the proposed framework.

A Novel Deep-Learning-Based QoS Prediction Model for Service Recommendation Utilizing Multi-Stage Multi-Scale Feature Fusion With Individual Evaluations

With the rapid development of service computing, the demand for service recommendation is increasing. Quality of Service (QoS) prediction has been one of the key challenges for service recommendation. Existing deep learning-based methods have been proposed for QoS prediction, but further improvement of their neural network structures is still needed to improve the prediction accuracy. This work introduces multi-stage multi-scale feature fusion with individual evaluations to a deep learning model for accurate QoS prediction. In our model, non-negative matrix factorization is used to extract three-scale (i.e., global, local, and individual) features; distance similarity is exploited to find similar users and services; a multi-stage deep neural network is designed to fuse multi-scale features, where individual evaluations are input to each stage to correct QoS features. Finally, our model is compared with often-cited prediction methods, and the experimental results show that it can more accurately predict QoS than its peers. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Accurate QoS prediction is very helpful to recommend the most suitable services to users among many similar services. Affected by the sparsity of historical data, the accuracy of existing QoS prediction methods is often limited. The multi-scale features of users and services can be used to improve prediction accuracy. This work proposes a new QoS prediction method to do so. Specifically, it first extracts global and individual features through non-negative matrix factorization and uses distance similarity to obtain local features. Then, it proposes a new deep neural network that fuses the extracted multi-scale features in each learning stage, thereby improving QoS prediction for services recommendation.

Demystifying AI: Current State and Future Role in Medical Education Assessment.

Medical education assessment faces multifaceted challenges, including data complexity, resource constraints, bias, feedback translation, and educational continuity. Traditional approaches often fail to adequately address these issues, creating stressful and inequitable learning environments. This article introduces the concept of precision education, a data-driven paradigm aimed at personalizing the educational experience for each learner. It explores how artificial intelligence (AI), including its subsets machine learning (ML) and deep learning (DL), can augment this model to tackle the inherent limitations of traditional assessment methods.AI can enable proactive data collection, offering consistent and objective assessments while reducing resource burdens. It has the potential to revolutionize not only competency assessment but also participatory interventions, such as personalized coaching and predictive analytics for at-risk trainees. The article also discusses key challenges and ethical considerations in integrating AI into medical education, such as algorithmic transparency, data privacy, and the potential for bias propagation.AI's capacity to process large datasets and identify patterns allows for a more nuanced, individualized approach to medical education. It offers promising avenues not only to improve the efficiency of educational assessments but also to make them more equitable. However, the ethical and technical challenges must be diligently addressed. The article concludes that embracing AI in medical education assessment is a strategic move toward creating a more personalized, effective, and fair educational landscape. This necessitates collaborative, multidisciplinary research and ethical vigilance to ensure that the technology serves educational goals while upholding social justice and ethical integrity.

Transportation Problems: Analysis and Solutions

Abstract: Transportation systems are fundamental to modern society, facilitating the movement of people and goods essential for economic development and social well-being. However, the efficiency and sustainability of these systems are often challenged by various factors, leading to transportation problems such as traffic congestion, inefficient route planning, and environmental degradation. This review paper comprehensively examines the analysis and solutions to transportation problems, encompassing a range of analytical techniques and innovative approaches. Beginning with an exploration of different types of transportation problems, including route optimization and capacity planning issues, the paper delves into the analytical techniques employed, such as linear programming and heuristic algorithms. Through case studies focusing on urban transportation networks and freight systems, key challenges such as data quality and technological constraints are identified. The paper then explores solutions, including the role of intelligent transportation systems, green initiatives, and emerging technologies like artificial intelligence and blockchain. Finally, it outlines future directions, including the potential of autonomous vehicles and the importance of sustainable urban mobility planning. This review provides valuable insights for policymakers, practitioners, and researchers seeking to address transportation challenges and build resilient and efficient transportation systems for the future.

Business intelligence tools in finance: A review of trends in the USA and Africa

This paper provides a comprehensive overview of the current trends in Business Intelligence (BI) tools within the finance sector, with a specific focus on the divergent landscapes of the USA and Africa. As the financial industry evolves, the integration of advanced BI tools has become imperative for informed decision-making and sustainable growth. This review examines key developments, challenges, and opportunities shaping the implementation of BI tools in both regions. In the USA, financial institutions are witnessing a surge in BI tool adoption, driven by the increasing need for real-time data analytics, regulatory compliance, and risk management. The integration of artificial intelligence and machine learning algorithms in BI tools is enabling predictive analytics, empowering financial professionals to make strategic decisions in a rapidly changing market environment. Additionally, the emergence of cloud-based BI solutions is enhancing scalability and accessibility, facilitating seamless collaboration and data-driven insights. Contrastingly, the African financial landscape is experiencing a gradual but promising shift towards BI tool utilization. The continent, characterized by diverse economies and regulatory frameworks, faces unique challenges such as infrastructure limitations and varying levels of technological readiness. However, the potential for BI tools to revolutionize financial operations, improve transparency, and facilitate financial inclusion is evident. Governments and financial institutions are investing in technology infrastructure and skill development to harness the transformative power of BI tools. This review underscores the importance of understanding regional nuances in the adoption of BI tools, recognizing that effective implementation requires tailored strategies. As both the USA and Africa navigate the evolving financial landscape, the convergence of technological advancements and strategic planning will undoubtedly play a pivotal role in shaping the future of BI tools in the finance sector

Survey on data replication in cloud systems

In a distributed environment, replication is the most investigated phenomenon. Replication is a way of storing numerous copies of the same data at different locations. Whenever data is needed, it will be fetched from the nearest accessible copy, avoiding delays and improving system performance. To manage the replica placement strategy in the Cloud, three key challenges must be addressed. The challenges in determining the best time to make replicas were generated, the kind of files to replicate, as well as the best location to store the replicas. This survey conducts a review of 65 articles published on data replication in the cloud. The literature review examines a series of research publications and offers a detailed analysis. The analysis begins by presenting several replication strategies in the reviewing articles. Analysis of each contributor’s performance measures is conducted. Moreover, this survey offers a comprehensive examination of data auditing systems. This work also determines the analytical evaluation of replication handling in the cloud. Furthermore, the evaluation tools used in the papers are examined. Furthermore, the survey describes a lot of research issues &amp; limitations that might help researchers support better future work on pattern mining for data replication in the cloud.