Abstract: Recently, state space models have exhibited strong global modeling capabilities and linear computational complexity in contrast to transformers. This research focuses on applying such architecture to more efficiently and effectively model point cloud data globally with linear computational complexity. In particular, for the first time, we demonstrate that Mamba-based point cloud methods can outperform previous methods based on transformer or multi-layer perceptrons (MLPs). To enable Mamba to process 3-D point cloud data more effectively, we propose a novel Consistent Traverse Serialization method to convert point clouds into 1-D point sequences while ensuring that neighboring points in the sequence are also spatially adjacent. Consistent Traverse Serialization yields six variants by permuting the order of x, y, and z coordinates, and the synergistic use of these variants aids Mamba in comprehensively observing point cloud data. Furthermore, to assist Mamba in handling point sequences with different orders more effectively, we introduce point prompts to inform Mamba of the sequence’s arrangement rules. Finally, we propose positional encoding based on spatial coordinate mapping to inject positional information into point cloud sequences more effectively. Point Cloud Mamba surpasses the state-of-the-art (SOTA) point-based method PointNeXt and achieves new SOTA performance on the ScanObjectNN, ModelNet40, ShapeNetPart, and S3DIS datasets. It is worth mentioning that when using a more powerful local feature extraction module, our PCM achieves 79.6 mIoU on S3DIS, significantly surpassing the previous SOTA models, DeLA and PTv3, by 5.5 mIoU and 4.9 mIoU, respectively.

Abstract: Cloud-based Intelligence of Things is significant for Augmented Enterprise Management Systems. Data integrity auditing is challenging in the intelligence of things environment, mainly when the newer versions in the public cloud environment update existing encrypted data. The related literature on cloud-based intelligence relies on encrypted data uploading or locally handling encryption and decryption using user keys. Considering the security risk, storage constraints at the edge, and realtime environment, both approaches have limited applicability in the intelligence of things environment. This paper presents the Privacy-Aware Secure Data Auditing (PASDA) framework at the cluster head for online data integrity verification. Specifically, the users hide data files by the blinding process with a generation of their corresponding signatures, which achieves data auditing by utilizing homomorphic techniques. A novel automated self-triggering/ Self-auditing-based data integrity auditing system is proposed, which detects the changes made in the cloud-stored data and sends alert messages to the trusted primary cloud server and users. A data dynamics method is developed containing a timestamp with a pointer to store multiple versions of the same file without signatures re-generation for the whole same file. The user is revoked due to prolonged absence or detection of the missed behaviour with system or service expiry. With these data dynamics, the proposed PASDA framework allows CH to regenerate signatures of the revoked user using its membership key for cloud-based stored data access and data integrity auditing. In-depth security analysis and extensive simulations based on comparative performance evaluation attest to the benefits of the proposed PA

Abstract: Genomic surveillance is extensively used for tracking public health outbreaks and healthcare-associated pathogens. Despite advancements in bioinformatics pipelines, there are still significant challenges in terms of infrastructure, expertise, and security when it comes to continuous surveillance. The existing pipelines often require the user to set up and manage their own infrastructure and are not designed for continuous surveillance that demands integration of new and regularly generated sequencing data with previous analyses. Additionally, academic projects often do not meet the privacy requirements of healthcare providers. We present Solu, a cloud-based platform that integrates genomic data into a real-time, privacy-focused surveillance system. Solu’s accuracy for taxonomy assignment, antimicrobial resistance genes, and phylogenetics was comparable to established pathogen surveillance pipelines. In some cases, Solu identified antimicrobial resistance genes that were previously undetected. Together, these findings demonstrate the efficacy of our platform. By enabling reliable, user-friendly, and privacy-focused genomic surveillance, Solu has the potential to bridge the gap between cutting-edge research and practical, widespread application in healthcare settings. The platform is available for free academic use at https://platform.solugenomics.com.

Abstract: The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy.

Abstract: Using the technology-organization-environment framework, this paper investigates elements that affect the adoption of e-commerce technology by Jordanian small- and medium-sized enterprises (SMEs). Cloud computing, logistics digitalization, and AI-driven customer engagement serve as elements for technological readiness assessment. In contrast, regulatory compliance readiness and supportive government measures are other significant factors for the study. Survey questionnaires for 425 respondents, including SME owners and managers alongside employees from different Jordanian industries, were distributed between March and July 2024 through stratified random sampling. SEM served as the model for evaluating and testing the hypotheses. The examination shows cloud computing readiness (β = 0.34, p < 0.01) combined with AI-driven customer engagement (β = 0.42, p < 0.001) along with logistics digitalization (β = 0.38, p < 0.001) to be the vital drivers that boost e-commerce adoption among SMEs. The findings show that readiness for regulatory compliance failed to impact enterprises’ adoption rates of e-commerce substantially. The government support strengthened the positive impact of technological enablers on e-commerce adoption. The research results present crucial knowledge that shows policymakers and SME managers why they should invest in specific digital infrastructure, improved financial support systems, and less complex regulatory frameworks to support successful digital transformation among Jordanian SMEs.

Abstract: Globally, pre-eclampsia is becoming a leading cause of maternal and fetal death, mainly due to late diagnosis and insufficient monitoring. The study proposes an advanced IoT and Machine Learning (ML) system to ensure timely medical intervention through a real-time monitoring system to detect pre-eclampsia in an early stage. The proposed system consists of a wearable pre-eclampsia watch and fetal kick sensor, which consistently monitors the major maternal and fetal critical indicators, like fetal heart rate, oxygen saturation, uterine contractions, and blood pressure. The data collected through LoRa and Bluetooth technology is safely sent to a cloud platform, where ML algorithms predict the eclampsia risk based on real-time trends. System forecast analysis and intelligent alert mechanisms strengthen healthcare providers with timely insights, reduce complications, and increase pregnancy results, especially in resource-limited settings. Unlike existing models, this solution provides end-to-end monitoring, increased data security, and future risk evaluation, while reducing the difference between maternal health requirements and technological signs of progress. By integrating TinyML for on-device processing and safe cloud analytics, the study lays a scalable, cost-effective, and data-driven approach to maternal healthcare. The novel IoT-ML Framework can bring a revolution in prenatal care, which significantly reduces pre-eclampsia-related mortality.

Abstract: Remote sensing target detection technology in cloud and mist scenes is of great significance for applications such as marine safety monitoring and airport traffic management. However, the degradation and loss of features caused by the obstruction of cloud and mist elements still pose a challenging problem for this technology. To enhance object detection performance in adverse weather conditions, we propose a novel target detection method named CM-YOLO that integrates background suppression and semantic context mining, which can achieve accurate detection of targets under different cloud and mist conditions. Specifically, a component-decoupling-based background suppression (CDBS) module is proposed, which extracts cloud and mist components based on characteristic priors and effectively enhances the contrast between the target and the environmental background through a background subtraction strategy. Moreover, a local-global semantic joint mining (LGSJM) module is utilized, which combines convolutional neural networks (CNNs) and hierarchical selective attention to comprehensively mine global and local semantics, achieving target feature enhancement. Finally, the experimental results on multiple public datasets indicate that the proposed method realizes state-of-the-art performance compared to six advanced detectors, with mAP, precision, and recall indicators reaching 85.5%, 89.4%, and 77.9%, respectively.

Abstract: The explosion of data in the digital era has posed major challenges handling, computing and analyzing enormous and complex datasets. Cloud computing has arisen as a revolutionary solution providing scalable and elastic infrastructure necessary to deal with incoming big data workloads. This study employs an empirical approach to evaluate the performance, cost efficiency, and scalability of the three dominant cloud service models, Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Function-as-a-Service (FaaS) on Amazon Web Services, Microsoft Azure and Google Cloud Platform. Standard big data analytics workloads running real-time stream processing and machine learning activities were then implemented in Apache Spark, Hadoop, and Kafka on harmonized cloud environments. Key performance metrics such as execution time, CPU utilization, memory, cost per task and throughput were taken, analyzed statistically using ANOVA and Tukey’s post hoc tests. Results show that FaaS configurations are always faster in execution speed, memory efficiency and cost compared to IaaS, while IaaS delivers better CPU usage for continual workloads. AWS and GCP platform performed relatively balanced when compared to Azure. It is concluded that serverless architecture is, in fact, optimal for modular and burst-oriented analytics, and hybrid models might be more appropriate for complex pipelines. These results can offer cloud architects practical directions towards scalable and cost-effective big data solutions.

Abstract: Digital Twin (DT) technology is revolutionizing healthcare by enabling real-time monitoring, predictive analytics, and highly personalized medical care. As a key innovation of Industry 4.0, DTs integrate advanced tools like artificial intelligence (AI), the Internet of Things (IoT), and machine learning (ML) to create dynamic, data-driven replicas of patients. These digital replicas allow simulations of disease progression, optimize diagnostics, and personalize treatment plans based on individual genetic and lifestyle profiles. This review explores the evolution, architecture, and enabling technologies of DTs, focusing on their transformative applications in personalized medicine (PM). While the integration of DTs offers immense potential to improve outcomes and efficiency in healthcare, challenges such as data privacy, system interoperability, and ethical concerns must be addressed. The paper concludes by highlighting future directions, where AI, cloud computing, and blockchain are expected to play a pivotal role in overcoming these limitations and advancing precision medicine.

Abstract: In the contemporary landscape of large language models (LLMs) development, it is crucial to address the challenges of deploying these models on hardware-constrained consumer electronic devices (CEDs), especially within complex dynamic task scheduling in multi-cloud environments (MCE). We propose a novel methodology leveraging a lightweight LLM to enhance task scheduling decisions in MCE.Our approach involves creating a task scheduling expert database informed by optimization objectives to fine-tune the lightweight LLM. This enables the model to generate a schedulable candidate set of tasks based on the current state of tasks and operational conditions within CEDs across MCE, optimizing scheduling decisions and enhancing overall efficiency. Simulations using both synthetic and real-world datasets demonstrate that our method outperforms three other algorithms in cost minimization, makespan reduction, and energy consumption. In summary, our methodology empowers CEDs to optimize the utilization of multi-cloud resources and harness the capabilities of lightweight LLMs to effectively minimize makespan, operational costs, and energy consumption during the task scheduling process, thereby facilitating efficient task scheduling.

Abstract: As 3D vision applications relying on point clouds rapidly develop, point cloud quality assessment (PCQA) has emerged as a significant research area. When observing a point cloud, people typically rotate it to different viewpoints to examine local details from various angles, ultimately synthesizing the overall quality score of the point cloud. In this process, different parts of the point cloud have varying impacts on the overall quality. However, existing PCQA methods often overlook the influence of local quality variations across different regions of the point cloud. To address the imbalance in quality distribution, we introduce COPP-Net, a no-reference point cloud quality assessment (NR-PCQA) method equipped with the capability for local area correlation analysis. Specifically, we segment the point cloud into multiple patches and enhance PointNet++ to generate accurate texture and structure features for each patch. These features are then combined to predict the quality of each patch. Subsequently, we conduct aggregation analysis on the features of all patches using the correlation analysis (CORA) network based on Transformer to determine correlation weights. Finally, we calculate the overall quality score by combining the predicted quality and correlation weights of all patches. Through comparisons with the latest state-of-the-art NR-PCQA models, as well as a series of tests on different distortion types, cross-dataset validation, and time complexity analysis, the high performance of COPP-Net is verified. The available source code for the proposed COPP-Net can be found at https://github.com/philox12358/COPP-Net.

Abstract: Abstract Virtualization technology enables cloud providers to abstract, hide, and manage the underlying physical resources of cloud data centers in a flexible and scalable manner. It allows placing multiple independent virtual machines (VMs) on a single server in order to improve resource utilization and energy efficiency. However, determining the optimal VM placement is crucial as it directly impacts load balancing, energy consumption, and performance degradation within the data center. Furthermore, deciding on VM placement based on a single factor is usually insufficient to improve data center performance because many factors must be considered, and ignoring them may be too expensive. This paper improves a new multi-objective VM placement (MVMP) algorithm using a quantum particle swarm optimization (QPSO) technique. We call it QPSO-MOVMP, and its objective is to find the Pareto optimal solution for the VM placement problem by balancing different goals. This algorithm generates Pareto optimal solutions that save power by minimizing the number of running physical machines, avoid performance degradation by maintaining service level agreement (SLA), and improve load balancing by keeping server loads at optimal utilization. The experimental results show that QPSO-MOVMP had superior performance in terms of power consumption and performance degradation compared to three other multi-objective algorithms and three conventional single-objective algorithms. Simulation results show that the proposed QPSO-MOVMP achieves a consumption of 2.4 × 10 4 watts in power. Furthermore, it outperformed the others, achieving a minimum of 12% SLA breaches while experiencing a significant surge in requests from VMs. Moreover, the proposed model generated Pareto solutions that had a better distribution than those derived from a comparative method.

Abstract: Inaccurate characterization of complex topography leads to the wet bias in climate models, particularly affecting terrain effects in regions like the Tibetan Plateau (TP). This study utilizes the Weather Research and Forecasting (WRF) model with multiple terrain datasets and introduces the terrain complexity index (TCI) to quantify the degree of terrain changes, aiming to evaluate how terrain complexity affects the cloud and precipitation processes over the TP. The results indicate that fine terrain complexity primarily causes earlier cloud formation and precipitation, resulting in more heavy precipitation on the southern slope of the TP (SSTP) and more light precipitation on the TP platform. The structure of moisture transport and microphysical processes further reveals that this promotes the formation of more medium and high clouds, increasing the proportion of solid precipitation over the SSTP. Over the TP platform, the restriction of medium and high cloud development with enhancing the proportion of low clouds for more liquid precipitation. These findings deepen the understanding of the TP's complex terrain effect on cloud and precipitation changes in the Asian water cycle.

Abstract: ABSTRACT Background Cloud Computing has established itself as an efficient and cost‐effective paradigm for the execution of web‐based applications, and scientific workloads, that need elasticity and on‐demand scalability capabilities. However, the evaluation of novel resource provisioning and management techniques is a major challenge due to the complexity of large‐scale data centers. Therefore, Cloud simulators are an essential tool for academic and industrial researchers, to investigate the effectiveness of novel algorithms and mechanisms in large‐scale scenarios. Aim This paper proposes CloudSim 7G, the seventh generation of CloudSim, which features a re‐engineered and generalized internal architecture to facilitate the integration of multiple CloudSim extensions within the same simulated environment. Methods As part of the new design, we introduced a set of standardized interfaces to abstract common functionalities and carried out extensive refactoring and refinement of the codebase. Results The result is a substantial reduction in lines of code with no loss in functionality, significant improvements in run‐time performance and memory efficiency (up to 25\% less heap memory allocated), as well as increased flexibility, ease‐of‐use, and extensibility of the framework. Conclusion These improvements benefit not only CloudSim developers but also researchers and practitioners using the framework for modeling and simulating next‐generation Cloud Computing environments.

Abstract: Cloud computing is such a revolution concerning the IT world offering computing as services capable of diminishing operational costs and complications. Recently, these service models, ranging from IaaS, PaaS, and SaaS, and deployment models in private, public, and hybrid clouds, offer users almost unlimited computing and storage capabilities on a pay-per-use basis. This elasticity of cloud systems makes it very easy to dynamically provision and de-provision resources to cater to very different needs. This facility has led to its widespread use within domains such as social networking, defense, scientific computing, financial services, and medical. IDG Communications has now announced that 73% of corporations are currently utilizing clouds, with a further 17% in the process of implementing. Service abstraction to increase usability raises yet a fresh set of issues in terms of operational costs, reliability, energy efficiency, and security. Especially in cases where the framework is applicable to critical ventures, as exhibited just a while back by Knight Capital in 2013, system failures may have serious financial and credibility repercussions. Fault tolerance strategies through resource redundancy increase the cost of downtime risk but lower energy consumption, hence less cost and less environmentally unfriendly; they affect profit. The bulk of the operational expense in data centers is associated with the use of energy, whereby the use of energy is environmentally unfriendly and poses environmental concerns; clouds are forecasted to contribute to 5.5% of carbon emissions globally by 2025. Balancing energy efficiency and reliability will require novel optimization approaches for today's and future cloud computing systems with robust fault tolerance

Abstract: Task scheduling in cloud computing environment aims to identify alternative methods for effectively allocating competing cloud tasks to constrained resources, optimizing one or more objectives. This systematic literature review (SLR) examines advancements in multi-objective optimization techniques for cloud task scheduling from year 2010 to October 2024, providing an up-to-date analysis of the field. Cloud task scheduling, critical for optimizing performance, cost, and resource use, increasingly relies on multi-objective approaches to address complex and competing scheduling goals. This comprehensive review presents a detailed taxonomy and classification of multi-objective optimization methods, highlighting trends and developments across various approaches. Additionally, we conduct a comparative analysis of key scheduling objectives, testing environments, statistical evaluation methods, and datasets employed in recent studies, offering insights into current practices and best-fit approaches for different scenarios. The findings of this SLR aim to guide researchers and practitioners in selecting appropriate techniques, metrics, and datasets, supporting effective decision-making and advancing the design of cloud task scheduling systems.