Abstract We conduct an in-depth investigation of the role of major climatological cloud systems in the well-known near symmetry at multiannual scales between the Northern and Southern Hemisphere reflected solar radiation (RSR). Here, we quantify the degree of symmetry for the entirety of a recent 22-yr period while also analyzing how its interannual variability is modulated by cloud systems which we have previously termed “regimes of regimes” (RORs). By pairing RORs with monthly RSR values from the Clouds and the Earth’s Radiant Energy System (CERES) EBAF dataset, we elucidate how several large RSR asymmetries at the ROR level, mainly due to strong ROR hemispheric population contrasts, combine in a compensatory way to produce near symmetry at the hemispheric level. We also investigate how interannual variations of small RSR asymmetries relate to interannual asymmetry variations at the ROR level, which we also find to be mainly because of trends in ROR populations. Overall, we find statistically significant darkening in both hemispheres but have lower confidence in the darkening’s hemispheric contrast. Finally, we probe the role of the complementary symmetry in absorbed solar radiation (ASR), and specifically of its atmospheric component, in the broader context of hemispheric energetics. Our analysis sheds light on the role of major global cloud systems in modulating the mean state and variability of the equitable contribution of the two hemispheres to the global RSR. Significance Statement The nearly equal amounts of solar energy reflected by Earth’s two hemispheres on a multiannual basis are a well-observed aspect of the planet’s energetics, but its significance remains unexplained. While the role of clouds in this phenomenon has been previously investigated, this work goes into more detail in investigating it with the help of a new classification of cloud systems. We find that even if the prevailing systems differ between the two hemispheres, the uneven contributions to reflected solar radiation cancel each other almost exactly to produce symmetric reflected solar energy, while other components of the energy budget are asymmetric.

Marine shallow cumulus clouds have long caused large uncertainty in climate projections. Such "trade cumuli" frequently organize into mesoscale (10 to 500 km) structures, through two processes that couple the clouds to shallow mesoscale circulations: i) mesoscale moisture aggregation and ii) cold pools, driven by mesoscale rain evaporation beneath the mesoscale cloud structures. Since global climate models do not capture these mesoscale processes, while the degree of mesoscale organization is observed to correlate to shortwave cooling, it has been suggested that mesoscale processes modulate contemporary estimates of cloud response to global warming. Here, we show that introducing mesoscale dynamics can indeed substantially alter top-of-the-atmosphere radiative budget, if the balance between the two circulations is upset. By homogenizing mesoscale rain evaporation patterns, we suppress the formation and effects of cold pools in a large ensemble of large-domain, large-eddy simulations. The experiments reveal that cold pool dynamics reduce mesoscale ascent in the cloud systems, thereby arresting a runaway self-aggregation of moisture into very moist, cloudy regions that occurs without them. This reduces the net rainfall of the cumulus fields, moistens the cloud layer and thus reduces the emission of clear-sky longwave radiation to space, giving an ensemble-averaged warming of 1.88 W/m2. Our results highlight that the proper interplay between mesoscale processes is critical for capturing radiative budgets-especially in kilometer-scale climate models that only partially resolve shallow cumulus aggregation and cold pools.

Structural characterization of water-soluble pectin in NFC orange juice during cloud destabilization.

Achieving cloud resource optimization with trust-based access control: A novel ML strategy for enhanced performance.

A new method known as Lionized Remora optimization based Recurrent Neural Network (LRObRNN) is recommended to enhance the safety of medical information stored on cloud servers to tackle these issues. To safeguard patient data, healthcare organizations must thoughtfully choose reliable and compliant cloud service providers while implementing robust security measures. Storing patient information in cloud systems raises issues with illegal access and data breaches. The LRObRNN generates a secret key using Lionized Remora optimization and employs cryptography to encrypt sensitive healthcare data. Continuous monitoring ensures the security of data transmission by identifying irregularities. Leveraging Recurrent Neural Networks the system analyzes sequential data, enabling the detection of patterns and potential security breaches during data transmission. The performance evaluation includes metrics such as encryption and decryption time, confidentiality rate, processing time, resource usage and efficiency, which are compared with other existing models.

ABSTRACT The convergence of cloud computing, machine learning (ML), and network function virtualization (NFV) offers significant opportunities for advancing network infrastructure management by providing efficient, flexible, and scalable resource utilization. This study aims to provide a comprehensive review of the primary challenges and explores state‐of‐the‐art solutions in cloud computing for resource allocation (RA) specific to NFV environments. The paper highlights the importance of adopting multifaceted strategies to optimize RA and enhance the efficiency, and adaptability of cloud systems that handle RA without ML, and with ML in NFV settings. In addition, gap identification is also discussed, emphasizing many needs: (1) the need for extending the NFV RA in the case of wireless networks; (2) the need for enhanced security protocols to fully harness the potential of ML within resource function virtualization (RFV) environments, ensuring that network infrastructures are not only efficient but also resilient and secure; and (3) the need to develop more efficient ML‐based RA for NFV, considering the trade‐off between performance and accuracy.

With the rapid development of 6G and the widespread adoption of cloud computing technologies, security issues in distributed cloud computing systems have become increasingly critical. Ensuring user anonymity, legitimate device access, communication security, and efficient authentication has emerged as an urgent challenge. To address these issues, this paper proposes an anonymous, secure, and efficient authentication scheme for 6G cloud computing. The scheme supports both user authentication and device access authentication by integrating Chebyshev chaotic mapping with a multi-factor authentication mechanism. It ensures secure verification of user identities and access devices and protects subsequent session keys. Furthermore, a Physical Unclonable Function (PUF) is deployed on the device side to leverage unique hardware features, providing strong identity recognition and resistance to physical attacks while improving system authentication efficiency. Performance evaluations demonstrate that the proposed scheme reduces computational overhead by an average of 30.45% and communication overhead by an average of 16.32% compared with the baseline scheme. These results confirm that the proposed scheme significantly enhances communication security between authorized users, legitimate devices, and cloud servers in 6G cloud computing environments. By combining chaotic mapping, multi-factor authentication, and PUF-based verification, the scheme achieves robust security, lightweight computation, and strong scalability suitable for next-generation distributed cloud systems.

The article is dedicated to analyzing the features of applying digital humanities (DH) in teaching humanities disciplines in higher education institutions, with an emphasis on comparing Western and Ukrainian experiences. The relevance of the topic is driven by the digital transformation of education, the need for adaptation to societal changes, and improving the quality of learning through innovative technologies. Analysis of recent studies shows that DH integrates cloud systems, open science, digital archives, and data analysis tools, promoting interdisciplinarity and interactivity in the educational environment, despite challenges of accessibility and faculty training. The theoretical foundations are based on the evolution of digital humanities from text digitization to methodology formation and integration into education with AI. Key implementation principles in Ukraine focus on interdisciplinarity, academic integrity, openness, practice-based learning, and continuity. Research results demonstrate a high level of DH implementation in EU and US countries. A crucial factor for effective DH integration is a methodological foundation that combines humanistic values with technological approaches. The Ukrainian experience shows gradual institutional integration of DH into curricula, development of digital educational courses, and student involvement in project-based research activities. Western and Ukrainian experiences reveal two different but complementary approaches: systematic implementation in EU and US countries and adaptive innovation under crisis conditions in Ukraine. The prospects for DH implementation in Ukrainian higher education include unified standards, a network of DH centers, interdisciplinary master’s programs, and methodological recommendations for instructors

Cloud computing techniques include various methods and practices aimed at enhancing the delivery, scalability, and efficiency of cloud services. These techniques are essential for managing diverse and fluctuating workloads while maintaining cost-effectiveness. Notable techniques include virtualization, which allows for the creation of virtual instances from physical resources; load balancing, which allocates workloads across multiple resources to optimize performance; and containerization, which encapsulates applications in lightweight, portable containers. Cloud computing techniques, including virtualization and containerization, facilitate the efficient management of computing resources. Research in these domains focuses on developing strategies to optimize resource utilization, minimize costs, and enhance overall system performance. Effective management of diverse and fluctuating workloads is critical, and techniques such as automated scaling and dynamic resource allocation play a key role in adapting cloud environments to shifting demands. By addressing these aspects, research ensures that cloud systems can adeptly handle peak loads and variable usage patterns, delivering smooth and reliable experiences for both users and businesses. Alternative: IaaS, PaaS, SaaS, FaaS, CaaS. Evaluation Parameters: Performance, Scalability, Cost, Complexity. The results indicate that FaaS achieved the highest rank, while SaaS had the lowest rank being attained. The value of the dataset for Cloud computing techniques, according to the Grey Relation Analysis method, FaaS achieves the highest ranking.”

Abstract. Ice microphysical processes are inherently complex because of their sensitivity to temperature and humidity, the diversity of ice crystal habits, and their interaction with supercooled liquid water (SCL) and turbulence. Long-term surface-based radar observations have been systematically used to unravel the different processes that affect ice particle growth. In this study, we present a statistical analysis of 6.5 years of Ka-band radar observations in Arctic cloud systems, combined with thermodynamic profiles derived from radiosonde measurements. For the first time, ice particle growth and sublimation – diagnosed from vertical gradients of radar reflectivity and mean Doppler velocity – are systematically mapped across a broad range of temperature and moisture conditions. These vertical gradients correspond closely to saturation levels relative to ice and exhibit a strong temperature dependence in supersaturated regions. Notably, distinct signatures near −15 °C are indicative of dendritic growth. Turbulence, quantified via the eddy dissipation rate (EDR), is most frequently observed in regions containing SCL. The co-occurrence of SCL and elevated turbulence results in significantly enhanced ice particle growth compared to conditions in which either is present alone. This work provides new observational constraints that are critical for improving the representation of ice microphysics in atmospheric models.

Abstract. Convective updrafts and downdrafts play a vital role in Earth’s energy and water cycles by modulating vertical energy and moisture transport and shaping precipitation patterns. Despite their importance, the characteristics of convective motions and their relationship to the near-storm environment remain poorly constrained by observations. Doppler radars, in principle, are able to measure the vertical air motion within clouds, thus providing critical insight into convective dynamics and enabling estimates of convective mass flux. The payload of the recently launched EarthCARE satellite mission includes a 94 GHz Cloud Profiling Radar (CPR) with Doppler capability. In this study, we present first-light CPR Doppler velocity observations in deep convective clouds. These early examples offer a first glimpse into the dynamic nature of cloud systems. The narrow footprint of the CPR helps reduce the impact of multiple scattering and non-uniform beam filling (NUBF) on the Doppler velocity measurements. However, the instrument’s low Nyquist velocity presents a significant challenge for recovering the true Doppler velocity profiles in deep convective systems. The CPR Doppler velocity observations are expected to challenge traditional methodologies for identifying deep convective cores, which typically rely on reflectivity-based thresholds. We showcase examples that demonstrate the synergy between CPR Doppler velocity measurements and geostationary satellite observations, illustrating how their combined use can help capture the evolution of the convective lifecycle. These results align with EarthCARE’s broader mission objectives and highlight the potential of spaceborne Doppler radars to significantly advance our understanding of cloud dynamics and convection in the climate system.

Abstract Cloud–aerosol interaction is a critical area of research in atmospheric science. Many numerical studies have primarily focused on one-way interactions, examining how aerosols influence the dynamics and microphysics of individual clouds and cloud systems. In this study, we simulate a mesoscale convective system using a mixed-phase spectral bin microphysics WRF model that explicitly incorporates two-way cloud–aerosol interactions. The model includes an aerosol budget that tracks aerosols as they are activated into cloud droplets, evolve through droplet collisions, and are incorporated into ice hydrometeors via immersion freezing. Aerosol mass increases in cloud droplets due to droplet–droplet collisions and in ice particles through drop–ice and ice–ice collisions. Eventually, aerosols are either released back into the environment through droplet evaporation and ice sublimation or removed from the atmosphere by precipitating to the surface. The results show that deep convective clouds efficiently transport aerosols to the upper atmosphere, shaping the vertical profiles of aerosol concentration. The release of aerosols significantly increases background aerosol concentrations across a broad area surrounding the mesoscale convective system both at the upper layer and in the boundary layer. Furthermore, the clouds are found to be efficient generators of giant cloud condensation nuclei (CCN). A portion of the released aerosols is re-entrained into the cloud through lateral boundaries, leading to notable changes in cloud microphysics. These include increased droplet concentrations, enhanced hail mass content in the convective region, and increased snow mass content in the stratiform region. The rise in droplet concentration strengthens cloud dynamics and influences the precipitation rate. These findings highlight that feedbacks within cloud–aerosol interactions are a crucial component of the overall cloud–aerosol system and must be considered in future modelling and observational studies.

Abstract The challenge of distinguishing convective anvil cirrus from in situ cirrus has long limited the quantification of their distinct roles in regulating upper‐tropospheric moisture and modulating Earth's energy budget. In this study, we address this ambiguity by introducing a physically constrained classification framework that applies advanced computer vision techniques to CloudSat‐CALIPSO observations. By tracking the complete physical evolution of cloud systems from their convective origins, this method enables a robust global separation of anvil and in situ cirrus. Our results illuminate stark contrasts in their macro‐ and micro‐properties, governed by fundamentally different mechanisms. Anvil cirrus extent is tightly coupled to dynamic factors, whereas in situ cirrus, while linked to local tropopause thermodynamics, exhibits strong modulation by remote atmospheric influences from the opposite hemisphere. This identified linkage shows a previously unrecognized interhemispheric teleconnection: wherein large‐scale deep convective systems in one hemisphere rapidly influence in situ cirrus formation in the other. We hypothesize that this coupling is mediated by planetary‐scale waves—likely fast‐propagating Kelvin waves that transmit energy across the equator, cooling the remote tropical tropopause layer, with subsequent interactions with the subtropical jet fostering mid‐latitude in situ development. This newly quantified atmospheric coupling provides a pathway for improving representation of cirrus in climate models and suggests a mechanism by which regional shifts in convection under global warming could reshape global cirrus distributions and their radiative impact.

The rapid increase of containerized applications in cloud environments has highlighted the critical need for efficient resource management and energy optimization. This article extends our previous work with an aim to enhance the performance of cloud systems. We propose an Extended Directed Container Placement (E_DCP) mechanism, a novel approach designed to enhance container placement efficiency in cloud systems when the number of container increases significantly. Leveraging the Whale Optimization Algorithm (WOA), the mechanism utilizes a scoring mechanism to evaluate various solutions with an aim to identify the best among them. By optimizing multiple objectives, including search time, resource utilization and energy efficiency, the mechanism achieves superior outcomes in heterogeneous and homogeneous cloud infrastructures in comparison to recent methods. The mechanism optimizes this solution to minimize overutilized physical machines. Extensive simulations demonstrate significant improvements in search time and resource utilization with acceptable energy consumption level.

Digital transformation requires visionary leadership to guide infrastructure modernization throughout organizations. The current technology landscape challenges executives to craft integrated approaches combining cloud systems, programmable networks, and advanced security frameworks. Effective transformation harmonizes technical progress with cultural evolution, establishing conditions where creative solutions coexist with reliable operations. Real-world results demonstrate how infrastructure choices strengthen business flexibility, endurance, and competitive standing. Forward-thinking leaders build lasting advantages through balanced governance structures, expanded organizational abilities, and heightened security consciousness. Companies mastering these transitions secure exceptional advantages, transforming the technology backbone from an expense item to a competitive weapon. Modernization builds platforms enabling business expansion while accelerating adaptation to market shifts, creating superior experiences for customers and returns for stakeholders. When executives prioritize business results above technical specifications, infrastructure renewal elevates from routine maintenance to strategic necessity, establishing firms for enduring prosperity within digital economies. Effective transformations reshape organizational capabilities, enabling rapid development, stronger adaptability during disruptions, and tailored customer services. Leading organizations recognize that infrastructure decisions determine product release timing, business adaptability, and information security—factors now essential for market leadership. Such advantages become more crucial as technology shifts competition patterns throughout economic sectors. Companies mastering infrastructure modernization seize emerging possibilities while rivals face technical constraints limiting available strategic choices. Modernized infrastructure provides strategic freedom to pursue innovative business models, enter adjacent markets, and respond decisively to disruptive forces. The technology foundation increasingly serves as either an accelerator or a barrier to business transformation initiatives across all functional areas.

AntPi: A Raspberry Pi based edge–cloud system for real-time ant species detection using YOLO

Cloud communication is a combination of distributed computing and parallel computing. Task scheduling is a major challenge in cloud communications due to the NP-completeness of cloud systems. To address this, various swarm intelligence-based approximation techniques have been developed. This paper proposes a novel method for efficient task scheduling with improved security in cloud computing. A Hybrid Convolutional Neural Network with Long Short-Term Memory (HCNN-LSTM) optimized using FABOA is proposed for task scheduling to maximize throughput and minimize make span. Additionally, an improved random bit-stuffing technique with a modified RSA algorithm ensures secure data transmission. A novel Hybrid Convolutional Neural Network with Long Short-Term Memory (HCNN-LSTM) algorithm which is optimized using FABOA is proposed, which complicates readability. While the introduction outlines general cloud computing challenges, it lacks a focused literature review that identifies specific gaps in existing work and clearly justifies the need for the proposed HCNN-LSTM-FABOA system. Finally, our proposed approach is simulated under a cloudlet simulator and the evaluation results are analyzed to determine its performance. In addition to this, the proposed approach is compared with various other task scheduling-based approaches for various performance metrics, namely, resource utilization, response time, as well as energy consumption.

Cloud computing is a complex and articulated semantic structure designed to facilitate the discovery, composition, and integration of cloud services offered by different providers. The cloud services reference ontology, in its current version, represents an articulated and complex semantic structure that facilitates the interoperability and portability between different cloud platforms. This work builds on previous research that has explored the use of semantic representations in cloud computing to improve the portability, interoperability, and automatic discovery of cloud systems. This paper proposes a series of improvements to simplify the class hierarchy, making it more readable and intuitive, and to enrich it with new classes, object properties, and data properties in order to expand the descriptive capacity and improve the accuracy and detail of the semantic description. The work presented here is based on a critical review of existing ontologies, accompanied by expansion to meet emerging challenges in the cloud computing.

Strong cybersecurity measures are becoming vitally crucial as cloud computing utilization rises. Advanced and dynamic cyberthreats are frequently difficult for traditional intrusion detection systems (IDS), which rely on preset signatures and rules, to identify. This study improves the detection of known and unknown intrusions in cloud systems using Machine Learning (ML) methods. The UNSW-NB15 dataset was used to train and assess a num- ber of ML classifiers, including Random Forest (RF), Decision Tree (DT), XGBoost, Naïve Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), and Gradient Boosting (GB). It uses full feature training across several classifiers and also investigates the implications of feature reduction, in contrast to many other studies that mainly employ full feature sets to train RF alone. Critical metrics including accuracy (ACC), precision, recall, and F1-score are used to analyze classifier performance and provide a thorough evaluation of their efficacy in in- trusion detection. The findings show that using all characteristics the RF and DT obtained perfect accuracy (1.00). In the case of less characteristics when using feature selection techniques (RF-based selection, information gain, or mutual information), the RF retained the best accuracy (0.94), whereas NB performed the worst overall. This study emphasizes the significance of feature selection in enhancing IDS performance and shows that ML-based techniques may greatly increase threat detection in cloud settings, even when feature dimensionality is lowered.

ABSTRACT In this paper, we present a novel approach to securing data stored in the cloud by incorporating DNA‐based cryptography. This methodology, known as cloud‐based DNA cryptography (CBDC), offers a cutting‐edge solution to enhance cloud data security by leveraging DNA's intrinsic data storage capacity and robust encryption capabilities. The primary objective of this study is to propose a comprehensive framework encompassing the foundations, methodologies, and potential benefits of integrating genetic cryptography into cloud systems. The research aims to deepen understanding and provide meaningful insights into the practicality and effectiveness of DNA encryption for strengthening the security of cloud‐based data storage. Additionally, machine learning techniques are employed for anomaly detection and performance evaluation, enabling dynamic system monitoring and optimization. Experimental results demonstrate the superiority of CBDC in terms of time efficiency, compression ratio, and computational performance when compared to traditional encryption techniques. This study also identifies key research gaps through a comprehensive literature review and highlights future directions for integrating bio‐cryptographic models with intelligent, scalable cloud infrastructures.